The uncertainty in the GEMINI-E3 Model

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

In the SynsCOP15 project we have evaluated the impact of different sources of uncertainties on the economic assessment of long term energy policies designed to mitigate climate change. We identify four classes of uncertainties related to climate, technology, economy and energy prices, respectively.

We propose a dual approach, based on the combined use of stochastic programming and Monte-Carlo (MC) analysis to deal with these uncertainties in a techno-economic analysis involving two complementary models. A stochastic programming approach is implemented on a bottom-up integrated assessment model, TIAM (Loulou and Labriet, 2008), to propose a hedging emission abatement policy for the time horizon 2030, followed by four typical recourse  abatement policies, compatible with a target of 2.1oC temperature increase in 2100,  under reasonable assumptions on the uncertainty on climate sensitivity (Cs) (Andronova and Schlesinger, 2001). The scenarios produced by TIAM take into account the Cs uncertainty but are based on perfect foresight assumptions for a lot of technological and economic parameters that could also impact the policy assessment.

To take into account the impact of these other sources of uncertainty on climate policy assessment we use MC analysis on a Computable General Equilibrium (CGE) model, GEMINI-E3 (Bernard and Vielle, 2008), specifically designed to assess climate policies and which is run in an harmonized way with TIAM. We take into account several sources of uncertainty pertaining to the general economic and technological environment, using MC simulations with Latin Hypercube sampling (Iman and Helton, 1988) to obtain probability density functions (pdf) for the output variables of GEMINI-E3 that concern welfare gains, emissions abatement, etc.

Recently, MC based approaches have been successfully implemented on the EPPA model which is also a world CGE model (Webster et al., 2008). The simulations in the EPPA model use the Latin Hypercube technique for analyzing the impacts of 100 uncertain parameters, including labor productivity growth rates, energy efficiency trends, elasticities of substitution, costs of advanced technologies, fossil fuel resource availability, and trends in emissions factors for urban pollutants. These simulations served to evaluate four climate policy scenarios and showed that energy demand parameters, including elasticities of substitution and energy efficiency trends are the sources of uncertainty impacting more significantly climate policies. A previous study, also involving the EPPA model (Webster et al., 2002), focused on the uncertainty of the projections of anthropogenic emissions. It reported a range of temperature change in 2100 comprised between 0.9 and 4.0oC. In Ref. (Scott et al., 1999) MC simulations have been also performed on the integrated assessment model MiniCAM 1.0 to analyze the sources of uncertainty and their relative importance in the decision policy process. The paper concludes that the ``current targets for atmospheric stabilization appear excessively ambitious" and that ``an adaptive policy of ``act, then learn, then act" appears to offer better prospects for balancing uncertain costs and benefits of controlling greenhouse gas emissions than do rigid precautionary measures". More recently MC simulations have been applied to the MERGE model (Kypreos, 2008) to produce probability distribution functions (pdf) of economic and climate related variables for different possible policies. Other Refs. concerning MC simulations on global assessment models are given in (Manne and Richels, 1994; Reilly et al., 1987; Edmonds and Reilly, 1885).

In the present study we identify four classes of uncertainties related to technology, economy, energy and climate, respectively. The first one regroups technological parameters, i.e., cost and date of availability of carbon capture and sequestration (CCS) technology, elasticities of substitution between energy forms, elasticities of substitution between production factors and technical progress factors. The second class deals with economic drivers such as GDP growth of emerging countries. The third one focuses on energy prices. Finally, the last category, related to climate, is summarized by the climate sensitivity (Cs) parameter. Recall that Cs is loosely defined as the temperature increase that would result from a doubling of atmospheric GHG concentration, compared with preindustrial level. So, in terms of climate policies, a variation in the assumed Cs value results in a different long term GHG concentration target and, as a consequence, in a different profile for the emissions abatement schedule resulting from an adaptation of the global energy system. From a policy point of view, one has to formulate a hedging emission trajectory which will be implemented now and eventually corrected or adapted when a more precise knowledge of Cs is available. We assume that the uncertainty on Cs will be resolved in 2030; we generate emission trajectories for different climate sensitivity values using the stochastic version of the model TIAM. By so doing, we get a single trajectory of emissions until 2030 and different profiles afterward depending on the revealed climate sensitivity. GEMINI-E3 is run for an ensemble of scenarios corresponding to sampled values for all uncertain parameters. In the case of Cs, the sampled value will determine an emission profile after 2030, obtained by interpolation of the typical emissions trajectories produced by TIAM stochastic. The simulation results represented by the economic indicators, like e.g. welfare loss, energy consumption and carbon price, are statistically analyzed using logit and standard regression models. This permits identification of the most sensitive parameters and of their role in the possible infeasibility of energy/climate policies.

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N. Andronova and M. E. Schlesinger. Objective estimation of the probability distribution for climate  sensitivity. J. Geophys. Res., 106, 2001.

A. Bernard and M. Vielle. GEMINI-E3, a General Equilibrium Model of International & National Interactions between Economy, Energy and the Environment. Computational Management Science, 5(3):173--206, May 2008.

J. Edmonds and J. Reilly. Global energy: assessing the future. New York: Oxford University Press, 1985.

R. Iman and J. Helton. An investigation of uncertainty and sensitivity analysis techniques for computer models. Risk Analysis, 8(1):71--90, 1988.

S. Kypreos. Stabilizing global temperature change below thresholds: Monte carlo analyses with merge. Computational Management Science, 5(1):141--170, 2008.

R. Loulou and M. Labriet. ETSAP-TIAM: the TIMES integrated assessment model Part I: Model structure. Computational Management Science, 5(1):7--40, 2008.

A. Manne and R. Richels. The Costs of Stabilizing Global CO2 Emissions: A Probabilistic Analysis Based on Expert Judgment. Energy Journal, 15(1):31--56, 1994.

J. Reilly, J. Edmonds, R. Gardner, and A. Brenkert. Monte carlo analysis of the IEA/ORAU Energy/Carbon Emissions Model. The Energy Journal, 8(3):1--29, 1987.

M. Scott, R. Sands, J. Edmonds, A. Liebetrau, and D. Engel. Uncertainty in Integrated Assessment Models: Modeling with MiniCAM 1.0. Energy Policy, 27(14):597, 1999.

M. Webster, M. Babiker, M. Mayer, J. Reilly, J. Harnisch, M. Sarofim, and C. Wang. Uncertainty in Emissions Projections for Climate Models. Atmospheric Environment, 36(22):3659--3670, 2002.

M. Webster, S. Paltsev, J. Parsons, J. Reilly, and H. Jacoby. Uncertainty in Greenhouse Emissions and Costs of Atmospheric Stabilization. Technical report, Joint Program Report Series, in press, 61 pages.$\_$id=974, 2008.