Combining Stochastic Optimization and Monte Carlo Simulation to Deal with Uncertainties in Climate Policy Assessment |
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Authors: | Frédéric Babonneau Alain Haurie Richard Loulou Marc Vielle |
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Institution: | 1. ORDECSYS, Geneva, Switzerland 2. Economics and Environmental Management Laboratory, Swiss Federal Institute of Technology at Lausanne (EPFL), Lausanne, Switzerland 3. KANLO Consultants, Lyon, France 4. Toulouse School of Economics (LERNA), Toulouse, France
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Abstract: | In this paper, we explore the impact of several sources of uncertainties on the assessment of energy and climate policies when one uses in a harmonized way stochastic programming in a large-scale bottom-up (BU) model and Monte Carlo simulation in a large-scale top-down (TD) model. The BU model we use is the TIMES Integrated Assessment Model, which is run in a stochastic programming version to provide a hedging emission policy to cope with the uncertainty characterizing climate sensitivity. The TD model we use is the computable general equilibrium model GEMINI-E3. Through Monte Carlo simulations of randomly generated uncertain parameter values, one provides a stochastic micro- and macro-economic analysis. Through statistical analysis of the simulation results, we analyse the impact of the uncertainties on the policy assessment. |
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