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Policy Scenarios and Stochastic Analysis with the Gemini-E3 Model

This work aims to perform stochastic analysis using the Gemini-E3 model in the framework of the Planets Project, analyzing the robustness of scenario results concerning energy mix and technological choices. Preliminary runs have been conducted, and the main characteristics of the Gemini-E3 model, such as its world general equilibrium representation and detailed indirect taxation, are highlighted. Uncertain parameters and infeasible runs are discussed, along with future steps involving uncertainty on climate sensitivity and linking results with stochastic parameters through econometric analysis.

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Policy Scenarios and Stochastic Analysis with the Gemini-E3 Model

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  1. Policy Scenarios and Stochastic Analysis with the Gemini-E3 Model F. Babonneau¹ – A. Haurie¹ – M. Vielle² (¹Ordecsys – ²EPFL) Switzerland www.ordecsys.com

  2. Aim of the work • Perform stochastic analysis in the framework of the Planets Project • Analyzing the robustness of the scenario results in particular concerning energy mix and technological choice • Preliminary runs

  3. Main Characterictics of GEMINI-E3 • World General Equilibrium Model • Total Price Flexibility • Detailed Representation of Indirect Taxation • Measure of Welfare Cost of Policies and Components

  4. GEMINI-E3 Production Function

  5. Electricity Generation CCS

  6. Cost of CCS • Information coming from Chalmers University of Technology - (Euro/ton CO2) K. Andersson, F. Johnsson « Process evaluation of an 865 MWe lignite fired O2/CO2 power plant » Energy Conversion and Management, 2006

  7. The climate constraint • The objective is to limit the radiative forcing to 3.5 W/m2, the emission profile is given by TIAM

  8. Methodology 1/2 • We retain the BAU scenario and the FP-3.5 scenario defined by the planets project • We use an aggregated version of Gemini-E3 model • Run Monte Carlo simulations on six parameters • We run 500 scenarios with Latin Hyper Cube sampling

  9. Regions • EUR : European Union (25) • OEC : Other industrialized countries • ASI : Asia • EEC : Energy exporting countries (FSU-MID) • ROW : Rest of the World

  10. Methodology 2/2 • Uncertain parameters : • Productivity factor (technical progress on labor) : • ASI • ROW • Correlation • Autonomous Energy Efficiency Improvements (AEEI) • Elasticities • Energy nest • Aggregated inputs • Oil prices • Price of carbon capture and sequestration (only for coal)

  11. Infeasible runs • 19% of runs are infeasible (i.e. Gemini-E3 does not converge) • We estimate a probit model: • 0 if convergence 1 otherwise • Endogenous variables : uncertain parameters

  12. Carbon tax in 2050 in US $ per ton

  13. Energy consumption (G toe) in 2050 Gas Coal In red: Standard deviation divided by mean 0.03 0.11 Elec Oil 0.02 0.06

  14. Macroeconomic Cost (GDP change wrt Baseline - 2050) 1/2 OEC EUR ASI ROW

  15. Macroeconomic Cost (GDP change wrt Baseline) – 2050 2/2 EEC

  16. Next Step : Uncertainty on climate sensitivity (1/2) • We propose to combine Monte Carlo and Optimal hedging strategy to «model» the dynamic process of the uncertainty. • We use the stochastic version of TIAM to generate emission profiles for different values of CS and assuming that the uncertainty is revealed in 2030. • Gemini-E3 : • Monte-Carlo sampling on CS • Generation of the emission profile by interpolation on the profiles given by TIAM.

  17. Next Step : Uncertainty on climate sensitivity (2/2) 3. Try to link results and stochastic parameters (econometric)

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