The Environmental, Social and Governance (ESG) sustainable performance evaluation framework dissects the multifaceted dimensions of sustainability, presenting a complex decision-making challenge that our study addresses by elucidating the ambiguity inherent in ESG assessments. Prior assessments have been criticized for overlooking the intricacies of real-world scenarios and neglecting the interplay between ESG metrics, a gap our study addresses by introducing a holistic evaluation approach. This study aims to address the aforementioned issues.
This study introduces a rigorous ESG sustainability evaluation model that employs a synergistic integration of Fermatean Fuzzy Linguistic Term sets (FFLTs) and the Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) methods, ensuring a comprehensive and nuanced assessment of ESG criteria.
FFLTs process fuzzy expert assessment data, followed by the FFLTs-Stepwise Weight Assessment Ratio Analysis (SWARA) method for criteria weight calculation and group evaluation matrix formation. Subsequently, the FFLT–CRADIS method prioritizes alternatives. Demonstrated through examples from new energy enterprises, sensitivity analysis and comprehensive comparison, this model flexibly handles complex expert information, considers metric interactions and reliably ranks corporate ESG sustainable performance. The results indicate that Geely (with a score of 0.350) is the highest-priority alternative, whereas Xiaopeng (with a score of 0.190) ranks the lowest. This conclusion is supported by the sensitivity analysis, which demonstrates the stability of the proposed framework under various criteria weight scenarios. Additionally, the comparative analysis with other multi-criteria decision-making methods (e.g. FFLT–TOPSIS and FFLT–CoCoSo) confirms the robustness of the FFLT–CRADIS method in evaluating ESG sustainable performance.
This study pioneers the integration of FFLTs with the CRADIS method, offering a novel ESG assessment framework that enhances the predictive accuracy and decision-making robustness in sustainable investments, thereby bridging a significant gap in the current ESG evaluation practices.
