Recent advancements in the availability of models and data to characterize the economic impacts of climate change have improved our ability to project both the physical impacts and economic effects of climate change across economic sectors of the United States. These advancements have in turn provided an opportunity to estimate these impacts across multiple economic sectors using a consistent set of damage functions. These functions can be used to inform decision making regarding the diversity and magnitude of future impacts and how adaptation and other actions can affect the risk of economic impacts. This article shows how damage functions can be developed from the results of detailed modeling studies and then used to estimate future economic impacts. We estimate damage functions based on 15 sectoral impact models that project the economic impacts of climate change on human health, infrastructure, and ecosystems and, with a focus on temperature, apply these functions to changes in economic impacts for seven U.S. regions through 2100. We also discuss the uncertainty of these results. We conclude that, although further research is needed, the methods presented here can be usefully applied to a range of alternative temperature trajectories to estimate the economic effects of climate change.

Workers in climate exposed industries such as agriculture, construction, and manufacturing face increased health risks of working on high temperature days and may make decisions to reduce work on high-heat days to mitigate this risk. Utilizing the American Time Use Survey (ATUS) for the period 2003 through 2018 and historical weather data, we model the relationship between daily temperature and time allocation, focusing on hours worked by high-risk laborers. The results indicate that labor allocation decisions are context specific and likely driven by supply-side factors. We do not find a significant relationship between temperature and hours worked during the Great Recession (2008–2014), perhaps due to high competition for employment, however during periods of economic growth (2003–2007, 2015–2018) we find a significant reduction in hours worked on high-heat days. During periods of economic growth, for every degree above 90 on a particular day, the average high-risk worker reduces their time devoted to work by about 2.6 minutes relative to a 90-degree day. This effect is expected to intensify in the future as temperatures rise. Applying the modeled relationships to climate projections through the end of century, we find that annual lost wages resulting from decreased time spent working on days over 90 degrees across the United States range from $36.7 to $80.0 billion in 2090 under intermediate and high emission futures, respectively.

Characterizing the future risks of climate change is a key goal of climate impacts analysis. Temperature binning provides a framework for analyzing sector-specific impacts by degree of warming as an alternative or complement to traditional scenario-based approaches in order to improve communication of results, comparability between studies, and flexibility to facilitate scenario analysis. In this study, we estimate damages for nine climate impact sectors within the contiguous United States (US) using downscaled climate projections from six global climate models, at integer degrees of US national warming. Each sector is analyzed based on socioeconomic conditions for both the beginning and the end of the century. The potential for adaptive measures to decrease damages is also demonstrated for select sectors; differences in damages across adaptation response scenarios within some sectors can be as much as an order of magnitude. Estimated national damages from these sectors based on a reactive adaptation assumption and 2010 socioeconomic conditions range from $600 million annually per degree of national warming for winter recreation to $8 billion annually per degree of national warming for labor impacts. Results are also estimated per degree of global temperature change and for 2090 socioeconomic conditions.

Abstract. Evidence of the physical and economic impacts of climate change is a critical input to policy development and decision making. The potential magnitude of climate change damages, where, when, and to whom those damages may occur across the country, the types of impacts that will be most damaging, and the ability of adaptation to reduce potential risks are all important and interconnected. This study utilizes the reduced-complexity model, Framework for Evaluating Damages and Impacts (FrEDI), to rapidly assess economic and physical impacts of climate change in the contiguous United States (U.S.). Results from FrEDI show that net national damages increase overtime, with mean climate-driven damages estimated to reach $2.9 trillion USD (95 % CI: $510 billion to $12 trillion) annually by 2090. Climate-driven damages are largest for the health category, with the majority of damages in this category from the valuation estimates of premature mortality attributable to climate-driven changes in extreme temperature and air quality (O3 and PM2.5). Results from FrEDI also show that climate-driven damages vary by geographical region, with the Southeast experiencing the largest annual damages per capita (mean: $9,300 per person annually, 95 % CI: $1,800–$37,000 per person annually), whereas the smallest damages per capita are expected in the Southwest region (mean: $6,300 per person annually, 95 % CI: $840–$27,000 per person annually). Climate change impacts may also broaden existing societal inequalities, with Black or African Americans disproportionately affected by additional premature mortality from changes in air quality. This work significantly advances our understanding of the impacts from climate change to the U.S., in what U.S. regions impacts are happening, what sectors are being impacted, and which population groups being impacted the most.

The text and associated Supplemental Materials contribute internally consistent and therefore entirely comparable regional, temporal, and sectoral risk profiles to a growing literature on regional economic vulnerability to climate change. A large collection of maps populated with graphs of Monte-Carlo simulation results support a communication device in this regard — a convenient visual that we hope will make comparative results tractable and credible and resource allocation decisions more transparent. Since responding to climate change is a risk-management problem, it is important to note that these results address both sides of the risk calculation. They characterize likelihood distributions along four alternative emissions futures (thereby reflecting the mitigation side context); and they characterize consequences along these transient trajectories (which can thereby inform planning for the iterative adaptation side). Looking across the abundance of sectors that are potentially vulnerable to some of the manifestations of climate change, the maps therefore hold the potential of providing comparative information about the magnitude, timing, and regional location of relative risks. This is exactly the information that planners who work to protect property and public welfare by allocating scarce resources across competing venues need to have at their disposal — information about relative vulnerabilities across time and space and contingent on future emissions and future mitigation. It is also the type of information that integrated assessment researchers need to calibrate and update their modeling efforts — scholars who are exemplified by Professor Nordhaus who created and exercised the Dynamic Integrated Climate-Economy and Regional Integrated Climate-Economy models.