A Bayesian framework for studying climate anomalies and social conflicts
A Bayesian framework for studying climate anomalies and social conflicts
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DOI：
10.1002/env.2778
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发表时间：
2022-11-21
期刊：
影响因子：
1.7
通讯作者：
Chatterjee，Snigdhansu
中科院分区：
文献类型：
--
作者：
Mukherjee，Ujjal Kumar;Bagozzi，Benjamin E.;Chatterjee，Snigdhansu
气候变化将对人类社会，特别是对政治和其他冲突产生深远影响。然而，关于理解气候变化和社会冲突之间关系的现有文献经常因使用数据而受到批评，这些数据受到抽样和其他偏差的影响，这些偏差往往是由于过于狭隘地关注一小块空间或一小组事件造成的。这些研究同样因为没有使用适当的统计工具而受到批评，这些工具(i$$i$$)处理时空相关性，(ii$$ii$$)获得概率不确定性量化，以及(iii$$III$$)导致一致的统计推论。在本文中，我们提出了一个贝叶斯框架来应对这些挑战。我们发现，在全球范围内，聚合的物质冲突和语言冲突的温度异常之间存在着强烈而实质性的联系。更深入地看，我们还发现大量证据表明，正温度异常与社会冲突有关，主要是通过政府与平民和政府与叛军之间的物质冲突，如平民抗议、叛军袭击政府资源或国家镇压行为。我们发现，与气候异常有关的大多数冲突是由反叛行为者引发的，其他人则对这种冲突行为作出反应。我们的结果显示了温度偏差和社会冲突之间的微妙关系，这在以前的研究中没有注意到。在方法论上，提出的贝叶斯框架可以帮助社会科学家探索涉及大规模空间和时间依赖的相似领域。我们的代码和合成数据集已经公开可用。
Climate change stands to have a profound impact on human society, and on political and other conflicts in particular. However, the existing literature on understanding the relation between climate change and societal conflicts has often been criticized for using data that suffer from sampling and other biases, often resulting from being too narrowly focused on a small region of space or a small set of events. These studies have likewise been critiqued for not using suitable statistical tools that (i$$ i $$) address spatio‐temporal dependencies, (ii$$ ii $$) obtain probabilistic uncertainty quantification, and (iii$$ iii $$) lead to consistent statistical inferences. In this article, we propose a Bayesian framework to address these challenges. We find that there is a strong and substantial association between temperature anomalies on aggregated material conflicts and verbal conflicts globally. Going deeper, we also find significant evidence to suggest that positive temperature anomalies are associated with social conflict primarily through government‐civilian and government‐rebel material conflicts, as in civilian protests, rebel attacks against government resources, or acts of state repression. We find that majority of the conflicts associated with climate anomalies are triggered by rebel actors, and others react to such acts of conflict. Our results exhibit considerably nuanced relationships between temperature deviations and social conflicts that have not been noticed in previous studies. Methodologically, the proposed Bayesian framework can help social scientists explore similar domains involving large‐scale spatial and temporal dependencies. Our code and a synthetic dataset has been made publicly available.