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Coal-exit alliance must confront freeriding sectors to propel Paris-aligned momentum

Nature Climate Change volume 13, pages 130–139 (2023)Cite this article

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Abstract

The global phase-out of coal by mid-century is considered vital to the Paris Agreement to limit warming well-below 2 °C above pre-industrial levels. Since the inception of the Powering Past Coal Alliance (PPCA) at COP23, political ambitions to accelerate the decline of coal have mounted to become the foremost priority at COP26. However, mitigation research lacks the tools to assess whether this bottom-up momentum can self-propagate toward Paris alignment. Here, we introduce dynamic policy evaluation (DPE), an evidence-based approach for emulating real-world policy-making. Given empirical relationships established between energy-economic developments and policy adoption, we endogenize national political decision-making into the integrated assessment model REMIND via multistage feedback loops with a probabilistic coalition accession model. DPE finds global PPCA participation <5% likely against a current policies backdrop and, counterintuitively, foresees that intracoalition leakage risks may severely compromise sector-specific, demand-side action. DPE further enables policies to interact endogenously, demonstrated here by the PPCA’s path-dependence to COVID-19 recovery investments.

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Fig. 1: DPE: a cyclical interface between techno-economic and sociopolitical analyses.
Fig. 2: Dynamic feasibility of national PPCA accession from COALogit.
Fig. 3: Annual PPCA-induced deviations from reference baseline.
Fig. 4: Evaluating PPCA scenario outcomes.

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Data availability

The data and analysis scripts that support the findings of this study are publicly available on Zenodo at https://doi.org/10.5281/zenodo.7335236.

Code availability

The source code of the REMIND–COALogit model version used in this study are available on Zenodo at https://doi.org/10.5281/zenodo.7335042. Source code for REMIND input data processing functions are openly available on GitHub at https://github.com/pik-piam/mrremind.

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Acknowledgements

The research leading to the results reported in this study was supported by the PEGASOS project (01LA1826C; S.B. and N.B.), made possible by funding from the German Federal Ministry of Education and Research (BMBF), and the MANIFEST project (950408; J.J.), funded by the European Commission’s Horizon 2020 ERC Starting Grant programme. We thank L. Merfort, V. Vinichenko, A. Malik and M. Pehl for correspondence and specific recommendations of data or code that enabled the implementation of certain elements of our study.

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Authors and Affiliations

  1. Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany

    Stephen L. Bi & Nico Bauer

  2. Technical University of Berlin, Berlin, Germany

    Stephen L. Bi

  3. Chalmers University of Technology, Gothenburg, Sweden

    Jessica Jewell

  4. University of Bergen, Bergen, Norway

    Jessica Jewell

  5. International Institute for Applied Systems Analysis, Laxenburg, Austria

    Jessica Jewell

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  1. Stephen L. Bi
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Contributions

S.B. and N.B. conceived of the research questions, while J.J. and S.B. conceptualized the methodology. All authors contributed to the literature review. S.B. led the implementation, analysis and manuscript writing with contributions from all authors. J.J. conceived of Figs. 1 and 2. N.B. conceived of Table 1. S.B. conceived of all other display items.

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Correspondence to Stephen L. Bi.

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Extended data

Extended Data Fig. 1 Historical coal power capacity in GW from 2000–2020 and near-term extrapolations with varied assumptions.

aggregated to REMIND regions (Supplementary Fig. A3.1) and globally. The ‘Literature’ scenario corresponds to global assumptions of 40-year lifetimes and 100% project completion, as is often used in prior studies on ‘committed emissions.’44 See Supplementary Table A1.5 for exact GW values per region.

Extended Data Fig. 2 The approximated PPCA-DFS in 2020.

which better illustrates the logit model’s ability to predict PPCA accession than the 2015 snapshot in Fig. 2a. REMIND source data licensing agreements unfortunately prevent us from using more recent data at the moment, so COALogit parameters are estimated using 2015 data. Coal-power-shares are derived for this figure from historical coal capacities, extrapolated utilization rates, and downscaled electricity generation from REMIND.

Extended Data Fig. 3 Depiction of the REMIND–COALogit framework.

Supplementary Table 3 lists all the specific variables passed from REMIND to COALogit, which vary by scenario. Policy stringency coefficients (PSCs) translate country-level coalitions into the fraction of each REMIND region’s coal demand (electricity or total) that the PPCA phases out. Their derivation is also scenario-dependent, as shown in Eqs. (1)-(2) and (5)-(11). The REMIND schematic (from Baumstark et al. 55) includes some pre-existing interfaces for context and illustration of model structure. The coupling routines vary from iterative co-optimization (REMIND-MAgPIE) to ex post calculations (MAGICC), but none are identical to the REMIND–COALogit soft-link.

Extended Data Fig. 4 REMIND–COALogit cascade for modelling multistage PPCA accession.

shown for six PPCA scenarios. Each Roman numeral corresponds to a distinct REMIND or COALogit run in the sequence, and numerals used throughout the Methods refer to this figure. Each REMIND run is a global Nash equilibrium solution in which regional welfare is intertemporally optimized across the time horizon shown (prior periods are fixed to the upstream run). The year in each COALogit oval indicates the REMIND period from which input data is received. This cascade is repeated for each COVID recovery, giving a total of 18 PPCA scenarios.

Extended Data Fig. 5 Impacts of the power-exit (a) and demand-exit (b) PPCA scenarios on final energy (FE) consumption in each sector.

Not shown are gas- and hydrogen-based mobility, and heat used in industry and buildings.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Tables 1–5, Appendices I–III, Appendix I Tables 1.1–1.6, Appendix II Fig. 2.1 and Appendix III Tables 3.1–3.3.

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Bi, S.L., Bauer, N. & Jewell, J. Coal-exit alliance must confront freeriding sectors to propel Paris-aligned momentum. Nat. Clim. Chang. 13, 130–139 (2023). https://doi.org/10.1038/s41558-022-01570-8

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