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Aviation fuel demand development in China

Author

Listed:
  • Chai, Jian
  • Zhang, Zhong-Yu
  • Wang, Shou-Yang
  • Lai, Kin Keung
  • Liu, John

Abstract

This paper analyzes the core factors and the impact path of aviation fuel demand in China and conducts a structural decomposition analysis of the aviation fuel cost changes and increase of the main aviation enterprises’ business profits. Through the establishment of an integrated forecast model for China’s aviation fuel demand, this paper confirms that the significant rise in China’s aviation fuel demand because of increasing air services demand is more than offset by higher aviation fuel efficiency. There are few studies which use a predictive method to decompose, estimate and analyze future aviation fuel demand. Based on a structural decomposition with indirect prediction, aviation fuel demand is decomposed into efficiency and total amount (aviation fuel efficiency and air transport total turnover). The core influencing factors for these two indexes are selected using path analysis. Then, univariate and multivariate models (ETS/ARIMA model and Bayesian multivariate regression) are used to analyze and predict both aviation fuel efficiency and air transport total turnover. At last, by integrating results, future aviation fuel demand is forecast. The results show that the aviation fuel efficiency goes up by 0.8% as the passenger load factor increases 1%; the air transport total turnover goes up by 3.8% and 0.4% as the urbanization rate and the per capita GDP increase 1%, respectively. By the end of 2015, China’s aviation fuel demand will have increased to 28 million tonnes, and is expected to be 50 million tonnes by 2020. With this in mind, increases in the main aviation enterprises’ business profits must be achieved through the further promotion of air transport.

Suggested Citation

  • Chai, Jian & Zhang, Zhong-Yu & Wang, Shou-Yang & Lai, Kin Keung & Liu, John, 2014. "Aviation fuel demand development in China," Energy Economics, Elsevier, vol. 46(C), pages 224-235.
    • Handle: RePEc:eee:eneeco:v:46:y:2014:i:c:p:224-235
    DOI: 10.1016/j.eneco.2014.09.007
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    as
    1. repec:bla:opecrv:v:34:y:2010:i:1:p:42-72 is not listed on IDEAS
    2. repec:bla:opecrv:v:33:y:2009:i:1:p:23-46 is not listed on IDEAS
    3. Taylor, James W., 2004. "Volatility forecasting with smooth transition exponential smoothing," International Journal of Forecasting, Elsevier, vol. 20(2), pages 273-286.
    4. repec:dau:papers:123456789/6792 is not listed on IDEAS
    5. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "A study of outliers in the exponential smoothing approach to forecasting," International Journal of Forecasting, Elsevier, vol. 28(2), pages 477-484.
    6. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    7. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    8. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    9. James W. Taylor, 2004. "Smooth transition exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 385-404.
    10. Chai, Jian & Guo, Ju-E & Wang, Shou-Yang & Lai, Kin Keung, 2009. "Why does energy intensity fluctuate in China?," Energy Policy, Elsevier, vol. 37(12), pages 5717-5731, December.
    11. Nygren, Emma & Aleklett, Kjell & Höök, Mikael, 2009. "Aviation fuel and future oil production scenarios," Energy Policy, Elsevier, vol. 37(10), pages 4003-4010, October.
    12. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Combination of long term and short term forecasts, with application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 870-886.
    13. Chèze, Benoît & Gastineau, Pascal & Chevallier, Julien, 2011. "Forecasting world and regional aviation jet fuel demands to the mid-term (2025)," Energy Policy, Elsevier, vol. 39(9), pages 5147-5158, September.
    14. Kick, Th. & Herbst, J. & Kathrotia, T. & Marquetand, J. & Braun-Unkhoff, M. & Naumann, C. & Riedel, U., 2012. "An experimental and modeling study of burning velocities of possible future synthetic jet fuels," Energy, Elsevier, vol. 43(1), pages 111-123.
    15. Babikian, Raffi & Lukachko, Stephen P. & Waitz, Ian A., 2002. "The historical fuel efficiency characteristics of regional aircraft from technological, operational, and cost perspectives," Journal of Air Transport Management, Elsevier, vol. 8(6), pages 389-400.
    16. Mohamed Boutahar, 2007. "Optimal prediction with nonstationary ARFIMA model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 95-111.
    17. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Combination of long term and short term forecasts, with application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 870-886, July.
    18. Kosei Fukuda, 2009. "Related-variables selection in temporal disaggregation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(4), pages 343-357.
    19. Hamid Baghestani, 2010. "Forecasting the 10‐year US treasury rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(8), pages 673-688, December.
    20. Hiroaki Chigira & Taku Yamamoto, 2009. "Forecasting in large cointegrated processes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 631-650.
    21. Jeong-Ryeol Kurz-Kim, 2008. "Combining forecasts using optimal combination weight and generalized autoregression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 419-432.
    22. Bianchi, Lisa & Jarrett, Jeffrey & Choudary Hanumara, R., 1998. "Improving forecasting for telemarketing centers by ARIMA modeling with intervention," International Journal of Forecasting, Elsevier, vol. 14(4), pages 497-504, December.
    23. Rob Hyndman & Muhammad Akram & Blyth Archibald, 2008. "The admissible parameter space for exponential smoothing models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(2), pages 407-426, June.
    24. Wadud, Zia & Graham, Daniel J. & Noland, Robert B., 2009. "Modelling fuel demand for different socio-economic groups," Applied Energy, Elsevier, vol. 86(12), pages 2740-2749, December.
    25. Vedantham, Anu & Oppenheimer, Michael, 1998. "Long-term scenarios for aviation: Demand and emissions of CO2 and NOx," Energy Policy, Elsevier, vol. 26(8), pages 625-641, July.
    26. Adams, Zeno & Gerner, Mathias, 2012. "Cross hedging jet-fuel price exposure," Energy Economics, Elsevier, vol. 34(5), pages 1301-1309.
    27. repec:bla:opecrv:v:32:y:2008:i:4:p:323-342 is not listed on IDEAS
    28. Sarah Gelper & Roland Fried & Christophe Croux, 2010. "Robust forecasting with exponential and Holt-Winters smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 285-300.
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    Cited by:

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    2. Chen, Zhongfei & Wanke, Peter & Antunes, Jorge Junio Moreira & Zhang, Ning, 2017. "Chinese airline efficiency under CO2 emissions and flight delays: A stochastic network DEA model," Energy Economics, Elsevier, vol. 68(C), pages 89-108.

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    More about this item

    Keywords

    Aviation fuel demand; Aviation fuel efficiency; ETS; ARIMA; Bayes;
    All these keywords.

    JEL classification:

    • L98 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Government Policy
    • L93 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Air Transportation
    • P48 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Legal Institutions; Property Rights; Natural Resources; Energy; Environment; Regional Studies
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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