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

文献类型：期刊论文

作者Ge, Quansheng7; Hao, Mengmeng1,7; Ding, Fangyu1,7; Jiang, Dong1,6,7; Scheffran, Juergen5; Helman, David3,4; Ide, Tobias2
刊名NATURE COMMUNICATIONS
出版日期2022-05-20
卷号13期号:1页码:8
DOI10.1038/s41467-022-30356-x
英文摘要Using machine learning, the authors reveal that stable background conditions explain most variation in armed conflict risk worldwide. Positive temperature deviations and precipitation extremes also increase the risk of conflict onset and incidence. 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.
WOS关键词SUB-SAHARAN AFRICA ; CIVIL-WAR ; VARIABILITY ; VIOLENCE ; ETHNICITY ; TEMPERATURE ; SHOCKS ; WATER
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040305] ; National Natural Science Foundation of China[42001238] ; German Research Foundation (DFG)
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:000799632400018
出版者NATURE PORTFOLIO
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; German Research Foundation (DFG)
源URL[http://ir.igsnrr.ac.cn/handle/311030/177751]  
专题陆地表层格局与模拟院重点实验室_外文论文
作者单位1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
2.Murdoch Univ, Ctr Biosecur & One Hlth, Harry Butler Inst, Perth, WA 6150, Australia
3.Hebrew Univ Jerusalem, Adv Sch Environm Studies, Jerusalem, Israel
4.Hebrew Univ Jerusalem, Robert H Smith Fac Agr Food & Environm, Inst Environm Sci, Dept Soil & Water Sci, IL-7610001 Rehovot, Israel
5.Univ Hamburg, Ctr Earth Syst Res & Sustainabil, Inst Geog, D-20144 Hamburg, Germany
6.Minist Land & Resources, Key Lab Carrying Capac Assessment Resource & Envi, Beijing 100101, Peoples R China
7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Ge, Quansheng,Hao, Mengmeng,Ding, Fangyu,et al. Modelling armed conflict risk under climate change with machine learning and time-series data[J]. NATURE COMMUNICATIONS,2022,13(1):8.
APA Ge, Quansheng.,Hao, Mengmeng.,Ding, Fangyu.,Jiang, Dong.,Scheffran, Juergen.,...&Ide, Tobias.(2022).Modelling armed conflict risk under climate change with machine learning and time-series data.NATURE COMMUNICATIONS,13(1),8.
MLA Ge, Quansheng,et al."Modelling armed conflict risk under climate change with machine learning and time-series data".NATURE COMMUNICATIONS 13.1(2022):8.

入库方式： OAI收割

来源：地理科学与资源研究所

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