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Chinese residential electricity consumption: Estimation and forecast using micro-data

Author

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  • Cao, Jing
  • Ho, Mun Sing
  • Li, Yating
  • Newell, Richard G.
  • Pizer, William A.

Abstract

Based on econometric estimation using data from the Chinese Urban Household Survey, we develop a preferred forecast range of 85–143 percent growth in residential per capita electricity demand over 2009–2025. Our analysis suggests that per capita income growth drives a 43% increase, with the remainder due to an unexplained time trend. Roughly one-third of the income-driven demand comes from increases in the stock of specific major appliances, particularly AC units. The other two-thirds comes from non-specific sources of income-driven growth and is based on an estimated income elasticity that falls from 0.28 to 0.11 as income rises. While the stock of refrigerators is not projected to increase, we find that they contribute nearly 20 percent of household electricity demand. Alternative plausible time trend assumptions are responsible for the wide range of 85–143 percent. Meanwhile we estimate a price elasticity of demand of −0.7. These estimates point to carbon pricing and appliance efficiency policies that could substantially reduce demand.

Suggested Citation

  • Cao, Jing & Ho, Mun Sing & Li, Yating & Newell, Richard G. & Pizer, William A., 2019. "Chinese residential electricity consumption: Estimation and forecast using micro-data," Resource and Energy Economics, Elsevier, vol. 56(C), pages 6-27.
    • Handle: RePEc:eee:resene:v:56:y:2019:i:c:p:6-27
    DOI: 10.1016/j.reseneeco.2017.10.003
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    7. Guefano, Serge & Tamba, Jean Gaston & Azong, Tchitile Emmanuel Wilfried & Monkam, Louis, 2021. "Forecast of electricity consumption in the Cameroonian residential sector by Grey and vector autoregressive models," Energy, Elsevier, vol. 214(C).
    8. Zhang, Yimeng & Wang, Feng & Zhang, Bing, 2023. "The impacts of household structure transitions on household carbon emissions in China," Ecological Economics, Elsevier, vol. 206(C).
    9. Song, Yanwu & Zhang, Jinrui & Song, Yingkang & Fan, Xinran & Zhu, Yuqing & Zhang, Chen, 2020. "Can industry-university-research collaborative innovation efficiency reduce carbon emissions?," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
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    15. Cansino, José M. & Dugo, Víctor & Gálvez-Ruiz, David & Román-Collado, Rocío, 2023. "What drove electricity consumption in the residential sector during the SARS-CoV-2 confinement? A special focus on university students in southern Spain," Energy, Elsevier, vol. 262(PB).
    16. Lyu, Xingbao & Yuan, Yi & Ning, Wenjing & Chen, Li & Tao, Wen-Quan, 2024. "Investigation and optimization of PEMFC-CHP systems based on Chinese residential thermal and electrical consumption data," Applied Energy, Elsevier, vol. 356(C).
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    More about this item

    Keywords

    Electricity; Demand; Estimation; Forecast; Residential; China;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • O44 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Environment and Growth

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