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

Author

Listed:
  • Quansheng Ge

    (Chinese Academy of Sciences)

  • Mengmeng Hao

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Fangyu Ding

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Dong Jiang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land & Resources)

  • Jürgen Scheffran

    (University of Hamburg)

  • David Helman

    (The Hebrew University of Jerusalem
    The Hebrew University of Jerusalem)

  • Tobias Ide

    (Murdoch University, Murdoch)

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.

Suggested Citation

  • Quansheng Ge & Mengmeng Hao & Fangyu Ding & Dong Jiang & Jürgen Scheffran & David Helman & Tobias Ide, 2022. "Modelling armed conflict risk under climate change with machine learning and time-series data," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30356-x
    DOI: 10.1038/s41467-022-30356-x
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    References listed on IDEAS

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    1. Jun Zhuo & Mengmeng Hao & Fangyu Ding & Jiping Dong & Dong Jiang & Shuai Chen, 2024. "The spatiotemporal patterns and driving factors of cybercrime in the UK during the COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    2. Zhenjie Liu & Jun Li & Haonan Chen & Lizhe Wang & Jun Yang & Antonio Plaza, 2024. "Prediction of changes in war-induced population and CO2 emissions in Ukraine using social media," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
    3. Racek, Daniel & Thurner, Paul & Kauermann, Goeran, 2024. "Integrating Spatio-temporal Diffusion into Statistical Forecasting Models of Armed Conflict via Non-parametric Smoothing," OSF Preprints q59dr, Center for Open Science.
    4. Mengmeng Hao & Fangyu Ding & Xiaolan Xie & Jingying Fu & Yushu Qian & Tobias Ide & Jean-François Maystadt & Shuai Chen & Quansheng Ge & Dong Jiang, 2022. "Varying climatic-social-geographical patterns shape the conflict risk at regional and global scales," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-8, December.

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