Exploring the worldwide impact of COVID-19 on conflict risk under climate change

Xiaolan Xie, Mengmeng Hao, Fangyu Ding, Tobias Ide, David Helman, Jürgen Scheffran, Qian Wang, Yushu Qian, Shuai Chen, Jiajie Wu, Tian Ma, Quansheng Ge, Dong Jiang

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: Understand whether and how the COVID-19 pandemic affects the risk of different types of conflict worldwide in the context of climate change. Methodology: Based on the database of armed conflict, COVID-19, detailed climate, and non-climate data covering the period 2020–2021, we applied Structural Equation Modeling specifically to reorganize the links between climate, COVID-19, and conflict risk. Moreover, we used the Boosted Regression Tree method to simulate conflict risk under the influence of multiple factors. Findings: The transmission risk of COVID-19 seems to decrease as the temperature rises. Additionally, COVID-19 has a substantial worldwide impact on conflict risk, albeit regional and conflict risk variations exist. Moreover, when testing a one-month lagged effect, we find consistency across regions, indicating a positive influence of COVID-19 on demonstrations (protests and riots) and a negative relationship with non-state and violent conflict risk. Conclusion: COVID-19 has a complex effect on conflict risk worldwide under climate change. Implications: Laying the theoretical foundation of how COVID-19 affects conflict risk and providing some inspiration for the implementation of relevant policies.

Original languageEnglish
Article numbere17182
JournalHeliyon
Volume9
Issue number6
DOIs
StatePublished - Jun 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Keywords

  • Boosted regression trees
  • COVID-19
  • Causal link
  • Conflict risk
  • Structural equation model

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