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A systematic map of machine learning for urban climate change mitigation

Nature Cities (2025)Cite this article

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Abstract

Many cities are interested in leveraging artificial intelligence and machine learning (ML) to help urban climate change mitigation (UCCM). Researchers and practitioners, however, are only beginning to understand how ML can contribute to achieving climate targets in cities. Here, we systematically map 2,300 peer-reviewed articles published between 1994 and 2024 that explore the use of ML in UCCM. We find that, despite fast growth in this research area, the use of generative artificial intelligence and large language models remains negligible, which contrasts to their increasing adoption in other urban domains. Among 40 identified application areas, ML research focuses predominantly on high-impact mitigation options denoted by the Intergovernmental Panel on Climate Change. This trend may partly be driven by data availability and commercial interest, which risk perpetuating geographic inequities and diverting efforts toward less impactful mitigation options. We therefore offer recommendations to guide the impactful deployment of ML solutions in UCCM.

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Fig. 1: The most researched impact areas by cluster.
Fig. 2: Geographic distribution of research and impact area clusters.

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Data availability

The data supporting the findings of this study are available within the article and its Supplementary Information. The steps for curating the data from the WoS and the ‘AI conferences info’ repository are provided in the text. The repository operates under a CC-BY-SA-4.0 license.

Code availability

Code developed for this study is available via Code Ocean at https://codeocean.com/capsule/6679595/tree.

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Acknowledgements

This research was supported through a scholarship for M.J.H. by the Heinrich Böll Foundation. We thank F. Pachter for supporting with the coding of the articles and M. Callaghan for advising on prioritized abstract screening.

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Authors and Affiliations

  1. Technical University Berlin, Berlin, Germany

    Marie Josefine Hintz & Felix Creutzig

  2. Hertie School of Governance, Berlin, Germany

    Marie Josefine Hintz & Lynn H. Kaack

  3. Potsdam Institute for Climate Impact Research, Potsdam, Germany

    Marie Josefine Hintz, Nikola Milojevic-Dupont, Felix Creutzig & Tim Repke

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  1. Marie Josefine Hintz
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Contributions

N.M.-D. and L.H.K. conceived of the study, and N.M.-D., L.H.K. and M.J.H. designed the search queries. T.R. managed the process for prioritized abstract screening and was responsible for the fine-tuning of the classifier. M.J.H., N.M.-D. and L.H.K. performed the manual coding. M.J.H. led the analysis of relevant papers. All authors analyzed the results and interpreted the findings. M.J.H. created the figures and developed the initial draft. M.J.H. and L.H.K. wrote the draft with the help of N.M.-D. L.H.K. and F.C. supervised the research. All authors participated in the editing of the paper.

Corresponding author

Correspondence to Marie Josefine Hintz.

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Competing interests

L.H.K., N.M.-D. and F.C. are affiliated with Climate Change AI. M.J.H. is affiliated with Urban AI.

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Nature Cities thanks Anne Sietsma and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Table 1 Numbers of studies per mitigation potential level
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Supplementary information

Supplementary Information

Supplementary Information 1–3.

Reporting Summary

Supplementary Data 1

Dataframe with all the relevant papers included in our sample, consisting of three tabs (the first tab includes all relevant papers from the first round of coding, the second tab those from the extended timeframe and search queries, and the third tab all relevant papers from conference proceedings).

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Hintz, M.J., Milojevic-Dupont, N., Creutzig, F. et al. A systematic map of machine learning for urban climate change mitigation. Nat Cities (2025). https://doi.org/10.1038/s44284-025-00328-5

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