IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v294y2024ics0360544224007424.html

Forecasting electricity consumption in China's Pearl River Delta urban agglomeration under the optimal economic growth path with low-carbon goals: Based on data of NPP-VIIRS-like nighttime light

Author

Listed:
  • Rao, Yanchun
  • Wang, Xiuli
  • Li, Hengkai

Abstract

To reflect the trend of electricity consumption (EC) in China's Pearl River Delta (PRD) under a balanced environment and economy, this paper proposes an EC forecasting framework from the perspective of optimal economic growth. The historical GDP statistics of the PRD are calibrated with data from the new "NPP-VIIRS-like" nighttime light dataset from 2000 to 2020, and two simulation scenarios of optimal economic growth are constructed according to the carbon emission reduction rate. Finally, based on the electricity data from 2000 to 2017, an error correction model is constructed to predict and compare the trend of EC in the PRD under different carbon emission scenarios. The results demonstrate the following. (1) The economic growth path under low-carbon constraints is more closely aligned with the actual economic development of the PRD. (2) The electricity demand required to sustain optimal economic growth in the PRD under low-carbon constraints is projected to reach saturation around 2037, approximately a decade earlier than the scenario without carbon constraints. The results of the projections are expected to guide future work on power system planning and economic development assessment in the context of carbon reduction.

Suggested Citation

  • Rao, Yanchun & Wang, Xiuli & Li, Hengkai, 2024. "Forecasting electricity consumption in China's Pearl River Delta urban agglomeration under the optimal economic growth path with low-carbon goals: Based on data of NPP-VIIRS-like nighttime light," Energy, Elsevier, vol. 294(C).
  • Handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224007424
    DOI: 10.1016/j.energy.2024.130970
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224007424
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2024.130970?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Ozturk, Ilhan, 2010. "A literature survey on energy-growth nexus," Energy Policy, Elsevier, vol. 38(1), pages 340-349, January.
    2. Nordhaus, William D, 1991. "To Slow or Not to Slow: The Economics of the Greenhouse Effect," Economic Journal, Royal Economic Society, vol. 101(407), pages 920-937, July.
    3. Das, Anupam & McFarlane, Adian, 2019. "Non-linear dynamics of electric power losses, electricity consumption, and GDP in Jamaica," Energy Economics, Elsevier, vol. 84(C).
    4. Clark, Hunter & Pinkovskiy, Maxim & Sala-i-Martin, Xavier, 2020. "China's GDP growth may be understated," China Economic Review, Elsevier, vol. 62(C).
    5. Zhao, Zhenyu & Zhang, Yao & Yang, Yujia & Yuan, Shuguang, 2022. "Load forecasting via Grey Model-Least Squares Support Vector Machine model and spatial-temporal distribution of electric consumption intensity," Energy, Elsevier, vol. 255(C).
    6. Haibing Wang & Bowen Li & Muhammad Qasim Khan, 2022. "Prediction of Shanghai Electric Power Carbon Emissions Based on Improved STIRPAT Model," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    7. Raymond W. Goldsmith, 1951. "A Perpetual Inventory of National Wealth," NBER Chapters, in: Studies in Income and Wealth, Volume 14, pages 5-73, National Bureau of Economic Research, Inc.
    8. Emi Nakamura & Jón Steinsson & Miao Liu, 2016. "Are Chinese Growth and Inflation Too Smooth? Evidence from Engel Curves," American Economic Journal: Macroeconomics, American Economic Association, vol. 8(3), pages 113-144, July.
    9. Moon, Young-Seok & Sonn, Yang-Hoon, 1996. "Productive energy consumption and economic growth: An endogenous growth model and its empirical application," Resource and Energy Economics, Elsevier, vol. 18(2), pages 189-200, June.
    10. Steinbuks, Jevgenijs, 2019. "Assessing the accuracy of electricity production forecasts in developing countries," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1175-1185.
    11. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    12. Moreno-Carbonell, Santiago & Sánchez-Úbeda, Eugenio F. & Muñoz, Antonio, 2020. "Rethinking weather station selection for electric load forecasting using genetic algorithms," International Journal of Forecasting, Elsevier, vol. 36(2), pages 695-712.
    13. Sheng, Pengfei & Guo, Xiaohui, 2018. "Energy consumption associated with urbanization in China: Efficient- and inefficient-use," Energy, Elsevier, vol. 165(PB), pages 118-125.
    14. Hao Lu & Wenqiang Qu & Shengnan Min & Jiaqi Chen & Eric Lefevre, 2022. "Inversion of Regional Economic Trend from NPP-VIIRS Nighttime Light Data Based on Adaptive Clustering Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, August.
    15. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    16. Ferguson, Ross & Wilkinson, William & Hill, Robert, 2000. "Electricity use and economic development," Energy Policy, Elsevier, vol. 28(13), pages 923-934, November.
    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. F. Chui & A. Elkamel & R. Surit & E. Croiset & P.L. Douglas, 2009. "Long-term electricity demand forecasting for power system planning using economic, demographic and climatic variables," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 3(3), pages 277-304.
    19. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    20. Rawski, Thomas G., 2001. "What is happening to China's GDP statistics?," China Economic Review, Elsevier, vol. 12(4), pages 347-354.
    21. Tang, Lei & Wang, Xifan & Wang, Xiuli & Shao, Chengcheng & Liu, Shiyu & Tian, Shijun, 2019. "Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory," Energy, Elsevier, vol. 167(C), pages 1144-1154.
    22. Li, Jinghua & Luo, Yichen & Wei, Shanyang, 2022. "Long-term electricity consumption forecasting method based on system dynamics under the carbon-neutral target," Energy, Elsevier, vol. 244(PA).
    23. Xuan Liu & Zehao Li & Xinyi Fu & Zhengtong Yin & Mingzhe Liu & Lirong Yin & Wenfeng Zheng, 2023. "Monitoring House Vacancy Dynamics in The Pearl River Delta Region: A Method Based on NPP-VIIRS Night-Time Light Remote Sensing Images," Land, MDPI, vol. 12(4), pages 1-21, April.
    24. Dave Donaldson & Adam Storeygard, 2016. "The View from Above: Applications of Satellite Data in Economics," Journal of Economic Perspectives, American Economic Association, vol. 30(4), pages 171-198, Fall.
    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. Li, Shoujiang & Wang, Jianzhou & Zhang, Hui & Liang, Yong, 2024. "Enhancing hourly electricity forecasting using fuzzy cognitive maps with sample entropy," Energy, Elsevier, vol. 298(C).
    2. Wang, Zhixin & Wang, Xiuli & Tao, Huan & Li, Hengkai & Rao, Yanchun, 2025. "China's energy consumption trends and structural transition pathways under synergistic cost and carbon constraints: Based on the perspective of optimal steady economic growth," Energy, Elsevier, vol. 335(C).
    3. Yang, Yu & Xue, Jiashun & Zhang, Xin & Li, Xiaochun & Cheng, Yan & Yang, Shuoguo, 2026. "Disparity between urban and rural electricity consumption in the Yellow River Basin, China," Energy, Elsevier, vol. 344(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. Steinbuks, Jevgenijs, 2019. "Assessing the accuracy of electricity production forecasts in developing countries," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1175-1185.
    2. Beyer, Robert C.M. & Franco-Bedoya, Sebastian & Galdo, Virgilio, 2021. "Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity," World Development, Elsevier, vol. 140(C).
    3. Clark, Hunter & Pinkovskiy, Maxim & Sala-i-Martin, Xavier, 2020. "China's GDP growth may be understated," China Economic Review, Elsevier, vol. 62(C).
    4. Ai, Hongshan & Zhong, Tenglong & Zhou, Zhengqing, 2022. "The real economic costs of COVID-19: Insights from electricity consumption data in Hunan Province, China," Energy Economics, Elsevier, vol. 105(C).
    5. Anna Sznajderska, 2021. "Should we recalculate the level of spillover effects if the alternative GDP measures for China are correct?," Bank i Kredyt, Narodowy Bank Polski, vol. 52(5), pages 437-456.
    6. Eeva Kerola, 2019. "In Search of Fluctuations: Another Look at China’s Incredibly Stable GDP Growth Rates," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 61(3), pages 359-380, September.
    7. Fernald, John G. & Hsu, Eric & Spiegel, Mark M., 2021. "Reprint: Is China fudging its GDP figures? Evidence from trading partner data," Journal of International Money and Finance, Elsevier, vol. 114(C).
    8. Hamed, Mohammad M. & Ali, Hesham & Abdelal, Qasem, 2022. "Forecasting annual electric power consumption using a random parameters model with heterogeneity in means and variances," Energy, Elsevier, vol. 255(C).
    9. Chor, Davin & Li, Bingjing, 2024. "Illuminating the effects of the US-China tariff war on China’s economy," Journal of International Economics, Elsevier, vol. 150(C).
    10. Zeng, Jiangnan & Zhou, Qiyao, 2024. "Mayors’ promotion incentives and subnational-level GDP manipulation," Journal of Urban Economics, Elsevier, vol. 143(C).
    11. Sinclair, Tara M., 2019. "Characteristics and implications of Chinese macroeconomic data revisions," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1108-1117.
    12. Fernald, John G. & Hsu, Eric & Spiegel, Mark M., 2021. "Is China fudging its GDP figures? Evidence from trading partner data," Journal of International Money and Finance, Elsevier, vol. 110(C).
    13. Brock, Gregory, 2019. "A remote sensing look at the economy of a Russian region (Rostov) adjacent to the Ukrainian crisis," Journal of Policy Modeling, Elsevier, vol. 41(2), pages 416-431.
    14. He, Canfei & Li, Jing & Wang, Wenyu & Zhang, Peng, 2024. "Regional resilience during a trade war: The role of global connections and local networks," Journal of World Business, Elsevier, vol. 59(5).
    15. Liu, Ping & James Hueng, C., 2017. "Measuring real business condition in China," China Economic Review, Elsevier, vol. 46(C), pages 261-274.
    16. Cruzatti C., John & Bjørnskov, Christian & Sáenz de Viteri, Andrea & Cruzatti, Christian, 2024. "Geography, development, and power: Parliament leaders and local clientelism," World Development, Elsevier, vol. 182(C).
    17. Klaus Andresen & Ursula Müller & Hans-Jörg Schmerer, 2026. "Energy for Growth: Satellite Synthetic Control Evidence from Indonesia," CESifo Working Paper Series 12502, CESifo.
    18. Menezes, Flavio & Figer, Vivian & Jardim, Fernanda & Medeiros, Pedro, 2022. "A near real-time economic activity tracker for the Brazilian economy during the COVID-19 pandemic," Economic Modelling, Elsevier, vol. 112(C).
    19. Fulong Xiao & Zini Liang & Yongbin Lv & Wei Wang, 2024. "The effect of government‐guided funds on target industries in development zones – Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(5), pages 4701-4722, December.
    20. Giorgio Chiovelli & Stelios Michalopoulus & Elias Papaioannou & Tanner Regan, 2025. "Illuminating the Global South," Working Papers 2025-009, The George Washington University, The Center for Economic Research.

    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:eee:energy:v:294:y:2024:i:c:s0360544224007424. 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.