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Global water security threatened by rising inequality

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Abstract

The global water-scarcity crisis is fundamentally driven by inequality, yet most forecasts overlook equity as a causal factor, leading to misdiagnosed problems and ineffective solutions. Here we develop a machine-learning-based global water-use forecasting model to project future water use and scarcity under distinct Shared Socioeconomic Pathways representing alternative development trajectories. Drawing on decades of historical data on human adaptation and resource use, the model predicts that by 2050, 6.5 billion people—equivalent to 65.5% of the global population—will face severe water scarcity under a high-challenge fragmentation scenario. By 2100, this figure is projected to rise to 8.0 billion, or 63% of the global population, far exceeding most previous estimates. Our analysis shows that a high inequality pathway directly amplifies water-scarcity risk. Critically, a technology-driven pathway improves aggregate water-use efficiency but concurrently deepens social and spatial inequalities. These findings underscore the need to move beyond purely technological fixes towards integrated, equitable water management, demonstrating that greater justice is inseparable from greater water security.

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Fig. 1: Regional distribution of water-scarce population and proportion under SSP scenarios.
Fig. 2: Forecasted global water use in 2100.
Fig. 3: Global water scarcity in 2100.
Fig. 4: Inequality in global water use.

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Data availability

All datasets generated and analysed during this study are available via Zenodo at https://doi.org/10.5281/zenodo.17445879 (ref. 67). Source data are provided with this paper.

Code availability

The source code for the ML-GWF model is available via Zenodo at https://doi.org/10.5281/zenodo.17445879 (ref. 67).

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Acknowledgements

This research received joint financial support from the National Natural Science Foundation of China (grants 72474065, 72074119), the National Social Science Fund of China (grant 23&ZD103), the Fundamental Research Funds for the Central Universities (grant B240207007) and the Jiangsu Provincial Water Conservancy Science and Technology Project (grants 205032, 205047). We gratefully acknowledge P. D’Odorico (University of California Berkeley), H. Jiang (The Nature Conservancy) and Y. Yang (Chongqing University) for their insightful comments.

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Authors and Affiliations

  1. Business School, Hohai University, Nanjing, China

    Jichuan Sheng 
    (盛济川) & Qian Cheng 
    (成茜)

  2. College of Economics and Management, Nanjing Forestry University, Nanjing, China

    Jichuan Sheng 
    (盛济川) & Hongqiang Yang 
    (杨红强)

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  1. Jichuan Sheng 
    (盛济川)

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  2. Qian Cheng 
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Contributions

J.S. and Q.C. contributed equally to this work. J.S. conceived the study, designed the model and wrote the manuscript. Q.C. performed the data analysis, ran the model and assisted in writing. H.Y. contributed to the initial draft and manuscript revision. All authors discussed the results and approved the final manuscript.

Corresponding author

Correspondence to
Jichuan Sheng 
(盛济川).

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Extended data

Extended Data Fig. 1 Actual water-scarce population (WSI > 2) in 2019 and forecasted water-scarce population (WSI > 2) in 2100 under multiple scenarios (the units are people per square kilometers).

a, Baseline scenario (2019). b–f, Forecasted water-scarce population in 2100 under the SSP1-SSP5 scenarios, respectively. These global maps are generated through a hybrid downscaling of national-level forecasts and serve to illustrate the potential spatial distribution of water stress, rather than providing locally validated predictions. Basemaps from Natural Earth.

Extended Data Fig. 2 The dominant factors of water use forecasting at the national scale.

a, Dominant factors identified from the Training set. b–f, Dominant forecasting factors under the SSP1-SSP5 scenarios, respectively. Note: Variable abbreviations in the figure are as below – GDP: Gross Domestic Product, POP: Population, IWE: Irrigation Water Efficiency, IWU: Industrial Water Use, MWE: Municipal Water Efficiency, URB: Urbanization Rate, IA: Irrigation Area, HFC: High-frequency sequence components of water consumption, LFC: Low-frequency sequence components of water consumption. Basemaps from Natural Earth.

Source data

Extended Data Fig. 3 Ranking of global water intensity and water inequality.

(a) and (c). Forecasted global water intensity in 2050 and 2100 (Water use per 10,000 USD of GDP). (b) and (d). Ranking of global water intensity and water inequality under different scenarios in 2050 and 2100.

Source data

Extended Data Fig. 4 Forecasted water-saving potential in 2100 relative to SSP1 (the units are cubic meters per square kilometer).

a–d, Forecasted water-saving potential in 2100 relative to SSP1 under the SSP2-SSP5 scenarios, respectively. These global maps are generated through a hybrid downscaling of national-level forecasts and serve to illustrate the potential spatial distribution of water stress, rather than providing locally validated predictions. Basemaps from Natural Earth.

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Supplementary Results, Supplementary Figures 1-24, and Supplementary Tables 1-16.

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Sheng, J., Cheng, Q. & Yang, H. Global water security threatened by rising inequality.
Nat. Geosci. (2026). https://doi.org/10.1038/s41561-025-01905-y

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