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Addressing partial identification in climate modeling and policy analysis.

Charles F Manski1, Alan H Sanstad2, Stephen J DeCanio3

  • 1Department of Economics and Institute for Policy Research, Northwestern University, Evanston, IL 60208; cfmanski@northwestern.edu.

Proceedings of the National Academy of Sciences of the United States of America
|April 10, 2021
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Summary

Addressing deep uncertainty in climate models is crucial for effective climate policy. This study proposes a new min-max regret (MMR) approach for cost-benefit analysis, avoiding problematic climate model weighting.

Keywords:
climate modelingclimate policydecision-makingpartial identificationstructural uncertainty

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Area of Science:

  • Climate Science
  • Environmental Economics
  • Computational Modeling

Background:

  • Computational climate models are vital for assessing climate change policies but suffer from significant structural uncertainty.
  • Current methods like averaging or subjective weighting of multimodel ensembles are problematic for policy-relevant analysis.
  • Climate model uncertainty can be framed as 'deep uncertainty,' where underlying system dynamics are not fully known or definitively modelable.

Purpose of the Study:

  • To propose a novel decision criterion for integrated assessment modeling under deep climate uncertainty.
  • To develop a theoretical framework for cost-benefit analysis of climate policy using this new criterion.
  • To computationally apply the framework with a simple integrated assessment model.

Main Methods:

  • Framing climate model uncertainty as a problem of partial identification or 'deep uncertainty'.
  • Proposing the min-max regret (MMR) decision criterion to handle deep uncertainty without weighting climate model outputs.
  • Developing and computationally applying a cost-benefit analysis framework based on MMR.

Main Results:

  • The min-max regret (MMR) decision criterion offers a method to account for deep climate uncertainty in integrated assessment.
  • A theoretical framework for cost-benefit analysis of climate policy using MMR was developed.
  • The MMR approach was computationally applied to a simple integrated assessment model.

Conclusions:

  • The min-max regret (MMR) criterion provides a robust alternative to traditional weighting methods for addressing deep uncertainty in climate models.
  • This framework facilitates more credible cost-benefit analyses for climate policies.
  • Further research is needed to explore the implications and applications of MMR in climate policy.