A hybrid MCDA framework for sustainability assessment: Integrating fuzzy normalization and weight reidentification based on external rankings

This study proposes a novel hybrid Multi-Criteria Decision Analysis (MCDA) framework integrating Stochastic Fuzzy Normalization (STFN) with Stochastic Identification of Weights (SITW) to enhance the modeling and replication of sustainability rankings. The methodology is applied to a dataset of 21 European countries, utilizing six key indicators related to material consumption, waste generation, circular economy, environmental value added, and energy productivity. The proposed STFN–SITW–TOPSIS model successfully reconstructs the structure of the Sustainable Development Report 2022 ranking, achieving high fidelity through reidentification of both fuzzy normalizations and criteria weights. Comparative analyses demonstrate that SITW-based weights outperform conventional objective weighting methods such as Criteria Importance Through Intercriteria Correlation (CRITIC), Entropy, and Equal weighting in terms of rank alignment and structural consistency. A comprehensive sensitivity analysis confirms the model’s robustness and identifies the most influential criteria. The findings highlight the importance of integrating external reference information into MCDA frameworks, offering a data-driven, flexible, and explainable approach to sustainability assessment and broader decision support systems.