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      Comprehensive evidence implies a higher social cost of CO 2

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          Abstract

          The social cost of carbon dioxide (SC-CO 2) measures the monetized value of the damages to society caused by an incremental metric tonne of CO 2 emissions and is a key metric informing climate policy. Used by governments and other decision-makers in benefit–cost analysis for over a decade, SC-CO 2 estimates draw on climate science, economics, demography and other disciplines. However, a 2017 report by the US National Academies of Sciences, Engineering, and Medicine 1 (NASEM) highlighted that current SC-CO 2 estimates no longer reflect the latest research. The report provided a series of recommendations for improving the scientific basis, transparency and uncertainty characterization of SC-CO 2 estimates. Here we show that improved probabilistic socioeconomic projections, climate models, damage functions, and discounting methods that collectively reflect theoretically consistent valuation of risk, substantially increase estimates of the SC-CO 2. Our preferred mean SC-CO 2 estimate is $185 per tonne of CO 2 ($44–$413 per tCO 2: 5%–95% range, 2020 US dollars) at a near-term risk-free discount rate of 2%, a value 3.6 times higher than the US government’s current value of $51 per tCO 2. Our estimates incorporate updated scientific understanding throughout all components of SC-CO 2 estimation in the new open-source Greenhouse Gas Impact Value Estimator (GIVE) model, in a manner fully responsive to the near-term NASEM recommendations. Our higher SC-CO 2 values, compared with estimates currently used in policy evaluation, substantially increase the estimated benefits of greenhouse gas mitigation and thereby increase the expected net benefits of more stringent climate policies.

          Abstract

          Coupling advances in socioeconomic projections, climate models, damage functions and discounting methods yields an estimate of the social cost of carbon of US$185 per tonne of CO 2—triple the widely used value published by the US government.

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          Most cited references75

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          Inference from Iterative Simulation Using Multiple Sequences

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                Author and article information

                Contributors
                anthoff@berkeley.edu
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                1 September 2022
                1 September 2022
                2022
                : 610
                : 7933
                : 687-692
                Affiliations
                [1 ]GRID grid.218364.a, ISNI 0000 0004 0479 4952, Resources for the Future, ; Washington, DC USA
                [2 ]GRID grid.16750.35, ISNI 0000 0001 2097 5006, School of Public and International Affairs, , Princeton University, ; Princeton, NJ USA
                [3 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Energy and Resources Group, , University of California, ; Berkeley, CA USA
                [4 ]GRID grid.418698.a, ISNI 0000 0001 2146 2763, Environmental Protection Agency, ; Washington, DC USA
                [5 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, Marron Institute of Urban Management, , New York University, ; Brooklyn, NY USA
                [6 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, NYU Grossman School of Medicine, ; New York, NY USA
                [7 ]GRID grid.418781.3, ISNI 0000 0001 2359 3628, EPRI, ; Palo Alto, CA USA
                [8 ]GRID grid.27860.3b, ISNI 0000 0004 1936 9684, Department of Environmental Science and Policy, , University of California, ; Davis, CA USA
                [9 ]GRID grid.16750.35, ISNI 0000 0001 2097 5006, Department of Economics, , Princeton University, ; Princeton, NJ USA
                [10 ]Independent researcher, Portland, OR USA
                [11 ]GRID grid.34477.33, ISNI 0000000122986657, Departments of Statistics and Sociology, , University of Washington, ; Seattle, WA USA
                [12 ]GRID grid.34477.33, ISNI 0000000122986657, Center for Statistics and the Social Sciences, , University of Washington, ; Seattle, WA USA
                [13 ]GRID grid.262613.2, ISNI 0000 0001 2323 3518, School of Mathematical Sciences, , Rochester Institute of Technology, ; Rochester, NY USA
                [14 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Economics, , Harvard University, ; Cambridge, MA USA
                Author information
                http://orcid.org/0000-0002-3205-5562
                http://orcid.org/0000-0002-9996-2183
                http://orcid.org/0000-0002-7745-2780
                http://orcid.org/0000-0001-8289-0980
                http://orcid.org/0000-0003-3866-9642
                http://orcid.org/0000-0003-3919-0836
                http://orcid.org/0000-0002-3236-5421
                http://orcid.org/0000-0002-7304-3883
                http://orcid.org/0000-0001-9319-2109
                Article
                5224
                10.1038/s41586-022-05224-9
                9605864
                36049503
                8b824336-9d86-49ff-8e86-fc2637edb143
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 December 2021
                : 11 August 2022
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                © The Author(s), under exclusive licence to Springer Nature Limited 2022

                Uncategorized
                climate-change impacts,environmental impact,environmental economics
                Uncategorized
                climate-change impacts, environmental impact, environmental economics

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