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Research on Green Management Effect Evaluation of Power Generation Enterprises in China Based on Dynamic Hesitation and Improved Extreme Learning Machine

1
School of Economics and Management, North China Electric Power University, Beijing 102206, China
2
Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China
3
State Grid Energy Research Institute Co., LTD., Beijing 102209, China
4
School of Economics and Management, Yan’an University, Yan’an 716000, China
*
Author to whom correspondence should be addressed.
Processes 2019, 7(7), 474; https://doi.org/10.3390/pr7070474
Received: 10 June 2019 / Revised: 8 July 2019 / Accepted: 16 July 2019 / Published: 22 July 2019
(This article belongs to the Section Green Processes)
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PDF [1652 KB, uploaded 22 July 2019]
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Abstract

Carbon emissions and environmental protection issues have become the pressure from the international community during the current transitional stage of China’s energy transformation. China has set a macro carbon emission target, which will reduce carbon emissions per unit of Gross Domestic Product (GDP) by 40% in 2020 and 60–65% in 2030 than that in 2005. To achieve the emission reduction target, the industrial structure must be adjusted and upgraded. Furthermore, it must start from a high-pollution and high-emission industry. Therefore, it is of practical significance to construct a low-carbon sustainability and green operation benefits of power generation enterprises to save energy and reduce emissions. In this paper, an intuitionistic fuzzy comprehensive analytic hierarchy process based on improved dynamic hesitation degree (D-IFAHP) and an improved extreme learning machine algorithm optimized by RBF kernel function (RELM) are proposed. Firstly, we construct the evaluation indicator system of low-carbon sustainability and green operation benefits of power generation enterprises. Moreover, during the non-dimensional processing, the evaluation index system is determined. Secondly, we apply the evaluation indicator system by an empirical analysis. It is proved that the D-IFAHP evaluation model proposed in this paper has higher accuracy performance. Finally, the RELM is applied to D-IFAHP to construct a combined evaluation model named D-IFAHP-RELM evaluation model. The D-IFAHP evaluation results are used as the input of the training sets of the RELM algorithm, which simplifies the comprehensive evaluation process and can be directly applied to similar projects. View Full-Text
Keywords: low-carbon sustainability and green operation benefits; evaluation index system for power generation enterprises; intuitionistic fuzzy analytic hierarchy process; dynamic hesitation; improved extreme learning machine low-carbon sustainability and green operation benefits; evaluation index system for power generation enterprises; intuitionistic fuzzy analytic hierarchy process; dynamic hesitation; improved extreme learning machine
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Qin, Y.; Li, M.; De, G.; Huang, L.; Yang, S.; Tan, Q.; Tan, Z.; Zhou, F. Research on Green Management Effect Evaluation of Power Generation Enterprises in China Based on Dynamic Hesitation and Improved Extreme Learning Machine. Processes 2019, 7, 474.

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