peak oil through the lens of a general equilibrium assessment
DESCRIPTION
Peak Oil through the lens of a general equilibrium assessment. Waisman H, Rozenberg J, Sassi O et Hourcade J-C (2012). “Peak Oil profiles through the lens of a general equilibrium assessment”, Energy Policy 48:744-753. Motivation. - PowerPoint PPT PresentationTRANSCRIPT
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Peak Oil through the lens of a general equilibrium assessment
Waisman H, Rozenberg J, Sassi O et Hourcade J-C (2012). “Peak Oil profiles through the lens of a general equilibrium assessment”, Energy Policy 48:744-753.
Motivation
Peak Oil = impending stagnation and decline of world oil production
Determinants of Peak Oil : what drives the occurrence of Peak Oil?
Date of Peak Oil : a signal for the urgency of changes in energy uses
Economic consequences : sudden shocks and long-term effects
Motivation
Two largely disconnected strands of literature
Geological-based analyses of Peak Oil
Hubbert-like approach = bell-shaped production curves at the field level Extrapolation at a global level is questionable (heterogeneous oil reserves and
producers’ rationale)
Economic dimensions of oil markets
Short-term effects of oil disruption econometric analyses after first oil shock no explicit accounting of resource depletion
Long-term effects of exhaustible resources exploitation production profile under perfect foresight (Hotelling) scarce representation of peaks
Objectives
Representing the macroeconomic dimensions of long-term oil markets
Endogenizing the interplay between determinants of long-term oil markets under inertia and imperfect anticipations Oil supply, Liquid Fuels demand, Alternatives to oil
Representing the impact of the economy on oil markets Consumer behavior driving oil demand in function of price signals Investment decisions in production capacities of oil and alternatives in
function of profitability prospects and capital availability
Capturing the feedback effects of oil markets on macroeconomy Energy trade (energy security for oil-importers, exportation revenues) Technical and structural change ( oil-dependency, industrialization) Aggregate effects on economic activity and welfare
Approach (1) A CGE approach embarking lessons from economic assessments of
oil shocks
mark-up pricing instead of competitive markets to capture a set of market imperfections
partial utilization rate of capital due to the complementarity between energy and capital services
putty-clay description of capital (with fixed energy intensity for old vintages) capturing the effects of inertias in the renewal of productive capital
frictions in reallocating capital and labor across heterogeneous sectors (in terms of sensitivity to oil prices) causing differentiated levels of idle production capacities and unemployment.
Approach (2)
A hybrid bottom-up/top-down approach
sectoral modules embarking expert-based information on the direction and magnitude of technical change, including the determinants of oil markets
a dialogue between technical/behavioral determinants and macroeconomic interactions
A representation of « second-best » economies
missing, imperfect or distorted markets noncompetitive behaviors technical and institutional inertias asymmetry or incompleteness of information
The IMACLIM-R model
Economic signals
(prices, quantities, Investments)
Static Equilibrium (t)
under constraints
Dynamic sub-modules
(reduced forms of BU models)
Static Equilibrium (t+1)
under updated constraints
Technical and structural parameters
(i-o coefficients, population, productivity)
Hybrid matrixes in values, energy and « physical » content (Mtoe, pkm) Secure the consistency of the engineering based and economic analyses Explicit accounting of inertias on equipement stocks Technical asymptotes, basic needs
Solowian growth engine in the long run but transitory disequilibrium Unemployment, excess capacities Investments under imperfect foresight (informed by sectoral models) Trade and capital flows under exogenous assumption about debts
Modeling oil supply geological dimension
In each region, 7 categories of conventional + 5 categories of non-conventional reserves size of the reservoir Q∞,i (ultimate reserves, including past production) threshold selling price pi
(0) above which production is profitable (proxy for costs of exploration/exploitation and accessibility)
Maximum rate of increase of production capacity for each category, given geological constraints
bi: steepness of the bell-shape profile (default value: b=0.061)t0,i : expected date of the maximum for oil category i, given past production
0,
0,
( )
max( )
. 1( , )
( , ) 1
i i
i i
b t ti
b t t
b eCap t i
Cap t i e
Modeling oil supply producers’ decisions
All regions except Middle-East = “Fatal producers” maximum deployment for profitable categories (poil > pi
(0) ) investment stopped for non-profitable categories (poil < pi
(0) )
Middle-East = “Swing producers” Fill the gap between demand and other suppliers World price depends on the utilization rate of production capacities ME deployment of production capacities in function of their price objective
Total production capacity= sum over all categories and regions
time
Production Capacity of oil category i
t t+1
Cap(t,i)
Cap(t,i)+Δcapmax(t,i)
Modelling oil demand
Liquid fuels’ demand (residential, industry, transport)
Utility and profit maximization under constraints• Short-term : inertia in the renewal of equipments and LBD• Long-term : consumption styles (preferences), technical potentials
(technology availability, asymptotes), location patterns
Alternatives to oil
Biofuels• Competition over oil-based fuels: supply curves increasing with oil price• Asymptotes on BF production at a given year (competition of land uses)• Evolve in time to represent technical progress
Coal-To-Liquid• backstop technology with capacity constraints• enter the market at high oil price• production limited by the cumul of past investments
Results
Two counterfactual scenarios of the world economy over 2010-2050 on production capacity expansion
Market Flooding scenario
ME expands production capacities to maintain oil price at 2009 level Intense demand in the short-termTechnical change towards oil-intensive patterns in the long term
Limited Deployment scenario
ME restricts capacity expansion to let short-term prices riseModeration of oil demandTechnical change towards oil-free patterns in the long term
ResultsPeak Oil profiles
Level
Date
Post-PO decrease
0
20
40
60
80
100
120
140
2010 2020 2030 2040 2050
Oil production (Million b/d)
Limited Deployment (LD) scenarioMarket Flooding (MF) scenario
Close dates but very different time profiles!
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
5
2010 2020 2030 2040 2050
World oil price (index 1=2009 level)
Limited Deployment (LD) scenarioMarket Flooding (MF) scenario
ResultsWorld oil prices
Controlled by OPEC in the short-term
Sudden rise at the Peak Oil date
Continuous increase in long term due to constraints on CTL
ResultsTime profile of oil revenues
Short-term revenues controlled by price targets
Bubble of long-term profits triggered by price increase after PO
0
200
400
600
800
1000
1200
1400
1600
2010 2020 2030 2040 2050
Middle-East annual oil profits (Billion $)
Limited Deployment (LD) scenarioMarket Flooding (MF) scenario
Room for Short-term vs. Long-term tradeoff!
ResultsTradeoff for oil producers
MF scenario profitable for oil producers at discount rates lower than 6%
Discount rateLimited Deployment
ScenarioMarket Flooding
Scenario
0% 38.9 43.6
1% 28.9 31.8
2% 21.9 23.6
5% 10.6 10.8
6% 8.7 8.6
7% 7.2 7.0
15% 2.4 2.2
Results Impacts on oil-importers (OECD)
Close average growth but different time profiles
Average(2010-2050)
Short-term Period(2010-2025)
Peak Oil Period(2025-2040)
Long-term Period(2040-2050)
Natural growth rates 1.42% 1.69% 1.30% 1.19%
Effective growth rates
Limited Deployment scenario
1.57% 1.93% 1.43% 1.24%
Market Flooding scenario
1.53% 2.00% 1.29% 1.18%
ResultsA sensitivity analysis on oil
determinants Amount of reserves Low bound: 1.6Trillion bbl conventional+ 0.8Trillion bbl non-conventional High bound: 2.3Trillion bbl conventional+ 1.2 Trillion bbl non-conventional
Inertia on non-conventional production Low inertia: b=0.04 vs. high inertia: b=0.07
2015
2020
2025
2030
2035
2040
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
Date (years)
m (reserves)
Date of the Peak Oil
LD ; b=0.041
LD ; b=0.07
MF ; b=0.041
MF ; b=0.07
Increasingreserves
60
80
100
120
140
160
180
200
220
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
Price ($/bbl)
m (reserves)
Post -Peak Oil price($/bbl)
LD ; b=0.041
LD ; b=0.07
MF ; b=0.041
MF ; b=0.07
Increasing reserves
Conclusion
A model to frame the debate on Peak Oil and long-term oil markets
representing the interplay between geological and macroeconomic dimensions of long-term oil markets
endogenizing the date of Peak Oil from market interactions between oil supply, liquid fuels’ demand and alternatives to oil
analyzing the effects of alternative pricing strategies and various assumptions on reserves and non conventional production
assessing the indirect macroeconomic effects of Peak Oil on long-term economic growth trajectories
Conclusion
Some important messages
Oil pricing trajectories hardly affect the date of Peak Oil … but have important consequences on its macroeconomic effects
(oil revenues, economic activity in oil importing countries)
strong variations of Peak Oil and related macroeconomic effects according to assumptions on oil reserves
Middle-East producers may prefer moderate short-term prices if they are able to bias technical change under imperfect foresight
High short-term oil prices may be beneficial in oil importing countries as a hedging strategy against long-term oil scarcity
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Thank you for your attention!
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34th IAEE International ConferenceStockholm - 19/23 June 2011
Approach (1) A CGE approach embarking lessons from economic assessments of
oil shocks
mark-up pricing instead of competitive markets to capture a set of market imperfections
partial utilization rate of capital due to the complementarity between energy and capital services
putty-clay description of capital (with fixed energy intensity for old vintages) capturing the effects of inertias in the renewal of productive capital
frictions in reallocating capital and labor across heterogeneous sectors (in terms of sensitivity to oil prices) causing differentiated levels of idle production capacities and unemployment.
Approach (2)
A hybrid bottom-up/top-down approach
sectoral modules embarking expert-based information on the direction and magnitude of technical change, including the determinants of oil markets
a dialogue between technical/behavioral determinants and macroeconomic interactions
A representation of « second-best » economies
missing, imperfect or distorted markets noncompetitive behaviors technical and institutional inertias asymmetry or incompleteness of information