Assessing the Sustainable Synergy Between Digitalization and Decarbonization in the Coal Power Industry: A Fuzzy DEMATEL-MultiMOORA-Borda Framework

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🔬研究者:Provides a novel multi-criteria decision framework integrating fuzzy logic and Borda count for energy transition scenario evaluation.

🏢実務担当者:Offers a practical tool for utilities and energy firms to assess digitalization-decarbonization synergy and prioritize investments.

🏛政策担当者:Highlights key drivers (carbon pricing, digital hedging) and critical factors (financial return, green marginal economy) for effective transition policy.

In the context of the “Dual Carbon” goals, achieving synergistic development between digitalization and green transformation in the coal power industry is essential for ensuring a just and sustainable energy transition. The core scientific problem addressed is the lack of a robust quantitative tool to evaluate the comprehensive performance of diverse transition scenarios in a complex environment characterized by multi-objective trade-offs and high uncertainty. This study establishes a sustainability-oriented four-dimensional performance evaluation system encompassing 22 indicators, covering Synergistic Economic Performance, Green-Digital Strategy, Synergistic Governance, and Technology Performance. Based on this framework, a Fuzzy DEMATEL–MultiMOORA–Borda integrated decision model is proposed to evaluate seven transition scenarios. The computational framework utilizes the Interval Type-2 Fuzzy DEMATEL (IT2FS-DEMATEL) method for robust causal analysis and weight determination, addressing the inherent subjectivity and vagueness in expert judgments. The model integrates MultiMOORA with Borda Count aggregation for enhanced ranking stability. All model calculations were implemented using Matlab R2022a. Results reveal that Carbon Price and Digital Hedging Capability (C13) and Digital-Driven Operational Efficiency (C43) are the primary drivers of synergistic performance. Among the scenarios, P3 (Digital Twin Empowerment and New Energy Co-integration) achieves the best overall performance (score: 0.5641), representing the most viable pathway for balancing industrial efficiency and environmental stewardship. Robustness tests demonstrate that the proposed model significantly outperforms conventional approaches such as Fuzzy AHP (Analytic Hierarchy Process) and TOPSIS under weight perturbations. Sensitivity analysis further identifies Financial Return (C44) and Green Transformation Marginal Economy (C11) as critical factors for long-term policy effectiveness. This study provides a data-driven framework and a robust decision-support tool for advancing the coal power industry’s low-carbon, intelligent, and resilient transition in alignment with global sustainability targets.

• semanticscholar https://doi.org/10.3390/su18031160first seen 2026-05-15 20:39:07 · last seen 2026-06-24 05:24:34