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Coal production forecast and low carbon policies in China

Author

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  • Wang, Jianzhou
  • Dong, Yao
  • Wu, Jie
  • Mu, Ren
  • Jiang, He

Abstract

With rapid economic growth and industrial expansion, China consumes more coal than any other nation. Therefore, it is particularly crucial to forecast China's coal production to help managers make strategic decisions concerning China's policies intended to reduce carbon emissions and concerning the country's future needs for domestic and imported coal. Such decisions, which must consider results from forecasts, will have important national and international effects. This article proposes three improved forecasting models based on grey systems theory: the Discrete Grey Model (DGM), the Rolling DGM (RDGM), and the p value RDGM. We use the statistical data of coal production in China from 1949 to 2005 to validate the effectiveness of these improved models to forecast the data from 2006 to 2010. The performance of the models demonstrates that the p value RDGM has the best forecasting behaviour over this historical time period. Furthermore, this paper forecasts coal production from 2011 to 2015 and suggests some policies for reducing carbon and other emissions that accompany the rise in forecasted coal production.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:enepol:v:39:y:2011:i:10:p:5970-5979
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    References listed on IDEAS

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    1. Lin, Bo-qiang & Liu, Jiang-hua, 2010. "Estimating coal production peak and trends of coal imports in China," Energy Policy, Elsevier, vol. 38(1), pages 512-519, January.
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    Citations

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    Cited by:

    1. 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.
    2. repec:eee:appene:v:197:y:2017:i:c:p:151-162 is not listed on IDEAS
    3. Wang, Jianliang & Feng, Lianyong & Tverberg, Gail E., 2013. "An analysis of China's coal supply and its impact on China's future economic growth," Energy Policy, Elsevier, vol. 57(C), pages 542-551.
    4. Hu, Yan & Hall, Charles A.S. & Wang, Jianliang & Feng, Lianyong & Poisson, Alexandre, 2013. "Energy Return on Investment (EROI) of China's conventional fossil fuels: Historical and future trends," Energy, Elsevier, vol. 54(C), pages 352-364.
    5. Yu, Shiwei & Zhang, Junjie & Zheng, Shuhong & Sun, Han, 2015. "Provincial carbon intensity abatement potential estimation in China: A PSO–GA-optimized multi-factor environmental learning curve method," Energy Policy, Elsevier, vol. 77(C), pages 46-55.
    6. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    7. Sun, Sizhong & Anwar, Sajid, 2015. "R&D status and the performance of domestic firms in China's coal mining industry," Energy Policy, Elsevier, vol. 79(C), pages 99-103.
    8. Nana Geng & Yong Zhang & Yixiang Sun & Yunjian Jiang & Dandan Chen, 2015. "Forecasting China’s Annual Biofuel Production Using an Improved Grey Model," Energies, MDPI, Open Access Journal, vol. 8(10), pages 1-20, October.
    9. Wang, Delu & Ma, Gang & Song, Xuefeng & Liu, Yun, 2017. "Energy price slump and policy response in the coal-chemical industry district: A case study of Ordos with a system dynamics model," Energy Policy, Elsevier, vol. 104(C), pages 325-339.
    10. Zhang, Yanfang & Zhang, Ming & Liu, Yue & Nie, Rui, 2017. "Enterprise investment, local government intervention and coal overcapacity: The case of China," Energy Policy, Elsevier, vol. 101(C), pages 162-169.

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