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

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 deviatio

SUBMITTER: Ge Q 

PROVIDER: S-EPMC9123163 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature