Environmental Modeling & Assessment

, Volume 17, Issue 1–2, pp 51–76

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

  • Frédéric Babonneau
  • Alain Haurie
  • Richard Loulou
  • Marc Vielle
Article

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.

Keywords

Climate change Uncertainties CCS Monte Carlo simulation Stochastic optimization General computable equilibrium Energy technology model 

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Frédéric Babonneau
    • 1
    • 2
  • Alain Haurie
    • 1
  • Richard Loulou
    • 3
  • Marc Vielle
    • 2
    • 4
  1. 1.ORDECSYSGenevaSwitzerland
  2. 2.Economics and Environmental Management LaboratorySwiss Federal Institute of Technology at Lausanne (EPFL)LausanneSwitzerland
  3. 3.KANLO ConsultantsLyonFrance
  4. 4.Toulouse School of Economics (LERNA)ToulouseFrance

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