A Novel Three‐Way Decision‐Based Approach for Environmental, Social, and Governance Performance Evaluation of Power Industry Under Pythagorean Fuzzy Multi‐Attribute Decision‐Making Environment

Evaluating the power industry's environmental, social, and governance (ESG) performance is critical for its low‐carbon transformation under the “dual carbon” goals, yet existing methods struggle to handle the sector's complexity, fuzzy expert judgments, and the need for performance classification. This paper proposes an integrated three‐way decision‐based approach under a Pythagorean fuzzy multi‐attribute decision‐making environment. A power industry–specific ESG evaluation index system with 20 indicators across environmental, social, and governance dimensions is first constructed. The Pythagorean fuzzy‐weighted zero‐inconsistency (PFS‐FWZIC) method then determines indicator weights, avoiding inconsistencies inherent in traditional pairwise comparisons. Subsequently, the PFS‐TOPSIS method evaluates and ranks corporate ESG performance, overcoming conventional TOPSIS's rigidity in processing fuzzy linguistic assessments. Finally, three‐way decision theory constructs decision thresholds from PFS‐TOPSIS–derived conditional probabilities, classifying enterprises into positive, boundary, and negative regions. A case study of five listed Chinese power companies reveals that environmental indicators dominate, with total carbon dioxide emissions and environmental protection investment ranking highest. Company A achieves the top position (closeness coefficient: 0.8221) in the positive region, whereas Company E falls into the negative region. The proposed framework provides reliable ESG rankings and meaningful classification, supporting investors in screening ESG targets, regulators in designing differentiated policies, and managers in allocating resources toward critical ESG dimensions.