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.
摘要：
了解武装冲突的风险对于促进和平至关重要。尽管研究界已经在不同的时空尺度上采用定量和定性方法研究了气候多变性与武装冲突之间的关系数十年,全球范围内的因果关系仍然知之甚少。在这里，我们采用基于机器学习的定量建模框架，从高频时间序列数据中推断潜在的因果关系，并模拟2000-2015年全球武装冲突的风险。我们的研究结果表明，武装冲突的风险主要受到稳定的背景环境和复杂模式的影响。其次是气候偏差相关的协变量。推断的模式表明，正温度偏差或极端降水与全球武装冲突的风险增加有关。我们的研究结果表明，在全球范围内更好地了解气候与冲突的联系可以增强对武装冲突风险的时空建模能力。
