A Hybrid MCDM and Clustering Framework for Evaluating Sustainable Competitiveness in OECD Countries

Sustainable competitiveness has increasingly become an important policy objective for OECD countries, as economic performance is expected to be balanced with environmental protection, social well-being, and effective governance structures. The aim of this study is to evaluate and compare the sustainable competitiveness performance of OECD countries from a holistic perspective. In the analysis, six criteria reflecting the main dimensions of global sustainable competitiveness were considered. Criterion weights were calculated using the CRITIC (Criteria Importance Through Intercriteria Correlation) method, an objective weighting technique that does not rely on subjective judgments. These weights were then integrated with the CoCoSo (Combined Compromise Solution) method to rank the sustainable competitiveness performance of countries. In the final stage, a clustering analysis was applied to group OECD countries exhibiting similar sustainability characteristics. The findings indicate that natural capital emerges as the most influential dimension within the evaluation framework. According to the ranking results, Finland, Sweden, Lithuania, Denmark, and Estonia are positioned among the countries with the highest sustainable competitiveness performance. The results reveal noticeable differences across OECD countries, demonstrating that environmental, social, economic, and governance-related dimensions affect country performance in distinct ways. A direct comparison with the 2025 Global Sustainable Competitiveness Index shows a strong but not perfect association between the two rankings (Spearman’s ρ = 0.977), indicating structural consistency alongside meaningful mid-ranking shifts. Furthermore, the clustering results enable the identification of country groups sharing relatively similar sustainability profiles. Overall, the study contributes methodologically to the sustainable competitiveness literature by integrating objective weighting, multi-criteria decision-making, and clustering analysis within a unified analytical framework, while also offering insights for comparative policy evaluation.