Title: Economic consequences of the spatial and temporal variability of climate change Show Chinese title

Title: 气候变化的空间和时间变异的经济后果

Abstract: Damage functions in integrated assessment models (IAMs) map changes in climate to economic impacts and form the basis for most of estimates of the social cost of carbon. Implicit in these functions lies an unwarranted assumption that restricts the spatial variation (Svar) and temporal variability (Tvar) of changes in climate to be null. This could bias damage estimates and the climate policy advice from IAMs. While the effects of Tvar have been studied in the literature, those of Svar and their interactions with Tvar have not. Here we present estimates of the economic costs of climate change that account for both Tvar and Svar, as well as for the seasonality of damages across sectors. Contrary to the results of recent studies which show little effect that of Tvar on expected losses, we reveal that ignoring Svar produces large downward biases, as warming is highly heterogeneous over space. Using a conservative calibration for the damage function, we show that previous estimates are biased downwards by about 23-36%, which represents additional losses of about US$1,400-US$2,300 billion by 2050 and US$17-US$28 trillion by the end of the century, under a high emissions scenario. The present value of losses during the period 2020-2100 would be larger than reported in previous studies by $47-$66 trillion or about 1/2 to 3/4 of annual global GDP in 2020. Our results imply that using global mean temperature change in IAMs as a summary measure of warming is not adequate for estimating the costs of climate change. Instead, IAMs should include a more complete description of climate conditions. Show Chinese abstract

Abstract: 在综合评估模型（IAMs）中的损害函数将气候变化的变化映射到经济影响，并构成了大多数碳的社会成本估计的基础。这些函数中隐含了一个不合理的假设，即限制了气候变化的空间变异（Svar）和时间变异性（Tvar）为零。这可能导致损害估计和来自IAMs的气候政策建议出现偏差。尽管Tvar的影响已经在文献中被研究过，但Svar及其与Tvar的相互作用尚未被研究。在这里，我们提供了考虑了Tvar和Svar以及各行业损害季节性的经济成本估计。与最近的研究结果相反，这些研究显示Tvar对预期损失的影响很小，我们发现忽略Svar会导致大的向下偏差，因为变暖在空间上高度不均。使用损害函数的保守校准，我们表明之前的估计值向下偏差约为23-36%，这在高排放情景下，到2050年将导致约$1,400-US$2,300亿美元的额外损失，到本世纪末将导致约$17-US$28万亿美元的额外损失。在2020年至2100年期间的损失现值将比以前的研究报告高出$47-$66万亿美元，大约相当于2020年全球GDP的1/2到3/4。我们的结果意味着在IAMs中使用全球平均温度变化作为变暖的总结指标不足以估计气候变化的成本。相反，IAMs应包含对气候条件更完整的描述。