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人工智能驱动下中国电力行业低碳绩效评价——以上海电力股份有限公司为例

Low carbon performance evaluation of China's power industry driven by artificial intelligence-Take Shanghai Electric Power Co., Ltd. as an example.

作者信息

Zhang Anyuan

机构信息

Sichuan College of Architectural Technology, China.

出版信息

Heliyon. 2024 Dec 9;11(2):e41066. doi: 10.1016/j.heliyon.2024.e41066. eCollection 2025 Jan 30.

DOI:10.1016/j.heliyon.2024.e41066
PMID:39897915
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11786636/
Abstract

Currently, China is vigorously promoting the transformation of industrial enterprises' manufacturing, and low-carbon development has become an important research direction in the transformation, especially in the energy industry. For China's electric power enterprises, it is urgent to create a performance evaluation system and method suitable for the characteristics of this industry. This paper uses the DPSIR-TOPSIS model to simplify the complexity of the overall problem, and fully considers the impact of the overall environment to establish an indicator system and framework. Taking Shanghai Electric Power Co., Ltd. as an example under the background of impact of AI on low-carbon production as an example. Through the DPSIR-TOPSIS analysis driven by AI, the enterprise should have a clearer planning and understanding of the future strategy. On this basis, according to result of evaluation, combining with analysis of each yearly work done by the company, suggestions like companies need to keep informed of their state and respond,and to encourage relevant research, and form special support funds or support policies were given to improve the further low carbon work of China's power industry.

摘要

当前,中国正在大力推动工业企业制造转型,低碳发展已成为转型中的重要研究方向,尤其是在能源行业。对于中国的电力企业而言,迫切需要创建一套适合该行业特点的绩效评估体系和方法。本文运用DPSIR - TOPSIS模型简化整体问题的复杂性,并充分考虑整体环境的影响来建立指标体系和框架。以上海电力股份有限公司为例,以人工智能对低碳生产的影响为背景。通过人工智能驱动的DPSIR - TOPSIS分析,企业对未来战略应有更清晰的规划和认识。在此基础上,根据评估结果,结合公司每年工作的分析情况,给出了诸如企业需随时了解自身状况并做出应对、鼓励相关研究以及形成专项支持资金或支持政策等建议,以推动中国电力行业进一步的低碳工作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8364/11786636/94eef3f9cbe8/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8364/11786636/455a8c89e8ff/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8364/11786636/94eef3f9cbe8/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8364/11786636/455a8c89e8ff/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8364/11786636/94eef3f9cbe8/gr2.jpg

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