IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v5y2012i3p577-598d16412.html
   My bibliography  Save this article

Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/5/3/577/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/5/3/577/
    Download Restriction: no
    ---><---

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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tehreem Fatima & Enjun Xia & Muhammad Ahad, 2019. "Oil demand forecasting for China: a fresh evidence from structural time series analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(3), pages 1205-1224, June.
    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. Malik, Afia, 2018. "Fuel Demand in Pakistan's TRansport Sector," MPRA Paper 103455, University Library of Munich, Germany.
    4. 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).
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tehreem Fatima & Enjun Xia & Muhammad Ahad, 2019. "Oil demand forecasting for China: a fresh evidence from structural time series analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(3), pages 1205-1224, June.
    2. Shao, Zhen & Chao, Fu & Yang, Shan-Lin & Zhou, Kai-Le, 2017. "A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 123-136.
    3. Gong, Xu & Lin, Boqiang, 2017. "Forecasting the good and bad uncertainties of crude oil prices using a HAR framework," Energy Economics, Elsevier, vol. 67(C), pages 315-327.
    4. Zhang, Yue-Jun & Zhang, Lu, 2015. "Interpreting the crude oil price movements: Evidence from the Markov regime switching model," Applied Energy, Elsevier, vol. 143(C), pages 96-109.
    5. Zeng, Yuan & Tan, Xianchun & Gu, Baihe & Wang, Yi & Xu, Baoguang, 2016. "Greenhouse gas emissions of motor vehicles in Chinese cities and the implication for China’s mitigation targets," Applied Energy, Elsevier, vol. 184(C), pages 1016-1025.
    6. Liu, Wen & Lund, Henrik & Mathiesen, Brian Vad, 2013. "Modelling the transport system in China and evaluating the current strategies towards the sustainable transport development," Energy Policy, Elsevier, vol. 58(C), pages 347-357.
    7. Sylvia Mardiana & Ferdinand Saragih & Martani Huseini, 2020. "Forecasting Gasoline Demand in Indonesia Using Time Series," International Journal of Energy Economics and Policy, Econjournals, vol. 10(6), pages 132-145.
    8. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    9. Adom, Philip Kofi & Bekoe, William, 2012. "Conditional dynamic forecast of electrical energy consumption requirements in Ghana by 2020: A comparison of ARDL and PAM," Energy, Elsevier, vol. 44(1), pages 367-380.
    10. Mirlatifi, A.M. & Egelioglu, F. & Atikol, U., 2015. "An econometric model for annual peak demand for small utilities," Energy, Elsevier, vol. 89(C), pages 35-44.
    11. Bracco, Stefano & Delfino, Federico & Pampararo, Fabio & Robba, Michela & Rossi, Mansueto, 2013. "The University of Genoa smart polygeneration microgrid test-bed facility: The overall system, the technologies and the research challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 442-459.
    12. Wang, H. & Zhou, P. & Zhou, D.Q., 2012. "An empirical study of direct rebound effect for passenger transport in urban China," Energy Economics, Elsevier, vol. 34(2), pages 452-460.
    13. Lu Wang & Feng Ma & Guoshan Liu & Qiaoqi Lang, 2023. "Do extreme shocks help forecast oil price volatility? The augmented GARCH‐MIDAS approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2056-2073, April.
    14. Velasquez, Carlos E. & Zocatelli, Matheus & Estanislau, Fidellis B.G.L. & Castro, Victor F., 2022. "Analysis of time series models for Brazilian electricity demand forecasting," Energy, Elsevier, vol. 247(C).
    15. Olsson, Linda & Hjalmarsson, Linnea & Wikström, Martina & Larsson, Mårten, 2015. "Bridging the implementation gap: Combining backcasting and policy analysis to study renewable energy in urban road transport," Transport Policy, Elsevier, vol. 37(C), pages 72-82.
    16. Márcio Poletti Laurini & Roberto Baltieri Mauad & Fernando Antonio Lucena Aiube, 2016. "Multivariate Stochastic Volatility-Double Jump Model: an application for oil assets," Working Papers Series 415, Central Bank of Brazil, Research Department.
    17. Charles, Amélie & Darné, Olivier, 2017. "Forecasting crude-oil market volatility: Further evidence with jumps," Energy Economics, Elsevier, vol. 67(C), pages 508-519.
    18. Laurini, Márcio Poletti & Mauad, Roberto Baltieri & Aiube, Fernando Antônio Lucena, 2020. "The impact of co-jumps in the oil sector," Research in International Business and Finance, Elsevier, vol. 52(C).
    19. Wang, Y.F. & Li, K.P. & Xu, X.M. & Zhang, Y.R., 2014. "Transport energy consumption and saving in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 641-655.
    20. Emami Javanmard, M. & Tang, Y. & Wang, Z. & Tontiwachwuthikul, P., 2023. "Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector," Applied Energy, Elsevier, vol. 338(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:5:y:2012:i:3:p:577-598:d:16412. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.