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

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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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Notes

  1. The Web site http://gemini-e3.epfl.ch/ provides all information about the model, including its complete description.

  2. We used a Dual 2.6-GHz Intel Xeon computer for the simulations; thus, we had four available CPUs.

  3. Labour in the generation activity is low compared to labour in the other activities (transport, distribution) and of a similar relative size for all plants. It is thus represented as a common factor.

  4. Note that these assumptions, imposed under the FP7 European project Planets, lead to lower GDP growth than those of the most recent forecasts [15] that incorporate the impact of the current economic crisis. This low GDP growth is primarily due to the conservative growth assumptions for developing countries and especially Asia. This source of uncertainty is discussed in Section 3.3.

  5. Some recent publications [1, 35] tend to affirm that it might be impossible to resolve uncertainty about Cs in the foreseeable future. If this is the case, the decision in 2030 should be based on the worst-case alternative.

  6. Note that the IPCC AR4 best estimate is 3.

  7. Johnsson et al. [20] has been prepared by the working group on the technology assessment of the PLANETS EU-project.

  8. Note that we take an uncertainty on the technical progress associated to energy, see Section 3.2.2.

  9. Because the elasticities are different among sectors, we use here the parameter σ which is used as a multiplier to the nominal elasticities (i.e. when GEMINI-E3 is used without uncertainties).

  10. Note that we suppose that the natural gas price is indexed on oil price, see Section 3.4.

  11. This corresponds roughly to a mean surface temperature increases between 2.23°C and 2.52°C with a climate sensitivity equal to 3 according to the TIAM WORLD model.

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Acknowledgements

This work was supported by the FP7 European Research Project PLANETS, by GICC Research Grant from the French Ministry of Ecology and Sustainable Development (MEDDTL) and by the Swiss-NSF-NCCR climate grant. For helpful comments and discussions, we thank A. Bousquet and R. Gerlagh. Two referees’ comments have been most useful to improve the paper.

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Authors and Affiliations

  1. ORDECSYS, Geneva, Switzerland

    Frédéric Babonneau & Alain Haurie

  2. Economics and Environmental Management Laboratory, Swiss Federal Institute of Technology at Lausanne (EPFL), Lausanne, Switzerland

    Frédéric Babonneau & Marc Vielle

  3. KANLO Consultants, Lyon, France

    Richard Loulou

  4. Toulouse School of Economics (LERNA), Toulouse, France

    Marc Vielle

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  1. Frédéric Babonneau
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Correspondence to Frédéric Babonneau.

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Babonneau, F., Haurie, A., Loulou, R. et al. Combining Stochastic Optimization and Monte Carlo Simulation to Deal with Uncertainties in Climate Policy Assessment. Environ Model Assess 17, 51–76 (2012). https://doi.org/10.1007/s10666-011-9275-1

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