Institute of Economics and EMbeDS-Economics and Management in the Era of Data Science, Scuola Superiore Sant'Anna Pisa, 56127 Pisa, Italy.
RFF-CMCC European Institute of Economics and the Environment, 20144 Milan, Italy.
Proc Natl Acad Sci U S A. 2019 Oct 22;116(43):21450-21455. doi: 10.1073/pnas.1907826116. Epub 2019 Oct 7.
Climate change has increased the frequency and intensity of natural disasters. Does this translate into increased economic damages? To date, empirical assessments of damage trends have been inconclusive. Our study demonstrates a temporal increase in extreme damages, after controlling for a number of factors. We analyze event-level data using quantile regressions to capture patterns in the damage distribution (not just its mean) and find strong evidence of progressive rightward skewing and tail-fattening over time. While the effect of time on averages is hard to detect, effects on extreme damages are large, statistically significant, and growing with increasing percentiles. Our results are consistent with an upwardly curved, convex damage function, which is commonly assumed in climate-economics models. They are also robust to different specifications of control variables and time range considered and indicate that the risk of extreme damages has increased more in temperate areas than in tropical ones. We use simulations to show that underreporting bias in the data does not weaken our inferences; in fact, it may make them overly conservative.
气候变化增加了自然灾害的频率和强度。这是否意味着经济损失也随之增加?迄今为止,对损害趋势的实证评估尚无定论。本研究在控制了许多因素后,证明了极端损害的时间性增加。我们使用分位数回归来分析事件级别的数据,以捕捉损害分布的模式(不仅仅是平均值),并发现随着时间的推移,损害分布右偏和尾部变厚的趋势非常明显。虽然时间对平均值的影响难以察觉,但对极端损害的影响很大,在统计上显著,且随着百分位数的增加而增加。我们的结果与气候经济学模型中常见的向上弯曲的凸性损害函数一致。这些结果在考虑不同的控制变量和时间范围的具体规定时也是稳健的,并表明极端损害的风险在温带地区比在热带地区增加得更多。我们使用模拟来表明,数据中的漏报偏差不会削弱我们的推断;实际上,它可能使推断过于保守。