一种测量气候适应性的机器学习方法
A Machine Learning Approach to Measuring Climate Adaptation
摘要：
我通过比较破坏性天气的短期和长期变化的弹性来衡量对气候变化的适应。我提出了一种去偏的机器学习方法来灵活地测量面板设置中的这些弹性。在一个模拟练习中，我证明了去偏机器学习相对于标准机器学习或普通最小二乘法有相当大的好处，特别是在高维环境中。然后，我测量了美国玉米和大豆生产对有害热暴露的适应性。利用丰富的温度和降水变化，我发现证据表明，从长远来看，破坏性的热量的短期影响是显着抵消。我表明，这是因为热暴露的长期变化的影响不遵循相同的功能形式作为短期冲击热暴露。
英文摘要：
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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