China's power industry has a single energy structure that heavily relies on coal,resulting in massive carbon emissions annually.The advent of the low-carbon era challenges the traditional operating models of power companies,making the balance between production and carbon reduction a critical issue.As the domestic carbon trading market improves and green finance becomes more active,power companies are increasingly focusing on carbon asset management and actively exploring related strategies.Effective carbon asset management has become a necessary condition for reducing carbon resource input and emissions.In this study,a comprehensive evaluation model for corporate carbon asset management was proposed based on the entropy weight method and Adaptive Neuro-Fuzzy Inference System(ANFIS).Publicly traded power generation companies in China were used as research subjects.Fuzzy rules were automatically generated from training samples,and the feasibility and effectiveness of the model were validated.Internal and external factors affecting corporate carbon asset management are explored,helping companies discover new profit growth points and providing guidance for carbon trading and strategy implementation.
Carbon TradingCarbon AssetsPower EnterprisesEntropy Weight MethodAdaptive Neuro-fuzzy Inference System
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