IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i11p2518-d1400413.html
   My bibliography  Save this article

Grid Model of Energy Consumption Using Random Forest by Integrating Data on the Nighttime Light, Population, and Urban Impervious Surface (2000–2020) in the Guangdong–Hong Kong–Macau Greater Bay Area

Author

Listed:
  • Yanfei Lei

    (State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
    University of Chinese Academy of Sciences, Beijing 101408, China)

  • Chao Xu

    (Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China)

  • Yunpeng Wang

    (State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China)

  • Xulong Liu

    (Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China)

Abstract

Energy consumption is an important indicator for measuring economic development and is closely related to the atmospheric environment. As a demonstration zone for China’s high-quality development, the Guangdong–Hong Kong–Macao Greater Bay Area imposes higher requirements on ecological environment and sustainable development. Therefore, accurate data on energy consumption is crucial for high-quality green development. However, the statistical data on local energy consumption in China is insufficient, and the lack of data is severe, which hinders the analysis of energy consumption at the metropolitan level and the precise implementation of energy policies. Nighttime light data have been widely used in the inversion of energy consumption, but they can only reflect socio-economic activities at night with certain limitations. In this study, a random forest model was developed to estimate metropolitan-level energy consumption in the Guangdong–Hong Kong–Macao Greater Bay Area from 2000 to 2020 based on nighttime light data, population data, and urban impervious surface data. The estimation results show that our model shows good performance with an R 2 greater than 0.9783 and MAPE less than 9%. A long time series dataset from 2000 to 2020 on energy consumption distribution at a resolution of 500 m in the Guangdong–Hong Kong–Macao Greater Bay Area was built using our model with a top-down weight allocation method. The spatial and temporal dynamics of energy consumption in the Greater Bay Area were assessed at both the metropolitan and grid levels. The results show a significant increase in energy consumption in the Greater Bay Area with a clear clustering, and approximately 90% of energy consumption is concentrated in 22% of the area. This study established an energy consumption estimation model that comprehensively considers population, urban distribution, and nighttime light data, which effectively solves the problem of missing statistical data and accurately reflects the spatial distribution of energy consumption of the whole Bay Area. This study provides a reference for spatial pattern analysis and refined urban management and energy allocation for regions lacking statistical data on energy consumption.

Suggested Citation

  • Yanfei Lei & Chao Xu & Yunpeng Wang & Xulong Liu, 2024. "Grid Model of Energy Consumption Using Random Forest by Integrating Data on the Nighttime Light, Population, and Urban Impervious Surface (2000–2020) in the Guangdong–Hong Kong–Macau Greater Bay Area," Energies, MDPI, vol. 17(11), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2518-:d:1400413
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/11/2518/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/11/2518/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jincai Zhao & Qianqian Liu, 2021. "Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China’s County-Level Cities," IJERPH, MDPI, vol. 18(8), pages 1-18, April.
    2. Zheng, Wei & Walsh, Patrick Paul, 2019. "Economic growth, urbanization and energy consumption — A provincial level analysis of China," Energy Economics, Elsevier, vol. 80(C), pages 153-162.
    3. Daniela Nicoleta Sahlian & Adriana Florina Popa & Raluca Florentina Creţu, 2021. "Does the Increase in Renewable Energy Influence GDP Growth? An EU-28 Analysis," Energies, MDPI, vol. 14(16), pages 1-16, August.
    4. Xiong, Ping-ping & Dang, Yao-guo & Yao, Tian-xiang & Wang, Zheng-xin, 2014. "Optimal modeling and forecasting of the energy consumption and production in China," Energy, Elsevier, vol. 77(C), pages 623-634.
    5. 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.
    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. Islam, Md. Monirul & Irfan, Muhammad & Shahbaz, Muhammad & Vo, Xuan Vinh, 2022. "Renewable and non-renewable energy consumption in Bangladesh: The relative influencing profiles of economic factors, urbanization, physical infrastructure and institutional quality," Renewable Energy, Elsevier, vol. 184(C), pages 1130-1149.
    2. 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).
    3. Hongyang Qiao & Sanmang Wu, 2025. "Decoupling Factor Analysis for Sustainable Development in China’s Four Municipalities Using the Tapio Model," Sustainability, MDPI, vol. 17(6), pages 1-26, March.
    4. Wei Wang & Kehui Wei & Oleksandr Kubatko & Vladyslav Piven & Yulija Chortok & Oleksandr Derykolenko, 2023. "Economic Growth and Sustainable Transition: Investigating Classical and Novel Factors in Developed Countries," Sustainability, MDPI, vol. 15(16), pages 1-15, August.
    5. Fei Tao & Guoan Tang & Yihao Wu & Tong Zhou, 2022. "Spatiotemporal Heterogeneity and Driving Mechanism of Co-Ordinated Urban Development: A Case Study of the Central Area of the Yangtze River Delta, China," Sustainability, MDPI, vol. 14(9), pages 1-23, April.
    6. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    7. İhsan Erdem Kayral & Tuğba Sarı & Nisa Şansel Tandoğan Aktepe, 2023. "Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
    8. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    9. Meixia Wang, 2024. "Predicting China’s Energy Consumption and CO 2 Emissions by Employing a Novel Grey Model," Energies, MDPI, vol. 17(21), pages 1-25, October.
    10. Ayoub Zeraibi & Daniel Balsalobre-Lorente & Khurram Shehzad, 2021. "Testing the Environmental Kuznets Curve Hypotheses in Chinese Provinces: A Nexus between Regional Government Expenditures and Environmental Quality," IJERPH, MDPI, vol. 18(18), pages 1-16, September.
    11. 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.
    12. Achuo, Elvis & Nchofoung, Tii & Asongu, Simplice & Dinga, Gildas, 2021. "Unravelling the Mysteries of Underdevelopment in Africa," MPRA Paper 111556, University Library of Munich, Germany.
    13. Li, Wei & Sun, Wen & Li, Guomin & Cui, Pengfei & Wu, Wen & Jin, Baihui, 2017. "Temporal and spatial heterogeneity of carbon intensity in China's construction industry," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 162-173.
    14. Wu, Jianxin & Xu, Hui & Tang, Kai, 2021. "Industrial agglomeration, CO2 emissions and regional development programs: A decomposition analysis based on 286 Chinese cities," Energy, Elsevier, vol. 225(C).
    15. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    16. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    17. Saldivia, Mauricio & Kristjanpoller, Werner & Olson, Josephine E., 2020. "Energy consumption and GDP revisited: A new panel data approach with wavelet decomposition," Applied Energy, Elsevier, vol. 272(C).
    18. Ding, Song, 2018. "A novel self-adapting intelligent grey model for forecasting China's natural-gas demand," Energy, Elsevier, vol. 162(C), pages 393-407.
    19. Maowen Sun & Boyi Liang & Xuebin Meng & Yunfei Zhang & Zong Wang & Jia Wang, 2024. "Study on the Evolution of Spatial and Temporal Patterns of Carbon Emissions and Influencing Factors in China," Land, MDPI, vol. 13(6), pages 1-24, June.
    20. Pruethsan Sutthichaimethee & Kuskana Kubaha, 2018. "The Efficiency of Long-Term Forecasting Model on Final Energy Consumption in Thailand’s Petroleum Industries Sector: Enriching the LT-ARIMAXS Model under a Sustainability Policy," Energies, MDPI, vol. 11(8), pages 1-18, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jeners:v:17:y:2024:i:11:p:2518-:d:1400413. 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.