IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v150y2018icp847-859.html
   My bibliography  Save this article

Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road

Author

Listed:
  • Shi, Kaifang
  • Yu, Bailang
  • Huang, Chang
  • Wu, Jianping
  • Sun, Xiufeng

Abstract

Fully understanding spatiotemporal patterns of electric power consumption (EPC) is one of the key questions related to sustainable socioeconomic and environmental development in countries along the Silk Road Economic Belt and the 21st-Century Maritime Silk Road (hereinafter referred to as the Belt and Road countries). However, studies about spatiotemporal patterns of EPC in the Belt and Road countries are still scarce due to the lack of reliable data. This study attempted to investigate spatiotemporal patterns of EPC in the Belt and Road countries from multiple perspectives. Firstly, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime stable light data were used to estimate EPC from 1992 to 2013. Subsequently, the mathematical statistic method, standard deviational ellipse, rank size rule, and correlation analysis were employed to evaluate the EPC change in detail. The results reveal that the EPC growth mainly occurs in the developing countries, especially in China. The geographical distribution of EPC in the Belt and Road countries is oriented in the Northwest-Southeast direction between 1992 and 2013. Based on the rank size rule analysis, the slope values of q are −2.392 and −2.175 between 1992 and 2013, with an average R2 value of 0.664, indicating a clear clustering pattern of EPC. It is also proved that GDP is a more important impact factor to EPC than the population. Our findings can offer an effective way to understand spatiotemporal evolution characteristics of EPC in the Belt and Road countries, and provide references for regional socioeconomic development and cooperation.

Suggested Citation

  • Shi, Kaifang & Yu, Bailang & Huang, Chang & Wu, Jianping & Sun, Xiufeng, 2018. "Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road," Energy, Elsevier, vol. 150(C), pages 847-859.
  • Handle: RePEc:eee:energy:v:150:y:2018:i:c:p:847-859
    DOI: 10.1016/j.energy.2018.03.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218304183
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2018.03.020?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. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Yang, Chengshu & Li, Linyi & Huang, Chang & Chen, Zuoqi & Liu, Rui & Wu, Jianping, 2016. "Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 184(C), pages 450-463.
    3. Pérez-García, Julián & Moral-Carcedo, Julián, 2016. "Analysis and long term forecasting of electricity demand trough a decomposition model: A case study for Spain," Energy, Elsevier, vol. 97(C), pages 127-143.
    4. Yu Liu & Xi Liu & Song Gao & Li Gong & Chaogui Kang & Ye Zhi & Guanghua Chi & Li Shi, 2015. "Social Sensing: A New Approach to Understanding Our Socioeconomic Environments," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(3), pages 512-530, May.
    5. Lean, Hooi Hooi & Smyth, Russell, 2010. "CO2 emissions, electricity consumption and output in ASEAN," Applied Energy, Elsevier, vol. 87(6), pages 1858-1864, June.
    6. Kaifang Shi & Yun Chen & Bailang Yu & Tingbao Xu & Linyi Li & Chang Huang & Rui Liu & Zuoqi Chen & Jianping Wu, 2016. "Urban Expansion and Agricultural Land Loss in China: A Multiscale Perspective," Sustainability, MDPI, vol. 8(8), pages 1-16, August.
    7. Ranjan, Manish & Jain, V.K., 1999. "Modelling of electrical energy consumption in Delhi," Energy, Elsevier, vol. 24(4), pages 351-361.
    8. Payne, James E., 2010. "A survey of the electricity consumption-growth literature," Applied Energy, Elsevier, vol. 87(3), pages 723-731, March.
    9. Christopher D. Elvidge & Daniel Ziskin & Kimberly E. Baugh & Benjamin T. Tuttle & Tilottama Ghosh & Dee W. Pack & Edward H. Erwin & Mikhail Zhizhin, 2009. "A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data," Energies, MDPI, vol. 2(3), pages 1-28, August.
    10. Wang, Wenchao & Mu, Hailin & Kang, Xudong & Song, Rongchen & Ning, Yadong, 2010. "Changes in industrial electricity consumption in china from 1998 to 2007," Energy Policy, Elsevier, vol. 38(7), pages 3684-3690, July.
    11. Chuanglin Fang & Zhenbo Wang, 2015. "Quantitative Diagnoses and Comprehensive Evaluations of the Rationality of Chinese Urban Development Patterns," Sustainability, MDPI, vol. 7(4), pages 1-26, March.
    12. Wang, Shaojian & Fang, Chuanglin & Guan, Xingliang & Pang, Bo & Ma, Haitao, 2014. "Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces," Applied Energy, Elsevier, vol. 136(C), pages 738-749.
    13. repec:asg:wpaper:1001 is not listed on IDEAS
    14. Huang, Min & He, Yong & Cen, Haiyan, 2007. "Predictive analysis on electric-power supply and demand in China," Renewable Energy, Elsevier, vol. 32(7), pages 1165-1174.
    15. Al-mulali, Usama & Binti Che Sab, Che Normee & Fereidouni, Hassan Gholipour, 2012. "Exploring the bi-directional long run relationship between urbanization, energy consumption, and carbon dioxide emission," Energy, Elsevier, vol. 46(1), pages 156-167.
    16. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Chen, Zuoqi & Liu, Rui & Li, Linyi & Wu, Jianping, 2016. "Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis," Applied Energy, Elsevier, vol. 168(C), pages 523-533.
    17. Xie, Yanhua & Weng, Qihao, 2016. "Detecting urban-scale dynamics of electricity consumption at Chinese cities using time-series DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) nighttime light imageries," Energy, Elsevier, vol. 100(C), pages 177-189.
    Full references (including those not matched with items on IDEAS)

    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. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Yang, Chengshu & Li, Linyi & Huang, Chang & Chen, Zuoqi & Liu, Rui & Wu, Jianping, 2016. "Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 184(C), pages 450-463.
    2. Hu, Ting & Huang, Xin, 2019. "A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 240(C), pages 778-792.
    3. Lu, Linlin & Weng, Qihao & Xie, Yanhua & Guo, Huadong & Li, Qingting, 2019. "An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery," Energy, Elsevier, vol. 189(C).
    4. Shi, Kaifang & Yang, Qingyuan & Fang, Guangliang & Yu, Bailang & Chen, Zuoqi & Yang, Chengshu & Wu, Jianping, 2019. "Evaluating spatiotemporal patterns of urban electricity consumption within different spatial boundaries: A case study of Chongqing, China," Energy, Elsevier, vol. 167(C), pages 641-653.
    5. Shi, Kaifang & Chen, Yun & Li, Linyi & Huang, Chang, 2018. "Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective," Applied Energy, Elsevier, vol. 211(C), pages 218-229.
    6. Yang, Di & Luan, Weixin & Qiao, Lu & Pratama, Mahardhika, 2020. "Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery," Applied Energy, Elsevier, vol. 268(C).
    7. Hu, Ting & Wang, Ting & Yan, Qingyun & Chen, Tiexi & Jin, Shuanggen & Hu, Jun, 2022. "Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS," Applied Energy, Elsevier, vol. 322(C).
    8. Zhong, Liang & Liu, Xiaosheng & Ao, Jianfeng, 2022. "Spatiotemporal dynamics evaluation of pixel-level gross domestic product, electric power consumption, and carbon emissions in countries along the belt and road," Energy, Elsevier, vol. 239(PA).
    9. Yongguang Zhu & Deyi Xu & Saleem H. Ali & Ruiyang Ma & Jinhua Cheng, 2019. "Can Nighttime Light Data Be Used to Estimate Electric Power Consumption? New Evidence from Causal-Effect Inference," Energies, MDPI, vol. 12(16), pages 1-14, August.
    10. Yongxing Li & Wei Guo & Peixian Li & Xuesheng Zhao & Jinke Liu, 2023. "Exploring the Spatiotemporal Dynamics of CO 2 Emissions through a Combination of Nighttime Light and MODIS NDVI Data," Sustainability, MDPI, vol. 15(17), pages 1-17, August.
    11. Wang, Shaojian & Li, Guangdong & Fang, Chuanglin, 2018. "Urbanization, economic growth, energy consumption, and CO2 emissions: Empirical evidence from countries with different income levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2144-2159.
    12. Shi, Kaifang & Yu, Bailang & Zhou, Yuyu & Chen, Yun & Yang, Chengshu & Chen, Zuoqi & Wu, Jianping, 2019. "Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels," Applied Energy, Elsevier, vol. 233, pages 170-181.
    13. Yang Zhong & Aiwen Lin & Zhigao Zhou, 2019. "Evolution of the Pattern of Spatial Expansion of Urban Land Use in the Poyang Lake Ecological Economic Zone," IJERPH, MDPI, vol. 16(1), pages 1-14, January.
    14. Salahuddin, Mohammad & Gow, Jeff, 2014. "Economic growth, energy consumption and CO2 emissions in Gulf Cooperation Council countries," Energy, Elsevier, vol. 73(C), pages 44-58.
    15. Wanchun Leng & Guojin He & Wei Jiang, 2019. "Investigating the Spatiotemporal Variability and Driving Factors of Artificial Lighting in the Beijing-Tianjin-Hebei Region Using Remote Sensing Imagery and Socioeconomic Data," IJERPH, MDPI, vol. 16(11), pages 1-20, June.
    16. Hui Wang & Guifen Liu & Kaifang Shi, 2019. "What Are the Driving Forces of Urban CO 2 Emissions in China? A Refined Scale Analysis between National and Urban Agglomeration Levels," IJERPH, MDPI, vol. 16(19), pages 1-19, September.
    17. Jie Zhao & Nguyen Xuan Thinh & Cheng Li, 2017. "Investigation of the Impacts of Urban Land Use Patterns on Energy Consumption in China: A Case Study of 20 Provincial Capital Cities," Sustainability, MDPI, vol. 9(8), pages 1-22, August.
    18. Li, Shuyi & Cheng, Liang & Liu, Xiaoqiang & Mao, Junya & Wu, Jie & Li, Manchun, 2019. "City type-oriented modeling electric power consumption in China using NPP-VIIRS nighttime stable light data," Energy, Elsevier, vol. 189(C).
    19. Kaifang Shi & Qingyuan Yang & Yuanqing Li, 2019. "Are Karst Rocky Desertification Areas Affected by Increasing Human Activity in Southern China? An Empirical Analysis from Nighttime Light Data," IJERPH, MDPI, vol. 16(21), pages 1-12, October.
    20. Kaifang Shi & Yun Chen & Bailang Yu & Tingbao Xu & Linyi Li & Chang Huang & Rui Liu & Zuoqi Chen & Jianping Wu, 2016. "Urban Expansion and Agricultural Land Loss in China: A Multiscale Perspective," Sustainability, MDPI, vol. 8(8), pages 1-16, August.

    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:150:y:2018:i:c:p:847-859. 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.