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.
翻译:我通过比较受破坏天气的短期和长期变化的弹性来衡量适应气候变化的程度,我提议采用有偏差的机器学习方法,灵活测量面板环境中的弹性。在模拟练习中,我显示,有偏差的机器学习对于标准的机器学习或普通的最小方形具有相当大的好处,特别是在高维环境中。然后,我测量适应美国玉米和大豆生产中损害的热暴露的程度。我利用丰富的温度和降水量变化,发现有证据表明,从长远来看,损害性热的短期影响大大抵消了。我表明,这是因为长期热暴露变化的影响与短期热暴露冲击的功能形式不同。