Global Sustainability Performance and Regional Disparities: A Machine Learning Approach Based on the 2025 SDG Index

Sadullah Çelik, Ömer Faruk Öztürk (), Ulas Akkucuk and Mahmut Ünsal Şaşmaz
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Sadullah Çelik: Department of Mathematics, Columbian College of Arts & Sciences, The George Washington University, Washington, DC 20052, USA
Ömer Faruk Öztürk: Department of Public Finance, Faculty of Economics and Administrative Sciences, Uşak University, Uşak 64000, Turkey
Ulas Akkucuk: Department of Management, Faculty of Economics and Administrative Sciences, Bogaziçi University, Istanbul 34342, Turkey
Mahmut Ünsal Şaşmaz: Department of Public Finance, Faculty of Economics and Administrative Sciences, Uşak University, Uşak 64000, Turkey

Sustainability, 2025, vol. 17, issue 16, 1-28

Abstract: Sustainability performance varies significantly across countries, yet global assessments overlook the underlying structural trends. This study bridges this gap using machine learning to uncover meaningful clustering in global sustainability outcomes based on the 2025 Sustainable Development Goals (SDG) Index. We applied K-Means clustering to group 166 countries into five standardized indicators: SDG score, spillover effects, regional score, population size, and recent progress. The five-cluster solution was confirmed by the Elbow and Silhouette procedures, with ANOVA and MANOVA tests subsequently indicating statistically significant cluster differences. For the validation and interpretation of the results, six supervised learning algorithms were employed. Random Forest, SVM, and ANN performed best in classification accuracy (97.7%) with perfect ROC-AUC scores (AUC = 1.0). Feature importance analysis showed that SDG and regional scores were most predictive of cluster membership, while population size was the least. This supervised–unsupervised hybrid approach offers a reproducible blueprint for cross-country benchmarking of sustainability. It also offers actionable insights for tailoring policy to groups of countries, whether high-income OECD nations, emerging markets, or resource-scarce countries. Our findings demonstrate that machine learning is a useful tool for revealing structural disparities in sustainability and informing cluster-specific policy interventions toward the 2030 Agenda.

Keywords: sustainable development goals; regional disparities; machine learning; K-Means clustering; sustainability performance; Random Forest; ROC analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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