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Forecasting world and regional aviation jet fuel demands to the mid-term (2025)


  • Benoît Cheze

    (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles)

  • Pascal Gastineau

    (IFSTTAR/LTE - Laboratoire Transport et Environnement - IFSTTAR - Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux - Université de Lyon)

  • Julien Chevallier

    (LEDa - Laboratoire d'Economie de Dauphine - Université Paris-Dauphine)


This article provides jet fuel demand projections at the worldwide level and for eight geographical zones until 2025. Air traffic forecasts are performed using dynamic panel-data econometrics. Then, the conversion of air traffic projections into quantities of jet fuel is accomplished by using a complementary approach to the 'Traffic Efficiency' method developed previously by the UK Department of Trade and Industry to support the Intergovernmental Panel on Climate Change (IPCC, 1999). According to our main scenario, air traffic should increase by about 100% between 2008 and 2025 at the world level, corresponding to a yearly average growth rate of 4.7%. World jet fuel demand is expected to increase by about 38% during the same period, corresponding to a yearly average growth rate of 1.9% per year. According to these results, energy efficiency improvements allow reducing the effect of air traffic rise on the increase in jet fuel demand, but do not annihilate it. Jet fuel demand is thus unlikely to diminish unless there is a radical technological shift, or air travel demand is restricted.

Suggested Citation

  • Benoît Cheze & Pascal Gastineau & Julien Chevallier, 2011. "Forecasting world and regional aviation jet fuel demands to the mid-term (2025)," Post-Print hal-00990189, HAL.
  • Handle: RePEc:hal:journl:hal-00990189
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    References listed on IDEAS

    1. Mohammad Mazraati, 2010. "World aviation fuel demand outlook," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 34(1), pages 42-72, March.
    2. Dermot Gately, 1988. "Taking Off: The U.S. Demand for Air Travel and Jet Fuel," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 63-91.
    3. Nygren, Emma & Aleklett, Kjell & Höök, Mikael, 2009. "Aviation fuel and future oil production scenarios," Energy Policy, Elsevier, vol. 37(10), pages 4003-4010, October.
    4. Karen Mayor & Richard S. J. Tol, 2008. "Scenarios of Carbon Dioxide Emissions from Aviation," Papers WP244, Economic and Social Research Institute (ESRI).
    5. Macintosh, Andrew & Wallace, Lailey, 2009. "International aviation emissions to 2025: Can emissions be stabilised without restricting demand?," Energy Policy, Elsevier, vol. 37(1), pages 264-273, January.
    6. 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.
    7. repec:eee:jotrge:v:16:y:2008:i:6:p:388-394 is not listed on IDEAS
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    Cited by:

    1. Zhang, Chi & Hui, Xin & Lin, Yuzhen & Sung, Chih-Jen, 2016. "Recent development in studies of alternative jet fuel combustion: Progress, challenges, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 120-138.
    2. 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.
    3. repec:eee:energy:v:140:y:2017:i:p2:p:1378-1386 is not listed on IDEAS
    4. repec:eee:transa:v:112:y:2018:i:c:p:71-84 is not listed on IDEAS
    5. Beria, Paolo & Laurino, Antonio, 2016. "Determinants of daily fluctuations in air passenger volumes. The effect of events and holidays on Milan Malpensa airport," Journal of Air Transport Management, Elsevier, vol. 53(C), pages 73-84.
    6. 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.
    7. González, Rodrigo & Hosoda, Eiji B., 2016. "Environmental impact of aircraft emissions and aviation fuel tax in Japan," Journal of Air Transport Management, Elsevier, vol. 57(C), pages 234-240.
    8. Benoit Cheze & Julien Chevallier & Pascal Gastineau, 2012. "Will technological progress be sufficient to effectively lead the air transport to a sustainable development in the mid-term (2025)?," Working Papers 1207, Chaire Economie du climat.
    9. repec:eee:intfor:v:35:y:2019:i:2:p:502-520 is not listed on IDEAS
    10. Benoît Chèze & Julien Chevallier & Pascal Gastineau, 2012. "Will technological progress be sufficient to stabilize CO2 emissions from air transport in the mid-term?," EconomiX Working Papers 2012-35, University of Paris Nanterre, EconomiX.
    11. Balli, Ozgur, 2017. "Advanced exergy analyses of an aircraft turboprop engine (TPE)," Energy, Elsevier, vol. 124(C), pages 599-612.
    12. repec:eee:transa:v:103:y:2017:i:c:p:525-540 is not listed on IDEAS
    13. Zhou, Wenji & Wang, Tao & Yu, Yadong & Chen, Dingjiang & Zhu, Bing, 2016. "Scenario analysis of CO2 emissions from China’s civil aviation industry through 2030," Applied Energy, Elsevier, vol. 175(C), pages 100-108.
    14. Jian Chai & Shubin Wang & Shouyang Wang & Ju’e Guo, 2012. "Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry," Energies, MDPI, Open Access Journal, vol. 5(3), pages 1-22, March.
    15. repec:eee:appene:v:242:y:2019:i:c:p:1460-1466 is not listed on IDEAS
    16. repec:eee:touman:v:40:y:2014:i:c:p:15-26 is not listed on IDEAS
    17. repec:eee:jaitra:v:63:y:2017:i:c:p:71-83 is not listed on IDEAS

    More about this item



    JEL classification:

    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • L93 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Air Transportation
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models


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