IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v37y2015icp44-56.html
   My bibliography  Save this article

Simulating the market penetration of cars with alternative fuelpowertrain technologies in Italy

Author

Listed:
  • Valeri, Eva
  • Danielis, Romeo

Abstract

This paper evaluates the market penetration of cars with alternative fuelpowertrain technologies in Italy under various scenarios. Seven cars on sale in 2013 are considered: the Ford Fiesta (diesel), the VW Polo (gasoline), the Fiat Punto Evo (bi-fuel – CNG), the Natural Power Alfa Romeo Mito (bi-fuel – LPG), the Toyota Yaris (hybrid – gasoline), the Peugeot iOn (BEV – owned battery), the Renault Zoe (BEV – leased battery). A Mixed Error Component Logit model is estimated based on data collected via a stated preference choice survey administered in 2013 in various Italian cities. The model's parameters are then used to build a Monte Carlo simulation model which allows evaluating, under different scenarios, the market penetration of the seven cars. The main findings are that (a) the subsidies enacted by the Italian government in favour of the low CO2 emitting cars appear to favour mostly the Ford Fiesta (diesel); (b) a three-fold increase in the BEVs range would not change their market share significantly (about 2%); and (c) only a combination of changes such as the introduction of a subsidy equal to €5000, the decrease of the purchase price for BEVs by €5000, the increase in the battery range, and the increase in the conventional fuel price would significantly increase the BEVs' market share, raising it to about 15%.

Suggested Citation

  • Valeri, Eva & Danielis, Romeo, 2015. "Simulating the market penetration of cars with alternative fuelpowertrain technologies in Italy," Transport Policy, Elsevier, vol. 37(C), pages 44-56.
    • Handle: RePEc:eee:trapol:v:37:y:2015:i:c:p:44-56
    DOI: 10.1016/j.tranpol.2014.10.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X14002029
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tranpol.2014.10.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    2. Daziano, Ricardo A. & Chiew, Esther, 2012. "Electric vehicles rising from the dead: Data needs for forecasting consumer response toward sustainable energy sources in personal transportation," Energy Policy, Elsevier, vol. 51(C), pages 876-894.
    3. Bliemer, Michiel C.J. & Rose, John M., 2010. "Construction of experimental designs for mixed logit models allowing for correlation across choice observations," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 720-734, July.
    4. Rusich, Andrea & Danielis, Romeo, 2013. "The private and social monetary costs and the energy consumption of a car. An estimate for seven cars with different vehicle technologies on sale in Italy," Working Papers 1301, SIET Società Italiana di Economia dei Trasporti e della Logistica, revised 2013.
    5. Horne, Matt & Jaccard, Mark & Tiedemann, Ken, 2005. "Improving behavioral realism in hybrid energy-economy models using discrete choice studies of personal transportation decisions," Energy Economics, Elsevier, vol. 27(1), pages 59-77, January.
    6. Rusich, Andrea & Danielis, Romeo, 2013. "The private and social monetary costs and the energy consumption of a car. An estimate for seven cars with different vehicle technologies on sale in Italy," Working Papers 13_1, SIET Società Italiana di Economia dei Trasporti e della Logistica, revised 2013.
    7. Nayum, Alim & Klöckner, Christian A. & Prugsamatz, Sunita, 2013. "Influences of car type class and carbon dioxide emission levels on purchases of new cars: A retrospective analysis of car purchases in Norway," Transportation Research Part A: Policy and Practice, Elsevier, vol. 48(C), pages 96-108.
    8. Bliemer, Michiel C.J. & Rose, John M., 2011. "Experimental design influences on stated choice outputs: An empirical study in air travel choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(1), pages 63-79, January.
    9. Achtnicht, Martin & Bühler, Georg & Hermeling, Claudia, 2008. "Impact of Service Station Networks on Purchase Decisions of Alternative-fuel Vehicles," ZEW Discussion Papers 08-088, ZEW - Leibniz Centre for European Economic Research.
    10. Berkovec, James & Rust, John, 1985. "A nested logit model of automobile holdings for one vehicle households," Transportation Research Part B: Methodological, Elsevier, vol. 19(4), pages 275-285, August.
    11. Louviere, Jordan J & Hensher, David A, 1983. "Using Discrete Choice Models with Experimental Design Data to Forecast Consumer Demand for a Unique Cultural Event," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 10(3), pages 348-361, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giansoldati, Marco & Danielis, Romeo & Rotaris, Lucia & Scorrano, Mariangela, 2018. "The role of driving range in consumers' purchasing decision for electric cars in Italy," Energy, Elsevier, vol. 165(PA), pages 267-274.
    2. Chen Wang & Ricardo Daziano, 2015. "On the problem of measuring discount rates in intertemporal transportation choices," Transportation, Springer, vol. 42(6), pages 1019-1038, November.
    3. Ricardo A. Daziano & Martin Achtnicht, 2014. "Forecasting Adoption of Ultra-Low-Emission Vehicles Using Bayes Estimates of a Multinomial Probit Model and the GHK Simulator," Transportation Science, INFORMS, vol. 48(4), pages 671-683, November.
    4. Haghani, Milad & Sarvi, Majid, 2018. "Hypothetical bias and decision-rule effect in modelling discrete directional choices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 361-388.
    5. Mariel, Petr & Meyerhoff, Jürgen, 2018. "A More Flexible Model or Simply More Effort? On the Use of Correlated Random Parameters in Applied Choice Studies," Ecological Economics, Elsevier, vol. 154(C), pages 419-429.
    6. Joan L. Walker & Yanqiao Wang & Mikkel Thorhauge & Moshe Ben-Akiva, 2018. "D-efficient or deficient? A robustness analysis of stated choice experimental designs," Theory and Decision, Springer, vol. 84(2), pages 215-238, March.
    7. Richard Yao & Riccardo Scarpa & John Rose & James Turner, 2015. "Experimental Design Criteria and Their Behavioural Efficiency: An Evaluation in the Field," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(3), pages 433-455, November.
    8. Parsons, George R. & Hidrue, Michael K. & Kempton, Willett & Gardner, Meryl P., 2014. "Willingness to pay for vehicle-to-grid (V2G) electric vehicles and their contract terms," Energy Economics, Elsevier, vol. 42(C), pages 313-324.
    9. Tanaka, Makoto & Ida, Takanori & Murakami, Kayo & Friedman, Lee, 2014. "Consumers’ willingness to pay for alternative fuel vehicles: A comparative discrete choice analysis between the US and Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 194-209.
    10. Daziano, Ricardo A. & Chiew, Esther, 2012. "Electric vehicles rising from the dead: Data needs for forecasting consumer response toward sustainable energy sources in personal transportation," Energy Policy, Elsevier, vol. 51(C), pages 876-894.
    11. Amirreza Talebi, 2024. "Simulation in discrete choice models evaluation: SDCM, a simulation tool for performance evaluation of DCMs," Papers 2407.17014, arXiv.org, revised Jul 2024.
    12. Nikhil Sikka & Paul Hanley, 2013. "What do commuters think travel time reliability is worth? Calculating economic value of reducing the frequency and extent of unexpected delays," Transportation, Springer, vol. 40(5), pages 903-919, September.
    13. Alexandros Dimitropoulos & Piet Rietveld & Jos N. van Ommeren, 2011. "Consumer Valuation of Driving Range: A Meta-Analysis," Tinbergen Institute Discussion Papers 11-133/3, Tinbergen Institute.
    14. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Review of hybrid, plug-in hybrid, and electric vehicle market modeling Studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 190-203.
    15. Daina, Nicolò & Sivakumar, Aruna & Polak, John W., 2017. "Modelling electric vehicles use: a survey on the methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 447-460.
    16. Grigolon, Anna B. & Borgers, Aloys W.J. & Kemperman, Astrid D.A.M. & Timmermans, Harry J.P., 2014. "Vacation length choice: A dynamic mixed multinomial logit model," Tourism Management, Elsevier, vol. 41(C), pages 158-167.
    17. Martin, Elliott William, 2009. "New Vehicle Choice, Fuel Economy and Vehicle Incentives: An Analysis of Hybrid Tax Credits and the Gasoline Tax," University of California Transportation Center, Working Papers qt5gd206wv, University of California Transportation Center.
    18. Daziano, Ricardo A., 2022. "Willingness to delay charging of electric vehicles," Research in Transportation Economics, Elsevier, vol. 94(C).
    19. Apurba Shee & Calum G. Turvey & Ana Marr, 2021. "Heterogeneous Demand and Supply for an Insurance‐linked Credit Product in Kenya: A Stated Choice Experiment Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(1), pages 244-267, February.
    20. Nadia Palmieri & Roberto Tomasone & Carla Cedrola & Daniele Puri & Mauro Pagano, 2023. "Factors Affecting Disabled Consumer Preferences for an Electric Vehicle for Rural Mobility: An Italian Experimental Study," Sustainability, MDPI, vol. 15(6), pages 1-16, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:trapol:v:37:y:2015:i:c:p:44-56. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30473/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.