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Estimating economic damage from climate change in the United States
Author(s) -
Solomon Hsiang,
Robert E. Kopp,
Amir Jina,
James Rising,
Michael Delgado,
Shashank Mohan,
D. J. Rasmussen,
Robert MuirWood,
Paul Wilson,
Michael Oppenheimer,
Kate Larsen,
Trevor Houser
Publication year - 2017
Publication title -
science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 12.556
H-Index - 1186
eISSN - 1095-9203
pISSN - 0036-8075
DOI - 10.1126/science.aal4369
Subject(s) - climate change , gross domestic product , environmental science , climatology , activity based costing , geography , range (aeronautics) , probabilistic logic , meteorology , natural resource economics , economics , statistics , economic growth , mathematics , ecology , biology , materials science , accounting , composite material , geology
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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