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.
The Social Cost of Carbon with Economic and Climate Risks
Becker Friedman Institute at the University of Chicago（芝加哥大学贝克弗里德曼研究所）；Hoover Institution（胡佛研究所）；Department of Business Administration, University of Zurich（苏黎世大学工商管理系）
Economic Impacts of Climate Change in the United States: Integrating and Harmonizing Evidence from Recent Studies
National Center for Environmental Economics, Environmental Protection Agency, USA（美国环境保护局国家环境经济中心）；Doerr School of Sustainability, Stanford University（斯坦福大学迪尔可持续发展学院）；Department of Agricultural and Resource Economics, University of California, Berkeley（加州大学伯克利分校农业与资源经济学系）
A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty
Arizona State University（亚利桑那州立大学）；University of Wisconsin（威斯康星大学）；University of Chicago（芝加哥大学）；University of California Santa Barbara（加州大学圣巴巴拉分校）；University of Pennsylvania（宾夕法尼亚大学）
Exploring the economic, social and environmental prospects for commercial natural annual grasslands by performing a sensitivity analysis on a multidisciplinary integrated model
