Estimates of global economic damage caused by carbon dioxide (CO2) emissions can inform climate policy1,2,3. The social cost of carbon (SCC) quantifies these damages by characterizing how additional CO2 emissions today impact future economic outcomes through altering the climate4,5,6. Previous estimates have suggested that large, warming-driven increases in energy expenditures could dominate the SCC7,8, but they rely on models9,10,11 that are spatially coarse and not tightly linked to data2,3,6,7,12,13. Here we show that the release of one ton of CO2 today is projected to reduce total future energy expenditures, with most estimates valued between −US$3 and −US$1, depending on discount rates. Our results are based on an architecture that integrates global data, econometrics and climate science to estimate local damages worldwide. Notably, we project that emerging economies in the tropics will dramatically increase electricity consumption owing to warming, which requires critical infrastructure planning. However, heating reductions in colder countries offset this increase globally. We estimate that 2099 annual global electricity consumption increases by about 4.5 exajoules (7 per cent of current global consumption) per one-degree-Celsius increase in global mean surface temperature (GMST), whereas direct consumption of other fuels declines by about 11.3 exajoules (7 per cent of current global consumption) per one-degree-Celsius increase in GMST. Our finding of net savings contradicts previous research7,8, because global data indicate that many populations will remain too poor for most of the twenty-first century to substantially increase energy consumption in response to warming. Importantly, damage estimates would differ if poorer populations were given greater weight14.
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The data for replicating the findings of this study are available on Zenodo at https://doi.org/10.5281/zenodo.5099834.
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This project is an output of the Climate Impact Lab consortium that gratefully acknowledges funding from the Carnegie Corporation, Energy Policy Institute of Chicago (EPIC), the International Growth Centre, the National Science Foundation (SES1463644), the Sloan Foundation and the Tata Center for Development. T.C. acknowledges funding from the US Environmental Protection Agency Science To Achieve Results Fellowship (FP91780401). J.R. acknowledges funding from the H2020-MSCA-RISE project GEMCLIME-2020 GA number 681228. We thank L. Alcocer, T. Bearpark, T. Chong, Z. Delgerjargal, G. Dobbels, D. Gergel, R. Goyal, S. Greenhill, I. Higuera-Mendieta, D. Hogan, A. Hussain, T. Kulczycki, R. Li, B. Malevich, M. Norman, O. Nwabuikwu, S. Annan-Phan, C. Schwarz, N. Sharma, J. Simcock, Y. Song, E. Tenezakis, J. Wang and J. Yang for research assistance during all stages of this project, and we thank S. Anderson, J. Chang, M. Landín and T. Mayer for project management. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for the CMIP, and we thank the climate modelling groups (listed in Extended Data Fig. 2b) for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank seminar participants at the UC Berkeley Energy Camp, the University of Chicago EPIC Lunch Series and the Mansueto Institute Lunch Colloquium, LSE Workshop in Environmental Economics, International Energy Workshop, International Workshop on Empirical Methods in Energy Economics, the University of Michigan Sustainability and Development Conference, the Berkeley/Harvard/Yale Environmental and Energy Economics Seminar, the NBER EEE Summer Institute, the UCLA Luskin Center for Innovation Climate Adaptation Research Symposium and the Federal Reserve Bank of Richmond Climate Change Economics Workshop for helpful comments.
A.R., T.C., A.H., A.J., R.K., I.N., J.R. and J.Y. declare no competing interests. M.D., T.H., K.E.M.: The Rhodium Group provides independent research and analysis on a range of global economic topics to clients in the public and private sectors. This includes analysis of global energy market trends. Although some of those clients could potentially be impacted by the results of this research, both positively and negatively, Rhodium staff contributions to this research were conducted completely independently. M.G., S.H. and R.K.: we received no financial or in-kind support for the research conducted in this paper, nor have we received funding or in-kind support from any ‘interested’ parties. As part of a diversified portfolio, we each hold more than US$10,000 in stocks and bonds of various companies including those within the energy sector. No other parties have the right to review the paper before its circulation. M.G. holds the position of director at the Energy Policy Institute at the University of Chicago and the Environment and Energy Lab at University of Chicago Labs, and is faculty director at the E2e Lab. From July 2015 to January 2017, M.G. was a member of the US Department of Energy’s Secretary of Energy Advisory Board. S.H. is the director of the Global Policy Laboratory at the University of California, Berkeley. R.K. previously served as a consultant to Rhodium Group, which provides independent research and analysis on a range of global economic topics to clients in the public and private sectors, including analysis of global energy market trends. R.K. currently serves as director of the Rutgers Institute of Earth, Ocean, and Atmospheric Sciences. R.K. provided technical support to and M.G. was a co-chair of the US Federal Government Interagency Working Group on Social Cost of Carbon between September 2009 and March 2010. R.K. was an author of the National Academies of Sciences, Engineering, and Medicine report Valuing Climate Damages Updating Estimation of the Social Cost of Carbon Dioxide published in 2017.
Peer review information Nature thanks Katrina Jessoe, William Pizer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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Extended data figures and tables
Extended Data Fig. 1 Map of the 24,378 “impact regions” for which location-specific projections are calculated.
Map is produced with R software, ggplot2 package, using Global Administrative Region dataset (GADM) basemap50. A clustering algorithm17 is used to form these impact regions from the full set of GADM administrative regions50, such that they are roughly similar in total population, and so that they are approximately internally homogenous with respect to mean temperature, diurnal temperature range, and mean precipitation.
Extended Data Fig. 2 Future climate projections used in generating probabilistic, empirically-based climate change impact projections.
Panel (a) shows local climate distributions under the 21 climate models (outlined maps) and 12 model surrogates (dimmed maps) (‘Data assembly’ in Methods, Supplementary Sections A.2.2, A.2.3) that are weighted in climate change impact projections so that the weighted distribution of the 2080 to 2099 global mean surface temperature anomaly (ΔGMST) exhibited by the 33 total models matches the probability distribution of estimated ΔGMST responses (blue-grey line) under a high (RCP8.5) emissions scenario. For this construction, the anomaly is relative to values in 1986–2005. Maps are produced with Python programming language, using data from ref. 43 and Global Administrative Region dataset (GADM) basemap79. Panel (b) lists all 33 models and model surrogates, and their corresponding model weights for both high (RCP8.5) and moderate (RCP4.5) emissions scenarios43. These are used to capture climate model uncertainty when generating climate change impact projections under a given emissions scenario (Supplementary Section B.5).
Extended Data Fig. 3 Sample overlap between present and future.
The density plots demonstrate the overlap in the joint income × long-run climate distributions at 2010 and 2090. Long-run climate is measured by heating degree days (a) and cooling degree days (b). Distributions are for 24,378 impact regions, in 2010 (grey-black) and 2090 under the RCP8.5 emissions scenario and SSP3 socioeconomic scenario (red-orange). All impact regions within a country are assigned the national per capita income. Although the future distribution is shifted towards higher incomes, greater cooling degree days, and fewer heating degree days, the substantial overlap in the two distributions allows for credible extrapolation of energy-temperature responses into the future (‘Projecting the impacts of climate change’ in Methods).
Extended Data Fig. 4 Climate-change induced changes in total energy expenditures at end-of-century, by present-day income deciles.
The bar chart above depicts annual climate-change induced changes in total energy expenditures at 2099 under a high emissions scenario (RCP8.5) and the SSP3 socioeconomic scenario, separately for each decile of 2012 national per capita income. Income deciles are calculated across all countries at 2012; representative countries in selected deciles are indicated. Expenditures are calculated under a 1.4% annual price growth scenario and are expressed in 2019 USD per capita based on each decile’s projected 2099 population. Bars represent mean estimates across an ensemble of 33 climate models. Intervals indicate 5th–95th percentiles of projected distributions, accounting for climate model and econometric uncertainty (Supplementary Section B.5). The chart demonstrates that heterogeneity in expenditure changes at end-of-century (Fig. 2a) is systematically correlated with present-day national income per capita. Over the upper half of the present-day income distribution, we find that countries with higher incomes today are generally projected to experience larger overall net savings at end-of-century. This partly reflects the fact that today’s richest countries tend to be in temperate climates, where energy savings from fewer cold days will more than offset increases in costs from more hot days. The smallest savings at end-of-century are projected to occur in middle deciles of the present-day income distribution, which is consistent with many of these countries being situated in the tropics and also attaining sufficiently high income levels at end-of-century to increase electricity consumption due to more hot days. The positive correlation between present-day income and net savings at end-of-century does not hold in the lower ranges of today’s income distribution. Net savings in today’s poorest deciles (i.e. first and second) are actually higher than in the third and fourth deciles, as many of the poorest countries are projected to remain too poor at end-of-century to increase electricity consumption on hot days.
Extended Data Fig. 5 The impacts of climate change on energy expenditures.
Time series of changes in total global energy expenditures under the SSP3 socioeconomic scenario for moderate (RCP4.5; Panel a) and high (RCP8.5; Panel b) emissions scenarios, assuming various energy price trajectories. Three of these trajectories are based on direct extrapolation of present-day price statistics at either moderate (1.4%), stagnant (0%), or high (3%) annual growth rates (Supplementary Section C.1), while five are based on price projections from integrated assessment models (Supplementary Section C.2) named in the legend. Expenditure changes in a given year are expressed as a percent of global GDP in that year. Aggregate global expenditure changes are obtained by monetizing and summing over the spatially disaggregated impacts across both electricity and other fuels. Regardless of the emissions scenario or assumed price trajectory, end-of-century changes (i.e. net savings) represent a minute fraction of the US $ 353 trillion end-of-century global GDP projected under SSP3.
Extended Data Fig. 6 Recovering local temperature-energy consumption relationships using aggregate energy consumption data.
An illustration demonstrating how the effect of local temperature on local per capita energy consumption can be recovered from observations of local temperatures and national per capita energy consumption. a: Let a hypothetical, linear response of daily temperature and energy consumption exist at a local (i.e. grid cell) level, depicted by the diagonal grey line. Let E denote baseline daily energy consumption on a 20 °C day. Average per capita energy consumption is observed on day d in countries i (blue circle) and j (pink circle), respectively consisting of 8 and 5 equally populated grid cells experiencing different temperatures. While the temperature is observed in each grid cell, only the national average per capita energy consumption is observed. b: Height of each bar represents unobserved energy consumption on day d within each grid cell. Pink bars are grid cells in country j and blue bars are grid cells in country i. Energy consumption within each grid cell responds to temperature within that grid cell. Averaging temperature and per capita energy consumption across grid cells within each country produces the country-level observations in Panel a. A regression using these observations recovers the grid cell-level response. Note that this illustration depicts a linear energy-temperature response for illustrative purposes, however a nonlinear temperature-energy consumption response can be recovered as well, if nonlinear transformations of temperature are computed at the grid-cell-level before being aggregated to the national level (‘Econometric estimation of energy–temperature responses’ in Methods, Equation 2).
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Rode, A., Carleton, T., Delgado, M. et al. Estimating a social cost of carbon for global energy consumption. Nature 598, 308–314 (2021). https://doi.org/10.1038/s41586-021-03883-8
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