A Machine Learning Approach to Measuring Climate Adaptation

• URL: http://arxiv.org/abs/2302.01236v1
• Date: Thu, 2 Feb 2023 17:16:48 GMT
• Title: A Machine Learning Approach to Measuring Climate Adaptation
• Authors: Max Vilgalys
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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