Skip to main content
SpringerLink
Log in

Damage function uncertainty increases the social cost of methane and nitrous oxide

  • Analysis
  • Published:

From Nature Climate Change

View current issue Submit your manuscript

Abstract

The social cost of greenhouse gases (SC-GHGs), indicating marginal damage from GHG emissions, is a valuable and informative metric for policymaking. However, existing social cost estimates for methane (SC-CH4) and nitrous oxide (SC-N2O) have not kept pace with the latest scientific findings in damage functions, climate models and socioeconomic projections. We applied a multimodel assessment framework, incorporating recent advances that are neglected by past studies to re-estimate SC-CH4 and SC-N2O. Models of gross domestic product (GDP) level effects reveal US$2,900 per t-CH4 (in 2020 US dollars) for SC-CH4 and US$49,600 per t-N2O for SC-N2O for the emissions year 2020, indicating a 2-fold increase over previous estimates. Models incorporating GDP growth effects over time present a further 15–25-fold increase in estimates, dominating the uncertainty in social cost estimates. Although substantial uncertainty remains, our findings suggest greater benefits from CH4 and N2O mitigation policies compared with those of previous studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1: A multimodel assessment framework for estimating SC-CH4 and SC-N2O.
Fig. 2: Frequency distributions of SC-CH4 and SC-N2O estimates for the emissions year 2020 obtained using an ensemble of models and the global damage potential for methane and nitrous oxide.
Fig. 3: SC-CH4 and SC-N2O estimates for the emissions year 2020 and global climate damage obtained with various damage models, with Hector as a representative climate model.
Fig. 4: Global mean surface temperature perturbations and social cost estimates simulated with different climate models.
Fig. 5: Global certainty-equivalent SC-CH4 and SC-N2O estimates and decomposition analysis.

Similar content being viewed by others

Data availability

All data used in this study are publicly available on Zenodo54 and GitHub (https://github.com/wtpeng22/SC-GHG-estimates). Source data are provided with this paper.

Code availability

All codes used in this study are publicly available on Zenodo54 and GitHub (https://github.com/wtpeng22/SC-GHG-estimates).

References

  1. IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al., Cambridge Univ. Press, 2021).

  2. Tokarska, K. B., Gillett, N. P., Arora, V. K., Lee, W. G. & Zickfeld, K. The influence of non-CO2 forcings on cumulative carbon emissions budgets. Environ. Res. Lett. 13, 034039 (2018).

    Article  Google Scholar 

  3. Mengis, N. & Matthews, H. D. Non-CO2 forcing changes will likely decrease the remaining carbon budget for 1.5 °C. npj Clim. Atmos. Sci. 3, 19 (2020).

    Article  CAS  Google Scholar 

  4. Pearce, D. The social cost of carbon and its policy implications. Oxf. Rev. Econ. Policy 19, 362–384 (2003).

    Article  Google Scholar 

  5. Marten, A. L. & Newbold, S. C. Estimating the social cost of non-CO2 GHG emissions: methane and nitrous oxide. Energy Policy 51, 957–972 (2012).

    Article  Google Scholar 

  6. IAWG Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis, Under Executive Order 12866 (US Government, 2010).

  7. IAWG Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis, Under Executive Order 12866 (US Government, 2016).

  8. Wang, T., Teng, F., Deng, X. & Xie, J. Climate module disparities explain inconsistent estimates of the social cost of carbon in integrated assessment models. One Earth 5, 767–778 (2022).

    Article  Google Scholar 

  9. Montzka, S. A., Dlugokencky, E. J. & Butler, J. H. Non-CO2 greenhouse gases and climate change. Nature 476, 43–50 (2011).

    Article  CAS  Google Scholar 

  10. Nordhaus, W. D. Estimates of the social cost of carbon: concepts and results from the DICE-2013R model and alternative approaches. J. Assoc. Environ. Resour. Econ. 1, 273–312 (2014).

    Google Scholar 

  11. Waldhoff, S., Anthoff, D., Rose, S. & Tol, R. S. J. The marginal damage costs of different greenhouse gases: an application of FUND. Economics 8, 1–33 (2014).

    Article  Google Scholar 

  12. Hope, C. Critical issues for the calculation of the social cost of CO2: why the estimates from PAGE09 are higher than those from PAGE2002. Clim. Change 117, 531–543 (2013).

    Article  Google Scholar 

  13. Diaz, D. & Moore, F. Quantifying the economic risks of climate change. Nat. Clim. Change 7, 774–782 (2017).

    Article  Google Scholar 

  14. Burke, M., Hsiang, S. M. & Miguel, E. Global non-linear effect of temperature on economic production. Nature 527, 235–239 (2015).

    Article  CAS  Google Scholar 

  15. Burke, M., Davis, W. M. & Diffenbaugh, N. S. Large potential reduction in economic damages under UN mitigation targets. Nature 557, 549–553 (2018).

    Article  CAS  Google Scholar 

  16. Ricke, K., Drouet, L., Caldeira, K. & Tavoni, M. Country-level social cost of carbon. Nat. Clim. Change 8, 895–900 (2018).

    Article  CAS  Google Scholar 

  17. NASEM Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide (The National Academies Press, 2017).

  18. Errickson, F. C., Keller, K., Collins, W. D., Srikrishnan, V. & Anthoff, D. Equity is more important for the social cost of methane than climate uncertainty. Nature 592, 564–570 (2021).

    Article  CAS  Google Scholar 

  19. Dietz, S., van der Ploeg, F., Rezai, A. & Venmans, F. Are economists getting climate dynamics right and does it matter? J. Assoc. Environ. Resour. Econ. 8, 895–921 (2021).

    Google Scholar 

  20. Rose, S., Diaz, D. & Blanford, G. Understanding the social cost of carbon: a model diagnostic and inter-comparison study. Clim. Change Econ. 8, 1750009 (2017).

    Article  Google Scholar 

  21. Howard, P. H. & Sterner, T. Few and not so far between: a meta-analysis of climate damage estimates. Environ. Resour. Econ. 68, 197–225 (2017).

    Article  Google Scholar 

  22. Dell, M., Jones, B. F. & Olken, B. A. Temperature shocks and economic growth: evidence from the last half century. Am. Econ. J. Macroecon. 4, 66–95 (2012).

    Article  Google Scholar 

  23. Schwarber, A. K., Smith, S. J., Hartin, C. A., Vega-Westhoff, B. A. & Sriver, R. Evaluating climate emulation: fundamental impulse testing of simple climate models. Earth Syst. Dyn. 10, 729–739 (2019).

    Article  Google Scholar 

  24. Rennert, K. et al. The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates. Brook. Pap. Econ. Act. https://www.jstor.org/stable/27133178 (2021).

  25. Rennert, K. et al. Comprehensive evidence implies a higher social cost of CO2. Nature 610, 687–692 (2022).

    Article  CAS  Google Scholar 

  26. Raftery, A. E. & Ševčíková, H. Probabilistic population forecasting: short to very long-term. Int. J. Forecast. 39, 73–97 (2023).

    Article  Google Scholar 

  27. Müller, U. K., Stock, J. H. & Watson, M. W. An econometric model of international growth dynamics for long-horizon forecasting. Rev. Econ. Stat. 104, 857–876 (2022).

    Article  Google Scholar 

  28. Newell, R. G., Pizer, W. A. & Prest, B. C. A discounting rule for the social cost of carbon. J. Assoc. Environ. Resour. Econ. 9, 1017–1046 (2022).

    Google Scholar 

  29. Kalkuhl, M. & Wenz, L. The impact of climate conditions on economic production. Evidence from a global panel of regions. J. Environ. Econ. Manage. 103, 102360 (2020).

    Article  Google Scholar 

  30. Newell, R. G., Prest, B. C. & Sexton, S. E. The GDP-temperature relationship: implications for climate change damages. J. Environ. Econ. Manage. 108, 102445 (2021).

    Article  Google Scholar 

  31. van der Wijst, K.-I. et al. New damage curves and multimodel analysis suggest lower optimal temperature. Nat. Clim. Change 13, 434–441 (2023).

    Article  Google Scholar 

  32. IAWG Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide. Interim Estimates under Executive Order 13990 (US Government, 2021).

  33. Pindyck, R. S. Climate change policy: what do the models tell us? J. Econ. Lit. 51, 860–872 (2013).

    Article  Google Scholar 

  34. Wagner, G. et al. Eight priorities for calculating the social cost of carbon. Nature 590, 548–550 (2021).

    Article  CAS  Google Scholar 

  35. Cai, Y. & Lontzek, T. S. The social cost of carbon with economic and climate risks. J. Polit. Econ. 127, 2684–2734 (2018).

    Article  Google Scholar 

  36. Ritchie, P. D. L., Clarke, J. J., Cox, P. M. & Huntingford, C. Overshooting tipping point thresholds in a changing climate. Nature 592, 517–523 (2021).

    Article  CAS  Google Scholar 

  37. Cai, Y., Lenton, T. M. & Lontzek, T. S. Risk of multiple interacting tipping points should encourage rapid CO2 emission reduction. Nat. Clim. Change 6, 520–525 (2016).

    Article  Google Scholar 

  38. Kanter, D. R. et al. Improving the social cost of nitrous oxide. Nat. Clim. Change 11, 1008–1010 (2021).

    Article  Google Scholar 

  39. Shindell, D., Fuglestvedt, J. & Collins, W. The social cost of methane: theory and applications. Faraday Discuss. 200, 429–451 (2017).

    Article  CAS  Google Scholar 

  40. Sarofim, M. C., Waldhoff, S. T. & Anenberg, S. C. Valuing the ozone-related health benefits of methane emission controls. Environ. Resour. Econ. 66, 45–63 (2017).

    Article  Google Scholar 

  41. Sampedro, J., Waldhoff, S., Sarofim, M. & Van Dingenen, R. Marginal damage of methane emissions: ozone impacts on agriculture. Environ. Resour. Econ. 84, 1095–1126 (2023).

  42. Hope, C. W. The marginal impacts of CO2, CH4 and SF6 emissions. Clim. Policy 6, 537–544 (2006).

  43. Marten, A. L., Kopits, E. A., Griffiths, C. W., Newbold, S. C. & Wolverton, A. Incremental CH4 and N2O mitigation benefits consistent with the US Government’s SC-CO2 estimates. Clim. Policy 15, 272–298 (2015).

  44. Bertram, C. et al. NGFS Climate Scenarios Database Technical Documentation (Network for Greening the Financial System, 2020).

  45. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An Overview of CMIP5 and the Experiment Design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

  46. Rogelj, J., Meinshausen, M., Sedláček, J. & Knutti, R. Implications of potentially lower climate sensitivity on climate projections and policy. Environ. Res. Lett. 9, 031003 (2014).

  47. Fricko, O. et al. The marker quantification of the Shared Socioeconomic Pathway 2: A middle-of-the-road scenario for the 21st century. Glob. Environ. Change 42, 251–267 (2017).

  48. Dellink, R., Chateau, J., Lanzi, E. & Magné, B. Long-term economic growth projections in the Shared Socioeconomic Pathways. Glob. Environ. Change 42, 200–214 (2017).

  49. Kc, S. & Lutz, W. The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 42, 181–192 (2017).

  50. EPA Report on the Social Cost of Greenhouse Gases: Estimates Incorporating Recent Scientific Advances (U.S. Environmental Protection Agency, 2022).

  51. Kay, A. L., Davies, H. N., Bell, V. A. & Jones, R. G. Comparison of uncertainty sources for climate change impacts: flood frequency in England. Clim. Change 92, 41–63 (2009).

  52. Chen, J., Brissette, F. P., Poulin, A. & Leconte, R. Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed. Water Resour. Res. 47, W12509 (2011).

  53. Bosshard, T. et al. Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections. Water Resour. Res. 49, 1523–1536 (2013).

  54. Wang, T. et al. wtpeng22/SC-GHG-estimates: SC-GHG-estimates, Zenodo, https://doi.org/10.5281/zenodo.8135620 (2023).

Download references

Acknowledgements

This work was supported by the National Key R&D Program of China (No.2022YFE0209200), the National Natural Science Foundation of China (72140003,71673162), Tsinghua University Initiative Scientific Research Program and the Environmental Defense Fund (EDF).

Author information

Authors and Affiliations

  1. Institute of Energy, Environment and Economy, Tsinghua University, Beijing, China

    Tianpeng Wang & Fei Teng

Authors
  1. Tianpeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Teng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.W. and F.T. conceptualized the project, developed the methodology, conducted the investigations and wrote the manuscript. F.T. acquired funding and resources and supervised the project.

Corresponding author

Correspondence to Fei Teng.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks David Kanter and Frank Venmansm for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Socioeconomic and emissions projections from the RFF-SPs scenarios.

Global population projections (a), average growth rate of global GDP per capital (b), and global CO2 emissions projections (c). The colored uncertainty bands show the 5th to 95th percentiles of projections, and the lines inside the ribbons show median projections.

Source data

Extended Data Fig. 2 Incremental climate damage simulated by different damage models with Hector as a representative climate model.

a, b, Incremental damage from one additional metric ton of CH4 in the emissions year 2020 by the level-based damage models (D-FUND, D-PAGE, D-DICE, and D-HS) (a) and growth-based damage models (BHM-lag0, BHM-lag5, and DJO) (b). c, d, Incremental damage from an additional metric ton of N2O in 2020 by the level-based damage models (c) and growth-based damage models (d). The colored uncertainty bands show the 5th to 95th percentiles of incremental damages, and the lines inside the bands are average values.

Source data

Extended Data Fig. 3 Country-level GDP and social cost estimates with Hector as a representative climate model.

a, Median baseline country-level GDP per capital (Canada, China, India, Russia, and the United States) and the GDP per capital after considering the climate damages by the BHM-lag0, BHM-lag5 and DJO models. b, c, Country-level SC-CH4 estimates (b) and SC-N2O estimates (c) by the BHM-lag0, BHM-lag5 and DJO models. The horizontal lines in the box plots show the 5th to 95th percentiles of the social cost estimates, and the box width represents the 25th to 75th percentiles. The vertical lines in the box plots indicate the median estimates and the inside points indicate the simple mean values.

Source data

Extended Data Fig. 4 Incremental climate damages by various combinations of climate models and damage models.

a, b, Incremental damage for an additional metric ton of CH4 in 2020 by the level-based damage models (a) and growth-based damage models (b). c, d, Incremental damage for an additional metric ton of N2O in 2020 by the level-based damage models (c) and growth-based damage models (d). All the estimates are simple mean values of the Monte Carlo simulations.

Source data

Extended Data Fig. 5 SC-CH4 and SC-N2O estimates under the RFF-SPs and SSP2-4.5 scenarios.

a, SC-CH4 estimates. b, SC-N2O estimates. Left panel: social cost estimates for level-based damage models after equally weighting each climate model. Right Panel: social cost estimates for growth-based damage models after equally weighting each climate model. The discount rate is a Ramsey-like stochastic discount rate.

Source data

Extended Data Fig. 6 SC-CH4 and SC-N2O estimates under the Ramsey-like stochastic discount rate and fixed 3% discount rate.

a, SC-CH4 estimates. b, SC-N2O estimates. Left panel: social cost estimates for level-based damage models after equally weighting each climate model. Right Panel: social cost estimates for growth-based damage models after equally weighting each climate model.

Source data

Extended Data Fig. 7 The IPCC AR5 consistent ECS distribution and probabilistic Global mean surface temperature anomalies by different climate models.

a, ECS distribution. b, Global mean surface temperature anomalies. The colored uncertainty bands show the 5th to 95th percentiles of GSMT anomalies simulations, and the lines inside the bands show the simple mean values.

Source data

Supplementary information

Supplementary Information

Supplementary Discussion and Tables 1–3.

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, T., Teng, F. Damage function uncertainty increases the social cost of methane and nitrous oxide. Nat. Clim. Chang. 13, 1258–1265 (2023). https://doi.org/10.1038/s41558-023-01803-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-023-01803-4

  • Springer Nature Limited

Search

Navigation