Carbon emission reduction contribution analysis of electricity enterprises in urban green development: A quantum spherical fuzzy sets-based decision framework

被引:11
|
作者
Du, Puliang [1 ]
Zhou, Bo [2 ,3 ]
Yang, Miaoheng [1 ]
机构
[1] Shanghai Univ Elect Power, Sch Econ & Management, Shanghai 201306, Peoples R China
[2] Nanjing Univ Finance & Econ, Sch Finance, Nanjing 210023, Peoples R China
[3] Nanjing Univ Finance & Econ, Sch Finance, Wenyuan Rd 3, Nanjing 210023, Peoples R China
基金
中国国家自然科学基金;
关键词
Carbon emission reduction contribution; Urban green development; Electricity enterprises; Quantum spherical fuzzy sets; Simplified group best-worst method (SGBWM);
D O I
10.1016/j.techfore.2023.123181
中图分类号
F [经济];
学科分类号
02 ;
摘要
As managers of urban power grids (UPGs), electricity enterprises are responsible for ensuring energy security and stability during the urban green transformation process. They are also a key driving force in promoting green power transmission and integration. Therefore, exploring the green attributes of UPGs and accurately enhancing their green support capabilities are of significant importance. This study addresses the perceived contributions of electricity enterprises to carbon emission reduction and proposes a decision framework within a quantumspherical fuzzy (QSF) environment. First, from the perspectives of economy, technology, policy, and management, a carbon emission reduction contribution evaluation system for electric enterprises was constructed. In the QSF environment, this study identifies the importance of various attributes in the evaluation system. Subsequently, an electric enterprise carbon emission reduction perception model was developed based on the preference ranking organization method for enrichment evaluations and prospect theory (PROMETHEE-PT). An empirical validation of the proposed model was conducted in Hefei, China, demonstrating the robustness and effectiveness of the evaluation framework. The study's results were verified by comparing them with those of other methods.
引用
收藏
页数:16
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