Global development patterns: A clustering analysis of economic, social and environmental indicators

Abstract

Understanding how countries differ in their environmental and social characteristics is critical for designing effective and targeted sustainability policies. This study aims to explore heterogeneity among countries using a data-driven approach that considers multiple indicators relevant to environmental, social and economic development. We apply K-means clustering and principal component analysis (PCA) to a dataset covering 206 countries and including variables such as CO₂ emissions, access to electricity, life expectancy, education expenditure, inflation, GDP, unemployment and democracy levels. The analysis identifies eight distinct clusters of countries that broadly reflect global disparities in socio-economic and environmental conditions, with clear regional and income-based patterns. Notably, high-income democracies form distinct groups with strong social indicators, while clusters of Sub-Saharan African countries tend to exhibit low access to electricity and education spending. These findings underscore the need for differentiated policy strategies that align with each cluster’s characteristics. Our results contribute to the growing literature on multidimensional sustainability assessment and offer a useful typology for international environmental cooperation and policy targeting.