Addressing Partial Identification in Climate Modeling and Policy Analysis

Charles Manski, Alan H. Sanstad and Stephen DeCanio ()

No 28449, NBER Working Papers from National Bureau of Economic Research, Inc

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 inter-model “structural” uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multi-model 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.

JEL-codes: D81 Q54 Q58 (search for similar items in EconPapers)
Date: 2021-02
New Economics Papers: this item is included in nep-cmp, nep-ene and nep-env
Note: EEE PE
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Published as Charles F. Manski & Alan H. Sanstad & Stephen J. DeCanio, 2021. "Addressing partial identification in climate modeling and policy analysis," Proceedings of the National Academy of Sciences, vol 118(15).

Downloads: (external link)
http://www.nber.org/papers/w28449.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:28449

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w28449

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().