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Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry

Author

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  • Jian Chai

    (Shaanxi Normal University, International Business School, Xi’an 710062, China
    Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
    The School of Management Xi’an Jiao Tong University, Xi’an 710049, China)

  • Shubin Wang

    (Shaanxi Normal University, International Business School, Xi’an 710062, China)

  • Shouyang Wang

    (Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China)

  • Ju’e Guo

    (The School of Management Xi’an Jiao Tong University, Xi’an 710049, China)

Abstract

In this paper, petroleum product (mainly petrol and diesel) consumption in the transportation sector of China is analyzed. This was based on the Bayesian linear regression theory and Markov Chain Monte Carlo method (MCMC), establishing a demand-forecast model of petrol and diesel consumption introduced into the analytical framework with explanatory variables of urbanization level, per capita GDP, turnover of passengers (freight) in aggregate (TPA, TFA), and civilian vehicle number (CVN) and explained variables of petrol and diesel consumption. Furthermore, we forecast the future consumer demand for oil products during “The 12th Five Year Plan” (2011–2015) based on the historical data covering from 1985 to 2009, finding that urbanization is the most sensitive factor, with a strong marginal effect on petrol and diesel consumption in this sector. From the viewpoint of prediction interval value, urbanization expresses the lower limit of the predicted results, and CVN the upper limit of the predicted results. Predicted value from other independent variables is in the range of predicted values which display a validation range and reference standard being much more credible for policy makers. Finally, a comparison between the predicted results from autoregressive integrated moving average models (ARIMA) and others is made to assess our task.

Suggested Citation

  • Jian Chai & Shubin Wang & Shouyang Wang & Ju’e Guo, 2012. "Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry," Energies, MDPI, vol. 5(3), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:3:p:577-598:d:16412
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    References listed on IDEAS

    as
    1. repec:dau:papers:123456789/6792 is not listed on IDEAS
    2. Forouzanfar, Mehdi & Doustmohammadi, A. & Hasanzadeh, Samira & Shakouri G, H., 2012. "Transport energy demand forecast using multi-level genetic programming," Applied Energy, Elsevier, vol. 91(1), pages 496-503.
    3. Dilaver, Zafer & Hunt, Lester C, 2011. "Modelling and forecasting Turkish residential electricity demand," Energy Policy, Elsevier, vol. 39(6), pages 3117-3127, June.
    4. Robèrt, Markus & Hultén, Per & Frostell, Björn, 2007. "Biofuels in the energy transition beyond peak oil," Energy, Elsevier, vol. 32(11), pages 2089-2098.
    5. Liimatainen, Heikki & Pöllänen, Markus, 2010. "Trends of energy efficiency in Finnish road freight transport 1995-2009 and forecast to 2016," Energy Policy, Elsevier, vol. 38(12), pages 7676-7686, December.
    6. Kumar, Satish & Kwon, Hyouk-Tae & Choi, Kwang-Ho & Hyun Cho, Jae & Lim, Wonsub & Moon, Il, 2011. "Current status and future projections of LNG demand and supplies: A global prospective," Energy Policy, Elsevier, vol. 39(7), pages 4097-4104, July.
    7. 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.
    8. Dilaver, Zafer & Hunt, Lester C., 2011. "Turkish aggregate electricity demand: An outlook to 2020," Energy, Elsevier, vol. 36(11), pages 6686-6696.
    9. Leung, Guy C.K., 2010. "China's oil use, 1990-2008," Energy Policy, Elsevier, vol. 38(2), pages 932-944, February.
    10. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    11. Jensen, Thomas C. & Møller, Flemming, 2010. "Incorporating the value of changes in price volatility into cost-benefit analysis--an application to oil prices in the transport sector," Energy Policy, Elsevier, vol. 38(1), pages 573-579, January.
    12. Narayan, Paresh Kumar & Wong, Philip, 2009. "A panel data analysis of the determinants of oil consumption: The case of Australia," Applied Energy, Elsevier, vol. 86(12), pages 2771-2775, December.
    13. Anable, Jillian & Brand, Christian & Tran, Martino & Eyre, Nick, 2012. "Modelling transport energy demand: A socio-technical approach," Energy Policy, Elsevier, vol. 41(C), pages 125-138.
    14. Zhang, Ming & Mu, Hailin & Li, Gang & Ning, Yadong, 2009. "Forecasting the transport energy demand based on PLSR method in China," Energy, Elsevier, vol. 34(9), pages 1396-1400.
    15. Lindfeldt, Erik G. & Saxe, Maria & Magnusson, Mimmi & Mohseni, Farzad, 2010. "Strategies for a road transport system based on renewable resources - The case of an import-independent Sweden in 2025," Applied Energy, Elsevier, vol. 87(6), pages 1836-1845, June.
    16. He, Kebin & Huo, Hong & Zhang, Qiang & He, Dongquan & An, Feng & Wang, Michael & Walsh, Michael P., 2005. "Oil consumption and CO2 emissions in China's road transport: current status, future trends, and policy implications," Energy Policy, Elsevier, vol. 33(12), pages 1499-1507, August.
    17. Sadorsky, Perry, 2012. "Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies," Energy Economics, Elsevier, vol. 34(1), pages 248-255.
    18. Kok, Robert & Annema, Jan Anne & van Wee, Bert, 2011. "Cost-effectiveness of greenhouse gas mitigation in transport: A review of methodological approaches and their impact," Energy Policy, Elsevier, vol. 39(12), pages 7776-7793.
    19. Meyer, Ina & Kaniovski, Serguei & Scheffran, Jürgen, 2012. "Scenarios for regional passenger car fleets and their CO2 emissions," Energy Policy, Elsevier, vol. 41(C), pages 66-74.
    20. Larsson, Karl & Nossman, Marcus, 2011. "Jumps and stochastic volatility in oil prices: Time series evidence," Energy Economics, Elsevier, vol. 33(3), pages 504-514, May.
    21. Ali Ahmed, Huson Joher & Bashar, Omar H.M.N. & Wadud, I.K.M. Mokhtarul, 2012. "The transitory and permanent volatility of oil prices: What implications are there for the US industrial production?," Applied Energy, Elsevier, vol. 92(C), pages 447-455.
    22. Li, Zheng & Rose, John M. & Hensher, David A., 2010. "Forecasting automobile petrol demand in Australia: An evaluation of empirical models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(1), pages 16-38, January.
    23. Aftabuzzaman, Md & Mazloumi, Ehsan, 2011. "Achieving sustainable urban transport mobility in post peak oil era," Transport Policy, Elsevier, vol. 18(5), pages 695-702, September.
    24. Hou, Aijun & Suardi, Sandy, 2012. "A nonparametric GARCH model of crude oil price return volatility," Energy Economics, Elsevier, vol. 34(2), pages 618-626.
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    Cited by:

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    2. Ekaterina Grushevenko, 2015. "Complex method of petroleum products demand forecasting considering economic, demographic and technological factors," Economics and Business Letters, Oviedo University Press, vol. 4(3), pages 98-107.
    3. Ahmat Khazali Acyl & Flavian Emmanuel Sapnken & Aubin Kinfack Jeutsa & Jean Marie Stevy Sama & Marcel Rodrigue Ewodo-Amougou & Jean Gaston Tamba, 2024. "Forecasting Petroleum Products Consumption in the Chadian Road Transport Sector using Optimised Grey Models," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 603-611, January.
    4. Malik, Afia, 2018. "Fuel Demand in Pakistan's TRansport Sector," MPRA Paper 103455, University Library of Munich, Germany.
    5. Kapustin, Nikita O. & Grushevenko, Dmitry A., 2020. "Long-term electric vehicles outlook and their potential impact on electric grid," Energy Policy, Elsevier, vol. 137(C).
    6. Zhao, Chunfu & Chen, Bin, 2014. "China’s oil security from the supply chain perspective: A review," Applied Energy, Elsevier, vol. 136(C), pages 269-279.

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