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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

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  • 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.

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  • 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
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    1. 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.
    2. Das, Anupam & McFarlane, Adian, 2019. "Non-linear dynamics of electric power losses, electricity consumption, and GDP in Jamaica," Energy Economics, Elsevier, vol. 84(C).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Ozturk, Ilhan, 2010. "A literature survey on energy-growth nexus," Energy Policy, Elsevier, vol. 38(1), pages 340-349, January.
    10. 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).
    11. 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.
    12. 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.
    13. 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.
    14. Ferguson, Ross & Wilkinson, William & Hill, Robert, 2000. "Electricity use and economic development," Energy Policy, Elsevier, vol. 28(13), pages 923-934, November.
    15. 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.
    16. Clark, Hunter & Pinkovskiy, Maxim & Sala-i-Martin, Xavier, 2020. "China's GDP growth may be understated," China Economic Review, Elsevier, vol. 62(C).
    17. Steinbuks, Jevgenijs, 2019. "Assessing the accuracy of electricity production forecasts in developing countries," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1175-1185.
    18. Sheng, Pengfei & Guo, Xiaohui, 2018. "Energy consumption associated with urbanization in China: Efficient- and inefficient-use," Energy, Elsevier, vol. 165(PB), pages 118-125.
    19. 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.
    20. Rawski, Thomas G., 2001. "What is happening to China's GDP statistics?," China Economic Review, Elsevier, vol. 12(4), pages 347-354.
    21. 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.
    22. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    23. 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).
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    Cited by:

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