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Distributional effects of expanding climate targets beyond CO2

Nature Climate Change volume 16, pages 558–565 (2026)Cite this article

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Abstract

Targeting non-CO2 GHG is promoted as cost-effective, yet distributional consequences remain unclear. Here we compare CO2-only and multi-GHG carbon pricing, calibrated to the same climate outcome, across 201 household expenditure groups in 168 countries. Using a global social accounting matrix tracing price and income effects through supply chains, we find that adding non-CO2 GHGs makes carbon pricing more regressive: the relative burden rises for poorer households and falls for richer ones. The mechanism is compositional: relative to CO2-only pricing, multi-GHG pricing lowers energy prices but raises food prices; because poorer households devote larger budget shares to food, they experience larger burden, while richer households, whose consumption is more energy-intensive and whose incomes are less exposed, face smaller relative loss. Regionally, richer households in low-income regions, especially sub-Saharan Africa, face sizable cost increases. Our findings highlight the need for equity-oriented design to keep carbon pricing socially acceptable.

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Fig. 1: Impacts of CO2-only (US$50 tCO2−1) and multi-GHG (US$26 tCO2e−1) pricing on global expenditure groups.
The alternative text for this image may have been generated using AI.
Fig. 2: Impacts of CO2-only (US$50 tCO2−1) and multi-GHG (US$26 tCO2e−1) pricing on product price, household expenditure and income.
The alternative text for this image may have been generated using AI.
Fig. 3: Total effect of CO2-only (US$50 tCO2−1) and multi-GHG (US$26 tCO2e−1) pricing across 168 countries.
The alternative text for this image may have been generated using AI.
Fig. 4: Difference in the burden rate between multi-GHG and CO2-only pricing scenarios across different income-level country groups.
The alternative text for this image may have been generated using AI.
Fig. 5: Impacts of CO2-only (US$50 tCO2−1) and multi-GHG (US$26 tCO2e−1) pricing on population deciles within countries.
The alternative text for this image may have been generated using AI.

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Data availability

The MRIO table used in the paper is derived from the GTAP v.11 database (https://www.gtap.agecon.purdue.edu/)75. Household expenditure data are collected from the WBGCD59 and other sources60,61. See Supplementary Section 2 or contact the corresponding authors for more details. For household income source, the LIS is available at https://www.lisdatacenter.org/our-data/lis-database/ (ref. 62). The data generated in this work are available via Zenodo at https://doi.org/10.5281/zenodo.19025897 (ref. 76).

Code availability

Code was developed in R to process and analyse the primary data, which is available via Zenodo at https://doi.org/10.5281/zenodo.19025897 (ref. 76).

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant nos. 72522023, 72303136 and 72534005), the Taishan Scholar Youth Expert Program of Shandong Province (grant no. tsqn202507015), the major grant in National Social Sciences of China (grant nos. 23VRC037, 24VHQ018) and Research Grants Council—Strategic Topics Grant (grant no. STG2/P-705/24-R). K.H. acknowdedges funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101137905 (PANTHEON).

Author information

Author notes

  1. These authors contributed equally: Yating Kang, Peipei Tian.

Authors and Affiliations

  1. School of Geography & Environmental Economics, Guangdong University of Finance & Economics, Guangzhou, China

    Yating Kang

  2. Institute of Blue and Green Development, Shandong University, Weihai, China

    Yating Kang & Peipei Tian

  3. Integrated Research on Energy, Environment and Society (IREES), Energy and Sustainability Research Institute Groningen (ESRIG), University of Groningen, Groningen, the Netherlands

    Yating Kang & Klaus Hubacek

  4. Department of Geographical Sciences, University of Maryland, College Park, MD, USA

    Laixiang Sun & Xiangjie Chen

  5. School of Finance & Management, SOAS University of London, London, UK

    Laixiang Sun

  6. State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing, China

    Yuru Guan

  7. Institute for the Environment and Health, Nanjing University Suzhou Campus, Suzhou, China

    Yuru Guan

  8. Department of Geography, The University of Hong Kong, Hong Kong, China

    Kuishuang Feng

  9. Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong, China

    Kuishuang Feng

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Contributions

Y.K., P.T., K.F. and K.H. designed the study. Y.K. and P.T. performed the analysis and prepared the paper. Y.K. and X.C. developed the model. P.T., K.F. and K.H. coordinated and supervised the project. All authors (Y.K., P.T., L.S., X.C., Y.G., K.F. and K.H.) participated in writing the paper.

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Correspondence to Peipei Tian, Kuishuang Feng or Klaus Hubacek.

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Extended data

Extended Data Fig. 1 Sensitivity of distributional impacts relative to the CO2-only baseline (US$50/tCO2) under alternative multi-GHG benchmarks: proportional reduction from synthesis study (US$26/tCO2e), emission parity (US$23/tCO2e) and revenue parity (US$35/tCO2e).

All effects are reported as burden rates (percent of total household expenditure). Dots represent calculated values for each expenditure decile. Shaded bands indicate the range between the emission parity and revenue parity scenarios.

Extended Data Fig. 2 Sensitivity of distributional impacts to CO2-only price levels (US$50, US$100, and US$185/tCO2) and their corresponding multi-GHG benchmark prices.

Results are reported as burden rates (percent of total household expenditure) by global expenditure group. Dots represent calculated values for each expenditure decile. Shaded bands indicate the range between the emission parity and revenue parity scenarios.

Extended Data Fig. 3 Sensitivity of household burden rates across global expenditure groups to the substitution elasticity (σ) under CO2-only (US$50/tCO2) and multi-GHG (US$26/tCO2e) pricing.

A value of σ = 1 implies constant expenditure shares with no substitution responses (main result); σ < 1 represents complementary across goods; and σ > 1 indicates substitutability, with higher values reflecting greater ease of substitution.

Supplementary information

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Supplementary Section, Figs. 1–4, Tables 1 and 2 and References.

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Kang, Y., Tian, P., Sun, L. et al. Distributional effects of expanding climate targets beyond CO2. Nat. Clim. Chang. 16, 558–565 (2026). https://doi.org/10.1038/s41558-026-02622-z

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