A Machine Learning Approach to Measuring Climate Adaptation

Max Vilgalys

Papers from arXiv.org

Abstract: I measure adaptation to climate change by comparing elasticities from short-run and long-run changes in damaging weather. I propose a debiased machine learning approach to flexibly measure these elasticities in panel settings. In a simulation exercise, I show that debiased machine learning has considerable benefits relative to standard machine learning or ordinary least squares, particularly in high-dimensional settings. I then measure adaptation to damaging heat exposure in United States corn and soy production. Using rich sets of temperature and precipitation variation, I find evidence that short-run impacts from damaging heat are significantly offset in the long run. I show that this is because the impacts of long-run changes in heat exposure do not follow the same functional form as short-run shocks to heat exposure.

Date: 2023-02
New Economics Papers: this item is included in nep-big and nep-env
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2302.01236

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