TY - JOUR
T1 - Addressing partial identification in climate modeling and policy analysis
AU - Manski, Charles F.
AU - Sanstad, Alan H.
AU - DeCanio, Stephen J.
N1 - Funding Information:
ACKNOWLEDGMENTS. A.H.S. thanks the NSF and the Center for Robust Decision-Making on Climate and Energy Policy at the University of Chicago for supporting previous research that contributed to this work. We thank the research team of Professor Elizabeth Moyer (Department of Geosciences, University of Chicago) for providing CMIP5 model output, and Valentyn Litvin (Department of Economics, Northwestern University) for his technical review. We have benefitted greatly from the comments of an anonymous reviewer.
Funding Information:
A.H.S. thanks the NSF and the Center for Robust Decision-Making on Climate and Energy Policy at the University of Chicago for supporting previous research that contributed to this work. We thank the research team of Professor Elizabeth Moyer (Department of Geosciences, University of Chicago) for providing CMIP5 model output, and Valentyn Litvin (Department of Economics, Northwestern University) for his technical review. We have benefitted greatly from the comments of an anonymous reviewer.
Publisher Copyright:
© 2021 National Academy of Sciences. All rights reserved.
PY - 2021/4/13
Y1 - 2021/4/13
N2 - 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.
AB - 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.
KW - Climate modeling
KW - Climate policy
KW - Decision-making
KW - Partial identification
KW - Structural uncertainty
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U2 - 10.1073/pnas.2022886118
DO - 10.1073/pnas.2022886118
M3 - Article
C2 - 33837154
AN - SCOPUS:85104212303
SN - 0027-8424
VL - 118
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 15
M1 - e2022886118
ER -