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Science. 2017 Jun 30;356(6345):1362-1369. doi: 10.1126/science.aal4369.

Estimating economic damage from climate change in the United States.

Author information

1
Global Policy Laboratory, Goldman School of Public Policy, University of California, Berkeley, CA, USA. shsiang@berkeley.edu robert.kopp@rutgers.edu.
2
National Bureau of Economic Research, Cambridge, MA, USA.
3
Department of Earth and Planetary Sciences and Institute of Earth, Ocean, and Atmospheric Sciences, Rutgers University, New Brunswick, NJ, USA. shsiang@berkeley.edu robert.kopp@rutgers.edu.
4
Department of Economics and Harris School of Public Policy, University of Chicago, Chicago, IL, USA.
5
Global Policy Laboratory, Goldman School of Public Policy, University of California, Berkeley, CA, USA.
6
Energy Resource Group, University of California, Berkeley, CA, USA.
7
Rhodium Group, New York, NY, USA.
8
Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA.
9
Risk Management Solutions, Newark, CA, USA.
10
Department of Geosciences, Princeton University, Princeton, NJ, USA.

Abstract

Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors-agriculture, crime, coastal storms, energy, human mortality, and labor-increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).

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PMID:
28663496
DOI:
10.1126/science.aal4369
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