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 ((Formula presented.)) address spatio-temporal dependencies, ((Formula presented.)) obtain probabilistic uncertainty quantification, and ((Formula presented.)) 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.
Bibliographical noteFunding Information:
This project is partially supported by US National Science Foundation Grants 1737865 (Benjamin E. Bagozzi), 1737915 (Ujjal Kumar Mukherjee), and 1737918, 1939916, 1939956 (Snigdhansu Chatterjee). The methodological details and entire data analysis significantly improved with inputs from the reviewers and editors, and we are thankful to them.
© 2022 The Authors. Environmetrics published by John Wiley & Sons Ltd.
- Bayesian model
- global trade
- social conflict
- temperature anomalies