IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this article

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

Listed author(s):
  • Naiming Xie

    ()

  • Alan Pearman

    ()

Registered author(s):

    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

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL: http://hdl.handle.net/10.1007/s11069-014-1200-x
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Article provided by Springer & International Society for the Prevention and Mitigation of Natural Hazards in its journal Natural Hazards.

    Volume (Year): 74 (2014)
    Issue (Month): 2 (November)
    Pages: 639-659

    as
    in new window

    Handle: RePEc:spr:nathaz:v:74:y:2014:i:2:p:639-659
    DOI: 10.1007/s11069-014-1200-x
    Contact details of provider: Web page: http://www.springer.com

    Order Information: Web: http://www.springer.com/economics/journal/11069

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    as
    in new window


    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.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:74:y:2014:i:2:p:639-659. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)

    or (Rebekah McClure)

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.