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Spatial Correlation Network of Energy Consumption and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration

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  • Huiping Wang

    (Western Collaborative Innovation Research Center for Energy Economy and Regional Development, Xi’an University of Finance and Economics, Xi’an 710100, China)

  • Peiling Liu

    (Western Collaborative Innovation Research Center for Energy Economy and Regional Development, Xi’an University of Finance and Economics, Xi’an 710100, China)

Abstract

Accurately understanding the correlation characteristics of energy consumption between regions is an important basis for scientifically formulating energy policies and an important entry point for realizing carbon peak and carbon neutrality goals. Based on the energy consumption data of the Yangtze River Delta urban agglomeration (YRDUA) from 2004 to 2017, the social network analysis method is applied to investigate the spatial correlation characteristics of the energy consumption of 26 cities and its influencing factors in the YRDUA. The energy consumption presents an obvious spatial correlation network structure. The network density fluctuates by approximately 0.3, and the network structure is relatively stable. Hangzhou, Suzhou and other cities are at the center of the network, playing the role of intermediaries. In the network, 10 cities, such as Shanghai and Shaoxing, have the characteristics of bidirectional spillover effects and act as “guides”, while Nanjing, Yangzhou and Chuzhou have the characteristics of brokers and act as “bridges”. The regional differences in geographical adjacency, FDI, industrial agglomeration and environmental regulation intensity are positively correlated with the network, and the impact coefficients are 0.486, 0.093, 0.072 and 0.068, respectively. Infrastructure differences are negatively correlated with the network, with an impact coefficient of −0.087.

Suggested Citation

  • Huiping Wang & Peiling Liu, 2023. "Spatial Correlation Network of Energy Consumption and Its Influencing Factors in the Yangtze River Delta Urban Agglomeration," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3650-:d:1070692
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    as
    1. Amoako, Samuel & Insaidoo, Michael, 2021. "Symmetric impact of FDI on energy consumption: Evidence from Ghana," Energy, Elsevier, vol. 223(C).
    2. Huiping Wang & Qi Ge, 2022. "Analysis of the Spatial Association Network of PM 2.5 and Its Influencing Factors in China," IJERPH, MDPI, vol. 19(19), pages 1-15, October.
    3. Shimei Wu & Xinye Zheng & Chu Wei, 2017. "Measurement of inequality using household energy consumption data in rural China," Nature Energy, Nature, vol. 2(10), pages 795-803, October.
    4. Wang, Ailun & Hu, Shuo & Lin, Boqiang, 2021. "Emission abatement cost in China with consideration of technological heterogeneity," Applied Energy, Elsevier, vol. 290(C).
    5. Jeetoo, Jamiil, 2022. "Spatial interaction effect in renewable energy consumption in sub-Saharan Africa," Renewable Energy, Elsevier, vol. 190(C), pages 148-155.
    6. Mi, Zhifu & Zhang, Yunkun & Guan, Dabo & Shan, Yuli & Liu, Zhu & Cong, Ronggang & Yuan, Xiao-Chen & Wei, Yi-Ming, 2016. "Consumption-based emission accounting for Chinese cities," Applied Energy, Elsevier, vol. 184(C), pages 1073-1081.
    7. Lv, Yulan & Chen, Wei & Cheng, Jianquan, 2019. "Modelling dynamic impacts of urbanization on disaggregated energy consumption in China: A spatial Durbin modelling and decomposition approach," Energy Policy, Elsevier, vol. 133(C).
    8. Li, Fengyun & Li, Xingmei, 2022. "An empirical analysis on regional natural gas market of China from a spatial pattern and social network perspective," Energy, Elsevier, vol. 244(PA).
    9. Wang, Na & Fu, Xiaodong & Wang, Shaobin, 2022. "Spatial-temporal variation and coupling analysis of residential energy consumption and economic growth in China," Applied Energy, Elsevier, vol. 309(C).
    10. Lv, Zhike & Liu, Wangxin & Xu, Ting, 2022. "Evaluating the impact of information and communication technology on renewable energy consumption: A spatial econometric approach," Renewable Energy, Elsevier, vol. 189(C), pages 1-12.
    11. Liu, Xiaorui & Guo, Wen & Feng, Qiang & Wang, Peng, 2022. "Spatial correlation, driving factors and dynamic spatial spillover of electricity consumption in China: A perspective on industry heterogeneity," Energy, Elsevier, vol. 257(C).
    12. Tian, Wei & Song, Jitian & Li, Zhanyong, 2014. "Spatial regression analysis of domestic energy in urban areas," Energy, Elsevier, vol. 76(C), pages 629-640.
    13. Huang, Junbing & Xiang, Shiqi & Wu, Panling & Chen, Xiang, 2022. "How to control China's energy consumption through technological progress: A spatial heterogeneous investigation," Energy, Elsevier, vol. 238(PC).
    14. Feng Wang & Mengnan Gao & Juan Liu & Wenna Fan, 2018. "The Spatial Network Structure of China’s Regional Carbon Emissions and Its Network Effect," Energies, MDPI, vol. 11(10), pages 1-14, October.
    15. Zhang, Xi & Geng, Yong & Shao, Shuai & Wilson, Jeffrey & Song, Xiaoqian & You, Wei, 2020. "China’s non-fossil energy development and its 2030 CO2 reduction targets: The role of urbanization," Applied Energy, Elsevier, vol. 261(C).
    16. Boqiang Lin, & Wang, Miao, 2019. "Possibilities of decoupling for China’s energy consumption from economic growth: A temporal-spatial analysis," Energy, Elsevier, vol. 185(C), pages 951-960.
    17. Leng, Zhihui & Sun, Han & Cheng, Jinhua & Wang, Hai & Yao, Zhen, 2021. "China's rare earth industry technological innovation structure and driving factors: A social network analysis based on patents," Resources Policy, Elsevier, vol. 73(C).
    18. Wang, Shaobin & Liu, Yonglin & Zhao, Chao & Pu, Haixia, 2019. "Residential energy consumption and its linkages with life expectancy in mainland China: A geographically weighted regression approach and energy-ladder-based perspective," Energy, Elsevier, vol. 177(C), pages 347-357.
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