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Global coastal human settlement retreat driven by vulnerability to coastal climate hazards

Nature Climate Change volume 15, pages 1060–1070 (2025)Cite this article

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Abstract

Retreating from coastlines is one potential human response to the increasing threats posed by coastal climate hazards. However, the global extent of coastal settlement retreat, its correlation with local vulnerabilities, and the adaptation gaps remain less understood. Here we analyse night-time light changes for 1992 to 2019 and show that settlements retreated from coastlines in 56% of coastal subnational regions, remained stable in 28%, and moved closer to coastlines in 16% of these regions. Retreat was weakly associated with historical experiences of coastal climate hazards but accelerated in regions with greater vulnerability to coastal climate hazards—indicated by lower infrastructure protection and less adaptive capacity. In 46% of low-income regions, particularly in Africa and Asia, settlements were forced to either maintain their current status quo or move closer to coastlines, revealing the large adaptation gap in addressing future climate change risks.

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Fig. 1: Global landscape of proximity of human settlements to coastlines.
Fig. 2: Global movement patterns of coastal human settlements.
Fig. 3: Heterogeneity in movement patterns of coastal human settlements across different continents and income groups.
Fig. 4: Movement pace of coastal settlements from 1992 to 2019, represented by the absolute value of the average annual rate of change in the proximity of human settlements to coastlines (km yr−1).
Fig. 5: Determinants of the movement pace of coastal human settlements.
Fig. 6: Global action gaps for coastal adaptation.

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Data availability

The source data supporting the findings of this study are available via Harvard Dataverse at https://doi.org/10.7910/DVN/OWYNB0 (ref. 120). Source data are provided with this paper.

Code availability

The code supporting the findings of this study is available via Zenodo at https://doi.org/10.5281/zenodo.16757827 (ref. 121).

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (42007418, 42377168, T2350710802 and U2039202), the International Innovation Cooperation Project of Department of Science and Technology of Sichuan Province (2024YFHZ0241), the National Key Research and Development Program of China (2023YFE0121900), the Shenzhen Science and Technology Innovation Commission Project (GJHZ20210705141805017 and K23405006), the Fundamental Research Funds for the Central Universities (SK2024-18), the Sichuan International Science and Technology Cooperation Base: International Joint Research Center for Multi-Hazard Resilience of Energy Infrastructure, the Center for Computational Science and Engineering at Southern University of Science and Technology, and Tsinghua University.

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Authors and Affiliations

  1. Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu, China

    Lilai Xu, Xue Yang & Baofeng Di

  2. MOE Key Laboratory of Deep Earth Science and Engineering, Sichuan University, Chengdu, China

    Lilai Xu, Xue Yang & Baofeng Di

  3. Department of Earth System Science, Tsinghua University, Beijing, China

    Deliang Chen

  4. Institute of Risk Analysis, Prediction and Management (Risks-X), Academy for Advanced Interdisciplinary Sciences, Southern University of Science and Technology, Shenzhen, China

    Didier Sornette

  5. Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

    Alexander V. Prishchepov & Shengping Ding

  6. School of Geography and Tourism, Shanxi Normal University, Xi’an, China

    Wang Pang

  7. Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Bandung, Indonesia

    Krishna Suryanto Pribadi

  8. Department of Civil and Environmental Engineering, Faculty of Engineering, Monash University, Melbourne, Victoria, Australia

    Xiaoming Wang

  9. Northwest Institute of Eco‐environment and Resources, Chinese Academy of Sciences, Lanzhou, China

    Xiaoming Wang

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L.X., X.W. and B.D. conceived and designed the study. L.X. and X.Y. collected data and performed the analysis. W.P. contributed analysis tools. L.X. drafted the manuscript, and X.W., D.C., D.S., A.V.P., S.D., B.D. and K.S.P. revised the manuscript. All authors contributed to the interpretation of the results.

Corresponding authors

Correspondence to Baofeng Di or Xiaoming Wang.

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The authors declare no competing interests.

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Nature Climate Change thanks Joyce Chen, Song Gao and Xuecao Li for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Time series of the proximity of human settlements to coastlines by continent from 1992 to 2019.

The solid lines represent linear least-squares fits; shaded bands indicate 75% confidence intervals.

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Extended Data Fig. 2 Time series of the proximity of human settlements to coastlines by income group from 1992 to 2019.

The solid lines represent linear least-squares fits; shaded bands indicate 75% confidence intervals.

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Extended Data Fig. 3

Settlement movement patterns across different income groups for the 1990s (a), 2000s (b), and 2010s (c), respectively.

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Extended Data Fig. 4 Top 20 countries and sub-regions with with fastest paces of retreat.

(a) Top 20 countries with fastest paces of retreat. Dots indicate individual coastal sub-national regions (n = 57 overall; n varies by country). Box hinges indicate the upper and lower quartiles; center lines indicate the median value; crosses indicate the mean value; whiskers extend to the minimum and maximum values. (b) Top 20 coastal sub-national regions with fastest paces of retreat.

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Extended Data Fig. 5 Top 20 countries and sub-regions with with fastest paces of approach.

(a) Top 20 countries with fastest paces of movement toward their coastlines. Dots indicate individual coastal sub-national regions (n = 50 overall; n varies by country). Box hinges indicate the upper and lower quartiles; center lines indicate the median value; crosses indicate the mean value; whiskers extend to the minimum and maximum values. (b) Top 20 coastal sub-national regions with fastest paces of movement toward their coastlines.

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Extended Data Fig. 6

Adaptation performance and gaps in regions with stable settlement distance in low- and high-income countries.

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Extended Data Fig. 7 Settlement proximity and movement patterns from ISA, and their comparison with NTL-based results.

(a) Average proximity of human settlements to coastlines from 1992 to 2019 based on ISA data; (b) Comparison of proximity values derived from ISA and NTL-based physical extents; (c) Coastal settlement movement patterns identified using ISA data; (d) Distribution of movement categories from ISA data and comparison with those derived from the NTL-based physical extents. Basemap in a,c from Natural Earth (https://www.naturalearthdata.com/).

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Extended Data Fig. 8 Consistency of movement categories derived from NTL-based physical extents and ISA data.

(a) Spatial distribution of consistency in movement categories between the two datasets; (b) Overall agreement of movement classifications between NTL-based physical extents and ISA-derived; (c) Variation in consistency across sub-national regions with different income levels; (d) Transfer matrix illustrating the correspondence of movement categories between results derived from NTL-based physical extents and ISA data, showing how each category identified using NTL-based physical extents aligns with the same or different categories in ISA; (e) Statistics showing the consistency between the two datasets in identifying specific movement categories. “Consistent” refers to cases where the same movement category was identified using both the NTL-based physical extents and ISA data. Basemap in a from Natural Earth (https://www.naturalearthdata.com/).

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Supplementary information

Supplementary Information

Supplementary Figs. 1–14 and Tables 1–3.

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Xu, L., Yang, X., Chen, D. et al. Global coastal human settlement retreat driven by vulnerability to coastal climate hazards. Nat. Clim. Chang. 15, 1060–1070 (2025). https://doi.org/10.1038/s41558-025-02435-6

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