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Combining Stochastic Optimization and Monte Carlo Simulation to Deal with Uncertainties in Climate Policy Assessment

Babonneau, Frédéric ; Haurie, Alain ; Loulou, Richard ; Vielle, Marc

In: Environmental Modeling & Assessment, 2012, vol. 17, no. 1-2, p. 51-76

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    Titre
    • Combining Stochastic Optimization and Monte Carlo Simulation to Deal with Uncertainties in Climate Policy Assessment
    Auteur
    • Babonneau, Frédéric. ORDECSYS, Geneva, Switzerland
    • Haurie, Alain. ORDECSYS, Geneva, Switzerland
    • Loulou, Richard. KANLO Consultants, Lyon, France
    • Vielle, Marc. Economics and Environmental Management Laboratory, Swiss Federal Institute of Technology at Lausanne (EPFL), Lausanne, Switzerland
    Type de document
    • Postprint
    Langue
    • Anglais
    Publié dans
    • Environmental Modeling & Assessment, 2012, vol. 17, no. 1-2, p. 51-76. Springer Netherlands
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:317486
    Summary
    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