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Statistical assessment and analyses of the determinants of transportation sector gasoline demand in Jordan

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  • Al-Ghandoor, Ahmed
  • Jaber, Jamal
  • Al-Hinti, Ismael
  • Abdallat, Yousef

Abstract

The main objectives of this study are to analyze past gasoline consumption in Jordan’s transportation sector and to identify main factors affecting its future demand. The sector is responsible for 39% of the total final energy consumption in Jordan, and is nearly totally dependent on oil consumption. The structure of this sector is analyzed with focus on passenger cars which represent 65% of total vehicles, and are responsible for nearly all of the national gasoline fuel demand. To achieve these objectives, the study develops a multi linear regression model using different independent variables based on 22-year historical data between years 1988 and 2009 refined from scattered data sources. The final model includes only the number of registered vehicles, income level, and gasoline price variables. A number of policy gaps are identified as contributors to the low efficiency composition of the fleet in terms of engine size, composition, availability of public transport, fuel prices, vehicle age, and type of ignition. To illustrate the importance of integrating energy policies within national energy plans, the impact of ending subsidies of gasoline was investigated and found to be significant. Without such policies, gasoline consumptions are predicted to rise by 1.81%/year. However, if such policies are implemented, over the same period, gasoline consumptions are forecasted to ascend at a lower rate of 0.53%/year.

Suggested Citation

  • Al-Ghandoor, Ahmed & Jaber, Jamal & Al-Hinti, Ismael & Abdallat, Yousef, 2013. "Statistical assessment and analyses of the determinants of transportation sector gasoline demand in Jordan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 129-138.
    • Handle: RePEc:eee:transa:v:50:y:2013:i:c:p:129-138
    DOI: 10.1016/j.tra.2013.01.022
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    1. Starr McMullen, B. & Zhang, Lei & Nakahara, Kyle, 2010. "Distributional impacts of changing from a gasoline tax to a vehicle-mile tax for light vehicles: A case study of Oregon," Transport Policy, Elsevier, vol. 17(6), pages 359-366, November.
    2. Al-Ghandoor, A. & Al-Hinti, I. & Jaber, J.O. & Sawalha, S.A., 2008. "Electricity consumption and associated GHG emissions of the Jordanian industrial sector: Empirical analysis and future projection," Energy Policy, Elsevier, vol. 36(1), pages 258-267, January.
    3. Anas, Alex & Hiramatsu, Tomoru, 2012. "The effect of the price of gasoline on the urban economy: From route choice to general equilibrium," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(6), pages 855-873.
    4. Jaber, J.O. & Al-Ghandoor, A. & Sawalha, S.A., 2008. "Energy analysis and exergy utilization in the transportation sector of Jordan," Energy Policy, Elsevier, vol. 36(8), pages 2985-2990, August.
    5. Azar, Christian & Lindgren, Kristian & Andersson, Bjorn A., 2003. "Global energy scenarios meeting stringent CO2 constraints--cost-effective fuel choices in the transportation sector," Energy Policy, Elsevier, vol. 31(10), pages 961-976, August.
    6. Hickman, Robin & Ashiru, Olu & Banister, David, 2010. "Transport and climate change: Simulating the options for carbon reduction in London," Transport Policy, Elsevier, vol. 17(2), pages 110-125, March.
    7. Rentziou, Aikaterini & Gkritza, Konstantina & Souleyrette, Reginald R., 2012. "VMT, energy consumption, and GHG emissions forecasting for passenger transportation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 487-500.
    8. Nolan, Anne, 2010. "A dynamic analysis of household car ownership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(6), pages 446-455, July.
    9. Small, Kenneth A., 2012. "Energy policies for passenger motor vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(6), pages 874-889.
    10. Lee, Jongsu & Cho, Youngsang, 2009. "Demand forecasting of diesel passenger car considering consumer preference and government regulation in South Korea," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 420-429, May.
    11. Leduc, Guillaume & Mongelli, Ignazio & Uihlein, Andreas & Nemry, Françoise, 2010. "How can our cars become less polluting? An assessment of the environmental improvement potential of cars," Transport Policy, Elsevier, vol. 17(6), pages 409-419, November.
    12. Haldenbilen, Soner & Ceylan, Halim, 2005. "Genetic algorithm approach to estimate transport energy demand in Turkey," Energy Policy, Elsevier, vol. 33(1), pages 89-98, January.
    13. 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.
    14. Al-Ghandoor, A., 2013. "An approach to energy savings and improved environmental impact through restructuring Jordan's transport sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 31-42.
    15. 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.
    16. Murat, Yetis Sazi & Ceylan, Halim, 2006. "Use of artificial neural networks for transport energy demand modeling," Energy Policy, Elsevier, vol. 34(17), pages 3165-3172, November.
    17. Haldenbilen, Soner, 2006. "Fuel price determination in transportation sector using predicted energy and transport demand," Energy Policy, Elsevier, vol. 34(17), pages 3078-3086, November.
    18. Limanond, Thirayoot & Jomnonkwao, Sajjakaj & Srikaew, Artit, 2011. "Projection of future transport energy demand of Thailand," Energy Policy, Elsevier, vol. 39(5), pages 2754-2763, May.
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    Cited by:

    1. Commander, Simon & Nikoloski, Zlatko & Vagliasindi, Maria, 2015. "Estimating the Size of External Effects of Energy Subsidies," IZA Discussion Papers 8865, Institute of Labor Economics (IZA).
    2. Ben Abdallah, Khaled & Belloumi, Mounir & De Wolf, Daniel, 2015. "International comparisons of energy and environmental efficiency in the road transport sector," Energy, Elsevier, vol. 93(P2), pages 2087-2101.
    3. Turgut Ozkan & Gozde Yanginlar & Salih Kalayci, 2019. "Testing the Transportation-induced Environmental Kuznets Curve Hypothesis: Evidence from Eight Developed and Developing Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 9(1), pages 174-183.
    4. Xie, Chunping & Hawkes, Adam D., 2015. "Estimation of inter-fuel substitution possibilities in China's transport industry using ridge regression," Energy, Elsevier, vol. 88(C), pages 260-267.
    5. Commander,Simon John & Nikoloski,Zlatko Slobodan & Vagliasindi,Maria, 2015. "Estimating the size of external effects of energy subsidies in transport and agriculture," Policy Research Working Paper Series 7227, The World Bank.

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