Spatially Correlated Network Structure and Influencing Factors of Carbon Emission Efficiency in the Power Industry: Evidence from China
As the largest carbon-emitting industry in China, the power industry has huge potential for carbon emission reductions. It is vital to study the spatial correlation of carbon emission efficiency in the power industry (CEEP) from a system perspective to understand the interaction mechanisms of CEEP i...
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MDPI AG
2025-01-01
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| Series: | Systems |
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| author | Baojun Sun Taiwen Feng Mingjing Du Yuqing Liang Tianbao Feng |
| author_facet | Baojun Sun Taiwen Feng Mingjing Du Yuqing Liang Tianbao Feng |
| author_sort | Baojun Sun |
| collection | DOAJ |
| description | As the largest carbon-emitting industry in China, the power industry has huge potential for carbon emission reductions. It is vital to study the spatial correlation of carbon emission efficiency in the power industry (CEEP) from a system perspective to understand the interaction mechanisms of CEEP in different provinces. This study applies the SBM-undesirable model to measure the CEEP in China, and a modified Gravity model and social network analysis (SNA) method are applied to analyze the interaction mechanism of the CEEP from a system perspective. Finally, the influencing factors of the CEEP’s spatial correlation are investigated using the quadratic allocation procedure (QAP) method. The results show that (1) the national CEEP is gradually increasing, while the CEEP gap between provinces is widening; (2) the overall network size shows an increasing trend, but the hierarchical structure is somewhat fixed; (3) the central province of a network has a high degree of consistency with the geographically central province, but the spatial spillover effect of the central node provinces on the peripheral provinces is not sufficient; and (4) differences in geographic proximity, energy intensity, and technical level of power generation significantly affect the formation of spatially correlated networks in the CEEP. |
| format | Article |
| id | doaj-art-6698475b6544466ca2066b024e759c86 |
| institution | Kabale University |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-6698475b6544466ca2066b024e759c862025-01-24T13:50:33ZengMDPI AGSystems2079-89542025-01-011313010.3390/systems13010030Spatially Correlated Network Structure and Influencing Factors of Carbon Emission Efficiency in the Power Industry: Evidence from ChinaBaojun Sun0Taiwen Feng1Mingjing Du2Yuqing Liang3Tianbao Feng4School of Computer Information and Management, Inner Mongolia University of Finance and Economics, Hohhot 010070, ChinaSchool of Economics and Management, Harbin Institute of Technology (Weihai), Weihai 264209, ChinaSchool of Computer Information and Management, Inner Mongolia University of Finance and Economics, Hohhot 010070, ChinaSchool of Computer Information and Management, Inner Mongolia University of Finance and Economics, Hohhot 010070, ChinaSchool of Computer Information and Management, Inner Mongolia University of Finance and Economics, Hohhot 010070, ChinaAs the largest carbon-emitting industry in China, the power industry has huge potential for carbon emission reductions. It is vital to study the spatial correlation of carbon emission efficiency in the power industry (CEEP) from a system perspective to understand the interaction mechanisms of CEEP in different provinces. This study applies the SBM-undesirable model to measure the CEEP in China, and a modified Gravity model and social network analysis (SNA) method are applied to analyze the interaction mechanism of the CEEP from a system perspective. Finally, the influencing factors of the CEEP’s spatial correlation are investigated using the quadratic allocation procedure (QAP) method. The results show that (1) the national CEEP is gradually increasing, while the CEEP gap between provinces is widening; (2) the overall network size shows an increasing trend, but the hierarchical structure is somewhat fixed; (3) the central province of a network has a high degree of consistency with the geographically central province, but the spatial spillover effect of the central node provinces on the peripheral provinces is not sufficient; and (4) differences in geographic proximity, energy intensity, and technical level of power generation significantly affect the formation of spatially correlated networks in the CEEP.https://www.mdpi.com/2079-8954/13/1/30electric power industrycarbon emission efficiencyspatial correlationinfluencing factorsSBM-undesirable modelsocial network analysis approach |
| spellingShingle | Baojun Sun Taiwen Feng Mingjing Du Yuqing Liang Tianbao Feng Spatially Correlated Network Structure and Influencing Factors of Carbon Emission Efficiency in the Power Industry: Evidence from China Systems electric power industry carbon emission efficiency spatial correlation influencing factors SBM-undesirable model social network analysis approach |
| title | Spatially Correlated Network Structure and Influencing Factors of Carbon Emission Efficiency in the Power Industry: Evidence from China |
| title_full | Spatially Correlated Network Structure and Influencing Factors of Carbon Emission Efficiency in the Power Industry: Evidence from China |
| title_fullStr | Spatially Correlated Network Structure and Influencing Factors of Carbon Emission Efficiency in the Power Industry: Evidence from China |
| title_full_unstemmed | Spatially Correlated Network Structure and Influencing Factors of Carbon Emission Efficiency in the Power Industry: Evidence from China |
| title_short | Spatially Correlated Network Structure and Influencing Factors of Carbon Emission Efficiency in the Power Industry: Evidence from China |
| title_sort | spatially correlated network structure and influencing factors of carbon emission efficiency in the power industry evidence from china |
| topic | electric power industry carbon emission efficiency spatial correlation influencing factors SBM-undesirable model social network analysis approach |
| url | https://www.mdpi.com/2079-8954/13/1/30 |
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