Using Integrated Assessment Models to Estimate the Economic Damages from Temperature Related Human Health Effects in the US

Friday, 19 December 2014
Elisabeth Gilmore1, Katherine V Calvin2, Robin Puett3, Amir Sapkota3 and Adria Schwarber4, (1)University of Maryland College Park, School of Public Policy, College Park, MD, United States, (2)Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, United States, (3)University of Maryland College Park, School of Public Health, College Park, MD, United States, (4)University of Maryland College Park, Atmospheric and Oceanic Science, College Park, MD, United States
Climate change is projected to increase risks to human health. One pathway that may be particularly difficult to manage is adverse human health impacts (e.g. premature mortality and morbidity) from increases in mean temperatures and changing patterns of temperature extremes. Modeling how these health risks evolve over decadal time-scales is challenging as the severity of the impacts depends on changes in climate as well as socioeconomic conditions. Here, we show estimates of health damages as well as both direct and indirect economic damages that span climate and socioeconomic dimensions for each US state to 2050. We achieve this objective by extending the integrated assessment model (IAM), Global Change Assessment Model (GCAM-USA). First, we quantify the change in premature mortality. We identify a range of exposure-response relationships for temperature related mortality through a critical review of the literature. We then implement these relationships in the GCAM by coupling them with projections of future temperature patterns and population estimates. Second, we monetize the effect of these adverse health effects, including both direct and indirect economic costs through labor force participation and productivity along a range of possible economic pathways. Finally, we evaluate how uncertainty in the parameters and assumptions affects the range of possible estimates. We conclude that the model is sensitive to assumptions regarding exposure-response relationship and population growth. The economic damages, however, are driven by the estimates of income and GDP growth as well as the potential for adaptation measures, namely the use and effectiveness of air conditioning.