presentations of the oecd 2nd circle technical workshop (2-3 oct. 2014)

Paris, 2-3 October 2014

Thursday 2 October 2014 (Day 1)

09:30 – 10:00 Opening session

Speakers Shardul Agrawala (OECD)

This short opening session presents the background for the workshop. It informs

participants of the general progress made so far in the CIRCLE project and the guidance

given by EPOC.

Background

material

• “CIRCLE: Assessing environmental feedbacks on economic growth and the benefits (and

trade-offs) of policy action; Scoping Paper”, ENV/EPOC(2013)15

• “CIRCLE: Overview, approach and update”, ENV/EPOC(2014)7

10:00 – 11:30 The land-water-energy nexus

Speakers Ton Manders, Netherlands Environmental Assessment Agency (PBL)

Rob Dellink (OECD)

Key questions How are the biophysical linkages between water, energy and land use represented in the

IMAGE model?

How can these biophysical aspects be coupled to an economic model?

Which biophysical aspects of the land-water-energy nexus are most crucial for economic

growth?

Background

material

“Economic impacts of the land-water-energy nexus; exploring its feedbacks on the global

economy”, ENV/EPOC(2014)15

11:30 – 12:00 Coffee break2

CIRCLE Worshop Outline – Day 1 (AM)

Second ad-hoc technical workshop on

CIRCLE, 2-3 October 2014, OECD, Paris

3

Economic Impacts of the Land-Water-Energy Nexus

Exploring its feedbacks on the global economy

The land-water-energy nexus

Fritz Hellman, Tom Kram, Ton Manders4

Content

• What is the nexus?

• Main bottlenecks

• Modelling framework

• Preliminary results

The nexus

Land-Water-Energy nexus

Strong linkages between land, water and energy

Competition for the same resources

Tension grow over time

An integrated analysis is needed

A desaggregated analysis is needed


Fritz Hellman, Tom Kram, Ton Manders

6

Main bottlenecks



7

Table 1 Existing links in IMAGE and ENV-Linkages

linkage importance

Water for agriculture

Water for energy

Agriculture for energy

Agriculture for water

Energy for agriculure

Energy for water

Land for agriculture

Bottlenecks: water use



8

Water stress matters



9

Bottlenecks: bioenergy



10

Bottleneck: land-use



11

Bottlenecks: land-use



12

Feedbacks between IMAGE and Env-Linkages

OECD-CIRCLE Workshop October 21 -22, 2013 | Ton Manders13

ENV-Linkages

IMAGE

Land supply, yield(water supply)(health)(biodiversity)

Economy (agriculturaldemand)population

Nexus in modelling framework

NEXUS-links: IMAGE ENV-Linkages CIRCLE

Water for agriculture Yes No Yes

Land for agriculture Yes Yes Yes

Agriculture for water Yes No No

Energy for agriculture No Yes Yes

Agriculture for energy Yes Yes Yes

Water for energy No No No

Energy for water No No No



14

Preliminary results: groundwater & irrigation Step 1:

– Baseline with plenty groundwater for irrigation

Step 2:

– Simulation without groundwater for irrigation.

– Agricultural production losses (IMAGE)

– “Shock” ENV-Linkages with production losses

– Economic impact of poduction losses (ENV-Linkages)

Step 3:

– Compare regional + sectoral production, trade, GDP, etc. between baseline and simulation variant-> cost of inaction!



15

IMAGE water & irrigation:



16

IMAGE water & irrigation:



17

Lower irrigationyields

Reallocationirrigatedagriculture

Increase area rainfedagriculture

Lower overall yields

Productionlosses to ENV-Linkages

World

Rice (yield)



18

Rice yield: India Rice yield: Indonesia



19

Next: other simulations

Bottlenecks regarding water availability:

– Water allocation variant

– Water efficiency techniques variant

Bottlenecks regarding land availability

– Land degradation variant

– Land supply variant

Other bottlenecks:

– Ozone variant

– Climate change variant

Thank you

[email protected]

[email protected]

www.pbl.nl



21

IMAGE

Energy supply/demand

Carbon cycleCropsNatural vegetation

Atmosphere Water cycle

Earth system

Impacts

Land use Emissions

Drivers

Agricultural demand/production



22

THE LAND-WATER-ENERGY NEXUS:

CONSEQUENCES FOR ECONOMIC

GROWTH

Rob Dellink

Environment Directorate, OECD

CIRCLE Ad-hoc expert workshop

Paris, 2 October 2014

• Soft-linking different models

– Using the output of one model as input to another

– Using a common baseline so models all share the same set of underlying common drivers (plus a set of model-specific drivers)

– Harmonise on other elements in the scenario storyline where possible

• Staged modelling approach

– ENV-Growth provides macroeconomic projections

– ENV-Linkages provides sectoral economic projections and emissions

– IMAGE provides biophysical impacts and bottlenecks

– Economics feedbacks to ENV-Linkages where possible

24

Linking different modelling tools

The first stages of the modelling track

Land-water-

energy nexus:

IMAGE model

suite

Structural economics & environmental pressure:

ENV-Linkages

Macroeconomics:

ENV-Growth

25

Stand-alone

modules for

e.g. natural

resources

Climate change:

ENV-Linkages

climate module

Air pollution:

range of models

26

• Computable General Equilibrium (CGE) model

• Multi-regional, multi-sectoral

• Full description of economies

• All economic activity is part of a closed, linked system

• Simultaneous equilibrium on all markets

• Structural trends, no business cycles

• Dynamics

• Solved iteratively over time (recursive-dynamic)

• Capital vintages

• Link from economy to environment

• Greenhouse gas emissions linked to economic activity

• Other pollutants forthcoming…

• Potential future work on water use?

The ENV-Linkages model

… and back

• Make use of the details of the CGE model where possible

– sectoral disaggregation

– explicit production function

– captures both direct and indirect effects

– relatively well-established for climate change damages, but for other environmental challenges the links to economic variables is much less clear

• Keep separate where needed

– Valuation of non-market damages

27

Incorporating feedbacks into a general

equilibrium model

28

Linking IMAGE output to ENV-Linkages

• The direct impacts are included in the IMAGE model

• ENV-Linkages calculates macroeconomic costs, which includes indirect impacts

Impacts on economic

growthIndirect impacts

Direct impact

Sector

Agricul-ture

Changes in crop

product-ivity

Changes in crop prices

Changes in food prices

Changes in trade specialization of agriculture / food products

Changes in prices and demands of other goods

Changes in household income and government revenues

…

Change in GDP

Change in

welfare

THANK YOU!

For more information:

www.oecd.org/environment/CIRCLE.htm

www.oecd.org/environment/modelling

[email protected]

Thursday 2 October 2014 (Day 1 - Continued)

11:30 – 12:00 Coffee break

12:00 – 13:00 Biodiversity and ecosystem services

Speakers Anil Markandya (BC3)

Key questions What is the state-of-the-art knowledge on the consequences of the loss of biodiversity and

ecosystem services for economic growth?

How to link loss of biodiversity and ecosystem services to economic growth?

What are the main opportunities and obstacles in including biodiversity and ecosystem

services into a dynamic CGE model?

Is it worthwhile to pursue this theme in the project through large-scale economic modelling

and if so, what should be the next steps?

Background

material

“The economic feedbacks of loss of biodiversity and ecosystems services”, ENV/EPOC(2014)16

30

CIRCLE Worshop Outline - Day 1(Cont.)

The economic feedbacks of loss of biodiversity and ecosystems

services Anil Markandya

Basque Centre for Climate Change

October 2014

Purpose of the Scoping Study• The cost of past economic growth in terms of loss

of biodiversity and functioning of ecosystems and has been studies in some detail.

• But less has been done on the effects these losses have in terms of reductions in economic performance.

• Or on what the benefits would be of shifting to green growth paths.

• This study aims to examine the evidence on the two questions and outline what further work is needed incorporate losses of biodiversity and ecosystem services within CGE models.

32

Ecosystem Services: A Key Concept

• The Millennium Ecosystem Assessment set up in 2005 a generic framework of ecosystem services (ESS), categorising them into four typologies: provisioning services, regulating services, cultural services, and supporting services.

• This has been adopted widely, with variations in the detailed definitions of the different services.

• If our interest is valuation it is useful to focus on final ecosystem services, while accounting for ecosystem processes and intermediate ESS as relevant in determining the final values.

• The categories of final services vary across studies.

33

Categories of ESS in TEEB

Provisioning ServicesFood

WaterRaw MaterialsGenetic ResourcesMedicinal ResourcesOrnamental Resources

Habitat ServicesNursery ServiceGenetic Diversity

Regulating ServicesAir QualityClimate RegulationDisturbance ModerationWater Flow RegulationErosion PreventionNutrient RecyclingPollination

Biological Control

Cultural ServicesEsthetic InformationRecreationInspirationSpiritual ExperienceCognitive Development

Empirical estimates have been made for all these categories.34

ESS and Biodiversity• Biodiversity: “the variability among living organisms from

all sources including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part.

• Ecosystem are “a dynamic complex of plant, animal and micro-organism communities and their non-living environment interacting as a functional unit” and ESS are benefits derived from ecosystems.

• Loss of biodiversity affects ecosystems significantly but links are complex and direct valuation of biodiversity is difficult.

• For this reason operational focus has been on ESS but some account of biodiversity loss on ESS has been taken through measures of Mean Species Abundance (MSA) in different habitats.

35

Valuation of ESS

• Considerable work on valuing final services by biome and geographical location.

• TEEB review documented 320 studies across 10 biomes, covering 300 locations. Derived from many databases such as EVRI, COPI etc. There are many more “studies” but details are not sufficient for them to be evaluated.

• Less work on valuing changes in final services when the ESS is modified or degraded.

36see www.es-partnership.org for information on most of these databases

Global Studies: 10 Biomes

Biome Biome

Marine (Open Oceans) Freshwater (Rivers/Lakes)

Coral Reefs Tropical Forests

Coastal Systems (1) Temperate Forests

Coastal Wetlands (1) Woodlands

Inland Wetlands Grasslands

(1) Coastal systems include estuaries, continental shelf areas and sea grass but not wetlands such as tidal marshes, mangroves and salt water wetlands

37

Main Valuation Findings for ESS

• Considerable work in reviewing and synthesizing valuation studies was done in the TEEB report.

• Values are generally expressed in terms of $/ha./yr.

• Some studies carry out a meta analysis giving these values as a function of site characteristics.

• The average values across studies are significant but with large ranges indicating the need to work at a spatially disaggregated level.

38

How Are the Values Derived?ESS Direct Market

ValuesCost Based Methods

Revealed Preference

Stated Preference

Provisioning 84% 8% 0% 3%

Regulating 18% 66% 0% 5%

Habitat 32% 6% 0% 47%

Cultural 39% 0% 19% 36%

• Direct Market Values include: market pricing; payment for environmental services; and factor income/production function methods

• Cost Based Methods include: avoided cost, restoration cost; and replacement cost

• Revealed Preference: hedonic pricing and travel cost• Stated Preference: contingent valuation, conjoint choice and

group valuation39

What Are the Numbers?

• Values are Int.$/Ha./Yr., 2007 price levelsESS Mean Median Min/Mean Max/Mean

Oceans 491 135 17% 339%

Coral Reefs 352,915 197,900 10% 603%

Coastal Systems 28,917 26,760 90% 145%

Coastal Wetlands 193,845 12,163 0.2% 458%

Inland Wetlands 25,682 16,534 12% 409%

Rivers & Lakes 4,267 3,938 34% 182%

Tropical Forest 5,264 2,355 30% 396%

Temperate Forest 3,013 1,127 9% 545%

Woodlands 1,588 1,522 86% 138%

Grasslands 2,871 2,698 4% 207%

De Groot et al, Ecosystem Services, 2012.40

Comments on Values• The values vary by biome, both means and

ranges.• Other review studies come up with different

mean values • Numbers of studies on oceans, coastal systems

and woodlands and grasslands are relatively few in number. Many more for wetlands and forests.

• Relatively few studies in developing countries (although there are some in most categories)

• Estimates can be targeted for a given site in a given location using meta analytical functions.

41

Meta Analytical Functions Estimated • Unit value as a function of site and user

characteristics have been made for:– Inland wetlands, Tropical and Temperate forests,

Grasslands, Mangroves, Coral Reefs

• Main explanatory variables include:– Size of the site, income level in the country, number

of people using the site, NPP in the area around the site, presence of other sites nearby, method of estimation used.

– Quality of the site rarely appears as a variable

• Functions not all well determined.

42

Application in Economic Models

• The usual databases are not so useful for estimating the impact of changes in the quality of biomes

• We have to look at more detailed studies of different ESS and how changes in their function due to external factors can effect the services they provide.

• A number of studies have attempted to do that using spatially disaggregated data but economic valuation is included only in some, and to a limited extent.

43

Incorporating ESS Values in Economic Models: Key Questions

• Does the model include ESS in both directions – i.e. the impact of economic changes on ESS and thereby on welfare as well as the impact of ESS changes on production possibilities for goods and services and thereby on growth?

• Does the model take account of the inter-relationships between markets – i.e. does it have a general equilibrium structure –allowing for market imperfections such as unemployment, trade barriers etc.?

• Does the model include a spatial dimension so that ecosystems impacts of growth can be taken into account different depending n where they occur?

• Is the coverage of ecosystems complete – i.e. are all biomes included in the system?

44

Models and Approaches Examined

Model Ecosystem Economics Other

GUMBO* 11 biomes, ESS feed into production and welfare functions

Economic output based on capital, labor, knowledge. Links from ESS to Economic module

No spatial modeling. Economic module not CGE. ESS valuation sketchy

GLOBIO-IMAGE

ESS from biomes affected by socio-economic drivers

LEITAP, extended version of GTAP, used to model land use changes

Changes in land for agriculture affects different biomes. Spatially explicit.

InVEST Production functions linking LULC type to ESS

Economic production functions determine demand for land & ESS

Still developing.Coverage not global as yet. Not CGE.

UK NEA ESS from different biomes spatially disaggregated scale

Scenarios estimate changes in ESS

No economic modeling but ESS changes valued for some services

* MIMES, spatial version of GUMBO is being developed45

Causality from ESS Changes to Economic Functions

• All the above models examine the implications of economic development growth on ESS in either physical or monetary terms.

• However, the only models that explicitly account for the impact of ESS changes on economic performance are the GUMBO-MIMES set. In these ESS services affect the measure of “natural capital”, which in turns enters as an input to the production function for other goods and services.

• But modelling is at a very aggregate level and there is a need to develop it further.

46

Use of a general equilibrium structure

• The only model that has a link with a general equilibrium structure is the IMAGE-GLOBIO model, which consists of an economic module which examines different development scenarios. It also has a spatial disaggregation.

• Effects of different growth paths on MSA-adjusted ESS are estimated for a number of services (but not all).

• But ESS do not directly enter the production of goods and services and so the feedback from a loss of ESS to the economy cannot be tracked in the model.

• It also does not have money values for ESS, although some parallel work has been done on these.

47

Inclusion of a Spatial Dimension

• The spatial dimension is incorporated into GLOBIO-IMAGE, InVEST and the UK NEA but not in GUMBO (although MIMES is working on developing that).

• The importance of including this aspect into the modelling is highlighted by the fact that the impacts of different scenarios on ecosystem functioning are found to vary considerably by location.

48

Coverage of Ecosystems in monetary terms

• The coverage of ecosystem services in monetary terms is not entirely complete in the models examined.

• E.g. Those models that do value ESS in money terms cover marine ecosystems to a limited extent if at all.

• Focus on valuation tends to be on forests, wetlands, lakes and rivers and croplands.

49

Need for Further Development

• More work is needed to model the linkages from changes in ESS to the functioning of the economy.

• Modelling that exists (e.g. GUMBO) is too aggregated and does not have a CGE structure.

• CGE models on the other hand do not have ESS in the production functions.

50

Possible Steps Forward• First a soft link can be made between the ESS

value changes and the economic models.• Alternative growth paths can be evaluated in

terms of the losses or gains they imply for different ESS and these values can be used to adjust the estimated GDP growth rate, to give a “corrected GDP”.

• This work can be based on the IMPAGE-GLOBIO Model, for example, with valuation work that has been done using that model, being linked to the typical OECD growth models.

51

Possible Steps Forward• At the same time a second approach needs to be developed, in

which the integrated CGE models include ESS as specific inputs into key sectors and where the output of these sectors affects the functioning of the ESS.

• The inclusion of ESS into some sectors such as agriculture and forestry should be relatively straightforward because linkages to marketed goods are well developed

• It will be more challenging to cover services such as recreation, tourism, and health (

• It will also be important to take account of connections between ESS (e.g. the quality of cultural services depend on how well the regulating services are functioning). This stream of work needs to be undertaken in conjunction with the dynamic modellers who are developing the combined framework of the OECD’s ENV-Growth model as well as the dynamic computable general equilibrium (CGE) OECD’s ENV-Linkages model.

52

Possible Work Plan?A. Set up a database of state-of-the-art estimates of the

value of ESS at a spatially differentiated level so it can be used in the economic models.

B. Calculate the losses of ESS associated with alternative growth paths and use these figures to calculate an adjusted GDP figure for each path, indicating the effect that the losses have on “true GDP”.

C. Initiate work on integrating ESS into the economic models. This can be done first for agriculture and forestry where there is considerable information and then go on to consider the more difficult sectors.

D. Combine the work on adjusted GDP with that on sectoralproduction links to produce an integrated system that includes both the effects of growth on ESS and the effects of declines in ESS on growth.

53

Useful Readings

• Ten Brink P. (ed.) (2012)The Economics of Ecosystems and Biodiversity in National and International Policy Making. London: Earthscan, 352pp.

• De Groot R. et al. (2012) Global estimates of the value of ecosystems and their services, Ecosystem Services, 1, 50-61.

• Hussain S. et al. (2013) “The Challenge of Ecosystems and Biodiversity”. in Lomborg B. (ed.) Global Problems, Smart Solutions, Cambridge University Press.

• Bateman, I. et al. (2013) Bringing Ecosystem Services into Economic Decision-Making: Land Use in the United Kingdom, Science, July.

54

Thursday 2 October 2014 (Day 1- PM)

14:00 – 15:30 Water-economy linkages

Speakers Tom Hertel (Purdue University)

Key questions What are the main economic implications of water scarcity and water stress?

How can water use and water supply be linked to economic growth?

What are the main opportunities and obstacles in including water into a dynamic CGE model?

Is it worthwhile to pursue this theme in the project through large-scale economic modelling and if

so, what should be the next steps?

Based on: “Implications of water scarcity for economic growth”, ENV/EPOC(2014)17


16:00 – 17:30 Resource Scarcity

Speakers Peter Börkey (OECD)

Renaud Coulomb (Grantham Research Institute at LSE)

Alexandre Godzinski (French Ministry of Environment)

Satoshi Kojima (IGES)

Key questions What are the key research/policy questions in the topical area of resource scarcity that are

relevant from the point of view of environmental protection?

Is resource scarcity an issue, and if so, what would be the consequences of supply disruptions,

long-lasting high minerals prices, or high price volatility on the economy and geopolitics?

What role can recycling policies play in helping to mitigate resource scarcity and the associated

impacts on the economy?

Is it feasible to include these themes into a dynamic CGE model and more generally what further

work could be developed within the CIRCLE framework to support efforts in this area?

Based on: “Critical raw materials in the OECD”, ENV/EPOC(2014)1855

CIRCLE Worshop Outline – Day 1 (PM)

Water Scarcity and

Economic Growth

Thomas Hertel and Jing Liu

Purdue University

Presented October 2, 2014 to the

OECD CIRCLE Workshop

Paris, France

Water Scarcity and Economic Growth

Thomas Hertel and Jing Liu

Purdue University

Presented October 2, 2014 to the

OECD CIRCLE Workshop

Paris, France

Three perspectives on water scarcity and economic growth

• Water as a publicly provided good, with reuse, but subject to congestion (Barbier, 2004)

• Water as a conventional input into the national production function (in the tradition of Solow)

• Water in a global CGE model (allocative distortions, second best effects and terms of trade changes)

Water as a publicly provided good with congestion

• Optimal growth model• Firms draw on common pool of water;

however, marginal productivity declines with increasing withdrawals (congestion)

• Cost of withdrawal rises at increasing rate

• Optimal rate of water utilization maximizes economic growth rate (Fig.1)

• Empirical results• Focus on 163 countries during 1990’s

• Positive elasticity of growth wrt water withdrawals (10% rise boosts growth rate from 1.3% to 1.33%)

• Most countries could increase growth rate by boosting water withdrawals

• Just 10% face extreme water scarcity

• However, sub-national story is surely different

Water as a conventional input into national production function: y = f(K,W)

• Central issue is the potential for substituting accumulating human and physical capital for water, summarized by

• If then, as K/W rises, water’s share of GDP will rise, eventually limiting growth

• If then, as K/W rises, water’s share of GDP will diminish and growth will not be constrained, as increasingly abundant capital is used to improve water efficiency as well as enhance available supplies of water to the economy

• But is an abstract concept – how can this be captured in a CGE model? It is determined by four different components:

• Sector level technologies

• Inter-sectoral responses to water scarcity

• Consumers’ willingness to substitute away from water intensive goods

• Potential for recycling/reuse and desalinization

1

1

• Calculating implied value of from CGE-water models would be a useful component of any assessment of impact of water scarcity on growth

(Cont. from previous slide…)

Water as a conventional input into national production function: y = f(K,W)

Water and real income growth in a global CGE model:

• Direct cost to economy of reductions in water availability depends on marginal value product of water in the CGE model; appropriate valuation of water, by sector/use is critical

• In many economies there are large (even 100x!) divergences in the MVP of water by sector; this opens the way for large second best effects in the face of any exogenous shock, provided it results in water reallocation

• Water scarcity can lead to reallocations across distorted sectors which can improve, or exacerbate losses (Liu et al. find the latter)

• Terms of trade effects can also be significant as the price of water intensive goods rises; welfare impact depends on geography of trade

Irrigated Agriculture: The Dominant Water Use

• Each calorie produced requires roughly 1 liter of water through crop evapotranspiration; feeding the world each year requires enough water to fill a canal 10m deep and 100m wide encircling the globe 193 times!

• Four-fifths is rainwater, one-fifth is irrigation water; accounts for 70% of global freshwater withdrawals

• Irrigated area accounts for nearly 20% of cropland and 40% of production

Groundwater irrigation has become increa-singly important

• Accessible without large scale

government initiatives at low

capital cost (although high

operating costs)

• Offers irrigation on demand

• Reliability in time and space:

low transmission and storage

losses

• Drought resilience; surface

water not available during

drought

• If undertaken in areas with

high recharge rates, then it is

also sustainable

But most rapid growth has been in arid areas with low recharge rates

65Source: cited in Burke and Villholth

There is substantial scope for increasing water use efficiency in agriculture, given appropriate incentives:• Improving delivery of water to plants: Global irrigation efficiency

= 50%

But not all losses are really lost – reuse of water further

downstream

Improved irrigation efficiency can also increase total use:

‘Jevons’ paradox’

• Increasing ‘crop per drop’: Water use efficiency of crops

themselves

Can be achieved by reducing non-beneficial evaporative losses

and limiting deep percolation of rainwater

Also by boosting grains share of total biomass, limiting pest

damage, and improving drought tolerance

Small-scale farms can boost production with less than

proportionate rise in water use; for commercial scale

operations, tend to rise in equal proportions

There is substantial scope for increasing water use efficiency in agriculture, given appropriate incentives (Cont.)

Evidence of conservation in the face

of scarcity:

The Australian experience

• Drought in 2002/3 led to a

29% drop in water usage in

the Murray-Darling Basin

• However, water used in

irrigated rice production

dropped by 70%

Flexibility facilitated by water trading:

when water is available, produce rice.

When it is scarce, sell water rights

instead of growing rice! (Will Fargher,

National Water Commission)

Evidence of conservation in the face of scarcity: The Australian experience (Cont. from previous slide)

• Early modeling work failed:

– predicted only modest declines in irrigation water usage

– Missed the potential for:

• Shifting land to rainfed production

• Shifting rice production to other regions

• Required significant modification of the TERM-H2O CGE model

Increasing irrigation scarcity will alter the geography of food trade

Irrigation. reliability index =

actual water consumption / potential irrigation demand

Red color means potential

irrigation demand is less satisfied

by actual irrigation consumption

Source: Liu et al. GEC, 2014

Focus on India results…

As output falls, consumers substitute low cost imports for domestic crops, exports & production decline

Source: Liu et al. GEC, 2014

Water use in power generation

• Hydropower consumes water

through evaporative demand

• Water for cooling is key water

demand

• World Bank report highlights

adverse impacts of water

scarcity: – “In the past five years, more than 50% of

the world’s power utility and energy

companies have experienced water-

related business impacts. At least two-

thirds indicate that water is a

substantive risk to business

operations.”

– In India, South Africa, Australia and the

United States, power plants have

recently experienced shut-downs due to

water shortages for cooling purposes.


• Projections for India

suggest that power

sector’s share of water

use could rise from 4%

today to 20% in 2050 –

primarily for cooling;

abstracts from potential

for installation of water

efficient capacity


• Hydropower consumes water

through evaporative demand

• Water for cooling is key

power demand

• World Bank report highlights

adverse impacts of water

scarcity: – “In the past five years, more than 50% of

the world’s power utility and energy

companies have experienced water-

related business impacts. At least two-

thirds indicate that water is a

substantive risk to business

operations.”

– In India, South Africa, Australia and the

United States, power plants have

recently experienced shut-downs due to

water shortages for cooling purposes.

• Projections for India suggest

that power sector’s share of

Residential, commercial & industrial

uses

• Residential demands well-studied:

– Average price elasticity of demand in industrialized

countries = -0.4

– In developing countries, households draw on multiple

sources of water: tap, wells, tankers, vendors, rain and

surface water – it is complicated!

• Urban formal: tap water – as with rich countries

• Urban slums: inadequate water and sewage svces; price is often

time

• Rural consumption: household labor required to collect water

Residential, commercial & industrial

uses

(Cont. from previous slide)

• Commercial sector is heterogeneous, difficult to

assess: assume same behavior as residential

demands

• Industrial demands vary greatly by industry:

– Water often self-supplied – hard to monitor

– Industrial steam is important source of demand for both

water and energy; conservation of energy leads to

reduced water use

– Scope for water savings, given incentives: elasticity= -

0.15 to -0.6 depending on sector

Environmental demands (in-stream

use)• Requirements depend on total volume as well as high/low flows

• Portion of flow reserved for environmental purposes varies

from 10% (IFPRI’s IMPACT-WATER model) to 50% (IWMI

– see map below)

Water Supply

• What is the relevant spatial unit for supply?

• Global models focus on river basin; take inputs from

hydrological model

• Reuse of water is key:

– Seckler et al. suggest that reuse will be one of the

most important sources of supply in the coming

decades

– Main barrier to reuse is pollution; therefore

pollution control is source of water supply

Water Supply

• Luckman et al study reduced water availability in

Israel emphasizing reuse

– seven different types of water separately, breaking

out: freshwater, seawater, brackish groundwater

(all natural resources), which can be converted to

potable water, brackish water and reclaimed water

via some production process; also allow for

desalinization

– 50% reduction in freshwater costs economy

0.2%GDP

• Rules for allocation across sectors are critical

Research Challenges & Priorities

• Main barrier to global CGE modeling of water scarcity

is data availability: not broken out in the typical social

accounting matrix:

– Break out activities by river basin

– Identify physical volumes by use – draw here on

hydrological models

– What price? Marginal value product varies widely

across and within sectors

• Important to distinguish different types of water:

endowments, outputs, byproducts and intermediate

inputs along with associated technologies

• Putty-clay treatment to capture impact of new

investments on efficiency

Research Challenges & Priorities

• Need to establish links to hydrological models which:

– Ensure that laws of gravity are enforced!

– Incorporate impacts of infrastructure development

and depreciation

– Deal with temporal and spatial variation

• Important to accommodate alternative allocation rules

(e.g., M-D Basin water reforms)

– How will scarcity be accommodated?

– Which sectors have priority?

– Will scarcity lead to institutional reforms?

RESOURCE SCARCITY– WHAT ARE THE KEY ISSUES?

Peter Börkey – OECD Environment Directorate

Static reserves life, 2011

Decoupling trends, 2000 to 2011

50

75

100

125

150

2000 2002 2004 2006 2008 2010

Index 2000=100

material consumption

GDP

OECD

50

75

100

125

150

2000 2002 2004 2006 2008 2010

Index 2000=100

material consumption

GDP

World

Copper mine grades and recoveries

Source: Citigroup (2011)

Commodity prices are increasing

Reserves and cumulative output - Copper

CO2 per tonne of metal production

• Physical scarcity is unlikely

• But it can be politically induced

• Rising opportunity costs appear likely

• A stronger constraint may come from a scarcity of environmental sinks

So what is resource scarcity?

• What is the potential impact of resource scarcity on the economy?– Increasing commodity prices

– Supply disruptions

• What are the potential environmental impacts fromresource scarcity?

• What is the role that circular economy policies canplay?– Growth

– Jobs

– Material security

• What is the impact that the transition towards green growth will have on resource scarcity?

What are the policy questions?

• Out of model approach

1. LSE – the critical materials approach (isresource scarcity real?)

• Macro-economic modelling

2. France – how to represent the circulareconomy in a CGE framework

3. IGES – how to include resource scarcity in a CGE framework

Three presentations

• What are the key research/policy questions?– Is resource scarcity an issue, and if so, what is

its impacts the economy and geopolitics?

– What role can circular economy policies play?

• Is it feasible to include these themes into a dynamic CGE model?

• And more generally what further work could CIRCLE develop in this area?

Questions for discussion

CRITICAL

MATERIALS

IN THE OECD TO 2030

Renaud Coulomb, Post Doctoral Researcher

[email protected]

AGENDA

I. The Challenge

II. Analytical Framework

a) Economic Importance

b) Supply Risk

III. Static Findings

a) Sectors Affected

IV. Introducing Dynamics

a) Sectorial Changes

b) Production shifts

V. Policy efforts

I) THE CHALLENGE

Raw materials are economically important as sectors such as energy, transportation, and communications crucially rely upon them.

Three mega trends:

1) Increasing demand driven by emerging markets (see Krausmann, 2009)

2) New technologies require large amounts of rare materials (DERA, 2012)

3) A slowdown in high-grade deposits discoveries after 2000

The current and future criticality of individual materials will depend on their economic importance and how likely they are to face supply disruptions.

In order to inform effective policy we set out to map material criticality for 54 materials in the OECD countries up until 2030.

II) ANALYTICAL FRAMEWORK

Our methodology draws on the previous research: EU (“Critical Raw Materials” 2010, 2014), US (“Minerals, Critical Minerals, and the US Economy” 2007), UK (“Material Security” 2008), etc., focusing on a new scope of countries and adding dynamics.

Criticality is assessed across two dimensions:

• Economic Importance determined by:

• Use of materials across sectors

• Value added of these sectors

• Supply Risk determined by:

• Concentration of production

• Distribution of reserves

• Political stability of major producers/holders of reserves

• Recycling rates

• Substitutability

II-A) ECONOMIC IMPORTANCE

• 𝐴𝑖𝑠 - The share of consumption of material i in end–use sector s

• 𝑄𝑠 - GVA of sector s

A material that is used heavily in a sector that constitutes a large part

of the economy will have a relatively high Economic Importance index

value.

Index is calculated for 54 materials in 17 Megasectors (Q) with total

GVA of 20% GDP.

Data sources: share of consumption (EU 2014, USGS 2014, etc), GVA

(OECD).

𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝐼𝑚𝑝𝑜𝑟𝑡𝑎𝑛𝑐𝑒𝑖 =1

𝑠𝑄𝑠

𝑠

𝐴𝑖𝑠𝑄𝑠i – material

s – sector

II-B) SUPPLY RISK

• 𝜎𝑖 - Substitutability = 𝑠𝐴𝑖𝑠𝜎𝑖𝑠

• 𝜌𝑖 - Recycling rate

• 𝑆𝑖𝑐 - Production shares by countries

• 𝑃𝑜𝑙𝑆𝑡𝑎𝑏𝑐 - Political stability by countries

The Supply Risk index is high if a material has few substitutes, low

recycling rates, and production is concentrated in politically unstable

countries.

Data sources: substitutability and recycling (EU 2014, USGS 2014 etc),

production (BGS 2014, WMD 2014 etc), political stability (WGI 2014)

i – material

s – sector

c – country

𝑆𝑢𝑝𝑝𝑙𝑦 𝑅𝑖𝑠𝑘𝑖 = 𝜎𝑖 1 − 𝜌𝑖

𝑐

(𝑆𝑖𝑐)2𝑃𝑜𝑙𝑆𝑡𝑎𝑏𝑐

III) STATIC FINDINGS

*Natural Rubber

III-A) SECTORS AFFECTED

21 critical materials are:

Antimony, Barytes, Beryllium, Borate, Chromium, Cobalt, Fluorspar,

Gallium, Germanium, Indium, Magnesite, Magnesium, Natural

Graphite, Niobium, PGMs, Phosphate Rock, REE (Heavy), REE

(Light), Silicon Metal, Tungsten, Vanadium.

The following Megasectors are affected (number of critical

materials affecting each Megasector):

Metals (Basic, Fabricated & Recycling) (18), Other Final Consumer

Goods (16), Chemicals (12), Electronics & ICT (10) ,Electrical

Equipment (7), Road Transport (7), Plastic, Glass & Rubber (6),

Mechanical Equipment (5), Construction Material (4), Refining (2), Oil

and Gas Extraction (2), Aeronautics, Trains, Ships (1), Beverages (1)

IV) INTRODUCING DYNAMICS

The project entails making projections up until 2030.

To meet this requirement the framework should be modified to account for the underlying dynamics of material supply and demand.

The team suggests that:

• The dynamics of Economic Importance are captured by incorporating the OECD forecast of sectorial composition into the analysis.

• The dynamics of Supply Risk are incorporated by introducing three supply scenarios based on current production shares and reserves.

Other factors that can affect criticality in the future: exploration of land to increase reserves and lower concentration, new extracting technologies etc.

IV-A) SECTORIAL CHANGES

Tomorrow’s economy will be different from today’s, criticality of

materials will be affected by changes in sectorial composition

driven by:

1) Emerging technologies

• Thin layer photovoltaics (gallium, indium), fibre optic cable

(germanium), seawater desalination (palladium, titanium,

chromium), micro capacitors (niobium, antimony), etc

2) General economic trends

• Diminishing share of agriculture

3) Policy focus

• Green policies

IV-B) PRODUCTION SHIFTS

The producers of the materials currently used in the OECD are likely

to change over time as reserves are depleted.

This should be accounted for in Supply Risk estimates and the team

therefore suggests evaluating three scenarios of future production:

1) production sources are assumed constant at current levels

(i.e. the countries of origins and their respective share of total supply

does not change over time)

2) production converges towards reserves distribution as stocks

deplete (i.e. the countries with abundant reserves become more

important for global supply in the future)

3) reserves distribution only matters (i.e. supply risk depends on the

origins of reserves NOT where current production occurs)

V) POLICY EFFORTS

To mitigate supply risk either recycling efforts need to increase

or new substitutes will have to be found.

The following changes will suffice to make materials non-critical:

*S – substitutability, higher S -> higher risk

*R – recycling, higher R -> lower risk

A1. PRODUCTION CONCENTRATION

S = 0.77

R = 0

S = 0.93

R = 0

A2. SUBSTITUTES AND RECYCLING

Potash

S = 0.32

R = 0

HHI = 2300

Barytes

S = 0.98

R = 0

HHI = 2603

Natural Graphite

S = 0.72

R = 0

HHI = 7300

Cobalt

S = 0.71

R = 0.16

HHI = 4600

A3. POLITICAL STABILITY INDEX

The main index used for Political Stability is the Worldwide

Governance Indicators (WGI) calculated by WB in 2014.

The index consists of six dimensions of governance:

• Voice and Accountability

• Political Stability and Absence of Violence

• Government Effectiveness

• Regulatory Quality

• Rule of Law

• Control of Corruption

A4. POLITICAL STABILITY VS WGI

A5. RULE OF LAW VS WGI

A6. POLITICAL RISK AND

CONCENTRATION IN OECD

• Average WGI among OECD countries – 2,7, among the rest

– 5.3.

Mexico

Fluorspar 18%

Silver 21%

Greece

Perlite 19%

Turkey

Borate 45%

Feldspar 21%

Perlite 18%

Share of production

0 1 2 3 4 5WGI_final

MEXICOTURKEYGREECE

ITALYISRAEL

HUNGARYS. KOREASLOVAKia

POLANDSPAIN

CZECH REPUBLICSLOVENIA

PORTUGALESTONIAFRANCE

CHILEJAPAN

United StatesBELGIUM

UNITED KINGDOMIRELAND

GERMANYAUSTRIA

AUSTRALIACANADA

LUXEMBOURGNETHERLANDSSWITZERLAND

DENMARKNORWAY

NEW ZEALANDSWEDENFINLAND

A7. SUBSTITUTABILITY

VS RECYCLING

A8. SUBSTITUTABILITY

VS CONCENTRATION

A9. RECYCLING VS

CONCENTRATION

A10. SUPPLY RISK FOR RESERVES

A11. ECONOMIC IMPORTANCE

USA VS OECD


JAPAN VS OECD


EU VS OECD

A14. STATISTICAL APPENDIX

Variable Mean

Std.

Dev. Min Max

Supply

Risk Subst. Recycling HHI HHI_wgi EI

Supply risk 1.11 1.04 0.1 4.61 1

Substitutability 0.69 0.18 0.32 0.98 0.27 1

Recycling 0.09 0.12 0 0.51 -0.16 0.25 1

HHI 3327 2344 629 9801 0.88 0.07 -0.14 1

HHI_wgi 1.73 1.51 0.22 5.99 0.95 0.09 -0.08 0.91 1

Economic

Importance 0.07 0.02 0.03 0.11 0.14 0.13 -0.04 0.14 0.14 1

Correlation matrix

A15. DATA ISSUES

• Economic importance index

• Sectorial composition (GVA of Megasectors)

• Data is currently available in GTAP breakdown

• Higher level of disaggregation is desirable for more accurate results (ISIC up to 4 digits)

• Breakdown of end-uses of materials can differ by countries and for OECD

• Data used currently is based on data in EU report (2014), USGS (2014)

• Supply risk index

• Input data may differ for the OECD countries: breakdown of end-uses, substitutability, recycling rates

• Alternative measures can be used: political risk (WGI vs PRS)

A.16 REFERENCES

DERA Rohstoffinformationen, 2012, Energy Study 2012, Reserves,

Resources and Availability of Energy Resources, Germany.

Krausmann, 2009, Growth in global materials use, GDP and population

during the 20th century

EU, 2010, Critical Raw Materials for the EU, Report of the Ad-hoc

Working Group on defining critical raw materials, 30 July

EU, 2014, Report on Critical Raw Materials for the EU

NRC, National Research Council, 2008, Minerals, Critical Minerals, and

the U.S. Economy, National Research Council of the National Academies

UK, 2008, Material Security Board Ensuring Resource availability for the

UK economy

U.S. Geological Survey, 2014, Minerals Yearbook 2010

World Mining Congress, 2014 World Mining Data

World Bank, 2014, World Governance Indicators

121

2 October 2014

Second ad-hoc technical workshopon CIRCLE

Alexandre Godzinski

French Ministry of Sustainable Development

Circular Economy:

A Computable

General Equilibrium

Approach

122

Model: why, how and what for• Motivation: explore and evaluate different instruments related to material

efficiency and waste treatment in France

• Computable general equilibrium model which includes:

– Material flows (virgin ore extraction, material in final products, waste, scrap metal)

– Material stocks (ore in the ground, productive capital stock, landfill stock)

• Stylized tool to assess policies related to material efficiency and waste

management, which are usually studied separately

• Model under construction! At the moment:

– World divided into two regions: France and the rest of the world

– Only one material: steel

• Output variables:

– Waste treatment (recycling rate, volume going to landfill…)

– Material efficiency (material productivity…)

– Usual economic outputs (GDP, consumption…)

123

Household

Generic good

Mines

Consumption

Primary material

Waste

treatment

service

Waste treatement

LandfillRecycling

Investment

Physical flows

Linear economy

124

Household

Generic good

Mines

Consumption

Waste treatement

LandfillRecycling

Investment

Circular economy

Waste

treatment

service

Secondary material

Physical flows

125

Model structure

Household

Generic good

Mines

Consumption

Primary

material

Secondary

material

Waste

treatment

service

Capital Labor

Waste treatment

LandfillRecycling

Investment

126

Application: reducing the volume of waste going to landfill

We consider two polar strategies:

• Taxing materials (both primary and secondary), so that final goods contain less material.

• Taxing landfill, so that more waste is recycled.

127

Tax on materials

0

2

4

6

8

10

12

14

16

18

20

0% 50% 100% 150% 200%

Tax rate

Vo

lum

e o

f w

aste

(m

illio

n to

ns o

f ste

el)

Volume of waste recycled

Volume of waste going to landfill

128

Tax on landfill

0

2

4

6

8

10

12

14

16

18

20

0% 50% 100% 150% 200%

Tax rate

Vo

lum

e o

f w

aste

(m

illio

n to

ns o

f ste

el)

Volume of waste recycled

Volume of waste going to landfill

129

Thank you for your attention

Comments welcome

CGE-MRIO analysis reflectingresource production costs, recycling and resource footprint

An input for the resource scarcity session

Satoshi Kojima, Ph.D.

Principal Researcher, Institute for Global Environmental Strategies (IGES)

Second Ad-hoc Technical Workshop on CIRCLEOECD, Paris, 2-3 October 2014

Institute for Global Environmental Strategies

Basic idea

Institute for Global Environmental Strategies 131

Economic impacts of resource scarcity of non-critical resources

Increasing resource production costs (low-hanging fruits first)

Historical decline of EROI (Energy Return On Investment)

Estimate economic impacts of not only resource scarcity but also “actions”

CGE (computable general equilibrium) model has advantages in assessing impacts of actions (policies)

Resource footprint can measure resource use based on the consumer responsibility principle ⇒MRIO (multi-regional input output) model serves for this purpose

Various policy options for sustainable resource use

Economic instrument such as natural resource tax

Recycling

Investment for resource saving/resource efficiency improvement

Empirical estimation of increasing mining costs

132

Mine cost database, World Mine Cost Data Exchange Inc.

(Operation cost data of 66 major iron ore mines in the world)

Estimated total cost curve for iron ore mining (fitted by cubic function)Reference:

Murakami S., Adachi T. and Yano T. (2012) An economic evaluation of resource supply constraint and its verification on material balance. Presentation at SEEPS 2012.

CGE-MRIO modelling: Progress


Develop global MRIO (and social accounting matrix for CGE)

Based on GTAP version 7

Iron-steel sectors (iron ore mining, pig iron, blast furnace steel, electric arc furnace steel) and steel scrap recycling sectors are disaggregated using national IO tables, UN-Comtrade, etc.

Develop recursive dynamic CGE model

Introduce sector-specific capital accumulation to reflect sector specific investment

Introduce substitutability between intermediate use of blast furnace steel and electric arc furnace steel

Conduct test run of CGE-MRIO linkage

Give policy shocks to CGE model and update MRIO based on the CGEsimulation results

Acknowledgement: This research is a part of the research project funded by the Ministry of the Environment, Japan.

CGE-MRIO modelling: Test run results

134

Policy impact on iron ore use: Japanese natural resource tax on iron ore use by pig iron producers (Source: Simulated results by the authors)

Note: I_M: Indonesia & Malaysia, EOG: Major exporters of oil & gas

Discussion for further research


Elaborate modelling of increasing resource production costs

Reflect impacts of declining EROI

Reflect per unit energy input for resource production may be increasing (analogous to EROI)

Reflect environmental costs (e.g. ecosystem destruction at mining sites)

Other channels?

Can we reflect physical limit of resource supply?

In the short run (e.g. a time step of simulation), resource supply capacity is effectively fixed ⇒ physical limit of resource supply

In case of scrap recycling, scraps are also limited resources. Scrap stock dynamics may set upper limit of available scrap for recycling.

But setting upper limits for resource stock in CGE may cause infeasibility problem …

Thank you for your attention!

[email protected]

http://www.iges.or.jp/Institute for Global Environmental Strategies 136

mailto:[email protected]

137

CIRCLE Worshop Outline – Day 2

Friday 3 October 2014 (Day 2)

9:00 – 10:30 Climate change

Speakers Rob Dellink (OECD)

Juan-Carlos Ciscar (IPTS)

Key questions What are the main policy insights from the preliminary analysis?

How can the analysis of the covered impacts be improved?

How can the analysis be extended to other climate impacts?

How to best evaluate the benefits of mitigation and adaptation policy action?

Background

material

“Consequences of climate change damages for economic growth – a dynamic quantitative

assessment”, OECD Economics Department Working Paper 1135.


11:00 – 12:45 Air pollution

Speakers Elisa Lanzi (OECD)

Mike Holland (EMRC)

Milan Ščasný (Charles University)

Key questions What is the state-of-the-art knowledge on the consequences of air pollution for economic

growth?

How can the health impacts from increased emissions of local air pollutants be projected for

major world regions?

How can these impacts be monetised and linked to specific economic activities and what

additional work is required to do so?

Based on: “CIRCLE progress report; local air pollution”, ENV/EPOC(2014)19

12:45 – 13:00 Closing session

Speakers Shardul Agrawala

Key questions What are the key synergies and trade-offs between the various themes that deserve priority

attention in the project?

What are the research priorities and next steps for the project?

What contributions by governments, experts and project partners can be further explored?

IMPACTS OF

CLIMATE CHANGE:


GROWTH

Rob Dellink




• 1st results published

– Economics Department Working Paper

– Used in OECD@100 and NAEC reports

• Continued support from EPOC

– Request to further improve analysis

– Request to prepare report in time for COP21

139

Current status: climate change

Climate change impacts and damages

• Coastal land losses and damages to capital

Sea level rise

• Changes in mortality & morbidity and demand for healthcare

Health

• Changes in productivity of production sectors

Ecosystems

• Changes in agricultural productivity

Crop yields

• Changes in productivity of tourism services

Tourism flows

• Changes in the demand for energy from cooling and heating

Energy demand

• Changes in catchment

Fisheries

• Extreme weather events, water stress, catastrophic risks, …

Not included

140

-4.0%

-3.5%

-3.0%

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

2010 2020 2030 2040 2050 2060

Global GDP impacts (% change wrt no-damages baseline)

Likely uncertainty rangeequilibrium climate sensitivity (1.5°C - 4.5°C)

141

Global assessment

-4.0%

-3.5%

-3.0%

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

2010 2020 2030 2040 2050 2060



Wider uncertainty rangeequilibrium climate sensitivity (1°C - 6°C)

Central projection

-4.0%

-3.5%

-3.0%

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

2010 2020 2030 2040 2050 2060



Wider uncertainty rangeequilibrium climate sensitivity (1°C - 6°C)

Central projection

Source: Dellink et al (2014)

142

Stylised analysis post-2060

-9%

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Global damages as percentage of GDP

Likely uncertainty range (Business as Usual)

Likely uncertainty range (Committed by 2060)

Central projection (Business as Usual)

Central projection (Committed by 2060)

Central projection (highly nonlinear damages)-9%

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100






Central projection (highly nonlinear damages)-9%

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100






Central projection (highly nonlinear damages)


143

Regional results (central projection)


-6%

-5%

-4%

-3%

-2%

-1%

0%

1%

2%

OECD America OECD Europe OECD Pacific Rest of Europeand Asia

Latin America Middle East &North Africa

South & South-East Asia

Sub-SaharanAfrica

World

Global GDP impact (% change wrt no-damages baseline, 2060)

Agriculture

Sea level rise

Tourism

Health

Ecosystems

Energy

Fisheries

Preliminary analysis of benefits of policy

action

• Assessment of benefits of policy action require insight into stream of future avoided damages

– Not straightforward to assess with ENV-Linkages

– Lack of sectoral adaptation information is also an issue

• As first step, use the AD-RICE model which is especially suited for this (as perfect foresight model)

– AD-RICE is an augmented version of Nordhaus’ RICE model, with explicit representation of adaptation

• Look at both adaptation and mitigation policies, and their interactions

144

145

Preliminary results: adaptation policies

Preliminary results; not to be cited or quoted

-10%

-9%

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

% change wrt no-damage baseline

Likely uncertainty range - Optimal adaptation Central projection - Optimal adaptation

Central projection - Flow adaptation Central projection - No adaptation

-10%

-9%

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100


Likely uncertainty range - No adaptation Central projection - Optimal adaptation


-10%

-9%

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100


Likely uncertainty range - Flow adaptation Central projection - Optimal adaptation


146

Preliminary results: mitigation policies


-10%

-9%

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

% change wrt no-damage baselineLikely uncertainty range - No mitigation Likely uncertainty range - Optimal mitigation

Central projection - No mitigation Central projection - Optimal mitigation

Weitzman damage function - No mitigation Weitzman damage function - Optimal mitigation

-10%

-9%

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100




-10%

-9%

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100




147

Preliminary results: discounting


-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100


Likely uncertainty range - Nordhaus discounting Central projection - Nordhaus discounting

Central projection - UK Treasury discounting Central projection - Stern discounting

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100


Likely uncertainty range - Stern discounting Central projection - Nordhaus discounting


-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100


Likely uncertainty range - UK Treasury discounting Central projection - Nordhaus discounting


148

Preliminary results: interactions


-10%

-9%

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100


Optimal adaptation - No mitigation Optimal adaptation - Optimal mitigationFlow adaptation - No mitigation Flow adaptation - Optimal mitigationNo adaptation - No mitigation No adaptation - Optimal mitigation

-10%

-9%

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100



-10%

-9%

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100



How to expand the list of impacts that

are covered?

• Extreme precipitation events– Floods, hurricanes

• Extreme temperature events– Heatwaves

• Water stress– But impacts on agriculture already largely included

• Large-scale disruptions– Shut-down of Gulf Stream, collapse of West-Antarctic

ice sheet

• Other discontinuities and tipping points

149

• Q4 2014 / Q1 2015– Finalise expanded baseline– Revise agricultural impacts– Carry out stand-alone assessment of the literature on

some of the major missing impacts (incl. heatwaves)– Finalise first stylised assessment of benefits of action– Updated report available in time for COP21

• Rest of 2015– Develop policy simulations in ENV-Linkages– Carry out integrated policy analysis for climate change

and air pollution– If possible extend the range of impacts covered in the

analysis

150

Timeline

THANK YOU!For more information:



IMPACTS OF

LOCAL AIR POLLUTION:


GROWTH

Elisa Lanzi




153

Impacts of air pollution

• Air pollution is one of the most serious environmental health risks

– WHO (2014) estimates that in 2012 around 7 million people died as a result of air pollution exposure

– OECD (2014) finds that the total economic costs of deaths from ambient air pollution amount to 1.6 trillion USD in 2010 in OECD countries

• Impacts also to crop yields, biodiversity and cultural heritage

154

The CIRCLE project approach

• Macroeconomic cost of the impacts of air pollution

– Include impacts of air pollution to the economy in the ENV-Linkages model

• Labour productivity

• Increased health expenditures

• …

– Adjustments take place in the model to finally give the final macroeconomic cost of air pollution

• Non-market costs

– Premature deaths

– Pain and suffering

155

Methodology

1. PROJECTIONS OF AIR POLLUTANTS EMISSIONS

2. CONCENTRATIONS OF AIR POLLUTANTS

3. IMPACTS OF AIR POLLUTION ON HEALTH

4. ECONOMIC CONSEQUENCES OF HEALTH IMPACTS

5. MACROECONOMIC IMPACTS OF AIR POLLUTION

OECD, IIASA

EU JRC (Ispra)

EMRC

EMRC

Methodological steps Project partners

OECD

156

1. Projections of air pollutants emissions

• Emission data from the sectoral GAINS model (IIASA)

– SO2, NOx, PM2.5, OC, BC, NH3

– Projections for Current Policy Scenario of IEA’s WEO 2012

• Link emissions to production activities in different key sectors

– Combustion of fossil fuels in energy and industrial sectors

– Production of goods

• Sector and region specific emission coefficients

• Projections of coefficients calculated using the WEO 2012 to 2035 and then linear extrapolation to 2060

• Calculating concentrations requires

– Downscaling from macro regions to local level

– Data on regional emissions, climatic and geographical variables (e.g. altitude, location of industrial areas, temperatures…)

• Calculations will be done by the EU JRC (Ispra)

– FAst Scenario Screening Tool (FASST), which describes relations between precursor’s emissions and pollutant’s concentrations

– Output:

• Concentrations of PM2.5, including from primary (BC and OC) and secondary (SO4 and NO3) emission sources

• SO2 and NOx

• Ozone

157

2. Concentrations of air pollutants

• Concentrations are used to calculate the impacts on health

• Demographic variables also needed as input – Population growth

– Ageing

– Fertility rates

• Impacts that would ideally be included are– increased mortality (premature deaths)

– increased morbidity (number of sick days, hospital admissions…)

158

3. Impacts of air pollution on health

• Once the health impacts are calculated, they need to be evaluated

• Market impacts– Additional health costs (from hospital admissions or healthcare)

– Changes in labour productivity

• Non-market impacts– Cost of premature deaths

– Costs of pain and suffering

• The challenge– Break down morbidity costs between market and non-market costs

159

4. Valuation of health impacts

160

5. Macroeconomic impacts of air pollution

• Health impacts will be modelled directly in the CGE model, as much as possible

• Production function approach– increased mortality: loss of labour supply

– increased morbidity: decreased labour productivity, increased demand for healthcare

• Aspects that cannot be captured in CGE models – Presented separately from the macroeconomic impacts

– Economic costs of premature deaths, costs of ‘pain and suffering’

– Challenge: how to combine market and non-market impacts?

• Policies can improve air quality and reduce the impacts on health

– Adoption of end-of-pipe technologies

– Shifting of economic activity away from polluting to less polluting sectors

– Improvements in production processes, e.g. energy efficiency improvements, fuel switching

• Potential air pollution scenario: Maximum Technically Feasible Reduction (MTFR) scenario, which reflects the implementation of the best available end-of-pipe technologies to reduce air pollution

– Need data on the costs of implementation of the policies, i.e. the costs of the adoption of new and more efficient technologies

• Interactions between air pollution and climate change mitigation policies

161

Benefits of policy action

• Model Marginal Abatement Cost Curves

– Identify how policies affect technology choice and then specify the position on the MACC

– The MACC reflects investments in abatement as a consequence of policies such as

• mandating specific end-of-pipe techniques

• incentives to adopt improved technologies

• road pricing schemes

• air quality targets

• Consider other impacts

– Agricultural yields

– Biodiversity

162

Possible future developments

• Q4 2014– Finalise the modelling of air pollutants in ENV-Linkages– Calculate concentrations– Finalise the methodology to calculate and evaluate impacts

• Q1 2015– Calculate and evaluate impacts

• Q2 2015– Quantitative assessment of the economic consequences of the health

impacts of air pollution– Develop relevant policy scenarios

• Q3 2015 – Calculate benefits of policy action

• Q4 2015– Finalise the work and draft a report, which should be ready in early 2016

• Q1 2016– Finalise the report

163

Next steps and timeline

THANK YOU!

For more information:



[email protected]

Calculating indicators for health

impacts of air pollution for

ENV-Linkages

Mike Holland [email protected]

September 2014

165


Tasks

• Calculate mortality and morbidity

indicators for SO2, NOx, PM2.5 emissions

• Quantify economic costs

– Health expenditure

– Labour productivity

– Non-market damage (pain, suffering,

premature mortality)

• Assess feasibility of extending quantitative

assessment to non-health pollution

impacts 166

Starting point for analysis

• Pollutant concentrations (PM2.5, others?)

• Previous studies

– Global burden of disease, USEPA, European

Commission, UN/ECE LRTAP Convention,

Chinese work

• Data on GDP, population, population

structure from OECD

• OECD recommendations on VSL

167

Same approach everywhere?

• Possible standard approach

– GBD for all, using cause specific mortality

functions

– 10% added for morbidity

• HRAPIE

– All cause mortality more reliable for Europe

(and USA)

– Detailed analysis of morbidity already

undertaken

168

Defining health endpoints

169

• Morbidity, Europe and USA, €2012

Quantifying outside Europe,

USA• Quantification

at higher

concentrations?

• Incidence,

prevalence data?

• Valuation data?

• Treatment

options?

170

Concentration

Response

Air pollution and healthcare

• EU, French, US studies

• Completeness? CV morbidity

• High costs associated with mortality in US

and French studies

171

Valuation of healthcare costs

172

Air pollution and productivity

• Functions for work loss days

– Limited, aged research

– How complete?

– ‘Presenteeism’?

173

Summary

• Quantification at global scale is possible

• Key decisions

– Treatment of morbidity

– Use of common

– Interpretation of effects

174

Health Benefits of Air Pollution

Milan Ščasný

Charles University in Prague

Second Ad-hoc Technical Workshop on CIRCLE

2-3 October 2014, OECD Paris

Contribution

Agenda: How can air pollution impacts be monetised and linked to specific economic activities and what additional work is required to do so?

• Linking the economic model with AQ-benefit assessment: Drivers of the pressures

• Identifying impacts: Going from pressure to impacts

• Deriving benefits: Moving from (health) impacts to monetary valuation

• Linking the modeling approaches on the top: Economic assessment within a general equilibrium framework

From econ model to AQ-benefits < Impact pathway approach >

POLLUTANT

& NOISE

EMISSIONS

MONETARY

VALUATION

TRANSPORT

& CHEMICAL

TRANSFORMATION

DIFFERENCES OF

PHYSICAL IMPATS

177

From econ model to AQ-benefits

Drivers

Output-

linked

coeff

Fuel-

linked

coeff

Fuel-linked

projections

(CIRCLE?)

ScaleThe change in performance of the

whole economy

Composition The change in relative sizes of sector

Fuel IntensityThe change in fuel consumption per

unit of value added

Fuel MixThe change in fuel-mix used in

production

Emission

Intensity

The change in emission volume per unit

of fuel used (affected by end-of-pipe)

1

• MR EE IOTs (EXIOBASE, CREEA) is very rich and useful source on fuel-specific country-specific emission coefficients, but it describes economy in the past (2007)

From pressures to impacts

CIRCLE:

• mortality, morbidity, pain attributable to airborne pollutants (SO2, NOx,PM2.5,OC,BC,NH3)

• primarily health benefits, but effect on crop, biodiversity, cultural heritage later

Comments

• building materials soiled or corroded the ExternE project series

• benefits can be valued only if reliable DRFs/ERFs/CRFs exist

PMcoarse, NMVOC, heavy metals ExternE (NEEDS, DROPS, HEIMTSA,…)

(GHGs health effects included in DICE, FUND, PESETA, GLOBAL-IQ, …)

Contribution of impact categories to total

externalities External costs from power sector in Czech Rep. (2005)

mil. €

% total externalities

% classic pollutants

mortality 956.75 32.4% 54.1% chronic YOLL 947.43 32.1% 53.6% acute YOLL 8.30 0.3% 0.5% infant mortality 1.02 0.0% 0.1% morbidity 484.89 16.4% 27.4% chronic bronchitis 150.07 5.1% 8.5% RAD 98.54 3.3% 5.6% LRS 82.87 2.8% 4.7% cough 3.02 0.1% 0.2% HA 0.95 0.0% 0.1% broncholidator 0.17 0.0% 0.0% WLD 149.27 5.1% 8.4% crops 16.07 0.5% 0.9% materials 75.74 2.6% 4.3% loss of biodiversity 184.32 6.2% 10.4% North hemispheric 50.00 1.7% 2.8% micro-pollutants 16.63 0.6% climate change (21€/t) 1 171.32 39.6% TOTAL 2 955.71 100.0%

Work-loss-days

Valuing benefits

< monetary valuation >CIRCLE:

• Market and non-market value

• GBD-based?

Comments

• GPD measured via QALY or DALY does not conform to welfare economics

• quantify welfare changes due to avoiding specific health outcome or risk

MEDCOST - Medical treatment costs medical costs paid by the health service (covered by insurance), and any other personal

out-of-pocket expenses

LOSSPROD - Indirect (opportunity) costs in terms of loss productivity work time loss, lower efficiency of performance, and the opportunity cost of leisure

DISUTILITY welfare loss due to inconvenience, suffer, pain, or premature death

Valuing benefits /2< Are they any values? WTP for other health

outcomes? >

• benefits can be valued only if monetary values (willingness-to-pay) are available

…reviews by Mike Holland & Anna Alberini

respiratory illness NEEDS (cough, hosp admission, etc.); HEIMTSA (COPD, chronic bronchitis), ECHA-WTP (asthma)

fertility Value of a Statistical Pregnancy of approx. €30,000 in ECHA-WTP study (Ščasný & Zvěřinová 2014)

developmental toxicity

WTP - €4,000 minor birth defects; €130,000 defects of internal organs, metabolic and genetic disorder; €125,000 very low birth weight ECHA-WTP

€5-20,000 loss of earnings due to one point IQ DROPS

carcinogens

VSL as well as VSCC for cancer, controlling for quality of life and pain impact (Alberini and Ščasný, 2014)

skin sensitisation and dose toxicity

WTP for dermatitis and renal failure by Máca and Braun Kohlová (2014)

Valuing benefits /3

< methodological issues >

VSL vs. VOLY (Value of a Statistical Life vs. Value of a Statistcal Life Year)

– due to shorter expected lifespans of elderlies, the VOLY assigns a lower value VOLY called as "senior death discount“

– EPA‘s SAB rejected using the VOLY approach (2008), similarly OECD CBA by Pearce et al. (2006) is recommending using VSL rather than VOLY

– Economic theory suggests to value changes in risk of dying WTP for ‘a micromort’ Value of a Statistical Life

– My suggestion:

use WTPs for mortality risk reduction and link it with Risk Rates estimated in epidemiological studies

If RR are transferred into Life Losts, use VSL

If RR are transferred into YOLLs, use VOLY if it was based on WTP for risk reductions (partly in Desaigues et al. (2007; 2011)

do not link VOLY on QALYs/DALYs, or make it with very caution

Valuing benefits /4

< methodological & normative issues >

• Premiums in a Value of a Statistical Case

10% ‘malus’ for morbidity associated with mortality risk

50% bonus for infants

no strong evidence for such premiums (Alberini and Ščasný 2012 for ‘child’ premium; Alberini and Ščasný 2014 for QoL in cancer risks)

but, benefits for premature death should include both DISUTILITY(hence VSL) and Cost-Of-Illness (for instance, MEDCOST of cancer treatment is €6,000 and LOSSPROD are €40,000 in Czech Rep; Ščasný & Máca 2008)

Linking the models on the topMEDCOST and LOSSPROD

• MEDCOST - Medical treatment costs

medical costs paid by the health service (covered by insurance), and any other personal out-of-pocket expenses

both public health service (sector in SAM) and personal out-of-pocket expenses (final use in SAM)

Premature death may reduce governmental expenditures on pensions and health care (final use in SAM)

public health system may affect the length of sickness leave LOSSPROD

• LOSSPROD - Indirect (opportunity) costs in terms of loss productivity

work time loss, lower efficiency of performance, and the opportunity cost of leisure

average wage, GDP per capita / employee – D(L)

costs of absenteeism (CBI 1999), direct and indirect – P(L), MPL

friction costs based on a concept of replacement (Koopmanschap et al. 1995)

Valuing benefits /5

< normative issue: social planner

perspective >

One value across countries and regions ?

• WTP for pain, inconveniences, or premature death

consensus

• MEDCOST

so far one ‘average’ value used, maybe for simplicity

• LOSSPROD

one value for whole EU, as far as I know, but the value is a population weighted average, at least for the EU

Linking the models on the top WTPs in GE framework /2

• One ‘EU-average’ WTP values used in EcoSenseWeb tool (ESW) using different values matter

Table: Health-related externalities due to pollution from power sector in the Czech Republic if different monetary values are used. Source: Máca and Ščasný 2009 (NEEDS project)

• one average value of MEDCOST and LOSSPROD is not consistent with SAM

• using one WTP value of DISUTILITY (pain, mortality, fertility) may be fine because there is no its counterpart in SAM, and no component in the CGE utility function

0

200

400

600

800

1000

1200

1400

1600

ESW

ESW

inde

x

ESW

inde

x CZ

ESW

wea

lth

ESW

wea

lth C

Z

LITRVin

dex

LITRVin

dex C

Z

LITRVw

ealth

LITRVw

ealth

CZ

mil. €

outside of CZ

within the CZ

PPP-

adjusted

GDP-

adjustedBased on our literature

review

EU-wide

values

Linking the models on the top WTPs in GE framework /3

• Impacts, and hence benefits, are NOT distributed among emitting-country residents only

Table: Health-related externalities due to pollution from power sector in the Czech Republic disaggregated according to the region where the impact would occur, % of total . Source: Máca and Ščasný 2009 (NEEDS project)

• To ensure consistency with SAM, physical impacts (health outcomes) should be derived for country/regions, as used in CGE regional structure

• Otherwise, one would need to assume that damage attributable to emissions released by region x are affecting residents from region x only

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

ESW LITRVindex LITRVwealth ESWindex ESWwealth

% o

f to

tal e

xte

rna

liti

es

rest

TR+YU+HR

UA+RUS

HU+RO+SVK

POL

CZ

NL+UK+BE

ITA+FRA+AT

DE

Linking the models on the top

WTPs in GE framework /4

• Keep WTP value over time constant (when income may increase)?

where g is percentage change in income per capita in period t (i.e. endogenous in CGE), ε is elasticity of WTP wrt income (invariant in time?)

• present value of WTPt to be consistent with CGE utility discounting (PRTP) vs. consumption discounting (PRTP + g*εy), where εy is the elasticity of the marginal utility of consumption

• consistency between variations (coming from CLI in CGE) and surpluses (CSU/ESU coming form stated preference valuation studies)

• WTP values reported in FINAL prices, however, expenditures in SAM are recorded in BASIC prices (i.e. excluding taxes) – to be consistent with national accounts, WTP values would have to be ‘cleaned’ (taxes put out)

𝑊𝑇𝑃𝑡 =𝑊𝑇𝑃 ∙ (1 + 𝑔𝑡 ∙ 𝜀𝑡)

Thank you for your attention.

Milan Ščasný

Univerzita Karlova v Praze

[email protected]


presentations of the oecd 2nd circle technical workshop (2-3 oct. 2014)

Documents