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

In this paper we explore the impact of several sources of uncertainties on the assessment of energy and climate policies when one uses in an harmonized way stochastic programming (SP) in a large scale bottom-up (BU) model and Monte-Carlo simulation (MC) in a large scale top-down (TD) model. The BU model we use is the Times Integrated Assessment Model (TIAM), 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 analyze the impact of the uncertainties on the policy assessment.