Addressing partial identification in climate modeling and policy analysis.

Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including intermodel "structural" uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multimodel ensembles, sometimes with subjective Bayesian assignment of probabilities across models. However, choosing appropriate weights is problematic. To use climate simulations in integrated assessment, we propose, instead, framing climate model uncertainty as a problem of partial identification, or "deep" uncertainty. This terminology refers to situations in which the underlying mechanisms, dynamics, or laws governing a system are not completely known and cannot be credibly modeled definitively even in the absence of data limitations in a statistical sense. We propose the min-max regret (MMR) decision criterion to account for deep climate uncertainty in integrated assessment without weighting climate model forecasts. We develop a theoretical framework for cost-benefit analysis of climate policy based on MMR, and apply it computationally with a simple integrated assessment model. We suggest avenues for further research.

Optimizing U.S. mitigation strategies for the light-duty transportation sector: what we learn from a bottom-up model.

Few integrated analysis models examine significant U.S. transportation greenhouse gas emission reductions within an integrated energy system. Our analysis, using a bottom-up MARKet ALocation (MARKAL) model, found that stringent system-wide CO2 reduction targets will be required to achieve significant CO2 reductions from the transportation sector. Mitigating transportation emission reductions can result in significant changes in personal vehicle technologies, increases in vehicle fuel efficiency, and decreases in overall transportation fuel use. We analyze policy-oriented mitigation strategies and suggest that mitigation policies should be informed by the transitional nature of technology adoptions and the interactions between the mitigation strategies, and the robustness of mitigation strategies to long-term reduction goals, input assumptions, and policy and social factors. More research is needed to help identify robust policies that will achieve the best outcome in the face of uncertainties.