Inequalities matter when prioritizing sustainable goals: Insights from applying a novel multi-criteria-clustering modeling to SDG Index Scores

Abstract The SDG Index aggregates country performance across 17 Sustainable Development Goals using arithmetic means, which allows high scores in some goals to compensate for poor performance in others, if weighted sum is applied as the aggregation function. To address this limitation, we develop and apply the KRP2 algorithm that combines K-means clustering with PROMETHEE II ranking to group countries based on their complete SDG performance profiles rather than overall scores alone. Applying this methodology to 2023 data reveals substantial disparities between high-performing and low-performing clusters. Countries in top-ranked categories show strong socioeconomic performance but score lowest on SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action). Conversely, bottom-ranked categories struggle with poverty and basic needs (SDGs 1, 7, and 9) yet exhibit the best performance on SDGs 12 and 13, reflecting their minimal historical contribution to environmental degradation. These trade-offs remain hidden when using compensatory aggregation methods. We extend the analysis with a nine-cluster sensitivity test that further isolates extreme cases, confirming the robustness of these patterns. Our framework provides decision-support inputs for policy deliberation, suggesting that differentiated approaches may better serve sustainable development objectives than uniform global policies.