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China’s rural electricity market—a quantitative analysis

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  • Yang, Ming
  • Yu, Xin

Abstract

The objective of this paper is to quantify the development of the rural electricity market at county level and below in China. A sectorial energy demand analysis and forecasting model was developed to analyze six Chinese provinces with different economic backgrounds. Historical data for over 20 years were collected on rural economic development, households, population, per capita income, community infrastructure development, capital investment, electricity consumption, output values in agriculture sector, and township and village enterprises (TVEs). This paper concludes that by 2010, annual electricity demand will increase at a rate between −1.40% and 15.60% (depending on the sectors and provinces). It also recommends a preferred order for future rural electricity investment: Jiangsu, Hebei, Henan, Shaanxi, Liaoning and Xinjiang, i.e. from the most to the least developed provinces, if the investment objectives are to find the best market return and the greatest impact on rural market development.

Suggested Citation

  • Yang, Ming & Yu, Xin, 2004. "China’s rural electricity market—a quantitative analysis," Energy, Elsevier, vol. 29(7), pages 961-977.
    • Handle: RePEc:eee:energy:v:29:y:2004:i:7:p:961-977
    DOI: 10.1016/j.energy.2003.12.002
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    References listed on IDEAS

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    1. Yang, Ming, 2003. "China's rural electrification and poverty reduction," Energy Policy, Elsevier, vol. 31(3), pages 283-295, February.
    2. Yang, Ming & Yu, Xin, 1996. "China's power management," Energy Policy, Elsevier, vol. 24(8), pages 735-757, August.
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    Cited by:

    1. Liang, Qiao-Mei & Fan, Ying & Wei, Yi-Ming, 2007. "Multi-regional input-output model for regional energy requirements and CO2 emissions in China," Energy Policy, Elsevier, vol. 35(3), pages 1685-1700, March.
    2. Ito, Toshihide & Chen, Youqing & Ito, Shoichi & Yamaguchi, Kaoru, 2010. "Prospect of the upper limit of the energy demand in China from regional aspects," Energy, Elsevier, vol. 35(12), pages 5320-5327.
    3. Shao, Zhen & Gao, Fei & Zhang, Qiang & Yang, Shan-Lin, 2015. "Multivariate statistical and similarity measure based semiparametric modeling of the probability distribution: A novel approach to the case study of mid-long term electricity consumption forecasting i," Applied Energy, Elsevier, vol. 156(C), pages 502-518.
    4. Pao, H.T., 2009. "Forecasting energy consumption in Taiwan using hybrid nonlinear models," Energy, Elsevier, vol. 34(10), pages 1438-1446.
    5. Cai, Wenjia & Wang, Can & Wang, Ke & Zhang, Ying & Chen, Jining, 2007. "Scenario analysis on CO2 emissions reduction potential in China's electricity sector," Energy Policy, Elsevier, vol. 35(12), pages 6445-6456, December.
    6. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    7. Pao, Hsiao-Tien, 2009. "Forecast of electricity consumption and economic growth in Taiwan by state space modeling," Energy, Elsevier, vol. 34(11), pages 1779-1791.

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