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Cross-national comparative assessment of synergies and conflicts in climate policy mixes

Nature Climate Change (2026)Cite this article

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Abstract

Carbon pricing is one of the most effective climate mitigation instruments, yet how it interacts with other climate policies and shapes their effectiveness remains unclear. Integrating the most comprehensive climate policy datasets covering more than 10,000 climate policies over 100 countries, this paper quantifies such policy interaction effects. We use synthetic control methods for policy effect estimation, construct a Global Climate Policy Index to reflect different policy designs and conduct global comparative studies on policy interactions. Our results show that under historical average levels of prices and coverage, emissions trading systems and carbon taxes reduce emission intensity by approximately 15.4% and 8.5%, respectively, and these reductions are significantly influenced by policy interactions, with both synergies and conflicts based on market maturity and policy intensity. Counterfactual simulations suggest that reducing such policy conflicts could improve carbon pricing effectiveness by up to 22.3%, highlighting the importance of policy coherence in climate mitigation strategies.

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Fig. 1: The treatment effects of carbon taxes and ETS policies in different countries.
Fig. 2: National climate change mitigation policy progress during 1996–2019.
Fig. 3: Direct and decomposed policy effect and interactions on ETSs.
Fig. 4: Policy effect simulation under different conflict policy scenarios.
Fig. 5: Counterfactual analysis and climate policy selection.

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Data availability

All data used in this study are publicly available. The climate policy data were obtained from the Global Climate Change Mitigation Policy Dataset. The carbon pricing data were compiled from the World Carbon Pricing Database (WCPD) and the Carbon Pricing Dashboard of the World Bank. The processed datasets generated and analysed in this study are publicly available via Zenodo at https://doi.org/10.5281/zenodo.18015601 (ref. 85). Source data are provided with this paper.

Code availability

A replication package is publicly available via Zenodo at https://doi.org/10.5281/zenodo.18015601 (ref. 85).

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Acknowledgements

We acknowledge financial support from the National Natural Science Foundation of China (grant nos 42341205, 72204049 and 72234002), the National Key Research and Development Program of China (grant no. 2020YFA0608600), the Shanghai Shuguang Project and the European Union under grant agreement no. 101137905 (PANTHEON). Part of the computations in this research were performed using the CFFF platform at Fudan University.

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Author notes

  1. These authors contributed equally: Libo Wu, Guolei Liu.

Authors and Affiliations

  1. School of Data Science, Fudan University, Shanghai, China

    Libo Wu, Guolei Liu & Zhihao Huang

  2. Shanghai Innovation Institute, Shanghai, China

    Libo Wu & Yang Zhou

  3. Shanghai Academy of AI for Science, Shanghai, China

    Libo Wu & Yang Zhou

  4. Institute for Big Data, Fudan University, Shanghai, China

    Libo Wu & Yang Zhou

  5. MOE Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, China

    Libo Wu & Yang Zhou

  6. School of Economics, Fudan University, Shanghai, China

    Libo Wu

  7. The Bartlett School of Sustainable Construction, University College London, London, UK

    Jing Meng

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Contributions

L.W. and Y.Z. conceived the idea and designed the study. L.W. and J.M. supervised the study. G.L., Z.H. and Y.Z. contributed to the methodology. G.L. and Y.Z. analysed the data and generated the figures. G.L. wrote the first draft of the paper, and all authors provided feedback. L.W., G.L., J.M. and Y.Z. contributed substantially to editing the manuscript.

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Correspondence to Jing Meng or Yang Zhou.

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Nature Climate Change thanks Adhurim Haxhimusa, Gregor Schwerhoff and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Analytical Framework for Assessing Synergistic and Conflicting Effects.

This figure illustrates our three-step analytical framework for systematically assessing carbon pricing policy interactions. Data Sources: Global Climate Change Mitigation Policy Database (GCCMPD), World Bank Carbon Pricing Dashboard (WBCPD), World Carbon Pricing Database (WCPD). The Robustness Check Framework ensures methodological rigor through comprehensive validation.

Extended Data Fig. 2 National Climate Change Mitigation Policy Index for 2019.

This figure reflects the global configuration of carbon pricing and complementary climate policy instruments, providing the analytical foundation for examining interaction effects between carbon pricing and complementary instruments. Superimposed stacked bars represent the relative weights of four basic policy instruments—subsidy, regulatory, information, and government provision—capturing their contributions to national mitigation portfolios. The European inset provides a magnified view of jurisdictions with denser and more heterogeneous policy mixes, facilitating comparisons of instrument layering and interaction across countries with varying levels of carbon pricing intensity. These divergent configurations indicate that the effectiveness of carbon pricing depends not only on price levels or coverage, but also on the surrounding mix of synergistic or conflicting policies. Basemap from Natural Earth (https://www.naturalearthdata.com).

Extended Data Table 1 Policy Interaction Effect on Carbon Emission Intensity
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Supplementary Figs. 1–21, Tables 1–64 and Discussion.

Source data

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Wu, L., Liu, G., Huang, Z. et al. Cross-national comparative assessment of synergies and conflicts in climate policy mixes. Nat. Clim. Chang. (2026). https://doi.org/10.1038/s41558-026-02574-4

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