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

Nature Climate Change (2023)Cite this article

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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.

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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.

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).

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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).

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Authors and Affiliations

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

    Tianpeng Wang & Fei Teng

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  1. Tianpeng Wang
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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.

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Correspondence to Fei Teng.

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

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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.

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

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