Sufficient statistics for climate change counterfactuals

Pierre Mérel (), Emmanuel Paroissien and Matthew Gammans ()
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Pierre Mérel: University of California, Department of Agricultural and Resource Economics - UC Davis - University of California [Davis] - UC - University of California
Matthew Gammans: Agricultural, Food, and Resource Economics - Michigan State University

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Abstract: Recent years have seen a growing interest among empiricists in exploiting random weather fluctuations to identify climate change impacts, yet a clear understanding of the conditions under which short-run weather effects can reveal long-run climatic impacts is lacking. We derive necessary and sufficient conditions for weather fluctuations to systematically identify the marginal effect of climate on an economic outcome. Under these conditions, empirical estimates of local marginal weather effects flexibly trace out a common long-run response function to climate that can be used for non-marginal climate change counterfactuals. Our application considers the effect of weather on county-level agricultural GDP in the United States. Depending on model specification, agricultural GDP is predicted to decrease by 6%–10% under a 2 °C warming scenario.

Keywords: Climate adaptation; Sufficient statistic (search for similar items in EconPapers)
Date: 2024-03
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Citations: View citations in EconPapers (4)

Published in Journal of Environmental Economics and Management, 2024, 124, pp.102940. ⟨10.1016/j.jeem.2024.102940⟩

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Journal Article: Sufficient statistics for climate change counterfactuals (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04498651

DOI: 10.1016/j.jeem.2024.102940

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