A Machine Learning Perspective on the Climatic and Socioeconomic Determinants of Mental Health in Southeast Asia

Teerachai Amnuaylojaroen () and Nichapa Parasin
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Teerachai Amnuaylojaroen: School of Energy and Environment, University of Phayao, Phayao 56000, Thailand
Nichapa Parasin: School of Allied Health Science, University of Phayao, Phayao 56000, Thailand

World, 2025, vol. 6, issue 2, 1-27

Abstract: The growing burden of mental health disorders necessitates a comprehensive understanding of their environmental and socioeconomic determinants. This study employs machine learning to analyze the relationship between mental health mortality and key socioeconomic and climatic factors across Southeast Asia. Using a Random Forest model (R 2 = 0.95), we identify the population size and the Physical Quality of Life Index (PQLI) as the strongest predictors of mental health mortality, while climate indices—the proportion of warm nights (TN90p) and hot days (TX90p)—exhibit weaker direct effects (importance < 0.1), but significant indirect effects through socioeconomic pathways. The regional disparities highlight Indonesia as the most climate-sensitive country, whereas the Philippines shows weaker climate–mortality correlations, suggesting that its socioeconomic resilience and healthcare infrastructure can mitigate climate impacts. These findings underscore the need for integrated climate–mental health strategies, particularly for vulnerable regions experiencing extreme temperatures and socioeconomic stressors.

Keywords: mental health mortality; climate change; machine learning predictive models; socioeconomic factors; Southeast Asia (search for similar items in EconPapers)
JEL-codes: G15 G17 G18 L21 L22 L25 L26 Q42 Q43 Q47 Q48 R51 R52 R58 (search for similar items in EconPapers)
Date: 2025
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