IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v197y2020ics0360544220303029.html
   My bibliography  Save this article

Spatial disparity and hierarchical cluster analysis of final energy consumption in China

Author

Listed:
  • Wang, Shaobin
  • Liu, Haimeng
  • Pu, Haixia
  • Yang, Hao

Abstract

This paper evaluated the spatial patterns and hierarchical clustering of final energy consumption in China from 2014 to 2016. For this purpose, exploratory spatial data analysis, kernel density estimation, and spatial hierarchical clustering were applied. The results presented various spatial characteristics of final energy consumption with different fuel types and consumption sectors. i) Coal and heat consumption showed significant positive spatial autocorrelations, whereas petroleum products exhibited significant negative spatial autocorrelations, and natural gas and electricity without any significant spatial autocorrelations. ii) The industry sector exhibited clusters of hot spots in eastern China and cold spots in southwestern China, which was obviously different from the north-south difference of coal and heat in the sector of residential energy and of transport, storage and post. iii) Residential coal consumption in rural areas exhibited the most significant disparity. In addition, northeastern, northern and eastern parts of China were identified as three spatial clusters of final energy consumption. Further, it indicated to improve the residential coal utilization level in key coal-producing regions such as Shanxi and Guizhou Provinces.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:197:y:2020:i:c:s0360544220303029
    DOI: 10.1016/j.energy.2020.117195
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220303029
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117195?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Blázquez Gomez, Leticia M. & Filippini, Massimo & Heimsch, Fabian, 2013. "Regional impact of changes in disposable income on Spanish electricity demand: A spatial econometric analysis," Energy Economics, Elsevier, vol. 40(S1), pages 58-66.
    2. Xiao, Hongwei & Ma, Zhongyu & Mi, Zhifu & Kelsey, John & Zheng, Jiali & Yin, Weihua & Yan, Min, 2018. "Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data," Applied Energy, Elsevier, vol. 231(C), pages 1070-1078.
    3. Hao, Yu & Peng, Hui, 2017. "On the convergence in China's provincial per capita energy consumption: New evidence from a spatial econometric analysis," Energy Economics, Elsevier, vol. 68(C), pages 31-43.
    4. 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.
    5. Niu, Shuwen & Li, Zhen & Qiu, Xin & Dai, Runqi & Wang, Xiang & Qiang, Wenli & Hong, Zhenguo, 2019. "Measurement of effective energy consumption in China's rural household sector and policy implication," Energy Policy, Elsevier, vol. 128(C), pages 553-564.
    6. Sachs, Julia & Moya, Diego & Giarola, Sara & Hawkes, Adam, 2019. "Clustered spatially and temporally resolved global heat and cooling energy demand in the residential sector," Applied Energy, Elsevier, vol. 250(C), pages 48-62.
    7. Tian, Wei & Song, Jitian & Li, Zhanyong, 2014. "Spatial regression analysis of domestic energy in urban areas," Energy, Elsevier, vol. 76(C), pages 629-640.
    8. Niu, Shuwen & Zhang, Xin & Zhao, Chunsheng & Niu, Yunzhu, 2012. "Variations in energy consumption and survival status between rural and urban households: A case study of the Western Loess Plateau, China," Energy Policy, Elsevier, vol. 49(C), pages 515-527.
    9. Hao, Yu & Liu, Yiming & Weng, Jia-Hsi & Gao, Yixuan, 2016. "Does the Environmental Kuznets Curve for coal consumption in China exist? New evidence from spatial econometric analysis," Energy, Elsevier, vol. 114(C), pages 1214-1223.
    10. Liu, Wen & Lund, Henrik & Mathiesen, Brian Vad & Zhang, Xiliang, 2011. "Potential of renewable energy systems in China," Applied Energy, Elsevier, vol. 88(2), pages 518-525, February.
    11. Bridge, Gavin & Bouzarovski, Stefan & Bradshaw, Michael & Eyre, Nick, 2013. "Geographies of energy transition: Space, place and the low-carbon economy," Energy Policy, Elsevier, vol. 53(C), pages 331-340.
    12. 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. Chen, Huadun & Du, Qianxi & Huo, Tengfei & Liu, Peiran & Cai, Weiguang & Liu, Bingsheng, 2023. "Spatiotemporal patterns and driving mechanism of carbon emissions in China's urban residential building sector," Energy, Elsevier, vol. 263(PE).
    2. Labbé, Martine & Landete, Mercedes & Leal, Marina, 2023. "Dendrograms, minimum spanning trees and feature selection," European Journal of Operational Research, Elsevier, vol. 308(2), pages 555-567.
    3. Song, Siming & Li, Tianxiao & Liu, Pei & Li, Zheng, 2022. "The transition pathway of energy supply systems towards carbon neutrality based on a multi-regional energy infrastructure planning approach: A case study of China," Energy, Elsevier, vol. 238(PC).
    4. Ling Yang & Kai Zhao & Yankai Zhao & Mengyuan Zhong, 2021. "Identifying Key Factors in Determining Disparities in Energy Consumption in China: A Household Level Analysis," Energies, MDPI, vol. 14(21), pages 1-20, November.
    5. 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).
    6. 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).
    7. Fan, Wei & Yan, Ling & Chen, Boyang & Ding, Wangwang & Wang, Ping, 2022. "Environmental governance effects of local environmental protection expenditure in China," Resources Policy, Elsevier, vol. 77(C).
    8. Valdes, Javier & Masip Macia, Yunesky & Dorner, Wolfgang & Ramirez Camargo, Luis, 2021. "Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications," Energy, Elsevier, vol. 215(PA).
    9. Cheng, Shulei & Fan, Wei & Zhang, Jian & Wang, Ning & Meng, Fanxin & Liu, Gengyuan, 2021. "Multi-sectoral determinants of carbon emission inequality in Chinese clustering cities," Energy, Elsevier, vol. 214(C).
    10. Fan, Min & Lu, Zhixi & Zhou, Yun & Wang, Jian, 2024. "Threshold and spillovers effects of fintech on China's energy dependence on fossil fuel," Resources Policy, Elsevier, vol. 91(C).
    11. 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).
    12. Djellouli, Nassima & Abdelli, Latifa & Elheddad, Mohamed & Ahmed, Rizwan & Mahmood, Haider, 2022. "The effects of non-renewable energy, renewable energy, economic growth, and foreign direct investment on the sustainability of African countries," Renewable Energy, Elsevier, vol. 183(C), pages 676-686.
    13. Zhihui Li & Chao Li & John Gibson & Xiangzheng Deng, 2024. "Two decades of inter-city migration in China: The role of economic, natural and social amenities," Working Papers in Economics 24/05, University of Waikato.
    14. Syed Ali Asad Naqvi & Muhammad Sajjad & Liaqat Ali Waseem & Shoaib Khalid & Saima Shaikh & Syed Jamil Hasan Kazmi, 2021. "Integrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan," IJERPH, MDPI, vol. 18(22), pages 1-30, November.
    15. Wang, Chengdong & Wang, Yutao & Tong, Xin & Ulgiati, Sergio & Liang, Sai & Xu, Ming & Wei, Wendong & Li, Xiao & Jin, Mingzhou & Mao, Jiafu, 2020. "Mapping potentials and bridging regional gaps of renewable resources in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    16. Seiya Maki & Satoshi Ohnishi & Minoru Fujii & Naohiro Goto & Lu Sun, 2022. "Using waste to supply steam for industry transition: Selection of target industries through economic evaluation and statistical analysis," Journal of Industrial Ecology, Yale University, vol. 26(4), pages 1475-1486, August.
    17. Yu Mao & Yonglin Li & Deyi Xu & Yaqi Wu & Jinhua Cheng, 2022. "Spatial-Temporal Evolution of Total Factor Productivity in Logistics Industry of the Yangtze River Economic Belt, China," Sustainability, MDPI, vol. 14(5), pages 1-16, February.

    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, 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).
    2. 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.
    3. 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).
    4. Kangjuan Lv & Yu Cheng & Yousen Wang, 2021. "Does regional innovation system efficiency facilitate energy-related carbon dioxide intensity reduction in China?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 789-813, January.
    5. 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).
    6. Huang, He & Hong, Jingke & Wang, Xianzhu & Chang-Richards, Alice & Zhang, Jingxiao & Qiao, Bei, 2022. "A spatiotemporal analysis of the driving forces behind the energy interactions of the Chinese economy: Evidence from static and dynamic perspectives," Energy, Elsevier, vol. 239(PB).
    7. Nguyen, Trung Thanh & Nguyen, Thanh-Tung & Hoang, Viet-Ngu & Wilson, Clevo & Managi, Shunsuke, 2019. "Energy transition, poverty and inequality in Vietnam," Energy Policy, Elsevier, vol. 132(C), pages 536-548.
    8. Cabral, Joilson de Assis & Legey, Luiz Fernando Loureiro & Freitas Cabral, Maria Viviana de, 2017. "Electricity consumption forecasting in Brazil: A spatial econometrics approach," Energy, Elsevier, vol. 126(C), pages 124-131.
    9. Nguyen, Trung Thanh & Nguyen, Thanh-Tung & Hoang, Viet-Ngu & Wilson, Clevo, 2019. "Energy transition, poverty and inequality: panel evidence from Vietnam," MPRA Paper 107182, University Library of Munich, Germany, revised 10 May 2019.
    10. Lu Jiang & Xingpeng Chen & Bing Xue, 2019. "Features, Driving Forces and Transition of the Household Energy Consumption in China: A Review," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
    11. Zi, Cao & Qian, Meng & Baozhong, Gao, 2021. "The consumption patterns and determining factors of rural household energy: A case study of Henan Province in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    12. Niu, Shuwen & Li, Zhen & Qiu, Xin & Dai, Runqi & Wang, Xiang & Qiang, Wenli & Hong, Zhenguo, 2019. "Measurement of effective energy consumption in China's rural household sector and policy implication," Energy Policy, Elsevier, vol. 128(C), pages 553-564.
    13. Shiwen Liu & Zhen Zhang & Junhua Yang & Wei Hu, 2022. "Exploring Increasing Urban Resident Electricity Consumption: The Spatial Spillover Effect of Resident Income," Energies, MDPI, vol. 15(12), pages 1-17, June.
    14. Erik Hille & Bernhard Lambernd, 2022. "Has Korean growth become greener? Spatial econometric evidence for energy use and renewable energy," Annals of Operations Research, Springer, vol. 313(1), pages 461-494, June.
    15. Liu, Tie-Ying & Lee, Chien-Chiang, 2020. "Convergence of the world’s energy use," Resource and Energy Economics, Elsevier, vol. 62(C).
    16. Gaivoronskaia, Elizaveta, 2020. "Electricity demand elasticity and regional effects: Spatial econometric approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 58, pages 76-95.
    17. Sun, Yeran & Wang, Shaohua & Zhang, Xucai & Chan, Ting On & Wu, Wenjie, 2021. "Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data," Energy, Elsevier, vol. 226(C).
    18. Yongqing Nan & Qin Li & Jinxiang Yu & Haiya Cai & Qin Zhou, 2020. "Has the emissions intensity of industrial sulphur dioxide converged? New evidence from China’s prefectural cities with dynamic spatial panel models," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(6), pages 5337-5369, August.
    19. Ma, Shaoyue & Xu, Xiangbo & Li, Chang & Zhang, Linxiu & Sun, Mingxing, 2021. "Energy consumption inequality decrease with energy consumption increase: Evidence from rural China at micro scale," Energy Policy, Elsevier, vol. 159(C).
    20. Chen, Feifei & Qiu, Huanguang & Zhang, Jun, 2022. "Energy consumption and income of the poor in rural China: Inference for poverty measures," Energy Policy, Elsevier, vol. 163(C).

    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:eee:energy:v:197:y:2020:i:c:s0360544220303029. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.