Structural Correlates of Global Sustainable Development Goals Achievement: A Cross-National Typological Analysis

Achieving the Sustainable Development Goals (SDGs) by 2030 remains highly uneven across countries, while the structural factors associated with this heterogeneity are still insufficiently understood. This study aims to classify 154 countries according to their full 17-dimensional SDG achievement profiles and to identify the structural indicators statistically associated with the observed typological differences. A two-stage analytical approach was applied. First, k-means cluster analysis based on the scores of all 17 SDGs was used to identify homogeneous groups of countries. Second, canonical discriminant analysis was performed for 64 countries with complete data for 17 indicators selected from international sources according to the “one indicator–one goal” principle. The cluster analysis identified three typologically homogeneous groups of countries that broadly correspond to differences in development level but are not reducible to them. The discriminant model achieved apparent classification accuracy of 90.63% (p < 0.0001), while the first canonical function explained 90.3% of the between-group variation. LOO cross-validation yielded an accuracy of 71.43%, confirming that the model retains meaningful discriminatory power beyond the estimation sample, while the difference between apparent and cross-validated accuracy reflects the constraints of a small sample relative to the number of predictors. The strongest differentiating indicators were the proportion of the urban population living in slums, the Global Peace Index, access to sanitation, and poverty. Overall, the results show that SDG achievement profiles constitute an independent analytical characteristic of countries and that typological differences are primarily associated with basic human development and institutional stability.