Addressing partial identification in climate modeling and policy analysis

Charles F. Manski*, Alan H. Sanstad, Stephen J. DeCanio

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article numbere2022886118
JournalProceedings of the National Academy of Sciences of the United States of America
Volume118
Issue number15
DOIs
StatePublished - Apr 13 2021

Keywords

  • Climate modeling
  • Climate policy
  • Decision-making
  • Partial identification
  • Structural uncertainty

ASJC Scopus subject areas

  • General

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