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Country-level social cost of carbon

Abstract

The social cost of carbon (SCC) is a commonly employed metric of the expected economic damages from carbon dioxide (CO2) emissions. Although useful in an optimal policy context, a world-level approach obscures the heterogeneous geography of climate damage and vast differences in country-level contributions to the global SCC, as well as climate and socio-economic uncertainties, which are larger at the regional level. Here we estimate country-level contributions to the SCC using recent climate model projections, empirical climate-driven economic damage estimations and socio-economic projections. Central specifications show high global SCC values (median, US$417 per tonne of CO2 (tCO2); 66% confidence intervals, US$177–805 per tCO2) and a country-level SCC that is unequally distributed. However, the relative ranking of countries is robust to different specifications: countries that incur large fractions of the global cost consistently include India, China, Saudi Arabia and the United States.

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Data availability

The database of the CSCCs with uncertainty bounds under all scenarios, model specifications and discounting schemes is available as a part of the Supplementary Information and via https://country-level-scc.github.io/.

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Change history

  • 25 March 2019

    In the version of this Article originally published, owing to a code error, the CSCC values for all income-dependent (that is, rich-poor) impact model specifications were incorrect, showing higher values relative to the preferred model rather than lower. These have now been recalculated for BHM SR RP, BHM LR RP and DJO RP, and Figs 1, 2b and 3b, as well as Supplementary Figs 2, 4–6 and 9, have been updated to reflect these recalculations. In addition, the text reading, “The GSCC tends to be similar in both pooled and rich/poor specifications of the damages model, with the exception of SSP3, in which the estimated GSCC is much higher in the rich/poor specifications. The DJO specification of the economic impact function yields significantly higher GSCC values” has been modified to reflect this recalculation and now reads, “The GSCC is always lower using the rich/poor specifications of the damages model with confidence intervals that, in most cases, extend into the negative SCC range. The DJO specification of the economic impact function, which also estimates distinct effects for rich and poor countries, yields significantly lower GSCC values.” The sentence beginning, “This results in higher (almost twice as much) global values of the SCC...” has also been modified to “In the case of the LR BHM specifications, this results in higher (almost twice as much) global values of the SCC...” The income thresholds and coefficients in the Supplementary Text, as well as the interpretation of the DJO sensitivity analysis results, have also been updated to reflect these changes. The authors thank Yixuan Zheng for identifying the code error.

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Acknowledgements

M.T. thanks M. Burke for an early discussion of these ideas and about the climate impact functions. K.R. thanks C. McIntosh and J. Moreno-Cruz for helpful discussions during the revisions of this manuscript. M.T. received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 336155 (project COBHAM). L.D received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement no. 642147 (CD-LINKS).

Author information

M.T. conceived the study. K.R. performed the climate data analysis. L.D. replicated the economic damage functions and performed the CSCC calculations and uncertainty analysis. K.R., M.T. and L.D. analysed the results. K.R. and M.T. wrote the manuscript. All authors discussed the results and provided input on the manuscript.

Competing interests

The authors declare no competing interests.

Correspondence to Katharine Ricke.

Supplementary Information

  1. Supplementary Information

    Supplementary Discussion, Supplementary Figures 1–13, Supplementary Tables 1–6, Supplementary References

  2. Supplementary Data 1

    CSCC Database

  3. Supplementary Data 2

    CSCC Database Readme

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Further reading

Fig. 1: GSCC in 2020 under various assumptions and scenarios.
Fig. 2: CSCCs.
Fig. 3: Lorenz curve and Gini coefficients for the country-level contributions to the GSCC in 2020.
Fig. 4: Winners and Losers of climate change among the G20 nations.