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Strategies to accelerate US coal power phase-out using contextual retirement vulnerabilities

Nature Energy volume 10, pages 1274–1288 (2025)Cite this article

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Abstract

Strategically planning the phase-out of coal power is critical to achieve climate targets, yet current approaches often fail to account for the context-specific barriers and vulnerabilities to retirement. Here we introduce a framework that combines graph theory and topological data analysis to classify the US coal fleet into eight distinct groups based on technical, economic, environmental and socio-political characteristics. We calculate each non-retiring coal plant’s ‘contextual retirement vulnerability’ score, a metric developed to quantify susceptibility to retirement drivers using the graph-based distance to a coal plant with an announced early retirement. Separately, we identify ‘retirement archetypes’ that explain the key factors driving announced retirements within each group, which are used to inform group-specific strategies for accelerating retirements. Our findings reveal the diverse strategies that are required to accelerate the phase-out of remaining coal plants, including regulatory compliance, public health campaigns and economic incentives.

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Fig. 1: Age-based retirement scenario versus current state of US coal retirements.
Fig. 2: Graph-based classification of the US coal fleet.
Fig. 3: Key group characteristics.
Fig. 4: Group vulnerability map.
Fig. 5: Retirement proximity graphs.
Fig. 6: Network analysis of Group 3.
Fig. 7: Coal fleet proximity to retirement.

Data availability

All datasets necessary to reproduce the results of this study are available via Zenodo at https://doi.org/10.5281/zenodo.15844521 (ref. 86). Our compiled coal plant dataset, including each plant’s group membership and vulnerability score, is available as Supplementary Data 1. Datasets include raw and processed coal plant data, imputed and cleaned datasets linked to the final graph model selected through our analysis pipeline, and literature review data on both planned retirements and plants that retired in the US between 2020 to 2023. These resources are also hosted on our GitHub repository, Retire, at https://github.com/Krv-Analytics/retire, where you will find data files, notebooks and scripts designed for ease of use. Source data are provided with this paper.

Code availability

THEMA is freely available at https://github.com/Krv-Analytics/Thema, with comprehensive documentation and a user guide hosted at https://krv-analytics.github.io/Thema/. Retire is available at https://github.com/Krv-Analytics/retire, and interactive visualizations that allow the user to identify specific plants in figures are available at https://krv.ai/academia.

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Acknowledgements

This work was partially supported by a Manalis Scholarship awarded to S.G. We thank D. Prull at the Sierra Club for his generosity in providing insights on analytical gaps to fill to achieve practical relevancy and data on announced retirements. In addition, we thank D. Khannan and J. Daniel at Rocky Mountain Institute (RMI) for helpful suggestions and J. Graham and C. Schneider at the Clean Air Task Force for access to coal cost and health impact data.

Author information

Authors and Affiliations

  1. Environmental Studies, University of California Santa Barbara, Santa Barbara, CA, USA

    Sidney Gathrid, Ranjit Deshmukh & Grace C. Wu

  2. Krv Analytics, Santa Monica, CA, USA

    Sidney Gathrid, Jeremy Wayland & Stuart Wayland

  3. Institute of AI for Health, Helmholtz-Zentrum München, Munich, Germany

    Jeremy Wayland

  4. Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, CA, USA

    Stuart Wayland

  5. Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, CA, USA

    Ranjit Deshmukh

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Contributions

S.G., J.W. and G.C.W. conceptualized the study. G.C.W. acquired funding. S.G., J.W. and S.W. developed the methodology and software and conducted the formal analysis. S.G. collected and curated the data with support from G.C,W., R.D., J.W. and S.W. S.G. wrote and edited the paper. G.C.W. and R.D. supervised the project.

Corresponding author

Correspondence to Grace C. Wu.

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Nature Energy thanks Ryna Yiyun Cui, Nada Maamoun 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 Representational Variability in Coal Plant Modeling via UMAP and Mapper.

(a) Each panel displays a low-dimensional embedding of coal plants using UMAP, with colors indicating cluster assignments via HDBSCAN. Varying locality parameters (for example, number of neighbors, minimum distance) leads to substantial differences in structure, despite identical input data. (b) Mapper graphs built on a fixed UMAP projection (with nneighbors = 16, min dist = 0.5, seed = 42) vary across cube counts (ncubes ∈ {5, 7, 10}) and percent overlaps (overlap ∈ {0.55, 0.6, 0.65}), illustrating how topology is sensitive to Mapper’s lensing. Nodes are colored by percent of plants with retirement plans. This variability motivates the use of THEMA to evaluate entire distributions of representations and automatically select models with the highest policy relevance.

Source data

Extended Data Fig. 2 Optimizing Model Selection for Policy Impact.

The x-axis represents the number of connected components (groups) in each graph, while the y-axis shows the variance of Total Nameplate Capacity (MW) within groups. The optimal model is chosen at the inflection point (elbow) where increasing the number of groups no longer significantly reduces the variance, balancing model complexity with meaningful group differentiation.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–3, Tables 1–11, detailed dataset breakdown, discussion and benchmarking analysis.

Supplementary Data 1

Coal plant dataset with groups, proximity to retirement and vulnerability stratifications included.

Supplementary Data 2

News review and target matching data: dataset used for the retrospective analysis of coal plants that have already retired, including identification of key drivers behind their retirement decisions.

Supplementary Data 3

News review of planned retirement coal plants: comprehensive news-based dataset of coal plants with announced retirement plans, serving as the basis for defining the retirement archetypes discussed in this paper.

Source data

Source Data Figs. 1–7

Tabular source data for Figs. 1, 3, 4 and 7. Graph construction and node-membership data for Figs. 2, 5 and 6.

Source Data Extended Data Fig. 1

UMAP + graph representational variability data.

Source Data Extended Data Fig. 2

Tabular source data.

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Gathrid, S., Wayland, J., Wayland, S. et al. Strategies to accelerate US coal power phase-out using contextual retirement vulnerabilities. Nat Energy 10, 1274–1288 (2025). https://doi.org/10.1038/s41560-025-01871-0

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