Model uncertainty and energy technology policy: The example of induced technical change

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 change - and their implications for CO"2 emissions abatement policy - as a phenomenon of model uncertainty. We apply a non-Bayesian decision rule - so-called ‘min-max regret’ - to this problem and computationally solve the model under the min-max regret criterion, yielding a policy - an emissions abatement path - that reflects a form of robustness to the model uncertainty.