Inequalities matter when prioritizing sustainable goals: Insights from applying a novel multi-criteria-clustering modeling to SDG Index Scores
针对SDG指数使用算术平均可能掩盖目标间权衡的缺陷，本文提出KRP2算法（结合K-means聚类与PROMETHEE II排序），基于2023年数据对国家进行分组，揭示了高绩效与低绩效集群间的显著差异，为差异化政策制定提供依据。
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.