A Machine Learning Approach to Measuring Climate Adaptation
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.
- Publication:
-
arXiv e-prints
- Pub Date:
- February 2023
- DOI:
- 10.48550/arXiv.2302.01236
- arXiv:
- arXiv:2302.01236
- Bibcode:
- 2023arXiv230201236V
- Keywords:
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- Statistics - Applications;
- Economics - Econometrics;
- Statistics - Machine Learning