Climate change expected to increase conflict risks over the next decades across sub-Saharan Africa
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PUBLIC SUMMARY
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The climate-conflict linkages derived from machine learning models exhibit threshold effects.
Climate change is expected to increase conflict risks over the next decades across sub-Saharan Africa.
The change of projected spatiotemporal distribution of conflict risk will be heterogeneous.
By the 2050s, 0.5-1.7 billion people may live at high risk zones of conflict across sub-Saharan Africa.
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ABSTRACT
While climate change is increasingly recognized as a driver of conflict risks, most research focuses on past correlations, hence limiting our ability to project future climate-related security risks. We use machine learning techniques to predict the spatiotemporal dynamics of conflict risks and estimate the human population that would be exposed to these risks in the 2030s, 2040s, and 2050s. Our results demonstrate that climate change is associated with increasing risks of conflict over the next decades and that the change of projected spatial and temporal distribution of conflict risk will be heterogeneous, depending on conflict types and climate scenarios. We estimate that 0.5-1.7 billion people may live at high risk zones of conflict across sub-Saharan Africa by the 2050s, which is an increase of at least 391 million compared to today. Detecting such risks early provides sufficient time to plan ahead and implement mitigation strategies. -
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Jiang D., Zhuo J., Chen S., et al. (2025). Climate change expected to increase conflict risks over the next decades across sub-Saharan Africa. The Innovation Geoscience 3:100139. https://doi.org/10.59717/j.xinn-geo.2025.100139
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Jiang D., Zhuo J., Chen S., et al. (2025). Climate change expected to increase conflict risks over the next decades across sub-Saharan Africa. The Innovation Geoscience 3:100139. https://doi.org/10.59717/j.xinn-geo.2025.100139
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Figure 1.
Analytic process overview. Successive steps starting from dataset preparation and leading to the model establishment, each processing step is described in greater detail in the Methods.
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Figure 2.
Precision comparison of modeling strategies A (the pairing of background contexts with climate deviations related covariates) and B (background contexts without climate factors) for the risk of armed conflict during the validation process
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Figure 3.
Marginal effect curves of the standardized temperature index on conflict risks: (A) armed conflict, (B) violent conflict, and (C) demonstrations
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Figure 4.
Marginal effect curves of the standardized precipitation index on conflict risks: (A) armed conflict, (B) violent conflict, and (C) demonstrations
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Figure 5.
Spatio-temporal pattern of the predicted armed conflict risk over time under the SSP5-8.5 scenario
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Figure 6.
The changes in projected armed conflict risk under the SSP5-8.5 scenario
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Figure 7.
Projected number of people exposed to the high risk zones of three types of conflict (A. Armed conflict B. Violent conflict, and C. Demonstrations) in sub-Saharan Africa in the 2030s, 2040s, and 2050s under different SSP scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5)
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