A Hybrid Fuzzy AHP–Machine Learning Framework for ESG-Based Sustainability Maturity Assessment and Decision Support Across Industrial Sectors

The growing emphasis on environmental, social, and governance (ESG) performance has increased the need for structured decision-support approaches that can assess sustainability maturity while accounting for both operational efficiency and broader sustainability priorities. However, existing sustainability maturity models are often static, sector-specific, or limited in their ability to integrate expert knowledge with data-driven analytical support. In this study, sustainability maturity refers to the extent to which ESG principles and practices are systematically embedded in organizational processes and decision-making, rather than merely reflecting current ESG performance or an aggregate ESG score. This study proposes an integrated sustainability maturity assessment framework that combines lean–green sustainability principles, ESG criteria, Fuzzy Analytic Hierarchy Process (Fuzzy AHP), and exploratory Random Forest analysis, linking expert-based criterion weighting with maturity assessment and feature-level interpretation within a common decision-support architecture. The framework evaluates sustainability maturity across ten industrial sectors using 33 ESG-oriented sub-criteria structured under environmental, social, and governance dimensions, derived from the sustainability literature and relevant standards and frameworks and refined through expert consultation. Twenty-four experienced professionals contributed to the assessment across the ten sectors, which were selected to reflect diverse sustainability practices and sector-specific conditions. Fuzzy AHP is employed to derive the relative importance of the criteria from expert judgments, while the resulting weighted assessment structure is used to determine sector-level sustainability maturity. Random Forest analysis is subsequently applied to the same weighted ESG criteria, using the resulting maturity classifications as target classes, to explore maturity-related patterns and identify influential criteria based on Gini impurity-based feature importance. The results show clear cross-sector variation: Information Technology (0.87) and Energy (0.84) achieved Level 5 maturity, whereas Food (0.55), Textile (0.48), and Logistics (0.46) were classified at Level 3. Regulatory Compliance Initiatives (0.109) and Certification Continuity (0.098) showed the highest feature importance. By integrating expert-based weighting, ESG maturity assessment, cross-sector benchmarking (i.e., comparison of sectors using the same weighted ESG assessment structure), and exploratory machine-learning-based feature prioritization within a single decision-support architecture, the proposed framework enables organizations to identify maturity gaps and prioritize sustainability improvement areas. Rather than serving as a deterministic predictive model, the framework is intended as an adaptable analytical and managerial decision-support tool for sustainability assessment and strategic planning across diverse industrial contexts.