Estimation the Price and Income Elasticities of Demand for the Gas and Diesel Fuel in Transportation Sector

Document Type : Original Article


1 Associate Professor of Economics, Faculty of Economics and Political Sciences, Shahid Beheshti University

2 Ph.D Candidate in Economics, Faculty of Economics and Political Sciences, Shahid Beheshti University


About a quarter of different types of energy consumed annually are in the transportation sector in Iran. Gasoline and diesel are the most important source of energy consumed in this sector. This study uses the Generalized Method of Moments (GMM) to evaluate the main factors affecting the gasoline and diesel for the transportation sector in different provinces in Iran during 2006- 2014. The evaluated factors are per capita income, the price of gasoline and diesel, per capita changes in gas and diesel cars stocks, and provincial urban bus fleets. The results show that the per capita gasoline and diesel consumed relative to the price changes in the short runare low elastic. The short-run price elasticity of gasoline is 0.14, the short-run price elasticity of diesel is 0.13; while the elasticity of income for gasoline and diesel is about 0.3 in both main models. An increase in per capita stock of gasoline cars has a positive effect on per capita gasoline and diesel consumption, while the per capita increase in diesel cars stock capacity has a negative impact on the per capita consumption of gasoline and diesel. The results also indicate that the urban bus fleet has not had an impact on fuel consumption per head. Based on the results of this study, the goal of a change in fuel consumption should be on people's per capita income, leading to reduced consumption and behavioral change.


- Anable, J., & Brand, C. (2012). Modeling transport energy demand: A socio-technical approach. Energy Policy, 41, 125-138.
- Arellano M. & Bover O. (1995). Another look at the instrumental-variable estimation of error components Models Journal of Econometrics, 68, 29–52.
- Arellano M. & Bond S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, 58, 277-297.
- Bandaranaike, R. D. & Munasighe, M. (1983). The Demand for Electricity Service and the Quality of Supply. Energy Journal 4 (2), 49-71.
- Berndt, E.R., & German, B. (1985). Energy demand in the transportation sector of Mexico. Journal of Development Economics, 17, 219-238.
- Bhattacharyya, S.C. (2011). Energy Economics. Springer Science & Business Media.
- Bhattacharyya, S.C., & Govinda R. T.(2009). Energy demand Models for Policy Formulation. Policy Research Working Paper No.4866.
- Baranzini, A., & Weber, S.(2013). Elasticities of gasoline demand in Switzerland. Energy Policy , 63, 674–680.
- Fetros, M. H., & Sahraei, R.(2014).estimation of energy demand function in road transportation for 1978-2013. Quarterly Journal of Strategic and Military Policies, 3, 26-42 (In Persian).
- Feng, A. (2012). Spread of English across greater china. Journal of multilingual and multicultural development, 33(4), 363-377.
- Ghasemi, R.(2014). Analysis of ICT effect on energy intensity in transportation sector. Iran Energy Economic Research Letter, 13, 169-190 (In Persian)..
- Lin, Y., & Chuanguo, Z. (2012). Panel estimation for urbanization, energy consumption and CO2 emissions: A regional analysis in China. Energy Policy, 49 , 488–498
-Mehregan, N., & Moradi, A. (2014). An Efficient Combined Predictor for Supply and Demand for Gasoline in the State, Combination of State-Space Patterns and Artificial Neural Network Models. Journal of Transportation, 11(3), 265-277 (In Persian).
- Kazemi, M.(2011). Prediction of Energy Demand for Transportation Using the Markov Gray Chain Model: A Case Study in Iran. Industrial Management Journal, 7,117-132 (In Persian).
- Polemis, M.L. (2006). Empirical assessment of the determinants of road energy demand in Greece. Energy Economics, 28, 385–403.
- Ming Z.(2010). Decomposition analysis of energy consumption in Chinese transportation sector. Applied Energy 88, 2275-2289.
- Roming ,N., & Marian L.(2015)" Econometric forecasting of final energy demand using in-sample and out-of-sample model selection criteria. Potsdam Institute for Climate Impact Research, Germany.
- Raymond, L., & Guy, C.K. (2012). Gasoline consumption in china: a dynamic panel data analysis. Economics Bulletin, 32(3), 2375-2382.
- Sung Y. P. (2010). An estimation of U.S. gasoline demand: A smooth time-varying cointegration approach. Energy Economics, 32, 110–120.
- Varian, H.R. (1992). Microeconomics Analysis. 3rd edition, Norton & company.