A Generalized Equilibrium Approach to Balance the Residual Abatements Resulting from COP-21 Agreement

Transcription

1 1 / 21 A Generalized Equilibrium Approach to Balance the Residual Abatements Resulting from COP-21 Agreement Frédéric Babonneau, Alain Haurie and Marc Vielle ETSAP Workshop - Cork May 30-31, 2016

2 2 / 21 Contents 1 Context and Objectives 2 A Dynamic meta-game model for climate negotiations 3 INDCs evaluation 4 Fair agreements for additional efforts 5 Conclusion

3 3 / 21 Context and Objectives 1 Context and Objectives 2 A Dynamic meta-game model for climate negotiations 3 INDCs evaluation 4 Fair agreements for additional efforts 5 Conclusion

4 4 / 21 Adressed questions Context and Objectives 1 What do INDCs mean? And what are the economic impacts of INDC implementation? 2 How an international carbon market might affect climate agreements? 3 How to share additional efforts on to reach the 2 o C target in 2100? How to design a fair agreement among groups of countries? 4 How each country will use its allocations on the horizon ? What will be the associated costs for each country?

5 5 / 21 Context and Objectives Methodology Propose a meta-game approach for assessing burden sharing agreements for the attainment of 2050 climate target. Methodology: 1 Identify a global emissions budget on compatible with a 2 o C temperature increase in Estimate abatement cost functions for each group of countries using simulations of the Computable General Equilibrium model GEMINI-E3 3 Define a meta-game in which each country minimizes its costs according to a global share of allocations. Each country decides strategically the timing of its emissions.

6 6 / 21 A Dynamic meta-game model for climate negotiations 1 Context and Objectives 2 A Dynamic meta-game model for climate negotiations 3 INDCs evaluation 4 Fair agreements for additional efforts 5 Conclusion

7 7 / 21 A Dynamic meta-game model for climate negotiations Meta-games for climate negotiations Global emissions Budget US EU China Coopera8ve Non Coopera8ve Interna8onal emissions trading scheme

8 7 / 21 A Dynamic meta-game model for climate negotiations Meta-games for climate negotiations Global emissions Budget US EU China Coopera8ve Non Coopera8ve The payoff (welfare loss) of player j at equilibrium satisfies : min ω j T 1 β t j (πt j (et j (Ωt )) p t (Ω t )(ω t j e t j (Ωt ))), t=0 Interna8onal emissions trading scheme subject to actions chosen by the other players and under the budget sharing constraint T 1 ω t j θ j Bud. t=0

9 7 / 21 A Dynamic meta-game model for climate negotiations Meta-games for climate negotiations The payoff (welfare loss) of player j at equilibrium satisfies : Global emissions Budget US EU China Interna8onal emissions trading scheme Coopera8ve Non Coopera8ve min ω j T 1 β t j (πt j (et j (Ωt )) p t (Ω t )(ω t j e t j (Ωt ))), t=0 subject to actions chosen by the other players and under the budget sharing constraint T 1 ω t j θ j Bud. t=0 Applying standard Kuhn-Tucker multiplier method, with multipliers ν j, we obtain the following first order necessary conditions for a Nash equilibrium: ν j = β t j (pt (Ω t ) + p t (Ω t )(ω t j e t j (Ωt ))) t j T 1 0 = ν j (θ j Bud ω t j ) t=0 T 1 0 θ j Bud ω t j t=0

10 A Dynamic meta-game model for climate negotiations Meta-games for climate negotiations The payoff (welfare loss) of player j at equilibrium satisfies : Global emissions Budget US EU China Interna8onal emissions trading scheme Coopera8ve Non Coopera8ve min ω j T 1 β t j (πt j (et j (Ωt )) p t (Ω t )(ω t j e t j (Ωt ))), t=0 subject to actions chosen by the other players and under the budget sharing constraint T 1 ω t j θ j Bud. t=0 Applying standard Kuhn-Tucker multiplier method, with multipliers ν j, we obtain the following first order necessary conditions for a Nash equilibrium: ν j = β t j (pt (Ω t ) + p t (Ω t )(ω t j e t j (Ωt ))) t j T 1 0 = ν j (θ j Bud ω t j ) t=0 T 1 0 θ j Bud ω t j t=0 Abatement cost functions π are estimated through statistical emulation on a large set of GEMINI-E3 simulations 7 / 21

11 8 / 21 A Dynamic meta-game model for climate negotiations A noncooperative meta-game approach Input Global budget Bud and allocations among countries (i.e., θ j ) Model Minimize the economic impacts for each country by deciding: 1 How to use the budget on the horizon 2 Permit sales and buyings on the trading market Output Emissions, Permit exchanges, Permit prices, Percentage of welfare losses,... By testing different allocations, one can find a fair burden sharing. For example if we adopt a Rawlsian approach to distributive justice, the optimal game design problem consists in finding the θ j s in such a way that one minimizes the largest welfare loss among the countries.

12 A Dynamic meta-game model for climate negotiations Estimation of the abatement cost functions We use the CGE model GEMINI-E3 as a the provider of data for the estimation of the abatement cost functions for each group of countries Estimations are based on statistical emulations of a sample of 200 GEMINI-E3 numerical simulations (4 periods 11 = nb estimations) The abatement costs are polynomial functions of degree 4 in the country abatement level AC j (t) = α j 1 (t) q j(t) + α j 2 q j(t) 2 + α j 3 (t) q j(t) 3 + α j 4 (t) q j(t) 4. (1) USA EU UMB CHI IND RUS OPE ROW ASI LAT LDC MAC Abatement (%) 9 / 21

13 10 / 21 INDCs evaluation 1 Context and Objectives 2 A Dynamic meta-game model for climate negotiations 3 INDCs evaluation 4 Fair agreements for additional efforts 5 Conclusion

14 11 / 21 INDCs evaluation INDC analysis and consolidation Difficulties to convert INDCs in consistent emissions abatements in 2030: Objectives are related to different reference emissions (Historical emissions, BAU emissions, Intensity target, etc) Conditional and unconditional targets Objective year: from 2025 to 2035 Missing information and unsubmitted INDCs We use conventional target related to GEMINI-E3 BAU scenario.

15 12 / 21 INDCs evaluation INDC targets in Mt CO2-eq in 2030 Unconditional Conditional Reduction compared to GEMINI-E3 BAU USA % EUR % UMB % CHI % IND % RUS % OPE % ROW % ASI % LAT % LDC % World

16 13 / 21 INDCs evaluation INDCs impacts on welfare losses on [2015, 2030] Without International carbon market With International carbon market Welfare loss CO 2 prices in $ /t Welfare loss CO 2 prices in $ /t in % of disc. HC in % of disc. HC USA EUR UMB CHI IND RUS OPE ROW ASI LAT LDC World International carbon market has a positive impact on global and all individual costs. Low welfare losses clearly reflect a lack of ambition of INDCs.

17 INDCs evaluation Decomposition of welfare losses LDC Abatement Costs LAT ASI GTT ROW OPE RUS IND CHI UMB EUR USA LDC Abatement Costs LAT Quotas buying ASI GTT ROW OPE RUS IND CHI UMB EUR USA / 21

18 15 / 21 Fair agreements for additional efforts 1 Context and Objectives 2 A Dynamic meta-game model for climate negotiations 3 INDCs evaluation 4 Fair agreements for additional efforts 5 Conclusion

19 16 / 21 Fair agreements for additional efforts Emissions budget on

20 17 / 21 Fair agreements for additional efforts Global welfare loss on

21 18 / 21 Fair agreements for additional efforts Example #1 of fair agreement (2 o C target) on [2015, 2050] Region Emissions budget Welfare loss Abatement cost Permit buying GTT in Mt CO 2 -eq in % of discounted household consumption USA EUR UMB CHI IND RUS OPE ROW ASI LAT LDC World

22 19 / 21 Fair agreements for additional efforts Example #2 of fair agreement (2 o C target) on [2015, 2050] Region Emissions budget Welfare loss Abatement cost Permit buying GTT in Mt CO 2 -eq in % of discounted household consumption USA EUR UMB CHI IND RUS OPE ROW ASI LAT LDC World

23 20 / 21 Conclusion 1 Context and Objectives 2 A Dynamic meta-game model for climate negotiations 3 INDCs evaluation 4 Fair agreements for additional efforts 5 Conclusion

24 21 / 21 Conclusion Conclusion and Perspectives Conclusion INDCs commitments are weak. It is possible to design fair agreements (eg, equalizing welfare costs between coalitions) The implementation of a tradable permits market is crucial as it allows to equalize marginal abatement costs and to reduce welfare losses Perspectives Extend the model to robust optimization to take into consideration statistical errors in the calibration of abatement cost functions Apply meta-game on alternative economic models (eg. TIMES)