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Forecasting energy consumption in China following instigation of an energy-saving policy

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  • Naiming Xie
  • Alan Pearman

Abstract

China is in a key stage of industrialization and urbanization, which brings a high economic growth rate accompanied by high energy consumption. To alleviate the unsustainable demand for energy consumption, China’s government has instigated an energy-saving policy to decrease energy consumption per unit gross domestic product (GDP) so as to improve energy efficiency. Based on analysing historical trends of energy consumption and GDP, we have applied an optimized single-variable discrete grey forecasting model [OSDGM (1, 1)] to measure the instigation effects of the energy-saving policy and forecast whether the planned reduction rate of energy consumption per unit GDP in the implementation stage could be accomplished or not. The results illustrate that China’s government has made major progress on energy saving even though the task is tough in the long run. The forecasting results indicate that it is difficult to accomplish the planned reduction rate of energy consumption per unit GDP at both the national and provincial levels. According to the economic growth rate of 2011 and 2012, nearly half of the provinces could not reach their planned reduction rate objectives. These conclusions are very important for China’s government both in terms of policy monitoring and development. Copyright Springer Science+Business Media Dordrecht 2014

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  • Naiming Xie & Alan Pearman, 2014. "Forecasting energy consumption in China following instigation of an energy-saving policy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 639-659, November.
    • Handle: RePEc:spr:nathaz:v:74:y:2014:i:2:p:639-659
    DOI: 10.1007/s11069-014-1200-x
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    2. Wang, Ce & Li, Bing-Bing & Liang, Qiao-Mei & Wang, Jin-Cheng, 2018. "Has China’s coal consumption already peaked? A demand-side analysis based on hybrid prediction models," Energy, Elsevier, vol. 162(C), pages 272-281.
    3. Xinhui Lu & Kaile Zhou & Felix T. S. Chan & Shanlin Yang, 2017. "Optimal scheduling of household appliances for smart home energy management considering demand response," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1639-1653, September.
    4. Li, Bing-Bing & Liang, Qiao-Mei & Wang, Jin-Cheng, 2015. "A comparative study on prediction methods for China's medium- and long-term coal demand," Energy, Elsevier, vol. 93(P2), pages 1671-1683.
    5. Zhiyu Fang & Ling Jiang & Zhong Fang, 2021. "Does Economic Policy Intervention Inhibit the Efficiency of China’s Green Energy Economy?," Sustainability, MDPI, vol. 13(23), pages 1-20, December.
    6. Renbo Liu & Yuhui Ge & Peng Zuo, 2023. "Study on Economic Data Forecasting Based on Hybrid Intelligent Model of Artificial Neural Network Optimized by Harris Hawks Optimization," Mathematics, MDPI, vol. 11(21), pages 1-28, November.

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