Abediasl, H., Ansari, A., Hosseini, V., Koch, C.R. and Shahbakhti, M., 2024. Real-time vehicular fuel consumption estimation using machine learning and on-board diagnostics data.
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering,
238(12), pp.3779-3793.
https://doi.org/10.1177/09544070231185609.
Akbari, F., Mahpour, A. and Ahadi, M.R., 2020. Evaluation of energy consumption and CO2 emission reduction policies for urban transport with system dynamics approach.
Environmental Modeling & Assessment,
25(4), pp.505-520.
https://doi.org/10.1007/s10666-020-09695-w.
Al-Ghandoor, A., Jaber, J., Al-Hinti, I. and Abdallat, Y., 2013. Statistical assessment and analyses of the determinants of transportation sector gasoline demand in Jordan.
Transportation Research Part A: Policy and Practice,
50, pp.129-138.
https://doi.org/10.1016/j.tra.2013.01.022.
Allen, T. and Arkolakis, C., 2022. The welfare effects of transportation infrastructure improvements.
The Review of Economic Studies,
89(6), pp.2911-2957.
https://doi.org/10.1093/restud/rdac001.
Ansarinasab, M. and Manzari Tavakoli, Z., 2020. Modeling Gasoline Consumption Behaviors in Iran Based on Long Memory and Regime Change.
Quarterly Energy Economics Review,
16(64), pp.125-149. URL:
http://iiesj.ir/article-1-1145-en.html. [in Persian].
Ayyıldız, E. and Murat, M., 2024. A lasso regression-based forecasting model for daily gasoline consumption: Türkiye Case.
Turkish Journal of Engineering,
8(1), pp.162-174.
https://doi.org/10.31127/tuje.1354501.
Bates, M. and Kim, S., 2024. Estimating the price elasticity of gasoline demand in correlated random coefficient models with endogeneity.
Journal of Applied Econometrics,
39(4), pp.679-696.
https://doi.org/10.1002/jae.3042.
Bayat, N., Davoodi, M.M. and Rezaei, A., 2023. Forecasting Gasoline Consumption in Iran using Deep Learning Approaches.
Iranian Economic Review,
27(2), pp.347-376.
10.22059/ier.2023.317681.1007087.
Ceylan, Z., Akbulut, D. and Baytürk, E., 2024. Forecasting gasoline consumption using machine learning algorithms during COVID-19 pandemic.
Energy Sources, Part A: Recovery, Utilization, and Environmental Effects,
46(1), pp.16623-16641.
https://doi.org/10.1080/15567036.2021.2024919
Chang, C.C., 2016. Causal analysis of carbon emissions, deadweight tonnage of global shipping fleet, fuel oil consumption, and economic activities in marine transportation.
Energy Sources, Part B: Economics, Planning, and Policy,
11(4), pp.303-308.
https://doi.org/10.1080/15567249.2011.607884.
Ford, A., 2018. System dynamics models of environment, energy, and climate change. In Encyclopedia of complexity and systems science (pp. 1-25). Springer, Berlin, Heidelberg.
Heidari, E., Bikdeli, S. and Daneshvar, M.R.M., 2023. A dynamic model for CO2 emissions induced by urban transportation during 2005–2030, a case study of Mashhad, Iran.
Environment, Development and Sustainability,
25(5), pp.4217-4236.
https://doi.org/10.1007/s10668-022-02240-7.
Kilian, L. and Zhou, X., 2024. Heterogeneity in the pass-through from oil to gasoline prices: A new instrument for estimating the price elasticity of gasoline demand.
Journal of Public Economics,
232, p.105099.
https://doi.org/10.1016/j.jpubeco.2024.105099.
Lee, C.C. and Olasehinde-Williams, G., 2021. Gasoline demand elasticities in the world’s energy gluttons: a time-varying coefficient approach.
Environmental Science and Pollution Research,
28(45), pp.64830-64847.
https://doi.org/10.1007/s11356-021-15615-6.
Mikayilov, J.I., Joutz, F.L. and Hasanov, F.J., 2020a. Gasoline demand in Saudi Arabia: are the price and income elasticities constant?.
Energy Sources, Part B: Economics, Planning, and Policy,
15(4), pp.211-229.
https://doi.org/10.1080/15567249.2020.1775325.
Mikayilov, J.I., Mukhtarov, S. and Mammadov, J., 2020b. Gasoline demand elasticities at the backdrop of lower oil prices: Fuel-subsidizing country case.
Energies,
13(24), p.6752.
https://doi.org/10.3390/en13246752.
Mosleh Shirazi, A.N. and Sotodeh, F., 2015. Simulating the role of subsidies reform in fuel consumption by system dynamics approach.
Journal of Applied Economics Studies in Iran,
4(13), pp.107-125. URL:
https://aes.basu.ac.ir/article_1036_en.html. [in Persian].
Nitoiu, C., Cofaru, C. & Popescu, M., 2025. Influence of road and traffic conditions on emissions and fuel consumption of light vehicles in a real urban driving cycle. Environmental Science and Pollution Research 32, pp. 15050–15065. https://doi.org/10.1007/s11356-025-36573-3.
Pourmatin, M., Moeini-Aghtaie, M., Hassannayebi, E. and Hewitt, E., 2024. Transition to Low-Carbon Vehicle Market: Characterization, System Dynamics Modeling, and Forecasting.
Energies,
17(14), p.3525.
https://doi.org/10.3390/en17143525.
Raei, H., Maleki, A. and Farajzadeh, Z., 2024. Analysis of energy policy reform in Iran: Energy and emission intensity changes.
Economic Analysis and Policy,
81, pp.1535-1557.
https://doi.org/10.1016/j.eap.2024.02.023.
Rogat, J. and Sterner, T., 1998. The determinants of gasoline demand in some Latin American countries.
International Journal of Global Energy Issues,
11(1-4), pp.162-170.
https://doi.org/10.1504/IJGEI.1998.071441.
Romero, C.A., Correa, P., Ariza Echeverri, E.A. and Vergara, D., 2024. Strategies for reducing automobile fuel consumption.
Applied Sciences,
14(2), p.910.
https://doi.org/10.3390/app14020910.
Roudbaraki, M., Valipour, M.S., Khorasani, A.F. and Salehi, G.R., 2025. Integrating Data Mining and System Dynamics to Model Energy Policy Development and Sustainability Assessment.
Petroleum Business Review (PBR),
9(1).
https://doi.org/10.22050/pbr.2025.475979.1348.
Salimi, M., Moradi, M.A. and Amidpour, M., 2022. Modeling and outlook analysis of gasoline supply and demand and sensitivity analysis of main economic and social drivers.
Energy,
256, p.124686.
https://doi.org/10.1016/j.energy.2022.124686.
Samavi, M., Panahi, M., Abedi, Z. and Ahmadian, M., 2024. Assessing the Economic Impact of Using ITSs (Intelligent Transportation Systems) on Gasoline Consumption in Iran (Case Study of Karaj-Chalous Axis).
Road,
32(120), pp.331-348.
https://doi.org/10.22034/road.2023.367900.2107. [in Persian].
Sapnken, E.F., Tamba, J.G., Essiane, S.N., Koffi, F.D. and Njomo, D., 2018. Modeling and forecasting gasoline consumption in Cameroon using linear regression models.
International Journal of Energy Economics and Policy,
8(2), pp.111-120. URL:
https://www.econjournals.com/index.php/ijeep/article/view/5985.
Shamsapour, N., Hajinezhad, A. and Noorollahi, Y., 2021. Developing a system dynamics approach for CNG vehicles for low-carbon urban transport: a case study.
International journal of low-carbon technologies,
16(2), pp.577-591.
https://doi.org/10.1093/ijlct/ctaa085.
Vafa-Arani, H., Jahani, S., Dashti, H., Heydari, J. and Moazen, S., 2014. A system dynamics modeling for urban air pollution: A case study of Tehran, Iran.
Transportation Research Part D: Transport and Environment,
31, pp.21-36.
https://doi.org/10.1016/j.trd.2014.05.016.
Yang, Y., Gong, N., Xie, K. and Liu, Q., 2022. Predicting gasoline vehicle fuel consumption in energy and environmental impact based on machine learning and multidimensional big data.
Energies,
15(5), p.1602.
https://doi.org/10.3390/en15051602.
Zareayan Mazrae Khosro, R. and Shakouri Ganjavi, H., 2016. System Analysis Gasoline Demand and Estimating the Price Elasticity in the Province of Tehran,
Iranian Energy Economics,
5(18), pp. 61–98.
https://doi.org/10.22054/jiee.2016.7193. [in Persian]
Send comment about this article