The responsiveness of fuel demand to gasoline price change in passenger transport: a case study of Saudi Arabia
- 211 Downloads
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.
KeywordsElasticity 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.
- Aircraft Commerce. (2005). Analyzing the options for 757 replacement. No., 42, 25–31.Google Scholar
- Alabbadi, N. M. (2012). Energy efficiency in KSA: Necessity and expectations. Yanbu: Royal Commission at Yanbu.Google Scholar
- Al-Ohaly, Abdulaziz. (2015). Public transport in Saudi Arabia. Global Competitiveness Forum.Google Scholar
- 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
- Gross, D., Shortle, J., Thompson, J., Harris, C. (2008). Fundamentals of Queueing Theory. Hoboken: John Wiley & Sons.Google Scholar
- Saudi Railways Organization (SRO). (2015). Fares and Terms. https://www.saudirailways.org. Accessed 10 Aug 2015.
- FlightStats. (2013). On-Time Performance Service Awards.Google Scholar
- Flynas. (2015). Flynas Schedule. http://www.flynas.com. Accessed 18 Aug 2015.
- General Authority for Statistics. (2013a). Household expenditure and income survey.Google Scholar
- General Authority for Statistics. (2013b). Labor Force Survey.Google Scholar
- General Authority for Statistics. (2014).Statistical Yearbook.Google Scholar
- Ghauche, Anwar. (2010). Integrated transportation and energy activity-based model. PhD diss., Massachusetts institute of Technology.Google Scholar
- 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
- 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
- International Civil Aviation Organization—ICAO. (2013). Annual Report of the ICAO Council.Google Scholar
- International Energy Agency. (2009). Transport energy and CO2: moving towards sustainability. OECD Publishing.Google Scholar
- Jacobs, Goff, Amy Aeron-Thomas and Angela Astrop. (2000). Estimating global road fatalities.Google Scholar
- 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. https://doi.org/10.1016/j.trc.2015.03.007.CrossRefGoogle Scholar
- Litman, Todd. (2009). Transportation cost and benefit analysis. Victoria Transport Policy Institute, 31.Google Scholar
- 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
- Ministry of Labor. (2013). Statistical Yearbook.Google Scholar
- Ministry of Municipal and Rural Affairs. (2013). Length of Paved Roads. Ministry of Municipal and Rural Affairs.Google Scholar
- Saudi Arabia Public Transport Company. (2015a). Annual Report 2014.Google Scholar
- Saudi Arabia Public Transport Company. (2015b). Ticket Reservation. Accessed 8 10 2015. https://www.saptco.com.sa.
- Saudi Arabian Monetary Agency. (2015). Appendix of Statistical Tables of the Forty-sixth Annual Report. Saudi Arabian Monetary Agency.Google Scholar
- Saudia. (2013). Passenger Flight Schedule.Google Scholar
- Saudia (2015) Saudia website. Accessed 8 10 2015. http://www.saudiairlines.com.
- Schäfer, Andreas. (2012). Introducing behavioral change in transportation into energy/economy/environment models. World Bank Policy Research Working Paper 6234 .Google Scholar
- 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
- 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
- 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
- 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
- Zahavi, Yacov and Antti Talvitie. (1980). Regularities in travel time and money expenditures. No. 750.Google Scholar