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Numerical modeling based on economic principles has become the dominant analytical tool in U. S. energy policy. Energy models are now used extensively by public agencies, private entities, and academic researchers, and in recent years have also formed the core of "integrated assessment" models used to analyze the relationships among the energy system, the economy, and the global climate. However, fundamental uncertainties are intrinsic in what has become the typical circumstance of multiple models embodying different representations of the energy-economy, and producing different policy-relevant outputs that model users are compelled to interpret as equally plausible and/or valid. Because the policy implications of these outputs can diverge substantially, policy-makers are confronted with a significant degree of model-based uncertainty and little or no guidance as to how it should be addressed. This problem of "model uncertainty" has recently been the focus of work in macroeconomics, where scholars have studied the problem of how a decision-maker should proceed in the face of uncertainty regarding the correct model of an economic system that is the object of policy. A unifying theme in this work is the identification of decision-rules that are robust to such uncertainty. This paper describes an application to energy modeling of the macroeconomists" insights and methods related to model uncertainty and robust analysis, focusing on the important example of model representations of technical change. Using a well-known model by Goulder and Mathai, we treat contrasting assumptions on technical changeand their implications for CO2 emissions abatement policyas a phenomenon of model uncertainty. We apply a non-Bayesian decision ruleso-called "min-max regret"-to this problem and computationally solve the model under the min-max regret criterion, yielding a policyan emissions abatement paththat reflects a form of robustness to the model uncertainty.
Numerical modeling based on economic principles has become the dominant analytical tool in U. S. energy policy. Energy models are now used extensively by public agencies, private entities, and academic researchers, and in recent years have also formed the core of "integrated assessment" models used to analyze the relationships among the energy system, the economy, and the global climate. However, fundamental uncertainties are intrinsic in what has become the typical circumstance of multiple models embodying different representations of the energy-economy, and producing different policy-relevant outputs that model users are compelled to interpret as equally plausible and/or valid. Because the policy implications of these outputs can diverge substantially, policy-makers are confronted with a significant degree of model-based uncertainty and little or no guidance as to how it should be addressed. This problem of "model uncertainty" has recently been the focus of work in macroeconomics, where scholars have studied the problem of how a decision-maker should proceed in the face of uncertainty regarding the correct model of an economic system that is the object of policy. A unifying theme in this work is the identification of decision-rules that are robust to such uncertainty. This paper describes an application to energy modeling of the macroeconomists" insights and methods related to model uncertainty and robust analysis, focusing on the important example of model representations of technical change. Using a well-known model by Goulder and Mathai, we treat contrasting assumptions on technical changeand their implications for CO2 emissions abatement policyas a phenomenon of model uncertainty. We apply a non-Bayesian decision ruleso-called "min-max regret"-to this problem and computationally solve the model under the min-max regret criterion, yielding a policyan emissions abatement paththat reflects a form of robustness to the model uncertainty.
International audienceThis paper considers the bilinear minimax control problem of an important class of parabolic systems with Robin boundary conditions. Such systems are linear on state variables when the control and disturbance are fixed, and linear on the control or disturbance when the state variables are fixed. The objective is to maintain target state variables by taking account the influence of noises in data, while a desired power level and adjustment costs are taken into consideration. Firstly we introduce some classes of bilinear systems and obtain the existence and the uniqueness of the solution, as well as stability under mild assumptions. Afterwards the minimax control problem is formulated. We show the existence of an optimal solution, and we also find necessary optimality conditions. Finally, to illustrate the abstract results, we present two examples of neutron fission systems. (c) 2006 Elsevier Inc. All rights reserved
International audienceIn this article, we consider a bioeconomic model for minimax control problems which are governed by periodic competing parabolic Lotka-Volterra equations. Firstly, the existence and uniqueness results to the state equations are proved as well as stability under mild assumptions. Afterwards, we formulate the minimax control problem. The optimality criteria are to minimize damage and trapping costs of the first species (nuisance), and to maximize the difference between economic revenue and cost of the second species. The existence of an optimal solution is obtained. Also, necessary conditions for a saddle point optimality are derive
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