Energy Efficiency

, Volume 11, Issue 6, pp 1341–1358 | Cite as

The responsiveness of fuel demand to gasoline price change in passenger transport: a case study of Saudi Arabia

  • Ibrahim M. AlgunaibetEmail author
  • Walid Matar
Original Article


Empirical estimates of fuel demand changes to price variation are based on historical consumption and prices, and can be applied as a single point estimate to a wide range of price movements. However, if fuel prices are set outside the boundaries of historical changes, policymakers may be concerned as to the validity of the empirically assessed price elasticity. We developed a transport model to provide a techno-economic estimate of the long-run price elasticity of fuel demand. It incorporates consumers’ choices as a result of several factors, including fuel substitutes, available transport modes, income, value of time and magnitude of price change. Our findings from the application of this transport model to Saudi Arabia show that policymakers can have confidence that the empirical estimates are broadly valid, even for large changes and if prices move outside historical variations. In general, gasoline demand in Saudi Arabia is price inelastic due to the lack of fuel and modal substitutes. However, our approach suggests that the response may become more pronounced when the magnitude of the change increases. The long-run cross-price elasticity of diesel is not constant. Demand for diesel will increase if gasoline price is raised significantly. The change in jet-fuel use is negligible.


Elasticity Gasoline Transport model Modal choice Travel behavior Energy systems modeling 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material


  1. Abrantes, P. A. L., & Wardman, M. R. (2011). Meta-analysis of UK values of travel time: An update. Transportation Research Part A: Policy and Practice, 45(1), 1–17.CrossRefGoogle Scholar
  2. Aircraft Commerce. (2005). Analyzing the options for 757 replacement. No., 42, 25–31.Google Scholar
  3. Alabbadi, N. M. (2012). Energy efficiency in KSA: Necessity and expectations. Yanbu: Royal Commission at Yanbu.Google Scholar
  4. Al-Ohaly, Abdulaziz. (2015). Public transport in Saudi Arabia. Global Competitiveness Forum.Google Scholar
  5. Arzaghi, M., & Squalli, J. (2015). How price inelastic is demand for gasoline in fuel-subsidizing economies? Energy Economics, 50, 117–124.CrossRefGoogle Scholar
  6. Axsen, J., Mountain, D. C., & Jaccard, M. (2009). Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles. Resource and Energy Economics, 31(3), 221–238. Scholar
  7. Baranzini, A., & Weber, S. (2013). Elasticities of gasoline demand in Switzerland. Energy Policy, 63, 674–680.CrossRefGoogle Scholar
  8. Belenky, P. (2011). The value of travel time savings: departmental guidance for conducting economic evaluations. Washington DC: Office of the Secretary of Transportation, US Department of Transportation.Google Scholar
  9. Bigazzi, A. Y., & Kelly, J. C. (2015). Modeling the effects of congestion on fuel economy for advanced power train vehicles. Transportation Planning and Technology, 38(2), 149–161. Scholar
  10. Bigerna, S., Bollino, C. A., Micheli, S., & Polinori, P. (2017). Revealed and stated preferences for CO2 emissions reduction: the missing link. Renewable and Sustainable Energy Reviews, 68, 1213–1221. Scholar
  11. Brand, C., Goodman, A., Rutter, H., Song, Y., & Ogilvie, D. (2013). Associations of individual, household and environmental characteristics with carbon dioxide emissions from motorised passenger travel. Applied Energy, 104, 158–169.CrossRefGoogle Scholar
  12. Burke, P. J., & Nishitateno, S. (2013). Gasoline prices, gasoline consumption, and new-vehicle fuel economy: evidence for a large sample of countries. Energy Economics, 36, 363–370. Scholar
  13. Cheng, Y.-H., Chang, Y.-H., & Lu, I. J. (2015). Urban transportation energy and carbon dioxide emission reduction strategies. Applied Energy, 157, 953–973. Scholar
  14. Dahl, C. A. (2012). Measuring global gasoline and diesel price and income elasticities. Energy Policy, 41, 2–13. Scholar
  15. Daly, H. E., Ramea, K., Chiodi, A., Yeh, S., Gargiulo, M., & Gallachóir, B. Ó. (2014). Incorporating travel behaviour and travel time into TIMES energy system models. Applied Energy, 135, 429–439. Scholar
  16. Gross, D., Shortle, J., Thompson, J., Harris, C. (2008). Fundamentals of Queueing Theory. Hoboken: John Wiley & Sons.Google Scholar
  17. Saudi Railways Organization (SRO). (2015). Fares and Terms. Accessed 10 Aug 2015.
  18. FlightStats. (2013). On-Time Performance Service Awards.Google Scholar
  19. Flynas. (2015). Flynas Schedule. Accessed 18 Aug 2015.
  20. Fouquet, R. (2012). Trends in income and price elasticities of transport demand (1850–2010). Energy Policy, 50, 62–71. Scholar
  21. General Authority for Statistics. (2013a). Household expenditure and income survey.Google Scholar
  22. General Authority for Statistics. (2013b). Labor Force Survey.Google Scholar
  23. General Authority for Statistics. (2014).Statistical Yearbook.Google Scholar
  24. Ghauche, Anwar. (2010). Integrated transportation and energy activity-based model. PhD diss., Massachusetts institute of Technology.Google Scholar
  25. Girod, B., van Vuuren, D. P., & de Vries, B. (2013). Influence of travel behavior on global CO2 emissions. Transportation Research Part A: Policy and Practice, 50, 183–197.Google Scholar
  26. Greenshields, B. D., Ws Channing and Hh Miller. (1935). A study of traffic capacity. In Highway research board proceedings, vol. 1935. National research council (USA), Highway Research Board.Google Scholar
  27. International Civil Aviation Organization—ICAO. (2013). Annual Report of the ICAO Council.Google Scholar
  28. International Energy Agency. (2009). Transport energy and CO2: moving towards sustainability. OECD Publishing.Google Scholar
  29. Jacobs, Goff, Amy Aeron-Thomas and Angela Astrop. (2000). Estimating global road fatalities.Google Scholar
  30. Keirstead, J., & Shah, N. (2013). Urban energy systems: an integrated approach. Abingdon: Routledge.zbMATHGoogle Scholar
  31. Krishnan, V., Kastrouni, E., Dimitra Pyrialakou, V., Gkritza, K., & McCalley, J. D. (2015). An optimization model of energy and transportation systems: assessing the high-speed rail impacts in the United States. Transportation Research Part C: Emerging Technologies, 54, 131–156. Scholar
  32. Litman, Todd. (2009). Transportation cost and benefit analysis. Victoria Transport Policy Institute, 31.Google Scholar
  33. Liu, Changzheng, and David L. Greene. (2013). Modeling the demand for E85 in the United States. Oak Ridge National Laboratory Report No. ORNL/TM-2013/369. Oak Ridge, TN: ORNL:52.Google Scholar
  34. Mau, P., Eyzaguirre, J., Jaccard, M., Collins-Dodd, C., & Tiedemann, K. (2008). The ‘neighbor effect’: simulating dynamics in consumer preferences for new vehicle technologies. Ecological Economics, 68(1), 504–516. Scholar
  35. Ministry of Labor. (2013). Statistical Yearbook.Google Scholar
  36. Ministry of Municipal and Rural Affairs. (2013). Length of Paved Roads. Ministry of Municipal and Rural Affairs.Google Scholar
  37. Saudi Arabia Public Transport Company. (2015a). Annual Report 2014.Google Scholar
  38. Saudi Arabia Public Transport Company. (2015b). Ticket Reservation. Accessed 8 10 2015.
  39. Saudi Arabian Monetary Agency. (2015). Appendix of Statistical Tables of the Forty-sixth Annual Report. Saudi Arabian Monetary Agency.Google Scholar
  40. Saudia. (2013). Passenger Flight Schedule.Google Scholar
  41. Saudia (2015) Saudia website. Accessed 8 10 2015.
  42. Schäfer, Andreas. (2012). Introducing behavioral change in transportation into energy/economy/environment models. World Bank Policy Research Working Paper 6234 .Google Scholar
  43. Schäfer, A., & Victor, D. G. (2000). The future mobility of the world population. Transportation Research Part A: Policy and Practice, 34(3), 171–205.Google Scholar
  44. Schäfer, A. W. (2015). Long-term trends in domestic US passenger travel: the past 110 years and the next 90. Transportation, 1–18.Google Scholar
  45. Sita, B. B., Marrouch, W., & Abosedra, S. (2012). Short-run price and income elasticity of gasoline demand: evidence from Lebanon. Energy Policy, 46, 109–115.CrossRefGoogle Scholar
  46. Sivakumar, A., J. Keirstead, and J. Polak. (2010). Integrated modelling of the demand and supply vectors in urban energy systems: Conceptual and modelling frameworks for the development of a new toolkit. In European Transport Conference, pp. 11–13.Google Scholar
  47. Srivastava, Leena, Ritu Mathur, Pradeep K. Dadhich, Atul Kumar, Sakshi Marwah and Pooja Goel. (2006). National Energy Map for India: Technology Vision 2030. New Delhi, India: The Energy and Resources Institute (TERI).Google Scholar
  48. Takeshita, T. (2012). Assessing the co-benefits of CO2 mitigation on air pollutants emissions from road vehicles. Applied Energy, 97, 225–237. Scholar
  49. Zahavi, Yacov and Antti Talvitie. (1980). Regularities in travel time and money expenditures. No. 750.Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.King Abdullah Petroleum Studies and Research Center (KAPSARC)RiyadhSaudi Arabia
  2. 2.Research and Development CenterSaudi AramcoDhahranSaudi Arabia

Personalised recommendations