基于熵权法和ANFIS的电力企业碳资产管理综合评价模型研究
潘军 1杨利鸣 1刘晓鸥 1杨皓然 2陆韬3
作者信息
- 1. 中国电力工程顾问集团有限公司,北京 100013
- 2. 中国科学院上海高等研究院低碳转化科学与工程中心,上海 200120
- 3. 上海易碳数字科技有限公司,上海 200441
- 折叠
摘要
我国电力行业能源结构单一,严重依赖煤炭,每年碳排放量巨大.低碳时代的到来对电力企业的传统经营模式提出了挑战,平衡生产与降碳成为关键问题.随着国内碳交易市场的完善和绿色金融的活跃,电力企业开始重视碳资产管理,并积极探索相关策略.有效的碳资产管理成为减少能源资源投入和碳排放的必要条件.本文基于熵权法和自适应模糊神经推理系统(ANFIS)提出了电力企业碳资产管理多维度综合评价模型,以国内发电上市公司为研究对象,通过训练样本自动生成模糊规则,验证了模型的可行性和有效性,并探讨了影响企业碳资产管理的内、外部因素,旨在为企业发掘新的利润增长点和碳交易与碳战略的实施,提供指导和调整依据.
Abstract
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.
关键词
碳交易/碳资产/电力企业/熵权法/自适应模糊神经推理系统Key words
Carbon Trading/Carbon Assets/Power Enterprises/Entropy Weight Method/Adaptive Neuro-fuzzy Inference System引用本文复制引用
出版年
2024
NETL
NSTL
万方数据