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How responsive is Saudi new vehicle fleet fuel economy to fuel-and vehicle-price policy levers?

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  • Sheldon, Tamara L.
  • Dua, Rubal

Abstract

This paper investigates the drivers of recent improvements in Saudi Arabia's fleet fuel economy for new vehicles including passenger cars and light-duty trucks. Vehicle choice models are estimated using both aggregate new vehicle sales data and disaggregate new vehicle buyer survey data. The estimated models are used to simulate counterfactual policy scenarios. Simulation results suggest that the Saudi gasoline price elasticity of demand for new vehicle fuel economy decreased slightly over recent years, but it is still more elastic than that of the United States. Moreover, the increase in domestic gasoline prices between 2014 and 2016 accounted for 42% of the increase in estimated new vehicle fleet fuel economy over that period. Finally, the estimated elasticities, and thus policy sensitivities, vary by income and household size. A revenue-neutral household income- and size-based feebate policy could be more progressive than a gasoline price increase for improving the new vehicle fleet fuel economy.

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  • Sheldon, Tamara L. & Dua, Rubal, 2021. "How responsive is Saudi new vehicle fleet fuel economy to fuel-and vehicle-price policy levers?," Energy Economics, Elsevier, vol. 97(C).
    • Handle: RePEc:eee:eneeco:v:97:y:2021:i:c:s0140988320303662
    DOI: 10.1016/j.eneco.2020.105026
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    Cited by:

    1. Bansal, Prateek & Dua, Rubal, 2022. "Fuel consumption elasticities, rebound effect and feebate effectiveness in the Indian and Chinese new car markets," Energy Economics, Elsevier, vol. 113(C).
    2. Prateek Bansal & Rubal Dua, 2022. "Fuel consumption elasticities, rebound effect and feebate effectiveness in the Indian and Chinese new car markets," Papers 2201.08995, arXiv.org.
    3. Kessler, Louise & Morvillier, Florian & Perrier, Quentin & Rucheton, Keyvan, 2023. "An ex-ante evaluation of the French car feebate," Energy Policy, Elsevier, vol. 173(C).
    4. Dhan Lord B. Fortela & Ashton C. Fremin & Wayne Sharp & Ashley P. Mikolajczyk & Emmanuel Revellame & William Holmes & Rafael Hernandez & Mark Zappi, 2023. "Unsupervised Machine Learning to Detect Impending Anomalies in Testing of Fuel Economy and Emissions of Light-Duty Vehicles," Clean Technol., MDPI, vol. 5(1), pages 1-18, March.
    5. Proque, Andressa Lemes & Betarelli Junior, Admir Antonio & Perobelli, Fernando Salgueiro, 2022. "Fuel tax, cross subsidy and transport: Assessing the effects on income and consumption distribution in Brazil," Research in Transportation Economics, Elsevier, vol. 95(C).

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