A Machine Learning Approach to Measuring Climate Adaptation

Max Vilgalys

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arXiv · 2302.01236

A Machine Learning Approach to Measuring Climate Adaptation

Max Vilgalys

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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.

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Max Vilgalys. 2023-02-02. A Machine Learning Approach to Measuring Climate Adaptation. https://arxiv.org/abs/2302.01236

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