Exploring the role of armed conflict in progress toward Sustainable Development Goals: Global patterns, regional differences, and driving mechanisms

Abstract

The Sustainable Development Goals (SDGs) represent a solemn commitment by United Nations member states, but achieving them faces numerous challenges, particularly armed conflicts. Here, we analyzed the impact of armed conflict on SDG progress and its driving mechanism through causal inference methods and machine learning technique. The results show that between 2000 and 2021, armed conflicts slowed overall SDG progress by 3.43 %, equivalent to a setback of 18 years. The Middle East was the most affected region, with a 6.10 % slowdown in progress. The impact of different types of conflict varies across specific goals: interstate conflicts primarily affect SDG 5 (Gender Equality) and SDG 7 (Affordable and Clean Energy), while intrastate conflicts have a larger impact on SDG 4 (Quality Education) and SDG 9 (Industry, Innovation and Infrastructure). Additionally, SDG 15 (Life on Land) is severely affected by both types of conflict, with long-term consequences. As armed conflicts increase, the development progress would regress rapidly in a non-linear manner. To achieve the SDGs by 2030, it is crucial not only to prevent conflicts but also to proactively address and mitigate their impacts on development.

Keywords

Geopolitical conflict / SDGs / Causal inference / Middle East / South Asia / Sub-Saharan Africa

CRediT authorship contribution statement

Di Wang: Writing – original draft, Methodology, Funding acquisition, Formal analysis, Conceptualization. Zhenci Xu: Writing – review & editing, Validation. Unai Pascual: Writing – review & editing, Validation. Lei Liu: Writing – review & editing. Waqar Ahmad: Writing – review & editing. Dong Jiang: Writing – review & editing, Writing – original draft, Investigation, Funding acquisition, Conceptualization.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research is supported and funded by the Special Research Assistant Program of Chinese Academy of Sciences (Grant No. E3S30015Y5). UP acknowledges Maria de Maeztu excellence accreditation 2023–2026 (Ref. CEX2021-001201-M), funded by MCIN/AEI/10.13039/501100011033.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2025.100355.

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