A Machine Learning Approach to Measuring Climate Adaptation

TL;DR

This paper introduces a debiased machine learning method to measure climate adaptation by comparing short- and long-term elasticities of weather impacts, revealing significant long-term adaptation in US agriculture.

Contribution

It develops a novel debiased machine learning approach for flexible elasticity estimation in panel data, improving accuracy over standard methods in high-dimensional settings.

Findings

Long-term impacts of damaging heat are significantly offset in US corn and soy production.

Long-run heat exposure impacts follow different functional forms than short-run shocks.

Debiased machine learning outperforms standard methods in simulation exercises.

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.