Modelling armed conflict risk under climate change with machine learning and time-series data

Quansheng Ge, Mengmeng Hao, Fangyu Ding (), Dong Jiang (), Jürgen Scheffran, David Helman and Tobias Ide
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Quansheng Ge: Chinese Academy of Sciences
Mengmeng Hao: Chinese Academy of Sciences
Fangyu Ding: Chinese Academy of Sciences
Dong Jiang: Chinese Academy of Sciences
Jürgen Scheffran: University of Hamburg
David Helman: The Hebrew University of Jerusalem
Tobias Ide: Murdoch University, Murdoch

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract Understanding the risk of armed conflict is essential for promoting peace. Although the relationship between climate variability and armed conflict has been studied by the research community for decades with quantitative and qualitative methods at different spatial and temporal scales, causal linkages at a global scale remain poorly understood. Here we adopt a quantitative modelling framework based on machine learning to infer potential causal linkages from high-frequency time-series data and simulate the risk of armed conflict worldwide from 2000–2015. Our results reveal that the risk of armed conflict is primarily influenced by stable background contexts with complex patterns, followed by climate deviations related covariates. The inferred patterns show that positive temperature deviations or precipitation extremes are associated with increased risk of armed conflict worldwide. Our findings indicate that a better understanding of climate-conflict linkages at the global scale enhances the spatiotemporal modelling capacity for the risk of armed conflict.

Date: 2022
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DOI: 10.1038/s41467-022-30356-x

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