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INTERN TION L ENERGY E ONOMI S



INT RN TION L

STU I S IN

ONOMI MO LLING

er

s

ditor

Homa Motamen Scobie

xe utive ire tor

urope n onomi s n in nci l ntr

on on

Economic

Modelling in the OE Countries

otamen Scobie

Modelling the

Labour

Market

eensto k

Input Output nalysis

i schini

Modelsof Disequilibrium

and

Shortage in Centrally Planned Economies

vis n h remz

Economic Modelling at the

Bank

of

England

nry n K tterson

Recent

Modelling pproaches

in pplied

Energy

Economics

jerkholt

lsen n

Vislie

International ommo ity Market Models

iiv n n

W bys n J esour

ynamic

Models

for the Inter relations of Real and Financial

Growth

kste t n est erg

Economic Models of

Trade Unions

ronn or n edes hi

International

Energy Economics

hom s t rn r

International Trade

Modelling

gen is n P

u k



ntern tion l

n rgy

onomi s

ite

hom s

t rn r

ep rtment of conomics othen urg niversity

we en

SPRINGER-SCIENCE BUSINESS MEDIA V



Contributors

Preface

1 Introduction

Thomas Sterner

Contents

VB

IX

2 Forecasting industrial energy use 11

Gale A. Boyd

3 Best-practice and average practice: technique choice and energy

demand in a vintage model 31

Lennart Hjalmarsson and Finn R. Forsund

4 The effects of changes in the economic structure on energy demand

in the Soviet Union and the United States of America 47

Yu.

D. Kononov, H. G. Huntington,

E.

A. Medvedeva and G. A.

Boyd

5 Modelling transport fuel demand

65

Thomas Sterner and Carol A. Dahl

6 Modelling the long-run supply of coal 81

Ronald P. Steenblik

7 Global availability of natural

gas:

resources, requirements and

location 105

Daniel A. Dreyfus

8 Modelling oil exploration 117

Victor Rodriguez Padilla

9 Environmental cost functions: a comparison between general and

partial equilibrium

141

Lars Bergman

10 Energy policies in a macroeconomic model: an analysis of energy

taxes when oil prices decline 157

P. Capros, P. Karadeloglou and G. Mentzas

11 A comparison of energy-economy models: the French experience 185

Ghislaine Destais



vi Contents

12

Models and projections of energy use in the Soviet Union

203

Yuri Sinyak

13

A detailed simulation approach to world energy modelling: the

SIBILIN and POLES experiences

221

Patrick Criqui

14

Inferred demand and supply elasticities from a comparison

of

world oil models

230

Hillard G. Huntington

15

World oil market simulation

263

Nick Baldwin and Richard Prosser

16

International Energy Workshop projections

297

Alan S. Manne and Leo Schrattenholzer

17 Environmental regulations and innovation: a CGE approach for

analysing short-run and long-run effects

299

Gunther Stephan

18

CO

2

emission limits: an economic cost analysis for the United

States of America

232

Alan S. Manne and Richard G. Richels

Index

347



Nick Baldwin

Lars Bergman

Gale

A.

Boyd

P.

Capros

Patrick Criqui

Carol

A.

Dahl

Ghislaine Destais

Daniel

A.

Dreyfus

Finn

R.

F orsund

Lennart Hjalmarsson

Hillard

G.

Huntington

P.

Karade1og10u

Yu.

D. Kononov

Alan

S.

Manne

G. Mentzas

E. A.

Medvedeva

Richard Prosser


Richard

G.

Richels

Leo Schrattenholzer

Yuri Sin yak

Contributors

Strategic Studies Department, PowerGen,

UK

Stockholm School of Economics, Stockholm, Sweden

Argonne National Laboratory, Argonne, Illinois,

USA

National Technical University of Athens, Greece

Institut d'Economie et de Politique de l'Energie,

Grenoble, France

Department of Mineral Economics, Colorado

School of Mines, USA

Institut d'Economie et

de

Politique de l'Energie,

Grenoble, France

Vice

President, Strategic Planning and Analysis Gas

Research Institute, Washington,

DC

Department of Economics, Oslo University,

Norway

Department of Economics, Gothenburg University,

Sweden

Energy Modeling Forum, Stanford University,

Stanford, California, USA


Siberian Energy Institute, Irkutsk, USSR

Department of Operations Research,

Stanford University, Stanford, California, USA


Siberian Energy Institute, Irkutsk, USSR

National Power, UK

Institut d'Economie et de Politique de l'Energie,

Grenoble, France

Electric Power Research Institute, Palo Alto,

California, USA

International Institute for Applied Systems

Analysis, Laxenburg, Austria

International Institute for Applied Systems

Analysis, Laxenburg, Austria



viii

Ronald P. Steenblik

Gunter Stephan

Thomas Sterner

Contributors

Organization for Economic Co-operation and

Development, Paris

Institute for Applied Micro-economics, University

of Berne, Switzerland

Department of Economics, Gothenburg University,

Sweden



Preface

The fact that editing a book is more work than you think it's going to be is a

'standard result' reported by most researchers. However many people forget to

tell you what a rewarding task it is. On occasion it has been a little hard on

my phone bill and even on

my

patience (the volume of E-mail, faxes and other

correspondence weighs several times more than the book) but the lasting

impression

is

that it has been a very stimulating experience to work together

with the authors of the individual chapters. I have learned a great deal and I

hope some of this

is

conveyed to the reader.

When Homa Motamen-Scobie first asked

me

if I wanted to edit a book on

energy modelling I must admit I was quite unsure how to go about the task.

I was sceptical of conference volumes or collections of papers where the

different chapters fail to interact and form a coherent book. My first idea was

therefore to draw up a rough structure and make a list of potential topics and

authors. The main challenge was to blow some life into the project and create

a dialogue between its different participants.

I am very grateful to Alan Manne who persuaded

me

that what I needed

was

a 'critical mass' of authors who would all agree at the same time to get the

book going. Alan, together with Leo Schrattenholzer, also provided me with

an excellent forum: the International Energy Workshop (lEW) meeting at the

International Institute for Applied Systems Analysis (IIASA) in June 1989.

Roughly half the chapters of this book are in some way related to that very

stimulating meeting. Although only two or three of the chapters actually bear

any direct resemblance to papers presented at the conference, a contact was

established which allowed me to discuss with the various authors the kind of

contribution I

was

looking

for.

This, in turn, made it much easier to find other

authors to fill in the missing links.

My ambition has been to make this a broad survey of energy modelling in

the sense of both original research and survey articles, both empirical and

theoretical work. It

covers demand, supply and energy-economy interaction;

models that are primarily short-run or primarily long-run, national or interna

tional, theoretical or oriented towards projections and policy analysis. The

book is also a reflection of the widely different academic and physical condi

tions in different countries. The authors come from a dozen countries and there

is

a fairly even spread at least across the Atlantic. There are also two Soviet



x

Preface

contributions and there were to

be

a couple of papers from authors in the

Third World but unfortunately various factors intervened. The most worrying

case

is

of the Chinese author with whom I have been unable to re-establish

contact after he went back to China.

A considerable portion of energy economics research is undertaken at a

limited number of specialized institutions and this

is

clearly reflected in the

book. I have already mentioned Alan Manne who, together with Hillard

Huntington and others associated with the Energy Modelling Forum (EMF),

and their link with the IIASA through the lEW, have provided something of

a cornerstone for this work. Among European centres, I would particularly like

to mention, and thank, the Institut Economique et Politique de l'Energie

(IEPE)

at

Grenoble. While I was a guest at the IEPE in

1989,

a significant part

of this volume

was

edited and three of its chapters

(8,

11,

13)

are written by

IEPE researchers. The IEPE was not only a very pleasant place to spend a

sabbatical but also a very appropriate one considering the prominent role

played by IEPE researchers such as Finon, Chateau, Lapillone and Criqui in

European energy-economy modelling (notably the MEDEE, EFOM, SIBILIN

and POLES models, see Chapter

13

and appendix to Chapter

10).

Another

obvious centre of energy analysis

is

the International Energy Agency (lEA), one

of whose researchers has contributed with an article. I would like, in this

context, to thank both the lEA and also the Oxford Institute of Energy Studies

for hospitality and stimulating discussions related to this book.

With such a dispersed set of authors, my role as editor has necessarily been

quite active and I would like to thank all the authors for putting up with my

many suggestions and question marks. Where appropriate I have encouraged

the authors to use similar tables briefly describing the main features of their

models to facilitate comparisons (see Chapters

2,

9, 10,

11, 13

and 14). In order

to keep the usual academic level, I have tried to have all the papers reviewed

in some way. The trouble

is

that the incentive to review a chapter for a book

is

much smaller than for a journal where you implicitly collect 'credits' for

future use. In several cases however I found that it was useful for the authors

to read each others' chapters in order to give more coherence to the book -

and thus, once again I would like to thank the authors to whom I have turned

in order to get comments on other chapters. In addition to these I would also

like to thank Dominique Anxo, Hans Bjurek, Per-Olov Johansson, Katarina

Katz, Catherine Locatelli, Geoffrey Harcourt, Homa Motamen-Scobie and

Peter Odell for constructive comments on various chapters or other help with

this book. I also want to thank Energy Economics, the OPEC review and the

Energy Journal for permission to reprint material in Chapters

15, 16

and

18

which are based on articles to which they hold copyright.

Thomas Sterner

Gothenburg



1

Introduction

Thomas Sterner

1.1

ENERGY MODELLING

As we enter the third decade of 'energy awareness' we discern both trends and

cyclical movements in the attention that different aspects of energy economics

receive. The political aspects of supply security were the hallmark of the 1970s:

this, in a

way,

was the period when energy economics was born, the Interna

tional Energy Agency (lEA) and various national Departments of Energy were

established. These and other organizations, such

as

the International Institute

for Applied Systems Analysis (IIASA), embarked upon large-scale and some

what grandiose modelling efforts motivated largely by the desire to ration

energy, optimize energy supply or trace the consequences of economic activity

on energy imports. They are epitomized by the US 'Project Independence'.

Their approach both to supply and demand was often technically biased and

much of the modelling was of the linear programming, input-output or

operations research type. This was the age of models such as EFOM and

MARKAL.

During the 1970s and early 1980s a number of impressive surveys of the

energy field were undertaken 1 and, as a result, there has come a gradual

awareness that energy as such is quite abundant albeit that various' costs (both

pecuniary and others) may

be

increasing. Naturally, supply and security

concerns regularly reappear during crises such as the latest one in the Gulf, but

it

is

still fair to say that interest has come to focus more on demand

management, including the role of prices and of environmental issues.

Modelling efforts appear to concentrate less on total optimization for the

whole system and more on detailed and in-depth understanding of incentives

and mechanisms of interplay between institutions, economics, energy and

environment. For the global models, demands have increased on their trans-

IFor

example, the IIASA study

Energy

in

a Finite World

(Hafele, 1981), Schurr

et

al.

(1979)

Energy

in

America's Future; Landsberg et

al.

(1979) Energy: The Next Twenty Years, Brooks et

al.

(1979)

Energy

in

Transition, or Stobaugh and Yergin (1979) Energy Future. For an excellent survey of

early modelling see Manne et

al. (1979).



2

Introduction

parency, comparability and the analysis of how results are conditioned by

model structure and assumptions.

1

The ambition of this book

is

to reflect, in a fairly broad fashion, the current

flavour of international energy modelling. We shall cover both the building

blocks of energy demand and supply

as

well

as

different types of market model,

world or c o ~ n t r y - w i d e system models and projections. We concentrate on

formalized models but do not want thereby to exclude

less

formalized judge

mental-political ones that may very well be appropriate to certain issues. The

approaches analysed include both 'bottom-up' and 'top-down', both pro

cess/econometric and recursive simulation or intertemporal optimization. The

purposes may be analysis, projections or application to other issues, such as

the environment.

Within a book of this format, one can, however, not possibly hope to do

justice to all the currents of energy economic modelling.

For

various reasons,

we

have kept away from certain areas: there is little on the particular problems

of the heavily infrastructure-dominated energy carriers electricity and natural

gas.

2

Likewise

we

have stayed relatively clear of energy saving, demand

management and sustainability. As mentioned in the Preface, the Third World

has unfortunately not been covered in any

way,

and finally the focus is more

on the interaction between economics and energy modelling than on the

technical aspects of econometric modelling.

1.2

ENERGY DEMAND

The first section of this book deals with demand modelling, and the contribu

tions have been chosen to represent not only distinctive types of energy

demand (industry, transport and general) but different methodologies. In

Chapter 2 Gale Boyd surveys a number of large-scale computerized industrial

energy demand models. Many of these are based on the academic 'KLEM'

type econometric studies using flexible functional forms. In this applied type of

work, however, these econometric models

face

competition from process

models using engineering-type data. Although the latter may be theoretically

less satisfying, they do have a number of advantages. Their 'bottom-up'

approach allows for the incorporation of considerable amounts of actual

engineering and planning information. On the other hand, they can generally

not, unlike the econometric models, be expected to take price-induced effects

into consideration. A growing number of models appear to take a very

pragmatic view and incorporate elements of both econometric and process type

into a 'hybrid' model structure.

'The efforts of the Energy Modeling Forum and of the International Energy Workshop are

particularly instrumental to this process.

2See Recent Modelling Approaches

in

Applied Energy Economics

ed.

O. Bjerkholt et

al. in

this same

series of books.



Energy

demand

3

One feature of reality that aggregate econometric models often gloss over

is

that once technical choices are made and capital takes on an actual physical

configuration, then flexibility

is

greatly reduced. In many industries, technology

can best be described as 'putty-clay' and hence the vintage structure of the

plants comprising an industry is a vital characteristic determining their short

and even medium-run responsiveness to changes in market conditions. To deal

with this aspect Lennart Hjalmarsson and Finn F6rsund in Chapter 3 analyse

choice of technology in a vintage model for an industry with endogenous

investment. This framework is then used to analyse how fast average practice

catches up with best-practice under various assumptions. The authors are

somewhat sceptical about the optimism aroused by demonstrations of energy

efficient best-practice technology, arguing that if capital turnover

is

slow, it

may take many years before average practice catches up with present best

practice.

At the other end of the spectrum, we find that not only for an industry, but

for a whole economy, structure again plays a vital role in determining energy

consumption. Using Divisia indexes, a mixed US/Soviet quartet of economists

compare, in Chapter 4, the respective roles of technological versus structural

change in their two countries. Structural change, measured as change in output

composition, is, together with overall changes in average technology (in each

industry), expected to provide major opportunities for future Soviet energy

saving.

One rather striking feature of the Soviet economy as distinct from Western

economies

is

the very small role, in energy demand, played by the 'non

production sphere' - i.e. domestic consumption - and by transport. Even in

their forecasts for the year 2010 these sectors continue to be minor fractions in

total energy consumption. This runs very much counter to the result one would

expect with a free and unregulated market. In Chapter

5,

Carol Dahl and I

discuss different ways of modelling transport fuel demand.

Our

main focus is

on the comparison between models, but practically all approaches find income

elasticities for transport fuels to be above one. Price elasticities by comparison

are often found to be below one, although it turns out to be difficult really to

estimate a true long run. The policy implications are anyhow clear, both for

our own OECD economies and for liberalizing Eastern European ones. If we

want to avoid the expected strong increase in consumer demand for transport

fuels, their relative price must rise faster than the rate of growth in income.

Without tough environmental policies, liberalization in the Eastern European

economies will probably imply a massive increase in transport fuel demand.

Energy demand

is

difficult to model for a number of reasons. One of these

is

the complex pattern of substitutability and complementarity between energy

carriers and labour or capital. On the one hand, commercial sources of energy

have historically often been employed precisely to substitute for human labour.

In this task they have been complementary to certain pieces of capital

equipment. On the other hand, new vintages of capital or additional capital

equipment may well save energy, in which case capital (and sometimes labour)



4

Introduction

may

be

substitutes for energy. Either way the processes of substitution take a

long time since the demand for energy

is

a derived demand and its changes are

intimately tied to the structure and technology of society.

1.3 ENERGY SUPPLY AND THE ENVIRONMENT

On the supply side, the picture is no

less

complicated. New sources of energy

are often characterized

by

indivisibility and uncertainty. They generally require

heavy investments and often have

a.

long gestation lag from initial project to

actual production. The next section of the book deals with energy supply. The

appropriate modelling approach depends crucially on the type of supply under

consideration. For long-term supply of nuclear energy, a model built on

historical statistics clearly runs the risk of being irrelevant. Statistics are best

used to study the past behaviour of many independent agents under various

conditions. In this case the main agents are the state (and maybe public

opinion) and the future 6f the nuclear industry is obviously a political matter:

any attempt to make long-run projections must be based on a social, political

and maybe even sociopsychological analysis. This might thus be an area where

less formalized 'modelling' would be appropriate.

Although there

is

a common logic to supply modelling for all three fossil

fuels considered, there are also significant differences. Ronald Steenblik starts

in Chapter 6 by discussing the long-run supply of coal. Since coal

is

abundant,

exploration in the ordinary sense of the word is not strictly necessary at least

for the short and medium term. We know where the coal is, but as Steenblik

points out, coal

is

pretty heterogeneous and actual field development will

generally entail numerous surprises about quality, quantity, depth, overburden

and other factors that are decisive for costs. A particularly useful feature of this

chapter is that it specifically shows how a supply curve in principle should be

derived from the reserve and resource categories conventionally given in this

field.

There are then numerous engineering cost and econometric cost estima

tion methods for producing such supply curves, but they all require detailed

geophysical/resource information and their performance is thus often impaired

by the deficient quality of available data.

Transport is another factor that is quite important for coal supply costs but

this factor turns out to be of even more importance to gas.

For

gas, exploration

does entail considerable uncertainty and, as Daniel Dreyfus points out in

Chapter

7,

resource or supply modelling is difficult because much of the best

data

is

treated as proprietary information by the respective companies or

governments. Large parts of the globe are poorly explored and in fact likely to

remain so in the foreseeable future because the geographical mismatch between

supply and demand would make the transport costs prohibitive. The necessary

infrastructure investments of some projects are such that private enterprise

alone will find them difficult to finance. Future supply will thus concentrate



Energy and the economy

5

where governments are stable, and where innovative schemes for financing and

risk-management are found.

While transport costs are

less

important

for

oil we are once again concerned

with large investments (in exploration and development of fields) and thus

political stability

is

still very important.

As

economists we generally assume a

positively sloping supply curve. In fact, a positive supply response to higher

prices seems pretty obvious. However, as Victor Rodriguez Padilla shows in

Chapter 8, actual supply reactions can be quite different - particularly in the

Third World. Concentrating on the most fundamental, and the most long-run,

aspect of supply, namely exploration activities, Rodriguez shows that there is

in fact only a very tenuous link to oil prices. The complicated interplay between

host governments and oil companies pursuing different objectives under

different perceptions of risk and uncertainty implies that there may be very

long lags between rising prices and more exploration. In many countries no

such relation at all can be observed and some of the most crucial factors turns

out to be the political aspects of petroleum jurisdiction, the contractual

conditions and tax regimes of each respective country.

Another factor of increasing importance for the general costs of energy

supply

is

environmental effects. In Chapter 9 Lars Bergman illustrates the

differences between a partial and a general equilibrium approach to modelling

environmental control cost functions. The example chosen, that of sulphur

dioxide (S02),

is

of particular relevance for the competition between different

potential sources of energy supply. Bergman contrasts the 'true' (but hard

to-calculate) general equilibrium cost, which takes into account all mechanisms

of adaptation within the economy, with various simpler cost functions. One

such function, that may lead to considerable overestimation of environmental

costs, assumes that energy demand is fixed at a certain level and then proceeds

to calculate the additional cost (for instance) of

SOz

reduction by

fuel

switching

without recognizing that higher energy costs should be allowed to induce a

lower overall level of energy use.

1.4

ENERGY AND

THE

ECONOMY

Recognizing that energy markets milst be analysed within the framework of a

complete model of energy-economy interaction leads us to a choice between

large and very complicated models on the one hand or interconnected groups

of models on the other. Chapter

10

by Capros, Karadeloglou and Mentzas

illustrates this point: in fact their contribution

is

almost two papers since the

Appendix

is

a very instructive survey of a number of energy and macro

economic models used, in different combinations, by the European Community

(EC)

to analyse economic aspects of environmental and energy issues.

The main body of the chapter itself is an application of the HERMES,

neo-Keynesian, multi sectoral model to the macro- and microeconomic policy



6

Introduction

issues related to falling oil prices in oil-importing countries. The main question

posed

is

whether domestic taxes should be increased to stabilize the domestic

product prices and, if

so,

how the resulting tax proceeds should be used. A

particularly attractive feature of this chapter is that it first explores the

analytical solutions of a simplified HERMES model and then goes on to present

the actual numerical scenario results using a full, empirical model for Greece.

HERMES is also one of the

five

major energy--economy models that has

been developed for France. In Chapter 11, Ghislaine Destais shows that

French modelling has a number of distinctive features: public sector involve

ment

(as

opposed to the significant proportion of consulting firm models in the

United States), and particular emphasis on the structure of the economy

(capacity utilization, post-Keynesian rather than neoclassical mechanisms, etc.).

Destais starts

by

introducing a useful typology with which she compares the

different models with respect to their level of aggregation, consumption and

production functions: 'bottom-up versus top-down', 'technical versus

econometric', supply functions and time horizon. She then points out that blind

reliance on anyone model is overly naive: many of their 'results' are merely the

transcription of the initial hypotheses adopted. A useful warning

is

provided

by

an example in which three models analysing the same oil-shock

give

completely different results.

Soviet energy planning in theory and in practice

is

the subject of Chapter

12

by Yuri Sinyak. Obviously this being the home of central planning, there was

originally considerable enthusiasm about the computerized planning of the

energy sector. As Sinyak writes, this sector was one of the first to be planned

in this way and the unique feature compared with Western modelling

is

the

web of models from perspective, federal, long-run projections, through the

national and sectoral 5-year plans and down to actual operative yearly or even

minute-to-minute load-management models for individual power stations. The

original belief that such complex systems could be optimized centrally has,

however, turned out to be an illusion. Interesting attempts

at

iterative solutions

of blockwise disaggregated models are carried out but are hampered by the

poor quality,

as

usual, of data, the lack of workable prices and apparently

by

the poor motivation of the researchers.

In the second half of his paper,

Sin

yak describes the contents of the current

Energy Plan and one

is

struck by the disparity between advanced methodology

on the one hand and poor quality of empirical evidence on the other.

Naturally, the usual difficulties are exacerbated by current uncertainties in the

Soviet Union. The forecasts are based on growth and system continuity (albeit

with big improvements in energy efficiency) but in reality the Soviet future will

depend decisively on the development of its economic system. Although much

mention

is

made of increased energy conservation through decreased material

intensity, the main focus of the Energy Plan is still supply. A supply, however,

that is coming up with many 'unplanned' surprises: scientific criticisms of

nuclear power, ecological barriers to hydro development, labour unrest in the



Forecasts

and

environment

7

coal

fields,

regional and transport infrastructure bottlenecks, etc. Thus the

actual future of, for instance, Soviet energy exports is at present very hard to

foresee.

1.5 INTERNATIONAL ENERGY MODELS, FORECASTS AND

ENVIRONMENT

Having started with individual market demand and supply models and then

looked at national energy-economy and forecasting models, we turn now to

world energy models and their applications. The next group of chapters, 13-16,

is

intended to provide an overview of how world oil models work. Chapters

13

and

15

present two such models while 14

is

a comparison of the elasticities

across models and 16 compares the latest set of forecasts using the models

participating in the

1989

International Energy Workshop. The final two

chapters of the book are applications of energy-economy models to environ

mental issues.

We start with Patrick Criqui who, in Chapter

13,

presents two models from

the IEPE institute in Grenoble: the medium-term SIBILIN and the long-term

POLES currently being built. The SIBILIN is based on the simulation of

national energy balances (for all the major energy consumers - others being

represented in a simplified aggregate

way).

The models are resolved by

simulation, as opposed to optimization, and both have an ambitious degree of

disaggregation,

by

sector and geographically. Given the level of detail -

and the length of the time horizon - they are basically more 'bottom-up' than

'top-down' which does not, however, preclude a number of 'hybrid' traits in the

model, notably the

use

of econometric relationships and price elasticities in

many subsectors.

The philosophy behind these modelling choices is that the models are to be

used as a mental aid in scenario analysis: detailed balances force the researcher

to be consistent in

his

scenarios but the evaluation of the many decisive

political processes

is

too complex to be left to any optimization or other

algorithm. This is nicely illustrated by an analysis of possible

OPEC

strategies

of price defence or defence of market shares. The POLES model promises to

be an exciting extension since it will keep and even increase some aspects of

the demand-side detail of the SIBILIN while endogenizing prices and supply

and tuning up the model for long-term environmental analysis.

Most forecasters would agree that models cannot be allowed to do all the

forecasting for them - the individual researcher must assume his or her

responsibility. The unfortunate implication of this is that when forecasts are

different it is often hard to disentangle which of the differences depends on the

models used and which on different hypotheses, assumptions or other adjust

ments made by the researcher. Thus the efforts of the Energy Modeling Forum

are invaluable since they allow for systematic comparison using common



8

Introduction

hypotheses. In Chapter

14,

Dr Huntington has found an extraordinarily

succinct way of summarizing the differences

(as

well as a certain degree of

consensus) between models by concentrating his presentation on demand and

supply elasticities.

Most of the models analysed by Hill Huntington are again recursive

simulation models in which three groups of agents - oil consumers,

OPEC

and

non-OPEC oil producers - have different response functions determining the

logic of scenario build-up. But two of them (DFI-CEC and ETA-MACRO, see

Chapter 18) are optimization models requiring some form of perfect foresight

by at least one agent.

All 11 models were represented 10 the Energy Modeling Forum (EMF-11)

comparison, and the elasticities were inferred from the differences in outcome

between high/average growth rate scenarios and between fiat/rising price

scenarios respectively. No changes in domestic taxes on petroleum products

were assumed (such as are being discussed with respect to environmental

effects). All

the scenarios show increasing demand on OPEC oil though there

is

quite a difference between the constant price/high growth and the rising price

(doubles by 2000) combined with low growth scenario.

The average results for demand were short-run price elasticities of -0.1 and

long-run elasticities of -0.4 which, considering the differences in crude and

product price, fit in reasonably well with what was found in Chapter 5 for

gasoline. Income elasticities averaged 0.8 but there were two separate groups

of models, one with values around 0.6 while many others had unitary income

elasticity. Generally the latter assumed some form of Autonomous Energy

Efficiency Increase (AEEI). Average supply elasticities started very low for the

short-run (0.03) but rose to around 0.4 in a long-run perspective.

Certain patterns of elasticities between models can be observed. Some

models have low elasticities of all types, some high. The World Oil Market

Simulation Model (WOMS) is an example in which both inferred price

elasticities of demand and supply are fairly low while the income elasticity of

demand is close to unity, thus implying that this model places some emphasis

on trends in autonomous technical development. In Chapter

15

WOMS is

described in detail, both the workings of the model and the estimation of its

main functions. The model is then used to carry out a fairly similar analysis of

the same question of OPEC strategies as that discussed by Criqui in Chapter

13.

In Chapter 16 Alan Manne and Leo Schrattenholzer summarize and discuss

the projections from the different models participating in the 1989 lEW. Over

the period

1 9 8 5 ~ 2 0 1 0

the median projections follow a

3-2-1

rule: economic

growth of 3% per annum, energy consumption increase of

2%

and just 1% for

oil consumption. One way of explaining these figures is to say that there

is

basically a unitary income elasticity of energy complemented by an AEEI of

1 % per year. This explains the 2% energy growth, and finally a price-induced

substitution effect explains lower growth for oil. Median projections for oil



Forecasts

and

environment

9

price imply a yearly growth rate of 1.5%, suggesting a price elasticity of about

-0.67 which

is

on the high side but not wholly inconsistent with the values

found in Chapter 14. An interesting point to note

is

that while all forecasts for

oil consumption are fairly consistent, the price forecasts vary widely, which

shows that people still basically think in terms of physical trends and the role

of prices

is

secondary at best. Average price projections in the

lEW

polls show

that researchers seem to believe in rising real prices

(by 1.5

or 2% per annum).

Whether this reflects expectations as to costs or other factors

is

not clear but

it may be yet another example of Hotelling's considerable influence on general

thinking in this area.

One of the gloomier aspects of these polls

is

their implicit median forecast

for global carbon emissions, which are expected to increase by more than 50%

by the year 2010. This

is

in sharp contrast to the rather drastic reduction

which, according to many scientists, would be necessary to moderate climate

change. In Chapter 17, Gunter Stephan illustrates how the level and distribu

tion of economic costs of reducing emissions (either by emission charges or, in

his

case,

by

imposing standards) may be calculated using a Computable

General Equilibrium (CGE) model. One distinguishing feature of this model

is

its Neo-Austrian approach to capital formation and one of its results

is

similar

to the conclusions from Chapter

3:

that in vintage type structures, the costs of

environmental improvements depend on the rate of turnover of capital equip

ment. Stephan also shows that even if total costs are not all that high, the

impacts on income distribution may be quite considerable.

Returning to the case of carbon in our final chapter, we do unfortunately

know that in this particular case emission reductions are expensive indeed. We

choose to conclude the book with this chapter not only because of the topical

nature and possible importance of the issue itself but also because its analysis

in a very challenging way brings together almost all the various aspects of

resource availability, supply cost, demap.d elasticities, model structure and

forecasting discussed in previous chapters.

In order to analyse the cost (in the United States) of a 20% reduction in

carbon emissions, Manne and Richels use the ETA-MACRO model. This, as

mentioned above,

is

not a recursive simulation model but one that builds on

intertemporal optimization. As such it

is

particularly appropriate for a dis

cussion of long-run costs assuming a far-sighted choice of policy. This

is

just a

first, although very important step. As the authors point out, a worldwide CGE

model would open up also the possibilities of analysing the distribution of costs

among different nations of various policy instruments such as carbon quota

rights. In order actually to get a handle on costs, the authors make a series

of assumptions on the costs and carbon emissions associated with various

alternative methods of producing available energy. Similarly, assumptions are

made on growth in the economy and specifically in energy demand. In order

to minimize the risks (discussed

by

Bergman in Chapter 9) of using an all too

partial supply-oriented cost function, Manne and Richels analyse the cost of



10 Introduction

carbon emission restrictions under various scenarios. Making optimistic as

sumptions as to technical progress (AEEI of 1%) as well as optimistic

assumptions as to the cost and availability of alternatives (such as coal

gasification with CO

2

removal and deposition or advanced nuclear power) the

discounted cost from the year

1990

to 2100 would be

$0.8

trillion. With the

most pessimistic assumptions this cost would be $3.6 trillion.

While this obviously is an enormous cost, it in no way implies a reduction

in present consumption levels. Only their rate of growth is affected. In the

$3.6

trillion case, consumption from 2030 to 2100 would still grow but lie roughly

5% lower with the carbon limit than otherwise. The required policy can be

illustrated in terms of the necessary carbon tax of around $250 per tonne. It is

not easy to judge whether these costs would be strictly motivated by the

potential damage of global warming since the scientific knowledge on the latter

is still too limited. The costs could of course be seen as an insurance premium.

(particularly if we consider the opinion of those who argue that we actually

need even more drastic cuts in emissions). It is anyway clear from the

magnitude of these costs that

we

still need to further our understanding by

research into increased efficiency, both in the supply and demand technologies

of the energy sector and their interconnection with the rest of the economy.

REFERENCES

Brooks, H. et

al.

(1979) Energy

in

Transition:1985-20JO. Committee on Nuclear and

Alternative Energy Systems, National Academy of Sciences, Washington, DC.

Hafele,

W. (ed.)

(1981) Energy in a Finite World. IIASA, Laxemburg, Austria.

Landsberg, H. H. et al. (1979) Energy: The Next Twenty Years, Ballinger, Cambridge,

Mass.

Manne, A.

S.,

Richels, R. G. and Weyant,

J.

P.

(1979)

Energy Policy Modelling: A

Survey. Operations Research,

27, 1.

Schurr, S. H., Netschart, B. c., Eliasberg, V. F. et

al.

(1979) Energy

in

America's Future.

Johns Hopkins University Press, Baltimore, Md.

Stobaugh, R. and Yergin, D. (1979) Energy Future. Random House, New York.



2

Forecasting industrial

energy use

Gale A. Boyd

2.1

INTRODUCTION

The term 'model' is a fragile one. To some it means a theoretical methodology

with equations and definitions, to others it is a simple mental reasoning

process, and to still others a computer with data bases and algorithms. In this

chapter

we

consider computer-based models that are used to forecast energy

use, but

we

will only describe their theoretical methods and underlying mental

reasoning processes. The technical descriptions of the algorithms and data

bases are left to their creators.

We

also consider only models whose primary

focus is forecasting. These models are often the arms of consulting firms and

government agencies. This paper draws much of its information base from the

Energy Modeling Forum Study 8 (EMF-8) on Industrial Energy Use and

Conservation. Some example forecasts from this study are presented as

well.

Other forecasting models that are not included in EMF-8 have been added to

the list of EMF 8 participants for presentation and discussion. This paper is

not intended to

be

a comprehensive

review

and the author apologizes in

advance to those modellers whose modelling activities have been overlooked.

It

may

well

be said that models, like certain mathematical objects, may be

completely described by a sequence. In this case the sequence

is

the questions

or problems that have been posed to the modeller. That sequence may

converge until the model's approach is clearly defined or the model may still

undergo evolutionary changes as new questions are posed and new information

in the form of data and methods is incorporated. The questions may determine

the number and type of industry sectors

(i.e.

aggregation level), the time

horizon (short, medium, or long), or the linkages the model has to energy

supply or economic feedback. The common, underlying question that all

industrial energy models

face is

how to treat the diversity that typifies this

sector; what aspects of industrial energy decision-making to abstract from;

which parts to overlook; and which parts to model in detail. All of the models



12

Forecasting industrial energy

use

start with a few common elements, energy prices and industrial growth. From

that point on they diverge on the path finally to arrive at some outlook

for

the

amount and composition of energy consumption at some future date.

2.2 FACTORS THAT INDUSTRY CONSIDERS IN

MAKING ENERGY DECISIONS

Trends in industrial energy use are affected by more than just cost; this

is

important to the behavior of computer models for industrial energy use. The

engineering, or 'process', models are often based on the life-cycle cost of a

generic application and include parameters to avoid extreme ('knife edge')

switching to the fuel that the model calculates as the least-cost. A market share

approach yields a distribution of different fuels, with the distribution sensitive

to relative costs. A dynamic approach is necessary to reflect momentum and

capital stock turnover rates in the process of adjusting to better fuel choice

options.

The following are some of the factors often cited by industry in the United

States as affecting

fuel

choices for new capacity or fuel-switching decisions for

existing capacity:

• Fuel capability, condition and age of existing equipment

(e.g.

boilers,

furnaces, coal handling equipment).

• Site-specific constraints such as availability of land for a coal pile and boiler

firing type. This was cited numerous times in the 1975 Major Fuel Burning

Installation (MFBI) survey (Alter, 1978).

• Direction and magnitude of change in fuel prices, e.g. world oil prices,

OPEC's ability to stabilize prices, effects of natural gas deregulation and

effects on delivered coal prices of railroad deregulation under the Staggers

Act.

• Availability of

fuel.

• Cost of capital (which

is

very important for capital-intensive investments and

for discounting future fuel savings), financial position of the firm, and

priorities

for

types of investments within the

firm.

The cost of capital for the

firm depends on the risk the firm faces in the marketplace.

• Regulatory uncertainty (for example, related to the enforcement of the Fuel

Use Act 1978), future pollution regulations such as the Industrial New

Source Performance Standards for

new

boilers or acid rain controls, and the

future regulatory and institutional environment for cogeneration.

• Reliability and performance of new technologies such as the atmospheric

fluidized bed, coal/water mixture (CWM), cogeneration and combined

cycle.

• Downtime to retrofit to coal or CWM, availability of replacement energy

during downtime, and resulting boiler derating.

• Coal experience. (The relevance of coal experience is unclear for long-term



How models deal with fuel choice 13

forecasts, because lack of experience can eventually be overcome if coal

is

fundamentally a better

fuel

option. Other 'hassle factors' are sometimes

mentioned in association with the use of coal.)

• Scale of operation.

These uncertainties, as well as the volatility of energy markets and policy

over the past ten years, all contribute to a cautious, wait-and-see attitude by

industry. In a previous paper, the value of this attitude was estimated

quantitatively (Hanson and Bauer, 1981). Models can incorporate these factors

implicitly or explicitly.

2.3

HOW MODELS

DEAL

WITH

FUEL CHOICE

Two basic model types are used to predict industrial fuel choice: process and

econometric. However, there is a grey area in modelling when both techniques

are applied. When some model uses a significant portion of

both

methods, the

term hybrid model is used. Models may use a bottom-up approach, computing

each component of energy demand (fuel choice for each sector

or

service

demand category) and adding them up to arrive

at

a total. An alternative

approach

is

'top-down'. In this case a total demand

is

computed and then

shared into

fuel

types

or

other categories. Significant opportunities for a hybrid

approach are available here as well.

For

example, electricity demand may be

determined

at

a 'bottom-up' sectoral or service demand level, but the decision

to cogenerate the electricity may be based on some 'top-down' sharing

procedure. The characteristics, advantages and disadvantages of process and

econometric modelling are described below.

2.3.1

Process models

These models explicitly list and characterize (by cost, performance etc.) all the

alternatives in a

fuel

choice decision. Once each alternative is characterized, the

model decides which is most economically attractive. The selection criterion is

usually lowest levelized cost.

For

example, the model

is

told how many tons of

steel must be produced; it must then decide the most attractive process and

energy-using technologies to employ. This process is inherently 'bottom-up' in

its approach. The listing of all of a company's alternatives and all of the factors

affecting the decision is enormously complex. In this lies both a strength and

a weakness. The strength is that new and emerging technologies may be

included in the list of alternatives, so that the model can be 'forward looking'.

However, process models must also compromise since the selection of a single

'best' technology is not always the result that occurs in the real world. Process

models can use several techniques like:

• Replacement of 'preliminary market shares' based on cost minimization with



14

Forecasting industrial energy use

'final market shares' that account for random (i.e., other unobserved) factors

through statistical sampling

• Behavioural lags used as surrogates for the many factors not considered

explicitly

• Random distributions for costs and other equipment performance character

istics.

Suppose a model estimates a 'preliminary market share' of 10% for a

technology or process. However, in 1985 this process may only have a 5%

market share. Without behavioural lags, the process model would predict

immediately (in its next forecasting year, say 1990) that the market share would

be 10%. Behavioural lags are an ad hoc method for adjusting the market share

from its current value

(e.g.

5%) to its predicted long-run value (defined above

as 'final market share'), which is 10%. For example, with adjustments for

behavioural lags, the market share would gradually approach the predicted

10% share, but not until the year 2000. In the marketing literature these

behavioural lags are studied extensively for individual products. The length of

the behavioural lag varies considerably

by

product, but can be as long

as

10-15

years.

A behavioural lag is defined as the time between the availability of a

new

competitive process and its adoption. Behavioural lags are surrogates for many

undefined or unknown factors that affect choices of

fuel

type and technology,

such as some of those listed above. They also reflect the desire of most firms

to follow, rather than lead, in market innovations. No firm, unless it is the only

beneficiary, wants to be the first to work out the 'bugs' of a new technology.

Random distributions of various costs of energy-using equipment can be

used to determine market shares of technology choice, rather than a 'winner

take all' knife-edge type of decision. These market shares can be obtained by

convolution of the cost distributions or, more commonly, via Monte Carlo

type techniques.

Process models may also use econometric (statistical) techniques to project

energy choice, particularly

fuel

choice. The distinction between process and

econometric is very fine in this case. However, because the projection is made

at the 'process' level rather than the 'industry' level, the model is still termed

'process'.

2.3.2

Econometric

models

Econometric models predict fuel choice

by

statistically analysing historical

relationships. They predict energy levels (or shares) from a given set of data on

energy, capital, labour prices and industrial output. Theoretically, econometric

models can capture the factors affecting fuel choice

by

assuming that those

factors continue into the future. That is, the models assume that our best guess

of the future

is

a continuation of past relationships. (Process models, in their



H

ow

models deal

with fuel

choice

15

attempts to represent the fuel choice explicitly, invariably omit factors and

hence produce biased results. However, process models may incorporate

econometric-like methods.) The US Department of Energy's Energy Informa

tion Administration feels that process models tend to be overly optimistic on

coal use and conservation (improvements in energy intensity).

Econometric models may be classified into two general categories; ad hoc

specifications and optimization-based. Ad hoc models use functional specifica

tions that relate energy use (or shares) to a set of explanatory variables that

impact energy use patterns. These variables usually include energy prices,

industrial activity, as well as other variables. Some popular ad hoc specifica

tions are linear, log linear and logit. Optimization-based models assume some

type of underlying production process and maximization behaviour and derive

the relationship between energy input demands and various prices and activity

variables. The basic form this approach takes

is

based on duality. Two specific

examples of the ad hoc

vs.

the optimization approach are examined in a

methodological experiment in Smith and Hill (1985). Examples of these

approaches and the background are presented below.

Ad hoc econometric models specify a simple functional form and estimate

the relationship between energy use (total level, shares or ratios) and relaxant

variables like energy prices, prices of competing energy forms, industrial

activity, specific technology variables and capacity utilization. Simple

econometric specifications may be used to derive short-run

vs.

long-run

relationships. Use of lagged variables, both dependent and independent,

is

a

possible specification. The emphasis

is

on the 'reasonableness' of the coef

ficients in terms of sign and magnitude and on goodness of fit. In other words,

'correct' signs for own-energy price coefficient (negative) and for cross-price

(competing) energy coefficients (positive) are the most important. Although the

functional form is usually linear in parameters for convenience, another

popular ad hoc approach is the

use

of the multinomial logit function.

The multinomiallogit function

is

based on a probabilistic choice model. In

other words, it assumes that the prices of energy and other institutional and

technological variables affect the decision-makers' probability of choosing an

energy form. This model can be used to derive a function that relates the ratio

of the share of two or more competing energy forms to the prices. Other

variables that affect the probability of choosing one energy form over another

can also be included in the model.

Optimization models assume a general form for an underlying production

process and that the decision-maker

is

cost-minimizing (or profit-maximizing).

Optimization models usually rely on duality theory and Shephard's lemma

(Shephard, 1970) that the optimal demand for an input (X)

is

the first

derivative of the cost function that is dual to the assumed production function.

Studies that examine total energy use (E) or total fuel and electricity use (EF)

often include capital, labour and materials

(KLM)

in prices included in the cost

function (C).



16

Forecasting industrial

energy

use

Total cost

=

qu,

P

K

,

P

L

, PE, PM)

or = qu, P

K

, P

L

, PE, P

F

, PM)

bC

Demand for X

=

~

uP

x

The demand system may be of energy types only,

by

assuming the energy

inputs are weakly separable or the system may include labour, materials and

capital inputs. Studies that seek to relate the choice of energy form, e.g. natural

gas

vs.

fuel oil

vs.

coal, usually assume that the energy form is weakly separable,

for example some aggregator function

(G) is

chosen.

This is often done due to data limitations. Whether materials is treated as an

input depends on the assumption of separability of material from other inputs.

If materials

is

separable then value-added

is

used as the measure of industrial

output.

I f

not, then total shipments may be used to measure industrial output.

Optimization models usually rely on an assumed form of the dual cost function

and derive a system of demand equations via differentiation. Common forms

of the cost or aggregator function are the so-called flexible functional forms,

which are based on second-order Taylor series expansions in output (U) and

prices

(P).

Specifying the function to be the natural log

gives

the TRANSLOG

form (1); specifying it

n n n n

C= L etJ(P

i

)+ L L fJiJ(PJf(Pj)+ 'hg(T)+ L M(T)f(Pd

i= 1

i=lj=l

i= 1

n

+

buf(U)

+ L

bJ(U)f(PJ

(1)

i= 1

to be the square root gives the Generalized Leontief form.l Shephard's lemma

applied to these forms yields cost share equations that are linear in the

parameters, hence estimatable with commonly available econometric packages.

2

Many empirical studies show that energy and capital in the aggregate have

tended to

be

complementary. When energy prices rise, there

is

less incentive to

automate, because automation requires energy-using capital. Similarly, when

capital prices

fall we

tend to invest in labour-saving devices, which increases

total energy use. There are specific conservation technologies for which capital

and energy are substitutes, but overall the complementary relationship (e.g.

automation) has dominanted the substitutability relationship

(e.g.

conserva

tion).

For

example,

see

Solow (1987) for a discussion.

1 Many variations of these forms involving technology (time) trends, homotheticity assumptions etc.

are possible. Only the simplest example is shown.

2We will

not discuss the additional economic or econometric applications in detail.



Selected models of industrial energy use

17

Econometric models may include a technology (time) trend

to

capture other

factors

that

increase energy use, such as continued electrification

and

the

long-run historical trend in this century toward automation and energy

intensive capital stock, because these models naturally identify trends and

continue them. Similarly the trends that contribute to lower energy use may

be included in a technology trend as well. Optimization models that treat the

technology trends differently for different inputs are called

factor-biased.

For

example, the factor-biased technical change in electricity may be electricity

using while the technical change in labour or fossil fuels may be

input-saving.

The performance of econometric models depends on the crucial assumption

that the analysis period represents the future.

For

example, if

we

ignore output

(U) and consider g(T) as a function of technology trends the signs of Yi

above determine the direction

of

technical change bias.

2.3.3 Similarity between process and econometric models

Both process and econometric models have the same purpose: prediction of

industrial fuel use, by fuel type. They

both

consider the same main factors:

• Industrial growth (measured by shipments [gross output], value-added or

industrial production indexes).

• Fuel prices.

• The underlying production process.

• Behaviour

of

the decision-maker.

2.4

SELECTED MODELS OF INDUSTRIAL ENERGY USE

The remainder of this chapater describes a number of industrial fuel-choice

models. Some of these models were participants in a study performed by the

Energy Modeling Forum. These models, by type, are:

Process models

ISTUM-l

ISTUM-2

PILOT

Econometric models

INFORUM

PURHAPS

Hybrid models

ORIM

A few additional models that have come into use recently

or

that simply did

not participate in the EMF-8 study are:



18

Process models

ICE

Econometric models

AEO-PCIM

INDEPTH


HJGEM (Hudson/Jorgenson General Equilibrium Model)

HKGEM (Hazilla/Kopp General Equilibrium Model)

Hybrid models

INRAD

The Industrial Sector Technology Use Model (ISTUM-1) (US Department

of Energy,

1979)

is a set of engineering and economic modules that integrate

information on industry characteristics,

fuel

prices, economic forecasts and

historical energy demands to develop predictions of industrial energy

use.

This

model is designed to project market penetration of energy technologies in the

industrial sector 20-30 years into the future.

The ISTUM-1 model

is

designed to test alternative scenarios and

is

used

frequently

for

various types of sensitivity analyses. The model can evaluate the

commercial viability of energy technologies in the industrial sector and test

their sensitivity to changes in macroeconomic activity, fuel price fluctuations

and

new

environmental regulations. It can analyse the fuel choices of the

industrial sector, including changes due to various economic and policy

scenarios.

The second Industrial Sector Technology Use Model (ISTUM-2)

(US

Department of Energy, 1983) is the result of a 4-year effort managed by the

Energy Productivity Center at Mellon Institute in cooperation with Energy

and Environmental Analysis, Inc. An analytical framework was developed to

address a wide range of policy issues related to industrial energy use. This

model is primarily a tool for determining how to provide energy services to the

industrial customer who bases purchase decisions primarily on cost.

The ISTUM-2 model produces output that provides much information on

industrial fuel and technology use. The model provides:

• Forecasts of industrial energy demand by service category, including indus

trial growth as well as changes in fuel mix.

• Projected fuel demands by ten federal regions.

• Projected market penetration for various technological options to improve

energy productivity.

• Evaluation of the impact of government policy on industrial fuel

use.

• Behaviour analysis that identifies, on an industry-specific basis, the factors

most important to industrial decision-makers.

The ISTUM-PC model (Jaccard and Roop,

1990) is

based on the ISTUM-2

code, but ported to the PC with data updated primarily for Canada. This

model has substantially the same structure, but

is

presumably more 'accessible'

than its mainframe predecessor.



Selected models

of

industrial energy

use 19

The Oak Ridge Industrial Model (ORIM) (Reister et

ai., 1980)

was initially

developed at Oak Ridge National Laboratory to help the Energy Information

Administration prepare mid-term (5- to 20-year) projections of industrial fuel

demand. The model forecasts demand for six industrial fuels: distillate fuel oil,

residual fuel oil, liquefied petroleum gas (LPG), natural gas, coal and elec

tricity. Demand

is

projected for 12 industrial sectors (nine manufacturing and

three other) in each of the ten federal regions in 5-year increments. The model

bases its projections on both statistical and engineering data. Types of forecasts

include energy demand by fuel, region, industry, energy service and vintage of

capital stock.

The following information is needed for input: (1) costs of capital, labour and

materials for each type of energy technology (for example, natural gas combus

tor, coal boiler, etc.) and (2) prices for the six fuels. Because fuel prices

sometimes change considerably from region to region, decisions based on

fuel

cost are also simulated separately for each of the ten federal regions. Outputs

from the model are regional, industry-specific fuel and electricity projections

for each five-year period and for four energy services: heat, steam, mechanical

drive and uniquely electrical service.

The Stanford Planning Investment Levels Over Time (PILOT) En

ergy/Economic Model

is

a l a r g e ~ s c a l e dynamic programming model that

calculates the time path of investments, production, consumption and imports

and exports so that the prices and quantities of all commodities make supply

and demand equal for the commodities. Policy issues addressed

by

PILOT

include the scheduling of various energy technologies to be built and used.

pollution abatement equipment to be installed and the nature and extent of

conversion to equipment types that use energy more efficiently. The model was

originally built to study interaction between the energy sector and the

macro economy.

Energy demands of the economy (industrial processing, consumers, exports,

government needs) are met in five energy forms: coal, crude oil, oil products,

natural gas products and electricity. The model contains detailed descriptions

of energy technologies; explicit descriptions of the depletion processes for oil,

gas and uranium; dynamics of capital formation and resource extraction;

accounting for trade with the rest of the world; and national consumption

trends.

The INFORUM Long-term Interindustry Forecasting Tool Model is an

input-ouput model developed in the University of Maryland's project for

interindustry forecasting. The model determines constant-dollar output for

78

sectors of the economy, consistent with given levels of final demands. These

final demands are forecasts of INFORUM's macroeconomic model. Inputs to

the macromodel include forecasts of population growth, government expendi

ture, money supply and primary energy and material prices. The

INFORUM

model also estimates

fuel

use by industry. Unlike models that represent only

the industrial sector, INFORUM ensures complete supply and demand bal

ance in energy use.



20


The Energy Information Administration (EIA) within the

US

DOE created

the Purchased Heat and Power System (PURHAPS) (US Department of

Energy, 1983) to project industrial energy demand by year for six major fuels

(electricity, coal, natural gas, distillate oil, residual oil and liquefied petroleum

gas).

The PURHAPS model

is

econometric - its estimates are based on

historical data.

Forecasts are made for 17 manufacturing industries and eight non-manufac

turing sectors at national, federal region and state levels. Industrial energy

demand is derived

as

a function of industrial production, current fuel prices

and price response lags (due to slow turnover of capital stock and other sources

of momentum). Other variables accounted for in predicting energy demand are

prices of labour, capital and materials;

effect

of economic recession; time trends;

and difficulties in using coal in small-scale industries. The basic equation

structure is hierarchical. Total energy demand

is

determined first, then a series

of energy shares are computed based on logit function estimates, e.g. energy is

shared into electricity and fossil fuels, fossil fuels are shared into coal

vs.

oil/gas,

etc. This makes PURHAPS a top-down type of model. The PURHAPS model

has been used extensively as a component of the Intermediate Future Forecast

ing System (IFFS), the EIA model on which the Annual Energy Outlook (US

Department of Energy, annual) was based until 1988 when a

new

model

AEO-PCIM was developed.

At the core of PURHAPS are two models: the manufacturing model and the

agriculture, construction and mining model (henceforth called the ACM

model). The manufacturing model produces econometric forecasts of energy

demand for

17

manufacturing industries at the two-digit Standard Industrial

Classification (SIC) level (all two-digit SIC industries are included except for

refineries; chemicals and rubber are combined as are textiles and apparel). The

ACM model predicts demand for eight separate non-manufacturing sectors

that have accounted for the bulk of growth in industrial electricity for the

period 1974-81.

Among the major inputs for PURHAPS are prices for each fuel

by

state. For

each industrial sector the real gross output by state and for the United States

as a whole

is

input. Prices of capital, labour and intermediate materials are also

input for each industry nationwide. Major historical data sources for model

inputs are the Bureau of Labour Satistics' Time-Series Data for the Input

Output Industries (published annually) and the Annual Survey of Manufacturers

(Bureau of the Census, annual)

PURHAPS

is

no longer used by EIA for forecasting. In its place

is

the

Annual Energy Outlook PC Industrial model (AEO-PCIM). This model is

based on a similar level of sectoral aggregation as PURHAPS, but without the

state-level detail. The structure of the model

is

ad hoc. Individual demand

equations are estimated and forecast without statistical or structural interde

pendence. For example, the oil equation would include industrial growth, oil

prices and gas prices, but is forecast without regard to the forecast for gas



Selected

models

of industrial energy

use

21

consumption. Total energy is bottom-up, rather than top-down. Unlike PUR

HAPS, no technology (time) trends are used

in

AEO-PCIM.

The Industrial Combustion Emissions (ICE) model (US Environmental

Protection Agency, 1988)

was

developed to forecast boiler fuel choice and

resulting pollution levels for the National

Acid

Precipitation Assessment

Program (NAPAP). While it does not cover the entire industrial sector and

has emission forecasting as its stated goal, the model uses several interest

ing features to forecast boiler

fuel

choice, i.e. coal vs. oil or gas. The model

incorporates process models features, but also uses econometric estimates. The

engineering factors that ICE considers are boiler size, utilization and the cost

of requisite pollution control devices. This data is used in addition to the

level

of industrial boiler

fuel

demand by seven industry categories in each state and

the corresponding fuel prices.

The Industrial Regional and Energy Demand (INRAD) model (Boyd,

Kokkelenberg and Ross, 1990) was developed as part of the Integrated Model

Set for NAPAP. INRAD

is

a set of econometric equations that forecasts

electricity and fossil fuel demand. The model

is

based on the Generalized

Leontief functional form with modifications to allow for capacity utilization

effects in the energy equations. Model estimates are based on national

level

data for eight industry groups, but are then benchmarked to state level energy

use and energy prices for

20

associated industries. The eight industries included

in INRAD are food, textiles, paper, chemicals, stone, clay and glass, steel,

aluminium, and all others. Forecasts of state

level

industrial activity and prices

indices for electricity, fossil fuel, labour, capital and materials are required to

drive INRAD. INRAD also incorporates penetration of two major electricity

intensive technologies, thermomechanical pulping and electric arc furnaces in

its forecasts. This makes this model a basically econometric model that

incorporates some process model features.

The Industrial End-use Planning Methodology (INDEPTH) is a series of

energy models developed by the Electric Power Research Institute (EPRI,

1990).

It

incorporates three levels of design. The 'top' level is based on

econometric model estimates. The 'middle' level

is

a process engineering model.

The 'bottom' level

is

an energy services model that operates at the level of the

local utility service area. INDEPTH is estimated at the national level for

202-digit SIC industries using several different flexible functional forms that the

user may choose from. Specifications include Generalized Leontief and

TRANSLOG in both a variable cost and KLEFM specification. The default

choice of functional specification differs from industry to industry. This is

somewhat of a departure in econometric models, which usually use the same

specification for all industries. The energy forms the

INDEPTH

models use are

the same as INRAD; electricity and fossil

fuel

aggregates. Regional detail has

been obtained in INDEPTH application within the Electric and Gas Utility

Modelling System (US Environmental Protection Agency, 1990).

Two general equilibrium models, one based on the work of Hudson,



22


Jorgenson and others (Hudson and Jorgenson, 1974, Jorgenson and Wilcoxen,

1990) (KJGEM) and the other based on the work of Hazilla and Kopp

(1990)

(KHGEM), both include within their structure energy demand equations based

on TRANSLOG cost-share functions.

For

each industry represented, a KLEM

cost function is estimated, E representing an energy aggregate. This energy

aggregate is broken into four energy forms, oil, gas, coal and electricity,

by

a

separable TRANSLOG aggregator subfunction. Unlike the other models

above, forecasting and analysis with these models allow for energy substitution

as well as feedback between the industry cost structure, energy prices and

industry growth. Both models represent

35

industries, corresponding to rough

ly the 2-digit SIC level of aggregation.

The key inputs and outputs of the models are summarized in Table

2.1.

All

of the models require some degree of detail on industrial sector activity inputs

(with corresponding detail on the output side). The industrial detail

is

usually

at the 2-digit SIC

level,

but some models, ISTUM-2 and INRAD, require 3-

or 4-digit detail for some large energy-using sectors. ISTUM-1, PILOT,

INFORUM

and AEO-PCIM are national-level models with varying degrees

of sectoral input detai1.

Forecasts of national-level industrial activity, energy

price etc. are usually more readily available and more easily adaptable to

scenario analysis. The remaining models require regional detai1. INRAD

includes some simple sharing routines to

go

from the national level to the

regionalleve1. Regional forecasts are

less

available from typical macroeconmic

forecasting services. This makes scenario analysis more difficult and time

consuming if consistent regional scenarios are to be constructed. The process

models require future outlooks

for

the performance of important process

equipment while the econometric models usually require forecast of capital,

labour and materials prices. Both of the inputs create their own problems in

forecasting. The ICE model accepts one input that

is

different from the other

models; environmental regulations and pollution control costs. As environ

mental controls become more important determinants of

fuel

and process

choice as well as overall energy

use,

more models will need to incorporate this

dimension.

Other characteristics of the models are summarized in Table 2.2. Almost all

of the models are long-run, with only two econometric models using ad hoc

treatment of short-run capacity utilization effects. The models are all FOR

TRAN code except ISTUM-1 and ISTUM-2, which are written in DYNAMO

and APL, respectively. None of the models include energy/economy interac

tions like the impact on higher energy prices on cost, hence industrial demand

or growth except PILOT,

INFO

RUM, HJGEM and HKGEM. PILOT

is

an

optimization model for the whole economy, including the energy sector, and

INFORUM is

an 10 model of the economy with a separate 10 'skirt' table to

account for fuel choice in various energy services. HKGEM and HJGEM are

general equilibrium models, which solve

for

an internally consistent set of

market prices and production for all industrial outputs, energy products being



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Conclusion

25

only one class of industrial outputs. These same four models include the supply

side explicitly in their forecasting approach, while PURHAPS, AEO-PCIM

and INRAD are usually run

as

part of some larger integrated system that

includes energy supply models.

Data sources for the major historical energy data bases are the Energy

Consumption

Data Base (ECDB) (EEA Inc.

1977)

for the process models and

either the Census Annual Survey, Fuels and Electric Energy Consumed

(CENSUS), or the National Energy Accounts (NEA). Some supplementary

sources and proprietary data-bases are also used. These are listed only as

'other'. They may be trade association data or results from other models. For

example ICE uses ISTUM-2 to calibrate its base year energy-use data which

is

derived from the American Boiler Manufacturers Association boiler sales

data. INRAD, ISTUM-l and ISTUM-2 all

use

Steel and Paper Trade

association data as well as other sources.

2.5

CONCLUSION

As shown above, several of the models were involved in a series of workshops

conducted by the Stanford Energy Modelling Forum.

EMF

Study 8 was

conducted to exercise the variety of models that participated under alternative

scenarios to gain insight into their similarities and differences and to under

stand issues facing both the industrial energy decision-makers and industrial

energy modellers. The results of EMF-8 are documented in a series of work

ing papers. The following observations on the participating models are from

the Final Summary Report for the study (EMF,

1987).

The models project that energy

use

per unit of industrial output will

continue to decline through

2010.

This trend is apparent in the results

from all the models for all scenarios. Energy use per unit of output

is

projected to decline by 0.5 to 1.5 percent per annum from 1985 to

2010.

The explanations for this trend are a shift towards the production of less

energy-intensive goods, the further penetration of

new

technologies that

are more productive in the

use

of all inputs, and a continuation of the

gradual adjustment to the rapid energy price increases of the past 15 years.

The much lower energy prices that emerged in 1986 may retard the last

trend, but will not significantly slow the other two.

The forecasted shift toward less energy-intensive goods

is

a function of the

macroeconomic inputs into the EMF-8 participants. This macro outlook was

another issue examined by EMF-8. The total growth of industry determines

the total growth in energy, conditional on the trend in energy intensity forecast

by the models

Despite this trend toward reduced energy consumption per unit output,



26

Forecasting industrial energy

use

growth in industrial output over the next two decades

is

expected to result

in a modest increase in total industrial energy demand over that time

period. Energy use for heat and power in industry

is

projected to grow by

1 to 2 percent per year from

1985

to 2010, or by about

40

to

80

percent

of the rate growth of industrial output

The models were in reasonable agreement that industrial growth would

outstrip the various improvements in energy efficiency.

The rate of growth of industrial energy demand in the future depends as

much on the projected level of total industry output and the projected mix

of energy-intensive and non energy-intensive goods produced as on

projected energy prices. The adjustment in total energy use per unit of

output to changes in energy prices tends to evolve over a long period of

time.

In

addition, long-term trends towards less energy intensive products

and towards more efficient use of all input in producing those products

continue somewhat independently of changes in energy prices, up or

down.

A significant source of disagreement in the models comes when one examines

the treatment of electricity demand.

Electricity use by industry

is

projected to increase more rapidly than its

use of fossil fuels. However, the engineering process models project a

gradual decrease in electricity

use

per unit of economic output (about -1 %

per year), while the econometric models show a gradual increase (about

+

1% per

year).

The econometric models assume that post-embargo trends

in the dependency of electricity use on fuel prices and output growth will

continue into the future. The process models explicitly represent individual

electricity-using technologies; thus, they can account for saturation effects

which could keep electricity growth rates below historical levels, but they

may not represent all future electricity-consuming technologies or subtle

process/product shifts towards greater or less electricity use.

This difference highlights the philosophical split between the models. INRAD,

which was not in EMF

8,

incorporates explicit penetration of two electricity

using technologies in its econometric framework. This type of hybrid may be

required in the future to address these types of forecasting concerns. Another

concern related to electricity demand

is

where the electricity comes from,

purchased power or cogenerated

Further penetration of cogeneration in the industrial sector would result

in more electricity used than purchased by the industrial sector. While such

penetration depends on a host of regulatory, institutional, and business

strategy issues, scenarios involving increased cogeneration are possible.

Thus, purchased electricity per unit of industrial output

will,

in fact, be

less than electricity consumption in industrial processes. Similarly, if some



Conclusion

27

cogenerated electricity

is

sold to utilities, this extra source of electricity

could also augment central electric generation in the decades to come.

The treatment of interfuel competition

is

another area that highlights the

differences in process versus econometric models. Process models rely on data

on dual firing capability, while econometric approaches rely on market-sharing

equations, like LOGIT or TRANSLOG cost shares. One captures important

near-term structural

effects,

the other captures some structural effects, but also

behavioural ones.

In the near term, the most intense interfuel competition

is

between oil

and gas.

- Most existing dual-fired capacity

is

oil-gas, with relatively low asso

ciated capital costs. Currently, gas

is

in

standard

use.

- There is a potential for greatly increased oil

use

in the industrial sector

in the medium-to-Ionger term if relative oil prices are low, although

there are indications that many users may not switch from gas to oil

as rapidly as aggregate

fuel

price data indicates.

The models' marked lack of energy/economy interaction was noted strongly by

the

EMF

participants.

The models included in this

EMF

study cannot be used to study dramatic

jumps in energy prices without external information/analysis regarding the

effects of these shocks on the overall economy.

- The most important effects of energy price shocks may be their

impacts on savings rates, inflation and economic output, which are not

explicitly represented in these models.

- Some of the models use 5-year time periods which does not allow for

a detailed representation of the macro dynamics of energy shocks.

As one can

see

from the above comments of the EMF-8 study participants

regarding sectoral shift and macroeconomic shocks, the need to integrate

industrial energy demand into an overall framework is an important avenue of

research. The class of general equilibrium models accomplishes this integration

completely, while other models that are used within some partially integrated

framework do so only partly. One important question remains unanswered

about the importance of this integration. That is the degree to which the same

factors, e.g. energy price shocks, impact on both energy decisions and industry

growth. I f these factors are different, then industrial energy forecasting can be

approached in a partial equilibrium framework, utilizing alternative economic

growth scenarios to measure the degree of uncertainty that the macro assump

tions give to any single forecast. However, if the same factors that affect energy

decisions are closely linked to industrial investment and growth, then the

partial equilibrium class of model presented above will need to move to a more

integrated, general equilibrium approach typified by the Hudson/Jorgenson

family of economic models.



28


ACKNOWLEGDEMENTS

The author would like to acknowledge the support of the United States

Department of Energy, Assistant Secretary for Fossil Energy, Office of

Planning and Environment, under Contract W-31-109-Eng-38. This paper

would not have been possible without the input of all the participants of the

Energy Modeling Forum Study 8 on Industrial Energy Demand Modelling,

particularly the model developers who provided comments on an earlier draft

review paper of the

EMF 8

models. I would like to thank ANL staff members

Ross Hemphill, Don Hanson and Don Jankowski for their input into that

earlier paper. Any omissions are entirely

my

responsibility.

REFERENCES

Alter,

S. L.

et al.

(1978),

Interim Validation Report: Major Fuel Burning Installation

System. Report prepared for

US

Department of Energy

by

Lawrence Berkeley

Laboratory, Berkeley, Ca.

Boyd, G., Kokkelenberg,

E.

and Ross,

M. (1990) Sectoral Electricity and Fossil

Fuel

Demand in

US

Manufacturing: Development of the Industrial Regional

and

Energy

Demand (lNRAD) Model. Argonne National Laboratory Report, ANLjEAISjTM-35,

Argonne.

Bureau of the Census (published annually) Annual Survey of Manufacturers. US

Department of Commerce, Washington, DC.

Bureau of Labor Statistics (published annually)

Time-Series

Data

for the Input-Output

Industries US Department of Labor, Washington, DC.

EEA (Energy and Environmental Analysis) Inc.

(1977)

Energy Consumption Data Base,

3 vols. TID-27981/TID-27986, TID-27988/TID-27990, TID-27992, Arlington, Va.

EMF

(Energy Modeling Forum)

(1987) Final Summary Report

for

EMF-8: Industrial

Energy Demand,

Working Paper. Stanford University Energy Modeling Forum,

Stanford, Ca.

Electric Power Research Institute

(1990)

Guide to

the INDEP1H Level I Econometric

M

ode/s:

Final Report. EPRI Customer Systems Division, Palo Alto, Ca.

Hanson, D. and Bauer, P.

(1981)

'Industrial Fuel Choice Under Uncertainty.' Presented

at ORSA-TIMS Conf.

Hazilla, M. and Kopp, R. (1986) Systematic Effects of Capital Service Price Definition

on Perceptions of Input Substitution. Journal of Business and Economic Statistics, 4,

209-24.

Hazilla, M. and Kopp, R.

(1990)

The Social Cost of Environmental Quality Regulation:

A General Equilibrium Analysis. Journal of Political Economy, 98(4), 853-73.

Hudson,

E. A.

and Jorgenson, D.

(1974)

US Energy Policy and Economic Growth,

1975-2000. Bell Journal of Economics and Management Science.

5,

461-514.

Jaccard, M. and Roop,

J. (1990)

The ISTUM-PC Model: Trial Application to the

British Columbia Pulp and Paper Industry. Energy Economics, pp. 185-96.

Jogenson, D. and Wilcoxen, P.

(1990)

'Reducing US Carbon Dioxide Emissions: the

Cost of Different Goals. Draft report, Harvard University, Cambridge, Mass.

Mansur, A. and Whalley, 1. (1984) Numerical Specification of Applied General Equilib

rium Models: Estimation, Calibration, and Data, in Applied General Equilibrium

Analysis (eds. H. Scarf and 1. Shoven), Cambridge University Press, Cambridge,

pp. 69-127.



References

29

Reister,

D.B.,

Edmonds,

J. A.

and Barnes,

R. W.

(1980)

The Oak Ridge Industrial Model:

vol.

II,

Model Description.

Oak Ridge National Laboratory, Oak Ridge, Tennessee.

Shephard, R.

W. (1970)

Theory

of

Cost and Production Functions. Princeton University

Press, Princeton, NJ.

Smith,

V. K.

and Hill

L. J. (1985)

Validating Allocation Function in Energy Models: An

Experimental Methodology. Energy Journal,

16,

29-47.

Solow, 1.

L. (1987)

The Capital Energy Complementarity Debate Revisited. American

Economic Review,

77,

605-14.

US


(1979)

Industrial Sectors Technology Use Model

(ISTUM),

vols 1-4, DOE/FE/2344-1 through DOE/FE/2344-4. Washington, DC.

US


(1983)

Industrial Energy Productivity Project: Final Report,

Vols 1-9, DOE/CS/40151-1 through DOE/CS/40151-9. Washington, DC.

US Department of Energy, Energy Information Administration

(1983)

A Statistical

Analysis of What Drives Industrial Energy Demand, DOE/EIA-0420/3. Washington,

DC.

US Department of Energy, Energy Information Administration (published annually)

Annual Energy Outlook, DOE/EIA-0383. Washington DC.

US Environmental Protection Agency (1988) Industrial Combustion Emissions Model

User's Manual, EPA-600/8-88-007a. Washington, DC.

US

Environmental Protection Agency

(1990)

'Electric and Gas Utility Modeling

System: Technical Documentation.' Draft report prepared

by

RCG/Hagler, Bailly,

Inc., Boulder, Col.



3

Best-practice

and

average practice:

technique

choice and energy

demand in a vintage model

Lennart Hjalmarsson and Finn R. Forsund

3.1 INTRODUCTION

Comparisons between best-practice use of energy and average practice have

become quite popular in debates and scenarios about future need for energy,

and sometimes fairly strong conclusions are drawn about long-run energy

demand on the basis of such comparisons

(see

for example Goldemberg et ai.,

1988

and Johansson

et

aI.,

1989).

Since in most sectors of an economy there

is

a substantial difference between the average and lowest energy input coef

ficients

(i.e.

the amount of energy used per unit output) an instantaneous

adoption of best-practice energy-using technology in all sectors would decrease

the use of energy radically.

However, most energy is consumed via capital goods and the longevity of

capital makes the transformation process from average practice to best-practice

an often slow and gradual one. Moreover, new and more efficient technologies

are embodied

in

new capital goods and the diffusion of new technologies

depends on the growth rates in different sectors. But a higher growth rate will

also increase energy demand. Therefore, the energy decreasing trend in the

input coefficients may be more than offset by the energy increasing impact of

a larger output volume so the net effect on energy demand cannot be

determined a priori. The purpose of this chapter

is

to study these offsetting

forces by analysing technology choice and energy demand in a vintage model.

The vintage approach

is

concerned with the dynamic process of structural

change in an industry producing a homogeneous good. Of particular interest

is the distance between best-practice and average productivity which is

determined by the development of the vintage structure over time through the

process of investments and the scrapping of old equipment.

Embodied technical progress yields a distribution of production units spread



The model

33

progress is represented by a Cobb-Douglas

ex

ante production function from

which the firms choose their input coefficients and capacity when investing in

new capacity. This

ex

ante function has wide substitution possibilities between

different inputs and is characterized by economies of scale, i.e. the elasticity of

scale exceeds one. Technical progress

is

assumed to be embodied in new

capacity. The firms in this industry are assumed to supply, in a cost-minimizing

way, a gradually expanding market by an optimal timing of investments in new

capacity.

On the basis of this process of capacity expansion we may investigate the

consequences for the choice of input coefficients and factor demand of different

assumptions about technology, relative prices and demand growth, deriving

the distance between best-practice and average practice productivity. Though

this may be regarded as a highly stylized model, it highlights the problem we

are addressing in a satisfactory way without loss of generality.

We shall assume the following:

1. Demand grows at a constant exponential rate g.

2. Initially there is just enough capacity, denoted

by

y(O,O), to meet demand.

3. The

ex

ante function at the micro level exhibits increasing returns to scale

and

is

a Cobb-Douglas function with neutral technical progress, labour,

energy and capital equipment. This

ex

ante function now reads:

(3.1)

where

and

y(v, v),

L(v,

v),

E(v, v)

and

K(v, v)

denote planned production

at

time v in vintage

v,

planned use of variable

inputs, labour, energy and planned capital investment respectively. Raw

materials are assumed proportional to output. b

is

the technical progress

parameter.

4.

The following functions describe the change in the factor prices:

i=L,

E, K

(3.2)

where qi

(0) is

the initial price.

5. Plant life and time horizon are infinite. This assumption makes the problem

more tractable without any significant loss of generality.

6.

Capacity utilization in the latest plant grows at the same rate

as

demand

until the next investment point at which there

is

no un utilized capacity. This

assumption makes the model more tractable but may also be defended by

the following consideration: if the time period between two investments is

not too long we may regard it as a learning period. During this period

capacity utilization grows continuously.



34

Technique choice

and

energy

demand

in a

vintage

model

7.

Discrete time periods are assumed. To distinguish between the different

vintages, successive time points of investment are denoted by

n=O,

1,2, ...

These may generally differ from the real time index. It

is

assumed that the

first time an investment is made coincides with the starting point zero. Input

coefficients are

fixed

at the full capacity level independent of capacity

utilization. The assumptions

(1}-(7)

above imply the following 'constant

cycle time' theorem:

Theorem:

An optimal policy consists of building successive plants at equi

distant intervals of time.

Proof:

See Hjalmarsson (1974).

The time interval between two investment points

is

denoted by

T and Tn=nT,

n=O,

1,2, ...

The growth in demand during the interval

Tn

to

Tn

+

1

is

y(O,

O)e

gtn

+

1

-

y(O,

O)e

gtn

= y(O,

O)engt(egt

-1)

(3.3)

This expression must be equal to the capacity installed at time

Tn

(3.4)

where the bars indicate

full

capacity values.

The full optimization problem consists of minimization of the discounted

stream of future capital and variable input costs under the constraint that the

increase in capacity equals demand growth. Depending on several parameters

there is a trade-off between the exploitation of scale economies and the cost of

excess capacity which determines the optimal length of time, denoted

by

T,

between two investments. A formal presentation of this problem and derived

expressions for factor demand functions and the development of input coef

ficients are given in the Appendix.

Since this model

is

not very tractable for analytical treatment, we will present

model results by numerical simulation examples.

Does this model provide a realistic description of a typical industry? There

is

a lot of empirical evidence supporting this type of model, both with regard

to the underlying assumptions about technology and to the assumptions about

firm behaviour. This type of model has also been tested empirically with

promising results (see, for example, Peck,

1974;

Hjalmarsson,

1976;

Gilbert and

Harris, 1984; Eaton and Ware, 1987; Gilbert and Lieberman,

1987.)

3.3

SIMULATION RESULTS

The purpose of the numerical examples

is

to illustrate the influence of different

parameters on the choice of technique and particularly the development of



Simulation results

35

best-practice technology compared to the development of average practice.

Since we are mainly interested in the time paths, the parameters influencing

these are varied while other parameters are kept constant.

As

a basis for the comparisons we proceed from a reference case (denoted

Ref) characterized by the following parameter values (r

=

real rate of interest,

I> =

elasticity of scale):

A

Y(O,O)

r

g e

%

j=L,E,K

1

100

0.08 0.05

1.2

b

aL aE aK

VL

VE

0.02 0.5 0.3 0.4

0.03 0

In

addition to the reference case, ten other cases are presented in Table

3.1,

where B denotes best, A average, L labour and E energy. In Table

3.1

we show

the optimal time cycle

T,

choice of input coefficients for the first investment,

year 0,

(BL-O and BE-O) and

the level

of

input coefficients for the most modern

plant in operation in year

30

(BL-30

and

BE-30) and the growth rates of the

best input coefficients

around

year 30 (BL-g and BE-g).

Due

to the constant

investment cycles this plant

is

usually taken into use one to three years earlier.

In

addition,

we

also present the average input coefficients in year

30

(AL-30

and

AE-30), their corresponding growth rates (AL-g

and

AE-g)

and

the ratio

between the best

and

the average input coefficients (BLjAL

and

BEjAE).

In

the third and second to the last rows of Table

3.1

the change in input

coefficients is put

on

index form (Index

=

100 year

0),

and in the last row

we

have also calculated the annual percentage rate of growth

of

demand for

labour

and

energy

around

year 30 (L-g%, E-g%).

3.3.1

Marginal and

scale elasticities

The first two cases illustrate the effects of variations in marginal elasticities

and

economies of scale.

In

Case 1 the elasticity

of

scale is increased from 1.2 to

1.3

by

an

increase in the marginal elasticity of capital. Compared

to

the reference

case a considerably larger plant covering the first

5.5

years

of

expected growth

of demand is erected.

The increase in scale economies makes it more profitable to build a larger

plant. Since there is no change in the price parameters the factor ratio is the

same in

both

cases in accordance with equation (3.21)

(see

Appendix). Larger

economies of scale, however, imply a more rapid decrease in

both

input

coefficients (BL-g, BE-g),

and

a slower growth ·of demand for

both labour and

energy (L-g, E-g).

In

Case 2 elasticity

of

scale

is

reduced to a very low level,

1.05.

Capacity

expansion will now take place in small steps

and

small-scale economies imply



Simulation results

37

a less rapid decrease in input coefficients and a higher growth of labour and

energy use. This also yields a somewhat higher ratio between best and average

input coefficients in year

30.

3.3.2 Expected demand growth

Cases 3 and 4 take the effects of changes in the rate of capacity expansion into

consideration. I t turns out that the length of the investment cycle is

not

very

sensitive to fairly large variations in g. On the other hand, a slower growth

yields a smaller total output in year

30

and consequently a slower growth in

labour and energy demand but higher input coefficients since scale economies

are less exploited.

A more rapid growth leads to a more rapid reduction of the input

coefficients compared to the reference case, but

at

the same time a higher

growth in factor demand.

3.3.3 Technical progress

The importance of technical progress

is

illustrated in Case

5.

A slow rate of

technical progress yields larger steps in the investment cycle since the relative

importance of scale economies increases. I f there

is

no time-dependent techni

cal progress the only source of productivity growth

is

the exploitation of scale

economies. Compared to the reference case it now pays to increase the size of

the plants. This

is

the opposite of Case 2 where the most important source of

productivity growth is the rate of technical progress that made it profitable to

expand capacity in rather small steps, taking advantage of the continuous flow

of more efficient techniques. Due to the increasing relative price of labour the

input coefficient of energy increases.

Note that the drop in technical progress in Case 5 has about the same effect

on percentage change in factor demand as the drop in economies of scale in

Case

2.

3.3.4 Energy price changes

In Cases 6-10 the relative price of energy is varied. The most important effect

of an increase in the relative price of energy (Case 6) is that it retards the

decrease in the labour input coefficient and raises the rate of decrease in the

energy coefficient. Moreover, the labour-energy factor ratio still increases but

it

is

lower compared to the reference case. The demand for labour is somewhat

higher than in the reference case, but the growth in demand for energy falls by

more than one percentage point.

If,

however, the relative price of energy is reduced, as in Cases 7-10, the

energy input coefficients may increase or decrease. In Case 7 the substitution

effect is stronger than the impact of scale economies and technical progress on



38 Technique choice and

energy

demand in

a

vintage model

the development of the energy input coefficients, so the energy coefficient

increases. The price decrease has about the same effect on energy demand as

the drop in technical progress in Case

5.

The force towards substitution of energy for labour is still more pronounced

in Case 8 where the elasticity of scale is reduced to a low level concomitant

with a falling energy price. In this case we notice the same small-step capacity

expansion as in Case 2, compared to the reference case a somewhat lower

demand for labour and an increased energy demand.

With regard to the development of the energy input coefficient the substitu

tion effect in Case 9

is

exactly balanced by the impact of technical progress and

economies of scale. At the same time the decrease in the labour coefficient

is

rather rapid, and

as

in Case 1 capacity expansion takes place in large steps.

With regard to the impact on factor demand, a comparison with Case 1 and

Case 7 reveals that labour demand

is

neither very sensitive to the change in

relative factor price nor to the change in elasticity of scale, while a drop in the

energy price growth from ° o - 2 per cent per year has a fairly large impact

on energy demand.

In Case 10, finally, the impact of zero technical progress

is

investigated again

(analogous to Case

5).

In this case the substitution

effect is

rather strong and

not at all balanced by the elasticity of scale, yielding a rather strong increase

in the energy input coefficient, but a somewhat more rapid reduction of the

labour input coefficient compared to Case 5. Still compared to Case 5, there is

a small decrease in labour demand and a rather strong increase in energy

demand.

In

none of the above cases have

we

assumed the price of energy to increase

faster than the price of labour. However, since labour and energy are treated

symmetrically in the model,

we

could analyse such a case by changing the

labels of the inputs without any change in the conclusions above.

3.3.5

Productivity structure

The structure of unit costs may

be

represented in a 'Salter like' diagram, called

a Heckscher diagram (Forsund and Hjalmarsson,

1987).

Such a diagram also

yields the structure of quasi-rents if the price level of output

is

known. The

quasi-rent structure of Case 7 above is depicted in Figure

3.1.

The oldest plant

is

to the right and the latest built to the

left.

The cost share

of labour and energy

is

also indicated. (The energy cost share line in Figure

3.1

is

not horizontal but slightly falling.) Let

us

assume that the first plant built

is

on

the zero quasi-rent margin. The surface between the rectangles and the price

line represents the total quasi-rent of the industry. Varying the price level

or

the input prices shows which units

will

have negative quasi-rents.

A modern plant

is

characterized by a high quasi-rent share of the total unit

product price. Compared to the older plants, unit labour cost

is

strongly

decreasing, while energy unit cost

is

slightly increasing which means that the



Current

unit

costs

0.70

0.60

0.50

0.40

0.30

0.20

-----

0.10

o

Simulation results

Quasi-rent area

r l

-

---

1---

--

- -

--

25

50

75

Uni t price

line

.--

Labou

cost

share

--

-

Energ

y

cost

share

100 % total capacity

Fig. 3.1 The Heckscher diagram of case 7

in

year

30.

39

energy cost share is strongly increasing. Thus, a ranking of units according to

quasi-rent shares or unit labour cost coincides with a ranking according to the

vintage of capital. This should also be the case in most industries where the

relative price between labour and energy has been increasing

in

the past.

Energy unit cost, on the other hand, may be positively or negatively correlated

with the vintage of equipment depending on the outcome of the trade-off

between economies of scale, technical progress and substitution.

An

important question concerns the empirical realism of the vintage model.

In the stringent sense this model holds for an industry producing a homogene

ous output. In practice, however, the model is also applied to industries at

more aggregated levels under headings like Salter analysis, productivity dis

tribution analysis, gross profit share analysis etc.

Although it may

be

difficult to predict exactly which plants will be closed

down and when they will

be

closed down, there is usually a steady and gradual

change of the structure of an industry

so

that best-practice today may be the

average practice at some future time. What does the distance between best and

average productivity tell us about future input coefficients?

If we

want to make predictions about future input demand both the

direction and speed of the process of structural change are crucial. Let

us

first

return to our vintage model and the numerical simulations above. We found

that technical progress 'breeds inefficiency' in the sense that the ratio between

best and average input coefficients increases when the rate of technical progress

increases. I t also turned out that important factors behind the rate of change



40 Technique choice

and energy

demand in

a

vintage model

of input coefficients were the rate of technical progress and the rate of change

of input prices. With regard to the direction of change of input coefficients, a

trade-off was noticed between substitution forces tending to move the choice

of technique along the isoquants in the cost-saving direction and the rate of

technical progress moving the isoquants towards the origin. The outcome of

this trade-off was dependent on the parameter values. Thus, a falling relative

price of energy in combination with slow technical progress and/or small

economies of scale should lead to an increase in the input coefficient of energy

and a decrease in that of labour. In such a case low input coefficients of labour

are correlated with high input coefficients of energy.

On the other hand, slightly falling energy prices in combination with a faster

technical progress and/or larger economies of scale should result in falling

input coefficients. In this case a ranking of plant input coefficients according

to labour intensity should give the same result as a ranking according to energy

intensity.

Even if the question of which development we should expect in a specific

industry is an empirical one, it is, however, reasonable to expect that the

substitution forces are stronger within the input aggregates of labour and

energy than between these aggregates. In Swedish industry there is a trend

towards higher input coefficients of electricity but lower input coefficients of

fuels within a decreasing aggregate input coefficient of energy (Bogren,

1984).

3.4 CONCLUSIONS

In this study we have analysed the choice of technique in a vintage model and

the potential usefulness of the vintage approach when forecasting future energy

demand. The result can be summarized as follows:

1.

Information about the distance between best and average productivities or

a full cross-section of industrial statistics for an industry at a certain point

of time cannot be used in a meaningful way for predictions about future

input coefficients. To obtain forecasts

we

need historical time series too.

2. The ratio between best and average input coefficients

is

a v a r i a b l e ~ in itself,

depending on a number of parameters. In the theoretical model it was

shown how the best-average ratio varied with important parameters. It

turned out that technical progress breeds inefficiency in the sense that a

more rapid technical progress increases the distance between best and

average input coefficients. A change in the relative price of labour and

energy had a large effect too. A decrease in this relative price reduced the

distance in the case of labour and increased the distance for the energy

input coefficients. A falling energy price even made the ratio between best

and average energy coefficients greater than one. Other parameter changes

were of less importance.

3.

The rates of change of input coefficients and of input demand also depend



42 Technique choice and energy demand in a vintage model

K ('m 'n)= [Y(O, O)A -lair;

aj-3

i

(1_e

VK

-r) KqK(o)-e

(

qj(O)

)3

l

Jl

/

n -ev1 r

1

i=L,E,K

For later use we define

and

LVjaj-VjI,-b+g

j

e

i=L,E,K

i,j=L,E,K

(3.8)

(3.9)

(3.10)

I f equations (3.6H3.8) are inserted in equation (3.5), the following cost function

is obtained

(3.11)

where

(3.12)

and

()=-'-j---- - r i=L,E,K

(3.13)

e

Summation over all

n

yields the total cost function for the whole horizon as

a function of the time interval, to be denoted by q,). q,) includes the

discounted stream of construction costs as

well

as operation costs:

(3.14)

where ()<O,

B>O

The optimal time interval

is

obtained by minimizing

q,)

with respect to ,.

Input per unit of output, the input coefficient, for vintage 'n is denoted by

~ ( ' n ) . It

is a variable

ex

ante, but a fixed coefficient

ex

post. From equations

(3.6H3.8) and

(3.3)

one obtains



Appendix

43

where

(3.16)

where

(3.17)

(3.18)

where

(3.20)

The development of the ratio between two input coefficients when new

capacity

is

built

is

given

by

i, j=L,E,K (3.21)

where

a· q.(O) [1-e

Vi

-

r

J

D i j = ~ q : ( O ) l_e

Vj

-

r

i , j=L,E

(3.22)

and

(3.23)

i.e. the development of the relative factor ratio

is

only governed by the

difference in factor price change between the two inputs. I f the factor prices

change at the same rate, Vj=V j , the factor ratio

is

constant.

The average input coefficients are obtained by the ratio of cumulated input

and cumulated output. From cumulated output at time

r,

Y(r)

is obtained as

Y(rn)

= y(O, O)eg(n

+

1)<

(3.24)

and from

(3.6)

and

(3.7)

cumulated inputs at time

rn, Vj(rn)

are

(3.25)



44 Technique choice and

energy

demand

in

a vintage

model

where

B

j

= y(O, 0)1/'H

1

/'aj (0)

-1(e

gt

-1)1/<(1-

e

Vj

-r)

(3.26)

and

Wi is

given in

(3.10).

Thus, the average input coefficient is obtained as

A

. _ Vlrn) ~ (e",,(n+ 1)<

-1 )

V.-

Y(rn) -

y(0,0)(e ';t-1)(e

g

(n+1)t-1)

i=L,E (3.27)

and the ratio between best-practice and average coefficients

as

A.

(e

g

(n+1)t_1)(e",;t_1)

BA

Vi

= -i

y(O,

0)

e",;(n +

1)t -1

en'nt

i= L, E

(3.28)

•

where

W i - g

i=L, E,

K

(3.29)

and

(3.30)

These expressions are fairly complicated and there seems to be no obvious

way to simplify. Depending on the sign and

size

of different parameters both

A

Vi

and BA Vi may decrease or increase over time, but not very much can be

said without information about the size of different parameters.

ACKNOWLEDGEMENTS

In the preparation of this paper valuable comments were received from

Thomas Sterner. Financial support from Jan Wallander's Research Founda

tion is gratefully acknowledged.

REFERENCES

Bogren, E. (1984) Energiproduktivitetens variationer inom industribranscher. Project

report from the Energy Research Board, Efn/AES, 1984:2.

Eaton,

B.

C.

and Ware,

R.

(1987)

A Theory of Market Structure with Sequential Entry.

RAND Journal

of

Economics, 18 1-16.

Forsund, F. R. and Hjalmarsson, L.

(1987) Analysis

of

Industrial Structure. A Production

Function Approach.

Almqvist and Wiksell International, Stockholm.

Gilbert, R.

J.

and Harris, R. G. (1984) Competition with Lumpy Investment.

RAND

Journal

of

Economics, 15

197-212.



References

45

Gilbert, R. 1. and Lieberman,

M.

(1987)

Investment and Coordination in Oligopolistic

Industries. RAND

Journal

of

Economics,

18, 17-33.

Goldemberg,

1.,

Johansson, T. B., Reddy A.

K.

N. et al.

(1988)

Energy for a Sustainable

World.

Wiley Eastern, New Delhi.

Heckscher, E.

F.

(1918)

Svenska produktionsproblem.

Bonniers, Stockholm.

Hjalmarsson, 1. (1974) The

Size

Distribution of Establishments and Firms Derived from

an Optimal Process of Capacity Expansion.

European Economic Review,

5, 123-40.

Hjalmarsson, 1. (1976) Capacity Expansion and its Implications for the Size Distribu

tion of Firms: Reply.

European Economic Review, 7,

287-92.

Hjalmarsson, 1. and Eriksson, S-G. (1985) 'Choice of Technology and Energy Demand

in a Vintage Framework.' Unpublished.

Johansen,1.

(1972)

Production Functions. Amsterdam, North Holland.

Johansson, T. B., Bodlund, B. and Williams, R. H. (eds) (1989) Electricity - Efficient

End-Use and New Generation Technologies, and their Planning Implications. Lund

University Press, Lund.

Manne, A.,

(1961)

Capacity Expansion and Probabilistic Growth. Econometrica, 29,

632-49.

Peck,

S. C.

(1974) Alternative Investment Models for Firms in the Electric Utilities

Industry,

Bell Journal

of

Economics and Management Science, 5, 420-58.



4

The effects

of

changes

in

the

economic structure on energy

demand in the Soviet Union

and the United States of America*

Yu. D. Kononov, H. G. Huntington,

E.

A. Medvedeva and G. A. Boyd

4.1 INTRODUCTION

It

is widely recognized that an economy can reduce its energy intensity through

many channels. Improvements in the energy efficiencies of certain processes in

individual industries are important but capture only a part of the response.

Shifts in the structure of an economy's production for energy-intensive sectors

have been and will continue to be an important factor in lowering the energy

use per unit output in many economies (Maday,

1984).

Analysts in both the

Soviet Union and the United States of America expect that the changing

economic structure

will

continue to reduce the aggregate energy intensity in

each country. There exists, however, considerable uncertainty about the rate of

decline in the energy intensity due to such shifts.

This paper focuses on studies conducted in both countries to measure the

relative importance of shifts within the economy on energy demand using the

same methodology - the Divisia index - for decomposing energy intensity

trends. Since conventions for measuring energy consumption and economic

output are different in the two countries, a comprehensive comparison of

international experiences

was beyond the scope of our collaboration. A

rigorous comparison of past trends in the two countries, if it can even be done

reliably, would require extensive revisions and adjustments to the existing data.

Instead, our objective was more modest: to use the most recent data available

*This paper was originally presented at the joint American-Soviet National Academy of Sciences

Symposium on Energy Conservation Research and Development, Yalta, USSR, 6-12 October

1988.



48

Effects of changes

in

economic structure on

energy

demand

internally within each country to help identify and understand the important

energy intensity trends in the two countries. Thus, this chapter summarizes two

separate but concurrent studies on aggregate energy intensities.

The decomposition of energy intensity trends into those due to changing

economic structure and those due to other factors, new processes within an

industry for example, is an important first step in understanding energy use

patterns. For a market economy, energy intensity trends within an industry are

governed largely by the prices of energy and other inputs as well as industry

specific technological progress. These same factors, however, may not be as

critical for economy-wide or aggregate energy intensity trends, which will be

strongly influenced

by

shifts in the relative importance of different economic

sectors.

For

example, aggregate energy intensity

is

significantly influenced by

the widespread substitution of newer, more versatile materials for the tradi

tional, energy-intensive raw materials, e.g. various plastics for primary metals.

Moreover, the indirect

effects

of energy price changes - the redistribution of

income and the shifting share of investment - are often at least as important

as the direct effect. Analyses that ignore these differences are likely to

misrepresent the factors determining aggregate energy-use trends in economies

that either now or

will

in the future depend upon markets.

Policymakers in the United States and USSR are increasingly interested in

strategies for reducing the aggregate energy intensity within each country.

In

the Soviet Union energy production is often extremely capital-intensive. In the

absence of market prices, many analysts perceive an overinvestment in energy

supplies in that country which retards economic growth

by

misallocating

capital. Thus, declining energy intensity in the economy would release capital

to other sectors where it could be employed more productively. As the Soviet

Union moves towards a market economy, energy efficiency gains achieved

through shifts among sectors of the economy are likely to respond to different

factors than those gains realized from new processes and technology implemen

ted within a sector

(see

further Chapter

12

by Yuri Sin

yak).

In the United States and other developed market economies, some analysts

argue that market prices undervalue the social cost of using additional energy.

Incomplete information, public utility regulation favouring supply options and

other market imperfections could bias aggregate economic consumption to

wards too much energy use. Moreover, increased energy consumption means

increased production from and dependence upon the Persian Gulf, the source

of marginal supplies in world energy markets. Growing dependence upon

insecure energy supplies increases the vulnerability of the world market

economies to oil market disruptions and price shocks.

In recent years, policy concerns in both countries have been broadened to

include environmental problems such as pollution and possible climate change

through increasing concentrations of carbon dioxide and other greenhouse

gases. Much recent attention has been focused on the forces influencing energy

use and the relationship between energy use and environmental degradation.

Estimates of the

effect

of different carbon-reduction strategies, for example,



Methodology 49

depend critically upon how rapidly energy will grow in the absence of such

limits as well as upon the degree of substitution away from energy as energy

use

is

restricted.

The two-country studies reported in this paper were done to provide an

initial perspective on past USSR energy intensity trends and possible future

developments, within the context of the US experience. A clear understanding

of the different channels for reducing energy in the major economies

is

a

prerequisite

for

an informed analysis and discussion of the relative benefits and

costs of various strategies for reducing energy use.

The concurrent studies of the two countries were conducted just as new 1985

data were becoming available on the use of energy in both economies. The

research also commenced at a time when the Soviet Union was just beginning

to contemplate major changes in its economy and energy sector. Since that

time, the challenge of this transition has grown substantially, creating consider

able uncertainty about future developments in both the Soviet economy and

its energy sector. Given these developments, one must

view

any attempt to

project future economic and energy trends as extremely risky, with large error

bands in either direction

(a

problem in any economy relying upon market

forces). Nonetheless, we view these projections and their decomposition into

sectoral shift and efficiency effects as a useful comparison of how these forces

could unfold in the two countries.

Our analysis reveals three key conclusions:

1.

The use of a Divisia index for separating the shift effect from other factors

is

a useful method when there are large or sudden changes either in energy

intensity within a sector or in the relative economic importance of a sector.

2.

Shifts in the composition of economic output have an important effect on

the trend in aggregate energy intensity of the economy. However, shifts

within the industrial sector away from energy-intensive industries have a

more pronounced effect that do shifts among major macro sectors

(e.g.

industry, transportation, agriculture).

3.

Within industry, there has been a dramatic decline in aggregate fuel

intensity in both countries over the last 20 years. During the post-embargo

period (after 1973), however, this trend accelerated in the United States but

slowed in the Soviet Union, due largely to little improvement in energy

intensity within industries.

After a brief discussion of the methodology,

we

discuss the Soviet and US

results in separate sections.

4.2 METHODOLOGY

Variations in aggregate energy intensity were decomposed into changes in

energy intensity at the industry level (measured

by

real energy intensity) and



The

Soviet

experience

53

The historical period is characterized by the high and increasing share of

sector 2 in industrial output. This trend reduces the aggregate energy intensity

within industry because the energy-output ratio for manufacturing industries

is much lower than for sector 1 (the materials-producing industries). The

electricity intensity of the output in sector

2,

for example,

is

four to ten times

lower than it

is

in sector 1 (the first figure corresponds to

1960,

the second one

to the end of the time period), and the

fuel

intensity is lower by a factor of

16-120 (calculated from the data of Pavlenko and Nekrasov,

1972;

Aksutin et

at.,

1981;

Melentiev and Makarov,

1983).

Such a large difference in the energy

intensity indexes for the two sectors causes changes in the relative shares of

industrial output in the two sectors to have a substantial effect

on

the changes

in the aggregate energy intensity of industrial output.

The contribution of the industries in sector 1 to energy demand within

industry

is

clearly seen from Figure

4.3,

showing the trends in the sectoral

demand for electricity, heat,

fuel

and aggregate energy (calculated from the

same data). The shape of sector 1 in electricity demand by industry for the

1960-85 period decreases from 67 to 60%, in heat demand from

61

to 54%,

and in aggregate energy from 77 to 68%. This sector's share in

fuel

demand

increases from

89 to 94%.

Energy use by households and service industries (the so-called non-produc

tion sphere in Soviet statistics) increases in relative importance over the

1960-85 period. This sector's share of electricity demand for the whole period

increases from 11 to 16% and its share of heat energy rises from

10

to 18%,

reaching its maximum of

21

% in

1980.

Its share of

fuel

demand decreases

somewhat. Agriculture's relative importance increases in total electricity use

but decreases in

fuel

and total energy use. Transportation's share falls for all

energy carriers during this period.

4.3.2 Contribution of sectoral shift to the change

in

energy intensity

The indexes calculated by the Divisia index approach for electricity and

fuel

intensity of NMp

3

and industrial output are given in Figures

4.4

and 4.5.

Figure

4.4

shows the shifting trends in the electricity-NMP ratio during the

1960-85 period. In the period between

1960

and 1965 the index was growing

at a rate of 4% per year, during the following 7 years its growth rate was low

(0.7% per year), and later - from 1972 to 1985 and in the near future - the

electricity intensity is practically stabilized.

The effect of sectoral shift is particularly pronounced in the early 1960s when

its share

is

more than 90%.

In

the period 1965-85, its contribution to the

change of electricity-NMP ratio

is

in the range from

40

to 85%. In the future,

the electricity-NMP ratio is expected to fall. The role of sectoral shift will also

decrease, to 32% by the end of the period. The fuel-NMP ratio

(see

Figure

3Households and services are excluded from the decomposition of the energy-NMP trends.



54 Effects

of

changes

in

economic structure

on

energy demand

(a)

100 % -rT ': 'T7 'r-:-r.,...,

': , 'T'-:-1,r:-r:-1,

: - r , - r - : r . ' ' 1

I

"""""'" 100 %

. , iTT I 'TTi 'TTTTT

......

r:-r.-r7'T ':' .,..,.-:'I

,', I' I', lilli', ,'I'

11111

50%

1960

1985 1960 1985

(c)

1960 1985 1660

1985

-

griculture Transport

Sector 1 Sector 2 Non-production

industry industry sphere

Fig. 4.3 Change in the structure of electricity (a), heat (b),

fuel

(c) and energy

(d)

demand in the economy.

4.4b) decreases by 2.8% per year in 1960-85 and

is

estimated to decline by

2.5% per year in the projections. Its pattern

is

almost completely determined

by the decrease in real energy intensity; the share of sectoral shift is 13% in

1960

and drops to 9% at the end of the period.

The electricity intensity of industrial output (see Figure 4.5a) decreases by

1.4% per year in 1965-85 and

is

expected to

fall

by 1.6% per year in the future.

Sectoral shift plays an essential role in declining aggregate electricity intensity,

accounting for 62% in the 1960s

and 68% at the end of the historical period.

It should be emphasized that during 1967-79 the real electricity intensity of

industry did not change and almost all of the decrease in aggregate electricity

intensity was due to sectoral shifts.



1.1

C)

1.0

II

_./

I t )

I

eX)

I

OJ

0.9

I

x

I

Q)

I

"0

I

-=

0.8

I

J

0.7

0.6

1960

1.8

C)

1.4

II

I t )

eX)

OJ

,...

1.0

x

---

)

"0

-=

0.7

0.5

0.3

1960

The

Soviet

experience

(a)

.... ' .

.--

/

1 ,,-

-:.....= : . '. -

- ~

I

......

1985

(b)

/ /Ds

/

. /

Da

.De

_ _ _ _ _ Os

--

1985

.

,oe

Da

55

Years

Years

Fig.

4.4

Changes in electricity (a) and fuel (b) intensities of NMP (Da) in production

sphere. Ds, sectoral shifts; De, changes in real electricity and

fuel

intensities.

A similar situation

is

observed in fuel consumption. Figure 4.5b shows that

between

1973

and

1985

the real

fuel

intensity changed very little; therefore, the

decline in the aggregate index was caused principally by sectoral shifts. The

total decrease in the fuel-industrial output ratio amounted to about 4% per

year in 1960-85 and is estimated approximately at 3% in the future. The

contribution of sectoral shift increases from 40% at the early 1960s to 52% at

the end of the historical period.



56 Effects of changes in economic

structure

on

energy

demand

(a)

1.3

-,

1.2

/

"

~

,

~

1.1

/

\

~

.....

II

1.0

I )

:...:. ...

CD

en

......

,

:::.

0.9 "-

x

"-

Q)

"-

...

Oe

:l

0.8

"-

.E

-

"-

"-

,...os

.7

0.6

1960 1985

Oa

Years

(b)

2.0

~

1.6

II

I I )

CD

en

:::. 1.2

x

Q)

l:l

1.0

E

--

--

"-

0.8

-

"-

....

.......

- ...... Os

....

0.4

1960

1985

Years

Fig.

4.5

Changes in electricity (a) and fuel (b) intensities of industrial output (Da). Ds,

sectoral shifts; De, changes in real electricity and fuel intensities.

Changes in the composition of output within the industrial sector have a

greater effect on aggregate energy intensity of the economy than do shifts

among major macro sectors (e.g., industry, agriculture and transportation).

As

revealed in Table 4.1, shifts among the major macro sectors actually increase

the economy's energy intensity during the 1965-85 period.

In

the projections,

the economy's energy intensity is expected to decline dramatically in the future,

with structural shift, both among the major macrosectors and within industry,



The

US

experience

Table

4.1

Impact of structural shifts and other factors on changes I I I the

energy-NMP ratio

(%)

in the Soviet Union

1965-1985

1986-2010

Structural shifts - total

- 3 (15) - 29 (57)

among four macro sectors

6 ( - 30) - 6 (12)

among ten industries

-9 (45) - 23 (45)

Changes with industries

-17

(85) -22(43)

(composition of outputs,

energy conservation, ...

)

Aggregate

-20(100%)

-51(100%)

Note: Percentage contribution to total

effect

appears

in

parenthesis.

For

ease of readability,

in Tables

4.1

and

4.2

percentage changes are reported in additive rather than multiplicative

form. Percentage charges within industries are equal to the difference between trends in

aggregate and sectoral shift intensities.

57

accounting for almost 60% of this decline. As the Soviet Union shifts towards

a market economy, it

is

likely that these shifts, if they are to occur, will be

induced by a set of factors different from those that determine trends in real

energy intensity.

Soviet studies in the coming years will examine in greater detail the

effect

of

sectoral shift on energy demand in the economy for the next 25 years. This

analysis

is

being based upon a set of simulation models described by Med

vedeva (1986).

It

comprises models of the economy and energy demand as well

as models of energy demand by separate sectors. Each model

is

based on three

groups of relations: technological (intersect oral) flows of products, financial

flows and energy demand of a sector or of the economy as a whole.

4.4

THE US EXPERIENCE

In the United States energy use has been maintained practically level from 1973

to

1985,

while economic activity (GNP) has increased about 30%. This trend

of declining

use

per dollar of output holds for total energy, for fossil fuels and

for

electricity. In the case of fossil

fuels,

this decrease represented an acceler

ation of the pre-embargo trend of declining fuel intensity. For electricity, on

the other hand, the post-embargo trend represented a reversal of the increasing

electrification observed before 1973.

A significant portion of this decline was due to shifts among sectors of the

economy, among industries (measured at various levels of disaggregation) and

among products. We estimate that these changes in the composition of

economic output account for almost one-half of the decline in energy use per

unit of output in manufacturing since the embargo. These new estimates are

based upon recently available data from the US Bureau of the Census, updated



The

US

experience

59

2.5

Aggregate

2.0

0'

0

II

co

I l )

1.5

l)

:::.

.

1

eal

. . . . . .

- - -

......--

- - - - - - --

x

Shift

CD

"0

C

ra

1.0

CIl

.:;

0

0.5

O ~ - - - - - - , - - - - - - - , - - - - - - - , - - - - - - - ~ - - - - - - ~ ~

1960

1965

1970

1975

1980

1985

Year

Fig.

4.6 Fuel intensity decomposition for US manufacturing.

energy-output ratio rises. This effect is quite pronounced in energy-intensive

industries.

During 1974-81, the sectoral shift caused real energy intensity improvements

to

be less

than the aggregate index would suggest. While the real energy

intensity averaged - 3.0% per year, sectoral shift contributed an additional

-

1.2

% to the aggregate trend.

For purchased fossil fuels, the sectoral shift effect

is

largely captured by

disaggregation to only two sectors (materials and non-materials industry

groups). The change in aggregate energy intensity from 1974 to

1981

was

33.4%, of which 9.2% (equivalent to 27.6% of the change) can be accounted

for by sectoral shifts among

20

2-digit SIC industries. Of this 9.2%, 5.5% can

be attributed to shifts away from the materials sector. Thus, about half of this

sectoral shift is captured by a two-sector disaggregation. The remaining effect

is largely between the energy-intensive industries themselves.

Electricity intensity

The overall picture for electricity 6 is quite different from that for fuels (see

Figure 4.7). The 1960s were characterized by an aggregate 2.5% per year

electrification trend. This trend was composed of a 0.8% shift towards

electricity-intensive industries - a sectoral shift very similar to that for fuel -

6 Includes purchased electricity and cogeneration for on-site

use.



60

Effects

of

changes

in

economic structure

on

energy

demand

1.4

Shift

1.2 --- . . /-------

II 1.0

co

10

C1l

..-

x

0.8

Q)

"tJ

C

. 0.6

(/)

.:;

Ci

0.4

0.2

Aggregate

.' . Real

O ~ - - - - - - . - - - - - - - r - - - - - - . r - - - - - - . - - - - - - - r ~

1960 1965 1970

1975 1980 1985

Year

Fig. 4.7 Electricity intensity decomposition for US manufacturing.

and 1.7% change in real energy intensity. After 1970 the aggregate electricity

intensity began to decline. This was the result of a fluctuating real electricity

intensity, characterized by large peaks in intensity as the result of capacity

utilization effects. However, the composition shift effect on electricity intensity

reversed itself, averaging -0.5% over the 1970-81 period, then accelerating

sharply to -2.8% from 1981-85. The sectoral shift away from electricity

intensive industries is a marked reversal of the pre-1970 trend.

There

is

evidence of shifts among the materials and non-materials industry

groups during 1971-81. Hence, a two-sector disaggregation incorporates only

some of the total sectoral shifts

effect.

After

1981,

however, the decline is largely

attributable (68%) to a two-sector disaggregation.

4.4.2 Projected

trends

in

energy

intensity

There exists no official forecast of the US economy and the associated energy

demands. Moreover, projections of changes in the structure of the US economy

can often differ by a substantial amount and these differences can result in

widely different outlooks for energy demand, as discussed in Boyd, Hochheiser

et al. (1987).

Nevertheless, for the purposes of comparing with the Soviet

projections, we have selected a forecast and have shown it together with our

historical analysis in Table

4.2,

much as

we

did for the USSR in Table 4.1. We

restrict Table

4.2

to manufacturing only, because reliable

data

on the other



The

US

experience

61

Table 4.2

Impact

of

structural shifts and other

factors on changes

in the energy-GNP

ratio (%)

in the

United States

of

America

1965-1985 1979-1985

1985-2010

Fuels

Structural

shifts

among industries

-36(47)

-28

(70) -9 (22)

Changes within industries

-40(53) -12 (30) -31 (78)

Aggregate

-76(100%) -40(100%)

-40(100%)

Total electricity

Structural shifts among industries

-21

-18

-9

Changes

within industries

17

6

-2

Aggregate

-4

-12

-11

Notes: Percentage contribution to total effect appears in parenthesis.

See

also note to Table 4.l.

Total electricity includes self-generated power.

sectors are not available.

In

addition, we have separated the trends in fuels and

electricity because this distinction

is

critical for the US trends. And finally,

we

have reported the 1965-85 as well as the 1979-85 period, because of the

dramatic acceleration in the sectoral shift effect during the latter interval.

Most economic projections show that the energy-intensive sectors do not

grow as rapidly as the rest of the economy. The projections from the Wharton

Annual Model, maintained by

Wharton

Econometric Forecasting Associates

(WEFA), are representative of such trends. These projections were used to

standardize the industrial energy demand projections of several models com

pared in a study by the Energy Modeling Forum (EMF, 1986).

They reveal a continuing trend towards less energy-intensive sectors, al

though at a slower rate

than

historically. Durable manufacturing, chemicals

and miscellaneous manufacturing grow more rapidly than aggregate industry

in these projections, while paper, petroleum refining

and

primary metals grow

more slowly. The slower decline in the shift away from energy-intensive sectors

is

due to more widespread economic growth than during the past

12

years. This

growth stimulates the demand for capital goods production, which is very

energy-intensive relative to other manufacturing sectors.

Shifts among six major manufacturing sectors contribute about 22%

of

the

total decline in fossile fuel use per dollar of

output

in the reference case

projections reported for the ISTUM-II model in the EMF study.7 Aggregate

fossil fuels intensity falls by 2% per annum between 1985 and 2010 (Table 4.2).

Meanwhile, sectoral shift causes aggregate intensity to decline by 0.4% over

this same 25-year horizon. The rate of decline due to sectoral shift is about a

quarter

of that

observed for the historical period 1965-85. Table 4.2 also shows

that most of the historical shift effect occurred during the 1980s.

7 The

fossil

fuel trends for this model were representative of those for the other models in the EMF

study.



References

REFERENCES

63

Aksutin, P.

K.,

Veretennikov, G.

A. et al.

(1981)

Energetika

SSSR

v

1981-1985

godah

(Energy of USSR in 1981-1985), (eds. A. M. Nekrasov and A. A. Troitski). Energoiz

dat, Moscow,

p.

352.

Anoshko, V. F., Borshchevski, M. Z., Krivorutski, L. D. et al. (1986) Sistemy energetiki:

upravlenie razvitiem i funktionirovaniem. (Modelling and system studies in energy

demand dynamics) T.2.-Irkutsk, SEI SO

AN

SSSR, pp. 41-7.

Bestuzhev-Lada, I. v., (ed.) (1982) Rabochaya kniga po prognozirovaniyu (Working

manual

on

forecasting). Mysl, Moscow,

p.

430.

Boyd, G. A., Hanson, D. A.

and

Sterner, T. (1988) Decomposition of Changes in Energy

Intensity: A Comparison of the Divisia Index and Other Methods. Energy Economics,

October, pp. 309-12.

Boyd,

G.,

Hochheiser, H. W., McDonald,

1.

F.

et

al.

(1987) Energy Intensity in

Manufacturing: A Comparison of Historical Results with Forecasts, in The

Changing

World Energy Economy (ed. D. O. Wood), Massachusetts Institute of Technology,

Cambridge, Mass.

Boyd, G., McDonald, J. F., Ross, M. et

al.

(1987) Separating the Changing Composition

of US Manufacturing Production from Energy Efficiency Improvements: A Divisia

Index Approach. Energy Journal,

8,

77-96.

EMF (Energy Modeling Forum) (1986) Industrial Energy Demand. EMF-8 Summary

Report (December 1986). Stanford University Energy Modeling Forum, Stanford, Ca.

Huntington, H. G., and Myers, J. G. (1987) Sectoral Shift and Industrial Energy

Demand: What Have We Learned? EMF-8.3, Stanford University Energy Modeling

Forum, Stanford. Also in

Forecasting Industrial Structural Change and

Its

Impact on

Electricity Consumption

(ed.

A.

Faruqui and

J.

Broehl), Battelle Press, Colombus,

Ohio.

Marlay, R. (1984) Trends in Industrial Use of Energy, Science,

226,

1277.

Medvedeva, E. A. (1986) Avtomatizatsiya issledovani razvitiya energeticheskogo kom

pleksa (A System of simulation models to study energy demand). Irkutsk, SEI SO AN

SSSR, pp. 92-105.

Melentiev,

L. A. and Makarov, A. A. (eds) (1983) Energetichesky kompleks SSSR

(Energy complex of the USSR). Ekonomika, Moscow, p. 264.

Narodnaye (1985) Narodnoye hozyaistvo SSSR v

1965-1985

godv (The USSR National

Economy in 1965-1985). Finansy i statistika, Moscow.

Pavlenko,

A. S.

and Nekrasov, A. M. (1972) Energetika SSSR,

v.

1971-1975 godah

(Energy of the USSR in 1971-1975). Energiya, Moscow,

p. 263.

Werbos, P. (1986) Industrial Structural Shift: Causes and Consequences for Electricity

Demand, in Forecasting Industrial Structural Change and Its Impact on Electricity

Consumption (eds A. Faruqui and J. Broehl), Battelle Press, Columbus, Ohio.



5

Modelling

transport

fuel

demand

Thomas Sterner and Carol A. Dahl

5.1

INTRODUCTION

Transport fuels account for an increasing share of oil consumption, and savings

appear to be both technically and socially harder to achieve than in many

other sectors where substitutes are more easily available. Large sums of money

are invested in trying to improve efficiency of vehicles but the really most

relevant issue

is

that of whole transport systems. These systems cannot be

planned in detail, however, but are the result of many individual actions.

It

is

therefore of particular interest to study the economics of the transport

fuel

market and thereby to evaluate the efficiency of the price mechanism as an

instrument of policy in this area. Taxes and hence domestic prices of transport

fuels vary considerably between countries (Sterner, 1989a,b; Angelier and

Sterner, 1990) and thus high demand elasticities would imply considerable

differences in consumption patterns.

A large number of different models have been conceived to explain how

gasoline demand

1

is related to price, income and other variables. This chapter

is

a systematic review of these models and draws heavily on two earlier pieces

of work: Dahl and Sterner (1991), which

is

a complete survey of more than one

hundred published studies, and Sterner (1990) in which a large number of

models are tested and compared using one and the same data set for the

OECD. The results are summarized in Table 5.1, which gives average values

from the survey and average OECD (1960-85) values from our own estima

tions.

We

find that different models tend to give very different estimates,

showing the need for careful model specification and interpretation of elastici

ties.

lThe analysis in this chapter applies to highway transport fuels in general. Empirical work

is,

however, nearly always focused on gasoline, presumably for reasons of better data quality.



66

Modelling transport fuel demand

Table

5.1

Summary

of

average elasticities by category

Price

Income

Model and data elasticity elasticity Vehicle

type

Cat. SR

IR

LR SR IR LR

elasticity

Panel data

S

-0.52

0.41

Static model TS

data

S

-0.53

1.16

Static model TS data

A

-0.28

1.37

Static vehicle model

CS data

S

-1.01

0.61 0.40

Static model CS data

A

-1.07

1.09

Dynamic models

Lagged endogenous S

-0.24 -0.80

0.45

1.31

Lagged endogenous A -0.25 -0.85 0.37

1.15

Polynomial distributed

lag A

-0.20 -0.96

0.35

1.15

Inverted V lag (5.8.1)

S

-0.22 -0.94

0.39 1.09

Inverted V lag (5.8.2)

A

-0.21

-0.54 0.52 1.25

Vehicle models

Simple vehicle S

-0.31

0.52

0.53

Simple vehicle A

-0.32

0.55 0.43

Vehicle characteristics

S

-0.16

0.29 0.48

Vehicle lagged

endogenous

S

-0.12 -0.29

0.38

0.60

0.19/0.32

Drollas model

S

-0.41 -0.77

0.42 1.11

Pooled estimators

Baltagi

and

Griffin (LE)

S

-0.15

-0.80 0.14

0.74

Lagged endogenous

OECD (1960-85)

A -0.18 -1.35 0.10 0.73

Hughes error

corrections

S

-0.18 -1.42 0.33 0.33

S,

Survey,

average results from

survey

of earlier work; A, Own average (OECD) estimates taken from

Sterner

(1990).

TS, time series;

CS,

cross-section;

SR,

IR, LR,

short-, intermediate- and long-run.

Model types

and categories etc. are explained

in

the

text.

Figures

are

presented

here

in

the

order they

appear in the chapter.

5.2 THE DETERMINANTS OF INDIVIDUAL MOTORING

FUEL DEMAND

In principle the demand for any particular commodity is determined simulta

neously with all the other variables of the economy. Assuming separability, the

demand for a product would just depend on its own price and the income of

the consumer. Naturally this

is

a very convenient hypothesis since it facilitates

estimation, interpretation and forecasting. The inclusion of additional explana

tory variables

will

often increase a model's realism and performance but - at

least for the forecaster - this is of little avail if the values of these variables



The level of aggregation 67

themselves are hard to predict.

For

gasoline, separability

is

clearly a question

able hypothesis but it

is

still commonly used.

Let

us

start with the individual consumer: his demand for gasoline

is

obviously a derived demand. The original demand is for transport together

with a number of other automobile-related characteristics such as comfort,

status etc. Thus gasoline demand

is

determined in a number of separate steps.

First there

is

the decision to buy a car (or how many to buy) and of what type

(in

other words which

efficiency);

then come the decisions

as

to its utilization

(how often, how far and also in what way to drive).

Decisions relating to the stock of automobiles are much less frequent than

decisions on utilization. They may be affected to some degree by gasoline prices

(historical or expected) but also, and more strongly, by the price of automo

biles, income and a whole range of other variables reflecting the habits, culture

and situation of the individual (or

family)

concerned. Among the most

important factors are the geographical location of residence in relation to

employment and other activities as well as to the availability of alternative

means of transport.

The daily decision on vehicle-utilization

is

obviously heavily constrained by

these factors but it is also likely to depend on gasoline price along with

innumerable other variables - such as the weather and various personal

variables such

as

health or mood, on which

we

can never hope to collect

information.

5.3 THE LEVEL OF AGGREGAn O N

When we use econometric methods to estimate demand for a product such as

gasoline we must assume the existence of a demand function that

is

constant

over a certain group of consumers (at some level of aggregation) and for a

certain period of time. Thus the modeller has to decide what type of data to

use. Basically there are four different types of data: at the level of aggregate

statistics there are time series, cross-sections and pooled cross-section time

series data. For individual consumers there are panel data (which are a

cross-section of individuals).

From the standpoint of a utility function it

is

easier to conceive of a function

for an individual consumer. Archibald and Gillingham

(1978,

1980) study the

influence of age, sex, race, martial status, education, area of residence and other

variables on individual gasoline consumption. In spite of all the variables

included they only explain a third of the variation in demand as measured by

R2.

This does not necessarily decrease the value of their results. Our interest,

on the other hand, generally concerns total gasoline consumption in a region

and explanations at the individual level are not necessarily sufficient at the

aggregate

level.

Furthermore their explanatory variables are interrelated so

that the elasticities found are quite low (see first row of Table 5.1).

Aggregation introduces, as usual, a series of additional difficulties. Macro



68


parameters are not, in general, related to the corresponding micro parameters

in a simple

way.

To deduce macro from micro parameters we would need

detailed, a priori information on all the explanatory micro variables. New

variables such as income distribution, gasoline supply factors or levels of

highway and public transport congestion appear at the aggregate level that are

not in any simple way deducible from micro-level information. At the same

time the micro-level variables are all, in principle, still relevant. Thus

if

young

people tend to consume more gasoline and the composition of population

changes, then this will

affect

aggregate gasoline consumption. In practice

analysts of aggregate data find it necessary to disregard the majority of

micro-level factors.

2

5.4 THE CHOICE OF ECONOMIC MODEL: THE SIMPLE

STATIC MODEL

The simplest model of gasoline demand begins with the assumption that utility

depends on motor fuel demand

(G)

and an aggregate of other goods

(0).

Consumers know the price of gasoline

P

g

and the price of the other good Po,

most commonly represented by the consumer price index, and choose G and

°

o maximize utility

U(G,

0)

subject to a budget constraint

P g G p o O ~ y

or

maximize equation

(5.1).

U(G,O)+h(y-pgG-poO) (h is a Lagrange multiplier)

(5.1)

With the usual assumptions regarding the utility function, this yields gasoline

demand as a function of gasoline price, other price and income.

It

is commonly

assumed that demand

is

homogeneous of degree zero in prices and income (so

that doubling nominal prices

will

leave demand unchanged).3 This allows

prices and income to be deflated into real terms yielding equation (5.2).

G=f(P,

Y)

(5.2)

Assuming log-linearity

gives

the simple static model

4

as in equation (5.3.1):

LnG=c+oclnP+/Jln Y+<; Where Y=Y/Po and Pg=pg/po. (Where there is

no risk of confusion P is used instead of P

g

for

the real gasoline price.) <; is a random error term.

(5.3.1)

lOn

the issue of aggregation

see

for instance Barker and Pesaran (\990) and Sterner (\990).

3

Hughes (1980), one of the very few who explicitly enters the rate of inflation along with nominal

prices instead of just using deflated prices, finds some evidence of money illusion on a quarterly

and monthly basis.

4This corresponds to

G=cp·yP.

I f

simple linearity

is

assumed the equation would

be

G=c+rxP+{JY.

We found no systematic difference between average elasticities for these two

functional forms, see further Sterner

(1990).



Dynamic

models

69

As already mentioned, aggregation is problematic.

We

must, however,

consider at least the number of people we are aggregating over to distinguish

between the effects of a rise in

GNP

due to an increase in population (with

constant per capita income) and an increase in GNP/capita (with constant

population). Even if individual income elasticity exceeds unity (with respect to

per capita income) intuition suggests that increases in population should not

increase gasoline consumption more than proportionately (in fact the increase

should be less than proportional if there are any effects on congestion or on

public transport availability).

5

Ln(G N)

=

c

+

J. Ln P

+

3 Ln (Y N)

+

£

(5.3.2)

The most crucial problem for the simple static model

is

that there may not

be sufficient time for total adjustment to changes in price and income within

the unit time period of our data. This implies that the adjustment captured

will

be

less

than total.

At

the top of Table

5.1

we see that this appears to be the

case for yearly time series data. Average price elasticities are - 0.5 in the survey

or -0.3 for Sterner's (1990) estimates. Income elasticities are, however, much

more like the long-run values found in other models. Monthly or quarterly

data estimates tend to give even lower values for both price and income (Dahl

and Sterner, 1991). Cross-sectional data do, however, give much higher values

(around

-1),

suggesting that these data reflect adjustment to long-run differen

ces in price.

5.5 DYNAMIC MODELS

Dynamic models are essentially used to capture the fact that adaptation takes

time. If circumstances such

as

income or price change this year and the

consumer reacts by buying a bigger or smaller car or moving from one area of

residence to another, then this will continue affecting gasoline consumption for

many years to come. Hence we may argue that today's consumption

is

not only

a function of today's income and price structure but of earlier incomes and

prices as well. In this case the omission of these variables amounts to a

misspecification of the model explaining the intermediate range values found

(see

Appendix).

One of the earliest and most widely used of the dynamic models was the

partial adjustment model which hypothesized that people adjust consumption

only partially to changes in price and income because of inflexibility in the

stock of consumer durables. Since information on this stock was often not

5The interpretation of equation (5.3.2) for instance assumes that (if per capita income

is

constant)

the population elasticity of gasoline consumption is 1. In some studies, population is added

separately. A better alternative would be explicitly to enter the characteristic of the population that

has changed such as age rather than total population but this is rarely done.



Other lag structures

71

plausible that the effects of a change in price or income have a gradually

decreasing effect through time.

9

Table 5.1 shows that average price elasticities

for this model are around

-0.25

in the short and

-0.8

in the long run.

For

income the corresponding figures are 0.4 and 1.2 or 1.3.

5.6 OTHER LAG STRUCTURES

Naturally, there are other less restrictive ways of modelling the process of

adaptation, which we call Distributed Lag models. In general they can be

written as equation (5.7).

f1

r2

G=c+ L (Xi

P

t - 1

+

L Pi Y

t

-

i

+Ct

(5.7)

i=O i=O

This formulation again allows

us

to distinguish between the short-run elastici

ties (which are elasticities of G with respect to the current period's variables

only) and the long-run elasticities (which are the sum of the elasticities of

G

with respect to each of the lagged values and the current value). A disadvantage

is that there are very many parameters to estimate and only a fairly limited

number of observations available but this can be partly remedied by imposing

restrictions on the structure of the lags. They could for instance be required to

follow a polynomial of a certain degree. In practice this type of model is not

so commonly used since it requires long series of data and because of

collinearity between the lagged values. Sterner's (1990) estimates with this

model gave similar values to the lagged endogenous (see Table

5.1).10

Another intuitively appealing lag structure is the inverted

V,

which implies

that adjustment is low in the earlier periods, increasing, and then decreasing.

The reasoning behind this lag structure is that adjustment is costly and takes

time and there may be a 'perception lag'; these factors account for a low

immediate rate of adaptation. Adjustment then picks up and reaches a

9We must, however, be aware that the assumption that they decline geometrically and identically

for income and price is quite a strong restriction.

If

the true lag structure is different then such

restrictions will obviously bias the results of both the short- and long-run elasticities.

It

is possible

to devise lagged endogenous models with different rates of geometric decline for the influence of

two (or more) exogenous variables. For instance

G,

=c+cc1:s

i

P'-i +

{J1:ri Y'-i

+s,

can be transformed into

where

1J,=s,-(s+r)s'_1 +srs'_2

(see Kmenta, 1971, p. 478). We have, however, not seen any empirical work using such a model.

IONote that the geometric lag is, itself, a simple example of the polynomial distributed lag (PDL).



72

Modelling transport fuel

demand

maximum after which it declines in the same way as with the geometric lag.

This type of lag structure can be modelled in a number of ways. One

alternative

is

shown in equation (5.8.1) which combines a lagged endogenous

term with one (or more) lags on the exogenous variables:

(5.8.1)

I f

we

allow

an

extra lag (or several lags) on the price term then lags can first

increase for one (or several) period(s). After

that

the lag structure, however,

again becomes that of a geometrically declining series.

11

Another practical

approach to modelling an inverted V lag is to assume that the weights

IX

and

f3

in

(5.7)

follow a Pascal lag which can be used to derive

an

estimation

equation such as

(5.8.2).12

(5.8.2)

Table 5.1 shows the average results found in the survey for studies using

(5.8.1)

to be quite close to the usual dynamic model values. Sterner (1990) found

(5.8.1) could be rejected using statistical testing. Results for (5.8.2) are reported

in Table 5.1

and

have a rather low long-run value for

priceY

5.7

VEHICLE MODELS

The rationale behind the assumptions

of

various complicated lag structures is

the process of adaptation, which to a large extent is reflected in the stock of

vehicles. Another approach to modelling gasoline demand is thus to include

directly variables that reflect this stock (or certain characteristics of the stock).

In

the simplest case we can assume that consumers purchase gasoline (G) and

automobile services (A) to produce transport miles - M = f(G, A) - thus

abstracting from other automobile-related characteristics such as comfort, as

well as the durability problems associated with automobiles. Now consumer

utility functions contain miles and another good which they maximize subject

to their income constraint. (Alternatively household production theory can be

applied.) Thus, they choose the 0, G and

A that

maximizes U{f(G,

A), O} +

h(Y

-POO-pgG-PaA).

The first-order conditions to this equation give the familiar consumer results

that marginal rates of substitution for miles and the other good must be equal

11 With h 'free' lags these will

be

written as Lr=os(l-s)iIXh-i' Note that these are unrestricted if

there

is

no restriction on the IXi' After h periods the lags

will

again decline geometrically so that

the lag at time t will

be:

h

(l-s)('-h) L s(l-s)i

IXh

_i

i=O

12Note that as usual with the Koyck transformation, serial correlation may be introduced into the

error term and this together with the overidentification of (5.8.2) can imply problems for

estimation.

See,

for instance, Kmenta

(1971)

p.

489

for details.

13Nested tests could not be used here given the parameter restrictions in (5.8.2).



Vehicle

characteristics models 73

to the ratio of their relative costs and the familiar production results that the

marginal rate of transformation of gasoline and automobiles must be equal to

their relative prices. Solving and assuming homogeneity of degree zero in

income and price gives:

G

=

c

+

cx(

pg/Po)

+

P(Y/Po)

+

r( P./Po) +t:

(S.9)

Aggregate formulations of equation (S.9) in either a static or dynamic frame

work have been used in a few studies.

In

practice, however, the most common

approach has been to assume, in the short run, that the stock of autos (A)

is

fixed. Under this assumption (and assuming that prices can be deflated) we

arrive at the simple vehicle model

(S.lO):

(S.10)

Extensive data on the stock of autos has made this formulation quite popular.

Estimated values are, however, small (Table S.1) and should be interpreted as

short-run.

In this context we should also mention those models that include the

availability or price of alternative modes of transport. These models are

generally derived as a reduced form in which the complete system of equations

reflects the demand for gasoline simultaneously with the demand for cars, other

transport, etc.

The advantage of these models

is

of course a degree of realism, which,

however, is achieved at a certain cost, including the difficulty of finding reliable

data. The principal problem however with the vehicle models

lies

in their

interpretation. They fail to capture the process of adaptation which takes place

through the replacement of vehicles.

In

models that hold fixed the number of

automobiles, we find that the price and income coefficients mainly pick up

short-term effects. Long-run decisions are embedded in the vehicle stock and

cannot be captured in such simple reduced-form equations. To capture

long-run elasticities in this context

we

would need a model with simultaneous

equations for gasoline and vehicle demand.

14

S.8 VEHICLE CHARACTERISTICS MODELS

The above formulations

fail

to take into consideration automobile size and

other characteristics. However, some studies of auto demand indicate that the

number of automobiles

is

not so sensitive to the price of gasoline, but rather

14Most conventional studies rest on the assumption that the supply curve shifts and the demand

curve

is

stable, no rationing occurs (sometimes rationing

is

accounted for by dummies) and thus

the observed data points will allow us to identify the demand curve. However demand may also

shift (due to changes

in

taste, technology or regulations - such

as

catalytic converters or mandatory

fuel

efficiency standards) and supply-side effects may contaminate the demand elasticities es

timated. The only way of tackling such problems

is

through the estimation of systems of equations

including both supply and demand for gasoline - and possibly equations for other petroleum

products, for vehicles, for other modes of transport etc.



74 Modelling transport fuel demand

people adjust through changes in the fuel consumption characteristics of the

vehicle stock. Holding the stock of vehicles constant may not be enough.

I f

there

is

some adjustment in vehicle characteristics in the observed time period,

the short-run elasticities may capture some of this effect as well as changes in

utilization of the stock. Some models therefore also include vehicle efficiency

(measured for instance

by

miles per gallon) or other measures such as the size

of the automobiles, the proportion of trucks, and so forth. This potentially

enables the researcher to include not only the adaptation of the vehicle stock

in terms of quantity but also of performance, thus leaving only the short-term

variation in utilization to be captured

by

the price and income terms. These

vehicle characteristic models capture an even less responsive short-run elastic

ity than the simple vehicle models (average price and income elasticities are

almost halved in Table 5.1), showing that all of the short-run adjustment in the

vehicle models cannot

be

due to changes in utilizationY

5.9

DYNAMIC VEHICLE MODELS

A number of researchers have estimated dynamic vehicle models including

both vehicles and lagged variables. A very general formulation allowing lags

on price and income is the vehicle-distributed lag model (5.11):

(5.11)

Sometimes the Koyck transformation

is

used analogously with equation

(5.5)

to give a 'lagged endogenous-vehicle model'

(5.12):

G=sc+saP+s 3Y+sDA+(I-s)G

t

-

1

+)1 (5.12)

The reader should note that equation (5.12) actually implies that vehicles (as

well as any other additional explanatory variables used) also have a geomet

rically declining influence on gasoline consumption (compare equations 5.5 and

5.6).

This

is

a little hard to accept and the resulting long-run estimate of

-0.29

in Table 5.1 should serve as a warning against simply 'sticking in' variables in

the equations to be estimated without properly considering the structural form

of the economic model.

5.10 SOME FURTHER EXTENSIONS

OF

GASOLINE

DEMAND MODELLING

There

is

a continuous

flow

of new articles in this area and it

is

hard to pinpoint

exactly anyone particular direction for research. However, more attention is

15Some studies model demand adjustment by estimating a miles travelled equation and miles per

gallon in place of or along with a demand for gasoline. For a discussion of some of the miles or

miles per gallon equations see Dahl (1986).



Use of pooled data

75

being paid in many studies to structural and functional form and at the same

time to lag structure and correct specification of the error structure.

Many models start by going back to fundamentals and by noting that

gasoline consumption G can be dis aggregated as follows:

G=A x (MjA) x

(GjM)

where A is the stock of autos or vehicles, MjA is the miles driven per vehicle

and

GjM

is

their average efficiency measured in gasoline consumption per mile.

To model this directly, one would need detailed data on the age structure of

cars and the efficiencies of different vintages. Another approach is taken by

Drollas

(1984),

who starts by dis aggregating gasoline use into U for utilization

(GjA)

and

A

for the stock of autos. He then goes on to model these separately:

In

G

= In

U

+

Ln A

In U =

c1 +

t

lIn

P

g +

P

In Y

In At - In At - 1

=

s(ln

At

- In At - 1 )

In At

=C2

+Ct21n(PgjPr) +

p21n Y

+ :51n P

a

where s

is

rate of adjustment in the stock of vehicles;

P

r

is

the real price of (non-auto) transport services;

and P

a

is the real price of automobiles.

(5.13.1)

(5.13.2)

(5.13.3)

(5.13.4)

Combining these gives the model (5.13.5) to

be

estimated (Drollas also

estimates the model with a more complicated Pascal lag which gives additional

terms such as G

t

-

2

in the equation estimated).

In

G=(CI

+C2S)+(Ctl

+

Ct2s)ln

P

g

-

Ct2s1n P

r

+ P 1 +P2

s

)ln

Y

+ :5s1n P

a

(5.13.5)

- Ct1(1-

s)ln P

g t - 1

- P1(1- s)ln 1 ' ; -1

+(1-

s)ln G

t

-

1

Because of the restrictions on the parameters of (5.13.5) (and the Pascal or

inverted V case), Full Information Maximum Likelihood estimates are used.

Unfortunately, this model needs the prices of autos and 'alternative modes

of transport', and particularly the latter is very hard to define properly.

Anyhow the estimates are not far from what

we

expect even if the long-run

price elasticity is a little low.

5.11 THE

USE

OF POOLED

DATA AND

THE

COINTEGRATION

OF

VARIABLES

As mentioned earlier, the use of cross-sectional data appears to give much

higher elasticity estimates. Baltagi and Griffin (1983) argue very strongly in

favour of the use of pooled cross-section time series data to gain efficiency.



76


They do, however, disregard adaptation of the number of vehicles to the price

of gasoline and model gasoline per vehicle GjA as utilization U

(=MjA)

divided by efficiency

E

(=

Mj

A).

Utilization

is

modelled by a formula analog

ous with what we have called the simple vehicle model (including income per

capita, gasoline price and the stock of vehicles per capita). Efficiency

is

modelled as a function of income per capita and gasoline price but with dis

tributed lags. In estimation both Koyck transformations and polynomial distri

buted lags are used. Table

5.1

shows their average

16

results with the lagged

endogenous model along with the values found

by

Sterner (1990) using a very

similar model. Sterner found that the inclusion of data

for

the

1980s

and for a

larger variety of countries increased the estimated elasticities considerably.

1

7

Hughes

(1988)

also uses a somewhat similar model, but noting that the

gasoline per car series in most countries follow some sort of Random Walk, he

tests for non-co integration of the dependent and explanatory variables and

finds that this can in general be rejected. He therefore

uses

an error correction

model and again finds, for a pooled sample of OECD countries 1955-85, very

high long-run price elasticities. His model appears, however, to have trouble in

determining the long-run income elasticities.

5.13

CONCLUSION

It should be obvious from Table 5.1 that we are not out to find a unique set

of consensus values since elasticities surely vary between regions and time

periods. On the other hand, we do find some degree of consensus between a

number of models when applied to the same data and we also find models that

consistently seem to give odd or unsatisfactory results. We also find it natural

to interpret the results of some models as short- intermediate- or long-run.

We found that long-run elasticities can be estimated with either dynamic

models on ordinary time series data or with simple static (or vehicle) models

on cross-sectional data. The average elasticities for the dynamic models on time

series data generally

fall

in the interval

-0.80

to

-0.95

for price and 1.1 to 1.3

for income. Simple static models using cross data

give

roughly unitary

elasticities (+ j

-1.0)

for income and price. The income elasticity

is

lower in

vehicle models because of the inclusion of vehicles.

The same static models on time series data

give

intermediate values,

particularly for price elasticities. Other elasticities that may be considered

intermediate include the dynamic models estimated with monthly or quarterly

data (Dahl and Sterner,

1991).

Recent models using pooled data indicate that price elasticities may be even

16The average is used since the authors use five different GLS estimators with different results.

17

Sterner does not

use

gasoline per vehicle but total gasoline which

is

an additional but minor

explanation for higher estimates.



Appendix

77

as high as -1.3 or -1.4. This can be compared to the results found by Baltagi

and Griffin (1983) in their extensive analysis of pooled estimators for the

OECD. They concluded that, depending on the method used, long-run

estimates should

lie

between -O.SS and -0.9 (for gasoline per vehicle, which

naturally gives lower values).

The dynamic models mentioned above all give estimates of short-run in

addition to the long-run elasticities. These estimates, together with the results

from the vehicle and the vehicle characteristics models, generally

fall

in the

range -0.1 to -0.3 for price and O.1S and O.S for income. The estimates for

the rate of adjustment show that we are up against long-run changes: as much

as 10% of total adjustment may still be left after

10

years.

IS

APPENDIX

Considering that there are so many alternative models estimated it is useful to

reflect on the effect of omitting a relevant explanatory factor. Suppose the true

model were (S.14.1) but we estimate (S.14.2) in which the variable X has been

omitted:

G=c+aP+ f3Y

+rx

G=c+aP+bY

(S.14.1)

(S.14.2)

Suppose furthermore that

X

is

correlated with Y but not with P. It can then

be shown

19

that the estimate b will be biased:

E(b) = f3 +PI>XY

(S.14.3)

Where <l>XY

is

the regression coefficient of the omitted variable X on

Y.

In practice it is not always clear which is the 'correct' model

(it

depends on

the purpose of the exercise).

It

is,

however, clear that the inclusion of additional

explanatory variables will systematically affect the elasticities depending on

how they are correlated with price and income. Thus the inclusion of vehicles

should reduce income and price elasticities.

A similar analysis can be applied to the omission of lagged values in the

simple static model

(S.3.2).

Since the income and price variables are positively

correlated with their own lagged values exclusion of the latter will tend to

give

intermediate elasticities: suppose the correct (log-linear) model is (S.lS.1) and

181[

the coefficient on the lagged endogenous variable

is 0.6

or

0.8

this implies

40%

or

20%

adjustment the first year and (with a geometric

lag)

99.4% or 89.3% adjustment in 10 years. Note

that the longest PDLs used are generally around 10 years.

19For detailed analysis see, for instance, Kmenta

(1971)

pp.

392-3.

It should also

be

observed that

omission of relevant variables not only causes the estimates to be biased but also inconsistent and

their variance will be biased even if the omitted variable is not correlated with the other

explanatory variables.



78

Modelling transport fuel

demand

the estimated model is

(5.15.2):

G=C+

X

1

P

t+

X

2

P

t-1 +f3Y

G=c+aP+bY

(5.15.1)

(5.15.2)

then the 'static' model's (5.15.2) price elasticity will

be

a. The 'dynamic' model

(5.15.1) has a short-run elasticity of

1X1

and a long-run elasticity of (1X1 + 1X2) and

since the regression coefficient

<I>

between P

t

and P

t

-

1

can

be

assumed to be

positive but smaller than 1

we

find that, in analogy with (5.14.3), the estimated

elasticity a is positively biased compared to 1Xl> the short-run elasticity, but

smaller than the sum of the two elasticities (1X1

+1X2)

that make up the long-run

elasticity:

(5.15.3)

ACKNOWLEDGEMENT

Thanks are due to the Swedish Transport Research Board for funding.

REFERENCES

Angelier, J. P. and Sterner,

T. (1990)

Tax Harmonization for Petroleum Products in the

Ee.

Energy Policy,

March.

Archibald, R. and Gillingham, R.

(1978)

'Consumer Demand for Gasoline: Evidence

from Household Diary Data.' College of William and Mary and Bureau of Labor

Statistics (mimeo).

Archibald,

R.

and Gillingham,

R. (1980)

An Analysis of the Short-run Consumer

Demand for Gasoline Using Household Survey Data. Review of Economics and

Statistics, 62

622-8.

Baltagi, Badi,

H.

and Griffin, James,

M.

(1983)

Gasoline Demand in the OECD: An

Application of Pooling and Testing Procedures.

European Economic Review,

22,

117-37.

Barker, T.

S.

and Pesaran,

M. H. (eds) (1990) Disaggregation

in

Economic Modelling.

Routledge, London.

Dahl, Carol

A. (1986)

Gasoline Demand Survey.

Energy Journal, 7

67-82.

Dahl, Carol A. and Sterner, T. (1991) Analyzing Gasoline Demand Elasticities. Energy

Economics, April.

Drollas, L. P.

(1984)

The Demand for Gasoline: Further Evidence.

Energy Economics,

6

January, pp.

71-82.

Griliches,

Z. (1967)

Distributed Lags: A Survey.

Econometrica,

35 pp. 16--49.

Houthakker, Hendrik, S. and Taylor, Lester D.

(1966) Consumer Demand

in

the United

States, 1929-1970.

Harvard University Press, Cambridge, Mass.,

p. 116.

Hughes, Warren

R.

(1980)

Price and Income Elasticities of Demand for Motor Gasoline

in

New Zealand, University of Waikato, Hamilton, New Zealand (mimeo).

Hughes, G. A.

(1988)

'On the Consistency of Short and Long Run Models of Gasoline

Demand', paper given at the Scottish Economic Society Conference, St Andrews, 9

April

1988.



References

79

Kmenta, Jan

(1971)

Elements of Econometrics. Macmillan, London.

Koyck,

L. M. (1954)

Distributed Lags

and

Investment Analysis. North Holland,

Amsterdam.

Sterner,

T.

(1989a) The Politics of Energy Pricing: Oil Products in L. America.

Energy

Journal,

10

25-45.

Sterner, T. (1989b) Le Prix des Produits petroliers en Afrique. Revue de I'Energie, no.

415, November, pp. 3-11.

Sterner, T. (1990) The Pricing of and Demand for Gasoline. Swedish Transport Research

Board, Stockholm (ISBN 91-87246-57-0).



6

Modelling

the long-run

supply

of coal

Ronald P. Steenblik

6.1 INTRODUCTION

There are many issues facing policy-makers in the fields of energy and the

environment that require knowledge of coal supply and cost. Such questions

arise in relation to decisions concerning, for example, the discontinuation of

subsidies, or the effects of

new

environmental laws.

The very complexity of these questions makes them suitable for analysis by

models. Indeed, models have been used for analysing the behaviour of coal

markets and the effects of public policies on them for many years.

For

estimating short-term responses econometric models are the most suitable (see,

e.g., Labys and Shahrokh

1981;

US EIA,

1986). For

estimating the supply of

coal over the longer term, however - i.e., coal that would come from mines as

yet not developed - depeletion has to be taken into account. Underlying the

normal supply curve relating cost to the rate of production is a curve that

increases with cumulative production - what mineral economists refer to as the

potential

supply

curve. To derive such a curve requires

at

some point in the

analysis using process-oriented modelling techniques.

Because coal supply curves can convey so succinctly information about the

resource's long-run supply potential and costs, they have been influential in

several major public debates on energy policy (see, for example, Layfield, 1987).

And, within the coal industry itself, they have proved to be powerful tools for

undertaking market research and long-range planning. The purpose of this

chapter

is

to describe in brief the various approaches that have been used to

model long-run coal supply, to highlight their strengths, and to identify areas

in which further progress

is

needed. The chapter starts with a review of

concepts and terminology, in order to provide a framework for discussing the

actual models.



82 Modelling the long-run supply of coal

6.2

POTENTIAL SUPPLY: A DIAGRAMMATIC EXPOSITION

Harris and Skinner

(1982)

state that the ideal model of long-term mineral

supply would consist of three separate but interconnected modules: an endow

ment inventory module, an exploration module and an exploitation module.

In the inventory module each deposit would

be described by key attributes,

including location, quality, and in situ geological characteristics, in a manner

whereby tonnage estimates could readily be aggregated according to desired

combinations of these attributes. The exploration module would then act upon

this inventory, examining each deposit from the standpoint of discoverability.

For most mineral products, such

as

oil, the modelling of exploration is

imperative (Chapter

8).

In the case of coal, however, the bulk of the endowment

is

already 'discovered'; hence the task

is

usually limited to estimating the

amount of effort, adjusted for uncertainty, that would be required (within the

reference period) to evaluate and develop various deposits or portions of

deposits. Finally, the exploitation module would simulate development deci

sions in these deposits, evaluating them for that optimal configuration of size,

extraction method, etc., that would maximize discounted profits. The ultimate

product would be a potential supply curve, or schedule, showing the amount

of coal that could be produced at different mine-mouth prices.

Before discussing the individual modules, an explanation of terms

is

in order.

An endowment inventory is essentially an aggregation of estimated quantities

contained in deposits that meet certain geotechnical limits with respect to

grade, thickness of seam, burial depth, size and suchlike. The classification of

endowment

is

analogous to the classification of reserves and resources, except

that in place of an economic criterion (cost per tonne), geotechnical criteria are

used. These geotechnical criteria are usually important determinants of mining

costs, but rarely are their effects on costs linear. Figure

6.1

shows how

endowment categories might be defined using a single geotechnical criterion,

say overburden ratio

(i.e.,

the ratio between the thickness of the rock and soil

between the surface and the top of the seam, and the thickness of the seam). In

classifying coal endowment, geologists often use two limits, one defining the

reserve base (limit 'A' in the figure) and one defining the rest of the total

endowment (limit 'B'). All other accumulations of material not satisfying these

inclusion criteria are contained in the remainder of the resource base. The

boundary values for excluding coal occurrences from the endowment should

be set below the values that would apply to resources, and near the foreseeable

technological limits of mining. The ideal endowment inventory would also be

probabilistic, describing the statistical distribution of key geological attributes

(rather than average values).

Mineral endowment is a relatively

new

term, and arose out of the need to

distinguish the geologist's concept of resources as a stock inventory from the

resource economist's concept of resources as a

flow

(Harris and Skinner, 1982).

Dorian and Zwartendyk

(1984:

p. 660) define it succinctly as 'the physical



Potential supply

Geotechnical

REMAINDER OF THE RESOURCE BASE

limit 'B'

L- - - - - J .I · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · : · · · · · · · · · · · · ·

..

············· .

Geotechnical

limit

'A'

I ~

IDENTIFIED

MINERAL

ENDOWMENT

RESERVE

BASE

UNDISCOVERED

MINERAL

ENDOWMENT

: Measured: Indicated: '

I

..

--- - - - - -- -- - -- -- - - -, Inferred : Hypothetical: Speculative

: Demonstrated: : :

~ - - - - - - - - - - - - - - - - - - - ~ - - - - - - - - - ~ - - - - - - - - - - - - - - - - - - - - - -

: IDENTIFIED : UNDISCOVERED

_____________________________ ______________________ _

Geological assurance categories

Fig. 6.1

Mineral

endowment classification

scheme.

(Source: Steenblik (1986),

Figure

2.4.)

83

envelope beyond which our technological capabilities do not reach, neither

now nor in the foreseeable future'. An important difference between an estimate

of mineral endowment and an estimate of resources is that an endowment

estimate is immutable with respect to changes in prices or technology. By

contrast, the term reserve should only be used in reference to 'those accumula

tions of a mineral that are known and have been explored to the extent that

there is a reasonable assurance that the mineral could be produced from them

economically' (Harris, 1984,

p.

13)

- that

is,

that it can be produced at current

prices and using currently available technologies. Reserves are a sub-set of

resources.

Potential supply differs from resources, reserves and endowment in that, by

definition, it considers explicitly the trade-off between the costs of producing

from known deposits and the (possibly lower) costs of finding, appraising, and

developing less well-identified deposits. And it is explicitly defined with respect

to time. Formally, potential supply represents 'that part of resources that

would be discovered by an optimum amount of exploration, given the specified

economics and unconstrained markets' (Harris, 1984,

p.

14).

While it

is

possible - indeed, preferable - to obtain an estimate of potential

supply from an endowment inventory, it is easiest to demonstrate the concept

graphically with reference to reserves and resources. Figure 6.2 shows a

hypothetical set of mineral deposits, classified according to degree of geologic

assurance and economic feasibility - as determined at the date of classification.



84 Modelling the

long-run

supply

of

coal

Cost of

extraction

~ ~ i r o

Me U' /Ubom,,:::::::,:,

01 O . . c /

30-year potential

supply

~ z : ~ +H

u 0 :":.:" . . . . . . . .

().

.,

~

~ : ~

:a

: : J :

·iii

Ul:

ca : ~

.

u

·e

o

c

............

( )

~

o

z

o

( )

UJ

......

90 per

cent

. confidence limit

+.:

F

.I-----r-----,-----.-------;r---

. . .

: Measured:

Indicated:

' ,

1---------->----------,

Inferred :Hypothetical: Speculative

: Demonstrated: , ,

r - - - - - - - - - - - - - - - - - - - - ~ - - - - - - - - - ~ - - - - - - - - - - - l - - - - - - - - - - _

: IDENTIFIED :

UNDISCOVERED

__________________

L

______________________

_

Geological assurance categories

Fig.

6.2

Relationship

between resources

and

potential

supply.

(Source: Adapted from

Steenblik

(1986), Figure

2.5a.)

Using the scheme suggested by McKelvey

(1972),

deposits A and B would be

classified as measured economic reserves, deposit C would be an indicated

economic reserve, deposit D would

be

a para-marginal (or subeconomic)

measured resource, and

so

on. Assuming rational economic behaviour, the

least-costly and most thoroughly appraised deposits

will

be developed first.

The trade-off between mining cost and the costs of exploration (the identifica

tion of deposits not yet discovered) and appraisal (the detailed measurement

and assessment of the characteristics of an identified deposit)

will

vary from

mineral to mineral and from place to place.

An estimate of potential supply makes sense only if it is defined with respect

to a stated period of time and corresponding assumptions about technology,

prices and exploration effort. In Figure

6.2,

an estimate of 30-year potential

supply might include deposits A through F, but exclude deposits G and H. The

cost of developing and mining deposit H would just

be

too high given the

assumptions about prices and technology; similarly, because of the uncertainty

surrounding the existence and characteristics of deposit G, this deposit is

unlikely to be developed within the specified timeframe.

No estimate of long-run potential supply can be expected to be precise.

Conceptually, this uncertainty can be depicted by enclosing the boundary of

potential supply within its associated confidence limits (the dotted curve in



Potential

supply:

a diagrammatic exposition

85

Figure 6.2). One would also expect that the dispersion of uncertainty as to

where the boundary lay would be greater

for

the less-well identified portions

of the resource - the right-hand area of the diagram - since the geological

uncertainties add to the uncertainties already accompanying the technical and

economic variables.

Figure

6.3

shows schematically how a potential supply curve might be

constructed for the set of hypothetical mineral deposits used for illustrative

purposes in the previous figures. Taking the six deposits, A through F, the task

is

to reduce the three dimensions by which they are classified - cost of

extraction, amount in place and the degree of assurance about the amount in

place - and compress them into two: cost and quantity. This is done by adding

to the expected extraction costs the costs of exploring and examining each

deposit to the point where the amount of information available about it

becomes sufficient to take a decision on development. Obviously, the

less

is

currently known about a deposit - that is, the further right it lies on the

geological assurance axis - the greater the expenditure (and risk) associated

with bringing it into production.

The final step is to arrange each deposit

in

increasing order of its total

expected unit costs. The recoverable quantity producible from each deposit at

an estimated cost forms a segment of width q and unit cost p that, when

arranged in increasing order, forms a stepped function, the potential supply

curve. The procedures required to translate coal endowment information into

an estimate of potential supply are more complex than when using resources

as the starting point, but because the assumptions and criteria used in

compiling a mineral endowment are apt to be less subjective and mutable, the

empirical results are likely to be superior. The major additional step is the

conversion of the geotechnical data into an estimate of extraction cost.

C

.2

i i c

. .

0

0·-

-a.ca

IIC::::

GI

0

- Q.

o

M

GI

III

O Q

1.1 C

01 as

as

III III

as .-

GI

~

.. Q.

1.1 Q.

.5

as

Cumulat ive 3D-year

potential supply curve

E

Cumulative production

F

Fig.

6.3

Idealized cumulative potential supply curve.

(Source: Steenblik

(1986),

Figure

2.6.)



86

Modelling the

long-run supply of coal

6.3

DISTINGUISHING CHARACTERISTICS OF COAL

While similar in many respects to other hard-rock minerals, coal has several

distinguishing characteristics that make the task of modelling its supply easier

than that of some other minerals. First, the structure of the coal supply

industry has generally been considered to be workably competitive. Second,

user costs are not considered to have an important bearing on price (Zimmer

man, 1981; Adelman et at.,

1990). By

not taking this element into account, the

computational aspects of estimating the supply response are greatly simplified.

Coal supply modelling

is

also facilitated by the existence of relatively

abundant data on the location and geological disposition of coal. Enough is

known about the whereabouts of coal in most regions that new discoveries play

a relatively small role in its supply. But though the location and approximate

amount of coal in the ground in many areas

is

well assured, this does not mean

that no geological risk is associated with mining: for coal, the uncertainty lies

in the detail. Especially in underground mining, the expensive surprises are

often not discovered until the mining machinery is already in place.

Decades of mining experience, confirmed by engineering-cost analyses, have

shown that any from a wide range of geometric, geomechanical and geochemi

cal characteristics can profoundly affect the costs of mining (see, e.g.,

Barnett,

1980;

Klein and Meany,

1984).

These include the size of the deposit; the

thickness of the coal seam; the depth of the rock and soil overlying the seam

(the overburden); the amount of tectonic disturbance; the angle from horizontal

in which the seam occurs; the friability of the overburden; and the rate of inflow

of water and methane into the mining section.

Two characteristics of coal complicate matters: its heterogeneity and its

bulkiness. Grade in the case of metallic ores is typically a one-dimensional

consideration - ultimately, the metal is refined and used in its pure state. While

it

is

possible to extract pure carbon from coal (as is done in small quantities

to produce carbon rods, for example), such processing

is

not yet economical

for producing fuel, coal's principal

use.

Generally, coal mined in its raw state

contains a number of undesirable impurities, such as ash and compounds of

sulphur or chlorine, the concentrations of which can be reduced at the

processing stage, but which cannot easily be eliminated completely. Finally,

coals differ considerably in their thermal content. These considerations mean

that, to be useful, coal supply curves must be distinguished according to quality

characteristics that affect the coal's market value.

The task of describing the distribution of the coal endowment is further

complicated by what might be loosely described as its fractal-like properties:

namely, that the variation of coal tonnages by certain quality or geotechnical

characteristics appears sometimes to be independent of the scale of the area

over which

it is

measured. The variation in coal seam thickness, for example,

can be as great within seams, and even within individual mines, as it is among

different seams (Newcomb and Fan, 1980). A coalfield with numerous mineable



Models

of

coal exploitation

87

deposits, each exhibiting large spatial variation in seam thickness, would have

quite different mining costs than one with the same variation overall but

wherein each deposit was homogeneous with respect to the thickness of its coal

seam. Moreover, predicting costs and the sequence of mine development is

much easier in the latter case than it

is

in the former.

Finally, because coal

is

bulky, and therefore transport costs weigh so heavily

in its final price, supply functions are meaningful only if they are specified for

fairly limited geographical areas.

6.4

MODELS

OF

COAL EXPLOITATION

Estimating a supply curve for a region involves three steps: (i)

the development

of relationships between the physical conditions of mining and costs; (ii)

defining that portion of the coal endowment that

is

developable and of

potential economic interest over the period covered by the analysis; and (iii)

using the cost relationships developed in step

(i)

to transform the geological

data into a potential supply schedule. The term 'exploitation model' refers to

all the procedures used to examine the deposits described in the coal endow

ment inventory and to calculate their expected costs of development and

production. Because the simulation of development decisions in the coal

endowment necessarily involves translating in situ into saleable quantities, it

also encompasses procedures to take into account losses associated with the

recovery and benefication of coal.

6.4.1 Modelling depletion

The focus of this section is on formal mine-costing models - that is, those that

use objective computational procedures for estimating long-run average coal

mining costs as functions of geotechnical and engineering factors.

It

may

be

observed, though, that many worthwhile empirical studies of coal-supply costs

have been published over the last 5 years that have been based on discounted

cash-flow models of individual or 'representative' model mines (see, e.g, Long,

1986;

Jamieson,

1990;

US Department of Commerce, 1990). Generally, the

analysts undertaking these studies have relied primarily and directly on the

informed judgement of mining engineers (often from the mining companies

themselves) in order to estimate the values of cost elements influenced by

geological variables. The results of these exercises may be accurate, but their

value

is

limited because they are difficult to replicate or to recalculate using

different assumptions. Some of the most interesting studies in the public

domain have been those undertaken for the purpose of comparing coal mining

costs among different countries. But too often the critical data inputs to the

models, especially those that would enable other analysts to regenerate the

supply functions using common assumptions, are not reported; this diminishes



88

Modelling the long-run supply of coal

their usefulness, for example, as inputs to models of world coal trade.

Quebral (1990) has reviewed a number of one-off empirical studies that have

used econometric techniques to explain the effects of changing technical

conditions and factor prices in particular coal industries. The discussion here

is limited to models that have been developed and maintained for extensive and

repeated use.

Much developmental work on formal models of long-run coal supply has

focused on the relationship between costs or productivity and in situ geological

conditions, in order to measure the changes that may be expected in costs as

these conditions deteriorate. Over the past

15

years, numerous mining-process

models of varying degrees of sophistication have been developed, particularly

in North America. There are over 3000 coal mines operating in the United

States alone - an unwieldy number to cost on a mine-by-mine basis. More to

the point, the United States boasts a demonstrated coal reserve base of some

226,000 million tonnes of bituminous coal, against an output of around 600

million tonnes annually. But a large part of this reserve base includes coal that

is

not yet - indeed may never be - profitable to extract.

It is

thus only with the

help of models that sense can begin to be made of the endowment figures.

Beginning in the early 1980s analysts began to apply these models to other

countries, often basing their analyses on US experiences. Notable examples

include Ellis's

(1979)

study of coal mining costs in South Africa, and ICF

Incorporated's study of Australia, Canada and South Africa (ICF, 1980a).

Basically, two approaches can be distinguished, though some models com

bine features of both: the engineering-cost approach and the statistical

econometric approach. The former

is

exemplified by the coal supply costing

programme, initially developed by the US Federal Energy Administration (now

the Energy Information Administration), and since modified and enhanced

by

the consulting firm ICF Incorporated as a component of their Coal and

Electric Utilities Model

(see

ICF, 1977), and

by

the EIA for their Resource

Allocation and Mine Costing (RAMC) Model

(see

SAIC,

1986,

1988a).

Al

though the two versions of the model differ in detail, they are similar in their

approaches. A quite detailed process-evaluation model was developed by NUS

Corporation for the Electric Power Research Institute (NUS,

1981;

1984),

though the application of this model in potential coal supply analysis has been

largely restricted to generating 'pseudo-data' for

use

in other, regional-scale

models. Finally, Skelly and Loy (CRA, 1986) have developed a model that uses

a subjective point system to evaluate individual deposits, and then calibrates

the results against the point rating of mines of known cost.

The second approach

is

exemplified by the models pioneered by

M.

B.

Zimmerman (1977,

1981),

and now used primarily by the consulting group

Data Resources Incorporated (DRI). The basic mine-costing relationships

derived by the Zimmerman model are also incorporated into a model that was

developed by another Cambridge (Mass.)-based consulting group, Charles

River Associates (see CRA, 1982). A slightly different mining-cost model, also



Models

of coal exploitation

89

employing a log-linear functional form, has been developed by Newcomb and

Fan

(1980).

The early versions of these models have already been reviewed by Gordon

(1979), and have been the subjects of detailed comparative assessments by the

Energy Modeling Forum (EMF

1978),

and by many others, including the

present author (Steenblik, 1985). The ICF/EIA coal supply models - mainly by

virtue of their central roles in US coal policy formation and analysis - have

also received special critical attention by a number of economists and model

analysts (e.g., see Vogely, 1979; Goldman and Gruhl, 1980; Wood and Mason,

1982). What follows below is therefore limited to a very brief description of

their salient features.

Engineering-cost models

In the ICF and EIA mine-costing frameworks, major cost elements (initial

capital, deferred capital and certain elements of annual operating costs), which

are presumed to be functions of the physical conditions of mining, are

estimated individually on the basis of relationships derived from engineering

cost models of representative mines. These model mines are hypothetical

constructs, distinguished by size, mining method (e.g., surface or underground),

and seam conditions. Rules are then developed to vary costs with variations in

mining conditions from those assumed for the base-case model mines; output

is assumed to adjust optimally so that average costs are minimized. Finally the

cost functions are used to evaluate discrete, pre-configured coal deposits.

Statistical-econometric

The statistical approach, as developed initially by Zimmerman and refined by

Barrett

(1982),

rather than estimating costs from physical conditions directly,

starts instead by defining the relationship between the productivity of the

relevant producing units comprising a mine and the mine's in situ seam

characteristics and output. In deep mining, a production unit is defined as a

mining machine and its complement of miners, i.e., a mining section. The

corresponding measure in the surface mining model is the capacity of the

overburden-removing equipment: the maximum usefulness factor (MUF) of

draglines (defined as the product of the volumetric capacity of the dragline's

bucket and the length of its dumping reach) for area, or strip mining; and the

shovel bucket capacity (SBC) for open-pit mines. Then, using either pseudo

data

(e.g.,

from detailed engineering-cost models) or, preferably, data for actual

operating mines, an equation relating output per production unit as a function

of mine size, and its geological and physical dimensions,

is

estimated. After

further manipulations, the model eventually yields an expression for minimum

average cost at some optimal level of output.

For the sake of brevity, only the



90

Modelling

the long-run

supply

of coal

equations for an underground mining model are described here; similar

procedures are used in the models for surface mining.

In

developing his original model, Zimmerman had data on only three

observable characteristics: number of mining sections, seam thickness and the

number of mine openings. Using public data from 1975 on 244 underground

mines in the United States, each of which produced at least

100,000

short

tonnes in that year, he estimated the following regression:

(6.1)

where q

=

average mining section productivity;

Q=

the total annual output of

a mine; S

=

the number of mining sections operating at the mine; Th

is

the coal

seam thickness;

Op

is

the number of openings (or shafts) to the mine; and

B

is

a random disturbance term for the unobservable geotechnical characteristics.

Using the productivity equation as a base, Zimmerman next estimated a

long-run cost function. He showed that necessary expenditures on labour,

operating supplies and capital could be approximated as a function of the

required number of mining sections. Zimmerman used engineering estimates of

expenditure categories

for

hypothetical mines, but it could also be fitted to

actual mine data, if available. Combining the cost equations yields an equation

for the average cost as a function of measurable geological parameters and

mine output.

On the assumption that, in the long run, the marginal cost of producing coal

will equal the minimum average cost of a mine, the final task

is

to establish

the minimum efficient scale (Q*) of a mine and then to evaluate the average

per-tonne cost at that point. (Somewhat different means are used to determine

(Q*)

for

underground and surface mines.) Ultimately, the procedure yields an

equation for the minimum average cost at some optimal level of output.

Following Church (1981), the resulting long-run marginal cost function may be

generalized as:

(6.2)

where Km

is

a constant corresponding to a particular mining technology.

A major result (and limitation) of the early modelling work by Zimmerman

was the large unexplained residual in the productivity equation. Zimmerman

(1983,

p.

308) concluded that 'if only the seam thickness

for

a given mine is

known, a

90

percent confidence interval includes productivity levels almost

seven times greater than those predicted by' the equation. Barrett (1982) has

shown that considerable improvements in the regression results can be ob

tained by performing the anaysis on a smaller, regional basis, using empirical

data from mines specific to that region. This makes sense, for as Brooks

(1976,

p. 168) observes: 'in one [group of deposits or a district] ... many of the

economic variables will hold constant. That is, though the relationship between

cost and certain physical characteristics may not be simple, it should at least

be stable for all of the deposits under consideration'.



Models of coal exploitation

91

Another of Barrett's enhancements was to expand the analysis to include

several technologies. Whereas Zimmerman assumed one type of mining tech

nology, continuous mining (whereby coal

is

ripped from the coal face and

loaded onto a conveyor belt in one continuous operation), Barrett looked at

three. The results (Figure

6.4)

are interesting because they indicate a moderat

ing, through technological substitution, of the long-run effects of depletion.

Thus, in the example, as thick seams are depleted, the preferred mining method

may be expected to change, from the

use

of continuous techniques to the use

of conventional mining techniques (wherein the blasting, extraction and trans

portation of coal are handled by different machines). In other coal fields, the

relationship of the curves may be different, with longwall mining playing a part

at some stage of the field's development.

6.4.2

RecoverabiIity and yield

Endowment refers to quantities in situ, but potential supply

is

concerned

ultimately with producible or saleable amounts. To translate from one to

another one has to take into account all losses or reservations of coal due to

geological, technological, economic and legal factors. Current potential supply

curve procedures treat these losses exogenously rather than incorporating them

as endogenous variables.

40

35

QI

C

c

.2

..

30

QI

Q.

~ 25

::;)

CI

00

0 )

20

a;

0

u

15

c

.2

U

10

::I

"tI

0

..

Q.

5

Longwall mining

mining

......------

' . . . . . . . . . . .

' . ,

Continuous mining

50 75 100 125 150 175

200

225 250

275 300

Coal seam thickness (cm)

Fig.

6.4

Minimum average cost of underground mining by mining method in the

midwest United States as a function of coal seam thickness. The shapes of the curves

are approximations of the originals. (Source: Adapted from Barrett (1982).)



92 Modelling the

long-run

supply

of

coal

Losses and reservations occur at all geographic levels and at every stage of

production. At the broad regional

level,

portions of the coal endowment

will

at any given time

be

unavailable where beds are overlain by urban structures,

parks and similar surface features with which mining would conflict. At the

mine-property level, coal will often be left unmined and therefore lost where it

underlies mine roads and buildings, or abuts other properties. Where under

ground mining methods are being used, some coal situated above or below the

bed being worked may be rendered unmineable and thereby lost. Losses

inevitably occur during extraction as well, as a result of spillage, accidents, fires

and so forth. Finally, a portion of the coal is lost or discarded whenever it

undergoes cleaning or beneficiation.

These concepts can be understood with the help of Figure

6.5,

which places

these losses in a sequential hierarchy. Implied by the reducing lengths of the

bars in Figure 6.5 is that at each stage the decisions or actions that affect a

Planning

losses

•

II

riginal

coal In

place

Lo en . eg. of Co./ recoll.ry

Legally accessible

and mineable coal

I

Layout Technically mineable

losses coal

Winning Economically

losses

mineable coal

Cleaning or

.beneficiation, ----,

losses

~

Run-of-mine

coal

Saleable

coal

Lo

g etlng

ectill/ty

Regional

and

urban planning

Mine

layout

Extraction

(winning)

Cleaning

or

beneficiation

Fig. 6.5 Relationships of concepts and terminology for designating losses and re

coverability of coal. Relative sizes of losses are only indicative. (Source: Steenblik (1986),

Figure 6.2.)



Models

of

coal exploitation

93

coal deposit

will

reduce the quantity available at the next stage. Viewed over

a finite period - that

is,

several years - the losses from one stage to another

are likely to be cumulative. Over the longer term, however, certain losses may

at least be

partially reversible. In a physical sense no losses (with the exception

of coal lost in fires) are permanent;

i.e.,

material

is

not destroyed.

It

may,

however, be so costly to recover that in a practical, economic sense it is lost

forever. Much of the losses that occur during the extraction process fall into

this category. On the other hand, certain types of planning losses may

be

regarded as temporary reservations - for example, coal underlying railroad

rights-of-way. Such coal may not

be

available to contribute to 20-year

potential supply, but some of it could very

well

contribute to 30-year supply

should the rights-of-way

be

sold or abandoned.

Between these two extremes may

be

found a large number of factors

influencing recovery of varying and changing degrees of irreversibility: time,

technology and prices interact in ways that are complex and largely indetermi

nate. As Harris and Skinner

(1982, p. 306)

explain, a deposit developed later

when prices for the mineral are high

is

likely to

be

mined more intensively than

an identical deposit developed earlier when prices are low. In some cases the

material left behind may

be

unprofitable to produce under any imaginable

price. In other cases, however,

as

the deposit

is

worked, higher prices or lower

production costs may justify expanding the initial mine in order to extract

previously excluded material. Similarly, a portion of the coal rejected as waste

during the screening and washing process and dumped in culms may become

profitable to mine at a later date. Such secondary recovery from culms

is

currenty taking place, for example, in eastern Pennsylvania and in Belgium.

Although improvements in technology generally increase the rate of recovery

from a deposit, changes in technology and factor costs can affect the level of

recovery in counteracting ways.

As

Fettweis (1983) and others have pointed

out, the level of recovery with respect to the coal endowment as a whole

decreased

with the introduction of mechanized room-and-pillar mining: seams

that previously would have been mineable using picks and shovels (at high risk

of injury to miners), became unmineable using mechanical cutters. Hence the

'recoverable' portion of the endowment was reduced. More recently, the

introduction of longwall mining machines has increased

the percentage of coal

that can be mined profitably from many seams,

as

compared with recovery

using room-and-pillar methods; but because the method requires more favour

able geologic conditions, its increasing use may have a neutral

or

even negative

effect on overall, ultimate recoverability.

Thus the relationship between the level of recovery and the coal endowment

is

largely an economic one. Under conditions of constant technology and factor

prices, mining costs are an increasing function of both deteriorating geological

conditions and increasing rate of recovery (Fettweis,

1983).

From

the static

perspective, the amount of coal that can

be

recovered at any particular cost

can come from different amounts in place. But given the partial irreversibility



94

Modelling the long-run supply

of

coal

of mining with respect to time, the decision of where to operate on the cost

surface at any moment will require choosing, consciously of unconsciously,

between accepting a low rate of recovery now and faster depletion, or higher

recovery now and slower depletion. This implies also that the level of recovery

is unlikely to remain constant through time.

Considering the complex nature of recovery, it

is

understandable that

applications of mine-costing models have generally ignored the economic and

time-dependent dimensions of recovery, treating it purely and simply

as

a

technology-determined matter. None the less, it

is

important to move away

from assuming global, and rather arbitrary recovery rates; wherever possible

one should instead

use

empirically derived estimates. There appears to be

strong evidence to suggest that the recovery rates commonly applied to

aggregate estimates of the coal endowment - 50% for deposits mineable by

underground methods, 80% for deposits mineable by surface methods (e.g.,

Averitt, 1975) - are over-optimistic by a factor of two (Schmidt, 1979).

Beneficiation, the removal of unwanted mineral matter (dirt and sulphur in

particular), invevitably results in some loss of material. Again, beneficiation

losses are a function of a number of technological and economic factors,

including coal quality (especially ash content) before and after cleaning, method

of mining, available beneficiation technology and existing cost-price condi

tions. Accordingly, a wide range of washery losses are possible from any

particular amount of coal in place - anywhere from a

few

percentage points to

over 50%. In general, the further the coal has to be transported, the more likely

it

will

undergo cleaning. And coal used

for

steel-making is normally cleaned to

a higher degree than coal used

for

power generation.

6.5

THE COAL ENDOWMENT INVENTORY

The preceding section described models for estimating the production costs of

mines. This section examines the data on geological variables that are required

for these models, and the problems involved when the data are incomplete.

An analyst seeking to model coal supply would ideally already have

available an inventory of the region's coal endowment, containing data

describing the characteristics of the coal in situ, by seam, in a manner whereby

tonnage estimates could readily be aggregated according to desired combina

tions of geographical units, geological attributes, quality attributes and so

forth. Such an inventory would enable the analyst to describe not only the

inter-seam distribution of these characteristics, but the

intra-seam

distribution

as well. It would also identify the surface land-use around the deposit,

indicating any structures that would inhibit mining (such as oil wells), so that

a judgement could be made about the accessibility of each deposit.

To date, however, detailed inventories of coal endowment have been

produced for only a few small areas of the world. This is not surprising: many



The coal endowment inventory

95

coalfields have not been thoroughly explored; others are controlled by state

monopolies that treat their geological data as commercial secrets. But even

where a large amount of data has been collected and made available to the

public, it has been rare that any attempt has been made to organize the

information systematically. And information that would allow the correlation

of data from different samples

is

often missing. Only with the advent of

computers have governments considered undertaking the expensive task of

compiling the data in a way that it may be used to construct detailed,

three-dimensional maps of their countries' coal endowments.

The US Geological Survey's (USGS) effort to inventory the coal endowment

of the United States is perhaps the largest programme of this kind. Since 1977,

the USGS, in cooperation with state geological bureaux, has been digitizing

data relating to the geophysical and quality characteristics of the nation's coal

deposits, along with information on land use and cultural features; to complete

the task, several hundreds of thousands of observations

will

have to

be

compiled (Carter et

al.,

1981;

Gluskoter,

1991).

A network of microcomputers

and software, known as the National Coal Resource Data System (NCRDS),

has been devised to process the data and, where the density of observations is

sufficient, to generate isoline maps or to calculate quantities of coal in relation

to seam thickness, depth of seam, and various quality attributes. The USGS's

goal

is

to set up the inventory and supporting software so that it can be

integrated with mining-cost models, such as those under development at the

US Bureau of Mines, to produce production-cost curves at the desired level of

aggregation.

The problem for the moment is that the coverage of the NCRDS is still

rather limited: only around

15

quadrangles (each measuring about

250

km

2

)

in

the central Appalachian coal province, out of an eventual total of 450, will be

completed by the end of

1991.

Work

is

continuing on mapping other areas of

the country as well; but, for the time being, any analyst seeking a comprehen

sive coal endowment inventory of the United States (or of other countries)

must make do with pre-aggregated tonnage estimates, distributed typically by

administrative units, coal rank, and by a small number of seam-thickness,

depth, and sulphur-content classes.

For the United States, at least, efforts have been made to assemble these

pre-aggregated inventories into a form that can be used as an input to

coal-supply models. This work has been carried out over the last

15 years by

or for the US Department of Energy and its predecessor agencies in an effort

to upgrade the Demonstrated Reserve Base (DRB) of coal, originally compiled

by the US Bureau of Mines in

1975

(for a description of this work

see

US EIA,

1989,

pp. 4-14). The method used has involved, essentially, partitioning each

region's coal endowment inventory into geographical sub-units of known or

calculable quantities and ascribing average values for the relevant physical and

quality characteristics to those sub-units. The quantities of coal identified

within each defined category (representing a range of values for any number of



96

Modelling the

long-run

supply

of

coal

physical and coal-quality characteristics) have then been aggregated across the

region.

Early efforts, limited by poor data and short deadlines, used rather simplistic

procedures for distributing the coal endowment by geotechnical and quality

characteristics ('coal type'). (Documentation of these efforts

is

to

be

found in

ICF Incorporated, 1977, 1980b; US EIA, 1982). Tonnages were assumed, for

example, to

be

distributed uniformally across a wide range of seam thickness

classes, rather than being skewed towards the thinner seams, which studies of

actual coalfields would suggest is the case. Over the last decade, however, both

the endowment data and the methodologies used to distribute it by coal type

have been steadily improved.

One important enhancement has been to introduce principles drawn from

geostatistics into the endowment assessments. Geostatistics differs from classi

cal statistics in that the latter assumes that observations have no spatial

relationship between them; the former recognizes the spatial correlation of

geological phenomena and provides a coherent set of probabilistic techniques

to characterize the degree of continuity (Sani,

1979).

Matheronian geostatistics

(Kriging), an interpolative method that

gives

a 'best' (i.e., the least biased and

with a minimum estimation error) estimate of an unknown spatial variable

(such as seam thickness), has been applied successfully at the level of an

individual deposit

or

coal bed to quantify the extent and variability of in-seam

coal characteristics,

(e.g.,

Kim

et

ai.,

1980;

Pauncz and Nixon, 1980; Tewalt

et

al., 1983).

But such methods require that the data points

be

relatively close

together and evenly spaced. Moreover, regional inventories can only

be

com

piled by aggregating the statistics of individually Kriged deposits.

Science Applications International Corporation, a consultant to the US EIA,

has evaluated the DRB and other available data sources to determine whether

geostatistical techniques - what they refer to as 'the optimal approach' - could

be used effectively to re-calculate the distribution of the DRB tonnages by

quality attributes (SAIC,

1986, 1988b).

Their conclusion was that 'the data

needed to support such an approach are generally not available' (SAIC, 1988b

4.4). However, SAIC

did

change the method of weighting the individual

observations -

i.e.,

the 'area of influence' over which the value associated with

a data point could

be

projected - to more closely approximate the way the data

would be handled using a formal geostatistical procedure. Testing this revised

procedure on the data for four regions (eastern and western Kentucky, and

northern and southern West Virginia), SAIC found that the distribution of the

DRB by thermal value differed significantly from that obtained using earlier

methods.

It seems likely that significant advancements in detailed, aggregative pro

cedures for estimating the distribution of regional coal endowments by import

ant in-seam characteristics must await the further development of com

puterized data bases (such

as

the USGS's Coal Resource Data System,

described above). The goal of developing a complete, detailed, computerized



Exploration,

discovery and

appraisal

97

inventory of the United State's coal endowment, much less the coal endow

ments of other countries, is many years away from realization. Such a detailed

mapping of the world's 'significant coalfields' was intended when lEA Coal

Research first set up its Coal Resources and Reserves

Data Bank Service in the

late 1970s (Gregory, 1979; lEA Coal Research,

1983),

but after several years the

enormity of the task was acknowledged and the project was abandoned.

In view of these problems other investigators have counselled using an

alternative, statistical approach. This entails obtaining detailed observations on

a well-explored area within a region, assuming (or, better yet, empirically

deriving) a functional form to represent the distribution of coal tonnages

according to each modelled geophysical and quality variable within the sample

area, and then deriving statistically the parameters of the (joint) distributions.

The parameters so derived would then be used to represent the distribution of

these variables across the whole region.

This approach has been used in modelling the mineral endowment of a

number of hard-rock minerals in the earth's crust (see

discussion in Kaufman,

1983, pp. 223-32), but only rarely for coal. The most notable examples are the

studies performed by Zimmerman (1977, 1981). On the basis of detailed

information on the coal endowment of several selected major coal-producing

counties in the eastern and midwestern United States, Zimmerman determined

that the distribution of coal by seam thickness (and also by overburden ratio)

was skewed and could be approximated 'fairly well' by log-normal or displaced

log-normal distributions. His major assumption was that the variance of this

distribution could be applied to describe all coal deposits in the United States.

Other observers

(see,

especially, ICF, 1980b), questioned the validity of descri

bing coal deposits across a country as large as the United States on the basis

of just one 'representative' area. However, this problem can presumably

be

overcome by increasing the number of areas' sampled in detail within the region

under study.

6.6 THE MISSING LINK: EXPLORA

nON,

DISCOVERY AND APPRAISAL

In most mineral extraction industries, the search for new and richer deposits,

and the discoveries that are the fruit of such activities, play an important role

in the long-run supply response (see Chapter 8). The simulation of this

exploration and discovery process therefore forms an integral link between the

cost modules and the endowment inventories in models of potential supply. In

the Harris and Skinner scheme, exploration refers to the activities involved in

searching for deposits where the presence of the target mineral is only

suspected.

By

this definition little exploration

is

carried out by the coal

industry: because of coal's shallow occurrence, its ease of identification and

information provided from the search for other minerals and water, knowledge



98

Modelling the long-run supply of coal

about the existence and rough magnitude of coal occurrences

is

already

extensive. In well-explored areas, such as Europe and the United States,

'exploration' activities carried out by coal industries are mainly aimed at

determining the grade and dimensions of discovered deposits - what would

be

called appraisal and development activities in other extractive industries

(Zimmerman, 1983). Coal supply modellers have therefore treated exploration

as

inconsequential: potential coal supply has been derived from demonstrated

(or proven plus probable) deposits, and mining company decisions have been

assumed to proceed as if knowledge about the extent and characteristics of all

the coal within this portion of the endowment was perfect and uniform.

How reasonable are these assumptions? To answer this question requires an

understanding of how geological assurance

is

defined. The practice in most

countries

is

to define geological assurance categories for coal according to one

simple measure: distance from an observation, generally either a core-hole

sample or a surface out-cropping (Todd,

1982). For

example, the US Geologi

cal Survey defines measured coal to include all coal within a radius of

0.4

km

of a data point;

indicated

coal extends in a band between

0.4

and 1.2 km; and

inferred

coal extends outwards to a radius of 4.8 km (Wood, et al., 1983).

Beyond

4.8

km from an observation, the presence of coal can only be

hypothesized. The category 'undiscovered' includes both this hypothetical coal

and

coal that

is

only speculated to

be

present. The dividing line between

inferred and undiscovered coal

is

thus somewhat qualitative.

As Zimmerman

(1983)

points out, the measured and indicated coal endow

ment categories may

be

treated as equivalent, without introducing a substantial

bias: the difference in knowledge

is

represented by five extra core-holes per

square mile. The extra cost of developing indicated coal is thus simply the cost

of drilling enough additional core-holes to attain the same data point density.

In 1983

he calculated that, on a levelized-cost basis, the added costs would

be

negligible - around $0.10/tonne for a bed of 1 m thickness, or only 0.5-2% of

the coal's final sales value.

The number of extra core-holes that have to be drilled in order to impound

coal from the inferred into the measured assurance category

is

many more than

the number required to go from indicated to measured. Also, the risk that the

actual tonnage is

less

than expected is higher. Zimmerman

(1983, p. 304)

suggests that this geological risk can

be

diversified away by drilling enough

separate parcels: 'unless the estimation process were biased, the "expected"

amount of coal would be found'. But the estimation process

is

biased. One

dimension, seam thickness, may turn out to

be

smaller or larger than expected.

But the aerial extent of coal within the delineated area can only

be

as expected

or smaller, since the calculation procedure normally assumes the presence of

coal over the whole area defined by the assurance category boundaries. Hence

all else equal, a mine developer

will

discount the amount of coal in an inferred

area compared with the amount that would be estimated if it were classified as

indicated or measured.



Summary

99

That coal classified as inferred

is

more risky and probably less economically

attractive than measured or indicated coal does not necessarily mean that it

should be ignored. The decision whether to include it in the analysis should

be

made on a case-by-case basis. For most of the well-explored coalfields of the

world, the portions of the coal endowment classified as inferred tend to lie in

beds that are thinner, deeper or more remote than those in the demonstrated

(measured plus inferred) categories. This sort of skewing in the data one would

expect: once enough information has been gathered to suggest that a particular

coalbed would be of substantially higher cost to develop than others in the

area, there remains no rational economic incentive to explore it further.

In

such

mature coalfields, therefore, one would probably not understate

20-

or 30-year

potential coal supply by ignoring the inferred portions of the endowment.

But the same cannot be said of newly developing coalfields. Although one

would expect some upward bias in costs towards the less well-defined deposits,

the bias in many cases may not be large. To have assessed the 30-year potential

coal supply from the Peace River coalfield in British Columbia, or the

EI

Cerrejon coalfield in Colombia, on the basis of the tonnages reported as

demonstrated in

1970

- 15 years before these

fields

were developed - would

likely have been to understate considerably the long-run potential supply from

these areas. If examination of the data suggests that there is likely to be a

substantial amount of good-quality coal that can be mined profitably in the

inferred portion of the reserve base, then that coal should be included in any

analysis of long-run potential supply. Clearly, however, some adjustment must

be made to the modelling procedure to reflect the higher appraisal costs that

would be incurred by mining companies in the process of developing these

deposits. One unsophisticated way to handle the problem might be to add an

exploration charge to the development cost estimates, based on an estimation

of the extra core samples that would have to be obtained in order to bring the

degree of geological assurance up to that of coal in the indicated or measured

reserve base.

SUMMARY

This chapter has provided an overview of the main approaches to modelling

the long-run supply of coal. The components of these modelling systems that

deal, respectively, with the estimation of mining costs, the description of the

coal endowment and the simulation of appraisal and development decisions in

the identified deposits, have been examined within the potential supply analysis

framework of Harris and Skinner (1982).

Strictly speaking, the systems for modelling long-run coal supply that have

been developed to date do not model potential supply, because they lack any

mechanisms for simulating exploration or appraisal, either at the intensive or

at the extensive margin. Rather, they treat one portion of the coal endowment



100

Modelling

the long-run

supply

of coal

as

well-enough appraised (usually the demonstrated reserve base), and ignore

the rest. Mining is then assumed to proceed sequentially through the reserve

base strictly in order of increasing mining costs. While ignoring the inferred

portion of the endowment may be reasonable when analysing medium-term

supply from a highly-developed coalfield, it may be inappropriate for less

developed fields and longer time horizons. Further research on the simulation

of exploration and appraisal decisions in coal mining might shed more light on

this problem.

The main challenge tackled by model developers so far has been to improve

the ability of models to predict costs of extracting coal given information on

variables known to affect mining. Undoubtably, the ability of the current

generation of models to estimate mining costs could still be improved. The

modelling of production and beneficiation losses, for instance, could be made

more sensitive to economic conditions and methods of mining. More generally,

dynamic feedback effects

of demand and prices on the coal industry's willing

ness and ability to search for new deposits or to invest in better exploration or

extraction technologies - i.e., not only on its willingness to invest in new

capacity - need to be integrated into the modelling process. The principal

features of a model that incorporates such feedback loops have been described

in general terms by Lee (1984), but there remain many obstacles to implemen

ting the model for a specific mineral, such as coal.

Because of data limitations, the usual procedure for describing the coal

endowment has been to make inferences about the distribution of key geologi

cal and quality characteristics from pre-aggregated inventory data that have

been developed for the purposes of calculating the

size

of a region's 'reserves',

not for the analysis of potential supply. The efforts of the US Geological

Survey (section 6.5 above) demonstrate what improvements can be made in the

collection and reporting of coal endowment data. Greater standardization

among countries of geotechnical criteria and terminology would also benefit

potential coal supply analysis. But so far,

few

comprehensive, detailed and

internationally comparable coal endowment inventories have yet been pre

pared of the world's most important coalfields. For this reason, there is an

urgent need for more research into the use of

less

data-intensive, statistical

inference methodologies for describing coal endowment.

ACKNOWLEDGEMENT

I would like to thank Thomas Sterner for his patience and his helpful

suggestions. Any errors or omissions, however, are my fault alone.

REFERENCES

Adelman, M. A., De Silva, H. and Koehn, M. F.

(1990)

'User Cost in Oil Production'.

MIT CEPR Working Paper 90-020, Cambridge, Mass.



References

101

Averitt, P. (1975) Coal Resources of the United States, January 1, 1967. US Geological

Survey Bulletin 1275. US GPO, Washington, DC.

Barnett, D. W. (1980) How Large are Our Coal Reserves? A Look at Australia and the

USA.

Materials and Society,

4,

225-38.

Barrett, S. A. (1982) 'Modeling Coal Supply: An Econometric Analysis of Competing

Mining Methods'. Unpublished manuscript prepared by Data Resources, Inc. for the

Electric Power Research Institute.

Brooks, D. B. (1976) Mineral Supply as a Stock, in

Economics

of

the Mineral Industries,

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Reserves and Potential Supply

of

Low-Sulfur

Appalachian Coal.

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A.M. (1981) Taxation

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Nonrenewable Resources. D. C.

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Dorian, 1.

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and Zwartendyk,

J. (1984)

Resource Assessment Methodologies and

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Materials and Society, 8,

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(1979)

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EMF


(1978)

Coal

in

Transition: 1980-2000,

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2,

Stanford University Energy Modeling Forum, Stanford, Ca.

Fettweis, G. B.

(1983)

'Considerations on Coal Deposits as Basis of Coal Production.'

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pp. 93-110.

Gluskoter,

H. (1991)

US Geological Survey, Reston, Virginia. Personal communication

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January

1991.

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R.

L.

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Assessment

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Modelling the

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A Cost Comparison

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Selected U.S. and

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Appendix

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Detailed [Mine Costing] Methodology.

Washington, DC. (Available from International Trade Administration by request.)

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(1982)

Documentation

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US EIA (US Department of Energy, Energy Information Administration) (1986)

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the Integrating Module and Stock Module

of

the Intermediate Future

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W.

A.

(1979)

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Bechtel's Regional Energy System for Planning and

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(RESPONS),

and ICF's National Coal Trade

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(NCM). EPRI Report No. EA-1133. Electric Power Research Institute, Palo

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(1982)

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(eds M. A. Adelman et al.), Ballinger, Cambridge, Mass., pp. 295-332.



7

Global availability of

natural

gas:

resources, requirements

and

location

Daniel

A.

Dreyfus

7.1 INTRODUCTION

In the past few years, the enormous significance of the largely untapped global

natural gas resource has received increasing attention. Initially, gas was viewed

as one component of a strategy to moderate the world's increasing dependence

upon liquid petroleum. More recently, the low air pollutant emissions and the

relatively low contribution to the so-called 'greenhouse' gases which result

from the combustion of natural gas compared to those of other fossil fuels,

have given the gas option greater emphasis.

Today, discussions of acid rain controls and of more comprehensive

measures to moderate man-made contributions to global warming usually

include some consideration of natural gas. Greater reliance upon gas

is

viewed

as

an interim measure to bridge the gap between business

as

usual and a future

energy balance which minimizes fossil fuel dependence.

The natural gas resource base

is

quite large in relation to the current levels

of demand upon it. Proved reserves of natural gas, those amounts which are

reasonably well known based upon drilling information, are estimated to be

the energy equivalent of about

740

billion barrels of crude oil. This approaches

the size -of global proved reserves of oil (896 billion bbls), but the annual

production of natural gas

is

only about half that of petroleum. The global

reserve to production ratio for natural gas, a measure which is often used as

an indication of near-term supply capability, is, therefore, about 60 to

1.

In

comparison the ratio for petroleum

is

40 to

1.

The estimated total remaining worldwide gas resource base which could be

economically recoverable with current technology

is

much larger than the

proved reserves. Estimates of total remaining recoverable reserves, which

include undiscovered portions of the resource base, are on the order of 1.5



106 Global availability of natural gas

trillion barrels of oil equivalent. Clearly, the natural gas resource is capable of

supporting much greater levels of production. I t will, undoubtedly, continue to

increase in its significance as an energy source in the coming decades.

The availability of natural gas to serve the requirements of global society,

however, involves more than the simple geological existence of the resource.

The geographical distribution relative to probable demand centres is also a

factor, particularly because transportation and storage are especially costly

where gaseous fuel

is

concerned.

7.2

THE NATURE OF RESOURCE ESTIMATES

Estimation of natural gas resources is done for a variety of purposes and at

widely varying levels of precision and detail. At one end of the spectrum lie the

resource estimates associated with reservoir engineering. They are based upon

considerable knowledge of the subsurface situation through detailed, on-site,

geological exploration, seismic investigation of the subsurface formations,

confirmatory information from the drilling of wells, and sometimes actual

production experience with the particular reservoir. These estimates usually are

made to support the technical and economic operation of the reservoir. The

extreme alternative is the appraisal of the remaining discovered and undis

covered recoverable resources which might exist in areas of national, regional,

or even global scope. Studies of the latter type are done for more abstract

economic motives, to support public policy decisions, or for general scientific

and academic interest. While they also rely upon a history of the exploration

and production experience with developed gas resources and upon specific

geological, seismic and drilling data where they are available, they also involve

a more generalized inference of the probable occurrence of producible gas

based upon broad geological conditions.

Authoritative estimates of the natural gas remaining to be produced are

generally characterized either as

proved reserves or recoverable resources. I t is

important to differentiate between the two classes of estimates.

Proved reserves are the recoverable quantities of gas which are estimated to

remain in known oil and gas reservoirs. Usually, these estimates are based

upon actual production experience or drilling data. Reserve estimates, there

fore, are reasonably reliable and represent an inventory of discovered gas.

Proved reserves, however, do not necessarily reflect the overall size or geo

graphical distribution of the global gas resource base. Exploration for gas has

been heavily concentrated in a few areas. More than 90% of all hydrocarbon

drilling experience has occurred in the United States and Canada alone. Low

estimates of proved reserves in some areas may indicate limited exploration

rather than limited geological potential.



Estimates

of

conventional

gas

resources

7.3

CONTEMPORARY ESTIMATES OF CONVENTIONAL GAS

RESOURCES

107

Table 7.1 summarizes some of the principal current estimates of proved gas

reserves and estimated resources. As might be expected, agreement among

estimates of proved reserves is quite good. From the point of view of the

longer-term global energy strategy, however, estimates of the remaining re

coverable resources, whether discovered or not, are more pertinent. Here a

wider degree of uncertainty concerning the undiscovered portion of the

resource base complicates the picture.

Estimates of undiscovered resources encompass some areas which are, as yet,

undrilled extensions of formations that are known to be productive and some

areas which are postulated to contain economically producible resources based

only upon judgements made by analogy to geological conditions that have

been productive elsewhere.

There are two major methodological techniques commonly used for the

estimation or appraisal of undiscovered gas resources. They can be character

ized as historical approaches and geological approaches (Office of Technology

Assessment,

1983).

Both approaches ultimately rely upon an extrapolation of

the knowledge of the resource base which has been garnered from past

exploration and development experience. They differ, however, with regard to

the historical data they emphasize and in the way in which the data are used.

Historical approaches extrapolate past trends in gas discoveries and produc

tion relative to the effort exerted by the producers. They rest upon an

Table 7.1

Estimates of worldwide natural gas resources (trillion cubic feet)

Western hemisphere

Western Europe

Middle East

Africa

Asia Pacific

CPE

World total

% OPEC

%CPE

"Includes proven reserves.

bSeparately estimated.

OGJ

518

200

1182

253

240

1561

3955

40%

39%

Proven reserves

WO

Cedi

516

535

227 192

1167

1065

206 257

245 327

1502 1532

3862

3909

37% 37%

39% 39%

Remaining recoverable

resources

Masters

IGT

1498 1902

423 377-395

2126

1903

570

453-653

630 671-833

2807 1966--2807

8107

b

7352-7830

b

References: OGJ, 'Worldwide Report',

1988;

WO, '43rd Annual International Outlook',

1988;

Cedi., CEDIGAZ

(1988);

Masters, Masters

et aI., 1987;

IGT, Institute of Gas Technology,

1986.



108 Global

availability

of

natural

gas

assumption that the nature of the resource base is the predominant factor

controlling discovery and production success. The historical trends in dis

covery, therefore, are strong evidence of the resource characteristics. Geo

logical approaches rely on data and assumptions about the physical size and

richness of the resource itself. In general, historical approaches tend to be

constrained

by

the quality and availability of relevant operational data and are

weak in dealing with frontier resources or changes in technology, economics,

or industry practices. Geological approaches are heavily dependent upon the

expert judgement of the estimators and are difficult to validate.

The historical approach is characterized

by

the estimates of the US oil

resource base done in the

1950s

by Hubbert (1967) of the United States

Geological Survey. Hubbert used historical trends in the drilling effort required

to discover new resources to produce decline curves that predicted the overall

extent of the resource yet to be discovered.

The approach rests upon the assumptions that (i) there

is

a continuous

relationship between the effort put into discovery and the amount of hydrocar

bons found; (ii) the discovery of an incremental unit of resource requires

increasing effort over time; and

(iii)

the ultimate resource is finite. These

assumptions can be represented by a continuous differentiable function relating

the quantity

(Q)

to effort

(E).

The first derivative is positive «dQ/dE) >0) and a

function of effort. The second derivative

is

negative.

These equations are used to develop 'find rate models', using mathematical

forms that conform to the basic assumptions, such as the logistical function or

some quadratic equations. Find rate equations or models are commonly used

to generate resource discovery success predictions. The PROLOG model used

by the US Energy Information Administration to project onshore US oil and

gas resource discoveries

is

one such model.

Such models are obviously critically dependent upon their fundamental

assumption of declining success per unit of effort over time. They do not

represent the potential impact of changes in the rate of technological advances,

potential additions to the resource base consisting of large new increments such

as frontier discoveries and unconventional resources, or changes in the econ

omic parameters of development.

Another mathematical approach to modelling the historical resource dis

covery experience

is

the Arps-Roberts model (Arps et at., 1971). The Arps

Roberts approach, known as the discovery process approach, is more directly

associated with the physical nature of the resource base. It assumes that the

probability of discovering a field with an exploratory

well

is directly propor

tional to the size of the field and inversely proportional to the extent of the

entire resource. Based upon exploration to date, it

is

possible to fit a

mathematical form to the historical data and compute the probability of

finding another field of a given size. The equations are of the form:

V

=F/(1-(1-A/B)W)



Estimates of conventional

gas

resources

where

F

=

number of fields found after 'W' wells have been drilled,

U

=

the ultimate number of fields to found in the region,

A

=

the geometric extent of a representative

field,

and

B = total region being explored.

109

The equation may be modified

by

a 'fitting parameter' which modifies 'W'

based upon exploratory experience with a particular region. The estimation of

this fitting parameter represents the critical problem in developing Arps

Roberts equations. As the region under consideration

is

disaggregated, the

relevance of the available data to the more intensively explored areas of the

resource becomes more powerful, but the sparsity of data in underexplored

areas reduces the approach to one of analogy from similar areas elsewhere. An

advanced application of this modelling approach is exemplified by the highly

dis aggregated GRI Hydrocarbon Model of the

US

oil and gas resource base

(Energy and Environmental Analyses Inc., 1990).

In contrast to the historical approaches which rely heavily upon the

statistical relationships between past discovery efforts and success, the geologi

cal approach focuses upon a physical description of the undiscovered resource

base derived from scientific geological theory and whatever measured evidence

is

available.

Parameter estimation methods strive to estimate the extent of potentially

productive formations, such as sedimentary basins, and the concentration of

oil or gas in place based upon surface and available drilling evidence (National

Petroleum Council, 1980). Structure count uses seismic data to evaluate

potentially productive formations (Energy Information Administration, 1973).

Analogy

is

used to impute potential resource availability of unexplored areas

reasoning from the observed geologic similarity to known producing areas.

The geological approach

is

commonly used by organizations; such as the US

Geological Service, that have extensive in-house geological expertise and

groups of experts assembled for the purpose of resource estimation. The

biennial estimates of the US natural gas resource base made by the Potential

Gas Committee are based upon the geological approach using teams of expert

geologists who have specific knowledge of the regions being evaluated. While

the geological approach has the flexibility to encompass notions of technologi

cal and economic changes, it

is

highly subjective and dependent upon the

capabilities and the attitudes of the estimators.

Compilations of the aggregate global remaining recoverable resources are

drawn from a broad range of disparate sources. Such sources vary greatly in

regard to the amount and reliability of geological data available, the definitions

used to decide upon the scope of resource types that are included in the

estimate, and, of course, the judgement and motivations of the estimators.

It should be evident that any effort to estimate undiscovered resources will



110

Global

availability

of

natural

gas

afford imprecise results, but strong agreement remains that the resource base

is double the proved reserves or larger.

In approaching estimates of the global gas resource, it

is

important to

remember that the

US

hydrocarbon resource has been more intensely explored

and drilled than any other in the world. Outside of the United States there is

much less historical experience, exploration drilling and production data are

scarce, and in many areas that data which does exist may be difficult to obtain,

of questionable validity, or both. Because aggregate global compilations are

drawn from diverse sources, consistency among the approaches must be

expected to be far less

than among US estimates.

Some general factors that will affect the validity of all resource estimates are

the following:

1. Data: Each of the estimates relies on the data available to the estimators;

there is not a single, authoritative and comprehensive data base used by all

estimators even in the well-explored and well-documented areas of the

mature US domestic gas producing regions. Significant discrepancies in the

publicly available data must be resolved by each estimator, and some have

access to exclusive, proprietary data unavailable to others.

2. Definitions and scope: Each estimator has chosen to include certain com

ponents of the resource base. Variations particularly are found in the

treatment of the less conventional resource components (coal bed methane,

Devonian shale, tight formations,

etc.).

The economic and technology

considerations affecting unconventional resources are more speculative, and

some estimates simply exclude some resource categories even where there

is

actual production experience. There are variations as well concerning the

limit on the depth of resource base considered, the assumed limitation of

water depth on the deep offshore potential, attitudes about the ultimate

accessibility of resources in remote and hostile regions, etc.

3. Judgemental attitudes: Estimators differ in their judgements even about the

potential for relatively well-known conventional resources. These

judgemental attitudes, in addition to differences in professional geological

interpretations, are coloured by attitudes about future economic parameters

(sales prices of gas and costs of factors of production) and relative optimism

about the future pace of innovation in exploration and production tech

nologies and their application in the field. While these differences are

particularly critical in the more judgemental geological approaches, they

can also affect the ways in which mathematical forms are chosen to

fit

historical data series.

Conventional resource estimates are customarily constrained to include only

methane contained in deposits that might be amenable to discovery and

production using current conventional practice and reasonably foreseeable

technical and economic extensions of that practice. This limitation excludes

significant portions of the longer-range resource potential from consideration.

Such gas resource estimates are reasonably comparable to similar conven-



Distribution of supply relative

to demand

111

tional estimates of alternative energy forms, such as the quantities of coal and

petroleum that could be provided using conventional practice. They are

appropriate estimates, therefore, for discussions of energy supply issues in the

near term, that is, for two or three decades.

For

discussions of the energy situation beyond the middle of the next

century, however, especially when the potential

for

gas is being compared with

significant advances in the technologies of alternative sources, such as commer

cial electric power from fusion energy reactions, power storage in superconduc

tive devices, and advanced solar concepts, the conventional gas resource

estimates are somewhat limiting. For example, substantial amounts of methane

are known to be present locked in hydrate form in permanently frozen arctic

areas and in the deep ocean floor. Large sedimentary basins are known to exist

in such circumstances and estimates of the methane present are enormous,

ranging as high as 3000 trillion cubic feet. Technologies required to exploit this

resource are currently beyond reach, but cannot be entirely dismissed as

long-term possibilities.

Resource estimates usually also omit from consideration those quantities of

gas that would require the investment of energy

for

production in amounts

approaching the energy value of the gas produced. To some extent, this

constraint also might be subject to revision should major breakthroughs in

production technology occur.

Even within the more conventional resource situations, definitions of re

coverable resources always include some notion of technological feasibility and

economic attractiveness. Although the precise criterion of technical recovera

bility

is

seldom explicitly stated by the estimator, statistical approaches as

well

as geological judgements generally capture the current, or conventional, techni

cal practice of the industry along with such evolutionary improvements in

technology as a knowledgeable practitioner can foresee. These limitations are

revised from time to time to reflect advances in practice and theory that are

evident in the trends, but the potential for scientific breakthroughs, or even

major innovations in engineering not yet apparent in practice, are usually

omitted.

Criteria of economic feasibility are even less explicit. I t seems clear, however,

that most estimators, in determining the recoverable portions of the gas in

place, have in mind the cost and price regimen that exists

at

the time the

estimate

is

made. Obviously, these assumptions might not be appropriate to a

future situation and might unduly constrain the estimate of recoverable

resources.

7.4

GEOGRAPHICAL DISTRIBUTION OF SUPPLY

RELATIVE TO DEMAND

Natural gas resources, until very recently, were discovered as a coincidence of

the worldwide search for liquid petroleum and were developed only when they



112

Global

availability

of

natural

gas

occurred in near proximity to energy demand centres. The aggressive explora

tion for additional gas reserves then tended to

be

concentrated in areas that

were convenient to an established market and transportation system.

Table

7.2

compares the current regional gas consumption patterns with the

outlook for the year

2010.

The 2010 scenario is adopted from the Global

Outlook for End-Use Energy Requirements (GOSSER

II).

Projections of

energy supply, demand and price are most commonly based upon historical

experience with the reactions of supply and demand to price changes. The

future requirements for energy are projected in terms of historical uses and

price elasticities. This approach captures the observed behavioural responses

that have influenced past consumption patterns, but it may not recognize the

constraints imposed upon the use of particular energy sources by infrastructure

which have not been tested historically.

It is

also unlikely to anticipate evolving

technologies or structural shifts in the nature of energy

uses.

Supply reactions, similarly, are predicted using assumed revenue optimiz

ation strategies and supply curves based upon the calculated costs of produc

tion and descriptions of the resource base previously discussed. As in the

demand analysis, this approach does not explicitly address the technical

limitations upon the choice among energy sources except to the extent they

have constrained historical activity.

An alternative approach to evaluating future energy demand, which con-

Table 7.2 Outlook for global consumption of natural gas (trillion cubic feet)

Consumption

Region 1987" 2010

b

United States

(17.1) (19.4)

Total,

North

America

19.4

22.9

Total, Western Europe

8.5

14.6

Total, Australasia 0.7 1.6

Japan (1.5) (3.8)

Total, Pacific Rime

2.9 6.9

Total, Latin America 3.0 6.7

Total, Middle East

2.1 9.7

Total, Africa

1.3

2.7

Total, China

0.5

4.8

Total, Soviet Bloc 25.3 51.4

Total, world

63.7

121.3

Interregional trade

aBP Statistical Review of World Energy, June

1988.

bDrawn from Ashby and Dreyfus

(1988).

'End

of

1987.

dAdopted from CEDIGAZ

(1987).

Exports (+

)/

Proved

imports ( - )

reserves

c

1987

d

2010

(186.7) (

-0.9)

(-2.6)

284.7 0.0 -1.0

218.8

-2.4

-6.3

23.8

+0.7

(1.0)

(

-1.4) (-3.8)

201.0 -0.1 -2.1

226.6

+0.0 +0.6

1084.0

+0.1 +2.8

248.6

+0.9

+ 1.8

30.7 0.0

0.0

1479.3

+1.5

+3.5

3797.5

2.2 9.4

eIncludes Japan, South and Southeast Asia and Pacific nations, but excludes Australia and China.



114

Global availability

of

natural gas

indigenous energy requirements and those regions that face growing needs for

natural gas relative to their resource bases.

The GOSSER projection already has been constrained in its forecast of

fuel

choices to reflect the regional availability of natural gas and the large capital

costs associated with long-distance transportation facilities. The projected

regional requirements

for

the year

2010,

therefore, are an indication of the

relative attractiveness of gas in competition with alternative energy sources

despite the costs of interregional transportation which will require additional

pipeline and LNG transportation facilities.

The projection indicates that the interregional trade in natural gas must

increase threefold by the year 2010 to accommodate the patterns of use that

are emerging. From only about

2.4

trillion cubic feet in 1987, interregional

transfers would increase to 9.4 trillion cubic feet in 2010. As much as 6.0 of this

amount would be intercontinental

LNG

shipments. The general patterns of

international transfers are shown in Figure 7.1. The arrows in the diagram are

directional only and do not imply any scale of magnitude.

7.5 CONCLUSIONS

The importance of interregional transportation as a limiting factor on global

gas consumption cannot be ignored.

For the foreseeable future, geological resources of natural gas that can be

produced at prices competitive with alternative energy forms are more than

ample to meet the probable market requirements. In the longer term, or in a

world that has decided

for

policy reasons to curtail another major energy

supply source, overall energy prices may be expected to be higher. Higher

prices will certainly expand the economic natural gas resource base beyond the

levels in contemporary estimates. Therefore, in a future in which more gas

is

called for, more probably can be provided.

The greater uncertainty

is

the capability of global markets to develop

rational approaches to the international gas trade and to underwrite the large,

long-term investments in transportation facilities that will be necessary to

moderate the geographical imbalances between supply and demand. Especially

in the developing countries, which will be the driving factor of future energy

demand growth, capital will be a scarce resource. I f even indigenous gas

resources are to play a role in the energy supply mix, investment in costly

transportation systems must somehow be accommodated.

Intercontinental trade in gas, by long-distance pipeline and by LNG,

will

depend upon innovative trade arrangements and investment schemes. The

focus of discussions about natural gas supply availability to meet global

requirements should include these infrastructure factors along with resource

estimates.



F

g

7

1

I

n

e

e

g

o

g

r

a

d

y

2



116

Global availability

of

natural gas

BIBLIOGRAPHY

'43rd Annual International Outlook'

(1988)

World Oil,

2,

31-126.

'Worldwide Report'

(1988) Oil and Gas Journal, 52,

43-119.

Arps,1. 1., Mortada, M. and Smith, A. E. (1971) Relationship between Proved Reserves

and Exploratory Efforts.

Journal

of

Petroleum Technology,

June, pp. 671-5.

Arps,1. 1. Mortada, M. and Smith, A. E. (1971) Relationship between Proved Reserves

and Exploratory Efforts.

Journal

of

Petroleum Technology,

June, pp. 671-5.

Ashby,

A.

B. and Dreyfus,

D. A. (1988) Global Outlook for Service Sector Energy

Requirements (GOSSER II):

Gas Research Institute, Washington, DC.

CEDIGAZ (1988) Natural Gas in the World in 1987.

Rueil Malmaison, Cedex, France.

Energy and Environmental Analysis, Inc.

(1990) Guide to the Hydrocarbon Supply Model

1990 Update. EEA, Arlington, V.

Energy Information Administration

(1983)

OCS Oil and Gas Supply Model.

EIA,

Washington, DC.

Geological Survey of Canada (1984)

Oil and Natural Gas Resources

of

Canada, 1983.

Energy, Mines and Resources Canada, Canadian Government Publication Center

Supply and Services, Ottawa, Canada.

Institute of Gas Technology (1986)

IGT World Reserves Survey as

of

December

31, 1984.

Chicago,

Ill.

King, Hubbert M.

(1967)

Degree of Advancement of Petroleum Exploration

in

the

United States. American Association of Petroleum Geologists Bulletin, 51, 2207-27.

Masters,

C. D.,

Attanasi,

E.,

Dietzman,

W. et al. (1987) World Resources of Crude Oil,

Natural Gas, Natural Bitumen and Shale Oil. Twelfth World Petroleum Conference,

Houston, Texas.

National Energy Board (1988) Canadian Energy Supply and Demand 1987-2005.

Minister of Supply and Services Canada, Ottawa.

National Petroleum Council

(1980) Unconventional Gas Source, vol.

5:

Tight Gas

Reservoirs, Parts I and II.

NPC, Washington, DC.

Office of Technology Assessment.

(1983) US

Natural Gas Availability. Congress of the

United States, Washington, DC. pp. 31-66.

Organization of the Petroleum Exporting Countries.

(1987) OPEC Annual Statistical

Bulletin.

OPEC, Vienna.

Potential Gas Committee. (1987) Potential Supply of Natural Gas in the United States.

Potential Gas Agency, Mineral Resources Institute, Colorado School of Mines

Foundation, Golden, Col.

University of Texas at Austin, Bureau of Economic Geology; ICF-Lewin Energy

Division, ICF Inc.; and Argonne National Laboratory (1988) An

Assessment

of

the

Natural Gas Resource Base

of

the United States.

Washington, DC.

US Department of Interior, US Geological Survey; and Minerals Management Service

(1988)

'National Assessment of Undiscovered Conventional Oil and Gas Resources.'

USGS-MMS Working Paper, Open-File Report 88-373.

World Energy Conference

(1986)

1986

Survey

of

Energy Resources.

Oxford, Holywell

Press.



8

Modelling oil

exploration


8.1

INTRODUCTION

Most oil supply models refer exclusively to North America. Models covering

other regions or the world situation

as

a whole are much rarer. In addition,

their forecasts of oil supply tend to be based on estimates of final resources and

cumulative production trends rather than on a sequential representation of

exploration, development and production.

Why should modelling be limited in this

way?

The answer

is

twofold. First,

outside the United States and Canada (and to a lesser extent the North

Sea),

data concerning oil activities are sorely lacking, especially for exploration.

Secondly, the phenomena involved are much more complex outside North

America. Since the early

1970s

the exploration market has split in

two:

on the

one hand the so-called, 'reliable' oil-producing regions in the industrialized

countries and, on the other, the

less

developed countries (LDC).

In

the first market there

is

a large number of active operators They have

virtually

free

access to the territories involved and, by law as

well

as in practice,

whatever hydrocarbons are discovered belong to whoever discovers them. In

addition, the tax situation

is

favourable, state intervention

is

relatively limited,

there are virtually no political risks, the oil market

is

well-developed and

operators have access to advanced technologies. In contrast, access to terri

tories in the second market

is

in general highly restricted. The owner of the

resources, the State, regulates the

flow

of companies seeking access to them.

The number of protagonists

is

thus limited and the authorities only have to

deal with a small number of international companies. In addition, because of

its lack of technology, skills and knowledge of the oil business, the State, or

national oil company, is

in general unable to carry out exploration without the

cooperation of foreign companies. On the other hand, state intervention in oil

activities can be

felt

at all levels. Finally, there

is

a twofold competition within

the market: among states possessing oil resources and seeking to attract

companies to invest in the search for oil, and among oil companies wishing to



118

Modelling

oil

exploration

gain access to zones which are promising in geological or contractual

terms.

Actual exploration diverges from the optimal exploration path described by

theoretical studies. Institutional factors, such

as

the contractual framework,

taxation and political risks, alter oil operators' expectations. Operators

have to take such factors into account with the result that their behaviour

is

different from what it would have been in the absence of such 'non-market'

factors.

Any extension to LDCs of the models constructed for developed countries

is thus limited because of this twin difficulty. The mathematical representation

of upstream oil activities in such models is very simple and their results not

very encouraging because of the generally poor quality of the available data

which the models have to use in their attempt to account for real processes

which are by nature extremely varied and complex.

Given the difference between oil-producing regions, several questions arise.

Can the same modelling approach be adopted in both cases? Are the factors

determining oil exploration the same? And, more specifically,

is

the price of oil

still the most appropriate factor when analysing oil exploration as theory

suggests?

The role of prices in driving upstream investment in the oil industry is

complex. Profit expectations are related to future prices, but companies' cash

flow (and hence investment capacity) is affected by current and past prices. It

should not be forgotten that the search for oil ~ a risky, capital-intensive

business ~

is

in most cases self-financed.

There can be no doubt that total exploration investment is somehow linked

with the price of oil. However, the price of oil

is

certainly not the main factor

determining the distribution of exploration investment among producing

regions. There may be a correlation between prices and exploration for some

regions, but in most cases the correlation

is

neither obvious nor immediate.

The impact of price changes on exploration activities varies from country to

country and from company to company. Institutional factors delay the

reactions of the various protagonists involved, to the point even that they have

been known to deny the existence of any simple relation between prices,

exploration, reserves and production. Such delays may be the dominant factor

in creating tensions within the oil market.

Our analysis will be developed in four parts. We first describe the way in

which exploration has been dealt with in oil supply models. We then look at

five recent models, paying particular attention to the explanatory variables

used when modelling exploration activities. This

will be

followed by a dis

cussion of the factors which have been shown by several empirical studies to

determine exploration in LDCs. Finally, we analyse the interdependence

between institutional factors, oil prices and exploration effort with a

view

to

drawing conclusions

for

modelling in the future.



The key factor

determining

oil supply 119

8.2 EXPLORATION: THE KEY FACTOR DETERMINING

OIL SUPPLY

Various approaches have been adopted to forecast the volume of undiscovered

oil.

Some modellers use exclusively economic hypotheses, others base their

approach on oilfield engineering or a combination of the two. Kaufman (1979,

1983)

distinguishes six main approaches. Several, such as those based on

life-cycles, effort rates, geological-volumetric methods, subjective probability or

econometric methods, can also be used to make supply forecasts.

Since each approach has some advantage, models often employ more than

one technique. Numerous comparative analyses have been made of the various

models, for example Adelman

et

al.

(1983)

provide a global analysis, as do

Clark

et al. (1981),

comparing ten models used for oil supply in the United

States.

Oil supply models generally cover three steps: first the global estimation of

undiscovered resources; secondly, the conversion of resources into reserves; and

thirdly, the elaboration of scenarios of how new

discoveries are brought into

production.

Some studies eliminate the exploration phase and go directly from final

resources to production. Production and discoveries are taken to be a function

of time only, thus reducing the task to that of adjusting a mathematical

equation to the cumulative production curve or the curve of cumulated

discoveries. This

is

the case with very general models in which the introduction

of exploration would increase operational difficulty (see, for example, Baldwin

and Prosser's oil market model in Chapter

15).

This is also true of country

based studies

in

which the exploration process is rendered irrelevant

by

the fact

that the proven reserves are extremely large (see,

for

example, the US

Department of Energy's estimates of oil potential in Nigeria (US DOE 1979)

and the Middle East (US DOE, 1981), see also Chapter 6 for the case of coal).

From a conceptual point of

view,

the most satisfactory models are those that

attempt to reproduce the relations between the various links in the 'chain' of

upstream oil activities: exploration, development and production (MacA voy

and Pindyck, 1973; US DOE, 1978; Adelman and Jacoby, 1979; Woods and

Vidas, 1983; CERI, 1988; and Choucri

et aI.,

1990).

Roughly speaking, the approach adopted by these models

is

as follows: an

oil operator decides to prospect in a given region

in

the light of geological and

economic data; exploration pays off with the discovery of an accumulation of

hydrocarbons; the operator then decides whether or not to go ahead and

develop the discovery; if the operation goes ahead, the discovery produces a

certain volume of oil and gas each year depending on the extraction rate. This

approach presupposes that the region or regions involved are homogeneous in

geological terms with the result that the operator does not have to arbitrate

between intensive and extensive exploration.



120

Modelling

oil

exploration

One hypothesis currently adopted

is

that for each year the oil discovered

is

proportional to the exploration effort. The key factor in this type of approach

is

the determination of the exploration effort. One of the principal tasks of oil

supply models attempting to reproduce the logic of upstream activities is thus

to determine which factors explain the level of exploration and to link them

together in a suitable manner.

There are two approaches to the construction of exploration models: one

based on optimization, the other on econometrics. The former,

e.g.

the

Department of Energy (US DOE, 1978) and Gas Research Institute (Woods

and Vidas, 1983) models, maximize complex objective function in order to

determine the way in which exploration responds to changes in prices, costs

and taxation.

1

These models are not particularly relevant to the present article

since they are not interested in establishing decision-making rules which fit

with historical data. Econometric methods are better suited to explaining the

successive phases in upstream oil activities and changes in exploration in

particular. These models are all more or

less

related to Hotelling's analysis

despite the numerous weaknesses which can be seen in this theory and its

extensions (see for example, Bohi and Toman's critical analysis (1986) of this

theory and its application to oil supply models).

8.3

FROM THEORY TO EMPIRICAL MODELLING

The rapid increase in the number of empirical models of oil supply during the

1970s was accompanied by a similar development on the theoretical side.

Improvements in Hotelling's model include the exploration process - for

mining basins or entire regions (Cairns, 1986).

Most theoretical models approach the problem of oil supply, including

exploration, either as one of constrained intertemporal maximization (cf.

Pakravan,

1977;

Peterson,

1978;

Pindyck,

1978),

or as a problem of stochastic

optimization under uncertainty (cf. Gilbert,

1979;

Pindyck,

1980;

Arrow and

Chang, 1982).

Pindyck (1978), for example, treats the fundamental problem of the firm

within a deterministic situation as one of maximizing the profit function given

the constraint imposed by the exhaustion of reserves (R) and that imposed by

the discovery of

new

reserves. The profit function is defined as the value of

'The United States Department of Energy model first of all calculates the quantity of economically

exploitable resources

in each of

12

oil regions in the United States. It then generates regional curves

for 'latent' or 'desirable' demand for exploration drilling at each price

level.

The relation between

latent exploration demand and price is unique for each region. Total demand is the sum of the

regional latent demands and falls with the reduction in the quality of discovered reservoirs and

cost increases. The model then looks at total desirable drilling

in

the light of several constraints

such

as

availability, capacity and the economic life of drilling equipment

in

order to obtain the

total quantity of 'realizable' exploration drilling. The model finally shares out the total effort

required to achieve the realizable drilling among the regions.



From theory to empirical modelling

121

output (PQ) minus production C

1

(R) and exploration C

2

(E) costs. Variations

in the levels of reserves (R) are a function of the rate of addition to reserves (X)

and the extraction rate. The rate of addition to reserves

is

a function of the

cumulative reserves

(x)

and the effort expended on exploration (E). In math

ematical terms this gives:

Max n= foo [PQ - C

1

(R)Q- C

2

(E)] e-

ol

dt

Q,E

0

R=x-Q x= f(E, x) R,

Q,

E, x ~ O

(8.1)

The actual objective function to be maximized depends on several factors.

For

example, does the firm undertake exploration in order to reduce uncer

tainty or rather to accumulate reserves;

is

the firm in a competitive market or

in a monopoly position; does it exploit one or several fields, etc.

In a competitive market exploration follows the rent in situ, the scarcity

value of the resource underground. The rent

is

the shadow value of one

additional unit of the resource. The marginal profit on the production of one

additional unit of reserves is the shadow price of the reserve. Thus the rent is

a measure of the real sacrifice the oil company

is

prepared to make to obtain

the marginal unit of the non-exploited resource. However, it

is

usually

impossible to observe this rent. How then can it

be

measured? In the ideal case,

costs are zero and the rent equals the price of the resource on the market.

Guadet and Hung (1986) point out that in reality the price is only an

approximate indicator of the rent because of the imperfections of the market

and because the price

is

a reflection both of changes in the extraction cost and

in the option cost which constitutes the value of the resource underground.

Another indicator is the marginal cost to extract the resource, or, even better,

the price at which known reserves are traded in the market. Their

use is,

however limited by the availability of statistical data. Devarajan and Fisher

[1982] point out that in a world without any uncertainties, the marginal

discovery cost

is

an appropriate estimate of the rent in situ. With uncertainty,

marginal costs differ from the rent. The difference depends on the extent of

uncertainty in exploration.

A large number of econometric models have been built in parallel with the

lessons learned from theory. These can be divided into two groups. The first

group takes the problem of inter-temporal maximization as its starting point,

i.e. it starts off from an equation similar to (8.1) (cf. Epple, 1975, 1985; Cox and

Wright, 1976; Nielssen and Nystand, 1986) in order to construct an exploration

function.

The second group combines the variable suggested by theory - for which

there exist historical series of data - with other technical and economic

variables recognized

by

the oil industry as capable of modifying exploration

related investment decisions. A linear or logarithmic equation

is

usually used

to establish an expected profitability function for exploration based on 'ra-



122

Modelling oil

exploration

tional expectations'. Assuming that the estimated coefficients for the past

remain unchanged, the equations can

be

used to forecast future exploration

levels. The expected profitability function

is

estimated in several ways. The

'Canadian school' for example (cf. Uhler, 1983; Scarfe and Rilkoff, 1984; Ryan

and Livernois, 1985; Bing, 1987) use the 'reserves price'; Moroney and Brem

mer (1987) and Desbarats (1989) use 'netbacks'; Attanasi (1984) uses the value

of undiscovered deposits; and MacAvoy and Pindyck (1973) use an index of

average deflated revenue.

8.3.1 Models based on the price of reserves

Several Canadian economists have attempted to construct statistical series of

'reserves price' in order to get as close as possible to theoretical (but unobserv

able) shadow prices. The reserves price has been defined as the price of

discovered but undeveloped reserves. I t

is

supposed to take overall account of

geological, price, cost and taxation

effects.

This concept is directly derived from

Epple's 'price of a discovery' [1975] which

is

equal to the discounted net profit

from a given discovery, divided by the amount of oil discovered. Uhler and

Eglington (1983) and Ryan and Livernois (1985) have built data series for

Canadian reserves prices. These are based on several hypotheses concerning

the operators' outlook in terms of production from given discoveries, the

evolution of oil and gas prices, development and operational costs, taxation

and inflation. Reserves price series thus vary from one author to another.

Several empirical models of exploration behaviour in Canada have sucess

fully used reserves prices. In Scarfe and Rilkoff (1984) the level of exploration

(E)

is

a function of reserves price and output. In the light of the uncertain

nature of oil prospection, the explanatory variables used by the model are: a

weighted average of oil and gas undeveloped reserves price (P) where the

weights are given by the proportion of exploratory wells intended to uncover

oil and gas; a weighted average of oil and gas production volumes (Q).

Exploration effort is measured through exploration expenditure. Desbarats

(1989) has carried out several estimations on this model, with the following

results:

Equation 1

Equation

2

LnQ

0.249

-0.019

LnP

0.128

0.024

Price elasticities

LnE

t

-

1

R2

Short-run Long-run

0.733 0.94 0.13 0.48

0.928 0.90 0.02

0.33

The results for equation 1 were obtained using Uhler and Eglington's

(1984)

reserves prices for the period 1980-91. The results for equation 2 were obtained

using Ryan and Livernois's (1985) reserves prices for 1951-81. The results show

that Scarfe and Rilkofs approach is encouraging but that Ryan and Livernois's

reserves prices series do not significantly contribute to explaining investments

in exploration.



From theory

to

empirical modelling

123

Bing's model [1987] is a variation of the previous one.

The

explanatory

variables are: expected profit on exploration, hydrocarbon

production

and

land

expenditures.

The

profitability

of

exploration is no longer measured in

terms of expected profit per unit of reserves, but rather by the expected profit

per unit of production. This concept makes it possible to take into account the

time necessary to produce reserves in the ground. Profit is calculated using

after tax development

and

operational costs only. The finding cost and any tax

incentives likely to reduce the ex post exploration costs are excluded. In this

way the author puts the emphasis on 'half-cycle profits' instead of 'full-cycle

profits'. The expected half-cycle profit per unit of exploration (UP) is calculated

using data on the size of discoveries, the probability of a discovery of a given

size, costs, prices, taxes, royalities

and

estimates concerning the evolution

of

prices and inflation:

(8.2)

where RVj(t), CJt) and Qi(t) are the present value of income, costs

and

production related to discovery i, and nt )

is

the conditional probability of

developing a reservoir of size

i

given

that

the discovery has been made. The

model estimates exploration activity (E) for each of the four provinces of

Western

Canada

over the period 1962-85. Exploration activity is measured by

land expenditures to begin with, and then by the number of metres drilled.

Straightforward regression is used to estimate the coefficients of the following

equation:

(8.3)

Q

is the output in million cubic metres of oil equivalent;

noil

and

n

gas

are the

expected half-cycle profit on oil

and

gas respectively; DU is a dummy variable

introduced to cover the effect

of

the National Energy Programme between

1981

and

1988. The expected profit

is

estimated as a weighted average for the

current year and the previous year using

an

Almon lag procedure. The model

fits reasonably well with historical data, but the explanatory power of expected

profit

is

low in

most

of the provinces studied.

8.3.2 Netback-based models

The net price for the producer or 'netback' is defined as the wellhead price of

oil (or gas) less production costs, taxes and royalties. In the absence of a

credible explicit measure

of

profits in the oil industry, the (observable)

producer's net price

is

a good approximation of companies' cash flow available

for future investment. It also provides a proxy cost of acquisition of reserves.

This indicator has the advantage

that

it does not require hypotheses concern

ing operators' (unobservable) outlook concerning the future of prices, costs,

taxation etc.



124

Modelling oil exploration

Moroney and Bremmer (1987) develop a complete model of oil supply for

Texas which can be used to forecast additions to reserves, the number of

exploration and development wells, the drilling success rate, hydrocarbon

reserves and output.

In

the part dealing with exploration, three econometric

equations are used to calculate the number of exploration

wells,

the success

rate and new discoveries. According to the authors, the factor driving new wells

for exploration and development

is

the discounted current value of expected

profit from these wells. They take net backs as a starting point for the

construction of a variable which they call 'profit', which

is

intended to sum up

exploration profitability. Thus, the producer's net prices for oil and gas are

calculated first before going on to estimate the production profile of the

discovery made in year

i.

Multiplying the netback for the year of the discovery

by

the volume of hydrocarbons extracted each year provides an estimate of the

annual revenue over the life of the discovery. Finally, future revenue is added

up and discounted in order to obtain a discounted net operating profit per

barrel of oil or million cubic feet of gas. In order to take geological factors into

account, the success rate and discovery rates (volume of oil and gas discovered

per successful exploration well) are introduced into the explanatory variable

'profit'.

(8.4)

where Pt

is

discounted net price, S successful oil (gas) wells, N total exploratory

wells,

R volume of oil and gas discovered and DC average after-tax drilling cost

per

well.

The tested equation is log-linear. The expected profit and exploration

drilling

(N)

for the preceeding year are explanatory variables. The following

results were obtained:

LnN

t

= -0.091

+0.824LnN

t

_

1

+0.134LnIl

t

-

1

(-0.124)

(9.190) (2.930)

(8.5)

Estimation: OLS (1959-83) R2 =0.86 DW=2.02 t-statistic in parentheses

The author thus finds that the short-run elasticity of exploration with respect

to profits

is

only 0.13, but the long-run response would be much higher:

0.13/(1---D,824)=0.76.

Desbarats [1989] puts forward a model for exploration expenditure in

Alberta in which exploration activities are taken to be an investment in new

production capacity. Additions to reserves are handled as analogous to

additions to capital stock in an investment model. A long-term equilibrium

relation is the starting point of a general self-regressive-distributed-Iag model

of exploration expenditure. The equation

is

simplified in order to take into

account reserves prices and several restrictions:



From

theory

to empirical

modelling

125

Ln

E

t

= -2.27 +0.60 Ln E

t

-

1

+ 1.24 P ~ 8 - 0 . 0 9 P t - l +0.003 X

t

-

1

+0.0021';-2

( - 6.S2)

(IS.03)

(14.67) (-9.17) (9.56)

(S.84)

+0.13 Q ~ ~ 1 -0.21 PW

t

- l

-0.87

dQog-0.20

dPW

t

- l

(8.6)

(4.79) (-2.41)

(-S.2S)

(-1.87)

Estimation: OLS (1949-82) R2

=

0.99 DW= 2.40 t-statistic in parentheses

E is real exploration expenditure, pog is the 2-year average of the sum of oil

and gas netbacks, P is the rate of inflation, X (= DO /Q

o

+ Dg/Qg) is the sum of

the ratio of additions to reserves (D) to production (Q) for oil and for gas,

Y( =D8/Q8) is the same ratio for gas, Q

0

8 is the sum of oil and gas production

and

Pw

is

the difference between domestic

and

world oil prices.

The model responds well to the statistical tests. To our knowledge, this is

the best empirical model of exploration behaviour to have been constructed for

a given oil province. Desbarats does not include expectations regarding

economic or geological factors, nor taxation. The model places emphasis on

feedback processes rather than forward-looking factors. This approach is

consistent with the point of view according to which firms' exploration

programmes are based

on

past

and

present data concerning capital costs,

output, netbacks and certain (e.g. financial) constraints.

8.3.3 Analyses

by

basin or region

We shall now examine two models with very different aggregation levels: that

of

Attanasi (1984), which deals with the American Permian Basin, and the

Candian Research Institute's model which deals with exploration in large

geographical regions.

According to Attanasi (1984), the decision to invest in exploration in the

short term depends

on

the field manager responsible, whose profit evaluation

will vary according to the size

of

the deposits discovered in the zone and to

drilling costs. In the long run the value of deposits still to be discovered in the

province is compared with that of other regions. The model assumes that the

exploration effort

(E)

is a function of the expected short-term return (II) as well

as of the expected value of deposits remaining to be discovered (0). II is

calculated as the value of the hydrocarbons discovered by successive explora

tion wells less the cost of the wells, multiplied by the exploration success rate.

In calculating cumulative discoveries X, Attanasi used a model of the discovery

process which predicts the number of oil

and

gas deposits to be discovered for

different sizes

and at

different depths.

On

the basis of prices for year t and

drilling costs, the value

of

each deposit can be estimated. There is a one-year

lag in the discovery process model's forecasts in order to take account of the

time which the operator needs to estimate correctly the size of new discoveries.

Exploration activity

is

measured first by the number of wells, then by



126

Modelling

oil exploration

expenditure:

I I n

R2

DW

Equation 1

(wildcats)

0.1005 0.0579

0.75 1.75

(2.1)

(15.2)

Equation 2 (expenditures)

0.337

5.805

0.50

2.44

(2.1)

(16.8)

Estimation: OLS (1950-1974)

t-statistic

in

parentheses

Although the

fit

is not particularly good, Attanasi concludes that American

firms have been guided by the history of past discoveries in their activity in the

Permian Basin. It should be noted that this model uses variables which

measure operators' expectations in terms of the quantity and size of deposits

only. It includes variables relating to other economic factors. The model

calculates the value of the deposits instead of expected discounted profit. Thus

it only uses observable variables.

One of the

few

models which examines each oil activity explicitly when

dealing with world oil supply and demand is the Canadian Energy Research

Institute's World Oil Market Model [CERI, 1988]. The supply part is divided

into four sub-models: drilling, additions to reserves, production and costs. The

drilling sub-model calculates the number of metres which will be drilled

annually in each of

16

regions worldwide. The model does not distinguish

between exploration drilling and development drilling, nor does it distinguish

between oil and gas prospecting. Within the model, the drilling effort (E) is a

function of profitability (11), the cost of capital (C) and the utilization rate of

reserves (U

R).

Since net backs are non-existent for each region, profitability is

estimated simply through prices of representative crude.

(8.7)

The results in Table

8.1

show that it is not easy to reconcile the equation

with historical data. The authors of the model explain this difficulty by the lack

of information concerning real profitability in each region and on the decision

making process as regards drilling in LDCs.

8.3.4

Explanatory variables of

empirical models

The ways in which models express firms' decision-making vary but the same

factors are nearly always used to explain the level of exploration.

Economic factors

Wellhead prices and technical costs are unavoidable. Some authors also

include other variables, such as drilling activity opportunity costs, operators'

debt servicing costs, the cost of reserves, the inflation rate etc.



From theory

to empirical modelling 127

Table

8.1

OLS estimates for the total drilling, 1960-1984

Supply

region

I I

C

UR

R

Canada

1080.1

-123.9

47.5 0.69

(2.33)

( -1.62)

(1.31)

United Sates 13701.5 18989.77

0.71

(5.21) (-2.99)

Mexico 95.9 -286.3

0 0.4

(1.73) (-2,05)

Brazil 44.6 0

0

0.54

(2.02)

Argentina

248.9

-829.7

-98.3

0.58

(3.98) (

-3.38)

(

-1.82)

Colombia

48.6

-128.1

0 0.33

(2.79) (

-2.07)

Peru

29.6 0 -32.3 0.72

(6.23) (-2.90)

Trinidad 16.3 -113.8

0 0.68

(2.94) (-5.27)

Egypt 13.8 0

0 0.60

(4.63)

Angola

27.1

0 0 0.53

(5.00)

India 35.8

-29.6

0 0.68

(1.91)

( - 2.01)

Malaysia

10.7

0 0 0.31

(2.45)

Australia

81.3 0 0 0.62

(1.52)

Other Middle East 47.2

0 0 0.69

(7.05)

North Sea

85.4 0 0 0.86

(4.78)

Western Europe 231

-905.9

0 0.72

(6.59) (-7.38)

Source: Modified from CERI (1988).

OLS modified where necessary for serial correlation.

t-statistic in parentheses.

Geological and technical factors

Exploration results are directly dependent on the volume of the resource

underground. Variables have to express not only the efficiency of exploration,

but also exhaustion of the resource. The drilling success rate, the discovery rate

and the cumulated drilling effort are commonly used. In some cases, global



128

Modelling

oil

exploration

growth in oil activities

is

brought in through technological development (e.g.

by introducing an improvement in the recovery or success rate.

Taxation

Perfectly competitive markets do not exist in reality. In particular the state's

influence can

be

very significant. Firms have to pay taxes and rights and are

subject to various rules and regulations (extraction rate, accounting rules,

etc).

Firms can also benefit from tax incentives such

as

a provision for the

reconstitution of a

field.

Can these variables explain exploration trends in LDCs? The answer to this

has to

be

no. The use of these variables

is

a necessary condition but not a

sufficient one. In the models covering developed countries' oil regions, institu

tional factors are represented by taxation alone. This

is

indeed a major

explanatory variable, but it is not the only one. Political risks, national

companies, non tax-related clauses in contracts, lack of infrastructure, etc., can

all affect exploration, often introducing strong discontinuities in the search

activities. The weaker the oil potential and/or the stronger state intervention

ism, the more discontinuous the exploration effort.

8.4

PARTICULARITIES

OF

EXPLORATION IN LDCs

Oil exploration in LDCs displays certain particularities due to institutional

and political factors.

By

institutional and political factors we mean respectively

those factors that relate to state management of natural resources and those

factors capable of altering

or

even upsetting this management. The former

depend on the legislative, contractual and tax framework governing explora

tion-production activities and the latter are the 'components' of political risk.

The heterogeneous nature of states implies a complex contractual and tax

framework, a diversity of management approaches and the permanent presence

of political risks.

8.4.1 A complex contractual and tax framework

The first feature of exploration in LDCs

is

that the state usually owns the

natural resources and foreign companies cannot engage in oil activities unless

they have either a licence or a contract with the authorities. Within the

contracts category,

we

can distinguish among other production-sharing con

tracts and risk-service contracts. There are also forms which combine the two

types, especially when it comes to taxation mechanisms.

Under licences, production belongs to the concessionaire who pays a

proportional mining royalty and a profit tax. In general, the company has to

go through one or more intermediate titles before being granted a

full



Particularities

of

exploration in LDCs

129

concession. Under the terms of the licence, the company takes responsibility

for all exploration risks and development, and supplies all the necessary finance

for operations. The oil extracted belongs to the company, though sometimes

the crude legally belongs to the state. In practice the company acts as if the

crude was its own. The national company often has the right to take a stake

in the concession, especially when a commercial discovery has been made. Even

if this mining title hardly resembles the extremely advantageous concessions

seen in the past and is less and less used in LDCs, it remains very widespread

in developed countries.

In production-sharing contracts

2

,

the discovered oil belongs to the state.

Production is divided in two: one part (the cost oil)

is

used to recover costs;

the other (profit oil)

is

given over in part to the contractor as remuneration for

services rendered. The rest belongs to the state. The company normally pays a

profits tax. Some contracts require payment of the royalty in cash as a

precondition. The company is responsible for all the necessary financing. The

national company can also take a stake.

In risk-service contracts, output remains the property of the state which then

repays and rewards the contractor for its services out of income gained from

the discovery. The 'risk capital' required for exploration is provided by the

company. Development expenditure

is

also the company's responsibility or

is

shared with the state. The contractor pays a tax related to the payments made

by the state. The contractor has the right to buy a share of output. Such

contracts began to appear initially in Brazil, around 1976-77, and developed

rapidly in other South American countries.

In association contracts (or joint operating ventures), the state company

joins up with a foreign company to prospect, and sometimes produce as well,

within a defined zone. The two partners are joint holders of a mining title

issued by the state, usually in the form of a licence. The national company

is

treated as a private company with all the rights and obligations which this

entails.

Output

is

shared between the partners according to predefined rules.

On the whole, those countries which already have an established oil industry

prefer to use risk-service contracts and joint operating ventures. Countries

which have proven oil potential or exsiting oil production use production

sharing contracts. Small or little-explored countries use concessions. This is not

a general rule, however. Each country choses a contractual system and tax

regulations closest to the aims of its oil policy in the light of technico-economic

limitations

The main feature of an oil contract is

its tax aspect. Under the concession

2Among the various production-sharing contracts should be noted the system based on profit

sharing (rate of return based profit sharing contract) which

was

introduced at the end of the 1970s

backed

by

the World Bank as a suitable system for both underdeveloped and developed countries.

Under this contract, the basic taxation mechanism is the resource rent tax or additional profit tax

(ATP). This tax essentially guarantees the investor a minimum profitability on his investment

before paying any tax. Profitability

is

calculated on the basis of the project's current cash flow.



130

Modelling oil exploration

system, the basic taxation mechanism

is

the royalty and profit tax. Within

production-sharing contracts, the rules governing the share out of profit oil

take precedence over other aspects. In service contracts, on the other hand, it

is factors such

as

the indemnity per barrel output which take precedence. Tax

mechanisms usually associated with one form of agreement are sometimes used

in other types of contract. In particular, some production-sharing contracts

include royalty clauses. Oil companies react to the whole tax structure not only

to the

level

of taxation but also the source of the taxation (Figure

8.1). If, for

. ~ I i I ( I I . 1 I

t. . ' n ~ , . l t l f ) sJt.rilt

IL PR IO

~ o U'IC'IOI .Jh.,.t or n n : . n d ~

c:::J

ROYllli., 01

lue

Profir

"./

Yurt

no

H

RI

G 0 TRA T

Fig. 8.1



Particularities

of

exploration

in

LDCs 131

example, the company can recover the same profit through either a concession

or a production-sharing contract, the financial indicators used (e.g. rate of

return, payback period, profit to investment ratio, etc.) are likely to vary

according to the particular situation.

As well as fixing the rules governing the sharing of profits between the state

and the company, contracts lay the foundations on which exploration activities

are based. They determine what work is to be carried out, the minimum

investment required on the part of the company, and the timetable for work

and expenditure. They specify the terms and conditions of renewal of the initial

exploration period and the zones which the company has to give back to the

state as exploration progresses. Several stipulations concerning monetary

transfers become effective during the exploration phase,

e.g.

bonus payments at

the time of signature and discovery, rights on the granting of a permit, leases.

etc. Some obligations imposed on the company have financial implications

which can discourage or encourage companies from committing themselves to

exploration within a given country. This depends essentially on three factors:

the extent of the financial burden implied by these obligations, the possibility

of recovering this initial financing once production has started, and the rigidity

of the timetable governing payments. Companies consider financial obligations

particularly burdensome during the exploration phase since they have to be

paid before production starts.

In

addition, such an investment does not bear

any real relation to profits. Considerable sums of money are tied up over long

periods of time and if exploration turns out to be fruitless, the investment is

lost. Financial burdens imposed on companies can render a project unprofit

able and block the development of marginal regions.

Contracts containing clauses covering operational restrictions and non-tax

related obligations can also reduce the overall profitability of a project. These

include the choice of the operator, the way in which operations are carried out,

checks during exploration activity, production targets, a preference for the use

of nationally/produced goods and services, exchange controls, marketing of

output, the sale of oil on the national market at reduced prices, the evaluation

of the oil, the use of natural gas, imports and exports of goods and services, etc.

To sum up the situation from the contractual point of

view,

the rhythm and

intensity of exploration in LDCs depends on (i) the structure of the tax system

and the level of taxation; (ii) clauses concerning the exploration phase,

particularly those that have financial implications for the company; (iii)

operational restrictions and non-tax-related obligations.

8.4.2 Diversity

of

management methods

in

the mining domain

The management of mining operations in LDCs bears hardly any resemblance

to that in developed countries. The state's monopoly power over resources

manifests itself in various ways. Some countries are closed to private explora

tion (e.g. Mexico, Iran, Iraq and more recently, Brazil). Some countries (e.g.

India, Venezuela, China) keep the best zones for state companies.

In

these



132

Modelling oil

exploration

countries, most of the exploration effort

is

undertaken by the national

company. Investment criteria as well as the financial resources given over to

national companies by the state for oil exploration are such that the rhythm

and intensity of exploration follow quite specific trends, sometimes in opposi

tion to the general trend. For example, during the 1970s and early

1980s

exploration declined considerably in Algeria, Ecuador, Mexico and Nigeria, in

spite of the two oil shocks.

In countries where companies are invited to take part in exploration, the

authorities choose the company according to several (generally secret) criteria

which are heavily influenced

by

political factors. The competitive bidding

system as practised in developed countries is not used in LDCs. Competitive

bidding

is

limited to certain contractual clauses, notably the signature bonus

and the minimum level of operation to be respected. In addition, the mining

domain

is

only accessible when the authorities choose to let it be so. Some

countries offer several exploration permits at a time, others, such as Angola,

prefer to divide the domain into blocks and open them up one by one.

Poor management in the mining domain sometimes leads to monopolization

of zones. This happened in Africa between 1975 and 1979. The phenomenal

extension of mining operations in Africa has not led to a proportional increase

in exploration activities

(see

Rodriquez Padilla, 1990). The continent did not

profit from the first oil shock. Oil companies acquired exploration zones with

the basic aim of getting a foothold in the territory in order to get a lead on

their competitors or to conserve their zones as an insurance policy or a

speculation for the future. Everywhere but in Africa, price rises and threats to

oil supply have given considerable impetus to hydrocarbon exploration.

Summing up from the point of view of the management of mining oper

ations, the rhythm and intensty of activities depend

on:

1. the extent of activities developed by the national company;

2. the speed at which the authorities offer exploration to private companies

and the number of permits;

3.

the degree of monopolization of the mining domain by the national

company or by foreign companies.

Ever-present

political factors

I f political risks are very slight in developed countries, this

is

not at all the case

in LDCs. Indeed this

is

one of the main reasons why international oil

companies retreated to their national base after the first oil shock. From an

exploration point of view, political factors can be divided into two subgroups:

those concerning changes in the legal and taxation aspects governing oil

companies' activities, and those which stem from political instability.

The first group of political factors covers total or partial nationalizations,

violations or unilateral renunciation of contracts, arbitrary tax in

creases/changes in tax mechanisms or suffocating bureaucracy leading to an

increase in costs. Companies pay a great deal of attention to what could be



Factors determining exploration in LDCs 133

called the 'fiscal risk' (i.e. the evaluation of the probability of changes in the

rules of the game) when deciding to commit themselves in a developing

country. The second group of political risks covers political instability due to

ethnic or religious rivalries, civil wars, frontier problems and hostile activities

towards foreigners (sabotage, bombings, etc.).

Most specialists

give

greater importance to the first set of factors, those

concerning changes in the rules of the game. Political stability is above all a

matter of stability in political behaviour, i.e. a matter of continuity in attitudes

towards foreigners rather than government stability.

I t would be impossible to explain changes in exploration activities in West

Africa, for example, without taking into consideration the impact of political

factors (Rodriguez Padilla,

1990).

These factors include: partial nationalizations

(Nigeria, Gabon, Angola, Cameroon, Congo, Zaire, Ivory Coast and Ghana);

compulsory changes in contract concessions (Cameroon); unilateral rupture of

agreements (Angola and Benin); constant leadership changes (Nigeria, Ghana);

sabotage and bombings of oil installations (Chad and Angola); civil wars

(Nigeria, Angola); and disagreements concerning the technical performance of

companies (Congo, Ivory Coast and Benin). In the case of the Angolan war of

independence and of the litigation between the Congo government and Elf

concerning the development of the Emerald oil field, exploration was totally

abandoned for between one and two years.

However, it has to be recognized that political factors do not always have a

negative effect, as can be seen in the case of Angola. The Angolan government's

success in maintaining a high level of exploration was based not only on a

flexible approach to taxation but also on a stable attitude with respect to

foreign investors. The government's marxist-leninist ideology did not get in the

way of business relations with the oil companies. Economic relations with the

Luanda government have been unanimously described as very satisfactory.

Finally, it has be said that external political factors have also altered

exploration behaviour in LDCs. One example

is

provided by the policy of

'diplomatic drilling' developed

by

French oil companies in several countries in

the Gulf of Guinea which accompanied French policy in these countries at the

end of the 1970s. A second example

is

provided by the anti-Angolan campaign

orchestrated

by

the Reagan administration in the mid-1980s which was aimed

at forcing the withdrawal of Cuban forces from the country and a general

weakening of the country's position within the region, especially with respect

to South Africa. Chevron, Texaco and Mobil had to leave the country as a

result of this policy.

8.5 FACTORS DETERMINING EXPLORA

nON

IN LDCs

In order to discover the relative importance of the factors determining oil

exploration activities in LDCs, pooled cross-section time series analyses have

often been used

(see

Siddayao, 1980; Broadman, 1985).



134

Modelling

oil

exploration

In her study of the renewal of reserves in South East Asia, Siddayao suggests

the following explanatory variables for exploration drilling (E): international

oil price

(P),

drilling costs

(C)

approximated by the US cost index on drilling

machinery equipment and supplies, success rate

(SR),

size of discoveries

by

type

of field (T), average depth of wells (DP) and institutional factors, dummy

variable (D U):

The index

j

refers to the countries - Brunei, Burma, Indonesia, Kampuchea,

Malaysia, Philippines, Thailand, Vietnam - and the index i to the four

categories of field

-

very

large, large, medium and small. The results

(see

Table

8.2)

suggest that the size of discoveries (approximated for a priori expected

return) and 'probably' the institutional factors are the principal explanatory

variables for exploratory drilling in this part of the world.

Using a larger and more varied sample of countries, Broadman comes to

similar conclusions. He suggests that the exploration effort undertaken in

developing countries is a function of geological potential, infrastructure type of

oil contract, taxation system, and of the degree of political risk. The final

specification of the model is as follows:

Eit=al +a2

SR

it-l

+a3

X

it-l

+a4INFi+asCSHit+a6JVSit+a7TXit

+asPRKit+a9DPit+alOPt-l +allAi+a12NOCit+eit

(8.9)

The number of

wells (E)

is again used for exploration. The geological potential

is approximated by the success rate (SR) and the rate of addition to reserves

(x)

(both calculated as running three-year averages). Infrastructure develop

ment

(INF) is approximated using the contribution of manufacturing to GDP.

The contractual framework

is

approximated by three dichotomous variables

each of which represents one of the currently used oil contracts: concessions

and production-sharing contracts (CSH), and risk-service contracts, non

risk-service contracts and joint ventures (JVS). These variables take the value

1 when the corresponding agreement is the dominant contract in year t and 0

when this is not the case. The impact of the taxation system on exploration

(TX)

is

approximated using the oil revenue taxation rate and the impact of

political risks (PRK) by the operation risk index proposed by the American

firm BERI Inc. Broadman also introduces four other explanatory variables,

two of which are intended to represent economic factors and two to express

certain particularities of LDCs: the depth of wells

(DP)

provides an approxi

mation of drilling costs; international oil price, calculated as a simple average

over the previous 2- and 3-year periods (P2 and P3 respectively); the size of the

country (A); and a dichotomous variable (NOC) indicating the existence of a

national oil company.



Factors determining exploration in LDCs

Table 8.2 Cross-country time series regression for exploration wells

drilled in South-East Asia, 1967-1977

Independant

variables

Equation I Equation

II

(lagged)

(auto)'

(auto),

Wildcat

0.54

N

(5.74)····

Price 0.89 0.82

(0.68)

(1.33)"

Cost

-0.15

N

( -1.11)'

Success ratio

0.12

N

(0.71)

Giant discovery

24.22

29.38

(7.14)····

(5.76)····

Large discovery 4.89

19.1

(0.79)

(2.30)'"

Medium discovery

3.16 6.71

(0.77) (1.51)'

Small discovery

5.06

10.42

(4.21)··

..

(7.42)····

Dummyb

2.32

9.82

(0.48) (1.89)

Dummy (not lagged) -6.66

N

( -1.56)"

R2=

0.98

0.92

Source: Adapted from Siddayao

(1980).

a

Least squares estimation by Cochrane-Orcutt type procedure (conver

gence

=

0.001).

"Dummy for institutional change.

t-statistic in parentheses.

N=not

used .

.... Significant at 0.005 level.

...

Significant

at 0.05 level.

"Significant at 0.10 level.

'Significant at 0.20

level.

135

Having analysed 47 LDCs over the period 1970--82 (Table 8.3), the author

concludes that the exploration market in LDCs appears to be segmented. The

factors which determine exploration in producer countries are not the same as

in non-producing countries.

In

the former, institutional factors (contractual

regime, taxation system, existence of a national company) and political risks

have a greater influence than factors related to the physical resource itself. The

opposite

is

the case in non-producing countries where geological factors play

a greater role.



136

Modelling

oil

exploration

Table 8.3 Cross-country time series regression models for exploration

wells

drilled in developing countries, 1973-82

Oil

Dependent variable producers

Success ratio of wildcats

4.81

(2.77)

...

Discovery size 2.30E-06

(1.04)

Infrastructure development 1.20

(3.97) . ··

Use of risk contracts or

-7.14

joint ventures

( - 3.47)

...

Use of production

-3.68

sharing contracts (

-1.72)'

Income tax rate

-0.18

(

-1.77)-

Political risk

0.26

(1.07)

Average well depth 7.30E-05

(0.69)

World oil price

3"

0.36

(3.78)

...

World oil price

2b

Area

0.Q2

(3.62) ...

Presence of a national 6.07

oil compagnie

(5.12) ...

R 0.62

F

37.54

...

n 250.00

Source: Adapted from Broadman (1985).

a P3(t-l)= [P(t-l)+P(t-2)+P(t-3)] -;. 3

b

P2(t

-1) =

[P(t)

+

P(t

-1)] -;.

2

t-statistic in parentheses

. Significant at 0.10 level.

.. Significant at 0.05 level.

... Significant at 0.02 level.

.... Significant at 0.01 level.

8.6 CONCLUSION

Non-oil

producers

1.15

(2.56)'"

-3.70E-03

(-0.10)

0.63

(1.26)

-1.80E-03

(

-0.11)

4.00E-03

(0.62)

-1.00E-06

( - 3.37)

...

-4.60E-03

(-1.04)

1.00E-05

(0.423)

0.32

(0.63)

0.03

1.664

220.00

This chapter has shown the complexity of the determinants underlying oil

exploration in various countries. Particularly for developing countries there

has been relatively little systematic research and results are certainly discourag-



138

Modelling

oil

exploration

Attanasi E.

D.

(1984) A Note on Petroleum Industry Exploration

Efficiency.

Energy

Journal, 5, 133-85.

Bing,

P.e.

(1987) A Model of Exploratory Drilling in Western Canada, IAEE, Ninth

International Conference, Calgary,

6-8

July,

p.

707-17.

Bohi

D. R.

and Toman, M.

A.

(1986) Analyzing Nonrenewable Resource Supply.

Research Project 1220--1. Electric Power Research Institute, Palo Alto, Ca.

Broadman,

H. G.

(1985) Incentives and Constraints on Exploratory Drilling for

Petroleum in Developing Countries, Annual Revue Energy,

no.

10,

pp.

217-49.

Cairns,

R. D.

(1986) 'The Economics of Energy and Mineral Exploration: a Survey.'

IIASA, Laxenburg, Austria (mimeo).

CERI (Canadian Energy Research Institute) (1988) World Oil Market Model; Documen

tation and Operating Procedures. CERI, Calgary, December, p. 65.

Choucri, N. Heye, Ch. and Lynch, M. (1990) Analyzing Oil Production in Developing

Countries: a Case Study of Egypt, Energy Journal, 1,91-115.

Clark,

P.

Coene,

P.

and Logan, D.

(1981)

A Comparison of Ten

U.S.

Oil and Gas

Supply Models, Resource and Energy,

3,

297-335.

Cox,

J.

e. and Wright,

A. W.

(1976) The Determinants of Investments

in

Petroleum

Reserves and their Implications

for

Public Policy, American Economic Review, 66

153-67.

Desbarats, e. M. (1989) Empirical Modelling

of

Canadian Petroleum Exploration

Activity. OIES Papers on Energy Economics, Oxford Institute for Energy Studies,

June,

p.

81.

Devarajan,

S.

and Fisher,

A.

e. (1982) Exploration and Scarcity Journal

of

Political

Economy, 90(6).

Epple, D.

N.

(1975)

Petroleum Discoveries and Government Policy: an Econometric

Study of Supply. Ballinger, Cambridge, Mass.,

p.

139.

Epple,

D. N.

(1985) The Econometrics of Exhaustible Resource Supply: a Theory and

Application, in Energy, Foresight and Strategy

(ed.

Thomas 1. Sargent). Resources for

the Future, Washington,

De.

Gaudet,

G.

and Hung, N.

M.

(1986) Theorie economique des ressources non re

nouvel abies: quelques elements

de

synthese, Quebec, Universite Laval.

Gilbert,

R.

(1979) Search Strategies and Private Incentives for Resource Exploration,

in

Advances

in

the Economics of Energy and Resources

(ed. R.

Pindyck).

vol 2,

JAI

Press, pp. 149-70.

Kaufman, G.

M.

(1979) Models and Methods for Estimating Undiscovered Oil and Gas;

What they Do and Do Not Do, in

Methods and Models for Assessing Energy

Resources (First IIASA Conference on Energy Resources, 20--21 May 1975)

(ed.

Michel Grenon), Pergamon Press, Oxford pp. 173-85.

Kaufman, G. M. (1983) Oil and Gas Estimation of Undiscovered Resources, in Energy

Resources

in

an Uncertain Future; Coal, Gas, Oil and Uranium Supply Forecasting (eds

M. A.

Adelman

et al.).

Ballinger, Cambridge, Mass.

Macavoy, P. and Pindyck, R. Alternative Regulatory Policies for Dealing with the

Natural Gas Shortage. Bell Journal

of

Economics and Management Science, 4, 454-98.

Moroney,

1.

and Bremmer,

D.

(1987) An Integrated Regional Petroleum Model, in

Advances in the Economics of Energy Resources

(ed. 1.

Moroney),

vol.

6, JAI Press,

Greenwich, Connecticut, pp. 187-220.

Nielssen,

T.

and Nystand,

A. (1986)

Optimum Exploration and Extraction

in

a

Petroleum Basin. Resources and Energy, no.

8,

pp. 219-30.

Pakravan,

K.

(1977) A Model of Oil Production Development and Exploration. Journal

of

Energy and Development, 3, 143-53.

Peterson, F. (1978) A Model of Mining and Exploring for Exhaustible Resource. Journal

of

Environmental Economics and Management, 5, 236--51.



References

139

Pindyck,

R. (1978)

The Optimal Exploration and Production of Nonrenewable Resou

ces. Journal

of

Political Economy,

86 841-61.

Pindyck

R. (1980)

Uncertainty and Exhaustible Resources Markets.

Journal of Political

Economy,

88 1203-25.

Rodriguez Padilla, V. (1990) 'L'impact

de

la fiscalite sur l'effort d'exploration-produc

tion

de

petrole;

Ie

cas des pays producteurs d'Africa

de

l'Ouest.

PhD

thesis, Institut

d'Economie

et

de Polique de l'Energie, Universite de Sciences Sociales de Grenoble,

p.520.

Ryan,

D.

and Livernois, 1.

(1985)

'Testing for non-lointness in Oil and Gas Exploration:

A Variable Profit Function Approach? Discussion Paper No. 85-6, University of

Calgary.

Scarfe

B. L.

and Rilkoff, E.

(1984)

Financing Oil and Gas Exploration and Development

Activity. Economic Council of Canada, Discussion Paper No. 274.

Siddayao,

C.

M. (1980)

The Supply of Petroleum Reserves in South-east

Asia;

Economic

Implications of Evolving Property Rights Arrangements. Oxford University Press,

Oxford,

p.

240.

Uhler,

R.

S. and Eglington, P.

(1983)

The Potential Supply of Crude Oil and Natural

Gas Reserves in the Alberta Basin. Monograph prepared for the Economic Council

of Canada, Ottawa.

US Department of Energy (DOE)

(1979)

Nigeria - An Assessment of Crude Potential.

Analysis Report DOEjEIA-0184jI4, Washington, DC,

p.

26.

US

Department of Energy (DOE)

(1981)

Middle East-Crude Oil Potential from Know

Deposit. Analysis Report DOEjEIA-0289, Washington, DC, p. 143.

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System Methodology Description. Technical Memorandum TMjESj79-05. Washin

gton, DC

p. 43.

Woods,

T.

1. and Vidas, H. (1983) Projecting Hydrocarbon Production and Cost: an

Integrated Approach.

Journal of Energy

and

Development,

Spring, pp. 267-82.



9

Environmental

cost

functions:

a comparison between general

and

partial equilibrium

analysis

Lars Bergman

9.1

INTRODUCTION

An emission control cost function, in the following denoted ECC function,

is

a condensed representation of the minimum costs associated with emission

control within a well-defined system such as a plant, a region or a country. In

models of trans boundary pollution, for instance IIASA's RAINS model

(see

Alcamo et

al.,

1987) of the so-called 'acid rain' problem, the ECC functions are

national cost functions. Thus the relevant cost functions are aggregate repre

sentations of the costs of reducing emissions from a large number of micro

units within each country.

The estimation of such functions involves two major steps. The first

is

to

identify emission control measures at the micro level and to estimate the costs

associated with these measures. In practice this amounts to combining engin

eering and economics. The issues raised in this work have attracted a lot of

attention in several studies during the past

few

years. Rentz

et al.

(1988)

give

a

thorough exposition of issues related to emission control costs in the energy

system. Johnsson

et al. (1988)

deal with related issues in local energy systems.

The papers in UN-ECE

(1988)

summarize the 'state of the art'.

H reliable micro data are available, however, it still remains to aggregate this

information in a consistent

way.

Thus the second step in the estimation of a

national ECC function is

to aggregate the micro information. Moreover, the

results should be presented in a closed form, suitable for direct integration in

decision-support models such as RAINS.

The natural point of departure when micro data are to be aggregated into

sectoral

or

economy-wide ECC functions

is

to use a general equilibrium

perspective. The reason is that emission control measures,

at

least when

ambitious emission reduction goals are to

be

attained, might affect prices and



142

Environmental cost functions

the allocation of resources in the economy as a whole, i.e. have general

equilibrium

effects.

The purpose of this study is to discuss the aggregation of

micro cost information into macro cost functions against the background of

the possibility of general equilibrium effects of emission control measures.

9.2

THE

DEFINITION

OF ECC FUNCTIONS

l

In general an ECC function is a minimum value function, i.e. the solution to a

cost minimization problem. It follows that the nature of the ECC function

depends on the specification of the underlying cost minimization problem. In

this section a simple taxonomy for national ECC functions will

be introduced.

For

simplicity it is assumed that the minimum cost of attaining any emission

reduction at each micro unit is known. I t thus remains to aggregate the micro

data.

The aggregate opportunity cost,

C,

for a country of reducing its

SOx

emissions can be written as a function of the parameters of the underlying cost

minimization problem. In the most general case the resulting macro cost

function can be written

C=C(E;Z,X)

(9.1)

where E is the emission level, Z is a vector of policy instrument values and X

is a vector of exogenously given economic variables.

The function C(

...

) is

decreasing in E and attains the value zero for some

finite value of E. As the definition of the cost function implies that any given

emission level

is

attained in a cost-efficient way, Z, i.e. the vector of policy

instrument values, does not include the policy instruments that are used to

affect the emission

level.

Within this very general framework it

is

possible to discuss several different

emission control cost concepts. Any choice of variables to include in the vector

X implies a specific partitioning of the economy into one part which is affected

by the emission control measures and one part which is not.

In

other words,

the specific partitioning adopted essentially determines the nature of the cost

minimization problem and thus what kind of model is needed for the

estimation of the national ECC function. In the following, three possible

alternatives will be briefly discussed.

For

simplicity it

is

assumed that all

emissions originate in the energy sector.

The general equilibrium perspective amounts to assuming that emission

control measures may affect prices and the allocation of resources in the entire

economy. This implies that the vector X in (9.1) only includes measures of the

economy's initial resource endowments and world market prices of traded

goods. In the following we will refer to this type of cost function as C

l ,

and

1

The exposition in section 9.2

is

to a large extent based on Carlsson

(1988).



The definition of ECC functions

the corresponding X and Z vectors as X

1

and Z 1. That is

C

1

=

C

1

E;X\Zl )

143

(9.2)

However, if variations of E within the relevant range do not affect the prices

of non-tradable goods and factors, these variables can be taken as exogenously

given and included in the vector X. This is the case if emission control

measures within the energy sector have small or negligible effects on other

prices in the economy. As a result a modified cost function, C

2

, is obtained.

This function can be written

(9.3)

with

X

2

and

Z

2

being obvious modifications of

X

1

and

Z

1.

It

is important to

note that as C

1

implies a wider range of adjustment possibilities than C

2

, it

must hold that C

2

is greater than or equal to C

1

for any given level of E and

compatible X and Z. This result follows from the so-called 'Ie Chatelier

principle' (see for instance Varian, 1984).

The cost function C 2 implies that any emission reduction is the result of the

combined effect of a number of adjustment mechanisms in the energy

producing sector as well as in the energy-consuming sectors of the economy.

More precisely, the emission level would be affected through direct measures

and/or

structural changes such as:

1. Installation of technical devices reducing the emissions from a given set

of

energy production units using given types of fuels.

2. Implementation of fuel switching measures and/or installation of new

production units in the energy sector.

3. Changes in the level and compositon of energy consumption.

The ECC function C

2

includes all the adjustment mechanisms (1) to (3) in

the list above. It

is

implicitly based on some kind of partial equilibrium energy

market model. Thus C

2

is

a measure

of

the loss of consumer

and

producer

surpluses resulting from emission control measures in the energy sector. As all

other prices by assumption are constant, there are no losses of consumer

and/or producer surpluses on other markets than the energy market. Conse

quently C 2 is a measure of the cost of emission control for the economy as a

whole.

However, if the cost-efficient combination

of

emission reduction measures

does not affect the consumption of energy,

we

can envisage a third, and from

an

economic point of view even more narrow, type

of

ECC function, C \

defined by

(9.4)

where X

3

includes exogenously given energy consumption levels and Z3 is an

appropriately modified version of

Z2.

In terms of adjustment mechanisms this

cost function includes

(1)

and

(2)

in the list above. By the same reasoning as



144 Environmental cost functions

above it holds that C

3

is larger than or equal to C

2

for

any given

E

and

compatible X and Z.

Obviously one can go on excluding additional adjustment mechanisms and

thus define even more narrow cost concepts. Thus a C

4

cost function would

only include type

(1)

adjustments only, i.e. installation of technical devices such

as flue gas desulphurization, fuel cleaning etc. As a result any specific cost

estimate based on the C

4

concept would be greater than or equal to the

corresponding C

3

estimate.

Cost estimates based on a narrow cost concept such as C

4

or C

3

can be

obtained from a simple model, but may be biased upwards. The question then

is

to what extent narrow cost concepts quantitatively affect the estimates of

emission control costs in practically relevant cases. Since some of the major

studies of sulphur emission control costs are based on narrow cost concepts,

2

this

is

not an unimportant issue.

In this study the evaluation of general equilibrium effects of sulphur emission

control and the estimation of C

1

ECC functions has been carried out

by

means

Character

Periodicity

Time

Use

Economic agents

Sectors of

economic

activity

Data base

Endogenous

Exogenous

Size

Software

Table

9.1

Brief model descriptions

CGE ENMARK

Computable general

equilibrium

Yearly

Long-term

Projections

Households, firms

Forest industry

Steel industry

Manufacturing industry

Construction

Services

Transport and communications

Public sector

National accounts

Factor and product

prices and quantities

Factor supplies and

world market demand

and prices

230

GAMSjMINOS

Partial equilibrium

Yearly

Long-term

Projections

Energy producers and

consumers

Central electricity

District heating

Specific data

Energy production,

consumption and prices

Nuclear and

fossil

fuel

prices, GDP, political

constraints

475

GAMSjMINOS

2The cost estimates generated within IIASA's RAINS model are based on the C

4

cost concept,

while the cost estimates generated within the BICRAM study are of the C

3

type. The IEA/ETSAP

study, which is based on several national variants of the so-called MARKAL model, is aimed at

estimating national cost functions of a type somewhere between C

3

and C

2

.



Equilibrium effects

of emission

control

145

of a separate CGE model of the Swedish economy. For the comparison of C

2

and C

3

an energy market model called ENMARK, implemented on Swedish

data, has been used

(see

Table

9.1

and Carlsson,

1988).

9.3

GENERAL EQUILIBRIUM EFFECTS OF EMISSION CONTROL

The general equilibrium effects of sulphur emission control are evaluated by

means of a computable general equilibrium (CGE) model of the Swedish

economy. A complete description of the model

is

given in Bergman (1989) and

Bergman (1990). As this study mainly

is

concerned with the choice of ECC

function concept, only a brief description of the model

will

be given here.

9.3.1 General features of the CGE model

The model pictures an economy with three tradables-producing and five

non-tradables-producing sectors. Power production

is

one of the non

tradables-producing sectors. Two of the tradables-producing sectors are price

takers on international product markets, while the third faces a downward

sloping, price-dependent export demand function. The economy's resource

endowment consists of capital, labour and electricity from existing hydro and

nuclear power plants, and the supply of these resources

is

completely inelastic.

Both production and consumption of goods and services lead to emissions

of SOx, NO

x

and CO

2

•

In particular both of the price-taking sectors, the forest

industry and the iron and steel industry, happen to be relatively energy

intensive and produce a relatively large amount of SOx emissions per unit of

output. Environmental policy takes the form of national maximum total

emissions of each one of these pollutants. The environmental policy objectives

are implemented by means of a system of tradable emission permits. In order

to comply with the regulations implied by the limited supply of emission

permits producers can change their technology, i.e. the proportions in which

various inputs are used, or implement direct abatement measures. However,

the emissions of CO

2

cannot be affected through direct abatement measures.

Consumers can change their pattern of consumption of final goods, and

thereby affect the pattern of production. In addition the real costs of emission

control tend to reduce disposable income and thus the level of final goods

consumption.

As

emission permits are assumed to be tradable, the equilibrium prices of

emission permits

will

be

equal to the marginal cost of emission control.

3

The

cost reflects the direct abatement cost functions, the substitutability of various

3 An alternative interpretation is that the calculated equilibrium emission permits prices represent

the emission taxes that would have to be implemented in order to comply with the given total

emission constraint.



146

Environmental cost functions

inputs in the production processes and the substitutability of various goods

and services in final consumption. Thus the national ECC function for SOx can

be estimated on the basis of the relation between national emission levels and

equilibrium

SOx

emission permit prices.

9.3.2 Some simulation results

The basic issue

is

whether contemplated measures to reduce emissions of

SOx

are likely to produce significant general equilibrium

effects.

I f they do, there is

a case for C

1

ECC functions. Otherwise the estimation of aggregate ECC

functions can be based on partial equilibrium sector models. In order to

elucidate this issue the CGE model of the Swedish economy was used to

simulate conditional general equilibrium resource allocations in the Swedish

economy in

1995.

Assumptions about the trends in exogenous variables such as the accumula

tion of capital, the growth of the labour force and world market conditions are

essentially the same as in the latest long-term economic survey published by

the Ministry of Finance. However, it was assumed that all nuclear power plants

currently in operation in Sweden will be in operation also in 1995.

4

One

consequence of this assumption is that there is no need for

new

power capacity

before 1995. Thus, as in the current situation,

5

electricity generation produces

no SOx emissions in 1995.

The basic issue with regard to this report

is

to what extent more stringent

constraints on

SOx

emissions affect the equilibrium factor prices. In order to

highlight this issue a series of simulations, where everything except the

constraint on

SOx

was kept constant, were carried out. The simulation results

are summarized in Table 9.2.

Table 9.2 Calculated marginal cost of SOx emission control and equilibrium

factor prices

at

various SOx emission

constraint

levels in 1995

Price index

C

of

Constraint

Marginal ECC

b

Capital Labour

324

300

275

250

225

200

175

o

9.2

17.1

29.2

42.7

57.2

72.3

"SO. emissions in 1000 tonnes.

bIn SEK/kg.

C 1985

=

1.000.

0.999

1.015

1.007

0.985

0.960

0.934

0.908

1.321

1.305

1.274

1.254

1.233

1.210

1.186

Electricity

1.580

1.619

1.668

1.720

1.778

1.841

1.906

4

According to a recent decision two nuclear reactors should be phased out around 1995.

5

In Sweden the bulk of the electricity generated comes, in roughly equal proportions, from hydro

and nuclear power plants,

i.e.

from processes that do not emit any SOx'



Equilibrium effects

of emission

control

147

As

a guide for the reader, it can be mentioned

that

the total

SOx

emissions

in Sweden in

1980,

measured as S02, were

483 000

tonnes. Thus a reduction

of

30%,

a number often referred to in relation to the so-called '30% Club', would

mean a reduction to annual emissions equal to

338 000

tonnes. The first row

of

Table

9.2

indicates the emissions when there are no additional constraints

on SOx

emissions.

As

can be seen the emission level in

that

case represents a

greater

than

35% reduction compared to the level in 1980.

It

is easy to see

that

increasing stringency of the SOx emission constraint

does affect factor prices. The question

is

how strong the impact needs to be

before one can say

that

the impact

is

significant. One arbitrary limit would be

a 10% change in relation to the no constraint case.

On

the basis

of

this norm

the emissions

of

SOx

can be reduced by almost 40% without 'significant'

impact

on

the prices of capital

and

labour.

I f

the limit is set at 5%, the

corresponding emission reduction

is around

25%.

As

the Swedish economy to a relatively large extent

is

specialized

on

energy

and SOx

emission-intensive production, the results are sensitive to the assump

tions

about

the

market

power

of

Swedish producers

on

international markets.

As

was mentioned above, the model

is

based

on

the assumption

that

the

Swedish producers in the energy-

and

emission-intensive industries are price

takers

on

international markets. I f they, instead,

do

have a certain degree of

market

power, this would reduce the general equilibrium effects

of

SOx

emission constraints.

It

would have been desirable to be able to generate a C

2

ECC

function

that

was directly comparable to the C 1

ECC

function implied by the results in

Table

9.2.

Unfortunately

that

is not

possible. However, Table

9.3

displays the

result of calculations based

on

two simplifying assumptions. These assumptions

are

that

the marginal emission control costs reported in Table 9.2 only consist

of capital and

labour

costs and

that

the proportions of these cost items are

9 to

1.

6

Table

9.3 Calculated C

1

and C

2

ECC functions for Sweden

SOx

emission

constraint

300

275

250

225

200

175

"1000 tonnes.

bSEKjkg.

Marginal cost

of

emission control

b

C

1

C

2

C

2

jC

1

9.2

17.1

29.2

42.7

57.2

72.3

9.3

17.3

30.2

45.2

62.2

80.8

1.011

1.012

1.034

1.059

1.087

1.118

6 Although these assumptions are quite arbitrary, they seem to be well in line with available micro

data on emission control costs.



Comparison between

C

2

and

C

3

cost concepts

149

The energy markets are modelled as competitive markets, and all energy

demand functions are linear. In view of the linearity of the demand functions

and standard economic theory ENMARK

is

specified as a quadratic program

ming model in which the sum of consumer and producer surpluses across

energy markets are maximized subject to market clearing and capacity con

straints. Numerical solutions are obtained by means of GAMS/MINOS.

The core of the model is a national market for electricity and a set of regional

markets for district heat. ENMARK is driven by exogenous assumptions about

real incomes and the prices of fuels and other inputs.

It

endogenously

determines the prices of electricity and district heat, the consumption of

electricity, district heat and various

fuels.

It also determines the emissions of

SOx,

NO

x

and

CO

2

originating in the energy and heating sectors.

The general structure of ENMARK implemented on Swedish data and the

corresponding energy flows in 1985 are shown in Figure 9.1. The demand

variables

V, Hand U

represent the electricity demand for direct electric heating

systems, the electricity demand for water-borne heating systems and all other

types of electricity demand, respectively. In other applications the demand

variable

U

is further subdivided. In analyses of energy taxation, for instance, it

is necessary to distinguish the electricity demand by the so-called electricity

intensive industries, i.e. the industrial electricity consumers for whom a special

reduction rule applies. Other symbols in Figure

9.1

should be self-explanatory.

If an upper limit on the total emissions of, say, SO., is imposed, the resulting

loss of consumer and producer surplus can

be

calculated by means of

EN MARK. The outcome of the calculation is a C

2

estimate of the cost of

emission control.

If,

in addition, the energy demand functions are replaced by

a vector of fixed energy consumption levels, the emission limit generates a C 3

ECC estimate. By comparing the two cost estimates a ceteris paribus compari

son of the two cost concepts is obtained.

9.5 A COMPARISON BETWEEN THE C

2

AND C

3

COST CONCEPTS

The purpose of this section

is

to

use

ENMARK for an explicit comparison

between a C 3 and a C 2 cost function for the Swedish energy markets. The

alternatives, define the production alternatives in the model. Thus conventional nuclear power is

the activity X(COND, NUC, NO), while combined heat and power production

in

coal fired plants

with flue gas desulphurization is represented by the activity X(CHP, COAL, FGD). It also follows

that the

set

of production activities can easily

be

changed and/or extended by changes in or

additions to the

T-,

F- and A-vectors.

Each one of the production activities is represented by an output vector, an input vector and an

emission vector. In addition there is a capacity limit that can be extended through investments.

The investment criterion is based on a comparison between scarcity rents on existing capacity and

annualized capital costs of new capacity. Thus

in

a simulation of energy market conditions at some

future point

in

time new capacity is added to existing capacity as long

as

the scarcity rents

in

question exceed the annualized capital costs of

new

capacity.



Comparison

between

C

2

and C

3

cost concepts

151

The experiment was designed as a comparison between two projections for

1995.

In one of these the

1995

sulphur emissions of the Swedish energy sector

were constrained to be at least 30% lower than the corresponding emissions

in 1980, while there was no constraint on such emissions in the other projection.

As

the sulphur emissions of this sector were 154000 tonnes in 1980, the 30%

reduction implied an upper limit for 1995 equal to 107800 tonnes in the

experiment. However, owing to endogenous emission reductions the forced

emission reduction implied by the constraint was only 19905 tonnes. Using

ENMARK a C 2 estimate of the cost of complying with this emission constraint

was made.

A critical assumption behind the simulation

is

that new investments in

nuclear power are not allowed, and that investments in natural gas or

biomass-based electricity production are more costly alternatives than invest

ments in coal power. In Table 9.4 we have summarized the contribution of

various adjustment mechanisms to the overall emission difference between the

two projections.

These results clearly suggest that emission reductions in the energy sector

are likely to lead to higher consumer prices of electricity and district heat, and

thus to consumption reductions. In this particular case the emission constraint

produced a 19% increase (in relation to base case projection for 1995) of the

marginal cost of high voltage electricity, which roughly corresponds to the

producer prices of electricity. As a consequence total electricity consumption

was reduced by 3.5% (in relation to base case projection for 1995). This

consumption reduction was sufficient to reduce the need for additional capac

ity in coal fired condensing plants by approximately 45%, which, as can be

seen in Table 9.4, implied a considerable saving in terms of sulphur emissions.

The results in Table 9.4 suggest that a more narrow cost concept than C

2

would,

by

definition, exclude important adjustment mechanisms and thus

overestimate the emission control cost. In this particular case it turned out that

the corresponding C

3

cost estimate was approximately 25% higher. Obviously

there are realistic cases where emission reduction cost functions of the C 3 type

seriously overestimate the true cost. We conclude that higher order cost

functions than C

2

should be used only when there is strong a priori evidence

that demand responses are likely to be insignificant.

Table 9.4 Allocation of projected emission diffe

rences

Emission difference (in tonnes) due to

Difference due to

fuel

substitution

Difference due to abatement measures

Difference in electricity consumption

Total difference in sulphur emissions

-30

-5510

-14365

-19905



:.::

LU

en

152

Environmental

cost

functions

9.6 ECC FUNCTIONS IN CLOSED FORM

In addition to the conceptual issues dealt with so far, there are some practical

issues related to the

use

of ECC functions. ENMARK is, like most energy

systems models, a relatively big model.

9

Thus it cannot easily be integrated in

a strategy model such as RAINS. The question then is whether the cost

function implied by a set of ENMARK solutions could be represented by a

continuous function of the type

(9.5)

In order to represent the cost functions generated by ENMARK, the model

has been used to generate a set of 'observations' on emission control costs at

various maximum levels of SOx emissions from the energy sector. Then each

1.2

1.1

1.0

0.9

0.8

0.7

c

Cil

0-0

- C

=

(1j

- I / )

E ::J

0.6

C 0

-£

'-

0.5

0

( )

0.4

0.3

0.2

0.1

0

5

10 15 20

D

25 30

Real

35 40 45 50 55 60 65 70 75 80 85 90 95 100105110115

Emissions (ktonne)

+ Estimated

Fig.

9.2

The ECC function, Sweden 1.

9The Swedish version consists of around

150

equations and inequalities.



ECC functions in closed form

153

2

1.9

1.8

1.7

1.6

1.5

1.4

~

1.3

w

C/J_

1.2

c

'"

0

1.1

c

ro

1.0

E

'"

::J

~ £

0.9

Ci l -

0.8

0

0

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

5

10 15

20 25 30 35 40 45 50 55 60 65

70

75 80 85 90 95100 105 110

115 120 125

130

135

Emissions (ktonne)

O R e a l + Estimated

Fig.

9.3

The

ECC function, Sweden

2.

of the cost functions was estimated by means of conventional econometric

techniques on the basis of these 'observations'.

Two examples of cost functions estimated in this way on Swedish data are

depicted in Figures 9.2 and 9.3. The cost function in Figure 9.3, 'Sweden l ' is

based on the assumption that

12

nuclear reactors are in operation, while the

cost function 'Sweden 2' in Figure 9.3 is based on the assumption that ten

nuclear reactors are in operation.

For

both of these cost functions it is assumed

that the maximum sulphur content in heavy

fuel

oil is

0.4%,

and that natural

bas is available in the production of district heat.

As can be seen in Figures 9.2 and 9.3, the cost functions tend to have a

quadratic form. The estimated equations are the following (t-values in parenth

eses):

Sweden 1 (Figure 9.2)

C=1235.371

(59.773)

20.912*E

( -14.462)

+

0.091*E2;

(2.995)

R2=0.999



154

Sweden

2 (Figure 9.3)

C=2088.952

(32.542)

Environmental

cost

functions

43.625*E

(

-11.260)

+ 0.349*E

2

;

(5.274)

R2=0.996

In

these ECC functions the cost is expressed in MSEK and the emissions in

1000 tonnes S02. The marginal cost of emission control

is

the negative of the

derivative of C with respect to

E.

As the estimated ECC functions are quadratic

it follows

that

the level of the marginal cost depends

on

the level of emission.

Thus, at the emission level 50000 tonnes the marginal cost of emission

reduction would be around 11 Swedish kronor (SEK) per kg S02. The

corresponding figure for Sweden 2 is 9 SEK per kg S02.

9.7 CONCLUDING REMARKS

The conclusions of this analysis can be summarized in a

few

points:

1.

ECC functions that only incorporate direct abatement measures can lead to

seriously upward biased cost estimates.

2. In some cases it is necessary to take general equilibrium effects into account

when ECC functions are to be estimated.

3.

In

a wide class of cases, however, it seems that C 2 is a sufficiently elaborated

cost concept.

4. It is a relatively straightforward task to estimate C

2

ECC functions and to

represent the results in a closed form, suitable for integration in strategy

models such as RAINS.

ACKNOWLEDGEMENT

The author is grateful to the Nordic Council of Ministers for financial support

and to Bo Andersson and Anders Carlsson for research assistance.

REFERENCES

A1camo,

1., Amann, M., Hetterlich,

1.

P.

et

al.

(1987)

Acidification in Europe. AMBIO,

16,

no. 5.

Bergman,

L. (1989)

'Tillvaxt och miljo - en studie

av

mAlkonflikter'. (Economic growth

and the environment - a study of goal conflicts). Bilaga 9, LU90, Ministry of Finance.

Bergman,

L.

(1990) 'General Equilibrium Effects of Environmental Policy: A CGE

Modelling Approach.' Research Report, EFI, Stockholm School of Economics.

Carlsson, A.

(1988) 'Estimates of the Costs of Emission Control in the Swedish Energy

Sector.' Research Report

273,

EFI, Stockholm School of Economics.

Johnsson,

1.,

Bjorkqvist, 0., Larsson,

T., et

al.

(1988)

'LAngsiktig kommunal energi-



References

155

och miljoplanering i Uppsala kommun'. (Long range energy and environmental

planning in Uppsala). Rapport A 88-173, Institutionen for Energiteknik, Chalmers

Tekniska Hogskola, Gothenburg.

Rentz,

0.,

Haasis,

H.

D., Morgenstern, T.,

et

al.

(1988)

'Energy and Environment:

Optimal Control Strategies

for

Reducing Emissions from Energy Conversion and

Energy Use.' Working Paper, Institute for Industrial Production, University of

Karlsruhe.

UN-ECE (1988) 'Air Pollution

in

Europe: Environmental Effects, Control Strategies

and Policy Options.' Discussion document from a conference in Norrtalje, Sweden.

Varian,

H. R. (1984) Microeconomic Analysis,

Norton,

New

York.



1

Energy

policies

in

a

macroeconomic model:

an analysis of energy taxes

when oil prices decline

P. Capros,

P.

Karadeloglou, G. Mentzas

10.1 INTRODUCTION

Economic planners and policy-makers have been acquainted, in the last fifteen

years, with unanticipated oil price increases. The energy economics literature

is

abundant with research exploiting alternative macroeconomic policy rules

that would mitigate the negative impacts of oil price shocks. However, the

unprecedented nosedive of oil prices in 1986 poses some new policy questions,

beside serving as a reminder about the volatility of the world oil market.

According to the literature the recession observed in the 1975-85

period and

the high inflation figures are well tied to the oil price shocks of 1973-74 and

1979-80 (see

for instance Eckstein,

1978;

Gordon,

1975,

Hamilton,

1983;

the

contributions in Mork, 1981; Bruno and Sachs, 1982, 1985). It

is

generally

admitted that oil price shocks impose a macroeconomic cost on oil-importing

economies through various mechanisms: reduction in real national income;

increase

in

the marginal cost of production; drop of labour and/or capital

productivity through factor substitutions; etc. These effects have been quanti

fied for many oil-importing countries by means of macroeconomic models

(e.g.

Hickman, Huntington and Sweeney,

1987).

In order to provide a means of mitigating the negative impacts of unan

ticipated oil price increases, various taxation instruments have been examined;

e.g.

consumption taxes, import tariffs, subsidies, etc. (see Hudson and Jorgen

son, 1974); Gilbert and Mork, 1984, 1986; Pindyck, 1980 and Mork, 1985).

Most authors conclude that establishing accommodating policies seems more

efficient than varying tax rates on the consumer price of oil; such accommo

dating policies include monetary accommodation, income tax reduction,

supply-side credits, etc.

If, however, the developed theories were applied

in

reverse, the result of an



158

Energy

policies

in

a macroeconomic model

oil price decline should

be

a substantial stimulus to real activity and a drop of

inflation. Macroeconomic models support such a conclusion, although one

should

be

aware of the fact that most of them experience an almost linear

symmetry around their solution point. lOne might then suggest that maximum

efficiency will

be

obtained by letting the market forces

free

to act. However,

significant policy questions are still put forward:

1.

Given that the tax rates

on

oil products were defined during a period of

upwartl trends of oil prices, one policy dilemma

is:

should one change these

tax rates and if so, in which direction?

2.

A number of countries facing significant public deficits need to maintain or

even increase the public revenues that stem from the taxation of oil

products.

In

the case of an oil price decline such countries tend to increase

tax rates on oil products. What

is

the macroeconomic impact of such a

policy?

3. Low oil product prices for domestic consumption may not comply with

long-term objectives, mainly with the reduction of the dependence on oil

imports. In fact low prices may delay or even cancel energy conservation

programmes or energy restructuring projects that aim at substituting

oil.

What

is

the macroeconomic cost of using tax rates on oil products in order

to avoid a shift of energy dependence on imported

oil?

4.

If,

for the above reasons, an increased taxation on oil products

is

decided,

what is the best accommodating policy,

i.e.

where should the increased

public revenues

be

allocated? The importance of this last question is related

to the policy response of the public sector when facing a variety of options

aiming at accelerating growth. Should the public sector try to boost the

economy by increasing demand

(e.g. public investment increase) or should

it follow a supply side oriented policy? Should the public sector reduce its

borrowing requirements (PSBR) or should it keep the PSBR at its initial

level and spend the additional resources for development purposes?

These issues have not been sufficiently analysed in the literature, although

most governments have changed their oil products pricing policies after the oil

price decline of 1986.

For

instance, in most European countries taxes on oil

products have been used to increase public revenues while energy restructuring

funding has been retarded.

In this paper we attempt

an

analysis of these issues within the context of

macroeconomic modelling.

We

retain a medium-term perspective and start by

assuming that the economy experiences unemployment and excess capacity

when the price decline occurs. The analysis excludes any response elements that

refer to long-term equilibria, optimum allocation of resources or welfare

1 See e.g. pages 42-5 of Hickman, Huntington and Sweeney (1987) who report results of 14 models

for the US economy. This symmetry, however, was not experienced in practice and has recently

been criticized by a number of research studies, although no concrete theoretical explanation of an

asymmetry has yet been offered;

see

Davis (1987), Loungani (1986) and Mork (1989).



160

Energy policies

in


large-scale neo-Keynesian models. The purpose of this construction IS to

attempt a standardization of models' properties with respect to oil price

variations and to illustrate the analysis based on large-scale modelling. The use

of the large-scale model in several policy scenarios

is

presented in Section 10.3.

Section

lOA

highlights the conclusions as well as the limitations of our study.

10.2 CONCEPTUAL ANALYSIS

The conceptual model reflects the

view

that macroeconomic equilibrium is

obtained in the short- to medium-term via quantity- rather than price

adjustments, by assuming that the economy experiences excess supply and

imperfect competition

in

all markets. The properties of all similar neo-Key

nesian models may be attributed directly to their basic structure: demand

drives production which in turn determines labour and capital requirements;

the latter are evaluated by means of factor demand relations derived from an

underlying production function (or cost minimization behaviour) that permits

factor substitutions. Labour supply

is

set by the households and the labour and

capital markets are quantity adjusted by assuming excess supply; within

imperfectly competitive markets, commodity prices are determined from pro

duction costs, but also influenced by demand-supply depending factors which

express disequilibrium pressures on markets. Labour demand, together with

wages adjusted for inflation, drives private income that mainly forms demand;

the economy as a whole

is

not financially constrained in foreign markets in the

sense that permanent deficits in its current account may be experienced.

In order to emphasize the role of energy, a tripartite representation of energy

is

included. Energy enters the underlying production function as a production

factor, creates economic activity as a producing sector and contributes to

private consumption and to inflation as a final commodity.

The conceptual model represents an open economy with two composite

goods, energy and non-energy, two sectors, energy and non-energy, as well as

three economic agents; namely a consumer, a producer and the government.

The non-energy good is produced

by

means of three production factors:

capital, labour and energy. Both goods are domestically produced, imported

and exported. The public sector (consumption and tax rates) is exogenous. The

monetary sector

is

neglected. However, note that the assumption of exogenous

ly defined interest and exchange rates is in line with the common hypothesis

of a small open economy with

fixed

exchange rates and free international

capital movements. The equations of the model and the definition of symbols

are given in Tables

10.1

and

10.2.6

The model includes a production possibility frontier for the non-energy

6 Recall that all specifications and type of determination of variables (i.e. endogenous or exogenous)

are valid also for the large-scale model used in the paper.



(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(19)

Table 10.1

The conceptual aggregate model

~ = < 1 >

[t ~ PEJ

dQc IN' C ' C

dL

N

= <1> [t ~ PEJ

dQc LN' C ' C

dEN

=<1>

[t

~

PEJ

dQc

EN' C ' C

K

N

= l -o

N

)(K

N

)_l

+IN

LN=dL

N

+

DdLN)-i

EN=dE

N

+

DdEN)-i

QC=.f(KN, L

N

, EN, t)

K

E

= l -o

E

)(K

E

)_1

+IE

LE=IEQE

ME=EN+CE-QE+X

E



162

Energy

policies

in


Table 10.1

-

contd.

(20)

(21)

(22)

(23)

(24)

(25)

(26)

(27)

U

_QN

c -

Qc

w=

[(1

+ t v ) p ) ] o U ~

c=p(l +bN+r-w)

QN = CN+G+ N + E +XN MN

Y=CN+CE+G+IN+IE+XN+XE-MN-ME

=QN-EN+QE

(28) SG=twWL+tvPCN+tXEPE(CEEN)

+tk[P(QN- E

N

)+ PEPQE-wL] -

pG

sector. Given the short/medium-term orientation of the model, we adopt a

putty-day formulation for the behaviour of firms. Hence the representative

producer decides only for the contribution of factors in the last vintage of

production inputs, while the remaining production factor stock remains rigid;

see

Artus

(1983),

Helliwell

et al. (1986),

Struck meier

(1987)

and Hogan

(1989).

The cost minimization behaviour of the producer leads to a system of vintage

factor share equations, a simple form of which

is

presented in equations

(6)

to

(8).

Factor stocks are determined following accumulation mechanisms;

see

equations

(9)

to

(11).

The ex-ante vintage production function is used for the

determination of production capacity;

see

(19); while the rate of utilization of

production capacities is determined as a ratio by (20).

By

assuming imperfectly competitive markets,

we

adopt a mark-up rule on

production costs for the determination of non-energy market prices. Produc

tion costs depend on factor prices and productivities. Non-energy prices bear

the influence of disequilibrium pressures in the goods market and of foreign

prices;

see

equation

(23).

The consumer price of non-energy goods

is

augmented

by the value added tax.

The supply of labour is determined exogenously. Labour demand is deter

mined from (21) as the sum of sectoral labour demand, which comes from

(7)

and

(17).

A Phillips curve formulation

is

employed for the determination of



Conceptual analysis

163

Table 10.2 Symbol definitions

ENDOGENOUS VARIABLES

C

private consumption

KE

capital stock in the energy sector

C

N

priv. cons. of non-energy goods

QE

production of the energy sector

C

E

priv. consumption of energy goods

X

E

exports of energy

MN

imports of non-energy goods

PEP

production price of energy

X

N

exports of non-energy goods

LE

labour demand by the energy

sector

IN

investment in the non-energy

ME

imports of energy

sector

LN

labour demo non-energy sector

PE

consumer price of energy

EN

energy demo non-energy sector

U

c

rate of capacity utilization

KN

capital stock non-energy sector

L

total labour demand

Qc

potential output non-energy

UN

rate of occupation

sector

P

market price of non-energy goods

w wage rate

c capital cost

QN

production non-energy sector

y

Gross Domestic Product

SG

public balance current prices

B

current account current prices

EXOGENOUS VARIABLES

R

f

net income from abroad

Q ~

P*

foreign prices of non-energy goods

e

(j

RE

Q:

Lo

G

replacement rates

energy reserves

foreign demand for energy

total labour supply

public investment and

consumption

tax on salary income

value added tax

IE

PEM

IE

r

TAX RATES

MISCELLANEOUS

<f),f functional forms

- i lagged variable

x =I.lx/x rate of variation of

variable x

t

I.l

foreign demand non-energy

goods

exchange rate

investment in the energy sector

foreign prices of energy goods

technical coefficient

interest rate

tax on non-salary income

tax on energy consumption

time

first difference

wages;

see

equation

(24).

The model thus assumes a flexible nominal wage rate.

The wage rate is however sticky because estimated long-term wage indexation

on prices is not different from one.? The rate of labour force occupation (one

minus the rate of unemployment) is derived

as

the difference between labour

7Long-terrn elasticity equals

1.1.



164

Energy policies in a macroeconomic model

supply and demand, while excess supply of labour is assumed to prevail (22);

this rate influences wage rates in (24). The cost of capital depends on the price

of non-energy (capital) goods and on the difference between the interest rate

and the anticipated growth rate of

wages;

see Ando, Modigliani, Rasche and

Turnovsky (1974).

Private consumption is a function of real income (eq. (1)). In the large-scale

model, the

effects

of the real interest rate and the unemployment rate are also

included. Total consumption

is

further allocated into energy and non-energy

goods via a typical expenditure allocation system which

is

driven by the effects

of relative prices

(eq. (2)

and (3)). Under the assumption of a small open

economy, imports and exports depend on the level of demand, domestic and

foreign respectively, and on the impacts of relative prices as

well

as on

short-term implications of demand pressures; the latter are proxied

by

the rate

of capacity utilization;

see (4)

and

(5).

Total demand, that is the sum of private consumption, (exogenous) public

expenditures, demand for capital goods by investments, and net exports,

determines the domestic production of non-energy goods, as

in

equation (26).

The gross domestic product, the public balance and the current account are

determined as an output from the model but do not have any feedback effect.

The energy sector extracts, and supplies primary energy

(e.g.

crude oil,

natural gas

at

the well-head, hydroelectricity, etc.). Thus, in the input/output

representation of our conceptual model we include only net energy production

(that is total energy treated minus energy conversion). Domestic energy

production depends on the capital stock in place (investments in the energy

sector are exogenously determined) and on available energy reserves.

Four tax instruments are included: a value added tax on non-energy goods,

two income taxes and a tax on energy consumption. The latter is applicable to

both households and firms. The government receives revenues from taxes and

allocates them to consumption and investment. Permanent deficits in the

public balance are allowed. The financing of such deficits, however, should

influence the interest rate if we assume neutrality of monetary policy. This

effect

is

neglected in the model but has been considered in one of the scenarios

with the large-scale model that are presented in the following section.

Below we apply a linearization of the conceptual model

by

evaluating the

growth rates of the variables.

8

Hence a comparative statics analysis is achieved

with respect to the steady-state solution of the model;

see

Capros, Karadelog

lou and Mentzas

(1989)

for details on the analytical solutions of the linearized

form of the model.

The direct effect of a 10% fall of the crude oil price (in terms of the model's

nomenclature this refers to the foreign price of energy goods)

is

a decrease in

8 In fact we evaluate the first difference of the logarithm of both sides of each equation of the model.

Thus, the linearization operator

is

as follows:

~ [ l o g ( x ) ] ',>;;

~ x / x .

By

applying this operator to the

product of two variables one obtains the sum of their growth rates, while by applying it to the

sum of two variables one obtains the sum of their growth rates weighted by their mean shares.



Conceptual analysis

165

the domestic price of energy, which is estimated to be about 7 to 8%. I f tax

rates on energy remain constant, this decrease

is

transmitted to the consumer

price of energy. Production costs are then diminished by a rate of about

-0.8%.

By assuming a full indexation of

wage

rate to prices, the wage rate is

also decreased by 0.8%. The cost of capital will also fall but less rapidly. Thus,

the direct effect of the crude oil price decline

is

deflationary.

Various kinds of substitution occur between production factors on the

supply side. These generally lead to increased productivity of labour which

implies a further reduction of prices by about -0.1 % in the short-term. Even

if

one assumes that there is no change

in

the foreign trade, this situation leads

to an increase in demand, especially through private consumption and invest

ment. Thus, gains in both the non-energy output and

GDP

growth rates are

obtained. These lead, however, to increased disequilibrium pressures coming

from the rate of capacity utilization and the unemployment rate that are both

rising. The latter contributes negatively to the deflationary process. In fact

demand pressures in the goods market are proxied

by

the capacity utilization

rate and have a positive influence on price formation. A high capacity

utilization ratio indicates the existence of backlogs in the goods market and a

short-term reduction of total demand by increasing prices

is

obtained. How

ever backlogs in the goods market increase investments in the medium-term

and the productive capacity of the economy.

Finally another effect is concerned with the increase in income due to

cheaper imports. In fact an energy price decline implies that the amount of

income transferred to abroad is reduced. The GDP growth which would result

from domestic price decline and factor substitution, will thus be strengthened

by this second effect.

I f the foreign prices are not affected

by

the drop of crude oil prices, or

if

they

are decreasing

less

than the

fall

of domestic prices, the economy experiences

gains in competitiveness. These have strong positive implications for real

output, employment and national income. Through the multiplier accelerator

mechanism of the neo-Keynesian model [see for instance equations (6) and (7)]

the positive growth

effects

are consolidated.

In the case that the energy tax rate remains

fixed,

the government bears a

reduction of real revenues from energy taxation of about

-7%.

Depending on

the structure of the tax revenues, this may lead to a degradation of the public

budget in constant monetary units, in spite of the increased revenues due to

the rising activity and to an improvement of the current account. This occurred

in our particular numerical example of the large-scale econometric model,

calibrated to the Greek figures. One might assume that the reduction of tax

income as a result of the

fall

in the oil price would be partially counterbalanced

by increasing energy consumption. This

is

however hardly the case for the

Greek economy because the price elasticities are estimated to be relatively low

compared to other European economies. In countries where energy consump

tion

is

more price sensitive, the overall negative effect on public budget could



166

Energy policies in a macroeconomic

model

be

less

important but on the other hand the positive

effects

on the current

account balance should also be

less

important.

I f

the government wishes to maintain the level of real revenues from energy

taxation, the tax rate on energy consumption should be augmented. Through

equation (19), this leads to a less important reduction of consumer energy

prices. This is transmitted to production costs and prices, and thus affects

competitiveness. Output gains and GDP growth rates become lower. Such a

situation, however, complies with energy policy objectives, since it leads to

lower levels of energy consumption.

The magnitude of the above interactions depends on the openness of the

economy and on the relative importance of the energy sector in the economy.

10.3

EMPIRICAL ANALYSIS

10.3.1

Scenario definition

The empirical analysis is carried out with the HGRV, which has about 370

equations and performs annual time-forward simulations.

It

includes four

production sectors (agriculture, energy, industry and services), four private

consumption categories and a full account of costs, incomes, tax revenues and

balances in the economy. From a methodological point of view, the HGRV

model

is

quite similar to the conceptual aggregate model presented in Section

10.2.

The HGRV model

is

used for building policy scenarios and deriving

conclusions

by

examining the deviations from a baseline projection. For each

scenario the model runs dynamically for

14

time periods (years). In all policy

scenarios we assume that the dollar price of the imported crude oil decreases

by

10% throughout the simulation period. Energy taxation and other accom

modating policies are, defined exogenously

for

each scenario, as

follows:

SCENARIO A: There is no change in any taxation or other exogenously

defined policy. Thus, this scenario provides numerical indications about

the impacts of the crude oil price drop (see Table

10.3).

Clearly the

objective of this scenario

is

to analyse the direct and indirect, first round

and induced effects of the energy price decline. All behavioural equations

of the model remain unchanged while the only variable which

is

exogenously modified concerns the crude oil price in dollars.

SCENARIO B:

The drop in the crude oil price

is

linked to an augmenta

tion of the tax rate on energy consumption.

I t is

assumed that the

government increases the tax rate in order to compensate the loss of real

revenues (see Table

lOA).

In this scenario the tax rate on energy products

is

exogenously increased by 9.2% in the beginning and 7.6% in the end of

the period while all other exogenous variables of the model remain the

same. The objective of this scenario

is

to quantify the effect of this type of

policy response on

GDP

growth and the public deficit.



Empirical analysis

167

Table

10.3 Scenario A: 10% decrease of the crude oil price

2

accomm. policy: none

1987 1988

1990

1995

2000


0.73

0.92 1.13 1.93

2.65

Private Consumption

0.51 0.59 0.72 1.24 2.06

Investment (industry)

-0.14 0.59

0.42 1.39

1.68

Labour demand (industry) 0.42 0.47 0.66

0.83

0.82

Energy demand (industry)

0.86

1.06

1.65 2.63

3.22

Total energy demand 0.76 0.93

1.30

2.13 2.76

Total imports

-0.02 -0.22 -0.12

0.11 0.85

Total exports 1.69 1.79

2.17

2.50

2.35

Imports of energy

0.95

1.06

1.39 2.18 2.81

GDP

Deflator

-3.00 -3.50 -3.95

-5.00

-5.50

Wage rate -3.00 -3.25 -3.46

-4.11

-4.60

Cost of capital 6.10 1.40

-3.30 -4.76 -5.40

Energy tax rate 0.00 0.00 0.00 0.00 0.00

Public investments 0.00

0.00 0.00

0.00 0.00

Interest rate 0.00 0.00 0.00

0.00 0.00

Budget def./GDP (abs. dill)

-0.09 -0.08

0.03

0.13

0.24

Current acc./GDP (abs. dill) 0.72 0.82 1.00

1.16

1.04

2 All results are reported

as

percentage

deviations

from the baseline

solution.

Table

10.4

Scenario B: 10% decrease of the crude oil price

accomm. policy: tax rates on energy consumption (9.2-7.6%)

1987 1988 1990 1995 2000


0.63 0.77

0.94

1.64

2.25

Private Consumption 0.43 0.49 0.58

1.00 1.68

Investment (industry) -0.25 0.41 0.26

1.17

1.43

Labour demand (industry) 0.36 0.40 0.55 0.69 0.65

Energy demand (industry) 0.74

0.91

1.41 2.29 2.82

Total energy demand

0.65

0.79

1.11

1.84

2.39

Total imports

0.03

-0.15

-0.01

0.15 0.77

Total exports 1.56 1.64 1.98 2.29 2.18

Imports of energy 0.81 0.91

1.19

1.88

2.43

GDP

Deflator

-2.61

-3.04

-3.49

-4.46 -4.96

Wage rate

-2.80 -2.94 -3.14 -3.74

-4.18

Cost of capital 5.49 1.29 -3.00 -4.38 -4.96

Energy tax rate

9.20

9.05

8.80

8.20

7.60

Public investments 0.00 0.00 0.00

0.00 0.00

Interest rate 0.00 0.00 0.00 0.00 0.00

Budget def./GDP (abs. diff.)

0.04 0.04 0.14

0.23 0.32

Current acc./GDP (abs.

diff.)

0.70 0.80

0.97 1.15 1.09

SCENARIO C: This

scenario

combines the

drop

in

oil price with an

increased tax rate on energy, as above.

The

increased government revenues

serve

to

finance the additional public investment,

at

a level such that the

public deficit is not significantly lower than in scenario A (see Table 10.5).



168

Energy policies in

a

macroeconomic model

Table 10.5

Scenario C: 10% decrease

of

the crude oil price

accomm. policy: tax rates on energy consumption (9.2-7.6%)

and

increased

public investment (3.8-2.5%)

1987 1988

1990 1995 2000

Gross Domestic

Product

0.81

0.98 1.14 1.90 2.57

Private Consumption

0.50 0.58 0.69 1.18 1.94


-0.19

0.63

0.40

1.31 1.59

Labour demand (industry)

0.43 0.47 0.64 0.81 0.81


0.83 0.99 1.52 2.44

3.01

Total energy demand

0.78

0.93

1.25 2.02 2.61

Total

imports

0.18

-0.01

0.15 0.32

0.97

Total

exports

1.51 1.65

1.99

2.29

2.17

Imports of

energy 0.95 1.04 1.33 2.06 2.65

GDP Deflator -2.72

-3.18

-3.61 -4.61

-5.15

Wage rate

-2.87 -3.03

3.22

3.83

-4.31

Cost of

capital

5.80

1.34

-3.09 -4.48 -5.07

Energy tax rate

9.20 9.05 8.80 8.20 7.60

Public investments 3.80 3.60 3.40 2.90 2.50

Interest rate 0.00 0.00 0.00 0.00 0.00

Budget def./GDP (abs.

diff.)

-0.10 -0.09 0.01 0.11

0.20

Current acc./GDP

(abs. diff.)

0.66

0.76

0.92

1.09

1.00

The aggregate public investment expenditure is exogenously determined in

the model used. Disaggregation of the total amount is undertaken in the

model using fixed technical coefficients. Total investments are disag

gregated to industrial and service goods while the agricultural and energy

sectors are not affected.

SCENARIO D: Here, the drop in oil price is again combined with an

increased tax rate on energy, but the additional funds are used to finance

the public deficit. I t is assumed that, through an appropriate credit policy,

the credit supply constraints are relaxed in the capital market. The latter

leads to a downward shift of the interest rate

(see

Table

10.6).

The purpose

of this scenario is to explore further the positive

effects

of the oil price

decline assuming that the financing of the public sectors borrowing

requirements (PSBR) by increased taxation

will

decrease interest rate by

2%.

In designing the scenarios no supplementary deficit in the public budget or in

the current account has been provoked, when results are compared to those

obtained by scenario A.

All

scenarios assume that the exogenous variables

related to foreign markets, other than the oil market, remain unchanged.

10.3.2 Analysis of results

The macroeconomic implications of the crude oil price decline are quite positive,

in all respects. A 10% fall of oil prices leads to additional real output of 0.73%



Empirical analysis

169

Table

10.6

Scenario D: 10% decrease of the crude oil price

accomm. policy: tax rates on energy consumption (9.2-7.6%) and decreased

interest rate by 2%

1987 1988 1990

1995

2000


0.65 0.80 0.96 1.67

2.28

Private Consumption

0.43 0.50 0.58

0.98

1.64


0.40

0.91

0.56

1.32 1.53

Labour demand (industry)

0.29 0.29

0.47

0.48

0.36


0.82 0.95 1.45 2.28 2.81

Total energy demand 0.69 0.83

1.14

1.84

2.38

Total imports

0.09

-0.09 -0.01

0.09

0.66

Total exports

1.54

1.69 1.99

2.31 2.25

Imports of energy

0.91

0.95

1.21

1.88 2.45

GDP

Deflator

-2.65 -2.71 -3.51 -4.52

-4.96

Wage rate

-2.76 -2.94 -3.16 -3.76 -4.23

Cost of capital

3.68

-1.11

-4.69 -5.53

-5.51

Energy tax rate

9.20 9.05 8.80 8.20

7.60

Public investments 0.00 0.00 0.00 0.00

0.00

Interest rate

-2.00 -2.00 -2.00 -2.00 -2.00

Budget def./GDP (abs. diff.)

0.05

0.05

0.14

0.23 0.31

Current acc./GDP (abs.

diff.)

0.69 0.80 0.97

1.17

1.11

in the short term while medium- and long-term increase is respectively 1.13 and

2.65%. Both output and lower prices raise energy demand by 0.8%, 1.3% and

2.7% in the short-, medium- and long-term. This leads to an equal rise of

energy imports. The deflationary spiral is important, leading to a decrease in

prices ranging from -3% to

-5.5%.

Both the energy tax revenues and the

public deficit are degraded in real terms. However, the current account is

improved.

If

the effects of

oil

price decrease on competitive countries are taken

into account the overall results on the national economy are rather limited.

This is confirmed by the results of a scenario (not presented here) in which

foreign prices are decreasing equally to the domestic ones. In this case, positive

growth effects are experienced during only four years after the oil price shock

and then vanish. This is mainly due to the fact that competitiveness gains,

which are a very important factor, disappear.

Scenario A illustrates numerically that although the economy profits from

growth and deflation, public revenues may diminish and energy dependence

may

be

aggravated.

As

can be seen in Table

10.3,

a worsening of the PSBR by

-0.09 points is observed in the beginning of the simulation period. This

negative effect

is

however reduced

in

the medium-term because of increased

revenues coming from the boosting of the economy. In the medium-term this

improvement reaches 0.03 points of GDP.

For these reasons increase in the tax rate on energy consumption is worth

discussing. This is examined in scenario B. Results show that positive growth

effects are not reduced to zero in this case. The economy achieves 2.26%



170


additional output with positive effects on the public deficit, and on the current

account as percentages of GDP. This

is

accomplished mainly at the expense of

deflation which

is

less important than previously

(-4.89%

in the long-term).

Households seem to bear most of the indirect impacts of the increased taxation,

since they gain significantly less in real income and consumption, compared to

scenario

A.

However no significant decrease in energy demand is obtained with

respect to scenario A. This is totally due to low price elasticity for Greek data.

Thus scenario B can only be justified for public budget policy reasons.

9

Following scenario B, overall government gains are estimated to reach 0.04 as

percentage of GDP.

By comparing the results of scenarios A and

B,

one can evaluate what is the

cost of reducing the budget deficit through increased expenditures. Clearly the

reduction of the public deficit (result of scenario

B)

would reduce GDP growth,

employment and increase the general level of prices. The reduction of the

public budget deficit would clearly have depressive effects on the economic

activity (potential positive financial effects are not examined at this stage).

The question which then arises is the following: should the public sector use

the increased funds from additional energy taxation to reduce its borrowing

requirements or should these funds be used

for

development purposes? Thus,

it may be interesting to investigate if, by using these increased funds, the

economy would achieve better growth results than in scenario

A,

while not

degrading the public deficit, compared to this scenario.

In fact the increased funds can be used by the public sector in different ways.

Government may increase public investments or consumption. Furthermore

subsidies and/or grants can help private firms to finance their investments.

Moreover increased resources can allow the reduction of income and/or

corporate tax rates. On the other hand the reduction of the PSBR can have

positive impacts on the interest rate.

All

the above policy options may have

positive

effects

on overall economic activity. The scenarios that follow examine

this issue. Scenario C uses the additional funds for financing public investment,

while in scenario D funds are used for relaxing credit supply constraints in the

capital market which leads to diminishing interest rates.

The additional public investments assumed within scenario C have positive

multiplier effects in the short-run. The gains in output, employment and real

income, compared to scenario A, however vanish in the medium-term. The

main reason

is

that increased public investments create demand-pull inflation.

The direct impact of a lesser decline of the general price

level

is a decrease of

competitiveness gains obtained in scenario A. Following this result, exports

perform less

well

than in scenario

A.

Moreover imports increase and

GDP

9We also examined the case of diminishing the tax rate on energy consumption. This is justifiable

III

the case of decreasing foreign prices of non-energy goods due to the possible gains in

competitiveness. In this case the economy experiences improved growth rates and employment, but

the public deficit is seriously aggravated.



Conclusions

171

growth

is

less than in the non accommodating policy scenario. Even if increased

public investments boost economic activity in the short-term, the results are

different in the medium- and long-term. The beneficial effects cannot

be

maintained because of inflationary pressures. However the results obtained in

this scenario, i.e. the case in which additional energy taxes are spent by the

public sector for investments, are more beneficial when compared to scenario

B where additional expenses are used to finance the public deficit.

10

It was shown above (scenario

B)

that if the energy price decline

is

followed

by an energy tax increase, the public deficit is significantly improved. However

a different option in macroeconomic policy would

be

to maintain the public

investments. The macroeconomic results of this scenario come from the

demand side. In this scenario the supply-side effects transmitted through

financial channels are examined. The reduction in public sector borrowing

requirements

will

decrease credit demand in the economy. Assuming that the

interest rate is determined by market forces according to demand and supply,

a fall in credit demand

will

equilibrate the market at a lower level. Interest rates

may thus be reduced and have supply-side macroeconomic impacts on econ

omic activity.

We

implicitly assume that the decrease in the interest rate

is

2%,

which corresponds to a decrease of 0.36 and 0.1 points in the short- and

long-term respectively. The supply-side effects consist of factor substitution

effects:

both energy and labour are substituted by capital, so investments

increase even in the first year. This complies with energy objectives but leads

to significantly less gains in real income for households. The results are globally

positive but the efficiency of the scenario

is

lower when compared to the case

where public investments are increased but higher than in the case where no

accommodating policy

is

undertaken.

11

lOA CONCLUSIONS

Referring to the policy questions put forward in the introduction,

we

conclude

as follows:

1. If the economy experiences excess supply when an oil price decline occurs,

the implications

will be

significantly positive for real output and employ

ment. Depending on the degree of openness of the economy, these results

will be maintained if the economy

is

able to transmit the competitiveness

gains on the domestic to foreign relative prices. This depends to a large

extent on wave indexation. If wage indexation is less than unity and

assuming a symmetric behaviour, real wage rate increases

will

result.

I f

however wage indexation is larger than unity, more competitiveness gains

'OThis is due to the absence of an explicit financial sector in the model variant used.

11

This result

is

related to the structure of the neo-Keynesian model and may be different if

price-induced equilibrium structure

is

adopted.



172 Energy

policies in

a

macroeconomic model

should

be

expected. Following the above linkage and if additional growth

is expected from foreign trade, the tax rate on energy consumption must

remain unchanged or even decreased. This would lead to a further price and

wage decline and extra competitiveness gains. However, such a decision has

significant consequences on the public deficit. If on the other hand, domestic

income effects constitute a constraint to the growth process, income policy

(structural change on wage indexation) and income distribution should

be

considered together with tax policy.

2. The macroeconomic cost of augmenting the energy tax rate does not seem

important. The growth of real output

is

maintained, although households

bear the main part of the indirect macroeconomic cost. Thus positive effects

on growth are maintained and improvement of the public deficit

is

achieved.

3.

The evolutions in the energy sector following an oil price decline clearly do

not comply with the long-term objectives of energy policy as usually

formulated in oil-importing countries. Increased taxation at the level

examined here does not seem able to restrain the growth of energy demand.

It

should however

be

mentioned again that this result

is

mainly due to the

fact that the estimated energy price elasticities for Greece are relatively low

compared to other countries. On the contrary, income elasticities are the

most important explanatory variables for energy demand.

If

price elastici

ties were higher, energy taxation should be more efficient.

4.

Policies that impose additional energy taxes and spend the increased funds

in public investment or in financing the public debt do not seem to perform

better than the 'doing nothing' case (i.e. scenario

A)

at a macroeconomic

level. However, a clear trade-off emerges between the demand and supply

sectors as receivers of the increased funds. Nevertheless, due to its underly

ing methodology, our model

is

not able to capture the

full

range of effects

of policies using the additional funds for financing past public debts.

The policy maker and the reader should

be

aware of two potential limitations

in the above analysis. First because the study focuses on the medium-term

perspective, any general equilibrium considerations are excluded. This is con

nected to the importance of neo-Keynesian macroeconomic modelling tools in

the EEC, which

we

preferred to use in the analysis; the need for an in-depth

inquiry into the properties of such tools; and the necessity to standardize their

behaviour. It is clear that the analysis should be complemented by a general

equilibrium model.

The second potential limitation

is related to the sectoral disaggregation and

micro economic aspects. The macroeconomic models currently used (ours

included) exhibit a symmetry in their behaviour concerning variations of the

energy price. This symmetry has been challenged theoretically and should

be

examined with scrutiny.

Beside the above limitations, it

is

important to quantify the margin within

which the impact of a certain policy

is

included. The modelling approach can



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Appendix

APPENDIX: ENERGY-RELATED MODELS OF THE

EUROPEAN COMMUNITIES

INTRODUCTION

175

In this note the energy-related models constructed by the DG/XII of the

Commission of the European Communities are described. Moreover, examples

of concrete applications of these models are presented together with a table

presenting the models' identity in a condensed way

The models were constructed with the financial support of CEC-DGXII but

their use has been extended to other DGs of the European Communities. The

HERMES model has been used for the evaluation of macroeconomic effects of

the completion of the European Internal Market in the Cecchini Report;

HERMES, EFOM and MEDEE have been used in the European Communi

ties Task Force report on

1992 and the Environment. Actually MIDAS is the

official model of DG/XVII for forecasting and analysing energy policy.

In the first section of this note a description of different models is presented.

The description includes the presentation of the models' main features, together

with references and a brief analysis of their functioning properties. In the

second section a resume of two examples of real-world use of these models

is

presented.

DESCRIPTION OF MODELS

HERMES is a European-wide large-scale neo-Keynesian multi sectoral

macroeconometric model. Its general features have been described in the text

and are recapitulated in Table lOA.l.

In the manufacturing sector a 'putty-clay' production function of the

C E S ~

Cobb-Douglas type is adopted, with three production factors: capital, labour

and energy (KLE). In the services sector a 'clay-clay' approach is used, with

two production factors, capital and labour (KL). A Phillips curve (unemploy

ment rate and inflation rate) is employed for the estimation of the wage rate.

Prices are evaluated as a mark-up of labour cost, intermediary demand cost

and capital cost, and affected by the evolution of the utilization rate.

The model is annual and dynamic, while the forecasting period is up to ten

years. All economic sectors are described in detail and all macroeconomic

1 HERMES stands for Harmonized European Research for a Multinational Economic and Energy

System. It was initiated by

d'A1cantara and Italianer

(1982).

The model

was

constructed

by

several

national European teams under the coordination of DG/XII of the Commission of the European

Communities.

For

a detailed presentation of the model see d'A1cantara and Italianer (1982),

Italianer (1986) and Valette and Zagame (1991).



Appendix 177

mechanisms are represented in the model. The latter and their interactions are

activated in a simultaneous way during model resolution and simulation.

MIDAS

is

a medium-term annual energy model, combining econometric

with process analysis formulations. It simulates the evolution of energy

demand, energy supply and prices. Energy is measured in physical units on the

basis of the energy balance system.

Energy demand is dis aggregated by sector,

i.e.

industry, households, trans

port etc. Demand is evaluated per sector, use and energy product and is a

function of real disposable income and the relative prices of energy products.

Energy demand as a production factor is influenced by relative prices (i.e. wage

rate and cost of capital) and the volume of production.

On

the supply side the electricity production module formulates a linearized

load duration curve which is constructed by using the energy demand results.

Electricity plants are allocated within the load curve following cost minimiza

tion while the marginal cost is approximated for each consumption category,

and

is

introduced in the energy prices module.

The refining sector module uses an aggregate representation of a typical

average refinery, including distillation, cracking and reforming. Desired pro

duction of each refining unit is compared to existing capacities in order to

obtain estimations of the rate of capacity utilization and of production cost.

Refinery throughput and production flows are estimated from supply-oriented

econometric formulations.

The production of coal is formulated by means of supply curves (involving

reserves), which also serve in the evaluation of mining profitability and coal

pnces.

Finally MIDAS evaluates energy prices by using the dollar price of crude oil

as the main exogenous variable. Most of the equations follow a rate of

variation formulation, connecting an energy product price with the price of a

product which is considered as a leading price and national and international

labour and capital factors. Concerning electricity pricing, the model adopts a

special algorithm which corresponds to marginal cost tariffication for each

consumer type. Taxes are then added to these prices, in order to obtain consumer

prices.

2

The MIDAS energy model project was initiated by N. Kouvaritakis

(see

ECOSIM, 1986) and supported by the Commission of the European Commu

nities. At that early stage, development was concentrated on the energy

demand module. Recently, Detemmerman

et

al.

(1988)

re-formulated MIDAS

energy demand module and re-estimated econometric equations for four

European countries. Finally energy supply and pricing modules and the

integration of the demand and supply MIDAS modules into a single model has

been effected by Capros, et al. (1990b). The complete MIDAS model is

2For more information see Capros

et

al. (J990b).



178


available for the United Kingdom, Italy, France, Germany, Greece, Holland

and Belgium.

MEDEE

is

a disaggregated technoeconomic energy demand

mode1.

3

The

main energy demand variables in MEDEE concern sectoral demand by industry,

residential and tertiary, passengers and goods transport. These sectors are

further disaggregated in many sub sectors that are homogeneous with respect

to energy consumption patterns. The variables determining sectoral energy

demand in MEDEE are linked with sectoral activity, GDP, disposable income

while relative and/or absolute price effects do not have any impact. The

methodology used in MEDEE in general follows three steps: evaluation of the

end-use energy requirements for each homogeneous consumption subsector;

determination of the energy technology and energy form mix for the satisfac

tion of energy needs with predetermined penetration rates of energy forms; and

evaluation of the final energy demand by the use of the efficiency coefficients

of energy appliances.

EFOM is an optimization model based on the energy flow representation of

the energy supply system of a country.4 The model provides a convenient

graphical portrayal of the

flows

of energy through the economy, with an

explicit accounting of energy losses at each stage of conversion, distribution

and end-use. The model aims at defining the structures of energy activities that

MEDEE

MIDAS

I

~ ' - - D - E - R - E ~

FRET

FOM

HERMES

Fig. lOA. Information

flow

between models.

3

See

Chateau and Lapillone (1978).

4For details on

EFOM see

Finon (1979) and Van der Voort

(1982).



Appendix 179

permit the energy needs of a country to be satisfied at minimum cost. The

environmental module of energy supply on air pollution emission and more

specifically on

S02

and

NO

x, EFOM can also produce the effects of emission

reduction on the energy system and propose solutions based on cost efficiency.

APPLICATION EXAMPLES

Introduction of a carbon tax

in the

UK for CO

2

reduction

The carbon tax has been put forward as a policy instrument to induce

substitutions and fossil energy savings aiming at reducing CO

2

overall

emissions. The carbon tax consists in applying taxes to fossil energy products

at a rate proportional to their CO

2

unit emission.

The work with MIDAS in the

UK

for carbon tax policy analysis comprised

the following tasks:

construction and incorporation into MIDAS of a 'C0

2

impact evaluation'

module;

definition of the carbon tax and adaptation of the corresponding consumer

energy price equations of MIDAS.

The use of the HERMES/MIDAS/Linked System introduces important mech

anisms in the evaluation of both the potential of CO

2

emission reduction and

the impacts induced by the carbon tax. The major additional mechanisms refer

to substitutions between energy and non-energy goods, services and produc

tion factors.

I f

this mechanism permits further energy savings, the CO

2

emission reduction potential may increase.

The MIDAS energy model

is

particularly suitable for energy system insights

associated with the carbon tax analysis. This is due to the very nature of the

model, as it

fully

represents, in the heart of its formulation, price-induced

interactions in energy demand, energy supply and energy demand-supply

adjustment. Energy prices are not shadow prices as in other models, while its

formulation and

use

as simulation tool allows for a straightforward treatment

of carbon tax-related issues. MIDAS' econometric behavioural equations

permit the full endogenization of carbon tax-induced substitutions, while its

fixed process analysis and econometric formulations ensure engineering orien

tated reliability of results. Moreover, MIDAS is a global energy system model

in the sense that it covers all energy subsystems, interactions and mechanisms

within the energy sector. MIDAS provides estimates of country energy bal

ances, costs, prices and capacities through dynamic annual simulations for

10-15 years.

The MIDAS-HERMES fully integrated linkage

is

a particularly suitable

tool for the assessment of carbon tax implications through the energy

economy interactions. The impacts of energy prices on the economy, factor



180

Energy policies in a

macroeconomic

model

substitutions, induced non-energy substitutions etc. are fully covered by the

MIDAS-HERMES linkage. In particular, in estimating the carbon tax CO

2

reduction potential, this tool ensures a global evaluation, in the sense of

accounting for all types of substitutions, interindustrial (I/O) exchanges and

supply contractionary effects. Because of its global character, this approach

is

clearly more reliable than partial evaluations,

for

instance sectoral KLEM

based estimates.

The HERMES/MIDAS/Linked System has been used to evaluate the

macroeconomic implications and served for the re-estimation of the expected

CO

2

reduction potential from carbon tax.

5

The main conclusions drawn are

summarized in the following:

1.

The stabilization of CO

2

emission

is

very difficult to attain by means of

a policy based solely on carbon-tax.

2. I f only a carbon tax is used, stabilization of CO

2

emissions may be

obtained with carbon tax rates implying a mean energy price as much as

two to three times higher.

3. The reduction of CO

2

emissions

is

obtained mainly through the decrease

of total energy demand and secondarily from inter-fuel substitutions.

4.

The elasticity of CO

2

emission reduction with respect to a unit increase

in the mean energy price (induced by the carbon tax)

is

very small,

ranging from

-0.2

to

-0.1;

moreover, the elasticity

is

diminishing when

the carbon tax rate increases.

5. The role of structural adjustment of power generation, consisting in accele

rating the combined cycle and natural gas programmes, is important in

CO

2

emission reduction. I f such a structural adjustment is combined

with carbon tax policy, CO

2

emission reductions are substantial.

6.

Within the energy system, the carbon tax induces substitutions that are

favourable to electricity and to a lesser degree natural gas; the consump

tion of liquid fuels

is

substantially reduced; if no structural adjustment

occurs in power generation, the share of solid fuels

is

maintained, with

adverse

effects

to CO

2

emissions.

7. The additive carbon tax seems significantly more efficient than the

multiplicative carbon tax.

8. The macroeconomic implications of the carbon tax are generally nega

tive; if the carbon tax

is

applied at an international level, results may

differ substantially.

The effects of the introduction of a CO

2

tax in the UK have been extended

to three other European tax countries, namely France, Germany and Italy.

Moreover, technological aspects are introduced and comparison of energy

versus CO

2

tax has been effected. The conclusions and numerical results are,

however, provisional and are not presented here. The whole work has been

5S

ee

Capros et al. (1990a).



Appendix

181

effected by the National Technical University of Athens and is a part of the

'Models for Energy and Environment' section of the Joule Programme

fi

nanced by the Commission of the European Communities - DG/XII.

Environmental

policy in

the

European

internal

market

A second application

is

concerned with the evaluation of the environmental

impacts of the completion of the European Internal Market. Environmental

costs and benefits were evaluated using three European-wide models: the

macro econometric model HERMES, the energy demand model MEDEE and

the energy supply and environmental model EFOM-Environment (hereafter

EFOM-E).

The analysis follows a global equilibrium approach and excludes partial

equilibrium estimates. First the macroeconomic impacts of the completion of

the Internal Market and the implications on energy demand and supply as well

as on production levels and consequently on the environmental system are

studied. Second, a variety of policy measures necessary to finance emission

abatement technologies are examined. The study focuses on the macro level of

analysis and uses the European-wide macro econometric model HERMES.

Such an approach is more accurate than a mere accounting of costs and

benefits since it takes into account the interdependences in national economies

which might reveal unexpected 'costs' and 'benefits'. The steps followed and the

models used are presented below:

1. The first step deals with the evaluation of the macroeconomic impacts of

the completion of the Internal Market for Greece with the

use

of the

HERMES model.

2. The second step analyses the environmental impacts of the completion of

the Internal Market. The macroeconomic estimations of the first step are

used to evaluate the impact of the Internal Market on energy demand with

the MEDEE model. This information

is

then used to evaluate the impact

on energy supply and the environment by means of the EFOM-E model.

The quantitative results concern the environmental impacts on S02 and

NO

x

, and the cost of policies aiming at the reduction of S02 and NO

x

emission. Moreover, different cost efficiency scenarios for the reduction of

the level of the emission are studied and the corresponding investment and

necessary costs for the protection of the environment are evaluated.

3.

The third step refers to the conceptual analysis of the alternative financing

modes for supporting the investment required for anti-pollution policies as

well as to the quantitative assessment of the macroeconomic implications

of such investment. The modelling device utilized in this step is the

HERMES model.

The analysis has been carried out in the context of the CEC Task Force on

the completion of the European Internal Market and the Environment and



182

Energy policies

in

a macroeconomic

model

concerned

five

European countries: Belgium, France, the former Federal

Republic of Germany, Greece and the United Kingdom.

The analysis for energy demand and air pollution has shown that if the past

trends of the energy and environmental system are maintained in the future,

energy demand, supply and the emission of S02 and NO

x

will increase with

the completion of the Internal Market. The evaluation of the macroeconomic

impact of the increase in emission-reducing investment and the possibilities

given by the completion of the Internal Market (more growth, gain in benefits)

to finance such investment to prevent or abate pollution, were studied by

assuming different policy options. These concern the effects of an increase in

pollution abatement investment with different forms of financing.

The analysis shows that the financing of environmental protection inves

tments will have minor impacts on the main economic variables. The increase

of emission-reducing investment by 1% of

GDP,

financed by price increase,

subsidies and a reduction of productive investment, will have a neutral impact

on GDP in the medium term. Environmental concern can give the incentive

for the creation of an entire sector dealing with environmental protection. The

above results will have positive impacts on overall economic activity and

employment.

The conclusion stresses the fact that the surpluses, which will be created by

the completion of the Internal Market and spent for the protection of the

environment, will constitute an 'investment' with positive long-term effects

both for the economy and the quality of

life,

which will overcome short-term

spending.

6

REFERENCES

d'A1cantara,

G.

and Italianer,

A. (1982)

'European Project for a Multinational Macro

sectoral Model'. Commission of the European Communities,

MSll,

XII/759/82.

Capros P., Karadeloglou P. and Mentzas G.

(1990a) Carbon Tax Policy and its Impacts

on CO

2

Emission.

Working Paper, National Technical University of Athens, April.

Capros P., Karade1oglou

P.

and Mentzas

G. (1990b).

'New Developments for the

MIDAS Medium-term Energy Modelling Project of the EEC: the Energy Supply

Model and the Supply-Demand-Pricing Linkage'. Paper presented at the 12th

Triennial Congress on Operations Research IFORS

90,

Athens 25-29 June

1990.

Chateau,

B.

and Lapillone,

B. (1978)

Long-term Energy Demand Forecasting: A New

Approach.

Energy Policy,

pp. 140-57.

Commission of the European Communities (1990) 1992:

The Environmental Dimension.

Economica Verlag, Bonn.

Detemmerman,

M.

V.,

Guillaume,

Y.

and Ledoux,

M. (1988)

'MIDAS Demand'. Report

to the Commission of the European Communities, DGjXVII.

ECOSIM Sarl (1986) 'The MIDAS Energy Model', Report to the Commission of the

European Communities, DGjXVII.

6For a detailed presentation see Commission of the European Communities (1990).



11

A

comparison

of energy-economy

models: the French experience

Ghislaine Destais

11.1 INTRODUCTION

From the mid-1970s on, there has been a clearly

felt

need to understand more

precisely the links between energy trends and the economy as a whole.

Particularly in France, with its strong tradition of central economic planning,

this led to a strong interest in the modelling of energy-economy interactions.

This chapter compares and analyses several energy-economy interaction

models. The first section situates energy-economy models in relation to partial

equilibrium approaches, and suggests a typology of these models. The chapter

then focuses on the French experience in the

field

of energy-economy

modelling.

11.2 MODELLING ENERGY-ECONOMY INTERACTIONS:

SOME ELEMENTS OF ANALYSIS

11.2.1 The limitations of partial equilibrium approaches

Models have always been used in the energy field. On the supply side, the most

frequently used technique

is

optimization, essentially for investment program

ming. Initially used at the level of the firm, this approach was then applied to

the energy sector as a whole, as in the EFOM model (Fin on, 1976). Supply

models, however, only result in partial equilibria because energy requirements

are taken to be exogenous and the interdependence between the energy sector

and the rest of economy

is

neglected.

Demand forecasting models, on the other hand, do establish relations

between the evolution of the economic system and energy consumption trends.

Be they econometric, like SIBILIN (Criqui, 1985), or technico-economic, like

MEDEE (Chateau and Lapillonne, 1982), they also remain partial models

for



186

A comparison

of

energy-economy

models

two reasons: first, because they take economic variables to be exogenous, and

secondly, because they neglect the possible influence of energy supply on

demand.

Supply-demand equilibrium models avoid some of the limitations that we

have just mentioned by calculating the equilibrium levels of supply and

demand interactively. The IFFS model of the US Department of Energy

(Murphy

et

ai., 1984) is an example of such a model which covers the whole of

the energy sector. However, these models also remain partial in that they give

a representation of the operation of energy markets without taking into

account the cross-effects with and on the rest of the economy.

Input-output models constitute an advance in this respect in that they situate

energy flows within the framework of exchanges between the various sectors of

the economy - the breakdown may include anything up to several hundred

branches of activity. They can be used to analyse the links between structure

of production and energy demand (see Hoch and Carson, 1984). Other

applications include the repercussion of energy prices on other prices or the

impact of energy scenarios on the rest of the production system (see CEREN

CERNA,

1983).

I f the model

is

static, it accounts for the indirect effects induced

by intermediate consumptions, whereas with the dynamic Leontieff model,

acceleration effects on investment can also be integrated. However, in neither

case are the effects on macroeconomic evolutions taken into account.

In

reality,

the simple input-output model has two limitations when used in forecasting:

it only allows for scenarios which do not substantially modify macroeconomic

equilibria, and its assumption of fixed

input-output

coefficients limits its

application to the short term.

11.2.2 Modelling interactions between energy

and

the economy

Since the energy sector, far from being marginal,

is

central to the economy,

partial equilibrium analyses

will

not

suffice.

Instead energy-economy models

are intended to include the feedback effects between energy and economic

trends. By making economic variables endogenous, these models render studies

of energy supply and demand more coherent, while at the same time opening

up the scope of the analysis to new questions. Will energy availability limit the

possibilities of economic growth? What has been the macroeconomic impact

of the oil shocks? Or, what economic consequences can be expected from a

given energy policy, for example a moratorium on nuclear power?

Most of these models analyse energy-economy interactions within a nation

al framework. They are very heterogeneous, ranging from the small aggregated

model, constructed by an individual researcher for the purposes of an academic

paper, to enormous model systems entailing pluri-disciplinary work by large

research teams over several years. For this reason, it is not easy to set up a

general analytical framework which would include all possible structures.

Attempts to do so have all been flawed (see, for example, Coates, et ai., 1979;



The

French

experience

187

Samouilidis and Mitropoulos,

1982;

Devezeaux

de

Lavergne and Ladoux,

1989).

11.2.3 A typology of energy-economy models

Our classification of these models is based on the following criteria: the way

in which energy supply, energy demand and the overall operation of the

economy are represented. To

be

complete, we

will

add a fourth criterion, which

is

the level of breakdown chosen, notably for exchanges between sectors and

the forms of energy involved.

Using these criteria we can readily obtain some idea of the structure of a

model. Without going into any detail,

we

simply recall here the main ways in

which each of these components may be represented. With supply and demand

modules there is a choice between a technico-economic and

an

econometric

approach; the latter is generally based on production and consumption

functions. These modules

use

either optimization or simulation techniques. The

representation of the overall operation of the economy can be based on one or

other of the various currents of economic theory. In practice, however, the

structure of the models

is

generally either of a neo-Keynesian or neo-classical

general equilibrium type.

The above classification makes it possible, for example, roughly to charac

terize the basic models in this field. The model used by Hudson and Jorgenson

(1974) is

a general equilibrium simulation model, made up of nine branches of

activity (including five energy branches). Energy demand is defined economet

rically and energy supply is seen as an exogenous variable. The model

developed by Manne

(1977) is

also of the general equilibrium type, but it

is

resolved by optimization and includes only one non-energy branch of activity.

Energy demand, which

is

broken down into two products, is derived from a

macroeconomic production function whose parameters are seen as exogenous.

Energy supply, on the other hand,

is

based on a detailed technological

representation.

11.3 THE FRENCH EXPERIENCE

It was not until the 1980s that energy-economy interaction models began to

be developed in France. Unlike experience in other countries, in France these

models were mainly elaborated by public bodies. The initial research work was

undertaken by the administration for economic planning purposes. Five

models in all have been developed.

The first of these,

CGP

(Commissariat General du Plan), is a small one and

was built and used solely in order to prepare the VIIIth Plan. According to its

initiators, Levy-Garboua and Sterdyniak (1980), it was intended to stimulate

discussion on four issues: the role of energy prices in economic growth, energy



188 A

comparison

of energy-economy

models

price and taxation policy, the behaviour of energy demand, and, lastly, the

penetration and financing of electric power.

The construction of the Mini-OMS-Energie (MOE) model took place

between

1979

and 1982, and involved several organizations: the INSEE

(Institut National de la Statistique et

des

Etudes Economiques), the Centre

d'Etudes et de Recherches Economiques sur l'Energie, the Forecasting Direc

torate of the French Ministry of the Economy, Electricite de France and the

Institut Fran9ais du Petrole. The declared objective

was

to produce forecasts

containing a description of macroeconomic trends which would fit into the

framework of the French national accounting system, and a representation of

the energy sector by means of complete and detailed balances (Brillet et al.,

1982; Insee, 1988). The model was put to substantial use in the preparation of

the IXth Plan (Commissariat General du Plan, 1983), and a group of users

made up of energy enterprises and public bodies continued to make

use

of it

(Caussat and Plateau, 1986). More recently, the new orientation in the

modelling policy of the INSEE has led it to hand over the management of

MOE to the Laboratoire d'Economie de l'Ecole Centrale.

This

was

the same laboratory that designed HERMES-FRANCE, i.e. the

French part of the HERMES model of the European Commission, between

1981 and 1984 (see Chapter 10 and Faubry et

al.,

1984; Moncomble and

Zagame,

1986).

The idea behind this project was to provide a medium-term,

multinational and multisectoral model aimed at informing European energy

choices.

The last body to be concerned with macroenergy modelling in France is the

Commissariat a l'Energie Atomique

(CEA).

An initial project, which

was

carried out between 1980 and 1983, was reformulated between

1983

and 1986

and gave rise to the MELOOIE model, designed to assist in defining the

long-term strategy of the CEA (see Berthelemy and Oevezeaux de Lavergne,

1987).

The CEA also uses a simplified version of this model, micro-MELODIE

(see

Ollevier,

1987).

11.3.1 A first difference between models: the level

of

aggregation

Table

11.1

shows that the French models do not all analyse energy-economy

interactions in the same detail. There are two small, highly aggregate models,

which do not take into account the evolution of intersect oral exchanges and

only distinguish between two energy carriers (domestic and imported energy in

the

CGP

model; electric power and fossil energy in micro-MELOOIE). MOE

and MELOOIE are medium-sized models. They include two non-energy

sectors and distinguish between five energy forms (coal, oil products, gas,

electricity and others). Lastly, HERMES allows

for

a far greater degree of

breakdown, since it accounts for seven branches of activity (outside the energy

sector), including three industries and eight energy carriers.



The

French experience

189

Table 11.1 The degree of breakdown of French energy-economy models

No.

of equations

No.

of No. of

No. of

exogenous sectors·

energy

Model

Total Behaviour variables

carriers

CGP

40 20 n.d.

1

2

Micro-MELODIE

90

50

110

1

2

MDE

700

175

450

2

5

MELODIE

>600

n.d.

n.d.

2

5

HERMES-FRANCE

1500

250

400

7

8

'Outside the energy sector.

n.d., Not determined.

These characteristics of the models are important in determining where they

are to be applied. They also reveal the assumptions of the analysis concerning

the possible feedbacks between energy trends and economic structures. Gen

erally, such effects can scarcely

be

taken into account when there is little

breakdown between sectors of activity.

11.3.2

Household

energy demand:

technical

versus economic

approaches

When looking at household energy demand, we should stress the contrast

between the MDE model, which adopts a technico-economic, 'bottom-up'

approach, and the other four models, which are purely econometric.

In MDE (Figure 11.1), energy use is divided up into four demand sectors:

transport, electrical appliances, domestic heating with hot water consumption

and cooking, which is exogenous. In each individual sector, energy demand

depends on three determining factors: the stock of equipment, its specific

consumption and the utilization rate. The last two variables are usually

exogenous. On the other hand, the evolution of the stock

is

specified en

dogenously by econometric relations, which employ various explanatory vari

ables: temporal trends, number of households, total household consumption

and price of equipment (housing units or personal vehicles). However, the

allocation of energy forms used by new housing stock remains exogenous. The

only energy price used in this part of MDE

is

the

fuel

price, which has an

impact on the rate of utilization of personal vehicles.

The four other models adopt a 'top-down' approach, abolishing all reference

to the stock of equipment in order to establish a direct econometric relation

between the economic variables and energy demand. Moreover, they determine

inter-energy substitutions endogeneously according to the relative prices of the

various energy forms. Consumption for each type of energy is deduced from

total household consumption and prices, in one single stage in the aggregate

models (CGP and micro-MELODIE), and after two successive breakdowns in

HERMES and

MELODIE

- the intermediate level providing total energy



190

A comparison

of

energy-economy models

Equipment costs

Consumption or

number of households

Equipment stock

Utilization

-automobiles

rate -household electrical equipment

-buildings

by type of heating

-gasoline

consumption

--electricity consumption for specific uses

--energy consumption for heating and water

by

energy

form (electricity, gas, coal,

fuel oil, renewables)

Distribution of new

buildings by type

of energy

Find trend for household

electrical equipment

Specific consumption

(by energy form)

for heating and water

Energy consumption

for cooking needs

(by energy form)

Fig. 11.1 Household energy consumption

in

MDE. (The variables in boxes are

endogenous, the rest is exogenous.)

consumption. The most frequently used allocation function

is

of a dynamic

translog type.

In comparison with the other models, the first advantage of MDE is that it

reveals the actual physical details of energy demand formation, whereas

breakdowns obtained from total household consumption do not correspond to

any technological reality. Moreover, the use of a large number of exogenous

variables in

MDE

makes it possible to test the effect of various energy

conservation policies. With the other models only price policies can

be

dealt

with.

On the other hand, the multiplicity of exogenous variables in the MDE

model increases the danger of inconsistency. Moreover, the user should

be

aware that many of the findings do not result from the operation of the model

as such but are merely the transcription of the scenario hypotheses. The

econometric models provide greater consistency for the analysis

as

a whole,

and are particularly

well

adapted to coupling with a macroeconomic module.



The

French experience

11.3.3 Energy demand in production

191

Here again MDE

is

quite different from the other models, but the differences

are expressed at another level. Given the difficulty of taking into account the

great diversity of industrial processes, no model has explicitly introduced the

stock of energy-consuming equipment. They all establish a direct relation

between economic indicators and the energy demand of the various economic

sectors.

The principal particularity of MDE

is

that it bases its analysis on the notion

of the energy content of value-added; the evolution of energy contents being

defined econometrically by a temporal trend (Figure

11.2).

In the aggregated

industrial sector, the model distinguishes between a specific electricity content

and a content in terms of substitutable energies for heating purposes. The use

Time

trend

t

Electricity

intensity

Electricity

consumption

Time

trend

t

Value-added

Fuel

of sector

intensity

Fuel consumption

Exogenous distribution

Consumption of

petroleum products

Gas consumption

Coal consumption

Fig. 11.2 Producers' energy consumption in MDE.



192 A

comparison

of


of energy as a raw material is exogenous. The rest of the economy - too

heterogeneous for any useful analysis - is broken down into three sectors

(services, transport, the farming and food industries along with building and

public works), and fuel and electricity demand are determined for each of these.

As in the case of household consumption, inter-energy substitutions are

exogeneous. Another particularity of the model is that it only uses energy

prices marginally (only in explaining demand in the tertiary sector).

The other models (Figure 11.3) all use production functions to estimate

energy demand along with the demand for other factors (capital, labour,

Final demand

addressed

to the sector

KLEM Relative price

production function

J

of factors

/

\ ~

Labour

Energy

Materials

/

Distribution function

Relative energy

prices

/

Consumption by energy form

(according to the level of

breakdown within the model)

Fig. 11.3 Producers' energy consumption in CGP, MELODIE, micro-MELODIE

and HERMES-FRANCE.



The

French

experience

193

material) in each of the sectors which have been identified. The determining

factors of energy demand are the volume of production, prices, technical

progress, scale and the rate of utilization.

As

in the case of households, energy

is

directly broken down into two products in the CGP and micro-MELODIE

models. In HERMES and MELODIE energy demand per product is obtained

by breaking down total energy demand by means of a translog allocation

function. In the CEA models the production function

is

also translog; it

is

dynamic in MELODIE and static in micro-MELODIE. HERMES and CGP

both use a model based on capital vintages. Technical choices are made on new

investments and then remain frozen for the life-span of the equipment. The

marginal production functions are of the Cobb-Douglas type in the CGP

model and of a two-level CES/Cobb-Douglas type in HERMES.

Finally, the approach adopted by the MDE model appears to give too little

importance to the possible impact of energy prices. On the other hand, the

macroeconomic framework of the other models appears to give too much

importance to prices and to neglect other factors. In addition, all of these

models seem to be inappropriate if we are to take into account certain energy

policy measures such as, for example, financial aid for investments involving

energy savings or substitution. Finally, disaggregation by sector is, on the

whole, insufficient if

we

are to take account of structural

effects

and contrasting

changes in energy content from one sector to another.

11.3.4 Energy supply: frequently exogenous

The following information can be ascertained (wholly or partly) from the

supply side of the various models: investment and capacity in the energy sector;

final energy production and primary energy demand of the energy sector,

energy import and export; and finally energy prices and financial accounts

within the sector. The methods used vary between the five French macro

economic models but what

is

striking overall

is

the number of exogenous

factors used. This feature can be explained not only by the fact that these

models are mainly intended to test energy supply policies but also by the

difficulties involved in integrating a complex energy supply model within a

macroeconomic framework.

As a result, investment is only endogeneous in two cases: aggregate invest

ment for the entire sector within the

CGP

model, and refining investment in

MDE. In both cases investment is dictated by changes in demand without the

intervention of any profit-related terms.

Supply and demand are interconnected in all models by adapting the

level

of supply to that of demand. Supply

is

determined for each energy product by

the following balance between use and resources:

production + mports = domestic demand +exports

in which domestic demand

is

calculated by the demand modules as explained



194

A comparison of energy-economy models

previously and exports are exogenous (except in the case of HERMES). Thus,

only in HERMES is the breakdown of supply between production and imports

endogenous. In the other models either production or imports

is

exogenous,

the other being deduced residually.

Energy import

prices

are always exogeneous. On the other hand, the

production price of energy is sometimes considered endogenous, but the way

in which it is determined varies from model to model. In other cases (mainly

in

CGP

and MDE), the average user price

is

entirely exogeneous, i.e. consider

ed as a command variable of energy policy.

In HERMES and in the non-electric sectors of the CEA models, the move

from final energy demand to

primary

energy

demand

also involves exogenous

input-output

coefficients. In the other cases, the way in which primary energy

demand

is

calculated

is

the result of a detailed representation of the behaviour

of the energy sector, but a different modelling technique is used in each case.

CGP

uses an aggregate production function for the whole energy sector,

based on capital vintages, as for the rest of the economy. In this way it can

incorporate demand for production factors in the energy sector (capital and

imported energy) as a function of their relative prices and technical progress

(see

Chapter

3).

The main problem raised by this approach

is,

in our

view,

related to the fact that the aggregate figure includes such diverse units as

electric power stations and refineries. In contrast, the following two types of

representation are interesting because they are based on the technological

reality of the sector.

The refining model used by MDE is made up of econometric relations which

use pseudo-data. These fictitious data are obtained from the results of sixty

odd projections produced by the French Petroleum Institute's large optimiz

ation models which show how to adapt refining facilities in order to satisfy

demand at least cost. I t is then possible with the resulting model to make

investment endogeneous, to determine oil demand and to calculate marginal

production costs using fewer equations while still taking the numerous com

plex technological constraints into account. However, though this method is

simpler,

it is

less transparent.

In

addition, its use

is

necessarily limited to the

study of scenarios which are more or less similar to those used in constituting

the reference sample.

In the MDE, MELODIE and micro-MELODIE models, the electricity

sector

is

represented by a simulation model for the management of power

station capacity. These modules can be used to determine the primary energy

used, given the level and structure of electricity demand and the exogenous

production capacity. The advantage of such a technological representation

is

that it takes account of variations in the input-output coefficients as a function

of production level. But it necessarily remains a rough approximation of the

way in which power stations are managed, since in reality the latter takes place

in real time.



196

A comparison

of


expenditure and exports. Having deducted imports, a matrix of

input-output

coefficients

is

used to calculate the required production, intermediate consump

tion and value-added in volume terms. The model then deduces factor demand

and the tensions between this demand and the corresponding supply deter

mine wages and prices. The distributed incomes then determine a new value

for final demand. Static equilibrium

is

reached when this indirect final demand

is equal to the initial demand. Thus there are two typically Keynesian factors

at

the heart of these models' resolution processes; the driving role attributed to

demand (the hypothesis being that there

is

a supply surplus) and the import

ance given to imbalances represented by the tension variables (unemployment

and capacity utilization).

The short-term

dynamics

of this kind of model are influenced above all by

the effect of income on consumption, of activity on foreign trade and by the

effect

of limited outlets on investment. In the medium term, this type of multiplier

accelerator movement is rectified by the growing influence of the wage-price

loop. In the longer term, when prices and tension-inducing variables tend

towards their equilibrium value, supply conditions become dominant.

Given this theoretical framework, these five models have several common

features in terms of the way in which they look at interactions between energy

and the economy. These mechanisms can be easily demonstrated by analysing

the way in which the different variables follow on from each other in an

oil

shock scenario. The shock has first of all an inflationary

effect

which acts both

through the direct effect on price levels and the wage-price loop.

The rise in price levels then decreases the commercial balance as com

petitiveness is affected - unless supplementary exogeneous hypotheses are

made concerning foreign price

levels.

In the short term the oil shock also has

a negative

effect

on real household incomes since wage rises do not entirely

make up for price rises. The result

is

a fall in consumption. Given the reduced

possibilities offered by outlets, the level of investment also falls. The shock thus

brings about a slowdown in growth, a reduction in employment and a fall in

the rate of utilization of productive capacities.

These recessive effects do, however, have beneficial effects on foreign trade

which tend to counteract the initial effect. They first of all reduce imports

because of the fall in domestic demand and the

fall

in the rate of utilization of

productive capacity. At the same time, firms start to look for ways in which to

offset this

fall

by increasing exports.

In

addition, recessive effects have complex consequences for inflation and

real household income. The increase in unemployment holds back wage

increases and leads to a decrease in the real wage bill, both of which amplify

the

fall

in nominal income. Then the wages-prices loop,

fed by

the increase in

unemployment and the fall in capacity utilization, leads to a slowdown in price

rises which helps stabilize real income.

Finally, it should be noted that though all the models determine the impact



The French experience

197

of the shock on the external balance, none of them takes account of its

deterioration as a constraint on growth.

11.3.6 A comparison of results

The fact that there is a common economic logic should not eclipse the

differences between the models. We have already mentioned the specifics of

each model as regards the level of aggregation and the way energy supply and

demand are represented. Other differences stem from the choice of explanatory

variables used in the macroeconomic equations and of specifications (the form

of the equations, presence of dynamic elements and the value of the coeffi

cients). Since it

is

beyond the scope of the present article to describe the models

in detail (see Destais, 1989, ch. VIII),

we

shall here only mention their most

salient characteristics.

One essential difference has to do with the production function used to

represent the behaviour of producers. This function is of the putty-clay type in

CGP

as well

as

in the industrial sectors of HERMES. It

is

fixed coefficients in

MDE

and the CEA models use a translog form. While substitution between

production factors as a result of changing relative costs is impossible in MDE,

it plays an important role in the other models. To be more exact, the CEA and

HERMES models assume complementarity between capital and energy, the

aggregate of these two factors being substitutable for labour. CGP, on the

other hand, works on the hypothesis that all factors can be substitutes.

Looking at the effects of

an

oil price shock, these mechanisms modify the

analysis given above which leads to a reduction in investment and employ

ment. In

CGP

the levelling off of investment

is

attenuated by the increasing

cost of other factors which encourages very capital-intensive processes (though

it

is

reinforced by the increase in real interest rates). In the industrial sectors of

the HERMES model and in the CEA models, the fall in investment is accen

tuated by the complementarity of energy and capital. In contrast, the price of

energy has no direct effect on investment in the

MDE

model and in the non

industrial sectors of HERMES. However, the formation of capital

is

also

slowed down because of the increase in production costs which induces a fall

in the profit rate. In addition, the unemployment caused by the oil shock

is

attentuated by changes in relative prices which lead to a redeployment of produc

tion factors in favour of labour in all the models (with the exception of MDE).

Price equations also differ quite clearly from one model to another while

consumption, foreign trade and wage rate equations are relatively similar.

However the other basic difference between models has to do with their

dynamics. These dynamics are the result of the existence of variables which are

out of phase by one or more periods (years) in order to express anticipatory

behaviour or the existence of lags in adjustment

as

a result of inertia. From

this point of view, the quasi-static nature of micro-MELODIE and CGP



198

A comparison of energy-economy models

(which are supposed to express long-term adjustments) contrast with the

systematic introduction of phase differences in HERMES (intended to improve

the handling of short-term changes). The other models

lie

somewhere in

between, MELODIE having the specific characteristic of adopting a single

delay structure for all the production factors.

All

in all, the particularities of each model have a certain

effect

on the results

it provides. To illustrate this, we shall describe a scenario which has been tested

using three of the models: MDE, HERMES and micro-MELODIE (Kalayd

jian and Maillet, 1989). The variation looked at corresponded to a 50%

increase in the price of crude oil maintained over 7 years. All the other

exogenous variables

were left

unchanged. The results

(see

Table 11.2) are given

as percentage differences

as

compared with a reference scenario for each model.

As our analysis of the models led us to expect, some common elements can

be found, such as the overall recessive and inflationary effect of the shock, but

at the same time important differences can be seen in the numerical results for

certain variables.

1. The quasi-static nature of micro-MELODIE leads to a larger fall in growth

in the short term. However, this trend

is

then modified, in particular by

substitution among the factors which make competitive gains possible and

act favourably on foreign trade.

2.

In MDE and micro-MELODIE inflationary effects are

felt

immediately,

whereas they spread slowly in HERMES. They are later attenuated in MDE

as the result of a group of factors (slowdown in activity, increase in

Table 11.2 Impact of

an

oil shock in

MDE, HERMES

and micro-MELODIE

Year

MDE

HERMES Micro-MELODIE

Volume

GDP

0

-1.6 -0.7 -2.6

(%)

7

-3.9 -2.2

-1.6

Consumer

0 +7.1

+1.4 +5.4

price

(%) 7

+6.3 +8.9 +2.3

Nominal wage

0

+6.4

+0.6 +4.8

rate

(%) 7

+3.2 +5.5

+0.8

Employment

0

-125

- 24

-241

(thousands)

7

-566

-215

+ 79

Primary

0

-7.0

n.a. -12.9

energy consump-

7 -2.5

n.a.

-10.7

tion (Mtoe)

Final electricity

0 -0.2

n.a.

+

0.6

consumption

7

-2.1

n.a.

+

3.8

(Mtoe)

n.a., Not available.

Mtoe=Million tonnes oil equivalent.



Conclusion 199

unemployment, fall in profit rates}. In micro-MELOOIE inflation

is

sub

stantially reduced by the end of the period because of substitutions.

3.

MOE and micro-MELOOIE both employ an immediate indexation of

wages to prices, while HERMES uses a time delay.

4.

The large

fall

in employment in micro-MELOOIE in the first year is linked

to the fact that there is no adjustment delay and the final improvement in

employment is due to substitution between factors. In HERMES the

worsening of the labour market as a result of the recession cannot be

counterbalanced by factor substitution within the 7-year period because of

adjustment delays. The results provided by MOE illustrate the postulated

rigidity between the level of activity and employment.

5.

The fall in energy consumption resulting from income and activity effects

is

accentuated by the price effect in micro-MELOOIE but attenuated in MOE

because of rigidities in production technology.

6. Consumption of electricity falls in MOE, while it increases in micro

MELOOIE because of substitution between energy forms.

These examples show how results reflect the structure of the models and how

important it is to be fully aware of this structure in order to appreciate their

relevance.

11.4 CONCLUSION: CORRECT USE OF THE MODELS

The main lesson which can be learned from the preceding analysis is that

although the models described are useful tools in the decision-making process,

they should be used with caution.

It

should be said at the outset that because they deal simultaneously with

mechanisms of energy supply and demand formation, exchanges between

sectors and macro-economic evolution, French energy-economy models defi

nitely allow us to widen the field of analysis of energy-economy interactions

as compared with partial equilibrium models. However, this possibility has

been obtained at the cost of a certain number of restrictions: the analysis

remains highly aggregated; information concerning the technical mechanisms

which govern the formation of energy demand

is

rarely exploited; and energy

supply

is

essentially exogeneous.

More seriously, there

is

a certain lack of consensus concerning the dynamics

of energy-economy interactions. In this respect, the challenges are, first, to

bring out the dynamics which affect economic and energy phenomena (time

required to implement investments, lagged or anticipatory behaviour, etc.), and

secondly, to integrate short- and long-term phenomena.

We

have shown that

the formulations used

by

the various models are disparate and lead to

constrasting overall dynamics, which

is

obviously not satisfactory. More



200

A comparison

of


generally, one can raise the question of the reliability of the quantitative

analyses provided by these models. Studying a common scenario over several

models has indeed shown that they can at times produce very different

quantitative results.

Should we conclude then, as Hoogdwin

(1987),

that energy-economy models

are above all heuristic and of limited

use?

In our view their importance in

helping us structure our analysis of the questions involved should not be

underestimated, for they have two major advantages. First they render the

hypotheses underlying the conclusions explicit. Secondly, they guarantee the

internal coherence of the analysis and can be used to evaluate the results of

various contradictory

effects.

But it should be clearly understood that the models are not a neutral

simplification of reality. For example, one must know that some questions

(such as the effect of the external constraint on growth) cannot be dealt with

at all using any of the French energy-economy models, since certain phenom

ena are not represented (for example the financing of the external deficit). These

models also have an appropriate field of utilization linked to some of their

hypotheses. Thus

we

have seen that their conclusions were only valid in a

Keynesian situation of surplus supply - a heavy constraint. In addition, it

is

our

view

that their time horizon should be limited to the short and medium

term, even if some modellers claim they can

be

used for long-term studies.

Consistency within the equations and stability over the long term do not

protect a model from its own econometric and aggregate nature, features which

make it impossible to incorporate long-term structural changes taking place in

the economy and in the energy sector.

Moreover, potential users should ask themselves whether or not the repre

sentation which has been adopted by the model in question actually corre

sponds to their own analysis of the situation. Thus, as Hourcade and Kalayd

jian (1987) have emphasized, 'within the representation of structural changes,

models using KLEM production functions

give

more weight to substitution

between production factors under pressure of prices - and this is only one,

controversial, way of looking at such changes'.

Finally, our analysis shows that the appropriate use of the models has to be

based on an extensive knowledge of the ways in which they work. In this

respect, the comparative study of several models is, in our view, an excellent

means of evaluating the explanatory potential and the limits of each model.

However, this approach is not always rendered easy by those responsible for

the design of the models. If they really want their models to be used in the most

appropriate situations,

we

would suggest, in conclusion, that they adopt a

homogeneous framework for the presentation of the models and terminology

in order to facilitate access to their models. If, in addition, the modellers were

to make an effort to situate their model in relation to the others available, then

potential users would be more than delighted.



References

REFERENCES

201

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C.

and Devezeaux de Lavergne,

1.

G.

(1987)

Le modele MELODIE: un

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72.

Brillet, J. L. et al. (1982) Energie et economie: Ie modele Mini-DMS-Energie, Economie

et Statistiques, July/August, pp. 73-85.

Caussat,

L.

and Plateau C. (1986) 'L'analyse comparee

des effets

economiques de

diverses mesures de politique energetique Ii l'aide du modele Mini-DMS-Energie.

World Energy Conference,

2.3.3.,

5-11 October, Cannes.

CEREN-CERNA

(1983)

Impacts macroeconomiques

de

la production et de la consom

mation de charbon-vapeur, 2 vol. Paris, p. 99 and appendices.

CGP (1983) Rapport du Groupe Long Terme Energie, Preparation du IXeme Plan

1984-1988. La Documentation Fran9aise, Paris,

vol.

1,

p.

435, vol.

2,

p. 236.

Chateau,

B.,

Lapillonne

B.

(1982)

Manuel de description

du

modele MEDEE-3.

IEJE,

Grenoble,

p. 53.

Coates, R., Hanson, D. Juenger,s. et

al.

(1979) Survey of the Research into Energy

Economy Interactions,

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internationales, construction du modele SIBILIN. CGP, Paris, p. 191.

Destais,

G. (1989)

'La modelisation

des

interactions energie-economie, une analyse

centree sur Ie cas fran9ais'.

PhD

Thesis, Social Sciences University of Grenoble, IEPE,

p.670.

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de

Lavergne,

J. G.

and Ladoux, N.

(1989)

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getiques?

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de

l'Energie,

no. 411, May, pp. 423-34.

Faubry,

E.,

Moncomble, 1.

E.,

Vidal

de

la Blache,

O. et al.

(1985)

Le

modele

HERMES-FRANCE.

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3-29.

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Paris.

Hoch

I.

and Carson, R. T.

(1984)

An Energy-oriented input-output Model. Electric

Power Research Institute, Palo Alto, Ca,

p.

314.

Hoogdwin,

L. (1987)

'On the character of macroeconomics, macroeconomic policy and

econometrics: the need for another macroeconomic policy conception.' Papers

presented at the Economic Modelling Conference, 21-22 October, De Nederlandsche

Bank, Amsterdam,

p. 24.

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J.

C. and Kalaydjian, R. (1987) Macroeconometrie et choix technologiques

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de

l'energie, Papers presented

at 19th Conference on Economic and Econometric Structures, ARAE, Sophia

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p.

37.

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(1988)

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70.

Kalaydjian, R. and Maillet, P.

(1989) Les

modeles au service

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trois modeles macroenergetiques fran9ais.

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and Sterdyniak,

H. (1980)

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Modelling Energy-economy interactions: Five Approaches (ed. C. 1. Hitch), Re

sources for the Future, Washington, DC.

Moncomble,

1. E.

and Zagame, P.

(1986) Les effets

d'une baisse du prix du petrole:

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11,

November, pp. 222-32.



12

Models

and

projections

of

energy

use in the Soviet

Union

Yuri Sinyak

12.1 ENERGY PLANNING AND MODELLING

IN THE SOVIET UNION

1

The use of mathematical computer models in the Soviet energy system goes

back to the late

1950s.

The energy systems were one of the first sectors of the

national economy in which these new tools were applied. There was, at that

time, considerable enthusiasm over the supposed ability of such models to

solve economic planning problems but this soon turned out to

be

an illusion.

It

was a common error in the centrally planned economies to believe that social

reality could be mechanically determined and described by mathematical tools.

But reality is infinitely more complicated than even the most sophisticated

computer model, and judgement by the human intellect can only

be

replaced

in the very simplest of mechanical applications. Another problem in this area

is

that the performance of these models can never

be

better than the quality of

the data input (which in general is quite poor).

Today it is generally realized that effective planning and management of

man-machine systems is possible only through a combination of formalized

and non-formalized models. Man must retain an active role at all planning

levels, both in the formulation of goals and restrictions and in the assessment

of final results.

A specific character of Soviet economic planning has until recently been the

emphasis on material balances. The purpose of modelling in this framework

has been mainly optimization and relatively little attention has been paid to

the role of pricing, consumers' preferences and substitution between products

and factors of production.

1

The author describes the main features of the centralized planning system existing at the time of

writing. It

is

difficult to say how it will develop with a decentralized market-oriented economic

system among the national republics. However, the author

is

convinced that the scientific

background for long-term energy planning will be the same in the future.



204 Models and projections of

energy

use

in

the Soviet Union

This 'optimization' has been carried out by a multi-stage process in which

plans at the national level are linked to those at the regional and local levels

while long-term plans for technology choice (over 10-15 years) are linked with

5-year perspective plans and with yearly plans as well as the models of current

operative management,

This grandiose optimization scheme meets with a number of severe obstacles

such as the great number of elements and linkages in the energy system that

are non-linear or discrete. Furthermore, energy demand is decided by a great

number of independent decision-makers and this decentralization requires, in

a programming context, the conveyance of correct price information from the

upper levels in the planning hierarchy to its lower levels. This has earlier been

a major obstacle but the principle of marginal pricing is now accepted and

correct

fuel,

electricity and heat prices by region are now available but only

used for the design of new energy facilities and projects, not for everyday

operation which results in the collision of current and long-term goals. Such a

dual situation reduces the effectiveness of the pricing system and helps

maintain an inefficient administrative command system.

Recently the use of price-type information together with simulation concepts

is gaining support among planners at the expense of relying solely on

traditional optimization methods.

12.1.1

National energy planning

The application of mathematical modelling to the Soviet energy system started

with long-term optimization (Figure 12.1). The initial phase of energy forecast

ing consists in elaboration of several options for growth in GDP. The results

of this phase are used for the assessment of energy demand and feasible

alternatives for energy production. This information together with technology

projections

is

the basis for the optimization of state and regional energy

systems and their main sectors - electricity, heat, nuclear, oil, natural gas and

coal production and distribution. The final phase deals with the development

of state technological programmes, which are essential for achievement of the

specified goals.

The main objectives of energy planning and forecasting are:

1.

to clarify major ways of improving energy efficiency;

2.

to investigate the most promising areas of electricity penetration as a

driving force of social and technological progress and the optimal structure

of electricity generating mix;

3. to study the detailed possibilities for energy production, transportation

(pipelines, electricity grids, railway, etc.) and fossil

fuel

processing and

conversion - taking into account economic and ecological aspects;

4.

to assess the material, labour and capital requirements of the energy sector;

5. to elaborate R&D programmes.



Energy

planning and modelling in the

Soviet

Union

205

Fossil fuel

reserves and

resources

Energy

production

options

Plans for

energy supply -

sectors

-

~

...J

t:: ( )

-

( )

...J

0

...J

a:

~ UJ C3

t3

cr: § ()

UJ ::)

cr:

...J

I - ( )

t tTT

GOP projections

Energy

demand

options

State energy balance

Regional energy

balances

Energy

balances of

industrial

enterprises and

complexes

-

Economics of

new and

conventional

technologies

R&D

programmes

Fig. 12.1 Approximate scheme of the Soviet energy optimization concept.

The procedure for the energy system optimization consists of four main

stages. At the first stage a simplified optimization of the state energy system is

used (Melentiev, 1979). The expected range for aggregate fossil fuel production

is

based on the available fossil fuel reserves and the planned allocation of

capital to develop these reserves which in its turn depends on economic growth

and energy demand.

The second stage requires the application of multi variant optimization

models (see below) developed for the selection of the best solutions for each

energy supply sector to satisfy, at minimal cost, the supply options defined in

the first stage.

At

this stage the decision parameters are: fossil fuel production

by basin, inter-regional

fuel

and electricity flows, location of new fuel process

ing facilities and electricity generation, and levels of regional and sectoral

energy consumption. Usually at this stage more disaggregated energy models

are used compared to the first stage.



206 Models and

projections

of

energy

use

in the Soviet

Union

The third stage consists of the investigation of uncertainties associated with

the probabilistic or uncertain character of parameters used at the previous

stages (Beliaev,

1978).

Different primary energy sources are compared with

respect to cost, export value, production scale, input requirement, etc.

As

a

result two or three reasonable strategies for the development of each energy

resource are selected. These strategies are then evaluated under various

scenarios including the most unfavorable conditions. Preference

is

then given

to the strategies with least total costs under a mixture of normal and

unfavourable conditions.

Finally,

at

the fourth stage the selected strategies are evaluated from the

point of view of their demands on other sectors of the economy. Simplified

dynamic energy--economy models (Kononov,

1981),

are used to compare their

capital and labour requirements. Lead times of construction are also taken into

account at this stage, providing the adjusted programmes of material and

equipment supply for investment in energy facilities.

The peak of modelling applications in energy-related tasks occurred in the

1970s (Aganbegian and Fedorenko, 1978; Melentiev, 1979). Since then interest

in energy modelling seems to have declined, mainly because of the lack of

feasible alternative solutions due to increasing strains on the national economy

in the early 1980s and the neglect of the economic aspects in compiling

perspective plans of industrial development. Until now energy modelling has

been considered an optional supplement to the main planning procedure based

rather on simulation than on optimization.

12.1.2 State and regional energy balance models

2

The aim of state or regional energy balance models is to investigate the main

proportions within the national energy system and major inter-regional energy

flows (USSR Academy of Sciences, 1975, 1977). A simplified linear program

ming approach

is

usually applied. The energy system is treated as static and

the models usually describe the state of the system

at

the end of the planning

periods (5-,

10-

or 15-years). The models cover large regions (e.g., European

Soviet Union, Ukraine, Volga Region, Caucasus, Middle Asia, West and East

Siberia, Far East). Each region is characterized by the same energy consumers:

industrial furnaces, cement kilns, power plants, industrial boilers, space heating

and

hot

water supply. These are divided into three categories - old, under

reconstruction or new - each with different input coefficients. Sometimes the

2The practical applications of state energy balance models are carried out by the Computer Centre

of the USSR State Planning Commission and its former Institute on Complex Fuel and Energy

Problems, and the research work on the elaboration and improvement of such models

is

done

mainly by the Siberian Energy Institute of the USSR Academy of Sciences. Regional energy

balances, which resemble the national energy balance, are studied practically

at

all energy-related

institutions in national republics.



Energy planning

and

modelling

in the

Soviet

Union 207

scale of energy consumers and load curves are taken into account. One of the

common points of dispute in the application of these models is the method of

demand description: in terms of useful energy or cost.

One of the most interesting attempts to overcome the limitations of ag

gregated models is the elaboration of a block-wise dis aggregated linear model,

in which each block has to be treated separately by different groups of

specialists and then linked up by a central coordinating module carrying out

the function of the centralized control unit (Makarov, 1989; Nekrasov, 1981).

However, practical implementation has not been very successful because the

large information flows between modules can hardly be handled with the

existing computers and because of low productivity of the teams working with

the different modules in the preparation of new versions in response to

changing input parameters specified by the central module. Therefore, many

institutions have ceased efforts in this area.

12.1.3 Electric system models

3

The first application of electric system models dates back to the late 1950s and

earlier 1960s with the elaboration of the linear planning model (Markovich

et ai.,

1962). More sophisticated static and dynamic models were later devel

oped in the Siberian Energy Institute of the USSR Academy of Sciences and

were applied for expansion planning of electricity grids in the European part

of the Soviet Union (static model) and in Central Siberia and the north-west

of the European part of the Soviet Union (dynamic models) (Syrov

et

ai.,

1966);

Melentiev, 1971). The first models were used for studying the impact of various

factors on the optimal structure of the generating capacities, the specific

features of hydroelectric stations or load curves for different seasons, optimal

rates of nuclear energy development, etc. Later new electric system models were

elaborated in the Central Economic and Mathematics Institute of the USSR

Academy of Sciences for planning the location of base-type thermal power

plants and for electricity sector development at the level of a Soviet republic

(Nekrasov et

ai.,

1973; USSR Academy of Sciences, 1977). A special class of

electric system models comprises the short-term planning models dealing with

minute load-meeting or fuel supply within short time periods (e.g., a day or

week) (Sovalov, 1983).

Considerable efforts have been undertaken to elaborate more advanced

models for this sector based on the concepts of dynamic programming or with

the use of gradient optimization procedures. But these approaches are still

at

an experimental stage.

3The most well-known organizations engaged in electric system modelling are the Research Institute

of Electric Grid Design and the Research Institute of Electric Energy, both of the Ministry of

Energy and Electrification, and the Siberian Energy Institute of the USSR Academy of Sciences.



208

Models

and

projections of energy

use

in

the

Soviet

Union

12.1.4 Fuel supply models

Starting from the

1960s,

several models for natural gas development have been

created (Smirnov and Garlyauskas,

1969;

Druzhinin and Kuznetsov,

1970;

Feigin

et

ai.,

1979}.4

They have been used for the elaboration of 5-year plans

with the main goal of investigating optimal reserve/production ratios for

different gas fields as

well

as the allocation between Eastern (West Siberia) and

Western (European) gas production. Recent interest has been expressed in

optimization models which require a multilevel description of the natural gas

production and distribution system (Stavrovsky and Sukcharev, 1978; and

Maksimov,

1979).

Similarly, oil models have been used to compile middle- and long-term plans

of this industrial sector (Makarov,

1989).

Some of these models have special

modules describing the petrochemical industry. As a result, it has been shown

that petrochemistry has a large impact on the production rates of crude oil in

different regions and the location of the oil refineries as well as on the

distribution of light and heavy oil products. There are also models for oil

refinery optimization depending on the quality of the oil input and the local

requirements on the refinery products, including petrochemistry (Starovoitov

et ai., 1974}.5

Modelling in the coal industry

6

has gone mainly in the direction of gradual

disaggregation of the input information on coal producing facilities (Astakhov

et ai., 1967;

Tsvetkov,

1967).

The latest versions of the models usually consider

the whole set of the coal mining and coal processing (mainly benefaction)

enterprises with several alternatives for their development. As a result of such

a detailed approach to the description of the coal production system the static

models contain 800-900 constraints and above 6000 variables. The dynamic

versions expand the model dimension two to three times and require the use

of more complex software.

12.1.5 Recent trends in Soviet energy planning

In

addition to the sectoral and regional energy models mentioned, the

development of large industrial enterprises and complexes has been studied

with specially designed models (Kluev, 1970; Nekrasov and Sinyak,

1979).

Recently energy planning based on simulation concepts, especially for

long-term applications, is getting increased attention, particularly in research

4Natural gas modelling is concentrated

in

the research institutions of the former Ministry of

Natural Gas Production.

SOil

sector modelling

was

carried out

in

the Central Economics and Mathematics Institute of the

USSR Academy of Sciences,

in

the Institute of Economics and Industrial Management of the

Siberian Branch of the USSR Academy of Sciences and now is concentrated mainly

in

several

research institutions of the Ministry for Oil Production and Processing.

6The best known modelling results have been achieved

in

the Central Institute of Coal Industry

Economics of the Coal Ministry in cooperation with academic institutions.



Soviet energy

demand and

supply prospects

209

institutions. The experience of the last 20-30 years has

known many

problems

but also some important achievements:

•

The treatment of

the energy system as a multilevel hierarchical structure

which requires the use of complex optimizing procedures and good informa

tion exchange networks.

• Decision-making in the energy system on the basis of optimization pro

cedures with incomplete

or

uncertain information which increases the

robustness of the final solutions.

•

Top-down

accounting of constraints and time dynamics in planning practice

at all levels of hierarchy which increases the internal consistency of the plans.

12.2 SOVIET

ENERGY DEMAND AND SUPPLY

PROSPECTS

(1990-2000)

12.2.1 Energy programmes and policies

The prospects for Soviet energy systems development to the year 2000 were

defined in the Energy Programme (1984), first adopted in 1982. The

Pro

gramme gave great importance to the production of

natural

gas, coal and

nuclear energy

and

energy conservation. Since then, the

Programme

has been

revised and its time frame extended to 2010. Since the plans for nuclear energy

development between 1980 and 1985 were not fulfilled and installation of

nuclear power plants slowed down after the Chernobyl accident (1986-90),

provisions have been made to compensate for the drop in nuclear electricity

production primarily through the accelerated growth of the national gas

industry and increased use of coal in electric power plants. Although practically

finished, the latest Energy

Programme

has not been approved by government

because of heavy criticism from the public and some scientists. Now the Soviet

Union

has

main

directions for the energy policy only for the next 10-15 years

(Makarov, 1989;

Makarov

and Bashmakov, 1989; Volfberg et al., 1989).

Central to the national energy policy is

energy conservation.

The serious

drawbacks for the country's economic structure are the ineffective high-energy

intensive economic structure resulting from the long policy of self-isolation, the

high level of material intensity compared to industrially developed countries

and the low efficiency of obsolete and outdated equipment.

Electrification is

considered as a driving force of social

and

economic progress and will proceed

rapidly from 30%

today

to 33-35% of primary energy demand

in

2000. Special

attention

is devoted

to

ecological aspects.

7

First, as concerns the use

of

low-grade fossil fuels (in particular coal), large investments are to be under-

7However, up to now the problem of global warming and possible abatement measures have not

been seriously studied in the Soviet Union. The importance of these issues is presently under

investigation and most likely the Energy Programme will be revised with a view to these subjects.



210

Models

and

projections of energy

use in

the Soviet Union

taken to reduce the negative impacts on environment and health. The new

energy policy also stresses the importance of implementing economic mechan

isms in the energy sector with a view to promoting self-supporting energy

supply in the future.

The policy suggests that the improvements should be carried out in two

phases. The first phase (up to the year

2000) will be

characterized by a

continuous growth in hydrocarbon production and particularly natural gas

(mainly Tyumen gas transported to the European Soviet Union). During this

period, efforts are to be focused on radical improvements in safety and

reducing costs of nuclear energy as well

as

stable coal production.

In the second stage (first decade of the twenty-first century and perhaps

beyond), hydrocarbon production

is

to

be

stabilized with a further growth of

natural gas production compensating for possible reductions in liquid

fuel

production. It is not excluded that energy conservation

will

stabilize nuclear

energy growth. Options for economic development without growth in energy

consumption are now being studied. But such a transition

is

likely to occur

only beyond

2 0 0 5 ~ 1 0 .

However, the last few years showed that the national energy policy has little

chance of success if the new situation in the USSR

is

not seriously taken into

account.

12.2.2 Energy saving

These goals are quite impossible to achieve with existing trends and tendencies,

which implies the need for new approaches for solving national energy

problems. Instead it seems more promising to follow the lines of enhanced

energy conservation. According to some evaluations, structural changes in the

productive system could provide a 50-60% reduction in the expected en

ergy/NMP ratio equivalent to savings of

0.6

billion tonnes coal equivalent (tce)

in 2000 and

1 . 8 ~ 2 . 1

billion tce in 2010

(as

compared to1985). The rest

is

to be

achieved by improvements in energy efficiency (technical limit of energy

conservation

is

equal to more than

1000

million tce/year in case of

full

utilization of all known technologies, i.e., not less than one-third of today's

total energy consumption).

8

When the energy policy was compiled, more than

5000 energy-saving measures were analysed and chosen, of which 70 could

achieve energy savings of

400

million tce in 2000. For example, 50 million

tce/year could be saved by improving industrial furnaces. The same level of

energy savings can be achieved by new small steam and water heating boilers

with automatic control and automatically controlled heat supply systems.

Installation of new lighting devices, controlled electric drives, electric compen

sating equipment and improved transformers could result in further savings of

8 According to the author's assessment, this saving potential is approximately equal to half of the

total primary energy consumption in the Soviet Union. These figures may be compared to those

in

Chapter 4.



Soviet energy demand and

supply

prospects

211

50 million tce. Waste energy resources, if properly utilized, could save over

20

million tce/year. Therefore, just the above mentioned measures

will

result in

savings equivalent to the total coal production of the Donetsck region (the

largest coal-producing region in the Soviet Union, with about 200 million

tonnes). Further energy savings are expected with improvements in total

material production. For example, higher quality steel products and changes

in the structure of steel production technologies could save about 70 million

tce/year. Another 40 million tce could be saved in the transportation sector.

Investments will also be needed to implement energy-saving measures.

According to the Institute of Energy Research of the USSR State Committee

on Technology and Science and the Academy of Sciences (Makarov, 1989),

about 200 million tce/year could be saved without additional investments. The

saving potential for a 2- or 5-year payback period

is

equal to 450-650 million

tce. This will demand new capital investments of 28-30 billion roubles and the

production of

new

energy-saving equipment should reach 270-300 billion

roubles. Moreover, for savings of up to

600

million tce the specific investments

in energy saving remain cheaper compared to the corresponding investments

in primary energy supply systems. Figure 12.2 shows the dependence of the

900 r---------------------,

~ 700

Q)

.l:l

.s

c:

o

:;

~ 5 0 0

Q)

til

c:

o

o

>

Cl

...

Q)

c:

w 300

100

a

5 yr

----------:;::::::

-

yr

100

200

300

Investments

in

energy improvements (rbi/tee)

Fig.

12.2

Energy saving potential in the Soviet Union. (Source: Makarov and

Bashmakov, 1989.)



T

a

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Soviet

energy

demand and

supply prospects

215

There are thus many alternatives but they inevitably result in a considerable

increase in oil production expenditures and therefore stabilization of oil and

gas condensate production (at a level of some 650 million tonnes annually by

2010) is technically feasible, although at a high price. Instead, it appears

expedient to have an absolute reduction in oil production levels beyond

1995-2000

by

a few tens of million tonnes per year. This means the develop

ment of

fields

with very low productivity can be avoided, as can the wide-scale

use

(at least until 2010) of the most expensive technologies. But even in this

case, investments would rise considerably from 210 roubles/tonne in 2000 to

290-300 roubles/tonne by 2010. Under these conditions the expediency of

maintaining higher production levels will depend on world oil prices. The funds

saved in oil industry development can

be

better directed to development of

alternative sources for motor fuels and chemical feedstocks, notably those

based on natural gas.

In the early

1980s,

motor fuel and fuel oil yields in oil refining amounted to

around 40% each. In the future, motor fuel yields will be up to 60-65%, fuel

oil will decline to 15-17%, and there will be a substantial increase in the share

of feedstocks for petrochemistry and non-fuel products.

This is due to expected changes in the pattern of petroleum product

consumption. Today vehicles account for only 40% of consumption; in the

future their share may rise to 65%. At the same time

fuel

use for electricity

generation and heat supply is expected to decline from 35 to 12%. Petroleum

product consumption will also be strongly influenced by a shift to more

economic vehicles, electrically powered railway and urban public transport,

and

by

the use of compressed methane for part of the intra-city freight traffic.

The refining industry

will

make greater use of thermo catalytic refining

processes and fuel oil hydrogenization by methane-based hydrogen. Great

importance

is

attached to the production of lead-free gasoline and low sulphur

diesel to reduce the ecological problems of the large cities.

Coal production

is

to reach

0.7-D.8

billion tce in 2010 (especially owing to

the development of coal deposits located in the eastern part of the Soviet

Union - Kuznetsk, Kansk-Achinsk, Ekibastuz basins). Coal production in the

European part will reach a stable level before 2000 and will start to decline

afterwards. The large-scale users in eastern Siberia and Kazakhstan (power

plants, iron and steel factories, cement works, etc.) consume local solid

fuel

which

is

cheaper than nuclear energy, but ecological constraints limit the use

of coal in those regions with access to natural gas. In western Siberia and

central Asia (in view of seismology and as a result of higher capital costs of

nuclear power plants), Siberian coal

will

keep its competitiveness with nuclear

energy but not with natural gas (at any rate until 2010-2020). Coal production

will grow very slowly during the next decades because of ecological and social

constraints.

Because of the serious difficulties encountered recently more moderate

nuclear energy growth rates are expected during the next decade as compared



References

217

amounted to 33% of the oil produced compared to 12.1 % in 1960; for gas,

shares were 11.4 and 0.5%, respectively.

In

1988, oil and oil products exports

to capitalist and socialist countries totalled

144

million tonnes, oil products

61

million tonnes and gas 88 billion cubic metres, accounting for about two-thirds

of total hard currency earnings.

Recently, the policy of exporting hydrocarbons, particularly to market

economies, was strongly criticized by Soviet economists and politicians. But

this policy will certainly have to continue over the next decades until the

national economy is restructured and the quality of products raised to world

market levels.

Here it should be noted that interpreting raw material exports as a sign of

backwardness of a country

is

hardly justified. The modern world

is

character

ized by the growing division of labour. Autarchy

is

no alternative. Developed

nations are striving for mutually advantageous cooperation in all fields, not

excluding trade in raw materials. Efficient production of energy resources

nowadays uses just as much high technology as for example, electronics.

Rough estimates show that average labour productivity will increase by 30%

over the next 20-25 years. This will result in NMP growth of 1 trillion roubles

over the period 1986-2000. The realization of the new energy policy

will

demand about the same level of capital investments (including 800 billion

roubles in the development of the fuel and energy complex, 50-80 billion

roubles in energy conservation and the use of unconventional sources of

energy, 30 billion roubles in the development of appropriate machinery

construction industries, and some 90-100 billion roubles to meet the social

needs of operational staff). However, the new energy policy with an orientation

towards energy conservation and natural gas results in higher economic

efficiency. Due to structural changes and improvements in the energy systems,

more than one million people now employed in the energy supply sectors will

be shifted

by 2000

to more productive and efficient employment. At the same

time the policy sets the task of improving the ecological situation in big

industrial cities of the country by reducing the amount of hazardous pollutants

emitted by power-generating units by a factor of

1.5

by 2000 and more than

twofold over the following decade.

Needless to say, the elaboration of national energy programmes and policies

has required extensive

use

of modelling efforts on all the levels of the Soviet

hierarchical energy system: from industrial and agricultural enterprises and

households to the top-level planning authorities.

REFERENCES 10

Aganbegian, A. G. and Fedorenko, N.

P.

(eds)

(1978) General Recommendations for

the

Optimization

of

Industrial Development, Nauka, Moscow.

10 All

publications cited are in Russian.



218 Models and projections of energy use in the Soviet Union

Astakhov, A.

S.,

Gitin, E. M.

and

Saratovsky, E. G. (1967) Long-Term Optimization of

Coal Industry with the Use of Linear Models, in Proceedings

of

the All-Union

Conference on the Application

of

Computers and Mathematical Methods

in

Industrial

Planning.

Tallinn.

Beliaev,

L.

S. (1978) Solution

of

Complex Optimization Problems under Uncertainty.

Nauka,

Novosibirsk.

Druzhinin,

E.

P.

and

Kuznetsov, Yu. A. (1970) The Optimization

of

the State Natural

Gas Supply System. Siberian Energy Institute, Irkutsk.

Energy Programme (1984) Main Principles

of

the

USSR

Energy Programme for the

Long-Run. Politizdat, Moscow.

Feigin, V. I., Frolova,

E.

P.

and

Pevzner,

E.

V.

(1979) Modelfor Long-Term Planning of

Natural Gas Supply Systems with the Optimization

of

the Seasonal Storage Capacity.

Research Institute

of

Economics and Management in the

Natural Gas

Industry,

Moscow.

Kluev, Yu.

B.

(1970) Long-Term Energy Development

of

an Industrial Enterprise.

Ural

Filia of the USSR Academy of Sciences, Swerdlowsk.

Kononov Yu. D. (1981)

Energy and Economic System. Transition to

New

Energy Sources.

Nauka,

Moscow.

Makarov

A. A. (1989) New Concepts

of

Energy Development in the USSR, Energia,

4,

14-17.

Makarov, A. A. and Bashmakov, I.

A.

(1989)

The

Soviet Union: A Strategy for Energy

Development with Minimum Emission

of

Greenhouse Gases. Institute of Energy

Research, USSR Academy of Sciences

and

State Committee for Science and Technol

ogy, Moscow.

Maksimov, Yu.

I. (1979)

Network Models

in

Long-Term Industrial Planning.

Nauka,

Novosibirsk.

Markovich,

I.

M., Brailov, V. P.

and

Denisov, V.

I.

(1962) Applications

of

Mathematical

Programming to Long-Term Electricity System Development (in Russia). Izvestia AN

SSSR. Energetika i Transport,

6

5-13.

Melentiev, L. A. (ed.) (1971) Mathematical Models for the Optimization

of

the Electricity

Generation Development. Siberian Energy Institute, Irkutsk.

Melentiev, L.

A.

(1979) Energy Systems Analysis.

Nauka,

Moscow.

Nekrasov,

A.

S. (ed.) (1981) Optimization of the Fuel-and-Energy Complex. Energoizdat,

Moscow.

Nekrasov, A. S.

and

Sin yak, Yu. V. (1979) Management

of

Plant Energy Systems.

Energia, Moscow.

Nekrasov,

A. S.,

Kretinina, Yu.

S.,

Ershevich V. V. (1973) Base Load Thermal Power

Plan

Siting (in Russia), Electric Stations, 8

33-6.

Smirnov, V. A.

and

Garlyauskas,

A.

V. (1969) General Approach to the Optimization and

Control of the Natural Gas Supply System. Research Institute of Economics

and

Management in the

Natural Gas

Industry, Moscow.

Sovalov, S. A. (1983) Regimes

of

the Unified Electricity Supply System. Energoatomizdat,

Moscow.

Starovoitov, S. N., Andreeva,

L.

A.

and

Barmina, S. N. (1974) Joint Optimization

of

Crude Oil Production, Refining

and

Processing, in Optimization

of

Industrial Devel

opment, N auka, Novosibirsk.

Stavrovsky,

E.

R.

and

Sukcharev, M. G.

(1978)

Hierarchical Modelling System for

Natural Gas Planning, in Hierarchy

of

Large Energy Systems, Siberian Energy

Institute, Irkutsk.

Syrov, Yu. P., Makarov, A.

A. and

Zeiliger

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N. (1966) Linear Models for the

Optimization

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USSR Academy of Sciences

(1975) Methodology

for

the Optimization

of

the

Fuel and

Energy Complex.

Nauka, Moscow.

USSR Academy of Sciences (1977) Recommendations

for the

Optimization of Regional

Energy

Balances.

Moscow.

Volfberg, D.

B.,

Demirchan, K. S., Klokova, T. I. et aI.,

(1989)

USSR Energy Balance,

Izvestia

AN

SSSR. Energetika

i

Transport,

1 3-7.



- - -13

A detailed simulation

approach

to

world energy modelling:

the

SIBILIN

and

POLES

•

experIences

Patrick Criqui

The 1970s and 1980s have been marked by drastic changes in the international

energy markets. The early

1990s

have seen the return of the oil dependency

problem, but

new

issues are arising, in particular the concern for the planet's

global environment. To understand these changes and to identify appropriate

strategies for the new challenges, structured models, relying on good quality

information and retrospective analyses, are highly valuable tools. In this paper,

we describe the effort that the IEPE (Institut d'Economie et de Politique de

l'Energie in Grenoble) has undertaken to attain these goals. In the first part,

we analyse the specific approach developed, that of dis aggregated models for

the simulation of the world energy system. We then describe the structure and

results of the SIBILIN (SImulation des BILans energetiques INternationaux)

model, which had been developed to carry out medium-term oil market

scenarios. The third and final part presents the POLES (Prospective Outlook

on Long-term Energy Systems) model, presently being built to analyse long

term energy scenarios and their potential consequences on the global environ

ment.

13.1

A DISAGGREGATED SIMULATION APPROACH TO

INTERNA TIONAL ENERGY MODELLING

The margins for improving our understanding of the state and dynamics of the

world energy system are

wide.

Energy models have been developed at the IEPE

precisely because modelling

is

a learning process. This explains why disag

gregated models of the recursive simulation type have been chosen.

It

also

accounts for the

use

of the scenario approach instead of predictive forecasts



222

The SIBILIN

and

POLES experiences

and, last but not least, for the fact that such a modelling effort relies on detailed

data bases and retrospective analyses. The basic structure and logic of the

models correspond to the concept of hierarchical 'nearly decomposable'

systems, as identified by Simon

(1962).

World energy markets

(level

1) are

fed

by imports and exports from a set of national and regional energy models

(level

2) whose subsystems correspond to the main elements of the energy balance,

i.e. final consumption by sector, energy transformation sector, primary energy

production (level 3). Finally these subsystems rely on exogenous information

stemming from more detailed sectoral or 'bottom-up' studies.

13.1.1 Geographical disaggregation:

the

national simulation

approach

Most world energy models are highly aggregated. The Energy Modelling

Forum Study 'World Oil' (EMF, 1982) shows that the most detailed model

includes

16

production and consumption regions. Most other models include

five regions, the least detailed have only two regions. Among more recent

models, dealing with climate change issues, the IEA-ORAU model (Edmonds

and Reilly, 1985) incorporates ten regions, Global

2100

(Manne, 1990) five

regions and Nordhaus and Yohe (1983) study long-term energy scenarios for

the world

as

a whole

(see

also Table 14.1). The problem

is,

in fact, to choose

an optimum disaggregation level while taking into account:

1. the cost of gathering and managing detailed information and the benefits of

being able to identify the particularities of the different elements of the

model;

2. the fact that the consistency of the model might decline when the number

of exogenous hypotheses and causal relationships increases.

National energy balances are the main tool

for

a disaggregated approach to

world energy modelling, since they enable us to take country-specific factors

and constraints into account. Some 94% of total world energy

is

consumed by

only 40 countries and energy balances for the last 20 years have been

constructed for most of these countries

by

national and international institu

tions (lEA, UN-EeE, OLADE, Asian Development Bank), as well as by

research groups (International Energy Research Group - Lawrence Berkeley

Laboratory or ENERDAT A-IEPE).

This

is

why

the IEPE's world models are based on the simulation of national

and regional energy balances. These balances are connected to the upper level

- international energy markets - by energy exchange

flows

and prices. Their

basic dynamics derive, however, from the lower level - national energy

subsystems (production, transformation and final consumption) in which

country-specific technico-economic variables and energy policies are taken into

account. In spite of its requirements in terms of data collection, this approach

leads to greater consistency than more aggregated world energy models. This



A disaggregated simulation approach

223

is especially true for medium- and long-term energy issues, where national

energy policies playa particularly important role.

13.1.2

Modelling

the

markets: simulation rather than optimization

As regards international energy markets, the pros and cons of simulation

versus optimization/wealth-maximization approaches have been thoroughly

discussed by Gately (1984), in his survey of world oil models. He addresses in

particular the controversy between theoretical models based on the Hotelling

tradition, and empirical models which

use

recursive simuiation processes to

determine world oil prices.

According to Gately, the intertemporal wealth-maximization approach,

although internally consistent, involves highly disputable hypotheses and

simplifications, such

as

perfect information about future price and income

elasticities, or the neglect of lags in the adjustment of demand and supply. On

the contrary, despite their apparent simplicity, recursive simulation models of

the oil market make it possible to take into account the unavoidable uncer

tainty which OPEC is currently facing,

as

well as the 'rule of thumb' reaction

functions (for instance, a rise in price when the capacity utilization rate

is

rising) which it is forced to adopt in a highly complex situation.

Similar reasoning was used by Criqui and Kousnetzoff

(1987).

Their main

findings for the 1970-87 period were as follows:

1.

Oscillations in oil price basically depend on the capacity utilization of the

world's swing-producers (OPEC) and more particularly of the large reserve

countries (OPEC Core,

i.e.

the Gulf).

2.

The mere direction in the movement of oil prices

is

given

by

market

fundamentals, but the magnitude of the variations and the levels obtained

very much depend on the current geopolitical context, as well as on the

organizational structure of the oil industry and international market.

3.

The price-production strategy of the swing-producers in the medium term

cannot be analysed simply within the framework of wealth-maximization,

but must also include such factors as absorptive capacity and political

rivalry.

This

is

why the combination of 'behavioural' simulation models, driven by

the market fundamentals, with scenario hypotheses concerning the tactics and

strategy of the core swing-producers seems to be the most adequate solution.

Thus, it

is

necessary to avoid a fully integrated model structure, in order to

leave room for judgement in the building of scenarios.

13.1.3 Modelling demand: bottom-up or top-down?

As

regards the dynamics of energy demand modelling, there

is

the well-known

'top-down versus bottom-up' controversy

(see

Chapter

2).

The first approach



224

The

SIBILIN and POLES experiences

emphasizes the role of strictly economic variables (mainly prices and income)

and strictly economic behaviour (cost minimization and expenditure optimiz

ation).

I t

is strongly rooted in the neoclassical economic paradigm. Final

energy demand forecasts derive from the use of price and income elasticities.

The second approach is often referred to as the engineering approach to energy

demand. In this case, demand forecasts are mostly based on assumptions about

energy using equipment (boilers, cars, domestic appliances). This bottom-up or

end-use approach implies a finer degree of disaggregation, in order to take into

account the basic needs which energy has to satisfy as well as the correspond

ing economic structure and the technology applied.

Clearly, strictly economic and technological variables are both important in

analysing and forecasting energy demand. This is why any energy demand

model should to some extent incorporate

and

link the two sets of variables.

One possibility may be to use strictly economic variables for short- to

medium-term studies, while structural and technological variables have to be

explicitly taken into account for long-term outlooks: empirical evidence is

given by long period historical analyses of energy intensity of GDP, which

show short periods of stability within a long-run increasing or decreasing

trend, as identified by Martin

(1988).

Thus intermediate models, placing greater

or

lesser emphasis on economic

and

technological variables according

to

the time horizon considered, would

appear to be best suited. One can note that the inclusion of non-economic

mechanisms or variables implies exogenous hypotheses on changes in the

technological or consumption patterns and hence, the need to use simulation

processes.

13.1.4 Energy simulation models and Strategic Planning methods

For strategic planners such as Dumoulin

(1988),

'the future cannot be foretold

but it can be structured'.

In

fact Single-Line Forecasting

is

not

adequate in

times of shocks and strong perturbations of the economic system. On the

contrary, the basic methods of Strategic Planning, which consists of checking

different strategies against different consistent states of the world (Le. scen

arios), proves more relevant in a situation of high uncertainty. This is of

paramount importance in corporate planning, an area where neglecting the

'phantom scenario' (the worst case) can be fatal to the organization. But it also

holds true for policy-making.

'Structuring the future' means first of all taking into account the pre

determined elements. Even for long-term studies, they are often more import

ant than first thought: demography is one example, but capital stocks,

equipment under construction and technologies under development also illus

trate this fact. Thereafter, structuring the future means concentrating on major

uncertainties and combining them, so as to identify those states of the world

which seem internally consistent. All this can be done without a completely



226

The SIBILIN and POLES experiences

Exchange Rates

Inflation

OIL PRICE

I NERDAT A data base I

Fig.

13.1

SIBILlN, linking international energy and economic scenarios.

tegies.

As

regards the energy markets representation, SIBILIN places itself

deliberately within the family of recursive simulation models and not among

the intertemporal optimization models.

The model

is

based on the energy balance accounting logic. I t first of all

works back from the simulation of final consumption by sector and source to

primary consumption, having integrated transformation losses in the energy

sector on the one hand, and the structure of the electricity production system

on the other. It then calculates the import demand or the potential export

supply of the zone under consideration as the difference between its consump

tion and its production capacity. The balance sheets are simulated up to

1995,

and individual results are given for 24 OECD countries, 20 developing

countries and eight Eastern European countries, including the Soviet Union.

Along with these 52 national balance sheets, simplified primary energy bal

ances are also provided for

16

geographical areas. These zones make it possible

to cover the whole world in a way which is compatible with the CEPII

CHELEM economic data base.



A study

of

the

medium-term

world oil market

227

The demand submodels have an intermediate structure between the classical

econometric models (top-down approach) and the technico-economic models

of the MEDEE-type bottom-up approach

(see

appendix to Chapter

10).

Indeed, the explanatory variables (prices, value-added and income) and func

tional relations are those of econometric models, but they are applied within

the framework of a disaggregation into homogeneous consumption modules,

as

is

the case in technico-economic models.

In fact, the logic is very similar to that used in the structural analyses of past

demand.

2

In the model applied to the OECD countries, the demand equations

are produced by dis aggregating final consumption into

20

modules (energy

sectors and/or groups of products) and then by breaking down the consump

tion of each module into three indicators (energy intensity, structure and

activity). Thus:

FE=

' iJEi= i,(FEi/VAi)

x

(VAi/GDP)

x GDP,

i

i

where FE stands for total final energy consumption,

FEi

and

VAi

for the energy

consumption and the value-added of sector i, and:

F Ei/VAi

=

f(VAi, EPi, T),

where

EPi

stands for the average price of energy in sector

i,

and

T

for a time

trend.

Moving from final consumption up to source-by-source primary consump

tion involves taking into account the efficiencies of the various energy chains

as

well

as the structure of the thermal power plant system. Efficiencies are

extrapolated from past trends on the basis of national energy balance sheets.

Market shares of the various categories of power stations (coal, oil, gas) are

drawn, whenever possible, from national energy programmes or plans.

The hypotheses concerning the production capacities of the various energies

are not derived from a proper supply model. Given the time horizon used in

the forecast (5-10 years) and the lead times for energy production facilities, it

was more efficient to use exogenous hypotheses. A first 'production capacity

data base' was therefore set up for all the major energy producing countries,

using national energy programmes or international studies. This base was later

completed, modified and updated after discussion with experts from major

French energy companies.

13.2.2 A look back at key uncertainties and oil scenarios

of

1987

The main assumption made on the basis of retrospective analyses and used to

build the scenarios was that oil price is a function of the ratio of OPEC

production to OPEC capacity on the one hand, and of the strategy adopted

2See for instance the analyses of Chateau et al. for Europe, Schipper et aI, for the United States

and Matsui for Japan, in

Energie Internationale 1990-1991,

Economica, Paris, 1990, pp. 87-98,

99-110 and 111-23.



228

The SIBILIN

and

POLES experiences

by the large reserve core-countries on the other. When the capacity utilization

ratio is high (over 80%, or

28

million barrels per day (Mbd) production on the

basis of a

34

Mbd OPEC capacity), there

is

a high probability of a price hike.

When it is low (under 60%,

20

Mbd production), the price is very likely to

drop.

As

regards the future as it appeared in

1987,

the first uncertainty concerned

economic growth. At

that time, analyses indicated that world medium-term

growth would

be

moderate or low, depending on whether the major economies

of the

OECD

managed to elaborate a concerted policy for re-absorbing current

trade imbalances. The second uncertainty concerned the future strategy of

OPEC: would the Organization manage to restore sufficient internal cohesion

in order to defend oil prices by limiting output? The lower the level of

economic growth, and therefore of oil consumption, the more acute this

question would become.

If

internal discipline could not

be

upheld, and if oil

prices fell once again in the short term, then a final and major uncertainty

appeared: what would

be

the impact on world supply and demand of a very

low oil price over several years? Taking these uncertainties into account

resulted in the tree-matrix of the scenarios (Figure 13.2).

What

strategy for OPEC: price defence or market share defence?

The lesson of the 1986 counter-shock was that the weaker the demand for

OPEC

oil, the greater the sacrifices required of swing-producers to defend the

price and the greater is the risk of declared or latent price wars aimed at

1. World economic growth

2.

OPEC discipline

(oil price)

3. Demand and supply

price response

1\

OW MODERATE

A \

IGH

LOW

HIGH

~

~ S H O ~

RUN

LONG

(Reference case)

o

R ~

Fig.

13.2

Three levels of uncertainty, four scenarios.



A study

of the

medium-term

world oil

market

229

defending a new target: production levels or market shares. The simulations

carried out in 1987 with the moderate growth scenario were taken to be the

reference case. They showed a relatively low but regular growth in demand for

OPEC

oil which could

be

a sufficient condition for a return to price regulation

by output control, since

OPEC

production levels would rise progressively to

26 Mbd, with a price of

$24

per barrel (1986 dollars) in 1995. But in the case

of low economic growth with internal discipline, the results did not show any

short-term improvement in OPEC's oil revenues and production levels

(18

Mbd in 1990,24 Mbd in 1995). This is why the exogenous hypothesis of a new

break in discipline and oil prices was introduced (10 $/bl between 1987 and

1990), followed by a rise to 25 $/bl in

1995.

Two scenarios were then built in

order to analyse the consequences of this new price war situation.

What price-response for oil supply and

demand:

short-term or long-term?

The impact of a low price on energy consumption was computed directly by

the model using the incorporated price elasticities. However, an additional

hypothesis was introduced for those sectors in which inter-fuel substitutions

are possible: the substitution of other fuels for oil was barred as long as the

price remained under the

15

$86/bl threshold. The overall result was an

additional 2 Mbd consumption in

1990.

As

regards oil supply, two hypotheses were considered

1.

The first involved a rapid reduction in non-OPEC production ( - 2 Mbd in

1990), but also a quick revival after the real price upturn; this short-run

response scheme resulted, paradoxically, in a fairly balanced situation for

1995, with OPEC production at 24 Mbd.

2. In contrast, the second hypothesis was a delayed response situation, with

no short-run impact of low prices, but, because of lack of investment, a

4 Mbd reduction of non-OPEC production ( - 9% on the initial hypothesis)

in 1995; the result was clearly a return to a tight market with OPEC

production at

28

Mbd in

1995.

The four scenarios briefly described here were an attempt to depict con

sistent dynamics on the oil market while combining clear-cut hypotheses

concerning the main uncertainties. They can now be compared with the actual

changes since

1987,

in order to identify mis-specified hypotheses, processes or

behaviour patterns and to enhance our understanding of the world oil market.

13.2.3 The SIBILIN scenarios as compared

with

actual events

From

1987 to the first half of 1990, world economic growth has been higher

than assumed in the SIBILIN scenarios. This can be explained by the stronger

than forecasted delayed macroeconomic effects of the

1986

counter-shock on



230

The SIBILIN

and

POLES experiences

the one hand and by the fact that the structural adjustments on the US trade

and budget deficits have still been postponed on the other.

During this period, and in spite of a relatively high-growth environment,

OPEC did not succeed, or maybe did not even intend, to establish a

firm

price-defence strategy. With some oversimplification one might say that 1987,

1988 and 1989 have been successively years of high, low and again high

discipline inside OPEC. Is this just the way cartels function (agreement

cheating-agreement)? Or is it in some way intentional, aimed at obtaining an

intermediate price-path between price defence and price war?

This cyclical evolution might in fact have provided the core-countries with

a means of obtaining the same results as would have been obtained by a

technically difficult and politically disputed 'fine-tuning' of the oil market

(see

AI-Chalabi, 1988). In any case, this price-path, with some very low points (less

than

12

$86/bl) in 1988 and again in the first half of 1990), clearly had an

impact on world oil supply and demand, accelerating the upturn in the call for

OPEC oil. This explains the fact that actual OPEC production levels have been

higher than proposed in the scenarios: 22.6 Mbd in 1989, against at most

22 Mbd in the low-growth, price war and short-run price response scenario.

The outlook has changed as a result of the Gulf crisis. A political event has

once again upset the oil market, demonstrating that oil importing countries

remain structurally vulnerable. But

we

have learned from the second oil shock

that oil prices can also go down. This has happened again since the crisis was

over, simply because an OPEC production level of 23 Mbd

is

not at all a

critical one in a medium-term perspective. During each period on the

oil

market, a 'consensus price' appears among experts, forecasters and companies.

This was of

18

$/bl between 1986 and 1989. It

is

currently of 25 $/bl. This

is

not very far from the 24 $/bl (20 1986 dollars) which

was

indicated for 1990 in

the SIBILIN scenarios reference case.

13.3 POLES, A TOOL FOR LONG-TERM

ENERGY-ENVIRONMENT STUDIES

Although the hypothesis of a possible climate change linked to

fossil

energy

consumption

was

first proposed in 1896 by S. Arrhenius (Grinevald, 1990), it

was given relatively little attention until the 1980s.

At

that time important

advances by climatologists began to show that something with important

consequences for the planet might be taking place. Energy forecasting and

modelling efforts also began specifically to address the issue of global climate

change.

A number of global energy models and forecasts already study this problem

and the strategies that might be elaborated to manage it. But their results in

terms of long-run energy consumption vary greatly:

for

example, the World

Energy Conference 1989 report points to a 14 million tonnes of oil equivalent

(Mtoe) consumption in 2020 (Frisch,

1989),

while the Energy for a Sustainable



232

The

SIBIL IN and POLES

experiences

13.3.1 The main goals: to reduce uncertainties and identify the margins for

action

The model will produce world energy scenarios for the long term

(2010)

and

the very long term (2030). This corresponds to short time periods for climatol

ogists and to very long ones for economists and decision-makers. However, it

is

extremely important to identify what are the predetermined elements for

these next decades, in order to concentrate, in a second stage, on those

variables which could provide scope for freedom in policy-making.

The long lead-time and life-time of energy production and conversion

facilities is a well-known characteristic of the energy sector. For instance,

power plants take

5-10

years to build and may remain in operation for 30

years or more. This means that many electricity generation plants whose

construction will

be

decided on over the next 10 years, will still

be

in operation

in

2030.

In the same

way,

given the lead-time for the research, development

and diffusion of new energy technologies, it is probable that the major elements

of the 2030 technological systems have already been identified, either at the

laboratory or at the pilot-plant stage.

Inertia also exists, but might be

less

important for consumption devices than

for production and conversion equipments (Chapter 3). A simplified end-use

approach to energy consumption, based on the analysis of industrial waves

(Piatier, 1989) and of corresponding consumption patterns (from the building

of infrastructures, to household and individual transport equipment and

information) will

be

used as a framework. Hence the simulation of final energy

demand

will

result from country-specific energy paths but also from interna

tional comparisons aimed at identifying trends or possible saturation levels for

each main sector: industry, transport and residentia1. This should of course be

linked to an analysis of the technological systems that might contribute to the

satisfaction of these needs.

As for possible areas of action, it is clear that the limitation of energy

consumption by way of constraints

or

scarcity does not provide a solution. The

low level of satisfaction currently observed in developing countries today

derives not only from the lack of consumer purchasing power, but also from

the mere unavailability of energy and infrastructures. Because of debt and

financial constraints, these situations might worsen and extend into the future.

These facts should of course

be

taken into account in the

mode1.

However, the

diffusion of energy-efficient technologies, not only at the level of consumption

but also at the production and conversion levels, clearly appears to be the

correct solution for energy policies addressing environmental issues. Many new

technologies are already known: their technical feasibility has been proved, but

the role of R&D policies remains crucial in making them cost-effective.

Different hypotheses on the diffusion rates of new energy technologies

will

be

at the core of the normative scenarios simulated using the POLES mode1.



POLES

233

13.3.2 The need for a global cost-effectiveness analysis

As concerns global climate change issues, strictly national greenhouse gas

(GHG) abatement policies make little sense unless they are integrated within

an international perspective. For a country willing to spend several billions of

dollars on reducing GHG emissions it should not matter if the investment is

made inside its own frontiers

or

in a distant foreign country (unless indirect

advantages are anticipated). A policy of cost-effective investment should imply

a comparison of such different options as overinsulation of buildings in

countries of the North versus improved cooking-stoves

or

hydro-electric

development in countries of the South. For a global problem, a global

assessment scheme is required and the concept of national energy policy has

to be revised,

or

at

least be placed in a wider perspective.

The global cost-effectiveness approach might become a key aspect of

international global climate change policy issues. In fact other mechanisms

such as a reduction in proportions to past emission levels or stabilization of

emissions

on

a per capita basis clearly appear to be unfair. They would prove

highly disputable in the eyes of the countries of the South, since per capita

fossil fuel consumption

is

ten to 20 times higher in industrialized countries.

3

In

contrast, the financing of low-GHG energy technologies, wherever they present

a low investment per unit of avoided GHG, may prove logical for countries

both

in the

North

and in the South.

It

might also ease the financial constraint

which in many cases prevents developing countries from choosing environ

mentally sound solutions. By enabling the study of the consequences of

technology diffusion scenarios, the POLES model aims to provide insights into

the analysis of these issues.

13.3.3 The structure

of

the model

4

The structure of the model is again based on the hypothesis that the world

energy system can be considered as a 'nearly decomposable' hierarchical

system. At the first level, energy markets are simulated by taking into account

the fundamentals for each internationally traded energy, as well as the

strategies of the main protagonists. At the second level, national energy

balances make it possible to

put

together primary energy consumption and

production in order to compute the exchanges. At a third level, submodels

simulate energy consumption, transformation and production in each country

as a function

of

technical

and

economic variables and hypotheses.

3S

ee

the analyses

in

'World Status: Environmental Taxation?',

in

Financial Times Energy

Economist, no. 100, February 1990, pp. 14--22.

4For further detail see POLES (Prospective Outlook on Long-term Energy Systems). Maquette,

IEPE, Grenoble, March 1990,

p.

112.



POLES 235

The logic of price determination is different in the very long term.

We

suppose less overcapacity in oil production and less inter-fuel competition as

oil consumption becomes concentrated on captive uses (transport

fuels

and

chemical feedstocks). Price levels should thus

be

explained in terms of fuel

specific conditions,

i.e.

the marginal cost of developing new resources for each

internationally traded

fuel.

In this case, the price model simulates the progress

ive

mobilization of more and more expensive resources, using long-term supply

curves and taking into account the impact of technological progress on costs

(Bourrelier, 1990).

Level 2: national energy balances

This level provides the accounting framework within which information

is

organized in the most relevant way. Within the POLES model it

will

also make

it possible to analyse the impact of the development of energy systems: degree

of energy dependency, macroeconomic cost of energy imports, investment for

energy production and transformation systems etc. At this stage it

will

be

possible to supersede the mere extrapolation of existing trends by simulating

different national or regional energy policies.

Level

3:

energy demand, transformation and production subsystems

The energy demand modules first take account of such nearly predetermined

elements as demography, for which international organizations (UN, World

Bank) provide structured and detailed long-term forecasts. Final energy

demand is broken down into a dozen end-uses or sectors. For each module

two standardized indicators - a specific consumption indicator and an activ

ity/income indicator - have been developed. They enable us to study the

country-specific dynamics of energy demand variables as a function of price

level. They also make it possible to take advantage of international compari

sons in order to construct hypotheses concerning long-term trends or satura

tion levels. Last but not least, these modules

will

simulate the consequences of

different rates of adoption of new energy-efficient technologies. This can

be

done either explicitly, through hypotheses on the specific consumption of

vehicles for instance, or else implicitly, through evolutions of the specific

consumption indicators based on more detailed bottom-up studies.

Electricity generation

is

at the heart of the transformation subsystems, at

least within the medium- to long-term horizon, while in the longer term other

conversion options (synfuels, hydrogen, fuels from renew abies) might acquire a

more significant weight. Two different methods are used to simulate the

evolution of electricity generation plants. In some countries, the intertemporal

cost-minimization of electricity supply, based on

fuel

prices and demand

expectations, is still the actual framework for investment planning. In this case,

a simplified linear optimization model is used to simulate utilities' investment



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237

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(Energy Modelling Forum)

(1982) World Oil,

EMF

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Standford Univer

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Frisch,

J. R. (1989)

Horizons energetiques mondiaux 2000-2020. Technip-Conference

Mondiale

de

I'Energie, Paris,

p. 378.

Gately,

D. (1984)

A Ten-Year Retrospective: OPEC and the World Oil Market.

Journal

of

Economic Literature, 22,

1100-14.

Goldemberg, J., Johansson,

T.

B., Reddy,

A. K. N. et al. (1988) Energy for a Sustainable

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517.

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J.

(1990) L'effet de serre de la Biosphere. De la revolution thermo-industrielle

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S.

(1990)

Global

2100:

An Almost Consistent Model of CO

2

Emissions

Limits, Stanford University, Stanford, Ca, February,

p.

20.

Martin,

J. M. (1988)

L'intensite energHique

de

l'activite economique dans les pays

industrialises:

les

evolutions

de

tres longue periode

...

Economies et Societes,

series

E,

no.

4,

April, pp. 9-27.

Nordhaus,

W. D.

and Yohe,

G. W. (1983)

Future Paths of Energy and Carbon Dioxide

Emissions, in

Changing Climate,

National Academy Press, Washington, DC, pp.

87-153.

Piatier, A. (1989) 1980-1990: dix ans

de

surf. Economie et Societes, series F, no.

31,

March, pp. 5-41.

Simon,

H. A. (1962)

The Architecture of Complexity,

Proceedings of the American

Philosophical Society,

106, 467-82.



14

Inferred

demand

and

supply

elasticities

from

a comparison

of world oil models

Hillard G. Huntington

14.1 INTRODUCTION

Analyses of oil markets frequently depend upon a relatively small set of

important parameters governing the response of oil supplies and demands to

prices and economic growth. Analysts must assign these parameter values from

a limited historical experience that includes several sharp shifts in oil market

and economic trends.

As

a result, one finds a range of plausible parameter

values being used by oil policy analysts that can often lead to quite different

perspectives on oil market trends and the effectiveness of various policies to

reduce dependence upon insecure oil supplies.

This paper summarizes the responses of oil supply and demand to prices and

income in

11

world oil models that were compared in a recent Energy

Modeling Forum (EMF) study. In May 1989, the

EMF

commenced a study of

international oil supplies and demands (hereafter, EMF-11) to compare alter

native perspectives on supply and demand issues and how these developments

influence the level and direction of world oil prices. In analysing these issues,

the EMF-11 working group relied partly upon results from

11

world oil

models, using standardized assumptions about oil prices and gross domestic

product (GDP). During the study, inferred price elasticities of supply and

demand were derived from a comparison of results across different oil price

scenarios with the same GDP growth path. Inferred income elasticities of

demand were derived from a comparison of results across different economic

growth scenarios with the same oil price-path. Together, these estimates

summarize several important relationships for understanding oil markets.

The next section provides some background on the EMF study and on

general trends in the scenarios of interest that help to understand the results.

The following sections explain the derivation and qualifications of the inferred

estimates, report the results and summarize the key conclusions.



240

Inferred demand and supply elasticities

14.2 THE EMF STUDY

14.2.1

Purpose

and

approach

The eleventh Energy Modeling Forum study (EMF-ll) analysed the factors

determining the long-run trends in the international oil market over the next

two decades. Such issues included the growth in world oil demand, the

prospects for supplies outside OPEC and the long-run implications of these

demand and supply trends for the world's dependence on oil from OPEC

member countries and particularly from the Persian Gulf. From its inception,

the study was not designed to focus on the short-run impacts of disruptions on

oil markets.

Nor

did the study attempt to provide just a single view of the likely

future path for oil prices. As in previous

EMF

studies, the research was

conducted by an ad hoc working group of more than 40 leading analysts and

decision-makers from government, industry, academia and other research

organizations. In the EMF process, the working group pursues the twin goals

of improving the understanding of the capabilities and limitations of existing

energy models, and using these models to develop and communicate useful

information for energy planning and policy.1 The group is guided in the pursuit

of these goals by a set of design principles:

1.

A model user orientation maintained by active user involvement in the

development of the study.

2. A comparison of the capabilities and limitations of many models rather

than a detailed evaluation of a single model.

3.

An issue focus that directs and guides the study by applying the models to

an important energy problem.

4. Broad participation by a number of people in selecting the topic, forming

the working group, analysing the results, and disseminating key findings.

5.

Decentralized analysis of scenarios by proprietors familiar with the individ

ual models.

The group met four times over the

1989-90

period - prior to the Iraqi

invasion of Kuwait - to develop a study plan with a set of carefully selected

scenarios, analyse model results and supporting analysis and develop

key

conclusions and insights. Eleven economic models of the world oil market were

run by their proprietors at their home institutions using standardized assump

tions for 12 different scenarios. These results were reported to the

EMF

staff

and formed the basis of the group's indepth comparison of alternative

perspectives on the world oil market.

IThe EMF process and key findings from previous studies have been discussed extensively in

several papers, e.g. Huntington

et

al. (1982).



The EMF study 241

14.2.2 Scenarios and models

Only six of the

12

scenarios are analysed in this paper. They were developed

to analyse differences in oil demand and supply projections based upon

standardized assumptions for the oil price and economic growth. Specific

model assumptions about OPEC's behaviour or responses to market condi

tions are excluded from these scenarios. These results help to interpret the

results from scenarios where supply and demand conditions, including OPEC

production decisions, are allowed to determine oil prices endogenously in each

model. Three cases assume a flat oil price-path with different GDP growth

assumption - low, base and high. Another three cases

use

the same three

economic growth assumptions with a rising oil price-path.

2

The models in the study were developed to prepare long-run projections of

oil prices, oil production and oil consumption and to study changes

in

these

variables under alternative scenarios. They incorporate the behaviour of three

distinct agents: oil consumers, oil producers outside the cartel, and oil pro

ducers within the cartel. Oil consumers respond to gross domestic product

(GDP)/ energy-saving trends in technology or economic structure (if present),

and oil prices. The response of oil producers outside the cartel is governed

by

assumptions about trends in resource depletion and technology in addition to

oil prices. In most models, the cartel's productive capacity is exogenous, based

upon modeller judgement of a combination of economic and political con

straints.4 The cartel sets a price based upon last period's price and rate of

utilization of its capacity. In this way, oil prices, production and consumption

are determined recursively; market conditions in one year influence those

in

the succeeding year.

The main model features of interest to the elasticity estimates in this paper

are summarized in Table

14.1.5

Most are simulation models that determine oil

prices recursively in the manner described above. ETA-MACRO and DFI

CEC are optimization models that endow oil producers and/or consumers with

perfect foresight. The first assumes that both oil producers and consumers

maximize the discounted utility of total consumption of all goods and services;

the latter assumes that producers maximize total discounted oil profits. These

models require explicit assumptions about resource cost curves - the amount

of recoverable resources ultimately available at different prices.

2The other

six

scenarios included three based upon an exogenous oil price-path and three in which

market-clearing prices were determined by each model. See Huntington et

al.

(1989) for more

information on the assumptions in all 12 scenarios.

3Shifts in the economies' structures are seldom incorporated explicitly, although a macroeconomic

model linked to the Penn-BU model contains such detail.

4Capacity

is

endogenous in Penn-BU,

IPE

and ETA-MACRO. DFI-CEC uses

an OPEC

resource

curve directly without any capacity constraint.

5This table

is

based upon a comparison of model structures reported by Kress

et al.

(1990). Beider

(1982) also provides a very useful comparison of similar modelling approaches used in a previous

EMF study, including the distinction between recursive simulation and intertemporal optimization

approaches.



242

Inferred

demand and

supply elasticities

Table 14.1 Key features of models

ETA-

OMS

[PE

MACRO"

Penn-BU

WOMS

Model Recursive Recursive Intertemporal

Recursive

Recursive

type simulation simulation optimization

simulation

simulation

Perfect

No No

Producers and

No

No

foresight consumers

Periodicity Annual

Annual 10 years Annual

Annual

Horizon

2010

2000 2100 2010

2010

Regions

Supply

7

10

4

Demand

6

5

Parameters

Supply Judgement Judgement Judgement

Econometric

Econometric

Demand Judgement Judgement Judgement Econometric

Econometric

Participating Mark Rodekohr Nazli Choucri Alan Manne

Robert Kaufmann Nicholas

Modeller

and Baldwin and

Peter Pauly

Richard

Prosser

Organization

b

US Energy MIT Stanford Univ. of Penn.

PowerGen

Information

University and Boston

Administration

University

"This version of ETA-MACRO is also called Global 2100.

bOrganization of individual

(s)

who developed

and

exercised the model during the study. Listed for identification

purpose; model and results do not necessarily reflect organization's views.

Table

14.1

also compares the models in terms of periodicity, horizon (last

year in the projection), number of supply and demand regions, and whether the

supply and demand parameters are direct econometric estimates or are

determined judgementally based upon a reading of the available literature on

energy demand responses. The institutional affiliation listed in Table 14.1 is

provided to identify the model and not to indicate an official modelling

framework of a particular organization. This caveat applies particularly to

BP-America, WOMS and the Federal Reserve Bank of Dallas (FRB-Dallas),

as

well as the various university models.

Most models report prices and supply-demand balances annually and focus

exclusively on world oil markets. Alternative fuel prices and interfuel substitu

tion are not explicitly represented but instead are implicitly incorporated

through the own-price elasticity for oi1.

6

(This assumes that both the relation

ship between oil and other fuel prices and the potential for interfuel substitu

tion will remain the same as in the past.) An exception to this general

paradigm, ETA-MACRO focuses on the interactions between electricity, fossil

fuels

and the economy in the very long run, embodying specific parameters for

6BP-America and Penn-BU are exceptions.

For

the latter,

interfue1

substitution

is

incorporated in

a detailed macroeconomic model linked to the world oil model.



The EMF study 243

PRB- BP-

CERI

HOMS Dallas

DFl-CEC

America Gately

Recursive Recursive Recursive Intertemporal Recursive Recursive

simulation simulation simulation optimization simulation

simulation

No No

No Producers No

No

Annual Annual Annual 5 years

Annual Annual

2010 2010 2010

2032

2010

2010

17 7

4

7

7

6

5

Econometric Econometric Econometric Judgement Econometric

Judgement

Judgement Econometric Econometric Judgement

Econometric Econometric

Anthony William Hogan Stephen Dale Nesbitt

Lakis Dermot Gately

Reinsch and P. A. Brown

Vouyoukis

Paul Leiby

Canadian Harvard Federal Decision

BP America New York

Energy

Reserve

Focus, Inc.

University

Research

Bank of

Institute

Dallas

substitution between energy and non-energy inputs as

well

as for substitution

within energy between electricity and fossil fuels.

14.2.3 Demands and supplies with the flat price-path

The

EMF-II

working group considered several very different sustained,

long-run paths for the world oil price. Current oil prices (December 1990) have

been driven far above these assumed paths by the Iraqi invasion of Kuwait and

could become quite volatile with military conflict. Eventually, however, many

analysts expect that once the situation is resolved, market forces

will

return oil

prices to substantially lower prices. Thus, these price trajectories should be

viewed as establishing a reasonable range for the long-run, sustained path over

the next several decades.

A flat oil price case assumes that the US refiner acquisition cost for imported

oil rises from $14.70 in

1988

to

$18

per barrel in

1989

(all prices are in

1988 $)

and remains at that level until

2010.

A rising oil price case assumes that this

oil price rises gradually to

$36

per barrel by 2000 and remains at that level

until

2010.

In both scenarios, GDP for the market economies is assumed to

grow by 2.9% per annum through the period, with higher economic growth

(4.1 % p.a.) outside the OECD countries. In both the flat and rising price



244

Inferred

demand and

supply elasticities

scenarios, OPEC

is

considered to be a residual supplier of oil, meeting all the

oil demand that remains unsatisfied by non-OPEC production.

It

should be emphasized that modellers were requested not to impose any

shifts in government policies in running these cases. Many working group

members thought that oil importing countries would impose taxes and other

conservation policies to limit their oil demands. Thus, the

EMF

scenarios

should be considered as revealing the pressures that would emerge under

alternative oil price and

GDP

paths if no such policies

were

implemented.

Table

14.2

summarizes the trends for OECD demand, market economies

demand, non-OPEC production and the residual demand for OPEC oil in the

Table 14.2 Consumption, production, and call on OPEC (MBD) with flat

oil price-path

Model averages % change

(p.a.)

1990 2000

2010

1990-2000 1990-2010

Flat oil price

Consumption

OECD 38.3

46.9 58.1 2.0% 2.1 %

Mkt econ.

52.6 66.3 85.6 2.3% 2.5%

Production

Non-OPEC 28.5 25.5

20.7

-1.1%

-1.6%

CPE exports

1.9

1.1

0.6

-5.4% -5.3%

Call on OPEC 22.2 39.7 64.3 6.0% 5.5%

OPEC share

42.3% 59.9% 75.1%

Flat price with high

GDP

Consumption

OECD 38.8 51.0

68.1 2.8% 2.9%

Mkt econ. 53.2

72.6

102.5 3.2% 3.3%

Production

Non-OPEC

28.5 25.6 20.7

-1.1% -1.6%

CPE

exports

1.9 1.0

0.4

-6.2% -7.1%

Call on OPEC

22.8 46.1 81.3 7.3% 6.6%

OPEC share 42.9% 63.4% 79.4%

Flat price with

low GDP

Consumption

OECD

37.9

43.0 49.7 1.3% 1.4%

Mkt econ.

51.9 60.5

71.8 1.5% 1.6%

Production

Non-OPEC 28.5

25.5

20.6

-1.1% -1.6%

CPE exports

1.9

1.2 0.8

-4.6% -4.0%

Call on OPEC 21.6 33.8 50.4

4.6% 4.3%

OPEC share

41.5% 55.9% 70.1%

The results are averages for all models that report all components

in

the table. ETA-MACRO is

excluded from the averages in this table because it did not report market economies consumption

and Non-OPEC production in the study. Penn-BU is excluded because it did not report OECD

consumption. IPE results are included for 1990 and 2000 but are unavailable for

2010.



The EMF study

245

three scenarios based upon the flat oil price-path.

7

The alternative scenarios

represent a high

GDP

case (GDP for market economies grows by about 1

percentage point higher) and a low

GDP

case

(GDP

grows by

about

1

percentage point lower). Although Table

14.2

reports model averages only,

there exists a wide variation in results across models in these scenarios.

The projected supply and demand levels for the flat oil price-paths reveal the

strong pressure for

OPEC

members either to expand production rapidly or

increase prices.

All

scenarios imply substantially higher oil demands, modestly

declining non-OPEC supplies and rapidly growing dependence upon

OPEC

sources. With the baseline

GDP

assumptions shown in the upper rows, OECD

oil consumption would grow from 38

MBD

in 1990 to 47 MBD by 2000 and

to

58

MBD

by 2010. Consumption by the market economies, which includes

the less developed countries (LDCs), would grow even more rapidly, reaching

86

MBD by 2010. Non-OPEC production would decline modestly through

2000 (to

25

MBD), falling more precipitously during the initial decade of the

next century. The call on OPEC production resulting from these above trends

would climb rapidly to 40 MBD by 2000 and to 64 MBD by 2010. Demand for

OPEC production with the flat price would increase by 6.0% p.a. between 1990

and 2000. I f OPEC were simply to meet this demand

at

the $18 price,

dependence upon OPEC sources would quickly increase to 70% or more by

2010.

In the projections immediately below these results in Table

14.2,

the higher

GDP

path would accentuate these trends by raising world oil demand,

increasing the call on OPEC to 46

MBD

in 2000 and to 81 MBD in 2010. The

lower

GDP

path would reduce significantly the level of OPEC production to

34

and

50 MBD, respectively, for these two years. This second scenario would

still require a 4.6% p.a. growth in OPEC production over the next decade.

Although not shown in this table, differences in demand projections among

models dominate differences in production outside OPEC. In 2000, demand in

the market economies varies by more than

30

MBD across models, while

non-OPEC supply varies by about 7 MBD. Thus, variations in demand have

a critical effect

on

the different calls

on OPEC

observed in the various models.

The range of demand projections is emphasized quite dramatically in Figure

14.1,

which shows the oil-GDP ratio for the

OECD

countries continuing its

historical decline of the last two decades in

six

of the nine models under the

flat price scenario.

By

2010, the oil intensity falls by 20--25%,

or

by 1.0% to

1.3% p.a. Three models - HOMS, ETA-MACRO and FRB-Dallas - show the

oil intensity as initially rising before leveling out with the flat oil price-path.

All three models assume

that

oil demand grows 1

%

for each 1

%

increase in

economic output, holding energy prices constant. The other models assume

further declines in oil intensity with future economic growth. Both HOMS and

7Reftecting traditional data collection procedures, the models (except ETA-MACRO) exclude oil

supplies and demands in the Soviet Union, Eastern Europe and China. Net exports from these

regions are an assumption.



The EMF study 247

Table 14.3

Consumption, production,

and

call

on OPEC

(MBD) with rising oil price

path

Model

averages

%

change

(p.a.)

1990 2000

2010

1990-2000

1990-2010

Rising oil price

Consumption

OECD 37.7 38.8 44.3 0.3%

0.8%

Mkt econ.

51.7 55.9

67.0 0.8% 1.3%

Production

Non-OPEC 28.2

29.8

28.2

0.5%

-0.0%

CPE exports

1.9

1.1 0.6 -5.3% -5.2%

Call on OPEC 21.6 25.0 38.2

1.5% 2.9%

OPEC

share 41.8% 44.8% 57.0%

Rising price with high GDP

Consumption

OECD

38.1 42.4

51.9

1.1% 1.5%

Mkt

econ. 52.3 61.4

80.1

1.6% 2.2%

Production

Non-OPEC 28.2 29.8 28.2

0.6%

-0.0%

CPE exports

1.9

1.0

0.4

-6.1% -7.2%

Call on OPEC 22.3 30.6

51.5

3.2% 4.3%

OPEC Share 42.5% 49.8%

64.3%

Rising price with low GDP

Consumption

OECD 37.2

35.3 37.9

-0.5%

0.1%

Mkt econ. 51.0 50.7

56.2

-0.1%

0.5%

Production

Non-OPEC 28.2 29.7

28.1

0.5%

-0.0%

CPE exports 1.9 1.3 0.9

-4.1%

-3.4%

Call on OPEC 20.9

19.7

27.1 -0.6%

1.3%

OPEC

share 40.9%

38.9% 48.2%

The results are averages for all models that report all components in the table. ETA-MACRO

is

excluded from the averages

in

this table because it did not report market economies

consumption and Non-OPEC production in the study. Penn-BU is excluded because it did not

report OECD consumption. IPE results are included for

1990

and 2000 but are unavailable for

2010.

As expected, the rising price-path encourages more non-OPEC production

than in the flat price scenarios. The mean estimate calls for a relatively stable

production path of 28-29

MBD

through the period. Meanwhile, oil demand in

the market economies grows noticeably slower than with flat prices. Flat

OECD consumption and moderately increasing demands for the market

economies and for OPEC production result when high oil prices are combined

with low economic growth.

Figure 14.2 shows that the

oil-GDP

ratio declines under the rising price

assumptions for all but two models.

By

2010,

the oil intensity (indexed to 1 in

1988)

declines by 30-40%, or by 1.6% to 2.3% p.a. The exceptions are HOMS



248

Inferred

demand and

supply elasticities

1.3

1.2

.fI-

HOMS

-+-

FRB-Dallas

1.1

·G·

Gately

1.0

-£J-

ETA-MACRO

OMS

0.9

WOMS

0.8

IPE

·x· BP-America

0.7

...,..

CERI

0.6

.....

DFI-CEC

0.5

1980

1985

1990 1995

2000 2005

2010

Fig. 14.2 OECD oil-GDP ratio with rising price (1988 = 1).

and FRB-Dallas, both of which reveal oil intensities

by 2010

that are not much

lower than those in

1988.

During the early 1990s in these models, oil intensity

increases in response to the price declines of the

1980s.

Later in the period, oil

intensities begin to fall as future oil prices move higher.

ETA-MACRO's oil intensity trend

is

substantially different with rising than

with flat oil prices. With higher prices,

it

follows the pattern set by most models

and declines throughout the period. This trend contrasts sharply with the oil

intensity trends for the rising oil price case (Figure

14.1),

where ETA-MACRO

joined HOMS and FRB-Dallas in showing rising or flat o i l ~ G D P ratios up to

2010. The sharp swing in this model from a falling intensity in the rising price

case to a rising intensity in the flat price case reflects a strong demand response

to price, as will be discussed in the next section on inferred estimates of price

elasticities.

14.3

ELASTICITY ESTIMATES

The general oil supply and demand trends associated with the several rising

and flat price scenarios were discussed above. In this section we report some

elasticity estimates that summarize the responses of oil supplies and demands

to changes in price and income based upon these scenarios. Price elasticities of

oil supply and demand

for

each model are derived implicitly from a compari

son of the quantity and price results from the rising and flat price scenarios.

Inferred elasticities are computed as the ratio of the percentage difference in

the quantity demanded or supplied between the two scenarios and the

percentage difference in the crude oil price in the same year. GDP levels are



Elasticity estimates

249

held constant across these two scenarios. Income elasticities of oil demand for

each model are derived implicitly from a comparison of the quantity, and

GDP

results from the flat and the high

GDP

(with flat price) cases. They are

computed as the percentage difference in oil quantity between the two

scenarios, divided by the percentage difference in GDP levels in the same year.

Oil prices are held constant across these two scenarios.

14.3.1 Uses

and

qualifications

These estimates are quite useful for understanding the pressure on long-run

oil prices to change in response to shifts in supply and demand conditions. For

example, the mean results in Table

14.2

indicated rising oil demand with

limited expansion in oil supplies outside OPEC with a flat oil price-path.

I f

OPEC producers also limit oil supplies, market pressure would push oil prices

upward over the long run - a result that

is

evident in the endogenous oil price

scenarios that the participating modellers ran during the study. How much

prices would increase depends partly upon the

size

of the supply and demand

shifts and partly upon the response of supply and demand to price. Limited

price sensitivity requires larger increases in oil prices to re-establish an oil

market equilibrium after the supply and demand shifts. Thus, price elasticities

of supply and demand play an important role in shaping long-run oil price

projections from any particular model.

These estimates also help elucidate how different production strategies

influence the income of cartel producers exercising monopoly control. While

cartel producers may have other objectives, income is

likely to remain an

important criterion in their decision-making. In any particular year, reduced

cartel production

will

generate additional revenue if prices rise proportionately

more than the cartel's output declines. Again, prices will tend to increase more

with a given reduction in cartel output when world consumers and other

producers outside the cartel are less sensitive to price and when the cartel's

market share

is

greater. Ignoring extraction costs that are likely to be minimal,

income for the period would be maximized when the net demand for cartel

output possesses a unitary price elasticity. Income over the planning horizon,

of course, would also depend upon the timing of revenue receipts and the

cartel's discount rate.

Elasticity estimates are also useful for evaluating the effects on oil markets

of various policies introduced in major oil-consuming countries to reduce

imports and prices. Taxes on petroleum consumption

will

have a smaller

impact on domestic national wealth when price-induced substitution away

from oil

is

more extensive. Moreover, taxes will have a greater depressing effect

on world oil prices, and hence a smaller impact on domestic prices (including

the tax), when domestic demands are more price-sensitive and supplies and

foreign demands are less price-sensitive.

While the inferred elasticities are quite useful summaries of the responses for



250

Inferred

demand and

supply elasticities

each model, they must be interpreted carefully. Oil demand adjusts slowly to

price as the capital stock turns over so that the complete adjustment to price

(i.e., the long-run response) is

not

observed for many years. This problem is

compounded by the fact that higher oil prices are phased in gradually over

12

years in the EMF rising price scenario. In most models, consumers are

assumed to consider current (and past) prices, but not to look ahead at future

prices. Thus, for over half the period, demand decisions are being made on the

basis of prices below $36, the price level used in estimating the inferred

elasticity for 2000 and beyond.

By

overstating the price change upon which

decisions are made, the inferred elasticities

will

be understating the true

elasticity. Finally, we should note that the elasticities need not be constant in

all relevant price ranges, but may in fact depend upon the price level.

It should be emphasized that the EMF-ll estimates are for crude oil and

not

for petroleum product price elasticities. When refinery margins and taxes

remain relatively stable in dollars per barrel, delivered product prices will

change proportionately less than crude oil prices. Under these conditions, the

crude price elasticity will be smaller, being approximately equal to the product

price elasticity times the ratio of the crude to product prices. Such conditions

appear to apply to US oil markets. Given current prices within the United

States crude elasticities are approximately one-half product elasticities.

14.3.2 Price elasticities of demand

Table

14.4

reports the average price elasticities of demand inferred from the

EMF scenarios for the United States, OECD, non-OECD countries and all

Table 14.4 Inferred crude oil price elasticities of demand

Average model responses

l-yr

lO-yr Long run/20-yr

US

OECD

Non-OPEC

LDCs

Market economies

Estimates for other studies

Crude oil

Gately-Rappoport (1988)

Brown-Phillips (1989)a

Gasoline

Sterner and Dahl (1990)b

-0.10

-0.33

-0.44

-0.12 -0.34 -0.47

-0.11

-0.21

-0.30

-0.10

-0.26

-0.38

-0.07 -0.38

-0.11 -0.56

-0.24 -0.80

Elasticities are derived from the

EMF

rising and flat oil price scenarios. See text for derivation and

qualifications.

'one-quar ter elasticity equals -0.08.

bS

ee

Table 5.1 (lagged endogenous, survey average).



Elasticity estimates

251

market economies. The table contains estimates for the demand response after

the first, tenth and twentieth years.9

The results reveal several conclusions. First, the responses for the United

States appear quite similar to those for all OECD countries. Price elasticities

are approximately

-0.1 after the first year, rising to -0.4

or

-0.5 after 20

years of adjustment in the capital stock. Secondly, these estimates appear

comparable to several recent econometric studies that have estimated the

demand response to crude oil price changes in the United States (shown at the

bottom of Table

14.4).

It is

not surprising that the Brown and Phillips

(1989)

estimates are similar because those estimates are precursors to the FRB-Dallas

model being used in EMF-II. Also, shown in Table 14.4 are the means

reported by Sterner and Dahl in their survey of gasoline demand studies

(Chapter

5).

The first-year and long-run responses of

-0.24

and

-0.8,

respectively, correspond roughly to crude oil price elasticities of - 0.15 and

- 0.50,

given recent crude oil and US refined product prices. And thirdly, the

estimated price elasticities are lower outside than within the OECD.

It

should

be

emphasized, however, that the modelling of oil demand in the developing

countries

is

very rudimentary given the existing data for these regions. Since

much less effort has been expended to estimate oil demand parameters for these

countries, one must be cautious in drawing conclusions from these estimates.

Estimated price elasticities are reported for each model in the appendix

(Table A.l) For the most part, long-run elasticities cluster in the

-0.3

to -0.5

range for US and

OECD

demand. ETA-MACRO and an alternative version

ofHOMS

(HOMS-I) have substantially higher long-run price elasticities in the

-0.8

range, while Gately and

IPE

reveal considerably smaller than average

responses.

The higher response in HOMS-I directly reflects the alternative assumptions

used to estimate oil demand from historical data. This version assumes that all

declines in oil intensities over the last two decades can

be

attributed to higher

oil prices operating with a considerable lag as the capital stock

is

replaced. The

version reported as HOMS

1o

in the EMF study assumes that the structure of

oil demand was permanently altered in 1980, resulting in a one-time improve

ment in oil use efficiency independent of the oil price. Thus, part of the price

effect in

HOMS-l

is attributed to other causes in HOMS.

The higher price response in ETA-MACRO may depend upon its focus on

all energy rather than oil alone, as in the other models. This model explicitly

incorporates the interfactor substitution between energy and nonenergy inputs

as

well

as interfuel substitution between oil and other energy forms. In

addition, the model's substitution response to various prices

is

not estimated

9The choice of initial year (1989 or 1990) depends upon how the price change was implemented in

each model. The lOth- and 20th-year estimates were calculated from results for 2000 and 2010,

respectively.

lOThe

HOMS

modellers do not prefer one specification over the other. The choice of which version

to use as the main HOMS entry in the EMF study was arbitrary.



252

Inferred demand and supply

elasticities

directly from historical data but instead is set judgementally based upon a

reading of estimates from other studies.

The lower price response in the Gately model results from assumed asym

metries in the demand response to price changes. Due to large capital costs,

investment in energy-conservation measures is not undone when prices

fall

from previously high levels, so that demand would not change very much. Nor

does such investment need to be added back when prices begin to recover and

rise again, resulting in very little decline in demand. However, if prices were to

exceed their historical maximum (which is not reached in the

EMF

scenarios),

the price response would increase as

new

opportunities for investment in

conservation would emerge.

14.3.3 Income elasticities of demand

Table 14.5 reports the average inferred income elasticities of demand for the

United States, OECD, non-OECD developing countries and all market econ

omies.

It

contains estimates for the demand response after the first, tenth and

twentieth years.

The mean long-run elasticities for these models lie in the 0.8-0.9 range for

all regions. This result suggests some improvements in oil efficiency in these

economies over time even without higher oil prices, because oil consumption

grows more slowly than economic output. As reported in the appendix,

however, the inferred income elasticities differ widely across models. Income

elasticities in the range of unity are found for both versions of HOMS,

Table 14.5 Inferred income elasticities of demand

US

OECD

Non-OPEC LDCs

Market economies

Estimates from other studies

Crude oil

Gately-Rappoport (1988)"

Brown-Phillips (1989)b

Gasoline

Sterner and Dahl

(1990)"

Average model responses

l-yr lO-yr Long run/20-yr

0.87

0.88

0.78

0.72

0.60

1.13

0.45

0.85

0.86

0.88

0.81

0.86

0.88

0.92

0.85

1.31


EMF

high

GDP

(with flat prices) and flat price scenarios.

See

text

for derivation and qualification.

"Estimated from annual data,

1949-85.

Long-run income elasticity equals the first-year elasticity.

bEstimated from quarterly data,

1972:1-1988:1.

Long-run income elasticity equals the first-year

elasticity.

CSurvey of other studies, lagged endogenous model: see Table 5.1.



Elasticity estimates 253

FRB-Dallas, WOMS, BP-America, and ETA-MACROY The 20-year income

elasticities for the remaining models average 0.6 for both the OECD countries

and

market economies.

Most models with a unitary income elasticity also revealed a trend effect

towards declining oil intensity that is unrelated to changes in either past or

future oil prices or income.

12

These autonomous improvements in oil efficiency

incorporate the adoption of newer more energy-efficient technologies or

processes for reasons other than oil prices. In addition, the trend includes shifts

in the economic structure away from energy-intensive industries. As a result,

these models joined the group of models with income elasticities below one in

projecting oil demand to grow less rapidly than economic growth, even with

constant oil prices. Only FRB-Dallas and

HOMS

(both versions) incorporate

a unitary income elasticity without any autonomous improvements in oil

efficiency. These two models also indicate the highest demand projections in

the EMF scenarios specifying exogenous oil price-paths.

Finally, the first-year elasticities are virtually the same as the long-run or

20-year responses for all regions. The appendix reveals that most models follow

this trend of relatively constant income elasticities over time. Exceptions are

CERI and Penn-BU, in which

both

price and income responses become

stronger over timeY The SternerjDahl survey of gasoline demand (Chapter 5)

provides some evidence that income elasticities are larger in the long run than

the short run, although the 1.31 long-term estimate can be consistent with the

EMF-ll

estimates only if other petroleum products are quite income-inelas

tic.

14

The other two studies included in the bottom of Table 14.5 incorporate

an instantaneous adjustment to the long-run income elasticity. They reflect the

two alternative views depicted in the EMF estimates. The Gately-Rappoport

study (1988) reports income elasticities of about 0.6 while the Brown-Phillips

study (1989) estimates an elasticity that is

not

significantly different from unity.

14.3.4 Price elasticities

of

supply

Price elasticities of supply for the non-OPEC regions were calculated from the

rising and flat price scenarios in a procedure analogous to the one used for the

price elasticities of demand discussed previously. The percentage difference in

llETA-MACRO assumes an income elasticity of unity but did not run the scenarios that would

reveal an inferred income elasticity. Its responses are not reported in the appendix tables.

12This

information was ascertained by comparing two separate scenarios based upon the flat oil

price-path that

(a)

eliminated any economic growth, and

(b)

eliminated both any economic growth

and any time trend towards improved oil efficiency independent of oil prices.

13The Penn-BU results cause the average income elasticities for the market economies in Table

14.5

to rise slightly. This model did not report consumption for other regions. Table

14.5

is based

upon averages that exclude IPE and DFI-CEC in order to emphasize the time pattern of the

response. DFI-CEC did not report short-run results, and IPE's horizon extends only up to 2000.

14The

surveyed studies generally did not control for the number of drivers. Gately (1990) argues

that incorporating this effect would lower the income elasticity by nearly one-half.



254

Inferred

demand and

supply elasticities

Table 14.6 Inferred crude oil price elasticities of supplies

US

OECD

Non-OPEC

Total

Excluding US

Average

model

responses

l-yr lO-yr Long run/20-yr

0.05

0.05

0.03

0.Q2

0.24

0.25

0.21

0.20

0.40

0.43

0.40

0.38


EMF

rising and flat oil price scenarios. Mean response excludes

DFI-CEC, an intertemporal optimization model. See text for derivation and qualifications.

quantity produced between the two cases

is

divided by the percentage dif

ference in crude oil prices. Results for

1-,

10- and 20-year responses appear in

Table 14.6.

Price elasticities of supply begin a little lower than their demand counter

parts

15

(Table 14.4) but increase over time until the two elasticity estimates are

roughly comparable after 20 years. Long-run price elasticities of supply average

about 0.4 in each of several regions for which responses could be calculated.

Long-run responses for total non-OPEC production range from

0.16

(CERI)

to 0.64 (HOMS-l), as reported in the appendix. The pattern of the

OFI

supply

elasticity deserves special consideration. Suppliers

in

the model optimize

production over time to maximize discounted profits. In the rising price case,

suppliers have incentives to withhold production and extract oil in later years

when profits (after discounting) become more attractive.

As a result, this model

predicts less production in most regions for the rising than for the flat price

case in the early years and substantially greater production in the later years.

16

14.4 CONCLUSIONS

The EMF scenarios were designed to analyse international oil supply and

demand trends under alternative market conditions. While they were not

specified explicitly to reveal precise estimates of the relevant elasticities, the

scenarios do offer a unique opportunity to examine the approximate responses

embodied in some of the major world oil models used

for

policy and planning

purposes. This information

is

likely to be of considerable interest to policy

analysts and to other world oil modellers.

From this comparison of scenario results, we conclude that the average price

15This

would

be

explained by the findings of Rodriguez Podilla in Chapter 8.

16The model would view the assumed rising and flat oil price-paths

as

being dynamically

inconsistent because producers can earn a higher discounted profit in one time period than in

another. This factor explains the wide swings in production observed

for

this model in response to

the two exogenous oil price-paths.



References

255

elasticity

of

demand (measured at the crude oil level) in these models

is about

-0.1

in the short

run

(after the first year),

about -0.3

in the intermediate run

(after 10 years)

and about

-0.4

in the long run (after 20 years).

Most

long-run

estimates lie between - OJ and - 0.5, although several estimates fall either

above

or

below this range.

The evidence

on

income elasticities is far more diverse. For the most part,

the models incorporate the full demand adjustment to income within the first

year

of

a change in

GDP.

The average estimate of 0.8 for all models

is

deceiving. Half of the models anticipate no further improvements in oil

efficiency as the economy grows, unless oil prices move higher. This result

is

summarized by

an

inferred income elasticity of unity for these models. The

remaining models show improvements in oil efficiency resulting from future

economic growth, reflected by

an

inferred income elasticity

of about 0.6. In

addition, several models incorporate

an

autonomous long-run trend towards

oil-saving goods, technologies

and

processes, independent of price and income

changes. The income effect

and

the potential for autonomous energy efficiency

improvements are particularly fruitful areas for future research on energy

demand.

Like their demand counterparts, the price elasticities

of

supply outside

OPEC

increase over time as the full adjustment to price changes

is

incorpor

ated. The average crude oil price elasticity of supply

is

well below

0.1

in the

short run (after the first year),

about

0.2 in the intermediate run (after

10

years),

and about

0.4 in the long run (after 20 years). Most long-run estimates lie

between 0.2 and 0.5, although several estimates fall either above

or

below this

range.

ACKNOWLEDGEMENTS

I would like to acknowledge the significant contributions

of

the

EMF-l1

Working Group, chaired by

W.

David Montgomery, for improving my

understanding of certain key issues. I

am

also very grateful to those researchers

who exercised their models during the study. These individuals include

Nicholas Baldwin, Stephen P. A. Brown, Nazli Choucri,

Dermot

Gately,

William Hogan, Robert Kaufmann, Alan Manne, Dale Nesbitt, Anthony

Reinsch,

Mark

Rodekohr

and

Lakis Vouyoukas. Interpretations

and

con

clusions are entirely my own.

REFERENCES

Beider, P. (1982) 'Comparison of the EMF-6 Models', in World Oil, EMF Report 6,

vol.

2,

Stanford University, Energy Modeling Forum. Stanford, C, pp. 349-428.

Brown,

S. P. A.

and Phillips,

K. R. (1989)

An Econometric Analysis

of us

Oil Demand.

Research Department, Federal Reserve Bank of Dallas, No. 8901, January.



256

Inferred

demand

and

supply elasticities

EMF (Energy Modeling Forum)

(1990)

'International Oil Supplies and Demands'.

EMF

Report 11 (draft), Stanford University Energy Modelling Forum, Stanford,

C.

Gately, D. and Rappoport, P.

(1988)

The Adjustment of US Oil Demand to the Price

Increases of the

1970s.

Energy Journal,

9,

93-108.

Gately, D. (1990) The US Demand for Highway Travel and Motor Fuel. Energy

Journal, 11 59-73.

Huntington, H. G., Sweeney, 1. L. and Weyant, J. P. (1982) Modeling for Insights, Not

Numbers: The Experiences of the Energy Modeling Forum.

OMEGA:

The Interna

tional Journal

of

the Management Sciences, 10,449-62.

Huntington,

H.,

Kress A. and Robinson, D.

(1989)

EMF-ll

Revised Scenario Design.

Stanford University Energy Modeling Forum, Stanford, C.

Kress,

A.,

Robinson,

D.

and Ellis,

K. (1990)

Comparison of the Structure of Interna

tional Oil Models, (draft). Stanford University Energy Modeling Forum, Stanford,

C.

APPENDIX TABLES

Table 14A.l Price elasticity of demand inferred from rising and flat price cases

1st year 1995 2000 2005

2010

United States

OMS (EIA)

-0.071

-0.232

-0.283

-0.327 -0.327

Gately

-0.137 -0.146 -0.154

-0.163

-0.171

IPE

-0.039 -0.078

-0.084

ETA-MACRO

-0.870

-0.778

CERI -0.139 -0.296

-0.353

-0.419 -0.440

HOMS

-0.074 -0.162 -0.224 -0.287

-0.308

FRB-Dallas

-0.088 -0.323 -0.405 -0.502 -0.537

DFI-CEC

-0.177 -0.171 -0.186

-0.184

HOMS-I -0.140 -0.341 -0.456

-0.630 -0.737

Average

-0.098 -0.219 -0.333 -0.359 -0.436

OECD

OMS (EIA)

-0.130

-0.215 -0.285

-0.360 -0.396

Gately -0.137 -0.151

-0.160

-0.171 -0.181

IPE

-0.104

-0.161

-0.164

ETA-MACRO

-0.783

-0.761

CERI -0.164 -0.311 -0.370 -0.431

-0.446

HOMS

-0.111

-0.205 -0.269

-0.332 -0.354

FRB-Dallas -0.101

-0.326

-0.404 -0.498 -0.531

DFI-CEC

-0.217

-0.258 -0.338 -0.362

WOMS

-0.063

-0.179 -0.208 -0.366 -0.490

BP-America -0.034 -0.181 -0.317 -0.349 -0.368

HOMS-I

-0.205 -0.439 -0.547 -0.713 -0.804

Average

-0.117 -0.238 -0.342

-0.395

-0.469



Appendix tables

257

Table 14A.l-contd.

1st year

1995 2000 2005 2010

Non-OPEC LDCs

OMS (EIA)

-0.106

-0.096

-0.122

-0.170

-0.199

Gately

-0.104 -0.130 -0.144

-0.163 -0.178

IPE -0.144

-0.133 -0.153

CERI

-0.126 -0.306

-0.388

-0.494

-0.535

HOMS

-0.098 -0.193 -0.232

-0.292

-0.328

FRB-Dallas

-0.228 -0.318 -0.347

-0.386 -0.400

DFI-CEC

-0.098

-0.125 -0.175

-0.191

WOMS

-0.045

-0.071

-0.083

-0.138 -0.178

BP-America

-0.058

-0.181

-0.255 -0.329 -0.357

HOMS-I

-0.102 -0.192 -0.232

-0.291

-0.326

Average -0.112 -0.172

-0.208

-0.271

-0.299

Market Economies

OMS (EIA)

-0.094 -0.167 -0.224

-0.288

-0.318

Gately

-0.131

-0.139

-0.147

-0.158 -0.165

IPE

-0.104 -0.143

-0.148

Penn-BU

-0.013 -0.149

-0.158 -0.243 -0.313

CERI -0.151

-0.298

-0.360 -0.431

-0.450

HOMS

-0.098

-0.190 -0.244

-0.305 -0.329

FRB-Dallas

-0.139 -0.323

-0.388

-0.464 -0.490

DFI-CEC -0.181 .

-0.219

-0.288

-0.309

WOMS

-0.025

-0.149

-0.171 -0.299

-0.396

BP-America

-0.046 -0.181

-0.295 -0.342

-0.364

HOMS-I -0.179

-0.362 -0.450

-0.579

-0.648

Average

-0.098

-0.208 -0.255

-0.340 -0.378

FRB-Dallas, WOMS and BP-America did not report for non-OPEC LDCs. Their estimates

have been derived as the difference in the responses for the market economies and OECD.

DFI-CEC's demand response to price was calibrated to first-round OMS results in this study.

Estimate for 1st year is for the year in which the initial demand response is observed - 1990 for

OMS, Gately, IPE and BP-America, and 1989 for all others. ETA-MACRO's demand response

begins after 1990, i.e. in

1991,

but is reported for every ten years only. Non-OPEC LDC response

begins

in

1990 for WOMS.



Table 14A.2 Income elasticities of

demand

inferred from high

GDP

(with flat price)

and flat price cases

1st year

1995

2000

2005

2010

United States

OMS

(EIA) 0.601 0.711 0.731 0.756 0.769

Gately 0.875

0.918 0.936 0.944 0.946

IPE

1.199

1.048 0.972

CERI 0.626 0.493

0.462

0.503

0.486

HOMS 1.000 1.000 1.000

0.994

1.000

FRB-Dallas 1.099 0.968 0.972

0.975 0.991

DFI-CEC

0.646

0.693 0.723

0.627

HOMS-1 1.000 0.984 0.982 0.987 0.995

Average

0.914 0.846 0.844 0.840 0.831

ex.

IPE

and

DFI

0.867 0.846 0.847 0.860 0.864

OECD

OMS

(EIA)

0.801 0.604

0.568 0.594 0.593

Gately 0.751

0.764 0.774 0.782 0.798

IPE

1.397 1.140 1.079

CERI 0.376

0.371 0.403 0.440 0.458

HOMS 1.000 0.969

0.973 0.976 0.978

FRB-Dallas 1.000

0.984 0.974 0.982 0.996

DFI-CEC 0.572

0.605 0.645 0.559

WOMS 1.000 0.980

0.991 1.006

0.996

BP-America

1.111

1.249

1.248 1.257 1.262

HOMS-1

1.000

0.969 0.973

0.988 0.991

Average 0.937 0.860

0.859

0.852 0.848

ex.

IPE

and DFI 0.880 0.861 0.863

0.878 0.884

Non-OPEC LDCs

OMS (EIA) 0.694

0.508

0.560

0.570 0.598

Gately 0.834 0.849

0.891 0.916 0.944

IPE

1.221

0.989

0.854

CERI 0.770

0.478 0.610

0.649 0.710

HOMS

1.000

1.000 0.994 0.996

0.997

DFI-CEC

0.377 0.390

0.379 0.325

HOMS-1 1.000 1.000

0.994 0.996

0.997

FRB-Dallas

1.000

1.045 1.062

1.043 1.054

WOMS

0.701 1.020 0.991

0.976 1.021

BP-America 0.201 0.744

0.934 1.012 1.045

Average 0.825

0.801 0.828 0.838

0.855

ex.

IPE

and

DFI 0.775 0.830

0.880 0.895

0.921



Appendix

tables

259

Table

14A.2-contd.

1st year

1995 2000 2005

2010

Market Economies

OMS (EIA) 0.401 0.508 0.522 0.549

0.565

Gately 0.779 0.840 0.872 0.895 0.914

IPE 1.000 1.044

0.975

Penn-BU 0.223 0.395

0.337 0.378 0.400

CERI 0.446 0.417 0.517

0.575 0.620

HOMS

0.900 0.902

0.929 0.933 0.948

FRB-Dallas

1.000 1.029 1.033

1.043

1.055

DFI-CEC 0.523 0.557

0.572

0.495

WOMS

0.900 0.980 0.991 1.000 1.004

BP-America 0.889

1.101

1.188

1.221

1.240

HOMS-l

0.900 0.902 0.922 0.928

0.937

Average

0.744 0.786

0.804 0.809 0.818

ex. IPE and DFI 0.715

0.786

0.812

0.836 0.854

Responses were not reported for ETA-MACRO. Elasticity equals 1 by assumption.

Estimates are approximate due to rounding of results reported to

EMF staff. Estimated

elasticities in the range of 0.95 through 1.05 are not distinguishable from unity.

DFI-CEC's demand response to income was calibrated to first-round OMS results in this study.

Estimate for 1st year is for the year in which the initial demand response

is

observed - 1991 for

WOMS, 1990 for Gately, CERI and Penn-BU, and

1989

for all other models. This response was

not available for DFI-CEC, which reports every five years.

FRB-Dallas, WOMS and BP-America did not report for Non-OPEC LDCs. Their estimates

have been derived

as

the difference

in

the responses for the market economies and OECD.

Response in BP-America was to unexpected income growth; its response to expected income

growth is less than unity.



Appendix tables

261

Table 14A.3-contd.

1st year 1995 2000 2005 2010

Non-OPEC

ex US

OMS (EIA)

0.000

0.061 0.088

0.143 0.170

Gately

0.052 0.174 0.283 0.435 0.553

IPE

0.000

0.026 0.101

Penn-BU

0.000 0.106 0.151 0.197

0.200

CERI 0.000 0.098 0.132 0.152

0.144

HOMS

0.000 0.130 0.247

0.408 0.510

FRB-Dallas 0.013 0.123 0.224 0.374

0.480

DFI-CEC

-0.011

0.460 0.780

0.980

HOMS-I 0.076 0.260 0.377 0.537

0.633

Average

0.018 0.122 0.200 0.321 0.384

Averages exclude DFI-CEC, an intertemporal optimization model in which the rate of increase in

oil prices is critical to the observed supply response. First-year response is not reported for this

model because results are reported for 5-year periods.

Estimate for

1st

year

is

for the year

in

which the initial supply response is observed - 1990 for

OMS, Gately and BP-America, and

1989

for all others. ETA-MACRO's supply response begins

after 1990, i.e. in 1991, but is reported for every 10 years only.



264

World

oil market simulation

Nevertheless, the price reaction function is able to explain both the

1973/74

and 1979/80 price rises, whereas intertemporal optimization models can only

satisfactorily explain the

1973/74

price rise. (Both types of model have difficulty

with the 1986 price fall.) Criticisms have also been levelled against the assump

tion of perfect foresight and the neglect of uncertainty about other market

actors' behaviour within intertemporal optimization models.

3

This paper presents a recursive simulation model of the world oil market -

the World Oil Market Simulation Model (WOMS).4 The objective was to

construct a computationally simple model which provides a transparent view

of the workings of the oil market. In the event WOMS has a number of features

which distinguish it from other published models:

1. the effect of exchange rate movements is incorporated in the supply and

demand functions;

2. both demand and supply functions are dynamic;

3. the non-OPEC supply functions account for the geological as well as the

economic aspects of supply;

4. oil prices can be determined either by OPEC setting prices

(as

normally

included in this type of model) or by OPEC setting volumes and market

forces determining the price; and

5.

consistency checks on consumer's and producer's behaviour are incorpor

ated to confirm the plausibility of model projections.

This chapter commences with an outline of the model structure followed by an

examination of the choice of the appropriate data. The main sections of the

chapter discuss the estimation of the demand and non-OPEC supply functions.

Finally the modelling of OPEC's behaviour

is addressed. Comparisons are

made throughout with other published work. As the model

was

estimated

using data covering

1960

to

1985,

brief comments are also made comparing the

events of

1986

with model determined values.

15.2 MODEL STRUCTURE

The structure of the model is shown in Figure 15.1. Although seemingly

complex it can be considered simply

as

a game being played

by

three actors -

oil consumers, non-OPEC oil producers and OPEC - all of whom trade a

homogeneous commodity within a framework that both directs and constrains

play. The 'rules' of the game require both oil consumers and non-OPEC

producers to act as

price-takers, with consumers maximizing the benefits of

their expenditure on oil and non-OPEC producers maximizing their profits.

3S

ee

Gately (1984) who provides a succinct critique of intertemporal optimization models.

4In this context world refers to the World Outside Communist Areas (WOCA).



r

r

.

-

I

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1

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266

World


OPEC

set either price or volume depending on market circumstance.

5

The

behaviour of the actors

is

determined through three equations and an identity:

Oil demand

(d)

d

=

fn(GDP, lag GDP, price, exchange rates, lag demand, conservation)

Non-OPEC supply (s)

s

=

fn(reserves, time, price, lag price, exchange rates, lag supply CPE)

Market clearing

OPEC

supply

=

d - s

+ / -

stock adjustment

OPEC price reaction function

(price-lag price)/lag price

=

fn(OPEC supply/capacity)

(15.3)

(15.4)

where

'GDP' is

gross domestic product and 'CPE'

is

the net exports of crude

oil and petroleum products from the centrally planned economies.

In

addition to the basic 'rules' there are also a number of consistency checks

that can act as constraints on the actors' freedom of movement. Consumers'

efficiency of oil usage is examined using the oil ratio (i.e. oil demand per unit

of GDP). The ability of non-OPEC producers to stay in operation

is

examined

using reserve to production ratios. The requirement to increase oil revenues

whilst retaining control of the market provides the basis of the choices open to

OPEC

when setting either price or volume. A more detailed discussion of

OPEC's 'trade-offs' between price, volume, revenues and market share is

included in section 15.6.

15.3 DATA

The main source of oil supply and demand data used in the analysis was the

BP

Statistical Review of Wodd Energy. In places BP data was supplemented

with data from the

OPEC

Annual Statistical Bulletin, the US Energy Informa

tion Administration's Annual Energy Outlook and Monthly Energy Review, the

Oil and

Gas Journal and Petroleum Intelligence Weekly.

15.3.1 Real exchange rate adjusted crude oil prices

The dramatic appreciation of the US$ between

1980

and

1985

meant that

non-US consumers saw oil prices rise when measured in terms of their own

currencies, at the same time as US$ denominated prices were falling. This

factor has been recognized by Brown and Phillips

(1984),

Chevron

(1985),

EIA

(1985), Huntington (1986) and the International Energy Agency (1985),

amongst others.

5The assumptions about the behaviour of market actors are adapted from those given in the

documentation to the model used by the US Energy Information Administration

(1985).



Data

267

To account for this phenomenon a real oil price index covering the six main

developed nations was developed. The official OPEC price of crude oil was

converted from nominal to real terms using the US

GNP

deflator. This was

then converted into the real local currency unit price of crude oil in France,

Italy, Japan, the

UK

and West Germany, using real bilateral exchange rates.

Price indices, with a base of 1980, were calculated for each of the six nations.

These were weighted together using current consumption weights to produce

an aggregate Paasche

6

index (PI) as follows:

6 6

Pl

t

=

L {dit,[(Xit.P$tXiSO,P$SO)]} / L

d

it

i= 1 i= 1

(15.5)

where

X

is

the real exchange rate and

d

the oil demand of country i against

the US$ and P$ is the real oil price denominated in US$ per barrel. The suffixes

t

and 80

refer to time t

and

1980 respectively.

The aggregate index was named the

OECD

index. Two other indices were

also used depending on the area being analysed: one for the US$ price alone,

called the NA index; and the other for the five nations excluding the USA,

called the OECDXNA index.

All

three indices are shown in Figure 15.2

The six nations chosen for the index covered 60% of the oil consumption in

the World Outside Communist Areas (WOCA)

and

80% of the consumption

140

130

120

110

100

a

0

90

II

80

0

<Xl

OJ

70

x

60

Q)

0

50

E

40

30

20

10

0

1960 1965

1970

Weighted

average

of

OECD excluding

US

prices

Weighted

average

of OECD

prices

1975 1980

Fig. 15.2

Real oil price indices.

1985

6When using the model to make projections of future price the price index becomes a Laysperes

index using base year weights. This switch will undoubtedly introduce some bias into the result.

However, in this context it should be noted that the EMF-4 study (Energy Modeling Forum, 1980)

found that elasticity estimates were not sensitive to the choice of index.



268


in the Organization of Economic Cooperation and Development (OECD)

countries during

1985. It is

particularly worth noting that Canada was

intentionally excluded from the index because the C$/US$ exchange rate

remained constant in real terms over the 1980-3 period; its inclusion would

have biased the index downwards. The exclusion of Canada was broadly

counterbalanced by the exclusion of those West European countries that were

subject to depreciating exchange rates against the

US$.

15.3.2 Crude oil price

vs.

petroleum product price

In the world oil market producers are faced by crude oil prices, whereas

consumers are faced by petroleum product prices which include government

taxes, oil company profits, distribution costs etc. In a simple model it

is

easier

if only one price

is

used; and tests showed that crude oil prices were a

statistically acceptable proxy for product prices in the demand functions.

15.3.3 Economic data

Real national accounts measures of gross domestic product were used in the

analysis because they represent a more valid picture of the volume of economic

activity than aggregates based on present or past market exchange rates. They

also enable real prices of internationally traded commodities - some of which

like oil are subject to the 'law of one price' - to

be

compared between

countries. The

GDP

data were derived from work published by Kravis et

al.

(1980)

and by Summers and Heston

(1984)

as part of the International

Comparison Project.

7

Nominal exchange rate and

GDP

deflators were taken from the ,IMF

International Financial Statistics Yearbook (1986).

This allowed real exchange

rates to be calculated.

I t is

recognized that for total consistency of approach

the exchange rates should have been constructed taking account of purchasing

power parity. However, at the time the initial work

was

undertaken, such data

were not available; this limitation no longer applies and future work

will

address this problem.

15.4

DEMAND FUNCTION ESTIMATION

Numerous models of energy and oil demand have been proposed in the

literature. EMF-4

(1980)

and Bohi

(1982)

both provide a comprehensive

overview of the model types and the functional forms employed. The complex

ity of these models varies from the static, based solely on own price and

income, through to the dynamic taking account of various fuels

in different

7For more details see Roy (1987).



Demand

function estimation

269

sectors across many countries or regions. The results reported here are

confined to the estimation of a simple single

fuel

dynamic model for highly

aggregated world regions. In making this choice it was recognized that factors

such as interfuel and factor substitution, producer and consumer tax regimes,

etc were being treated implicitly rather than explicitly.

Two alternative approaches

were

followed when modelling the demand side

- these can be characterized as 'top-down' versus 'bottom-up'. The top-down

approach involved estimating the demand function for WOCA directly. For

the bottom-up approach separate demand functions were to be estimated for

North America, OECD excluding North America and the Less Developed

Countries using the price index appropriate

for

each region. The separate

demand functions could then be aggregated to provide WOCA demand.

Despite repeated attempts, however, it proved impossible to estimate any form

of satisfactory demand model for the LDCs. This difficulty, combined with

problems associated with the other disaggregated demand functions and the

superior statistical characteristics of the WOCA demand function, led to the

top-down approach being incorporated in WOMS.

As

a consistency check, a

hybrid top-downjbottom-up approach was followed to provide demand esti

mates for OECD in aggregate and the LDCs. Only the results of the

estimations for the WOCA and OECD demand functions are reported here.

A dynamic linear regression (DLR) model of oil demand can be expressed in

terms of both current and lagged income and price measures and lagged values

of oil demand:

n n

Dt=oc+

L

(Jlj I

t

-

j

+c5

j

PI

t

-J+

L

(Jj D

t

-

1

+e

t

(15.6)

j=O

j= 1

with all variables expressed in logarithms and where

I is

GDP,

PI is

the oil

price index,

D is

oil demand and

e

is

the error term. In order to preserve

sufficient degrees of freedom during estimation, the value of

n

was limited to 2

in this exercise.

According to Spanos

(1986)

a DLR model with this functional form

encompasses a large proportion of the empirical econometric models in the

literature. Drawing on the work of Hendry and Richards it

is

possible to

identify at least nine special cases of this DLR depending on which parameter

constraints are imposed. Included with these special cases are the finite

distributed lag model, the partial adjustment model and the autoregressive of

order one model.

Following the imposition of various parameter constraints and the applica

tion of various statistical tests, it was decided that the most appropriate

statistical model to

use was:

(15.7)

BCited in Spanos (1986).



270

World oil

market simulation

which can

be

rearranged to

give:

R

t

=

/30 - /33' PIt + 32' R

t

-

1

+e

t

(15.8)

where

R is

the oil demand:

GDP

ratio (DjI) or 'oil ratio'.

This functional form can be interpreted as either a partial adjustment model

or the Koyck transformation of the finite distributed lag model. The only

difference between the two forms is the structure of the residuals. In the partial

adjustment model the residuals are serially independent, whereas in the Koyck

transformation of the distributed lag model they are serially correlated. This

distinction

is

important because it leads to both different estimation techniques

and a different interpretation of the results.

The models were estimated using ordinary least squares regression and

rigorously statistically tested with particular attention being paid to first and

higher order serial correlation. In models with a lagged dependent variable the

Durbin-Watson statistic

is

not an appropriate test for first order serial

correlation as the results

will

be biased. Instead, a small sample version of the

Lagrange multiplier test

was

used to demonstrate whether serial correlation

was present.

I t

was found that the residuals,

e,

were serially independent, thus

confirming that the demand function represented a partial adjustment process.

The models were also found to

be

structurally stable over the period

1960

to

1985. The results of the estimation and testing process are given in Table

15.1.

Having decided to

use

a partial adjustment model, it was important to

understand the implications. As commonly reported in the literature partial

adjustment models are based on the idea that consumers aim at some desired

Table 15.1 Demand side estimation results for the period 1961-1985

WOCA demand

model

D

t

= 1.0G

t

-0.960G

t

_

1

+0.960D

t

_

1

-0.040P

t

+0.140

(36.028) (-11.007) (10.157)

iP=0.987; DW=1.974; LM=3.217-F(1,

20);

SE=0.014;

SSR = 0.0042; BP=6.014-xi; ARCH=0.009-xi;

GQ=0.088-F(9, 10); C=0.782-F(3,

19);

W=212-xi.

OECD demand model

D

t

=

1.0G

t

-0.976G

t

_

1

+0.976D

t

_

1

-0.046P

t

+0.157

(35.284) (-10.297) (9.407)

iP=0.986; DW=1.723; LM=0.376-F(1, 20); SE=0.017;

SSR=0.0062;

BP=5.793-xi;

ARCH=0.066-xi;

GQ=0.089-F(9,

10); C=0.757-F(3, 19); W=105-xi

D,

oil demand;

G,

GDP;

P,

real oil price.

Notation for test statistics: DW, Durbin-Watson; LM, Lagrange multiplier for first order serial

correlation; GQ, Goldfeld Quant for constant variance; C, Chow for parameter time invariance;

BP, Breusch Pagan and ARCH for heteroscadasticity; W, Wald for common factor restrictions



Demand

function estimation

271

or target level of demand; but due to a lack of perfect information and

structural rigidities they can only adjust towards this target gradually. How

ever, the existence of a perceived target level to be achieved in the long run

gives the partial adjustment model of consumer behaviour a limited degree of

foresight. Admittedly this foresight is based on past and present information

but it does imply some direction to consumers' decisions.

Taking R ~ as the target or desired level of the oil ratio as seen at time

t

and

R

t

as the actual level at time

t,

it can be demonstrated mathematically that if:

(15.9)

where r is the adjustment factor such that 0 < r 1 and if the target level is a

function of price:

R ~ = ( I . + 8 · P t

then combining equations

(15.9)

and

(15.10)

and rearranging:

R

t

=r·(I. +r ' 8 ·

PI

+( I - r ) ' R

t

-

1

+ e

t

and hence comparing equations

(15.8)

and

(15.11) gives:

f3o=r·(I.;

f33= - r '8 ; f32=(1-r)

which yields a long-run equilibrium price elasticity

of:

8= -/33/(1-f32)

(15.10)

(15.11)

The characteristics of the demand functions are given in Table 15.2. The short

and long-run income elasticities are

1.0

as a result of the statistical tests for the

parameter constraints. Income elasticities of

1.0

are not surprising and have

been commonly reported in the literature: e.g. Adelman

(1980),

EMF-6 (EMF,

1982)

and Gately

(1983).

The price elasticities reported are short-run and

long-run equilibrium elasticities.

As

a way of making comparisons with other

published estimates, price elasticities were calculated after specific time inter

vals and these are shown in Table 15.3.

The fact that the

OECD

price elasticities are larger than the comparable

values for WOCA is consistent with the large reduction in OECD oil demand

Table

15.2 Demand

function

characteristics

Elasticities

Short-run

income

Price

Long-run

income

Price

Median lag

Adjustment factor

'l:

WOCA

1.0

-0.040

1.0

-1.000

17 yr

0.040

OECD

1.0

-0.045

1.0

-1.875

29 yr

0.024



272 World

oil

market

simulation

Table

15.3

Price elasticities at discrete time in

tervals

Region

WOCA

OECD

10 years 20 years

25

years

-0.335 -0.558 -0.640

-0.404 -0.722 -1.022

since the peak year of 1979. Between

1979

and

1985

OECD demand fell

7.2 bbl x 10

6

jday whilst WOCA demand fell

by

5.8 bbl x 10

6

jday. This indicates

that demand growth in the LDCs partially offset the fall in demand in OECD.

Three comparable estimates of price elasticities for the WOCA region found

in the literature are shown in Table 15.4. Clearly the WOMS demand function

for WOCA has a smaller short-run price elasticity than other models. However,

the events of 1986 do appear to confirm that WOCA oil demand

is

indeed

inelastic in the short run. The estimate of additional WOCA oil demand during

1986

calculated by WOMS is 1.4 bbl x 10

6

jday which corresponds closely with

the actual outturn of

1.2

bbl x 10

6

jday.9 The 20-25-year elasticities show that

the WOMS price elasticities are broadly similar to those of others.

The median lag reported is a measure of the time taken for half of the

adjustment to occur in response to a change in price. This

is

sometimes referred

to as the 'half-life' of the demand response. As a first approximation it

represents half the time it takes for the entire capitaJ stock of the economy to

be replaced; it can also be taken as half the time needed to reach equilibrium.

The half-lives cited are longer than usually reported, but it

is

difficult to find

rigorous defences of shorter time periods. Adelman (1980) uses 10 years by

assumption and Huntington (1987) asserts that

17

years is too long. Hogan and

Leiby

(1985)

rely on their 'judgement' when imposing capital stock turnover

times that imply half-lives between 2 and

13.5

years. Daly, et

al.

(1982) also

make an assumption based on capital stock turnover times which leads to a

half-life of

6.5

years. These results (particularly for the WOCA model) compare

Table 15.4

Comparison of price elasticities for WOCA region

Model Short run

10 years 20 years

25

years

WOCA

WOMS (1987) -0.040

-0.335 -0.558 -0.640

Morrison

-0.065 -0.35

-0.60

n.a.

EMF-6 (1982) n.a.

n.a. n.a.

-0.60

Sweeny

-0.09

n.a.

n.a.

-0.60

and Boskin (1985)

9BP gives WOCA demand in

1985

as

45.27

bbl x

10

6

/day and in

1986

as

46.44

bbl x

10

6

/day.



Non-OPEC

supply

function estimation 273

favourably with the

view

given in EMF-4 (EMF,

1980)

that 25-35 years

approximates to the time needed to reach equilibrium.

The adjustment factor (r) also gives an indication of the rate of capital stock

turnover.

It

can

be

taken

as

an approximation to the net amount of

new

or

replacement energy consuming capital stock installed each year. The results

suggest that 4.0% of WOCA and 2.5% of OECD stock are either newly

installed or replaced each year. This compares with average real

GDP

growth

rates between 1960 and 1985 of 4.2% and 3,9% per annum in WOCA and

OECD respectively. The difference in capital stock turnover times between

WOCA and OECD can be explained

by

the replacement of existing stock and

the addition of new stock from the LDCs within WOCA; whereas OECD

is

primarily replacing existing capital stock. Again it

is

difficult to find compar

able estimates that are rigorously defended. The report of

EMF

4

stated that

'since the fraction of capital stock newly installed each year is small - certainly

less than

0.25 (i.e.

25%) and probably

less

than

0.1 (i.e.

10%) - the one year

adjustment parameter can be expected to be correspondingly small' (EMF,

1980).

The results confirm the 'probably

less

than 0.1' element of this statement.

One way chosen to assess the validity of the results was to construct

'backcasts' of the past. These backcasts used actual data for price, income and

demand for

1960,

together with the actual data for price and income in

subsequent years and the fitted values for demand. The backcasts indicate that

the WOCA model has a mean absolute error (MAE) of

0.5

bbl x

10

6

/day within

the range of a maximum error of

1.7

bbl x 10

6

/day and a minimum of

0.0

bbl x 10

6

/day. This

gives

a mean absolute percentage error (MAPE) of 1.2%

for the WOCA model when operating between

1961

and

1985.

The comparable

values for the OECD model are a MAE of 0.8 bbl x 10

6

/day within the range

0.1-2.7 bbl x 106/day and a MAPE of 2.5%.

It was

recognized that there

is

potentially an asymmetric demand response

to falling prices compared with rising prices: put another

way,

although

demand has fallen in response to sharply rising prices it will not necessarily

respond in an equal and opposite way to rapidly falling prices. Accordingly, a

time trend was included to test the hypothesis that a structural change within

the world economy could account for this type of response. The trend was not

found to

be

statistically significant. Hence, the demand functions presented

here incorporate a symmetric response to price movements; but because of the

great uncertainty surrounding this issue, the overall model was structured to

allow exogenous assumptions to

be

made on structural change. This question

will be

re-examined when more post-1986 data are available.

15.5

NON-OPEC SUPPLY FUNCTION ESTIMATION

Non-OPEC supplies are taken to encompass all non-OPEC oil producers in

WOCA plus the net exports from the centrally planned economies (CPE). The



Non-OPEC

supply

function estimation

275

way to employ a time trend in this analysis reference was made to the work of

Hubbert

(1979)Y

Hubbert's work on US crude oil reserves postulated that cumulative oil

discoveries would follow a logistic curve. During the initial phase cumulative

discoveries rise only slowly because knowledge of the underlying geology is

poor, exploratory technology

is

rudimentary and demand for oil

is

low.

As

geological understanding grows and technology improves, cumulative discove

ries rise at an exponential rate with most of the large fields in the province

being found in this period. After the largest fields have been discovered,

increasingly sophisticated and expensive techniques are employed to find the

remaining fields which have often been missed in earlier exploration. In this

final period, the rate at which cumulative discoveries are made approaches the

ultimate discovery level asymptotically.

This pattern for cumulative discoveries can be used to describe the rate of

oil production. Essentially the cumulative production curve is the same as the

cumulative discovery curve, but shifted to allow for the time needed to develop

fields.

The annual rate of production is the first derivative of the cumulative

production curve. This produces a bell-shaped curve which represents the

geological determinants of oil production; and it

is

this curve which was used

as the appropriate time trend for the analysis.

Theoretically the correct approach should have been to determine the

appropriate logistic curve for cumulative discoveries, lag this by a predeter

mined time interval and then take the first derivative to determine the annual

supply curve. In practice this approach presents a number of problems. First,

there was the question of what lag to

use

between the discovery and production

curves. Second, the data concerning discoveries was limited to annual levels of

proven reserves, and therefore to annual additions to reserves rather than

discoveries. Although this was

considered an acceptable proxy another prob

lem was encountered using this data. The published level of proven reserves

shows considerable variations with time as reserves are re-evaluated, account

ancy practices change or nations try to change their international credit rating.

In addition, as noted by BP (1979) and emphasized by Odell and Rosing (1980),

true levels of proven reserves at a given time can often be determined only 20

or 30 years after the initial assessment.

To resolve these problems supply, rather than reserve, data were used to

provide the necessary logistic curve, which thereby also provided the annual

production curve directly. Using the level of the ultimate oil resource derived

from the supply data it was then possible to estimate the cumulative reserve

curve. From this, identities were used to derive the annual level of proven

reserves and reserve production ratios as consistency checks.

"The inspiration to use Hubbert's work came from a report

by

Chase Econometrics

(1986).



276


The logistic curve for cumulative supply is given by:

CS

t

= CS*/[1 +A "exp( -t/(X)]

(15.12)

where CSt

is

the cumulative supply at time

t,

CS* is the ultimate recoverable

supply of oil and A and

X

are both constants.

The cumulative supply logistic curve was estimated in the form:

(15.13)

where Po

=

InA; P = -1/(X and

e

is

the error term.

The geological annual production curve

is

given by the first differential of

the logistic curve:

dCS

t

GSt=Tt =CSt-CS

t

-

1

(15.14)

where

GS

is the geologically determined supply.

Following the same procedure as for the demand side estimation, the

economically determined supply can be expressed in terms of a dynamic linear

regression model. This includes current and lagged values of geological supply

and price, and lagged values of economic supply as follows:

n n

St=(X+

L

(J,tj"GS

t-

1

+bj PIt- j

)+

L

O j St-j+et

(15.15)

j=O

i = l

where S

is

economically determined supply,

GS

is as above, PI

is

the oil price

and

e

the error term. Estimations were conducted using

n

= 3 to preserve

degrees of freedom.

The cumulative discovery logistic curve was estimated in the form

(15.16)

where CS*

is

determined from equation (13), CD

is

cumulative discoveries (as

proxied by additions to proven reserves) and the other terms are as above.

The proven reserves,

R"

were determined from the identity:

(15.17)

t= 1

When estimating the logistic curves for cumulative supply and cumulative

discoveries it was necessary to have estimates of cumulative supply before 1960.

Data from Jenkins

(1985)

and Grossling and Neilsen (1985) were used to

provide estimates of cumulative production of

64

bbl x

10

9

in North America

and 8 bbl x 10

9

in the ONO nations prior to 1960.

The logistic curves were estimated using a grid search for the value of CS*

which minimized the sum of square residuals of the regression. This approach

worked successfully for the North American data where the level of discoveries

is

already past the half-way point to finding the ultimate level of reserves. The



Non-OPEC supply function estimation

277

ultimate level of oil reserves in North America was estimated as 256 bbl x

10

9

by this procedure.

However,

it

was less successful in the

ONO

region where discoveries and

supply are still on the rising part of the respective logistic curves. In this case

it was not possible to find a

level

of CS* which minimized the sum of squared

residuals. Accordingly two alternative approaches were adopted. The first used

the Weyant and Kline (1982) estimate of total level of non-OPEC ultimate

reserves at

500

bbl x 10

9

and then subtracted the North American level of

256

bbl x 10

9

to give a rounded estimate of

250

bbl x 10

9

for the ONO region.

The second approach involved constructing a logistic curve for Non-OPEC in

aggregate which was found to minimize the sum of squared residuals at

650 bbl x

10

9 .

By

differencing the Non-OPEC and North American estimates

it was possible to arrive at a second estimate of the ultimate level of reserves

in the ONO region of 400 bbl x 10

9

• The results of the estimations of the

various logistic curves are shown in Table 15.5.

Table 15.5 Logistic curve estimation results, 1960-1985

NA cumulative supply

In[(256

CS)

-

1] = - 0.0704t

+

1.044

( - 366.65) (373.05)

iF

=0.9998; DW=0.261; SE=0.0073; SSR=O.0013

NA cumulative reserve additions

In([256

CR]-I)=

-0.0724t+0.3413

( - 366.65) (373.05)

iF

=

0.998; DW

= 0.62;

SE

=

0.057;

SSR = 0.0786

ONO

cumulative supply

(250

bbl x 10

9

ultimate reserves)

In([250 CS] -1)= -0.0858t + 3.475

( -

57.86) (160.723)

iF

=0.993; DW=0.105; SE=0.056; SSR=O.077

ONO cumulative reserve additions (250 bbl x 10

9

ultimate reserves)

In([250

CR]-I)=

-0.135t+2.705

(-79.29) (108.68)

iF = 0.996; DW = 1.458; SE = 0.065; SSR = 0.102

ONO

cumulative supply

(400

bbl x 10

9

ultimate reserves)

In([ 400 CS]

-

1) = - 0.0825t

+3.945

(-65.84) (215.89)

iF =0.994; DW=0.11; SE=0.048; SSR=0.055

ONO cumulative reserve additions (400 bbl x 10

9

ultimate reserves)

In([400 CR]

-1)=

-0.116t+ 3.115

(-66.21) (122.26)

iF =0.994; DW=1.005; SE=0.067; SSR=O.l07

For

notation, see Table

15.1.



278

World

oil

market simulation

When considering these estimates of the ultimate level of the oil resource in

the non-OPEC region it is important to note that these are the implicit levels

being assumed by the producers of oil derived from their production behav

iour; that is, oil producers are supplying oil as if they believe the ultimate level

of the North American oil resource

is 256 bbl x 10

9

or for the

ONO

region is

in the range 250-400 bbl x

10

9

. It is not intended as an estimate of the resource

base derived from a geological analysis of the regions.

Comparable estimates of non-OPEC ultimate reserves were not readily

available from the literature, because estimates are usually provided by

geographical regions rather than by producer groupings. However, it was

possible to make comparisons with estimates for North America. These

estimates of ultimate crude oil reserves are given by Nehring

(1978),

Master

et

al.

(1987)

and for the lower

48

states of America by Hubbert (1979). Remem

bering that the WOMS estimates include NGLs as well as crude oil, a

comparison of estimates is given in Table

15.6.

The approach used to estimate the economically determined supply function

was the same as that used for the demand functions. A generalized model was

postulated, parameter constraints imposed and tested, the error term examined

and the model tested for structural stability. The results of this estimation

process are shown in Table 15.7.

This process revealed that the supply functions for both regions were

unstable either side of the

1973

oil price rise. Between 1960 and 1973 supply

was independent of the geological production curve and negatively correlated

with price. For this period a simple partial adjustment model based on lagged

supply and current prices adequately reflected supply behaviour. This suggests

that prior to 1973 supply was demand rather than geologically or price-driven.

The opposite applies post 1973, when supply is driven by geology and price

with the model based on geology, current price and price-lagged 3 years.

The divergence in behaviour either side of

1973

is not a surprising finding.

During the 1960s oil demand was growing at a rate of some 7% per annum

whilst prices declined in real terms; and oil supplies were developed according

to lowest cost of production. From 1973 onwards

OPEC

set prices and from

1982 onwards also controlled production to defend those prices. Prices have

Table 15.6 North American reserve estimates

Hubbert

(1979)'

Masters

et at

(1987)

WOMS

Nehring (1978)b

'Crude oil in lower

48

states

0[- USA.

bCrude oil in Canada. Mexico and

USA.

170

242.5

256

289-380



Non-OPEC supply function estimation

279

Table

15.7 Non-OPEC supply estimation results for each region divided 1961-1973

and 1974-1985

NA supply (1961-73)

St=0.914S

t

_

1

-0.061Pl

t

+0.58

(30.44)

( -

3.12) (3.56)

iF =0.99; DW=2.62;

L M = 1 . 1 2 ~ F ( 1 . 8 ) ;

SE=O.013; SSR=O.0017

NA supply (1974-85)

St

= 1.66GS

t

-1.66GS

t

_

1

+ St-l +0.052Pl

t

_

3

-0.191

(2.026) (5.219)

( -

5.82)

iF =0.78; DW=2.42; L M = 0 . 3 9 2 ~ F ( 1 . 7 ) ; SE=0.014; SSR=0.178.

ONO

supply (1961-73)

St

=0.924St_

1

-0.301Pl

t+

1.199

(28.05) ( - 3.52) (3.91)

iF =0.989; DW =2.192; LM =0.118 F(1.8); SE=0.046;

SSR

=0.021.

ONO

suppy (1974-85)

(250 bbl x 10

9

ultimate reserves)

St = 1.345GS

t

+0.149Pl

t

+O.lOPl

t

_

3

-2.695

(10.245) (2.786) (3.073) ( - 8.215)

iF =

0.995;

DW =

1.43; SE

=

0.026; SSR

= 0.0058.

ONO

supply (1974-85)

(400

bbl x

10

9

ultimate reserves)

St = 1.178GS

t

+0.147Pl

t

+0.117Pl

t

-

3

+ 1.983

(7.255) (1.961) (2.668) ( - 5.332)

iF =0.991; DW=1.29; SE=0.037; SSR=0.012.

S, annual supply; GS, geological supply; PI, oil price index. For other notation see Table 15.1.

been at levels considerably higher than the cost of production that would have

been incurred if oil reserves had been developed rationally. Non-OPEC

producers have responded to higher prices by developing resources previously

considered supra-marginal. With OPEC prepared to limit output non-OPEC

producers have been able to sell all the oil that they could produce subject to

the limitations of geology and profitability.

Following the procedure adopted for the demand side estimation, 'backcasts'

were again constructed to examine the accuracy of the various supply models.

Because the supply models were found to be structurally unstable either side

of 1973 the backcasts were constructed using the appropriate model for each

period to give a combined back cast for the entire period 1961-85.

For

North

America the mean absolute error (MAE) of the backcast was

0.1

bbl x 106/day

within the range 0.0-0.3 bbl x

10

6

/day. This gives a mean absolute percentage

error (MAPE) of 0.9. For the ONO region based on an ultimate resource of

250 bbl x 10

9

% the comparable figure was a MAE of

0.1

bbl x 10

6

/day within

the range of 0.0-0.7 bbl x 10

6

/day and a MAPE of 2.5%. Slightly different

results were obtained from the ONO model with an ultimate resource of



280


400 bbl X 10

9

• Here the MAE was 0.1 bbl x 10

6

/day within the range of 0.0-

0.6

bbl x

10

6

/day giving a MAPE of 2.5%.

It

is

particularly worth noting that the North American supply function does

not include a current year price term; the analysis suggested that only 3-year

lagged price effects were statistically significant. The experience of 1986

indicates that current year prices are important. According to BP, North

American production fell

by

some 0.3 bbl x 10

6

/day during 1986 whereas

calcultions with the North American supply function show production only

marginally below the 1985 level. At the same time the

ONO

demand function,

which does include current as well as lagged prices, shows a

fall

of almost

1 bbl x 10

6

/day in

1986,

when BP shows it actually increased 0.2 bbl x 10

6

/day.

This issue

will

be re-examined as more data becomes available.

15.6 STRATEGIES FOR OPEC

Analysis of the econometrically estimated functions shows that supply and

demand can be brought into balance for a range of mutually dependent values

of oil price and OPEC output. This allows OPEC to adopt one of two

strategies: e i t ~ e r it can set price, in which case its output

is

constrained; or it

can set output, in which case the price

is

constrained. This section addresses

OPEC's behaviour in either case and proposes a framework for assessing the

appropriate strategy.

15.6.1 OPEC sets

price

Prices can be set either exogenously or endogenously using an empirically

derived price reaction function. As the first method

is

self explanatory only the

second method will be discussed.

Whilst there

is

a lack of consensus about non-OPEC supply models there

is

better agreement on an appropriate model to simulate OPEC's price-setting

behaviour. Most models reported in the literature are similar to that used by

the EIA

(1985),

which is based on the view that OPEC sets (official) prices so

as to achieve an optimum level of capacity utilization. This

is

modelled using

a price reaction function in the form:

(15.18)

where PS

is

US$ the oil price and

V

is the level of capacity utilization (i.e.

production/maximum sustainable capacity).

The price reaction function was estimated using the EIA data of maximum

sustainable capacity; it proved difficult to achieve a good fit with other data.

The estimation results are shown in Table

15.8.

The estimated reaction

function is compared with the data

in

Figure 15.3. Although the statistical

characteristics of this estimation are not wholly acceptable, this function does



282


prices were being determined by fluctuations in the crude and petroleum

product spot markets. This was not the first time, since OPEC took control of

the market in

1973,

that spot markets have effectively determined official prices.

Observation shows that during 1979-81 spot prices led official prices by one

or

two quarters. Verleger

(1982)

has demonstrated a statistical relationship

between spot and official prices between

1975

and 1980.

Clearly an alternative price-setting mechanism was required to account for

price excursions. It was assumed therefore, that sudden price excursions

occurred when OPEC either constrained output or attempted to flood the

market.

13

The mechanism adopted to simulate price excursions uses an

exogenously set OPEC production level and then iteratively solves for a

market clearing price to balance overall supply and demand. In effect a price

is found which stimulates demand and constrains non-OPEC production, or

vice

versa,

to bring the market into balance. The inherent short-run price

inelasticity of both demand and non-OPEC supply ensure that quite small

changes in OPEC output lead to large price movements.

15.6.3 OPEC trade-offs

A framework

is

required to test the success of various strategies for OPEC.

Their concern to increase revenues

is

self-evident; but their concern about

market share has emerged in recent years and was most explicitly stated in the

so-called 'market share' policy adopted

at

OPEC's December 1985 meeting

which led directly to the 1986 price collapse.

14

Market share in this context

was defined in terms of OPEC output as a percentage of WOCA oil supply.

(OPEC's market share fell from

60%

in

1979

to

less

than

40%

in 1985.)

Taking revenues and market share

as

the two key indicators, two comple

mentary measures of success or failure were developed. The first looks at the

trade-off OPEC makes between price and volume to achieve revenue, and the

second plots the trade-off between revenue and market share.

The price/volume trade-off was taken from Gately (1986) and is similar to a

method used by Shell

(1986)

for demonstrating what is described as 'OPEC's

dilemma'. Oil export revenues are the product of price and export levels - a

given level of revenues can be achieved by a variety of combinations of price

and volume.

By

plotting the locus of intersection with revenue isoquants

through time, it

is

possible to see how successful OPEC

is

in choosing the

13The

US Department of Energy

(1987)

provides evidence that during the 1973/74 Arab oil

embargo some 1.5 bbl x 10

6

/day was withdrawn from the market over a 6-month period and that

during the Iranian Revolution in 1978/79 an average of some 2.5 bbl x 10

6

/day was withdrawn for

a similar period. During 1986

OPEC

successfully increased output by 2.2 bbl x

10

6

/day. Calcula

tions with WOMS suggest that this is some 1.5 bbl x 10

6

/day more than they would have managed

if prices had remained constant in normal terms at the 1985 level.

14The communique issued at the end of the OPEC meeting of 7-9 December 1985 stated 'the

conference decided to secure and defend a fair share

in

the world oil market consistent with

necessary income for member countries development' (emphasis added). Cited

in

Petroleum

Intelligence Weekly, 16

December 1985.



Acknowledgements

283

Constant

40

300 400

revenue

isoquants

(i)

35

j

Q) 50

1981

1980

C.

30

0

co

( j )

25

.0

.0

~

20

Q)

Ll

15

0

co

10

Q)

0:

5

1963

1969

0

8

12 16 20

24

28

32 36

OPEC exports (million bbl/day)

Fig.

15.4 OPEC trade-off

No. 1.

correct

mix

of price and volume. This trade-off can be seen in Figure

15.4.

The second trade-off plots the locus of intersection with OPEC 'utility'

isoquants through timeY The term 'utility' is taken to represent OPEC's set

of preferences at a point in time and is defined as the product of export revenue

and market share. As with the first trade-off, OPEC are faced with the need to

choose the appropriate combination of revenues and market share which

satisfies their implicit utility function. The second trade-off

is

shown in Figure

15.5.

Both trade-offs are consistent with Gately's view

(1984),

mentioned previous

ly, that OPEC 'is groping toward an unknowable "optimal" price-path'. The

loci shown in Figures 15.4 and

15.5

seem to indicate that

OPEC is

learning

from its efforts to manipulate the oil market. Indeed, it seems plausible that

OPEC will be able to learn enough about the adjustment processes at work

on both the supply and demand sides to gradually move towards some

'optimal' level of revenues and market share.

ACKNOWLEDGEMENTS

The authors are grateful for the comments they received from Robert Bacon,

Dermot Gately and Aris Spanos on the model and on earlier drafts of this

15The idea for this trade-off came from

Dr

R. W. Bacon and the Oxford Institute for Energy

Studies.



284

280

~

260

c:

.12

240

1i

220

<I>-

I f )

200

::J

0

180

co

OJ

160

~

(/)

140

0

c.

120

Q)

'0

100

'0

80

Q)

60

J

iii

40

20

World


150

Constant

'utility'

isoquants

\

1963

19651967 1969

1971

1964

1966 1968 1970

36 38 40 42 44 46 48 50 52 54 56 58

60 62 64

66

OPEC

market share

(%)

Fig. 15.5 OPEC trade·off No.2.

paper

which

has

earlier appeared in

Energy

Economics, July 1988.

They

also

acknowledge

the

help and

comments

they have received from

their

colleagues.

The views expressed

in

this

paper are those of the authors alone and

do not

necessarily reflect those of Power-Gen and National Power.

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16

International

Energy Workshop

projections

*

Alan

S.

Manne and Leo Schrattenholzer

The International Energy Workshop (lEW)

is

a loosely organized group of

energy analysts who all produce and/or make

use

of energy projections (see the

OPEC

Review

Autumn

1986

and Spring

1988

issues for earlier presentations).

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Applied Systems Analysis (IIASA), the lEW collects energy projections submit

ted by its members from all over the world on a standard poll form. These poll

responses include projections of the international crude oil price, gross national

product growth, electricity generation, primary energy consumption, produc

tion and trade.

For

reasons of comparability, the responses are grouped into

the following regions:

Soviet Union and Eastern Europe (SU/EE)

China

OECD

OPEC

Non-OPEC developing countries (NODC)

Market economies, subtotal

World, total

Individual countries and other regions.

The poll responses are collected continually, and they are published semi

annually. The publications normally include only those responses that are no

more than 3 years old. Another recurrent lEW activity

is

the organization of

annual meetings at which the

lEW

poll results are selected and focal themes

discussed. This chapter reports on the June

1989

meeting at the IIASA,

attended by some 120 participants. In additon to a presentation of the poll

results, this report gives an overview of the topics presented at that meeting.

The original idea behind the

lEW

was to learn from the diversity of different

*An earlier version of this chapter has appeared in the OPEC Review, Winter

1989

issue.



288

International Energy

Workshop projections

projections for the same item, the diversity stemming, of course, from the

different views taken by the individual authors. Due to the continued cooper

ation of the

lEW

members, this diversity in space can now be extended by a

diversity in time,

i.e.

by different views taken at different times. Moreover, what

used to be a medium-term projection in one year becomes short-term in

subsequent years. Due to its neutral position as an observer, the

lEW

thus

provides a valuable record of forecasting history. Since it would be inappro

priate to classify projections as wrong or right, we shall instead speak of the

stability of forecasts (over time) and the degree of agreement between forecas

ters (at a given point in time). We shall present the lEW poll results in the light

of these two criteria, permitting the reader to judge how easy it was to foresee

the individual items.

16.1 THE

INTERNATIONAL PRICE OF CRUDE

Figure

16.1

shows the histogram of several recent projections of oil prices. Each

poll response is marked by an asterisk, except that the median response is

marked by an 'M' (in the case of an even number of responses, the median lies

between the two responses marked with 'M'). The medians for the years 1990

and 2000 are $19 per barrel and $26jbl, corresponding to an average annual

growth rate of almost

3.2

per cent during the 1990s. The implicit annual growth

for the following decade is more than 4%, leading to a median price projection

of just under $40/bbl for 2010 (all prices are expressed in terms of constant

dollars of 1985 purchasing power).

$/b (1985)

~ r - - - - r - - - - - - - - - - - - - - - - ~ - - - - - - - - - - - - - - - - - T - - - - - -

40

1----+------------------+-----------------+

..

M -

M

3 2 ~ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

·MM· ·

24

I - - - - + - - - - - - - - - - - - - - - - - - + - - - - - - - - - - - - - - - - ~ - - - - - -

16 I - - - - - ~ ~ - - - - - - - - - - - - - ~ - - - - - - - - - - - - - - ~ - - - - - -

8 ~ - - ~ - - - - - - - - - - - - - - - - ~ - - - - - - - - - - - - - - - - ~ - - - - - -

1990

Medians: 19.00

2000

26.05

2010

39.35

Fig. 16.1 Oil price projections: 1988 responses only.



The international price

of crude

289

We regard the median as the most informative single number to describe

more than one poll response. A second informative statistic is the range.

For

the oil price projections in

20()(),

the range stretches from

$15fbl

to $45/bl. A

statistical measure of this range

is

the standard deviation, calculated under the

assumption that the responses are distributed log-normally. The result is

shown in Figure 16.2 which, for each point in time, gives the interval which

is

two standard deviations wide and centred around the mean. If the projections

were to describe the underlying distribution correctly, then this range covers

about two-thirds (68%) of the total probability. In any case, the range describes

the degree of agreement of the responses with each other and the inherent

uncertainty. With this definition, the range for the year 2010 is smaller than

for

20()()

- as if the uncertainty decreased with increasing distance into the future.

This may be attributed to some peculiarity in the poll responses, but it has also

been observed in past samples of oil price projections (see Manne and

Schrattenholzer, 1988).

The record of oil price projections for 1990

is

depicted in Figure 16.3, in

which the lEW poll medians in a given year are compared with the current

price. Apparently, the current price has a significant influence on the forecasts.

Although it is understandable that expectations are adapted to changing

current conditions, this at the same time indicates that these conditions have

changed in a largely unforseen way. The most recent projection of $19/b (at

1985 prices) corresponds to about $23fb at

1990

prices.

$/b (1985)

50

~ ~ - - ~ ~ - - - - - - - - - - - - - - - - - - - ~ - - - - ~

30 I---+-----t-----:::

20 1 - - - - - - - ' 1 - - - - -

10

~ - - - - I - - - - - - - - - ~ - - - - - - - - - - ; _ - - - - - - - - - - r _ - - -

o

1985

1990 2000

2010

Fig.

16.2

Ranges of oil price projections:

1988

responses only.

'This means that we assume the logarithms of the

oil

price to be distributed normally. This

assumption not only excludes negative prices, but also assures that the range (100%, 200%) is as

probable as (50%, 100%) all percentages expressed in terms of the average value. The untrans

formed normal distribution would result

in the meaningless projection that 'plus and minus 100%'

are equally probable.



290

International Energy Workshop

projections

$/b

(1985)

~ r - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~

40

20

10 f -

:;

~ ~

I

~ . :

;:

~

o

~ ~ 4 : ~ · __ ~ ~ ~ ~ ~ __ ~ ~ ~ ~ ~ ~ ~ ~ __ ~ ~

1983 1984 1985

1986 1987 1988

1989

~

Current price

f;;;;;;:::1 Forecast for

1990

Fig. 16.3 Current oil price and projections for 1990.

16.3 TOTAL PRIMARY ENERGY CONSUMPTION

Figure 16.4 shows, again expressed as poll medians, the projected consumption

of total commercial primary consumption for five lEW regions which comprise

virtually the entire world. The values are given for 1985 and 2000. The

1 0 ~ o e

5

4

3

r--------------------t

2

Su/EE

China

OECD

OPEC

NO

DC

~ 1985

Fig.

16.4

Total primary energy consumption in 1985 and 2000: 1989 poll medians in

five regions.



292

International

Energy Workshop projections

10&toe

1200

1000

800

600

400

200

0

-200

SUlEE

China

OECD

OPEC

NODC

_ Oil 1:::::::;:;1 Gas rzl2ZI Coal Ed.dJ Nuclear ~ 'Conservation'

Fig.

16.6

Changes in consumption rates of primary energy sources plus 'conservation',

1985

and 2000: poll

1989.

addition to the supply sources (coal, oil, gas and nuclear), the figure also shows

'conservation', The quotation marks indicate that this

is

a derived value,

calculated as the difference between the hypothetical growth of total primary

energy consumption at the same rate as those implied by the GDP medians of

1985 and 2000 and the actual medians of total primary energy consumption

for the year

2000. A

number of points are worth notiqg:

• Despite significant reductions in the growth of the rate of energy consump

tion in the past, there remains great potential for energy savings in the

OECD region exemplified by the overwhelming share of 'conservation' in

Figure

16.6.

• The regions SU/EE and China also show large amounts of 'conservation'.

• The energy-rich OPEC region is the only lEW region to which energy

consumption growth is projected to exceed GDP growth.

• The region SU/EE

is

the only one for which a decrease in oil consumption

has been projected. This

is

compensated for by enormous increase in

projected natural gas consumption. Gas attains a 43% share of total primary

energy consumption in this region by the year 2000.

This last point was implicit to a presentation by Professor Peter Odell

at

the

June meeting of the lEW. He argued that the consumption of natural gas in

Europe

falls

far below its potential because the marketing and distribution

systems have not been modernized since the era of city gas (see also Chapter

7). Thus inflexibility of old structures, underestimation of available supply and

subsequent regulatory measures aiming at a restriction of natural gas demand



Carbon emissions

293

to premium uses have reduced considerably the potential growth of natural

gas. Although Professor Odell views the continuation of the status quo as the

most likely scenario for the future of natural gas in Western Europe, he

mentioned the following new elements

as

possible factors to bring its share up

to 30% of total primary energy consumption (Odell,

1989):

• The Single European Act, promoted by the Energy Directorate of the

European Economic Community and likely to

be

supported by environ

mental interests, could lead to greater competition - and eventually to the

abolition of national monopolies in the gas industry.

• Changes in the Soviet Union are likely to encourage greater gas exports.

Perestroika is

paving the way for Soviet gas to

be

used to earn foreign

exchange for Western European consumer and capital goods, and the export

infrastructure to export up to one-and-a-half times the current volume of gas

will be

in place by the early 1990s. Up to 90 billion cubic metres could

be

carried via pipelines to Germany, Italy and Scandinavia by the year

2000.

• Many large manufacturing groups in Western Europe, as

well

as utility

companies, are interested in responding to larger gas supplies from the Soviet

Union.

•

An

enhanced gas trade between Eastern and Western Europe is in line with

current East-West political developments and with environmental concerns.

16.4 CARBON EMISSIONS

The programme of the June

1989

lEW meeting reflected the worldwide

concern that increasing global concentrations of CO

2

and other 'greenhouse'

gases (such as methane and chlorofluorocarbons) may lead to significant

changes in climate. Figure

16.7

provides the point of departure for some lively

discussion.

It

shows the calculated amounts of global carbon emissions. These

amounts are based on the lEW poll medians of fossil

fuel

consumption and on

average carbon emission coefficients reported in Manne and Richels (Chapter

18). It can clearly be seen that the carbon emission problem is of a truly global

scale - no single world region (let alone an individual country) can hope to

solve this problem independently.

Furthermore, Figure

16.7

shows that much of the burden of a global

reduction in carbon emission would rest on the shoulders of the developing

countries, in particular China. It

is

highly questionable - to say the least -

whether these countries are able and prepared to undertake the necessary

economic effort to reduce the global carbon emissions significantly Manne and

Richels give estimates of between

0.8

and 3.6 trillion (million million) US

dollars (until the end of the next century, discounted at 5% per year) as the

economic cost of stablizing US carbon emissions to 80 % of the 1990 level by

the year

2020.



294

International Energy Workshop

projections

10

9

tons/year

10 . - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~

8 r - . - - - - - - - - - - - - - - - - - - - ~ i ~ i ~ i ~ i ~ i ~ i ~ i ~ i ~ i ~ i ~ i ~ i ~ i ~ i ~

6

I - - - - - - - . r : ~ : : : : : ~ : ~ : : : : : ~ : ~ : : : ' : ~ : : : : ' ~ : ~ : : : : = ~ : ~ : : : : = ~ ; ~ : : : : : ~ : ~ : : : J : ~

---

~ ~ ~ ~ ~ ~ ~ ~

---

= , ,

2

1985

1990 2000 2010

flmI

US

rn Other OECD

~

Su/EE

o

China

f::::::::J

Rest of the world

Fig.

16.7

Carbon emissions implied

by

poll medians.

The discussants did not agree on the measures that should be taken in the

near future. The opinions ranged from the immediate introduction of drastic

constraints on the

use

of fossil

fuels

to a 'wait-and-see' policy with respect to

the adverse consequences of future climatic changes. There was general

agreement on the necessity to monitor climatic change and to further under

standing of the underlying processes.

16.5

POLL MEDIANS - A '3-2-1' HYPOTHESIS

ON

THE DEMAND FOR ENERGY

The conventional wisdom is sometimes described as a '3-2-1' hypothesis. That

is, for the market economies as a whole, GDP will grow at the annual rate of

3 %, total commercial primary energy consumption at 2 % and oil consumption

at 1%. I f we consider the 25-year period extending from

1985

to

2010,

the poll

medians are roughly consistent with these numerical values (Figure

16.8).

The

poll medians also imply that oil prices

will

grow at the average annual rate of

1.5% over this period.

Is there any underlying rationale to the seemingly independent projections

of these four growth rates? One approach would be to undertake a detailed

'bottom-up' analysis of the end-uses of energy within individual regions (see

Chapter

13).

A bottom-up analysis provides realistic details that are not

available from aggregative economic models, but it is data-intensive, time

consuming and does not always account for

new

uses of energy.



Poll

medians

295

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0

GOP Energy consumption

Oil consumption

Fig.

16.8

Growth rates of poll medians in market economies, 1985-2010.

As an alternative, it is instructive to see that there is a highly aggregative

economic model that provides a logical link between the four growth rates. The

model is parsimonious. It depends upon just two parameters - one that

summarizes the effects of price-induced energy conservation and the other

that summarizes the effects of autonomous (non-price, 'structural' or policy

induced) conservation.

Let the symbol AEEI denote the annual rate of autonomous energy

efficiency improvements and let ELAS denote the elasticity of the derived

demand for crude oil with respect to its international price. Also, let the

symbols OIL-PRICE, OIL-CON, TPE and GOP refer, respectively, to the

annual rates of growth of the international oil price, oil consumption, total

primary energy and GOP.

The 3-2-1 hypothesis is consistent with the two following equations. The first

indicates that, if overall energy prices remain constant, autonomous energy

conservation

is

the only factor that can account for a decoupling between

GOP

growth and total primary energy consumption. Autonomous energy conserva

tion must then occur at the annual rate of 1%:

TPE=GOP-AEEI

2=3-1

(16.1)

The second equation refers to oil consumption - and to the impact of oil

prices upon the growth in demand. Implicitly, equation (16.2) refers to interfuel

substitution between oil and other forms of primary energy.

I f

crude oil prices

grow at the average annual rate of 1.5%

(in

real terms) from their 1985 level,

equation (2) implies that ELAS (the price elasticity of demand for oil) must be



296

2/3:


OIL-CON

= GDP

- AEEI - (ELAS) OIL-PRICE

1 3 1 -

(2/3)

1.5

(16.2)

These two aggregate equations are easy to understand, and they provide a

cross-check on more detailed models of energy futures. Together, they are

consistent with the view that both autonomous and price-induced conservation

are significant determinants of the growth in oil demand. They depend

critically, however, upon the estimates of AEEI and ELAS.

In the following set of controlled comparisons between models participating

in the lEW poll, we shall see that there is a great deal of variability in the

implicit values of these two parameters. Accordingly, one should not be

surprised to

see

that there

is

variability in the poll responses.

We

shall also

see

that there is a much wider dispersion in the projections of price than in the

quantities supplied and demanded.

16.6 A CONTROLLED COMPARISON OF POLL RESPONSES

In conjunction with the Energy Modeling Forum, the lEW has undertaken a

controlled comparison of oil demand under two scenarios - an 'upper' and a

'lower' trend of international crude oil prices between 1985 and

2000.

For

purposes of this comparison, the rate of

GDP

growth was to be held

approximately constant. As of the June 1989 lEW meeting, responses had been

received from eight modelling groups:

AMOCO

CEGB

CONCO

ECON

EIA

GRI

lEA

LTM

AMOCO Corporation

Central Electricity Generating Board,

UK (now Powergen)

Conoco Corporation

ECON, Centre for Economic Analysis, Oslo

Energy Information Administration, USA

Gas Research Institute,

USA

International Energy Agency

Long-Term Model, Stanford University, USA

Because several of the modelling groups reported oil demand, but not total

primary energy consumption, there is no direct

way

to

use

their results to infer

the AEEI. There is, however, a close relationship between the AEEI and the

oil consumption-GDP elasticities that are implicit in the individual models.

For

example, under the

3-2-1

hypothesis - with constant oil prices - the oil

consumption-GDP elasticity would be

2/3

(recall equation (16.2) and insert

zero as the rate of oil price growth).

Figure

16.9

indicates the GDP elasticities of oil consumption at three

different levels of regional detail: the US, the OECD and the market economies

as a whole (see further Chapter

14).

In most cases, these elasticities are close to



A controlled comparison of poll responses

1.0.-- - - - - - - - - - - - - - - - - - - - - - . ,

I : : ' ~

0.8

I-------=... , .=,=-------------------I :{

,

::::1'

,

~ ~ ~

, r- ....

I - I ' - , - - ' L ~

- - - = , ~ - r _ - - I ' : ' - - ~ - - - I - - - ~ - : : : : - - t ~ ~

", ~ , 1::::1- ~ r < ,

::::

::::1-

:0:0'"

':':1-'

..

"'1-.

: : : , , ' - -1 - '1 -

I«'

,-

: : : : ~ , - :::: ,-

I - - - I ~ ~ . ~ : . ' -

t . : ~ . · . :

::: , : : : : ,

.:.:

::.::. '

..

,

.. :::: ::: ::: ·:·.:v,.

, ::::...

.. . ..

...

:::::: , .:.: It. : :.1-,

. . :.:

v

::::, ~ } _ :::: : : : I t ~

::: ,

"r-

:::: ' . '-

:.::.:1-.

~ : : ~ ' , - ---I?- : : : v ~

.'." "::.::::.::

1-'

.:.:

:::v.

::: ' , , , ~ ' : " . ~ . : :::

It' ::::

· : ~ · : ~ V

o

~ : : : L ' ~ __ ~ ' ~ ~ : : ~ : : ~ ~ ~ ~ ~ ~ : ~ : : L ' L _ ~

______

__

~ ~ ~

0.6

0.4

0.2

AMOCO CEGB

CON CO

ECON

EIA GRI lEA LTM

o us (:;:;:;J OECD

~

Market economies as a whole

Fig. 16.9

GDP

elasticities of oil demand.

297

the value of 2/3 that is implicit in the 3-2-1 model. The principal exception

is

LTM, where the authors (Manne and Rutherford,

1989)

adopted unitary

long-term

GDP

elasticities and made no provision for the role of autonomous

energy conservation. The econometric evidence on this issue

is

conflicting.

Both Hogan (1988) and Brown and Phillips

(1989)

found no reason to reject

the hypothesis of unitary

GDP

elasticity.

0.7.---------------------------------------,

..

- - - - - - - - - - - - ~ ~ = . - - - - - ~

~ h r ~

0.6

r - ~ - - - - - - - - I

f::

:::: , 1 - - - - - - - - -1

:.:

---

::::.' ~ tt

/::-)

- ~ - - - 1 r - -

.. ... H:

?:::::::: : . ~ : ~ : . ~ : - r-

~ ~ ~ :

- / . , .

: : : : : ~ : : : : :

_-

\l.J.l----l

:::v

--

rnb-

t

-; ~

~

O u - ~ ~

__ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ____ W ~ _ L ~ ~ ~ ~ ~ : : : L ~ 1 J

0.5

r - - - - - - - - - - I

0.4

r - - - - - - - F ~ ~ - - - - I

0.2

0.3

t-------

0.1

AMOCO CEGB CONCO

ECON

EIA GRI

lEA

LTM

SPIL

D u s

E : : : : : : : : : ~ OECD

~

Market economies as a whole

Fig. 16.10 Price elasticities of oil demand.



298


There

is

a wide variation in the oil demand price elasticities that are implicit

in the eight models (Figure

16.10).

Only a few of these are high enough to be

consistent with the long-run value of 2/3 suggested by the

3-2-1

model. Various

interpretations were suggested by workshop participants. One possibility

is

that there

is

a significant difference in demand elasticities for the IS-year period

1985-2000 and the 25-year period 1985-2010. Tax policies differ from one

region to another, and this may in turn affect demand elasticities. Another

possibility is related to the difference in the elasticity of demand for white and

black petroleum products. This would suggest that the price elasticity of the

derived demand for crude oil

is

very different at different price levels.

REFERENCES

Brown, S. P. A. and Phillips, K.

R.

(1989)

An Econometric Analysis of us Oil Demand.

Federal Reserve Bank of Dallas.

Hogan,

W. W.

(1988) Patterns of Energy Use Revisited. Harvard University, Cambridge,

Mass.

Manne,

A.

S. and Rutherford, T. F. (1989) A Long-term Model of Oil Markets, Economics

Growth and Balance of Payments Constraints. Stanford University, Stanford, Ca, and

University of Western Ontario, London, Ont.

Manne, A. S. and Schrattenholzer, L. (1986/88) The International Energy Workshop: a

progress report.

OPEC

Review,

Autumn

1986

and Spring

1988.

Odell, P. R.

(1989)

Der westeuropiiische Gasmarkt: Gegenwiirtige Situation und

alternative Zukunftsaussichten, Zeitschrift fur Energiewirtschaft, 12(2).



17

Environmental

regulations

and

innovation: a CGE approach

for analysing

short-run and

long-run

effects

Gunter Stephan

17.1

INTRODUCTION

As

is

well known, there

is

a substantial gap between the rigorous and

theoretically derived definitions of the costs and benefits of environmental

regulations and their empirical estimates. Nevertheless in applied cost-benefit

analysis four criteria have proved convenient, if environmental improvements

forced by governmental interventions are to be evaluated (see Siebert, 1987)

1. economic efficiency;

2.

ecological efficiency;

3. distributional effects;

4. political feasibility.

Although these criteria are important in quantifying the overall impact of

environmental regulations, they are static and

will

typically

be

used in

comparative static analyses. Environmental problems, however, have a tem

poral dimension. Today's emissions influence the quality of the environment in

the short- and in the long-run; an economy cannot adjust to a change of

environmental policy without short-run frictions; and environmental regula

tions feed back into economic growth which

is

closely related to invention,

innovation and technical progress on the one hand, and to resource

use

and

pollution on the other.

The interplay between invention, innovation, resource use and pollution

is

particularly important, since technological progress in pollution abatement can

significantly expand the boundaries of environmental policy design. Some

authors (for example,

see Milliman and Prince, 1989) argue that the impact of



300

Environmental regulations and innovation

regulations on the pace of innovation is over the long run the most important

criterion to judge environmental policies and the

key

to an effective solution

of environmental problems.

With this perspective in mind and considering the importance of environ

mental restrictions on the production and consumption of energy, it

is

the aim

of this paper to explore an intertemporal computable equilibrium framework

which allows us to analyse the intertemporal impact of environmental regula

tions on the allocation of resources, the distribution of income, on economic

growth and innovation. After discussing in section

17.2

how policy instruments

may differ with respect to their short-run and long-run effects on innovation

and technological change, section

17.3 is

a verbal presentation of the theoreti

cal framework.

1

Section

17.4

illustrates the functioning of the theoretical

approach by means of a numerical example.

17.2

INNOVATION INCENTIVES

OF

EFFLUENT

CHARGES AND STANDARDS

In

economic literature it

is

generally argued that market conform applications

of the so-called 'polluters pay' principle are the best policy tools for controlling

environmental externalities (for example,

see

Baumol and Oates,

1988).

Via the

invisible hand, effiuent charges and environmental taxes guarantee an efficient

and Pareto-optimal allocation of resources. They stimulate firms to lower

emissions down to the level where the marginal costs of reduction are equal to

the unit rate of the charge

or

tax. Direct governmental regulations like

standards on emissions or technology are

less

likely to promote static effi

ciency.

There are, however, conditions in which direct governmental interventions

by standards are preferred. This might

be

the case in situations where toxic

pollutants have to

be

reduced immediately, where high probabilities exist that

emissions exceeding certain limits lead to irreversible damages, but also for

situations in which cost functions are hard to estimate or when market

conform instruments for various reasons would

be

impractical or very costly

to operate. Moreover, studies on the political feasibility of policy instruments

stress that direct governmental regulations have higher chances to

be

accepted

than market conform applications of the polluters pay principle

(see Lee, 1984).

Important reasons for this observation are that standards on emissions

or

technology are easier to implement in existing legal regulations, seem to

be

more reliable than an effiuent charge and can

be

changed more efficiently and

rapidly in urgent situations.

Despite these typically short-run advantages, environmental standards seem

to have the disadvantage that they provide almost no dynamic incentive for

1The reader interested

in

a formal presentation should consult the Appendix.



Modelling innovation

and

adjustment

in

applied analyses 301

innovating less polluting technologies.

It

is argued in most articles that under

direct regulations polluters have to abate only those emissions that exceed the

upper limits set by government, whereas the remaining emissions can

be

discharged at price zero. Consequently, firms have no economic incentive to

lower emissions below the given level by employing less polluting technologies

and/or advanced abatement technologies.

The situation is even worse if technology standards are prescribed.

An

economic agent who intends to implement more sophisticated production and

purification technologies would have to change the existing technology stan

dards and the other polluters

will

then

be

forced to adjust their techniques.

Hence, producers are motivated to avoid new technologies becoming standard

which

is

a disincentive for technological change, invention and innovation

(Faber and Stephan,

1987).

One should

be

aware, however, that technological change is performed in at

least two steps (for a reference in the industrial organization literature,

see

Greer,

1984): (1)

invention of the initial idea, including a crude proof that the

new technology works on an industry

level;

and

(2)

innovation which

is

defined

as the process of capital accumulation and structural change in order to adopt

the new and approved techniques.

With respect to innovation the traditional argument overlooks that impos

ing a standard on emissions and or technology has at least two effects. First,

emissions have to

be

reduced by employing suitable reduction strategies. The

strategies at hand are either to install an end-of-the-pipe-abatement technol

ogy, or to reduce production and consumption or to substitute inputs. The last

two options can usually be used only to a limited extent, and if

so,

over the

very short-term only. Secondly, implementing an end-of-the-pipe emission

abatement technology and satisfying purification standards implies additional

costs. Under perfect competition producers cannot avoid these costs. Hence,

the only way to get rid of regulation-induced 'environmental' costs

is

to look

for alternative and less polluting production technologies. In other words, if

the overall costs of installing and operating less polluting technologies are

lower than the costs of fulfilling effluent standards by using suitable end

of-the-pipe abatement technologies, standards have a long-run effect on inno

vation, investment and the development of an economy which in our particular

case,

is

similar to the long-term impact of effluent charges. Exactly such an

outcome will

be

observed in section 17.4.

17.3

MODELLING INN

OVA

n O N

AND ADJUSTMENT

IN

APPLIED

ANALYSES

Most of the existing literature dealing with the interplay between technological

change and environmental regulations

is

of a partial nature and neglects the

impact of pollution control policy on the economy as a whole (for an overview,



302


see

Milliman and Prince, 1989).

It

seems therefore necessary to establish a

general equilibrium approach which enables us to take the temporal structure

of production and investment explicitly into account, to illustrate the innova

tion process and its effects

on an economy

as

a whole, and to analyse efficiency

as well as distributive consequences of a specific environmental regulation.

The instruments at hand for analysing environmental problems on the

economy level are input-output models, the optimization approach and the

computable general equilibrium (CGE) framework. The first two methodolo

gies neither satisfy the three requirements above nor do they contain variables

which can be regarded as policy instruments in a market economy.

Input-output models are generally characterized by fixed production coeffi

cients and demand structures. Hence, price-dependent substitution or the

innovation of new production techniques cannot

be

analysed and the criterion

of economic efficiency is not applicable.

An

optimization approach permits a more flexible treatment of the produc

tion side of an economy, but is still rigid in the requirements placed on the

consumers' side. Generally, it starts from a centrally planned economy with a

single decision-maker who is optimizing a social welfare function. This implies

that the analysis can be concentrated on efficiency and long-run development,

but

is

not designed for analysing distributive and short-run effects.

The most consistent way to capture allocational as

well

as distributional

effects

is to model all agents in a general equilibrium framework. As a

qualitative description of a competitive economy, the general equilibrium

model based on Walras's conceptional idea

is

already in its second century of

intellectual life. But as a quantitative tool its history is much more recent. It

was not until Scarf's pioneering work (1967) that rigorous techniques became

available to solve such models numerically. And to our knowledge no other

tool developed so far has the ability to be applicable for theoretical as well as

empirical analyses, to trace the consequences of a change of policy through the

entire economy, to provide a unified framework for analysing the trade-offs

between economic efficiency and equity and to illustrate the operation of an

economic system in which all decisions are price-guided on a micro level.

Despite these advantages computable equilibrium models are unsatisfactory

in many respects, since they rely on abstractions which can be regarded as

unrealistic and have been questioned

by

many economists (for a discussion, see

Hahn, 1989). For example, it

is

generally supposed that economic agents

behave as price-takers and maximize profits or utilities. Equally unrealistic are

the assumptions about the organization and functioning of markets: markets

are perfect and

well

organized, the market process depends on price signals

only and automatically assures an equilibrium.

17.3.1 Time and

adjustment

in computable

equilibrium

models

More important for our aim of providing a unified framework for analysing

innovation and adjustment, efficiency and equity

is

that there exists no easy




and

adjustment in applied analyses 303

way

to introduce time and dynamics into a computable equilibrium model.

In

formulating dynamic computable equilibrium models it

is

state of the art

to distinguish between two approaches (Stephan,

1990).

One

is

called 'clairvoy

ant' and the other is labelled 'myopic'. The first implies the concept of an

intertemporal equilibrium in the sense of Arrow and Debreu, and the second

leads to a recursive structure in which the economy evolves in a sequence of

(intertemporally uncoordinated) temporary flow equilibria.

Within an intertemporal Arrow-Debreu framework it is assumed (1) that all

commodities are traded on a complete set of well-organized future markets,

and (2) that at the initial moment economic plans are made and coordinated

for the whole economic horizon. After that markets are closed and will never

be

reopened.

These assumptions significantly influence how time, innovation, adjustment

and the evolution of an economy are reflected by a clairvoyant approach.

Broadly speaking, an important role of future markets is to disseminate

information regarding future supply and demand conditions. Hence, with

future markets for all commodities, economic agents can correctly forecast the

future states of the economy and fully utilize this knowledge in decision

making. Furthermore, since all commodities, including all types of durable

factors of production (capital goods) and pollutants, are traded on perfect and

well-organized markets, the existing production system can be adjusted at any

time to changes in the state of the economy simply by selling used and buying

new capital goods.

Adding these two

effects,

a clairvoyant approach implicitly supposes (1) that

an economy can react instantaneously to any change of prices and exogenous

parameters, and (2) that capital stocks, hence technologies, can be changed

from period to period, and innovation, the diffusion of new techniques into an

economy, is almost timeless. Consequently, the clairvoyant approach may be

used to study economic development which

is

based on long-run equilibrium

price formations, but short-run adjustment, temporary frictions and the irre

versibility of economic processes cannot be analysed within such an approach

(Stephan,

1989).

Additionally, an intertemporal equilibrium framework usually neglects that

intertemporal information structures are asymmetric. In reality, the past and

present events may be certain, but future ones are definitively not known for

sure. Some of the future events can be associated with objective or subjective

probability distributions based on past experience, but the future may also

contain novelty that is definitely unknowable, and thus cannot be associated

with probability distributions.

In contrast to a clairvoyant approach, myopic models suppose

(1)

that all

decisions are made period by period, and (2) that agents use only the

information available in the current period. One reason for such behaviour in

decision-making is that no future markets exist. Hence, there is no institutional

structure which enables exchange of information about the future or coordina

tion of intertemporal plans via market processes. Economic transactions take



304


place on spot markets only and the economy evolves in a sequence of flow

equilibria.

Some authors (for example,

see

Faber

et

a .,

1990)

argue that a myopic model

is a more realistic approach, since it allows for reopening of markets and

accounts for the fact that agents might be completely ignorant during decision

making. Such a myopic model structure captures the second feature of time

mentioned above. The advantage of a clairvoyant approach is, however, that

the economic development and decisions are handled in a logically and

intertemporally consistent way. Savings and investment result from intertem

poral optimization which incorporates future development and expectations

systematically. A myopic approach may easily lead to implausible behaviour

with economic agents repeating the same mistakes from period to period.

17.3.2 Innovation

and

production:

neoclassical

and

neo-Austrian

approaches

Independent of whether a clairvoyant or a myopic approach is used, the

resulting general equilibrium structures share one important property: for

every period and commodity a well defined market exists: spot markets in the

case of a myopic model, future markets in an intertemporal framework.

This allows us to employ a conventional approach to intertemporal produc

tion theory, where the intertemporal course of production

is

vertically disag

gregated into a sequence of one-period production activities which are linked

by market transactions (Stephan, 1988a). Formally, intertemporal production

is described

by

two elements: (1) a technology set V which represents the

technological knowledge available, and (2) a sequence of one-period produc

tion activities {(x(t),y(t)), t= 1, 2, ...

}.

At the beginning of period t=

1, 2,

. . . the

input vector x(t)

is

used to produce the output vector y(t) subject to the

condition that the pair (x(t),y(t))

is

an element of the technology set V. x(t) and

y(t) are vectors of the same dimension and include a complete list of all goods

in process; this means, final goods such

as

consumption goods, primary inputs,

intermediate factors, but also all types of capital goods, pollutants etc.

Figure 17.1 illustrates how a conventional approach characterizes the

intertemporal course of production. A possible interpretation is that produc-

Markets

t -1

Markets

Markets

t+ 1

___________________________________

• Time

x(t-1)

---. y(t-1) y(t)

VI

x(t)

Fig.

17.1

Conventional approach to intertemporal production theory.



Modelling

innovation

and adjustment in

applied

analyses 305

tion

is

carried out by a sequence of producers. Each producer operates his

enterprise for only one period. At the beginning of the period he buys

at

existing equilibrium prices all input factors which are technically necessary to

produce the desired output starting from scratch. At the end (i.e., the beginning

of the following period)

he sells his

total output at current prices, including

his

stocks of used, depreciated and semi-finished capital goods, emissions etc.

Seen theoretically, the vertically disaggregated characterization of intertem

poral production has the advantage of being transparent and logically consist

ent (Burmeister, 1974). But with respect to an adequate treatment of time,

innovation or adjustment, and in

view

of potential empirical applications, these

assumptions become crucial for at least three reasons (Stephan, 1988b):

1. Describing production in such a way means that the model-builder must

have complete knowledge about all goods and services used during produc

tion, independent of whether these goods are marketed or not. On the

contrary, this approach does not allow us to distinguish between goods

which are actually bought or sold by firms and factors which are owned by

firms, which are never sold or for which no markets exist. Equilibrium

prices are generated for each commodity and period and any commodity is

marketed.

2.

Since durable factors of production can

be

sold or bought at any point in

time at existing equilibrium prices, the temporal structure of constructing

and demolishing capital stocks is not explicitly modelled. Hence, such an

approach does not reflect the fact that particular goals, like innovating

less

polluting technologies, require time and a particular temporal sequence of

actions: for example, if a specific pollution abatement technology

is

to be

operated, the necessary capital equipment has to be constructed first.

3.

Every production activity embodies a certain production process and thus

a certain technological knowledge. As discussed above, the conventional

approach permits the use of different production processes from one period

to another. This means that production technologies can change abruptly.

For

this reason, a conventional production theory can neither consider

short-run restrictions resulting from the immobility of capital stocks nor

is

it suited to analyse the time profile of an innovation process.

To overcome some of the short-comings just mentioned, this chapter

presents an alternative formulation of a computable equilibrium model which

is

built on two elements (Stephan, 1988b, 1989, Appendix).

1. In order to determine the long-run behaviour of economic agents in a

logically consistent way a conventional market equilibrium framework

is

employed as far as possible. That means: all individuals are clairvoyant (i.e.,

they have identical and certain price expectations) and behave as price

takers. Households optimize intertemporal utilities, producers maximize



306


present values of profits. Money

is

neutral and the system

is

driven by

relative prices. Environmental regulations are imposed by governmental

interventions and have to be obeyed by every producer

at

any time.

2. In order to allow for time lags in adjustment and the innovation of

techniques there is, however, an important point of departure from the

neoclassical paradigm. We suppose in the following that the set of commod

ities

is

divided into two disjunct subsets: complete and well-organized spot

and future markets exist for all non-durables such as consumption goods,

labour, imports and exports, but do not exist for durables like used or

semi-finished capital goods and emissions.

By

this assumption, markets for

durable factors of production might be tight and restrict production

possibilities in the short-run. Hence, time

is

required to adjust to changes

in prices or exogenous parameters.

It should be obvious that such an assumption is incompatible with a

conventional approach to intertemporal production theory. Instead of assum

ing that production can be vertically disaggregated into a sequence of one

period activities with a complete reference back to the market, we now have to

employ a vertically aggregated characterization of production where producers

cannot sell or buy the necessary capital equipment

at

equilibrium prices, but

have to build up their own capital stocks.

The vertical aggregation of production

is

the specific feature of the Austrian

capital theory (see Hicks,

1973;

Faber,

1986).

In the Austrian view, production

is a process in time starting from a sequence of dated primary inputs which

mature to final outputs. The delay between inputs and outputs is subject to a

variable time distribution and pure intermediate goods as well as capital goods

are subsumed in the description of the production process.

Describing intertemporal production formally, the principal difference be

tween a conventional and the neo-Austrian approach can be summarized as

follows: in conventional production theory the characterization of production

is mainly based on the concept of production processes (activities). In this way

a technically feasible correspondence between inputs and output

is

defined, but

the fact is not reflected that applying a certain technology requires a suitable

structure and organization in the production system. From a neo-Austrain

viewpoint production has to be formalized by applying two concepts: the

notion of a production process which

is

similar to the conventional one, and

the concept of a production technique. Following Faber and Proops (1990) (see

also Stephan,

1988b),

a production technique is a minimal combination of

production processes which allows

us

to produce the final output (consump

tion goods) from non-produced (primary) inputs. Such a production technique

contains all stages of production including the construction of the necessary

intermediate goods and capital equipment. Applying the concept of a produc

tion technique means therefore that in characterizing the intertemporal course




and

adjustment in applied analyses

307

of production a third phenomenon

2

of time

is

reflected: innovation and

technological change are time-consuming, since technologies and production

structures have to be provided in a temporal structure before a specific

production process can be applied.

In this analysis we adopt the Austrian interpretation of a production

technique. But, instead of dealing with primary and final goods only, we

develop a characterization of production which

is

based on marketed and

non-durable commodities. To this end, we modify the concept of a production

technique (for the original definition,

see

Hicks, 1973).

In our view, a production technique which

is

implemented in period

t

consists of three elements:

1.

A production process (k(t),x(t),y(t)) chosen from the technology set V which

is

as usual the sum of technological knowledge available over the time

horizon considered. In contrast to the conventional notion above, durable

inputs are separated from non-durable ones. Hence,

k(t)

denotes the services

of a capital stock which

is

technically necessary to operate the production

process (k(t),x(t),y(t)), and the vectors x(t) and y(t) represent the inputs and

outputs of non-durable and marketable commodities only.

2.

A sequence of non-durable inputs x(t - s), s= 1, ...

S,

which go into a capital

gestation process

(x(t-S),

. . .

,

x(t-l))

to build up the capital stock

k(t).

This

reflects the fact that there are no markets

for

new capital goods. Hence,

capital stocks have to be constructed

by

each producer.

3. A sequence (x(t+ 1),y(t+ 1), ... ,x(T),y(T)) of non-durable inputs

x(t+s)

and

outputs y(t +

s),

s

=

1, ... , T -

t,

to describe the production which is feasible

from this technique in subsequent periods. Once a capital stock

is

estab

lished, it cannot be sold and the production process

(k(t),x(t),y(t))

has to be

applied in future periods. But capital goods may deteriorate and additional

inputs are necessary to maintain the service of an existing capital stock

(Stephan, 1988b). Hence, the level of production in future periods depends

on the depreciation of the existing capital stock, and the expenditure for

maintenance.

We

thus have a vintage production function similar to the one described

in

Chapter 2 of this volume.

Because of the lag structure of inputs and outputs it takes time to implement

new

production processes and the existing technologies cannot be abandoned

immediately

(see

Figure 17.2, in which the gestation of capital stocks takes one

period). Thus the short-run substitution potential, and adjustment possibilities,

are restricted by the set of production techniques which already exist. But over

2As mentioned earlier the other two features of time are asymmetry between future and past, and

novelty (Faber and Proops, 1990).



308

Environmental regulations

and innovation

Production technique (used first in period t)

Gestation period Production process Depreciation

_ _ _ _ _ _ _ _ _ _

__________

_ L I

__________

I _ _ . . Time

t -1

t t+1

t+2

(x(t-1 ). k(t))

(k(t) .x(t) .

y(t)) (x(t+

1) .

y(t+

1))

Fig. 17.2 Neo-Austrian concept of a production technique.

the long term, new production processes can be introduced and the short-run

substitution possibilities are small compared to the economy's long-run adjust

ment and innovation potential. This

is

in contrast to a conventional approach

to production theory, where the short-run and the long-run substitution

potential are identical (Figure 17.1).

17.4 NUMERICAL ILLUSTRAnON

For a simple general equilibrium model which consists of a

few

equations only

it might be possible to examine analytically the effects of environmental

regulations on economic growth, the development of emissions and the

innovation of

new

and

less

polluting production technologies. However, the

theoretical model discussed so far

is

too complex to allow for an analytical

investigation. It

is

therefore the purpose of the following exercise to illustrate

the operation of the theoretical framework in a simulation procedure. As such

this analysis does not aim for a very detailed and realistic simulation, and it is

not designed for policy analysis or recommendations. It simply serves as a

numerical illustration of the functioning of a model that cannot be solved

analytically.

Solving general equilibrium models numerically requires strong interactions

between model-building and the development of solution methods. On one side

the formulation of a numerically solvable model

is

influenced by the numerical

tools available and on the other side the existing economic structures motivate

the search for suitable computer codes. Among the various solution methods

available there are those that depend on gradient evaluations such as the

Newton ones and non-gradient methods such as the various versions of

fixed-point algorithms. The former have advantages in computational speed

while the latter are guaranteed to find a solution provided the assumptions of

Kakutani's fixed-point theorem are fulfilled (for an overview,

see

Manne,

1985).

We are employing the so-called MPS solution method, drawing from the work

of Mathiesen

(1985)

and Rutherford (1989), which combines the strengths of

both approaches.



Numerical illustration

17.4.1 Description

of the numerical

illustration

309

The analysis distinguishes three regions.

For

vividness let Region 1 be the

northern and highly industrialized part of Western Europe covering the United

Kingdom, France, Belgium, the Netherlands, the former West Germany,

Switzerland and Austria. Region 2 should correspond to the southern, less

industrialized countries, Portugal, Spain, Italy and Greece. Region 3 represents

the rest of the world, including Western Europe's main trade partners, the

United States and Japan.

The time horizon

is

divided into

six

periods, each of a 5-year.length except

for the first one which contains 7 years. Base year for calculations

is 1973,

the

last year reported

is

1995,

when the common market

will be

established.

It is assumed that each region produces a single, but specific commodity

which can be consumed domestically, exported and might be used as input

factor into capital gestation processes. Besides services of domestically con

structed capital stocks further inputs into regional production are labour and

environmental services. It is supposed that labour cannot be transferred

between regions, and inputs of environmental services reflect the use of nature's

ability to degrade pollutants from production and power generation. On this

basis, there are 12 different types of commodities and services in each period.

Since it was supposed in section 17.3.2 that there are no markets for durable

commodities and environmental services, for each period non-durables, pro

duced outputs and regional labour inputs are obtained which are traded on

perfect future markets

(see

Appendix).

As mentioned at the beginning,

we

are interested in answering the question

as to how governmental regulations affect the innovation of less polluting

technologies.

For

this purpose a counterfactual analysis

is

employed and

simulations are carried out under two scenarios.

In

Scenario 1 it is supposed

that environmental regulations remain unchanged over the time horizon

studied. Hence, Scenario 1 supposes that only those standards on emission

abatement have to

be

fulfilled which were implemented prior to 1973.

Economic growth means, however, that more and more raw materials and

energy have to

be

put into circulation, producing an increasing amount of

pollutants. At a limited extent nature has the ability to degrade pollution, but

without some kind of pollution control, nature's assimilative capacities could

be reached or even

be

surpassed in the long-run and could lead to an

irreversible destruction of the ecological system. Under this perspective it seems

reasonable to suppose that the national governments have to set higher

environmental standards to assure the environment's quality.

In

practice, the

implementation of such regulations would mean requirements

on

the emission

abatement technology and equipment.

Given these considerations, for the alternative case (Scenario

2)

it

is

assumed

that in every region the standards on the emission purification technology are



310

Environmental regulations and

innovation

changed during the time horizon considered.

3

In Scenario 2 from 1980

onwards each region has to run end-of-the-pipe pollution treatment plants

which reduce emissions by 95%.

Setting higher requirements on end-of-the-pipe emission treatment implies a

more then proportional increase of environmental costs for those who have to

treat their emissions in such equipment (see Appendix, where cost functions

for

end-of-the-pipe abatement are discussed). I t will be shown in the following that

such an increase of regulation-induced costs provides an economic incentive

for innovating

less

polluting technologies and technological change.

17.4.2 Results

Figures 17.3, 17.4 and

17.5

show the development of annual gross domestic

production under both scenarios. Under reference case assumptions (Scenario

1),

gross domestic production is growing continuously in each region. The

highest growth rates are for Region 3 and are close to 4.5%. The lowest growth

rates are for the southern part of Western Europe (Region 2). On average they

are lower than 1.5%. Growth rates in the northern countries of Western

Europe are intermediate, below 3% per year.

As one expects, tightening standards on abatement technology has only little

impact on economic growth and production in total, but there are important

GNP

6000

5000

4000

3000

2000

1000

0

1973 1980

1985 1990

1995

_ Scenario 1

~

Scenario 2

Fig.

17.3

Economic growth

in

the northern part of Western Europe (Region

1).

3Note, that in a conventional counterfactual analysis changes of governmental regulations happen

typically at the beginning of the time horizon. However, assuming that governmental regulations

are changed during the time horizon considered provides in our view a better understanding of

the time-path of adjustment.



Numerical illustration

311

GNP

1800

1600

1400

1200

1000

800

600

400

200

0

1973 1980

1985

1990

1995

_ Scenario 1

~ Scenario 2

Fig. 17.4 Economic growth in the southern part of Western Europe (Region 2).

GNP

3 0 , - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~

25

20

15

10

5

o

1973

1980

1985 1990

1995

_ Scenario 1

~

Scenario 2

Fig. 17.5 Economic growth in the rest of the world (Region 3).

differences across single regions when emission standards are changed simul

taneously. Region 2 and Region 3 are worse off, if higher purification

requirements are imposed: in the southern part of Western Europe (Region

2)

economic growth

is

cut back almost half in the long run, whereas Region 3,

Japan and the United States, suffers only a slight reduction of economic

development. In contrast to these two regions, economic growth is increased

in the northern countries of Westen Europe (Region

1)

under Scenario 2 .

This last result and the differences between regions are of particular interest.



312


and innovation

Industrial organization studies usually indicate that although the impact of

environmental regulations on economic growth and productivity is fairly small,

it

is

always negative (for example,

see

Barbera and McConnell,

1990).

Our

calculations parallel the first part of these findings, but they contrast with the

second: compared to the reference case, the northern part of Western Europe

has extended its exports to other regions under Scenario 2, whereas tightening

effluent standards has the opposite effect on the export prospects of Regions 1

and

3.

Hence, a distributional effect across regions

is

observed.

4

Figures 17.6,

17.7

and 17.8 show the development of annual emissions.

Under both scenarios a gap between economic growth and the development of

pollution generation

is

observed. But a significant reduction of emissions can

be obtained only if higher emission standards are imposed in each region.

Under reference case assumptions (that

is,

no change in the existing environ

mental regulations), emissions develop at almost half the rates of economic

production. I f the

new

standards have to be fulfilled from 1980 onwards, the

overall picture changes significantly. Only Region 3

is

still characterized by an

increase of the annual aggregate emissions. In Western Europe as a whole, the

aggregate volume of annual pollution declines over time, but with a different

timepath in each region. In the northern part (Region

1)

a peak for emissions

is

calculated for 1980, whereas in the south of Western Europe (Region 2)

emissions

will

be reduced continuously over the whole time horizon.

How can the drastic reduction of emissions be explained? What are the

Emissions

200

150

100

50

0

1973 1980 1985

1990 1995

_ Scenario 1

~

Scenario 2

Fig.

17.6

Annual emissions

in

the northern part of Western Europe (Region

1).

4In our view one reason for these different results

is

that industrial organization studies usually

employ a partial equilibrium analysis which does not allow us to capture distributional effects in

a systematic and consistent way.



Numerical

illustration

313

Emissions

70

60

50

40

30

20

10

0

1973

1980

1985

1990

1995

_ Scenario 1

~ Scenario 2

Fig.

17.7

Annual emissions in the southern part of Western Europe (Region 2).

Emissions

1000

800

600

400

200

0

1973 1980 1985 1990 1995

_ Scenario 1

~

Scenario 2

Fig. 17.8 Annual emissions

in

the rest of the world (Region 3).

reasons for the emission peak obtained in the period in which the emission

standards are changed?

As

discussed above, in principle two sets of strategies are available to reduce

pollution generation. In the short run, emissions can be reduced by cutting

back production or by input substitution; this means a higher percentage of

foreign produced commodities as inputs into production, hence an export of

environmental pollution into other regions. In the long run changing the

production technologies and implementing less polluting production processes

seems to be a suitable strategy.



314


This explicit distinction between short-run and long-run strategies serves as

an explanation for the 1980 emission peak in the northern part of Europe.

From

the model formulation

(see

Appendix) it follows that time

is

required to

adjust to a change in environmental regulations for two reasons: first, construc

tion of capital stocks, hence the innovation of new production techniques

consumes time, and secondly capital stocks and production techniques are

fixed over the short run and cannot be abandoned

(see

section

17.3.2).

Consequently, the time until

1980

was too short to construct a sufficient

amount of new and less polluting capital equipment: production is mainly

based on old techniques and the only emission reduction policy that could

work sucessfully was to apply short-run strategies. But with each investment

less polluting production processes can be implemented, and a switch from

short-run to long-run emission reduction strategies can be observed. This

explains the drastic reduction of emissions after the new emission standard

came into force.

To support this argument Table 17.1 reports the emission coefficients for the

sequence of production processes introduced over the time span

1973-95.

Emission coefficients measure the volume of emissions discharged per unit of

output produced by a specific production process. Hence, to allow for com

parison between different vintages, production processes are normalized to

produce one unit of specific regional output. Table

17.1

shows that emission

coefficients do not change significantly over time in the reference case.

Obviously, the historically given emission standards are too low to enforce

further implementation of less polluting production techniques over the time

span studied. But, in Scenario 2, from vintage to vintage emission coefficients

decrease for each region. Hence in all regions, less polluting production

processes are implemented over time.

The reason for this outcome is quite obvious. As the input requirement

function for end-of-the-pipe abatement

(see

Appendix) indicates, end-of-the

pipe pollution treatment costs will rise significantly if the emission standards

are tightened. This drastic increase of the end-of-the-pipe treatment costs

provides an economic incentive for implementing less polluting techniques:

Table 17.1 Development of emission coefficients

Region/year 1973 1980 1985 1990

1995

Emission coefficients: scenario 1

1 0.036 0.032 0.029

0.033 0.039

2

0.042 0.038 0.043 0.043 0.043

3

0.040 0.038 0.032 0.036 0.036

Emission coefficients:

scenario

2

1 0.036 0.021

0.019 0.017 0.016

2 0.042

0.031

0.029

0.027

0.027

3 0.040 0.028 0.023 0.020 0.020



Acknowledgements

315

implementation of less polluting technologies leads to a reduction of emissions

which have to be treated in an end-of-the-pipe abatement plant and thus

implies a reduction of 'environmental' costs. However, this

is

not an everlasting

process. As Table

17.1

shows, the emission coefficients stay almost constant at

the end of the time horizon. This seems to indicate that tightening the emission

standards only motivates implementing

less

polluting production techniques in

the medium term (15 years).

The actual time-path of emissions (see Figures

17.6, 17.7,

17.8), however, not

only depends upon the implementation of less polluting production processes.

In each period a certain amount of total pollution is generated by old

production techniques continuing in existence. Hence, the quantities emitted

per period depend upon the mixture between old and new production

possibilities. The faster old production capacities evaporate, the lower the

amount of old capital goods and the faster new processes can be implemented,

thus the faster total emissions from production and power generation will

decline. As shown by Forsund and Hjalmarsson in Chapter

3,

the development

of emissions depends upon the speed of substitution between new and old

production equipment. The present author has analysed how different assum

ptions about the speed of short-run adjustment affect the temporal structure of

pollution generation (Stephn,

1988b).

Summing up, the numerical analysis shows the following important results:

1. Tightening environmental standards has only little impact on economic

growth and productivity, but can have significant distributional effects.

International trade, imports and export prospects of single regions may

change significantly, as the counterfactual analysis indicates.

2.

The finding of many industry studies that governmental interventions for

regulating environmental externalities necessarily lead to a slow-down of

economic growth and productivity cannot be supported by this analysis.

3.

In contrast to the conclusions of most theoretical studies, it

is

shown that

not only charges but emission standards can provide a dynamic incentive

to reduce pollution generation in the long run by implementing

less

polluting production techniques. The economic reasoning for this outcome

is

quite obvious.

We

assume filter technology to be needed and thus that

complying with standards implies (shadow) prices for emissions. I f stan

dards are high enough, their shadow price can equal the optimal effluent

charge. In the absence of uncertainty, as in this chapter, imposing standards

and

effluent

charges may therefore lead to the same behaviour on the part

of the polluters.

ACKNOWLEDGEMENTS

Helpful comments and suggestions have been provided by T. Sterner,

R.

van

Nieuwkoop and T. Wiedmer. The usual disclaimer, however, applies.



316


and

innovation

REFERENCES

Barbera,

A.

and McConnell,

V.

(1990)

The Impact of Environmenal Regulations on

Industry Productivity: Direct and Indirect Effects. Journal oj Environmental Econ

omics and Management, 18 50-65.

Baumol,

W.

and Oates,

W.

(1988) The Theory oj Environmental Policy. Cambridge

University Press, Cambridge.

Burmeister, E.

(1974)

Synthesizing the Neo-Austrian and Alternative Approaches to

Capital Theory. Journal

oj

Economic Literature, 12 413-56.

Faber, M. (ed.) (1986) Studies

in

Modern Austrian Capital Theory, Investment and Time.

Springer-Verlag, Berlin.

Faber, M., Niemes, H. and Stephan,

G.

(1987) Entropy, Environment and Resources.


Faber,

M.

and Proops,

1.

(1990)

Evolution, Time, Production and the Environment.


Faber, M., Proops, 1. Ruth, M. and et al. (1990) Economy-Environment Interactions in

the Long-Run: a Neo-Austrian Approach.

Ecological Economics,

2 27-55.

Faber, M. and Stephan, G.

(1987)

Umwe1tschutz und Technologiewandel.I, in

Tech

nologie, Wachs tum und BeschiiJtigung

(ed.

R.

Henn), Springer-Verlag, Berlin, pp.

933-49.

Greer, D. (1984) Industrial Organization and Public Policy. Macmillan, New York.

Hahn, F. (ed.)

(1989)

The Economics oj Missing Markets, InJormation and Games.

Claredon Press, Oxford.

Hicks, J. (1973) Capital and Time: a Neo-Austrian Theory. Oxford University Press,

Oxford.

Lee, D.

(1984)

The Economics of Enforcing Pollution Taxation.

Journal

oj

Environment

al Economics and Management,

11, 147-60.

Manne,

A.

(1985)

Economic Equilibrium: Model Formulation and Solution.l, in

Mathematical Programming Study

23 (ed. A. S. Manne), North Holland, Amsterdam,

pp.I-22.

Mathiesen,

L.

(1985) Computation of economic equilibria by a sequence of linear

complementarity problems, in Mathematical Programming Study 23 (ed.

A.

S. Manne),

North-Holland, Amsterdam, pp. 144-62.

Milliman, S. and Prince R.

(1989)

Firm Incentives to Promote Technical Change in

Pollution Control.

Journal

oj

Environmental Economics and Management, 17

247-

65.

Rutherford, T.

(1989) General Equilibrium Modelling with MPSjGE.

Department of

Economics, University of Western Ontario, London, Canada.

Scarf, H. (1967) The Computation of Equilibrium Prices.l, in Ten Economic Studies

in

the Tradition oj Irving Fisher (ed. T. C. Koopmans), Wiley, New York, pp. 68-93.

Siebert,

H.

(1987) Economics oj the Environment. Springer-Verlag, Berlin.

Stephan, G. (1988a) Economic impact of emission standards: A computational approach

to waste water treatment in Western Europe, in Welfare and Efficiency in Public

Economies (D. Bos, M. Rose and C. Seidl eds.), Springer-Verlag, Berlin etc., 401-

422.

Stephan, G. (1988b) A Neo-Austrian Approach to Computable Equilibrium Models:

Time to Build Technologies and Imperfect Markets. Schweizerische Zeitschrift Jur

VolkswirtschaJt und Statistik, 124 48-64.

Stephan,

G. (1989) Pollution Control, Economic Adjustment and Long-run Equilibrium.


Stephan, G. (1990) Comments, in Heidelberg Congress on Taxing Consumption (ed. M.

Rose), Springer-Verlag, Berlin pp. 178-180.



Appendix 317

APPENDIX: MATHEMATICAL FORMULATION

OF

THE MODEL

17

A.l

Basic assumptions

To

translate ideas into a formal model

it is

supposed:

(1)

time is measured in

discrete units (periods)

and

evolves over a horizon

of T

periods.

(2)

Space is

split into

R

regions.

(3)

Each region

r = 1,

. . .

,

R

produces a specific commodity

which can be consumed, exported

or

used as input into domestic capital

gestation processes

and

into end-of-the-pipe emission abatement (see section

17.4.1). For each period

t = 1, . . .

, T, region r's gross output, consumption,

exports to the other regions j

= 1, . . . ,R,

inputs into the capital construction

and

emission treatment are denoted by

Yr(t),

cr(t),

mt(t),

( m ~ ( t )

=

0),

xr(t)

and

fr(t),

respectively.

(4)

Since

an

Armington rule for modelling interregional trade

is used, region r's exports enter into the other regions' production,

but not

into

consumption (see Stephan, 1988a).

Further

inputs into production are

exogenously supplied labour lr(t), services of domestically owned capital stocks

kr(t) and

environmental services which are converted into emissions er(t).

(5) Given the model assumptions from section 17.3.2, there are

no

markets for

capital goods and environmental services.

As

such in each period

t = 1, . . .

,

T

there are

2R

non-durables (produced

output

and labour) which are traded

on

perfect future markets. The present value prices of produced commodities and

labour

will be denoted by

Pr(t) and

wr(t),

r =

1,

. . .

,

R, t = 1, . . .

, T.

(6)

In each

region

r= 1, . . .

, R there

is

a representative consumer

and

a representative

producer. Both act as price-takers with households optimizing intemporal

utilities

and

producers maximizing present value of profits.

17

A.2.1

Consumers' decision

As

is usual in a microeconomic framework, the representative consumer of

region

r

=

1,

...

, R owns the endownment of the exogenously given

labour

force

Lr(t), t =

1,

. . .

, T, receives profits from production G

r

and

chooses

an

intertem-

poral consumption bundle

{cr(t),

t =

1,

. . .

,T}

which maximizes his utility

subject to his budget constraint. Let region

r's

labour force grow exponentially

with the rate

r and

let

T

U

r

=

L ~

log(cr(t))

(17 A.1)

t= 1

reflect the preference ordering of the representative consumer in region

r

with

the utility discount rate

Or.

Then his optimization problem

is:

T

Max

L

~

log(cr(t))

t= 1



318


and

innovation

subject to

T T

L

Pr(t)cr(t):::;

L

wr(t)Lr(t)

+

G"

(17A.2)

(=

1 (= 1

where G

r

denotes the present value of profits in region

r.

17A.3 Producers

decision

For modelling the producers' decision problem it is assumed that producers

behave as present value maximizers: in each region r=

1, . . .

, R the representa

tive producer selects a sequence of technically feasible production techniques

such that the present value of production less costs for end-of-the-pipe emission

treatment is maximized.

As

mentioned in section 17.3.2 a production technique consists of:

(1)

a

technically feasible production process, and

(2)

a lag structure of inputs and

outputs into the capital gestation process and into the utilization of the

production process after its installation.

For

applications, these elements are

specified as follows:

(1)

(kr(t),

lr(t), m ~

(t), . . .

, mi(t),

Yr(t),

er(t))

denotes a technically feasible production process, if

Yr(t)

=

{Dr [ k r ( t ) ~

lr(t)P

1tjmj(tr]b - Erer(t)b}

l/b.

(17A.3)

With this formulation it is technically feasible to combine the non-environ

mental inputs labour, imports and services of the capital stock as in a

Cobb-Douglas production function.

1

This index of inputs is in turn linked with

environmental services (expressed in terms of emissions) through

aCES

formulation with substitution elasticities which are less than unitary as the

entropy law tells

(see

Faber

et al., 1987).

(2)

I f we

assume that the temporal structure of capital gestation and depreci

ation are identical for all production techniques, then the lag structure of

inputs and outputs can be represented by an array of matrices {A(s), B(s),

s=

- T,

. . . , T}

such that

with

Xr(t

-s)

= A(s)kr(t),

A(s)

=

0, s>O,

Yr(t

+

)

=

B(s)Yr(t),

Ir(t

+

)

=

B(s)lr(t),

(17AA)

(17 A.5)

(17A.6)

'Note that region r's imports of produced goods from regionj equal regionj's exports to region r.



with

Appendix

mj(t

+

) = B(s)mj(t),

er(t

+

)

= B(s)er(t),

B(s)=O, s<O.

319

(17

A.7)

(17A.8)

Equation 17

A.4

determines the sequence of inputs x(t -

s)

into the capital stock

gestation process (xr(t- T), . . . , x

r

(t-1),

kr(t))=(A(

-

T)kr(t), . . . , A(

-1)k

r

(t),

kr(t));

equations (17

A.S)

to (17 A.8) denote the depreciation once the capital

stock kr(t) and thus production process (kr(t),

lr(t), m ~

(t), . . .

,

mHt), Yr(t),

er(t))

is

established.

Let

{(kr(t),

lr(t),

m'i{t),

. . .

,

~ ( t ) ,

Yr(t),

er(t)),

t=

1, ...

,

T}

be a sequence of

technically feasible production techniques. The net output zr(t) produced from

this choice of techniques in period

t =

1, ... , T

is

defined by

1 T

Zr(t)=

I

B(t-s)Yr(S)- L A(t-s)xr(s).

(17A.9)

s=

1

s=l+

1

L =l B(t-s)Yr(s)

denotes the gross output produced by means of the produc

tion techniques which have been implemented prior to period

t.

LJ=I+

1

A(t

-

s)xr(s) is

the investment in capital construction in period

t

which

is

expected to produce capital stocks in future periods.

Since inputs into production during period

t

also depend on the choice of

production techniques which have been installed in prior periods, we observe

the labour inputs (see

17

A.6).

1

L B(t

-

s)lr(s),

(17A.I0)

s= 1

and the inputs of produced commodities from other regions

(see

17 A. 7)

1

L

B(t - s)mj(s).

(17A.l1)

s= 1

Given these conventions the firms' present value maximization problem can

now be written as

Max

Jl {Pr(t ( t l B(t-s)Yr(S)-

Jl A(t-S)Xr(S)]}

Jl

Wr(t)Lt

B(t-S)lr(S)]-

Jl ttl pj(t{st

B(t-s)mj(S)]}-

Jl

Fr(t).



320


Fr(t)

denotes the present value of end-of-the-pipe emissions abatement costs in

region

r.

17A.4 End-of-the-pipe treatment and

equilibrium

conditions

As

mentioned in section

17.4.1,

it

is

supposed that each region

r

=

1, ... ,R faces

an emission's standard which requires the application of suitable pollutant

abatement technologies. Engineering analyses of such end-of-the-pipe systems

suggest that the principal determinants for purification are the volume of total

emissions Er(t), the concentration of waste and the emission standard

Sr

(see

Faber, et al., 1987). (1) For a fixed standard Sr emission treatment is usually

characterized by economies of scale. Hence, marginal costs of purification

decline with increasing flow

size.

(2) For fixed volumes of pollution Er(t),

however, marginal costs gradually increase, if the standard

Sr

is raised. This

reflects increasing difficulties of waste extraction.

To cope with this technical relationship in a computable general equilibrium

framework, a two-argument three-parameter function

is used to express the

input requirements for an end-of-the-pipe pollution treatment system

fr(t)

=

Er(t)(c - b In(100-

Sr

)).

(17A.12)

The parameters c and b determine marginal costs of waste extraction depend

ing on the effluent standard

Sr.

Since total emissions are determined by the emissions due to the chosen

mixture of technologies, and since only domestically produced goods enter as

inputs into end-of-the-pipe pollution abatement, for each period t = 1,

...

,

T

the costs of running the end-of-the-pipe equipment in region

rare

Fr(t)

=

Pr(t) ttl [B(t -

s)er(s)](c

- b In(100 - Sr))}.

(17A.13)

Summing up,

we

are able to formulate the equilibrium conditions:

Definition:

Let (P, W)=

{Pr(t),

wr(t), t= 1,

...

, T, r= 1,

...

,

R} be

a sequence of present

value price vectors. (P, W)

is

a (long-run) equilibrium price system, if for any

region

r

and period

t (17

A.14-17

A.16)

are fulfilled.

1 T

L

B(t

-

s) Yr(s)

- L

A(t

-

s)x(s)

(17A.l4)

s= 1

s=l+ 1

1

L

[B(t - s)er(s)](c - b

In(100 -

Sr )),

s= 1



Appendix

321

1

L B(t

- s)lr(s)

~ Lr(t),

(17A.15)

8= 1

T 1

L wr(t) L B(t-s)lr(s)-

(17A.16)

1=1

8=1

T 1

L Pr(t) L [B(t-s)er(s)](c-b

In(100-S

r

)).

1=1 8=1

Equation (17A.14) means that in each period demand for produced non

durable commodities (i.e., consumption, exports and inputs into end-of-the

pipe emission treatment)

is

covered

by

net supply. Equation

(17A.15)

says that

demand for labour is less than the exogenously given endowment. Equation

(17A.16)

is a restricted formulation of Walras's law and implies that in an

equilibrium the existing markets for non-durables clear at positive prices.

2

2 For a proof of existence and computability,

see

Stephan (1988b).



- - -18

CO

2

emission

limits:

an

economic

cost analysis for

the

United States of America

Alan S. Manne and Richard G. Richels

18.1

INTRODUCTION

Within the scientific community, there is a growing consensus that rising

concentrations of certain trace gases in the earth's atmosphere may lead to

significant changes in climate. The greenhouse effect has evolved from a purely

scientific issue to an important public policy debate. During the 100th

US

Congress (1988-9), more attention was devoted to hearings on the climate than

to any other single environmental issue, including acid rain. The result has

been a steady flow of legislative proposals to limit emissions of the major

greenhouse gases: carbon dioxide (C0

2

), methane (CH

4

), nitrous oxide (N

2

0)

and chlorofluorocarbons (CFCs).

Figure

18.1

shows the estimated current contribution of the various man

made greenhouse gases to global warming. CO

2

(believed to be responsible for

approximately half the problem)

is

produced primarily from the burning of

fossil fuels.

The energy sector therefore plays a central role in proposed

strategies to delay climate change. Over the next

few

decades, such strategies

typicaly call for a concerted push toward greater energy efficiency, and - to

whatever extent is possible - switching away from coal and oil toward natural

gas with its lower carbon emissions per unit of energy. For the longer term,

proposed strategies tend to emphasize greater dependence on carbon-free

alternatives such as solar (in several different

forms),

fission and fusion.

Although many of the legislative proposals have set physical targets

for the

reduction of emissions, little attention has been paid to the costs of meeting

these targets. This presents a serious dilemma to policy-makers. Without

information on the cost of emissions abatement, it

is

difficult to assess the

feasibility of alternative proposals, and to determine which measures are

cost-effective. Moreover, a reduction in emissions is not the sole policy

response to potential climate change. There is a point at which further



324

49%

CO

2

emission limits

Nitrous oxide

6%

Fig. IS.1 Man-made contributions to the greenhouse effect. (Source: Rind

(1989).)

reductions could become so expensive that it would be preferable to shift to

other options such

as

adaptation. Without careful analysis, it

is

difficult to

know where that point might be.

This paper describes Global 2100, an analytical framework for estimating the

costs of a carbon emissions limit. The model is designed to evaluate CO

2

-

energy-economy interactions. Although the analysis is being implemented on

a global scale, this paper

will

report only upon its initial application to the

United States. Because of the worldwide scope of the problem, it is clear that

a unilateral limit would be futile unless there

were

parallel abatement actions

by other nations.

Sections 2 and 3 contain a description of Global 2100, the rationale for its

design and a discussion of key macroeconomic p ~ r a m e t e r s that affect the

demand for energy. The next two sections summarize the cost and performance

characteristics of both electric and non-electric supply technologies. We then

present a quantitative assessment, estimating the costs of a carbon limit under

five alternative scenarios. Results are also presented

on:

CO

2

emissions in the

absence of any limits, the size of the carbon tax that would be required to

reduce emissions to the target levels, and the benefits of various alternatives for

reducing CO

2

emissions. In the final section, we conclude with some general

observations on the costs of a carbon constraint to the United States.

17.2 MODEL STRUCTURE

The name Global

2100

has been adopted in order to emphasize both the global

nature of the carbon emissions problem and also the need for a long-term



Model structure

325

perspective. There are long time lags inherent in the build-up of CO

2

and in

the transition away from carbon-based fuels.

Our

model is benchmarked

against

1990

base year statistics, and the projections cover

11

ten-year time

intervals extending from 2000 to 2100. This is an intertemporal rather than a

recursive model.

It

is

assumed that producers and consumers

will be

sufficiently

farsighted to anticipate the scarcities of energy and the environmental restric

tions that are likely to develop during the coming decades.

In its present form, Global 2100

is

based upon

parallel

computations for

five

major geopolitical groupings: the United States, other

OECD

nations (Western

Europe, Canada, Japan, Australia and New Zealand), the Soviet Union and

Eastern Europe, China and ROW (rest of world). Each of these areas is

endowed with limited amounts of oil and gas resources, and each

is

a

contributor to global carbon emissions. Because each region is likely to pursue

its own individual interests rather than the global welfare - and because there

are differences in the relative costs of emission abatement - it would be

desirable to analyse this problem within a computable general equilibrium

framework. As an initial step in this direction,

we

make a series of assumptions

on the future path of international crude oil prices - and also place bounds on

the willingness of each region to import or export

oil.

Moreover, it

is

assumed

that if a carbon emissions quota

is

assigned to each region through interna

tional negotiations, there

is

no practical way to trade these quota rights. At

some point in the future, we hope to adopt a computable general equilibrium

framework. A CGE framework would allow

us

to deal explicitly with issues

such as trade in carbon quota rights, trade in carbon-intensive commodities

and the impact of carbon quotas upon the international division of labour.

In undertaking a global analysis,

we

have avoided the data-intensive

approach required for end-use models. Because of the difficulties of gathering

a consistent international data set and then arriving at a meaningful summary

of results,

we

have adopted a much more aggregative approach than would

be

appropriate for analysing the United States by itself. The categories are

consistent with those of the International Energy Workshop, and the projec

tions have been benchmarked against the poll's median results (see Chapter 16).

Within each region, the analysis is based upon ETA-MACRO, a model of

two-way linkage between the energy sector and the balance of the economy.

1

This is a merger between ETA

(a

process analysis for energy technology

assessment) together with a macroeconomic growth model providing for

substitution between capital, labour and energy inputs

(See

Figure 18.2).

ETA-MACRO

is

a tool for integrating long-term supply and demand projec

tions.

It

is

designed to compare the options that are realistically available to

each region as the world moves away from its present heavy dependence upon

oil and gas resources - toward a more diversified future energy economy. This

type of model may help to promote 'second order' agreement.

For

example,

two analysts may disagree on the costs of solar electricity generation, but might

lFor

a detailed description of ETA-MACRO, see Manne (1981).



326

CO

2

emission limits

Exhaustible resources

(petroleum, natural gas)

Electric and

non-electric

energy

conversion

technologies

(coal, nuclea

and renewab

----.

r

les)

electric, non-electric

energy

ETA

energy costs

Labour

MACRO

Fig.18.2 An overview of ETA-MACRO

consumption

investment

capital

agree on a logical framework within which to estimate the impact of these cost

estimates.

ETA-MACRO allows explicitly

for:

• Energy-economy interactions: rising energy costs and limited supplies will

prevent the economy from achieving its

full

potential

GNP

growth rate, and

this in turn will

slow

down future capital accumulation.

• Cost-effective conservation: rising prices will induce substitution with capital

and labour, thereby reducing energy demands below the amounts projected

from historical trends.

• Autonomous conservation: changes in government policy and in the struc

ture of the economy

will

help to reduce the amount of energy required per

unit of GNP.

• Interfuel substitution: changing relative prices will induce consumers to

replace oil and gas with electricity,

e.g.

heat pumps in place of

fuel

burners.

• New supply technologies: each has its own difficulties and uncertainties on

dates and rates of introduction.

For each region that is parallel, a dynamic non-linear optimization is

employed to simulate either a market or a planned economy.2 Supplies and

demands are equilibrated within each individual time period, but there are

'look-ahead' features to allow for interactions between periods. These interac

tions are particularly important for the depletion of exhaustible resources and

for the accumulation of capital over time. Savings and investment decisions are

m o d ~ l l e d

so

that consumers

will

receive equal benefits from an additional

2The model

is

formulated and solved by means of the GAMS/MINOS system, (see Brooks et al.

(1988)). In a representative example, there are approximately 200 constraints, and 400 variables.

The solution of two successive cases - with and without a carbon constraint - requires 5 minutes

on a 25 Mhz desk-top computer.



Model structure

327

dollar's worth of current consumption and a dollar's worth of investment.

In order to focus upon the long-run issues of energy-economy interactions,

resource exhaustion and the introduction of new technologies, each region

is

described in highly aggregative terms. Outside the energy sector, all economic

activity

is

represented in terms of dollars of constant real purchasing power.

Within the energy sector, only two end products are distinguished: electricity

and non-electric energy.

Figure 18.2 provides an overview of the principal static linkages between the

sectoral and the macro submodels. Electric and non-electric energy (denoted,

by the symbols

E

and

N

respectively) is supplied

by

the energy sector to the

rest of the economy. Like the material balance equations of an input-output

model, aggregate economic output

(Y) is

allocated between interindustry

payments for energy costs

(EC)

and 'final demands' for current consumption

(C) and investment (I). Thus:

Y=C+I+EC.

(18.1)

Each component of equation (18.1)

is

measured as an annual flow (measured

in trillions of constant dollars).

For

an economy-wide production function,3 we

assume that gross output (Y) depends upon four inputs: K, L, E, N - capital,

labour (measured in 'efficiency' units that represent the sum of labour force

growth and productivity gains), electric and non-electric energy. To minimize

the number of parameters that require either calibration or econometric

estimation, the long-run static production function is described

by

a nested

non-linear form:

Y

=

[ a ( K ~ L l - ~ y +

b(EP

N1-Py]1/p

where p=(a-1)/a (for a;60,1,00).

Equation (18.2)

is

based upon the following assumptions:

• there are constant returns to scale in terms of these four inputs;

(18.2)

• there is a unit elasticity of substitution between one pair of inputs - capital

and labour - with ex being the optimal value share of capital within this pair;

• there

is

a unit elasticity of substitution between the other pair of inputs -

electric and non-electric energy - with

f3

being the optimal value share of

electricity within this pair;

• there is a constant elasticity of substitution between these two pairs of inputs

- the constant being denoted by a(

=

ESUB);

• the scaling factors a and b are determined so that energy demands in the base

year are consistent with the 'reference price' for non-electric energy; and

• there are autonomous energy efficiency improvements (AEEI) that are

summarized by growth in the scaling factor b.

3For the concepts and terminology of macroeconomic production functions and neoclassical

growth theory,

see

Allen (1968).



328

CO

2

emission

limits

Energy-economy interactions occur in two ways. According to (18.2), energy

is an input to the economy. According to (18.1), energy costs represent one of

the claims upon the economy's output. Tighter environmental standards

and/or an increase in energy costs will reduce the net amount of output

available

for

meeting current consumption and investment demands. This is

why the potential growth rates of GNP do not uniquely determine the

realized

rates. Since investment determines the accumulation of capital stocks, a lower

rate of current investment will in turn reduce the amount of output available

for meeting current consumption and investment demands. This

is

why the

potential growth rates of

GNP

do not uniquely determine the realized rates.

Since investment determines the accumulation of capital stocks, a lower rate of

current investment

will

in turn reduce the amount of output available for future

consumption. Alternative carbon emission scenarios will therefore be measured

in terms of their impact upon present and future levels of consumption.

In addition to resource depletion, there are the following intertemporal

elements of the model. First savings and investment decisions are determined

so

as

to maximize the discounted utility of consumption. For simplicity, the

utility function is logarithmic in form. The utility discount rate

is

chosen so

that the rate of return on capital

will

remain constant

if

the realized growth

rate of the economy coincides with the potential rate determined by the growth

of the labour force. Second, the rate of depreciation of equipment and

structures governs the time lags of demand in response to changing prices. This

is sometimes termed a 'putty-clay' approach. That is, the input-output

coeffi

cients for each successive age cohort of equipment are optimally adjusted to

the future trajectory of prices, but there are no changes possible in these

coefficients subsequent to the installation of a given cohort. Third, upper

bounds are imposed upon the rates of market penetration for new supply

technologies. Moreover, there are lower bounds to ensure that older technolo

gies will not be phased out too rapidly.

18.3 KEY DEMAND PARAMETERS

Three of the demand parameters (potential

GNP

growth, ESUB and AEEI)

are crucial to the debate over energy and environmental futures. There is no

easy way to estimate these coefficients econometrically. The values adopted

here have been determined so that our model will track closely with the

conventional wisdom expressed, for example, by the median poll responses of

the International Energy Workshop.

One key parameter

is

the rate of growth of the labour force and potential

GNP. In parallel with the slowdown of population growth during the twenty

first century, there

will be

a diminishing rate of growth of GNP, and hence a

slowdown in the demand for energy.

It

is assumed that the United States'

potential annual GNP growth rate will be nearly 3% from 1990 to

2000,

and



Assumptions for electricity

generation

329

that it

will

slow down to 1% during the latter half of the twenty-first century.

Even with a stationary population, this would allow for a modest increase in

per capita living standards.

ESUB represents the elasticity of price-induced substitution between capital

labour and energy. (For a demonstration of the importance of this parameter,

see EMF, 1977.) Over the long run, there is a good deal of possible substituta

bility between the inputs of capital, labour and energy. The degree of sub

stitutability

will

affect the economic losses from energy scarcities and price

increases. One example of such a trade-off would be insulation to replace

heating fuels in homes and other structures. A second example would be the

increased use of heat exchangers and of cogeneration within industry. In the

aggregate, the ease or difficulty of these trade-offs

is

summarized by ESUB,

here taken to be 0.40. The higher the value of ESUB, the less expensive it is to

decouple energy consumption from GNP growth during a period of rising

energy prices. When energy costs are a small fraction of total output, ESUB is

approximately equal to the absolute value of the price elasticity of demand.

Finally, there is AEEI, the rate of autonomous (non-price-induced) energy

efficiency improvements. In econometric investigations of the post-1947 histori

cal record, there has been no evidence for autonomous time trends of this type

(see

Hogan,

1988;

Brown and Phillips, 1989; Jorgenson and Wilcoxen, 1989).

Technologically orientated end-use analysts, however, have suggested that

non-price efficiency improvements may

be

induced by changes in government

policy,

e.g.

a mandatory doubling or quadrupling of the average fuel efficiency

of automobiles during the course of several decades

(see

Goldemberg

et

ai.,

1987). Clearly the AEEI parameter is highly controversial. In order to represent

two distinct viewpoints, we begin with a zero value for this parameter, and then

explore the implications of a high efficiency scenario.

18.4

SUPPLY AND COST ASSUMPTIONS

FOR

ELECTRICITY

GENERATION

Table

18.1

identifies the alternative sources of electricity supply that are

included in Global 2100. The first five technologies represent the sources of

electricity that exist within the United States today: hydroelectric and other

renewables, gas-, oil- and coal-fired units and nuclear power plants. The second

group of technologies includes the new generation options available for the

future. These differ in terms of their projected costs, carbon emission rates and

dates of introduction. Table

18.2

contains a summary of our cost and

performance estimates for new electricity supply technologies.

Figure 18.3 shows the breakdown of electricity generation by source for

1985.

In that year, natural gas-fired plants produced 12% of the electricity in

the United States, and coal-fired units produced 57%. Coal produces almost

twice as much carbon per kilowatt hour as natural gas.

If

sufficient natural gas



330

Technology

Existing

Hydro

Gas-E

Oil-E

Coal-E

LWR

New

Gas-N

Coal-N

Coal-R

ADV-HC

ADV-LC

CO

2

emission

limits

Table

18.1

Identification

of

electricity generation technologies

Earliest possible

introduction data

1995

1990

2005

2015

2010

2010

Identification

Hydroelectric

Remaining initial gas-fired

Remaining initial oil-fired

Remaining initial coal-fired

Remaining initial nuclear

Advanced

combined cycle

New coal-fired

Coal gasification

(20%

CO

2

removal)

Coal gasification (93% CO

2

removal)

High-cost non-carbon-based

Low-cost non-carbon-based

aEstimated year when the technology could provide 0.1 trillion kWh (approximately 20 GW of

installed capacity at 60% capacity factor).

were available, a conversion from coal to natural gas would therefore make it

possible to achieve a substantial reduction in carbon emissions.

I t

is

expected that new gas-fired capacity for base load electricity will be

produced by combustion turbine combined cycle plants. These units have a

high thermal efficiency

and

relatively low costs. If natural gas prices remain at

their 1988 levels, this technology would represent

an

attractive source of

electricity. In the absence of large-scale discoveries, however, domestic natural

gas resources will gradually become exhausted, and fuel prices will rise. For

example, in a baseline projection for the Gas Research Institute, Woods (1988)

projects a tripling of wellhead prices by 2010. With such an increase, gas-fired

electricity would lose its competitive advantage over coal,

see

also section

7.3.

Two categories

of

coal-fired technologies are considered - those without and

those with CO

2

emissions control. The first category includes

both

existing and

new pulverized coal technologies. Most of the existing pulverized coal plants

do not have flue gas desulphurization units.

As

a result, they have much lower

operating costs than new pulverized coal plants.

CO

2

can be separated either from the flue gas of an atmospheric boiler or

from the fuel gas produced within an integrated gasification-combined cycle

(IGCC) plant. According to the studies reviewed by Vejtasa and Schulman

(1989), the latter appears to be more cost-effective for new power plants. Table

18.2 contains the cost and performance

data

for coal gasification technologies

with 20% and 93% CO

2

recovery.

Separation

of

CO

2

does not solve the problem of permanent disposal.

Technically, this gas could be injected into the oceans or into depleted natural

gas fields. For purposes of Table 18.2, disposal costs are based upon compress

ing the recovered CO

2

at the generating station, transporting it by a

new



T

a

b

e

1

2

C

a

n

p

o

m

n

e

m

e

f

o

n

w

p

w

g

n

a

o

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h

o

e

C

b

C

c

o

m

n

s

T

h

o

H

a

r

a

e

C

a

e

m

s

o

F

c

o

n

m

(

B

u

k

W

c

o

(

k

W

c

o

c

e

n

(

M

u

F

O

&

M

C

a

T

a

G

N

7

5

0

1

2

3

1

5

3

3

1

4

3

2

C

-

N

9

1

0

2

1

5

1

7

1

2

2

7

5

6

C

-

R

C

g

f

(

2

%

C

O

r

e

m

)

9

1

0

1

1

5

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0

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4

3

3

5

7

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d

s

p

a

-

1

m

e

p

p

n

7

1

0

1

2

2

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T

a

1

5

9

C

g

f

(

9

%

C

O

r

e

m

)

1

3

1

0

0

1

5

1

5

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3

2

6

4

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d

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p

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m

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p

p

n

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3

0

3

6

6

6

T

a

2

7

0

A

H

1

0

2

0

0

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0

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9

1

9

A

L

C

1

2

1

0

0

0

8

8

2

1

5

3

3

5

O

'

C

a

p

o

m

d

a

a

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f

o

m

V

a

a

S

m

(

1

b

C

b

E

m

s

o

C

c

e

(

C

s

d

n

a

b

o

o

o

o

c

b

e

m

e

o

h

a

m

p

e

p

o

k

W

h

(

b

o

o

T

W

h

o

e

e

c

y

g

a

e

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m

(

D

m

1

M

h

d

B

o

p

c

o

g

n

1

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p

c

a

e

n

o

h

o

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a

h

r

s

o

m

f

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s

s

m

a

o

A

B

a

I

G

w

h

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c

o

.

h

p

n

a

2

%

c

y

i

A

so

a

a

b

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m

b

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A

n

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w

h

p

v

s

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f

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e



332

Coal

CO

2

emission limits

Gas

12%

Fig. 18.3 Electricity generation

(by

source,

1985)

pipeline for 100 miles, and then disposing of the gas either in the oceans or in

distant natural gas fields via the existing pipeline network. The feasibility of

these disposal options

is

highly speculative. In the following analysis, we

calculate the benefits of solving the problem of permanent disposal.

ADV-HC and ACV-LC respectively refer to high- and low-cost non

carbon-based electricity generating technologies. Although any of a number of

technologies could

be included in these categories, the cost and performance

data contained in Table 18.2 are based upon specific designs considered in

EPRI's

Technical Assessment Guide

(1989).

The representative high cost source

is an advanced solar technology with cost and performance characteristics

similar to those for concentrator photovoltaic cells. (Alternatively, this might

be a biomass-based generating unit or some combination of the two.) The low

cost source is an advanced nuclear design with passive safety features. In our

judgement, 2010

is

the earliest availability date for 0.1 TkWh of electricity from

this technology.

18.5

NON-ELECTRIC SUPPLY TECHNOLOGIES

The non-electric energy supply technologies are listed in Table 18.3. The

individual fuels are ranked in ascending order of their cost per million BTU of

non-electric energy. The least expensive

is

CLDU. This category accounts for

direct uses of coal in industries such as iron and steel, cement, etc. Its growth



T

a

b

e

1

3

N

e

e

r

c

e

n

g

s

u

e

U

b

T

h

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a

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c

p

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c

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(

q

c

o

c

e

n

C

D

C

-

d

r

e

ue

2

0

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o

w

h

a

2

%

h

0

0

r

a

e

o

G

O

-D

O

-

d

m

c

2

5

C

a

n

r

a

o

m

0

0

G

N

u

a

g

1

5

+

C

a

n

r

a

o

m

0

0

1

2

a

d

o

d

s

r

b

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co

s

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-M

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m

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e

x

s

5

0

n

2

2

q

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r

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d

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Y

S

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s

1

0

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o

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4

1

%

p

a

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B

A

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e

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o

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o

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8

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3

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%

5%



334

CO

2

emission limits

rate is taken to be only 20% that of the GNP. Next in the 'merit order' are

domestic oil and gas. These exhaustible resources are available at constant

marginal costs, but are subject to upper bounds based on a model of reserves

and resource depletion.

For

specifying the upper bounds on exhaustible hydrocarbon resources, we

draw a sharp distinction between current

reserves

and the remaining stock of

undiscovered resources. Because cost estimation is exceedingly hazardous

in

this area, we do not attempt to provide an explicit economic rationale through

rising marginal cost curves. Instead, a constant ratio model

is

employed to

determine an upper bound on the annual rate of oil and gas production. There

is also the possibility of

delaying

the exploitation of these resources.

Reserves of exhaustible resources are depleted by current production, and

are augmented by

new

discoveries. Production

is

a fixed fraction of reserves

(the

1990

production-reserve ratio), and new discoveries are a

fixed

fraction

(5% per year) of the remaining undiscovered resources. When the production

reserve ratio exceeds the resource depletion factor (RDF), it can be shown that

the RDF governs the ultimate rate of decline. Figure

18.4

illustrates the

comparison between a 3% versus a 5% value of the RDF. With 3%

for

this

parameter, production drops off even more rapidly during the years up to

2030.

Our reserve and resource estimates are taken from the 5th percentile point

along the probability distributions available from the Geological Survey work

by Masters et al.

(1987).

This source provides a modal (i.e., most likely) estimate

of resources along with the 5th and 95th percentile. For practical purposes, the

95th percentile point indicates a lower bound on undiscovered resources, and

the 5th percentile indicates an upper bound. That is, according to the USGS,

there is only a 5% probability that undiscovered conventional resources will

exceed the 5th percentile values. For Global

2100, we

have adopted the USGS

upper

bound on natural gas resources. Had our calculations been based upon

the modal or the 95th percentile, the prospects for domestic natural gas

production would

be

considerably more pessimistic than the case examined

here. According to Figure

18.4,

there

is

no prospect that conventional domestic

gas resources will permit a significant expansion of consumption above its 1990

level. Since the production-reserve-resource ratios for domestic crude oil are

similar to those

for

natural gas, a similar conclusion also holds

for

this

hydrocarbon resource.

Excluding economic rents, it is assumed that oil imports net of exports

(abbreviated OIL-MX) are more expensive than domestic supplies. Interna

tional oil prices are projected to rise over time, but are assumed to be

independent of the quantities imported by the United States at

anyone

point

in time. Typically, this option is also pushed to its upper bound - a limit of 20

quads based upon national security considerations. In the absence of this

bound, the United States would import significantly higher quantities of oil.

For modelling purposes, it

is

assumed that oil imports would be limited either

by tariffs or quotas, not by 'voluntary export restraints'.



A constrained energy supply scenario

335

quads

25r------------------------------------------,

20

15

10

5

o

__ _ ~ _ ~ _ ~

1960 1980 2000 2020

2040

2060

2080 2100

Fig.

18.4

Natural gas consumption (two alternative

RDF

values)

According to Table 18.3, there are two high-cost backstop options - both

available in unlimited quantities: SYNF (synthetic

fuels

based on coal or shale

oil) and NE-BAK

(e.g.,

biomass fuels or hydrogen by electrolysis, using a

non-carbon-based source of electricity). NE-BAK emits no carbon, but is likely

to be more expensive than synthetic

fuels

based upon coal or shale oil. One or

the other of these high-cost technologies will impose an upper bound upon the

cost of non-electric energy - depending on whether or not there is a carbon

constraint.

18.6 A CONSTRAINED ENERGY SUPPLY SCENARIO

In this section,

we

explore the implications of a carbon constraint for the

energy sector and for the economy as a whole. Carbon constraints can take a



336

CO

2

emission

limits

variety of shapes and forms. Legislative proposals have ranged from slowing

the future growth rate to reducing CO

2

emissions to half their current levels.

Because of the wide range of options under consideration, Global 2100 has

been designed with a great deal of flexibility regarding the imposition of carbon

constraints. Here we illustrate the capabilities of the model by calculating the

economic costs associated with just one set of emission reduction targets.

Specifically, we investigate the costs of restricting carbon emissions to

1.37

billion tons (their 1990 rate) up to 2000, reducing them gradually to 80% of

this level by 2020, and stabilizing them thereafter. Although these targets are

not as stringent as those contained in some proposed legislation, they never

theless represent a substantial reduction in future emissions when compared

with a business-as-usual

view.

The impacts of a CO

2

limit will depend on the technologies and resources

available for meeting energy demands as well as the demands themselves. Table

18.4 summarizes five energy supply-demand scenarios under which the impacts

of this carbon constraint will be analysed. Scenario I represents the most

constrained case - both from the perspective of supply enhancement and

demand conservation. On the supply side, we have excluded the coal technolo

gies with CO

2

removal capabilities (Coal-R) and the low cost non-carbon

based sources of electricity (ADV-LC). We begin with such a highly con

strained supply scenario in order to establish a basis for calculating the benefits

of alternative generation options having lower CO

2

emissions. These alterna

tives include advanced nuclear power and also coal gasification with CO

2

removal capabilities.

On the demand side, the distinguishing characteristic of scenario I

is the rate

of autonomous energy efficiency improvements. We assume a zero value for

the AEEI parameter. As in the case of electricity supplies, we start with the

most constrained case and then assess the benefits from measures which reduce

CO

2

emissions.

A carbon constraint

will

have both direct and indirect consequences for the

economy. Because of the absence of low cost alternatives, the economic impacts

Table 18.4

Five energy supply-demand scenarios

Scenario Supplies Demands

I

Constrained case AEEI = 0.0% per year

II Constrained case AEEI = 0.0% per year

plus Coal-R

III Constrained case AEEI = 0.0% per year

plus ADV-LC

IV

Constrained case AEEI

=

1.0% per year

V Constrained case AEEI = 1.0% per year

plus Coal-R and ADV-LC



A

constrained energy supply scenario

337

will be highest in scenario I. To understand the source of these impacts and to

gain some insight into the workings of the model, it will be useful to explore

how the energy sector might evolve over the next century under such a highly

constrained scenario.

Figures 18.5a and 5b show a snapshot of the energy sector at two points in

time, 2010 and 2030, under scenario I. In each instance, the optimal combina

tion of supply alternatives is shown - with and without the carbon constraint.

Within the electricity sector (Figure

18.5a),

note the importance of price

induced conservation. A carbon constraint limits the options for electricity

generation, raises the price of electricity, and this in

tum

drives down demand.

With a carbon constraint and in the absence of an economical CO

2

removal

capability or a low cost carbon-free alternative, the options for meeting

electricity demand are severely limited. One alternative is greater reliance on

natural gas. Indeed, Figure

18.5a

shows a significant rise in the use of this low

carbon fuel in the electric sector. Recall that gas-fired plants produced only

12% of total electricity in 1985. According to Global 2100, the share of gas will

rise to 27% by 2010 if we are in a carbon-constrained environment.

Increased demands for natural gas from the electric sector will eventually

place tremendous pressure on natural gas markets. Our calculations show that

by 2010 the price of natural gas will approach the point where high cost

renewables (ADV-HC) become an attractive source of electricity. Despite the

cost,

we

see a substantial role for this supply category if carbon emissions are

constrained, and low cost alternatives are unavailable. In such a world, high

cost renewables would by necessity become the marginal source of electricity

supply.

Without a carbon constraint, the picture is entirely different. According to

Figure

18.5a,

coal would once again be the fuel of choice and would supply an

increasingly larger share of the load. Coal-fired electricity is plentiful and

relatively inexpensive. Although natural gas prices would not rise as rapidly as

with a carbon limit, the geological resource constraints and competing de

mands from the non-electric sector will nevertheless lead to significant price

increases. The results of Global 2100 are broadly consistent with the estimates

that appear in Woods (1988). In his baseline projection for the Gas Research

Institute, he projects a tripling of wellhead prices by 2010.

Now consider the non-electric side of the overall energy balance (Figure

18.5b). In the short term, up to

2010,

a carbon constraint would be felt mainly

through its impact on the price of natural gas. High prices will in tum lead to

lower demand through price-induced conservation.

The impacts of a carbon constraint on the non-electric sector become more

pronounced over time. As indicated by Figure

18.5b,

if

CO

2

emissions were not

a concern, the burden of meeting non-electric demands would eventually shift

to carbon-based synthetic

fuels.

Such a shift is infeasible, however, if there are

stringent limitations on carbon emissions. We would then have to rely upon a

non-carbon source such as hydrogen produced by electrolysis (NE-BAK).



E

e

c

e

g

2

T

W

h

1

r

8

6

N

c

b

m

4

C

b

m

2 o

I

r

n

T

T

U

F

T

T

F

@

f

p

f

@

f

@

_

H

o

[

L

W

R

E

G

I

Z

m

§

A

H

N

e

e

c

e

g

2

q

8

i

N

c

b

m

6

C

b

m

4 2

I

i

I

~

~

.

1

'

1

'

1

'

1

'

1

'

1

1

o

[

g

W

j

p

_

I

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2

I

C

D

0

G

S

r

n

O

-

D

D

O

M

§

N

E

B

A

K

E

e

c

e

g

2

T

W

h

1

8

N

c

b

m

6

C

b

m

4

2

o

[

0

.

-

1

/

6

6

(

6

(

(

6

(

(

6

6

6

(

6

6

_

H

o

t

G

IZ

Z

Z

J

C

§

A

H

N

e

e

c

e

g

2

q

8

i

N

c

b

m

C

b

m

6 4

2

I

~

~

o

3

P

B

±

f

W

%

~

L

D

U

m

G

a

S

Y

N

F

r

J

O

i

D

C

O

M

§

N

E

B

A

K

F

g

1

5

E

e

r

c

en

g

p

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(

s

c

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I



A constrained energy supply scenario

339

According to Table 18.3, this type of

fuel

is likely to be considerably more

expensive than, for example, gasoline from oil shale or from direct coal

liq uefaction.

Using Global 2100, we may add together the costs throughout the economic

system and calculate the annual losses in consumption due to the carbon

constraint. Figure 18.6 shows the losses in each time period for scenario I.

The effects of a carbon constraint do not begin to have measurable macro

economic consequences until 2010. At that point the rise in energy prices

begins to have a significant

effect

upon the share of gross output available for

current consumption. By 2030, roughly 5% of total annual macroeconomic

consumption is lost as a consequence of the carbon constraint. This percentage

remains relatively constant for the remainder of the time horizon. Adding over

all the years from 1990 to

2100,

the present value of these losses would

be $3.6

trillion, discounting to 1990 at 5% per year.

4

It

is instructive to look at the time-path of CO

2

emissions in the absence of

a carbon constraint. From Figure 18.7, we see that there could be a six-fold

increase between 1990 and

2100.

This represents an average annual growth rate

$

trillion

1 0 0 c - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~

10

Annual losses due

0.1 _ _ _ _ _ _ _ _

_ _ _ _

_ _

_ L

_ _ _ _ _ _ _ _ _ _ _ _

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Fig. 18.6 Aggregate consumption and losses due to carbon limit - scenario I

4 A 5% discount rate

is

consistent with the numerical assumptions that underlie the economy-wide

production function, equation (18.2).

For

all cases reported here, we employ 24% as capital's share

of the GNP, 2.4 as the initial capital-GNP ratio and 5% as the net annual rate of depreciation of

the capital stock.



340

CO

2

emission limits

Billion tonnes of carbon

10

r--------------------------------------------,

8

6

No carbon limit

2

Carbon limit

o ~ - - ~ - - ~ - - ~ - - ~ - - ~ - - ~ - - ~ - - ~ - - ~ - - ~ - - ~

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Fig. 18.7 Carbon emissions -

scenario

I

of about 1.7% per year. Although the increase

is

large, it should come as no

surprise. In the absence of a carbon constraint, carbon based fuels are the most

economical source of supply in both the electric and non-electric sectors.

There are a variety of policy instruments available

for

reducing emissions to

the desired levels. One frequently discussed option

is

to impose a uniform tax

upon those activities responsible for carbon emissions. Figure 18.8 shows the

size of such a tax for scenario I. The tax

is

relatively low in 2000

($29

per ton

of carbon), then rises sharply as emission limits are tightened. In the absence

of low-carbon supply alternatives, consumers are willing to pay a high price to

burn carbon-based fuels.

The tax must be sufficiently high to discourage these

demands.

By

the middle of the twenty-first century, sufficient additional

capacity is available to stabilize the tax at about $250 per ton of carbon. In

this scenario, the long-run equilibrium tax

is

determined by the cost and

emission coefficients of the synthetic fuels and non-electric backstop supply

technologies.

18.7

REDUCING

THE

COSTS

OF

A CARBON CONSTRAINT

Of

our cases, scenario I

is

the most constrained - from the perspective of

supply enhancement and demand conservation. Experience has shown that

energy forecasting, even over a few decades, is a highly inexact art.

At

best, one

can ask a series of 'what if' questions' in the hope of gaining some insights into



Reducing the costs

of

a

carbon

constraint

341

Dollars per tonne of carbon

700 . - - - - - - - - - - - - - - - - - - - - - - - - - ,

600

500

400

300

200

100

O ~ k ~ ~ ~ ~ ~ ~ ~ ~ ~ ~

2000 2010 2020 2030 2040 2050 2060 2070

2080 2090 2100

Fig. 18.8 Carbon tax - Scenario I

the relative attractiveness of various means of reducing CO

2

emissions.

I t

is

in

this spirit that we have examined several alternatives - first individually and

then in combination.

In scenario II, we calculate the benefits that might accure from the successful

development of a cost-effective means of CO

2

removal and disposal of the gas

in a manner that prevents it from reaching the atmosphere. Recall that the cost

and performance data contained in Table 18.2 are

for

coal gasification with

20% and 93% carbon removal.

Scenario III explores the benefits from a low-cost non-carbon-based source

of electricity. Here our costs and performance data are based on those for an

advanced nuclear technology with passive safety features

(see

EPRI's

Technical

Assessment Guide, 1989).

Figure 18.9 shows these benefits in terms of reductions in the costs of a

carbon constraint. For developing a CO

2

removal and disposal capability with

the assumed characteristics, the discounted benefits are about

$0.6

trillon (the

difference in consumption losses between scenarios I and II). The discounted

benefits from a low-cost non-carbon-based source of electricity are

$1.2

trillion.

The benefits from these two technologies would not be additive. To a certain

extent, the two technologies would

be

substitutes for each other. However,

because of constraints on the rate that any single technology can be introduced,

there are benefits from deploying both.

On the demand side, a major issue

is

the rate of autonomous energy

efficiency improvements. Up to this point,

we

have assumed that the AEEI is

zero. That is, there are no energy efficiency improvements except those that are



342

CO

2

emission

limits

$

trillion

6r---------------------------------------------,

5

4 Constrained

3

2

Scenario I Scenario II Scenario III Scenario IV Scenario V

Fig.

18.9.

Consumption losses (1990-2100)

price-induced.

For

scenario IV - the high efficiency case - we assume an AEEI

of 1.0% per year.

By

comparison with scenario I, energy demands in 2050

would be nearly halved. Figure 18.9 shows that this huge reduction in energy

requirements would significantly lower the cost of a carbon constraint. The

total discounted consumption losses would drop to $1.8 trillion. By definition,

no consumption losses are imputed to autonomous energy efficiency im

provements. It is highly controversial whether such rapid improvements are

indeed achievable. Through scenario analysis, we have sought to bound the

broad range of viewpoints on this issue.

The rightmost bar on Figure 18.9 shows the costs of a carbon limit in the

best of all worlds defined by these

five

scenarios. That

is,

both Coal-R and

ADV-LC are available, and autonomous (non-price) demand reductions occur

at the rate of 1.0% per year. In this case, the discounted costs of the carbon

limit

fall

to $0.8 trillion. This would be only 22% of the losses incurred under

the constraints of scenario I.

Finally, we compare the emissions for all five scenarios - both with and

without a carbon limit (Figure

18.10).

When carbon emissions are uncon

strained, the profiles for scenarios I and II are identical. There would be no

purpose in deploying the carbon removal and disposal technology (Coal-R) if

carbon emissions were unconstrained.

Scenario III includes a low-cost non-carbon-based source of electricity

(ADV-LC). Such a technology would be attractive for economic reasons alone.

I f

it were introduced in 2010, it would assume an increasing share of the electric

load thereafter. This automatically leads to a substantial reduction in the rate

of growth of carbon emissions.



The potential benefits

from

Rand D

Billion tonnes

of

carbon

1 0 ~ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

8

6

--

-

:-;

..

. ·o

. . , . .;..'.:-' "AEEI=1.0

/o

- . .....-......: .-: . . .::. . ..:.--

AOV-LC

and AEEI=1.0%

4

2

Carbon

limit

O ~ - - . - ~ r - - . - - - . - - - - - - r - - - r - - . - - - . - - - - ~

1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Fig. 18.10 Carbon emissions with and without carbon limit

343

Scenario

IV

differs from scenario I only on the demand side.

It

includes

energy efficiency improvements

at

the annual rate of 1.0%. This leads directly

to lower demands for electric and non-electric energy - and therefore to lower

carbon emissions. When these lower demands are combined with a low-cost

and non-carbon-based source of electricity, carbon emissions are reduced still

further (scenario

V).

Even with the technological advances implied by scenario

V,

unconstrained carbon emissions would still exceed our carbon limit

by

a

factor of two.

18.8.

THE POTENTIAL BENEFITS

FROM

R&D

In this chapter, we have calculated the macroeconomic impacts oflimiting CO

2

emissions under alternative scenarios.

I f emission controls are required,

there

will be significant costs, but it

is

clear that the nation can reduce the size of the

ultimate bill through R&D in both the supply and demand sides of the energy

sector. According to our calculations, the combination of potential im

provements could reduce the costs of a carbon constraint - perhaps by several

trillion dollars.

These savings

will

not occur unless there are sustained research and

development programmes on a wide variety of fronts - both in the public and

the private sector. On the supply side, consistent long-term funding is needed

to promote non-carbon energy sources such as solar, fission and fusion. R&D

on CO

2

emissions control could have a substantial payoff - provided there

is

an economical solution to the problem of permanent disposal. There are also



344

CO

2

emission limits

gains to be anticipated from more efficient processes for the conversion of

conventional

fuels into secondary energy forms such

as

electricity.

On

the demand side, there may also be an enormous potential. Following

the oil price explosion of 1973, there has been a remarkable improvement in

the efficiency of energy utilization. It is unclear how long this trend will

continue. Some of this may be the result of an autonomous time trend, but a

good deal may

be

attributed directly to the price mechanism. Research is

needed to clarify the role of conservation, and to ensure that cost-effective

options are available to the greatest extent possible.

This paper has focused on the costs associated with one set of emission

reduction targets. Given the current state of knowledge, it

is

unclear whether

it would be justified to incur these costs. There remain a number of uncertain

ties in our understanding of the greenhouse effect and in the likely effectiveness

of various countermeasures. As observed by Clark (1985), 'every responsible

scientific assessment of the last several years has noted (if not always empha

sized) how thoroughly uncertainties pervade the carbon dioxide question'.

These uncertainties

will

not be resolved in the near future.

It would be extremely costly to wait for scientific certainty on the impact of

greehouse gases upon global climate before committing to a vigorous R&D

programme. New technologies require many years for market penetration. If it

turns out that substantial reductions in

CO

2

emissions are needed, it will be

important to have the means available for achieving such reductions in a timely

manner. This can only be accomplished through a sustained commitment to

R&D.

ACKNOWLEDGEMENTS

The research reported in this paper was funded by the Electric Power Research

Institute (EPRI). The views presented here are solely those of the authors, and

do not necessarily report the

views

of EPRI or its members. The authors are

much indebted to Diane Erdmann and to Lawrence Gallant for research

assistance. Helpful comments have been provided by George Booras, Jae

Edmonds, George Hidy, William Hogan, Dale Jorgenson, Stephen Peck, Scott

Rogers, John Rowse, Chauncey Starr, Stanley Vejtasa, Gary Vine and Robert

Williams.

An

earlier version of this chapter appeared as a paper in

The Energy

Journal,

2(2), April

1990.

REFERENCES

Allen, R. G. D.

(1968)

Macroeconomic Theory. Macmillan, New York.

Brown, S. P. A. and Phillips,

K.

R. (1989) An Econometric Analysis

oj

US Oil Demand.

Federal Reserve Bank of Dallas.

Brooke, A., Kendrick, D. and Meeraus, A.

(1988)

GAMS: A User's Guide. Scientific

Press, Redwood City California.



References

345

Clark,

W. C.

(1985) On the Practical Implications

of

the Carbon Dioxide Question.

International Institute for Applied Systems Analysis, Laxenburg, Austria.

Edmonds,

J.

and Reilly,

J.

M. (1985) Global Energy - Assessing the Future. Oxford

University Press, New York.

EIA (Energy Information Administration) (1989) Monthly Energy Review, US Depart

ment of Energy, Washington, DC.

EMF (Energy Modeling Forum) (1977) Energy and the Economy. Report 1, Energy

Modeling Forum, Standford University, Stanford, Ca.

EPRI, (1989) Technical Assessment Guide. Electric Power Research Institute, Palo Alto,

Ca.

Goldemberg, J., Johansson, T. B., Reddy, A. K. N. et al. (1987) Energy for a Sustainable

World. World Resources Institute, Washington, DC.

Jorgenson, D.

W.

and Wilcoxen, P.

J.

(1989) Environmental Regulation and US Economic

Growth.

Harvard University, Cambridge, Mass.

Hogan,

W. W. (1988)

Patterns

of

Energy Use Revisited. Harvard University, Cambridge,

Mass.

Manne,

A.

(1981) ETA-MACRO: A User's Guide. EA-1724, Electric Power Research

Institute, Palo Alto, Ca.

Masters,

C.

D., Attanasi, E. D., Dietzman,

W.

D.

(1987)

World Resources of Crude Oil,

Natural Gas, Natural Bitumen, and Shale Oil. 12th World Petroleum Congress,

Proceedings, vol.

5.

Rind, D. (1989) A Character Sketch of Greenhouse. EPA Journal,

15,

no.

1.

Vejtasa,

S.

A. and Schulman, B. L. (1989) Technology Datafor Carbon Dioxide Emission

Model: Global 2100.

SFA Pacific, Inc., Mountain View, Ca.

Woods,

T.

J. (1988)

The

Long-Term Trends

in

US Gas Supply and Prices: the

1988

GRI

Baseline Projection

of us

Energy Supply and Demand to 2010. Gas Research Institute,

Washington, DC.



Index

Abatement 314

Accommodation 157

Adjustment mechanism

143

Advanced supply technology

330

AEEI 295, 326, 342

Aggregation 11, 188

Agriculture 53

Alternative models of transport 75

Appraisal

97

Asymmetric elasticities 158

Autonomous energy efficiency, see AEEI

Average practice 31

Backstop 335

Balance sheets 227

Behavioural lag 14

Beneficiation

94

Best-practice 31

Bias 77

Budget deficit

170

Capacity 32

expansion 38

utilization 33,

228

Captive markets

235

Carbon

emissions 293

quota rights 325

tax 179,340

see also CO

2

Cartel

263

see also OPEC

CGE 145,299

CGP 187

China 287

CO

2

emission 180, 323

CO

2

removal 336

Coal 81

characteristics

86

depletion

87

seam thickness 90

supply modelling 86

transport cost 87

Cobb-Douglas 33

Co integration 75

Comparative statics

164

Composition of economic output 49

Computable general equilibrium,

see

CGE

Concession 129

Conditional factor demand 41

Conservation

209, 244, 292,

326, 340

Continous mining

91

Contract form

129

Cores

98

Cost function 16

Cross-section 69

Cumulative discoveries

275

Decomposable systems 233

Deflationary spiral 169

Demand

distributed lag 71

dynamic model 2, 68-74

elasticities

8-9

growth 37

Koyck model

70

partial adjustment 69

static model

68

Demography 224

Derived demand 67

Detailed simulation

221

Developing countries,

see LDC

Disaggregation

227

Discovery 97, 124

Distributed lag

270

Distibution infrastracture

113

Distributional effects

315

Divisia index 47

Drilling effort 108

Dynamic adjustment 198

Dynamic linear regression 269

Eastern European 226

EC (European Community)

180,293

ECC

141

Ecological efficiency

299

Economic planning 187

Economic structure 47

Efficiency

227



348

Index

Effluent charges

300

EFOM 1, 175, 185

Elasticity

inferred

239

oil and product 248-50

scale 35

Electricity demand 26, 53

Electricity generation 330

Electricity intensity 59

Electrification 57, 209

Embodied technical progress 31

EMF 17,61,239

Emission

abatement 323

permits 143-5

Endowment inventory 82

End-use energy service

113

Energy 191, 227

aggregate intensity 48

balance 206

efficiency

26,

204

exchange flows 222

household 189

industry intensity 49

output ratio

51

policy 157

price 37

production 191

programmes 209

saving, see Conservation

supply 193

tax 157-64

Energy Modeling Forum, see EMF

Energy-economy interaction 27, 186,326

Energy-environment 230

Engineering approach

224

ENMARK

148

Environmental cost functions, see ECC

Environmental policy 180,299, 312

Error correction

76

ETA-MACRO 242, 325

Excess supply

171

Exhaustible resources 334

Exogenous supply 193

Expected profit 123

Exploration 97, 118

Extraction cost

85

Flexible functional forms

16

Forecast 11, 60

Fuel choice

13

Fuel intensity 58

Fuel prices

19

Gas

resources

107

transportation 112

Gasoline,

see

Transport

fuel

Geographical distribution

111

Geographical disaggregation 222

Geological and technical factors 127

Geological assurance 83

Geological risk 98

Global 2100 324

Global warming 323

Greenhouse gas 233, 323

GRI (Gas Research Inst) 296

Growth impact 315

Gulf 234

Heat 52

HERMES 159, 175, 188

Hierarchical models 209

Historical trends 58

Hydrogen 337

lEA 1,

296

lEW

287,328

IIASA

1,

141, 287

Incentives

128

Income elasticity 76,

253

Individual fuels 291

Industrial organization 312

Innovation incentives 300

Input coefficients 31

Interetemporal equilibrium 303

Interfuel competition

27

Interfuel substitution 326

Internal market, see EC

International energy workshop,

see

lEW

Intertemporal production theory 306

Inverted

V 72

ISTUM

18,61

Judgmental attitudes 110

Kakutani's theorem 308

Koyck 270

Kuwait 243

Lag structure

71

LDC 133-7,226

Less polluting technologies 309

Level of recovery

93

Licence 129

Linked models 179

Long-run

251



Macroeconomic

cost 160, 339

effects 229

modelling

158

shocks 27

structures 195

MARKALI

Market economy 57

Market power 147

Maximum usefulness factor

89

MDE

189

MEDEE 175, 185

Median lag

272

Median poll 289

MELODIE 188

Methane

111

MIDAS 175

Model

bottom up 189, 223

disaggregated 221

duality 15

dynamic 207

econometric 14, 89, 120

equilibrium 21, 186

fuel

supply

208

hybrid 17, 224

input-output

186

macroeconomic 157-77

neo-Keynesian

159

optimization

15, 120, 179

probabilistic 15, 108

process and technical 13, 89, 178

top-down 13, 189, 223

typology 185

Monetary

157

Monopolization

132

Motoring, see Transport

fuel

Multilevel planning

209

Myopic models 303

Natural gas

interregional trade 114

resources 105

Neo-Austrian school 304

Netback 123

NMP-

53

Non-carbon

337

Non-conventional resources

111

Non-linear optimization 326

Non-OPEC supply 273

Nuclear 292

OECD

226, 287

Index

Oil exploration 117

Oil products 158

Oil supply 117-18

see also

World

oil

Oligopolistic game 137

Omitted variable 77

OPEC

227, 240, 263

dilemma 282

strategy 228

Optimal policy

34

Optimal price-path

283

Optimization

1,

263

Overburden

89

Perestroika

293

Phillips curve 177

Planning 203

POLES 221-30

Political factors 132

Political risk 128

Poll responses 287

Pollution treatment 310

Polynomial distributed lags 76

Pooled data 75

Post-Keynesian

195

Power parity 268

Price elasticity 76, 271

Price hike

228

Price of crude 288

Price of reserves

122

Price reaction function

264

Price-dependent substitution 302

Price-response 229

Price-takers 264

Primary energy 194, 290

Production

average 31

best-practice

31

putty-clay

177

sharing

130

structure

38

technique 306

Projections 203

Public sector 170

Purchasing 268

Putty-clay 197

Quasi rent 38

R & D 343

RAINS 141

Recoverability 91

Reduced form 73

349

international energy economics

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