IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p3650-d1070692.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/3650/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/4/3650/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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).
    3. 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.
    4. 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).
    5. Amoako, Samuel & Insaidoo, Michael, 2021. "Symmetric impact of FDI on energy consumption: Evidence from Ghana," Energy, Elsevier, vol. 223(C).
    6. 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).
    7. 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.
    8. 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.
    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. 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.
    12. 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.
    13. 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).
    14. Tian, Wei & Song, Jitian & Li, Zhanyong, 2014. "Spatial regression analysis of domestic energy in urban areas," Energy, Elsevier, vol. 76(C), pages 629-640.
    15. 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).
    16. Wang, Ailun & Hu, Shuo & Lin, Boqiang, 2021. "Emission abatement cost in China with consideration of technological heterogeneity," Applied Energy, Elsevier, vol. 290(C).
    17. Jeetoo, Jamiil, 2022. "Spatial interaction effect in renewable energy consumption in sub-Saharan Africa," Renewable Energy, Elsevier, vol. 190(C), pages 148-155.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Huiping Wang & Peiling Liu, 2024. "Characteristics of the urban environmental regulation network and its impact on carbon emission efficiency in China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-16, December.
    2. Wang, Huiping & Liu, Peiling, 2023. "Spatial correlation network of renewable energy consumption and its influencing factors: Evidence from 31 Chinese provinces," Renewable Energy, Elsevier, vol. 217(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Huiping & Liu, Peiling, 2023. "Spatial correlation network of renewable energy consumption and its influencing factors: Evidence from 31 Chinese provinces," Renewable Energy, Elsevier, vol. 217(C).
    2. Wang, Shaobin & Liu, Haimeng & Pu, Haixia & Yang, Hao, 2020. "Spatial disparity and hierarchical cluster analysis of final energy consumption in China," Energy, Elsevier, vol. 197(C).
    3. Wang, Na & Fu, Xiaodong & Wang, Shaobin & Yang, Hao & Li, Zhen, 2022. "Convergence characteristics and distribution patterns of residential electricity consumption in China: An urban-rural gap perspective," Energy, Elsevier, vol. 254(PB).
    4. Limei Ma & Qianying Wang & Dan Shi & Qinglong Shao, 2023. "Spatiotemporal patterns and determinants of renewable energy innovation: Evidence from a province-level analysis in China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    5. Sun, Shuyu & Tong, Kangkang, 2024. "Rural-urban inequality in energy use sufficiency and efficiency during a rapid urbanization period," Applied Energy, Elsevier, vol. 364(C).
    6. Teng Ma & Silu Zhang & Yilong Xiao & Xiaorui Liu & Minghao Wang & Kai Wu & Guofeng Shen & Chen Huang & Yan Ru Fang & Yang Xie, 2023. "Costs and health benefits of the rural energy transition to carbon neutrality in China," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    7. Wang, Shaobin & Zhao, Chao & Liu, Hanbin & Tian, Xinglei, 2021. "Exploring the spatial spillover effects of low-grade coal consumption and influencing factors in China," Resources Policy, Elsevier, vol. 70(C).
    8. Huang, Junbing & Luan, Bingjiang & He, Wanrui & Chen, Xiang & Li, Mengfan, 2022. "Energy technology of conservation versus substitution and energy intensity in China," Energy, Elsevier, vol. 244(PA).
    9. Park, Jongmun & Yun, Sun-Jin, 2022. "Social determinants of residential electricity consumption in Korea: Findings from a spatial panel model," Energy, Elsevier, vol. 239(PE).
    10. Li, Jiapeng & Zuo, Xuguang & Sun, Chuanwang, 2023. "The effect of urban renewal on residential energy consumption expenditure--the example of shantytown renovation," Energy Policy, Elsevier, vol. 183(C).
    11. Xu, Jie & Lv, Tao & Hou, Xiaoran & Deng, Xu & Li, Na & Liu, Feng, 2022. "Spatiotemporal characteristics and influencing factors of renewable energy production in China: A spatial econometric analysis," Energy Economics, Elsevier, vol. 116(C).
    12. 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).
    13. Gong, Yuanyuan & Sun, Hui & Wang, Zhiwei & Ding, Chenxin, 2023. "Spatial correlation network pattern and evolution mechanism of natural gas consumption in China—Complex network-based ERGM model," Energy, Elsevier, vol. 285(C).
    14. Nan, Shijing & Huo, Yuchen & Lee, Chien-Chiang, 2023. "Assessing the role of globalization on renewable energy consumption: New evidence from a spatial econometric analysis," Renewable Energy, Elsevier, vol. 215(C).
    15. Teng, Meixuan & Burke, Paul J. & Liao, Hua, 2019. "The demand for coal among China's rural households: Estimates of price and income elasticities," Energy Economics, Elsevier, vol. 80(C), pages 928-936.
    16. Peng, Yue & Wang, Wei & Zhen, Shangsong & Liu, Yunqiang, 2024. "Does digitalization help green consumption? Empirical test based on the perspective of supply and demand of green products," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    17. Liu, Xinglei & Liu, Jun & Ren, Kezheng & Liu, Xiaoming & Liu, Jiacheng, 2022. "An integrated fuzzy multi-energy transaction evaluation approach for energy internet markets considering judgement credibility and variable rough precision," Energy, Elsevier, vol. 261(PB).
    18. Sun, Lu & Liu, Wenjing & Li, Zhaoling & Cai, Bofeng & Fujii, Minoru & Luo, Xiao & Chen, Wei & Geng, Yong & Fujita, Tsuyoshi & Le, Yiping, 2021. "Spatial and structural characteristics of CO2 emissions in East Asian megacities and its indication for low-carbon city development," Applied Energy, Elsevier, vol. 284(C).
    19. Nyiko Worship Hlongwane & Realeboga Mahapa & Tselane Confidence Nthebe, 2023. "The Nexus between Foreign Direct Investment and Electricity Consumption in South Africa," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 213-220, September.
    20. Zhonghua Cheng & Xiaowen Hu, 2023. "The effects of urbanization and urban sprawl on CO2 emissions in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1792-1808, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3650-:d:1070692. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.