IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v167y2019icp641-653.html
   My bibliography  Save this article

Evaluating spatiotemporal patterns of urban electricity consumption within different spatial boundaries: A case study of Chongqing, China

Author

Listed:
  • Shi, Kaifang
  • Yang, Qingyuan
  • Fang, Guangliang
  • Yu, Bailang
  • Chen, Zuoqi
  • Yang, Chengshu
  • Wu, Jianping

Abstract

The timely and effective evaluation of spatiotemporal patterns of urban electricity consumption is a critical prerequisite for establishing policy on sustainable electricity utilisation in China. However, calculating urban electricity consumption in China is difficult due to the confusion generated by the various city definitions and corresponding urban boundaries. Using Chongqing as a case study, this study was the first attempt to evaluate and compare the spatiotemporal patterns of urban electricity consumption within different spatial boundaries. Four urban boundaries, including the city administrative area, city district, urban centre, and urban built-up area, were defined using the administrative boundaries and urban built-up area data. Then, the electricity consumption was estimated at a 1-km spatial resolution from 1992 to 2013 using the nighttime light data and statistical electricity consumption data. Finally, the temporal and spatial evolution of urban electricity consumption within different boundaries were evaluated and compared from multiple perspectives. The results showed that a rapid increase in urban electricity consumption occurred in the four urban boundaries in Chongqing from 1992 to 2013. The urban electricity consumption in urban built-up area accounted for 34.34%–45.69% of that in city administrative area from 1992 to 2013, which indicated that urban built-up area was still the centre of electricity consumption in Chongqing. There was a very low-gridded urban electricity consumption with significant spatial variability in city administrative area, city district, and urban centre; however, there was a wide distribution from 10.03 to 20.21 million kWh in urban built-up area. Special attention should be given to urban built-up area, which presented the highest per capita urban electricity consumption among the four urban boundaries, with values from 18,470 kWh in 2005 to 20,370 kWh in 2010. Our results also noted that the urbanisation rate has become the strongest driver of urban electricity consumption within the different urban boundaries in Chongqing, with R2 values larger than 0.95. This study suggested that decision makers should explicitly state the accounting boundaries to avoid data gaming and inaccurate results when designing benchmarks or plans or when analysing the performance of urban electricity consumption.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:energy:v:167:y:2019:i:c:p:641-653
    DOI: 10.1016/j.energy.2018.11.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218322266
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2018.11.022?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. Liu, Da & Ruan, Liang & Liu, Jinchen & Huan, Huang & Zhang, Guowei & Feng, Yi & Li, Ying, 2018. "Electricity consumption and economic growth nexus in Beijing: A causal analysis of quarterly sectoral data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2498-2503.
    3. 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.
    4. Sovacool, Benjamin K. & Brown, Marilyn A., 2010. "Twelve metropolitan carbon footprints: A preliminary comparative global assessment," Energy Policy, Elsevier, vol. 38(9), pages 4856-4869, September.
    5. Lixiao Zhang & Zhifeng Yang & Jing Liang & Yanpeng Cai, 2010. "Spatial Variation and Distribution of Urban Energy Consumptions from Cities in China," Energies, MDPI, vol. 4(1), pages 1-13, December.
    6. 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.
    7. Waite, Michael & Cohen, Elliot & Torbey, Henri & Piccirilli, Michael & Tian, Yu & Modi, Vijay, 2017. "Global trends in urban electricity demands for cooling and heating," Energy, Elsevier, vol. 127(C), pages 786-802.
    8. Pachauri, Shonali & Jiang, Leiwen, 2008. "The household energy transition in India and China," Energy Policy, Elsevier, vol. 36(11), pages 4022-4035, November.
    9. He, Yiming & Fullerton, Thomas M. & Walke, Adam G., 2017. "Electricity consumption and metropolitan economic performance in Guangzhou: 1950–2013," Energy Economics, Elsevier, vol. 63(C), pages 154-160.
    10. 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.
    11. Shahbaz, Muhammad & Chaudhary, A.R. & Ozturk, Ilhan, 2017. "Does urbanization cause increasing energy demand in Pakistan? Empirical evidence from STIRPAT model," Energy, Elsevier, vol. 122(C), pages 83-93.
    12. Cai, Bofeng & Zhang, Lixiao, 2014. "Urban CO2 emissions in China: Spatial boundary and performance comparison," Energy Policy, Elsevier, vol. 66(C), pages 557-567.
    13. Meng, Lina & Graus, Wina & Worrell, Ernst & Huang, Bo, 2014. "Estimating CO2 (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program's Operational Linescan System) nighttime light imagery: Methodological challenges and a ," Energy, Elsevier, vol. 71(C), pages 468-478.
    14. 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.
    15. 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.
    16. Cai, Bofeng & Wang, Jinnan & He, Jie & Geng, Yong, 2016. "Evaluating CO2 emission performance in China’s cement industry: An enterprise perspective," Applied Energy, Elsevier, vol. 166(C), pages 191-200.
    17. Su, Yongxian & Chen, Xiuzhi & Li, Yong & Liao, Jishan & Ye, Yuyao & Zhang, Hongou & Huang, Ningsheng & Kuang, Yaoqiu, 2014. "China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 231-243.
    18. 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.
    19. 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.
    20. Dhakal, Shobhakar, 2009. "Urban energy use and carbon emissions from cities in China and policy implications," Energy Policy, Elsevier, vol. 37(11), pages 4208-4219, November.
    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. Qiangyi Li & Lan Yang & Shuang Huang & Yangqing Liu & Chenyang Guo, 2023. "The Effects of Urban Sprawl on Electricity Consumption: Empirical Evidence from 283 Prefecture-Level Cities in China," Land, MDPI, vol. 12(8), pages 1-27, August.
    2. Wei Shi & Wenwen Tang & Fuwei Qiao & Zhiquan Sha & Chengyuan Wang & Sixue Zhao, 2022. "How to Reduce Carbon Dioxide Emissions from Power Systems in Gansu Province—Analyze from the Life Cycle Perspective," Energies, MDPI, vol. 15(10), pages 1-15, May.
    3. Li, Peiran & Zhang, Haoran & Wang, Xin & Song, Xuan & Shibasaki, Ryosuke, 2020. "A spatial finer electric load estimation method based on night-light satellite image," Energy, Elsevier, vol. 209(C).
    4. Juchao Zhao & Shaohua Zhang & Kun Yang & Yanhui Zhu & Yuling Ma, 2020. "Spatio-Temporal Variations of CO 2 Emission from Energy Consumption in the Yangtze River Delta Region of China and Its Relationship with Nighttime Land Surface Temperature," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    5. Guo, Jinyu & Ma, Jinji & Li, Zhengqiang & Hong, Jin, 2022. "Building a top-down method based on machine learning for evaluating energy intensity at a fine scale," Energy, Elsevier, vol. 255(C).
    6. Liu, Xiaorui & Sun, Tao & Feng, Qiang & Zhang, Di, 2020. "Dynamic nonlinear influence of urbanization on China’s electricity consumption: Evidence from dynamic economic growth threshold effect," Energy, Elsevier, vol. 196(C).
    7. 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).
    8. Zhang, Pengfei & Cai, Wenqiu & Yao, Mingtao & Wang, Zhiyou & Yang, Luzhen & Wei, Wendong, 2020. "Urban carbon emissions associated with electricity consumption in Beijing and the driving factors," Applied Energy, Elsevier, vol. 275(C).
    9. Lin, Boqiang & Huang, Chenchen, 2023. "How will promoting the digital economy affect electricity intensity?," Energy Policy, Elsevier, vol. 173(C).
    10. Wang, Jiaxin & Lu, Feng, 2021. "Modeling the electricity consumption by combining land use types and landscape patterns with nighttime light imagery," Energy, Elsevier, vol. 234(C).
    11. Chuanlong Li & Yuanqing Li & Kaifang Shi & Qingyuan Yang, 2020. "A Multiscale Evaluation of the Coupling Relationship between Urban Land and Carbon Emissions: A Case Study of Chongqing, China," IJERPH, MDPI, vol. 17(10), pages 1-13, May.
    12. 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).
    13. Lu, Xiaonong & Zhang, Qiang & Peng, Zhanglin & Shao, Zhen & Song, Hao & Wang, Wanying, 2020. "Charging and relocating optimization for electric vehicle car-sharing: An event-based strategy improvement approach," Energy, Elsevier, vol. 207(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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Cui, Yuanzheng & Zhang, Weishi & Wang, Can & Streets, David G. & Xu, Ying & Du, Mingxi & Lin, Jintai, 2019. "Spatiotemporal dynamics of CO2 emissions from central heating supply in the North China Plain over 2012–2016 due to natural gas usage," Applied Energy, Elsevier, vol. 241(C), pages 245-256.
    8. Wang, Shaojian & Shi, Chenyi & Fang, Chuanglin & Feng, Kuishuang, 2019. "Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model," Applied Energy, Elsevier, vol. 235(C), pages 95-105.
    9. 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).
    10. Zhao, Jincai & Ji, Guangxing & Yue, YanLin & Lai, Zhizhu & Chen, Yulong & Yang, Dongyang & Yang, Xu & Wang, Zheng, 2019. "Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets," Applied Energy, Elsevier, vol. 235(C), pages 612-624.
    11. 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.
    12. Xiaofeng Lv & Kun Lin & Lingshan Chen & Yongzhong Zhang, 2022. "Does Retirement Affect Household Energy Consumption Structure? Evidence from a Regression Discontinuity Design," Sustainability, MDPI, vol. 14(19), pages 1-14, September.
    13. Yu, Yantuan & Zhang, Ning & Kim, Jong Dae, 2020. "Impact of urbanization on energy demand: An empirical study of the Yangtze River Economic Belt in China," Energy Policy, Elsevier, vol. 139(C).
    14. 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.
    15. 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.
    16. Jasiński, Tomasz, 2019. "Modeling electricity consumption using nighttime light images and artificial neural networks," Energy, Elsevier, vol. 179(C), pages 831-842.
    17. 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.
    18. 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).
    19. 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.
    20. Chen, Qianli & Cai, Bofeng & Dhakal, Shobhakar & Pei, Sha & Liu, Chunlan & Shi, Xiaoping & Hu, Fangfang, 2017. "CO2 emission data for Chinese cities," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 198-208.

    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:167:y:2019:i:c:p:641-653. 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.