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

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  • Naiming Xie

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  • Alan Pearman

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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

Suggested Citation

  • 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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    References listed on IDEAS

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    1. Pao, Hsiao-Tien & Yu, Hsiao-Cheng & Yang, Yeou-Herng, 2011. "Modeling the CO2 emissions, energy use, and economic growth in Russia," Energy, Elsevier, vol. 36(8), pages 5094-5100.
    2. Wang, Yuanyuan & Wang, Jianzhou & Zhao, Ge & Dong, Yao, 2012. "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China," Energy Policy, Elsevier, vol. 48(C), pages 284-294.
    3. Kaza, Nikhil, 2010. "Understanding the spectrum of residential energy consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 38(11), pages 6574-6585, November.
    4. Wang, Jianzhou & Dong, Yao & Wu, Jie & Mu, Ren & Jiang, He, 2011. "Coal production forecast and low carbon policies in China," Energy Policy, Elsevier, vol. 39(10), pages 5970-5979, October.
    5. Yuan, Chaoqing & Liu, Sifeng & Wu, Junlong, 2010. "The relationship among energy prices and energy consumption in China," Energy Policy, Elsevier, vol. 38(1), pages 197-207, January.
    6. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    7. Yao, Ming-Jong & Chu, Weng-Ming, 2008. "A genetic algorithm for determining optimal replenishment cycles to minimize maximum warehouse space requirements," Omega, Elsevier, vol. 36(4), pages 619-631, August.
    8. Cadenas, E. & Jaramillo, O.A. & Rivera, W., 2010. "Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method," Renewable Energy, Elsevier, vol. 35(5), pages 925-930.
    9. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    10. Talha Yalta, A. & Cakar, Hatice, 2012. "Energy consumption and economic growth in China: A reconciliation," Energy Policy, Elsevier, vol. 41(C), pages 666-675.
    11. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
    12. Pao, Hsiao-Tien & Tsai, Chung-Ming, 2011. "Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil," Energy, Elsevier, vol. 36(5), pages 2450-2458.
    13. Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
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    1. repec:spr:nathaz:v:88:y:2017:i:3:d:10.1007_s11069-017-2937-9 is not listed on IDEAS
    2. Che-Jung Chang & Liping Yu & Peng Jin, 2016. "A mega-trend-diffusion grey forecasting model for short-term manufacturing demand," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(12), pages 1439-1445, December.
    3. 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.

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