Electric, plug-in hybrid, hybrid, or conventional? Polish consumers’ preferences for electric vehicles

Abstract

Poland aims at stimulating the market to reach a target of 50,000 plug-in and battery electric vehicles by 2020. However, as in other Eastern European countries, the market penetration stays very low. In Poland, there were only 475 battery electric vehicles and 514 plug-in electric vehicles registered in 2017. To identify effective support measures, this paper examines the preferences of Polish consumers for three types of electric vehicles: battery, hybrid, and plug-in hybrid vehicles. We use a discrete choice experiment to estimate the willingness to pay of a representative sample of consumers intending to buy a car in Poland. We find that electric vehicles are significantly less preferred than conventional cars, even under a public programme that would enable slow-mode charging in places where respondents usually park. We quantify the marginal willingness to pay for increasing the driving range, reductions in charging time, the availability of fast-mode charging stations, and the provision of policy incentives. The novelty of the paper lies in presenting a scenario with the slow-mode and availability of several levels of fast-mode charging stations and examination of the extent to which the heterogeneity of consumer preferences is driven by place of residence (urban, suburban, rural), intention to buy a new versus a used car, and the annual mileage. This is also the first discrete choice experiment on electric vehicles conducted in Eastern Europe. To stimulate the electric vehicle market, we recommend a pricing policy that affects the operating costs and other incentives along with an effective up-front price incentive scheme.

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Notes

  1. 1.

    Stated preferences make it possible to measure willingness to pay for configurations of goods which do not currently exist, such as new characteristics of existing or new products to be introduced in the market (Hanley and Czajkowski 2017). In addition, the ability to exogenously and systematically vary attributes of alternatives from which the respondent chooses serves the joint purpose of allowing for clean identification (e.g. allaying endogeneity and collinearity concerns associated with market-observed attribute level combinations; Earnhart 2001; Freeman et al. 2014; Phaneuf and Requate 2017) and increasing the efficiency of preference parameter estimation (Scarpa and Rose 2008).

  2. 2.

    To elicit this information, we used 13 price categories for a new car and 16 price categories for a used car (as the used cars are typically cheaper), and we asked each respondent to choose the one (s)he is most likely to expect to pay for their next car. In the design, the price of a CV equalled the midpoint of the selected price category. If a respondent did not know the purchase price, we attributed randomly one of these prices, PLN 45,000; 55,000; 65,000; or 80,000, for a new car or PLN 12,500; 17,500; 25,000; or 35,000 for a used car or for a car if the respondent did not know whether their next car should be new or used (these ranges were based on the Polish market purchases). Of the respondents, 4.7%, 6.1%, and 16.6% did not know the price of a new, a used, or a non-specified car, respectively.

    Hereinafter, to convert PLN to EUR, we use the purchasing power corrected exchange rate of 0.2325 EUR per PLN.

  3. 3.

    Specifically, fast-mode recharging infrastructure was described as “Recently, very fast recharging devices have become available, which make charging faster. Recharging an electric vehicle entirely takes only 10 minutes, compared to 6 to 8 hours if recharged from an AC socket at home. A hybrid vehicle with a plug-in can then be recharged within 5 minutes only. The fast-mode charging stations can be available to users to various degrees. They can be located at some of existing petrol stations, for example, 20%, 60%, or 90% of petrol stations, or other frequently visited places (e.g. supermarkets, cinemas and sport stadiums).”

  4. 4.

    All prior estimates were assumed to be normally distributed, with the exception of the priors for alternative specific constants, which were assumed to be uniformly distributed to represent potentially larger heterogeneity of respondents’ preferences with respect to propulsion technologies. The means of the Bayesian priors were derived from the MNL model estimated on the dataset from the pilot survey, and standard deviations equal to 0.25 of each parameter mean (with some arbitrarily selected minimum level).

  5. 5.

    There is a direct translation between asymptotic parameters in models estimated in preference space and WTP space (Scarpa et al. 2008), and the two expressions of utility are behaviourally equivalent. Any distribution of parameters in preference space implies some distributions in WTP space, and vice versa. In some cases, however, the resulting distributions can lead to implausible values for WTP or preference parameter estimates (Carson and Czajkowski 2013).

  6. 6.

    The software codes for estimating the MXL model were developed in Matlab and are available at http://github.com/czaj/DCE under Creative Commons BY 4.0 licence.

  7. 7.

    For the identification of speeders, we followed the recommendation of Survey Sampling International (Mitchell 2014) and excluded those who completed the survey in 48% of the median time. Following this strategy, 5.6% of respondents were excluded.

  8. 8.

    These weights should not be based on the quota for the general population nor on the quota for the automotive population, which are generally both available, compared to the population of Poles who intend to buy a car that is unknown. In order to have our dataset representative of the population who intend to buy a car, we utilise the data from sample B (representative of the general population) and derive the relative frequencies for the quota variables using observations from respondents with this intention. Since sample A is representative of the general Polish population and background information about the characteristics of car purchasers was missing, we believe this approach is sufficiently sound to provide information about the shares and hence the weights. The resulting shares after weighting are compared to the quota for the general population of the Poles in Table 2.

  9. 9.

    This subsample provided a basis to derive the weights for the dataset used in this paper. From the general population sample, 9% of respondents do not have a car and also do not intend to buy one in the future, and 5% do not have a car now but would like to have one later.

  10. 10.

    According to the Polish 2017/2018 Automotive Industry Report (PZPM 2017), in 2015–2017, the first registrations of passenger cars by market segment were 17–19% (B class), 29–30% (C class), 10–11% (D class), 1.7–2.6% (E–G class), 24–28% (SUV), and 6–7% (MPV).

  11. 11.

    Average equivalised household income in 2015 was PLN 2223, taking 2.6 persons a household (Eurostat) and assuming 1.8 “equivalised persons” give PLN 4000 a month per household.

  12. 12.

    The supplementary results, such as the estimation results of other models, including Conditional logit and Mixed logit without fully correlated parameters, are provided in the on-line Supplementary Information material. Code is available from http://czaj.org/research/supplementary-materials.

  13. 13.

    Note that the alternative specific constants capture the utilities associated with the baseline levels of all the attributes (e.g. range = 0, charging time = 0, operating cost = 0, low availability of fast-charge stations and no policy incentives) and the otherwise uncontrolled perceived differences between labelled alternatives.

  14. 14.

    Note that this interaction may to some extent control for the differences between urban, suburban, and rural respondents. We find, however, that on average mileage does not differ across these three residence segments, whereas it is significantly larger for the used-car buyers than for the new-car buyers.

  15. 15.

    All USD values reported in this paragraph are PPP-adjusted 2005 USD based on OECD statistics.

Abbreviations

BEV:

Battery electric vehicle, a vehicle set in motion by an electric motor. Powered by electricity, it has a battery which can be recharged from a regular electric socket.

PHEV:

Plug-in hybrid vehicle, a vehicle with an internal combustion engine (petrol or diesel) and batteries that can also be charged from a regular electric socket. The car can drive several tens of kilometres solely on electricity. When the batteries are empty, the car will automatically switch to the internal combustion engine.

HEV:

Hybrid vehicle, a vehicle with batteries but without a plug. It has both an internal combustion engine and an electric engine. The combination allows the electric motor and batteries to help the conventional engine operate more efficiently, reducing fuel use. Switching between the two engines occurs automatically without the driver’s intervention. The battery is charged from the energy produced by the combustion engine during driving or while braking. A hybrid car drives several kilometres solely on electricity.

EV:

Electric vehicle, includes BEV, PHEV, and HEV

CV:

Conventional vehicle, drives on an internal combustion engine that can be fuelled by petrol, diesel, or oil derivatives such as LPG.

References

  1. Aasness, M. A., & Odeck, J. (2015). The increase of electric vehicle usage in Norway—incentives and adverse effects. European Transport Research Review, 7(4).

  2. Achtnicht, M. (2012). German car buyers’ willingness to pay to reduce CO2 emissions. Climatic Change, 113(3–4), 679–697.

    Article  Google Scholar 

  3. Axsen, J., Mountain, D. C., & Jaccard, M. (2009). Combining stated and revealed choice research to simulate the neighbor effect: the case of hybrid electric vehicles. Resource and Energy Economics, 31(3), 221–238.

    Article  Google Scholar 

  4. Axsen, J., Bailey, J., & Castro, M. A. (2015). Preference and lifestyle heterogeneity among potential plug-in electric vehicle buyers. Energy Economics, 50, 190–201.

    Article  Google Scholar 

  5. Bahamonde-Birke, F. J., & Hanappi, T. (2016). The potential of electromobility in Austria: evidence from hybrid choice models under the presence of unreported information. Transportation Research Part A: Policy and Practice, 83, 30–41.

    Google Scholar 

  6. Bateman, I. J., Carson, R. T., Day, B., & Hanemann, W. M. (2004). Economic valuation with stated preference techniques: a manual. Cheltenham, UK: Edward Elgar ISBN: 1840649194.

    Google Scholar 

  7. Bjerkan, K. Y., Nørbech, T. E., & Nordtømme, M. E. (2016). Incentives for promoting battery electric vehicle (BEV) adoption in Norway. Transportation Research Part D: Transport and Environment, 43, 169–180.

    Article  Google Scholar 

  8. Bliemer, M. C. J., Rose, J. M., & Hess, S. (2008). Approximation of Bayesian efficiency in experimental choice designs. Journal of Choice Modelling, 1(1), 98–127.

    Article  Google Scholar 

  9. Brownstone, D., & Train, K. (1998). Forecasting new product penetration with flexible substitution patterns. Journal of Econometrics, 89(1–2), 109–129.

    MATH  Article  Google Scholar 

  10. Bunch, D. S., Bradley, M., Golob, T. F., Kitamura, R., & Occhiuzzo, G. P. (1993). Demand for clean-fuel vehicles in California: a discrete-choice stated preference pilot project. Transportation Research Part A: Policy and Practice, 27(3), 237–253.

    Article  Google Scholar 

  11. Carson, R., & Czajkowski, M. (2013). A new baseline model for estimating willingness to pay from discrete choice models. Sydney: Presented at International Choice Modelling Conference.

    Google Scholar 

  12. Carson, R., & Czajkowski, M. (2014). The discrete choice experiment approach to environmental contingent valuation. In Handbook of choice modelling (p. 202–235).

  13. Caulfield, B., Farrell, S., & McMahon, B. (2010). Examining individuals preferences for hybrid electric and alternatively fuelled vehicles. Transport Policy, 17(6), 381–387.

    Article  Google Scholar 

  14. Champ, P. A., Boyle, K. J., & Brown, T. C. (ed). (2003). Collecting Survey Data for Nonmarket Valuation. A primer on nonmarket valuation (Vol. 3). Dordrecht: Springer Netherlands

  15. Chorus, C. G., Koetse, M. J., & Hoen, A. (2013). Consumer preferences for alternative fuel vehicles: comparing a utility maximization and a regret minimization model. Energy Policy, 61, 901–908.

    Article  Google Scholar 

  16. Czajkowski, M., & Budziński, W. (2017). Simulation error in maximum likelihood estimation of discrete choice models protection regulation (GDPR). University of Warsaw Faculty of Economic Sciences Working Papers, 1–47.

  17. Dagsvik, J. K., Wennemo, T., Wetterwald, D. G., & Aaberge, R. (2002). Potential demand for alternative fuel vehicles. Transportation Research Part B: Methodological, 36(4), 361–384.

    Article  Google Scholar 

  18. van der Vooren, A., Alkemade, F., & Hekkert, M. P. (2012). Effective public resource allocation to escape lock-in: the case of infrastructure-dependent vehicle technologies. Environmental Innovation and Societal Transitions, 2, 98–117.

    Article  Google Scholar 

  19. Dimitropoulos, A., Rietveld, P., & van Ommeren, J. N. (2013). Consumer valuation of changes in driving range: a meta-analysis. Transportation Research Part A: Policy and Practice, 55, 27–45.

    Google Scholar 

  20. EAFO (2017). The European Commission initiative to provide alternative fuels statistics and information (electricity, hydrogen, natural gas, LPG). European Alternative Fuel Observatory. http://www.eafo.eu. Accessed January 2018.

  21. EAFO (2018). The European Commission initiative to provide alternative fuels statistics and information (electricity, hydrogen, natural gas, LPG). European Alternative Fuel Observatory. www.eafo.eu/content/netherlands. Accessed on 10h July 2018.

  22. Earnhart, D. (2001). Combining revealed and stated preference methods to value environmental amenities at residential locations. Land Economics, 77(1), 12–29.

    Article  Google Scholar 

  23. European Commission (2014a). Directive 2014/94/EU of the European Parliament and of the Council of 22 October 2014 on the deployment of alternative fuels infrastructure. Text with EEA relevance.

  24. European Commission (2014b). Regulation (EU) No 333/2014 of the European Parliament and of the Council of 11 March 2014 amending Regulation (EC) No 443/2009 to define the modalities for reaching the 2020 target to reduce CO2 emissions from new passenger cars.

  25. Egbue, O., & Long, S. (2012). Barriers to widespread adoption of electric vehicles: an analysis of consumer attitudes and perceptions. Energy Policy, 48, 717–729.

    Article  Google Scholar 

  26. Ewing, G., & Sarigöllü, E. (2000). Assessing consumer preferences for clean-fuel vehicles: a discrete choice experiment. Journal of Public Policy & Marketing, 19, 106–118.

    Article  Google Scholar 

  27. Ferrini, S., & Scarpa, R. (2007). Designs with a priori information for nonmarket valuation with choice experiments: a Monte Carlo study. Journal of Environmental Economics and Management, 53(3), 342–363.

    MATH  Article  Google Scholar 

  28. Figenbaum, E. (2017). Perspectives on Norway’s supercharged electric vehicle policy. Environmental Innovation and Societal Transitions, 25, 14–34.

    Article  Google Scholar 

  29. Fluchs, S., & Kasperk, G. (2018). The influence of government incentives on electric vehicle adoption: cross-national comparison (pp. 25–29). Gothenburg Sweden: Paper presented at the 5th World Congress of Environmental and Resource Economists.

    Google Scholar 

  30. Freeman, A. M., Herriges, J. A., & Kling, C. L. (2014). The measurement of environmental and resource values: theory and methods (third ed.). Abingdon, Oxon; New York, NY: RFF Press.

  31. Gallagher, K. S., & Muehlegger, E. (2011). Giving green to get green? Incentives and consumer adoption of hybrid vehicle technology. Journal of Environmental Economics and Management, 61(1), 1–15.

    Article  Google Scholar 

  32. German, R., Pridmore, A., Ahlgren, C., Williamson, T., & Nijland, H. (2018). Vehicle emissions and impacts of taxes and incentives in the evolution of past emissions: report to EEA. Bilthoven: Eionet Report – ETC/ACM 2018/1. European Topic Centre on Air Pollution and Climate Change Mitigation.

    Google Scholar 

  33. Golob, T. F., Torous, J., Bradley, M., Brownstone, D., Crane, S. S., & Bunch, D. S. (1997). Commercial fleet demand for alternative-fuel vehicles in California. Transportation Research Part A: Policy and Practice, 31(3), 219–233.

    Google Scholar 

  34. Hackbarth, A., & Madlener, R. (2013). Consumer preferences for alternative fuel vehicles: a discrete choice analysis. Transportation Research Part D: Transport and Environment, 25, 5–17.

    Article  Google Scholar 

  35. Hackbarth, A., & Madlener, R. (2016). Willingness-to-pay for alternative fuel vehicle characteristics: a stated choice study for Germany. Transportation Research Part A: Policy and Practice, 85, 89–111.

    Article  Google Scholar 

  36. Hanley, N., & Czajkowski, M. (2017). Stated preference valuation methods: an evolving tool for understanding choices and informing policy. University of Warsaw Faculty of Economic Sciences Working Papers, 1–43.

  37. Hardman, S., Chandan, A., Tal, G., & Turrentine, T. (2017). The effectiveness of financial purchase incentives for battery electric vehicles—a review of the evidence. Renewable and Sustainable Energy Reviews, 80, 1100–1111.

    Article  Google Scholar 

  38. Haugneland, P., Bu, C., & Hauge, E. (2016). The Norwegian EV success continues. Montréal, Quebec, Canada: Presented at EVS29 symposium.

    Google Scholar 

  39. Heffner, R.R., Kurani, K.S., & Turrentine, T.S. (2005). Effects of vehicle image in gasoline-hybrid electric vehicles. UC Davis Institute of Transportation Studies, Davis. European Alternative Fuels Observatory (2018). Netherlands. Retrieved from www.eafo.eu/content/netherlands on 10h July 2018.

  40. Helveston, J. P., Liu, Y., Feit, E. M., Fuchs, E., Klampfl, E., & Michalek, J. J. (2015). Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the U.S. and China. Transportation Research Part A: Policy and Practice, 73, 96–112.

    Article  Google Scholar 

  41. Hess, S., Fowler, M., Adler, T., & Bahreinian, A. (2012). A joint model for vehicle type and fuel type choice: evidence from a cross-nested logit study. Transportation, 39(3), 593–625.

    Article  Google Scholar 

  42. Hidrue, M. K., Parsons, G. R., Kempton, W., & Gardner, M. P. (2011). Willingness to pay for electric vehicles and their attributes. Resource and Energy Economics, 33(3), 686–705.

    Article  Google Scholar 

  43. Hoen, A., & Koetse, M. J. (2012). A choice experiment on AFV preferences of private car owners in the Netherlands (vol. 3). PBL working paper.

  44. Hoen, A., & Koetse, M. J. (2014). A choice experiment on alternative fuel vehicle preferences of private car owners in the Netherlands. Transportation Research Part A: Policy and Practice, 61, 199–215.

    Google Scholar 

  45. Jensen, A. F., Cherchi, E., & Mabit, S. L. (2013). On the stability of preferences and attitudes before and after experiencing an electric vehicle. Transportation Research Part D: Transport and Environment, 25, 24–32.

    Article  Google Scholar 

  46. Kim, J., Rasouli, S., & Timmermans, H. (2014). Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: application to intended purchase of electric cars. Transportation Research Part A: Policy and Practice, 69, 71–85.

    Google Scholar 

  47. Koetse, M. J., & Hoen, A. (2014). Preferences for alternative fuel vehicles of company car drivers. Resource and Energy Economics, 37, 279–301.

    Article  Google Scholar 

  48. Kurani, K. S., Turrentine, T., & Sperling, D. (1996). Testing electric vehicle demand in ‘hybrid households’ using a reflexive survey. Transportation Research Part D: Transport and Environment, 1(2), 131–150.

    Article  Google Scholar 

  49. Lebeau, K., Van Mierlo, J., Lebeau, P., Mairesse, O., & Macharis, C. (2012). The market potential for plug-in hybrid and battery electric vehicles in Flanders: a choice-based conjoint analysis. Transportation Research Part D: Transport and Environment, 17(8), 592–597.

    Article  Google Scholar 

  50. Li, S., Tiong, L., Xing, J. & Zhou, Y. (2017). The market for electric vehicles: indirect network effects and policy design. Journal of the Association of Environmental and Resource Economists, 4(1), 89–133.

  51. Liao, F., Molin, E., & van Wee, B. (2017). Consumer preferences for electric vehicles: a literature review. Transport Reviews, 37(3), 252–275.

    Article  Google Scholar 

  52. McFadden, D. L. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics. New York: Academic Press.

    Google Scholar 

  53. Mersky, A. C., Sprei, F., Samaras, C., & Qian, Z. S. (2016). Effectiveness of incentives on electric vehicle adoption in Norway. Transportation Research Part D: Transport and Environment, 46, 56–68.

    Article  Google Scholar 

  54. Mitchell, N. (2014). The changing landscape of technology and its effect on online survey data collection. Survey Sampling International. https://www.surveysampling.com/site/assets/files/1583/the-changing-landscape-of-technology-and-its-effect-on-online-survey-data-collection.pdf. September 2017.

  55. Phaneuf, D. J., & Requate, T. (2017). A course in environmental economics: theory, policy, and practice. Cambridge, United Kingdom; New York, NY, USA: Cambridge University Press.

    Google Scholar 

  56. Potoglou, D., & Kanaroglou, P. S. (2007). Household demand and willingness to pay for clean vehicles. Transportation Research Part D: Transport and Environment, 12(4), 264–274.

  57. PZPM (2017). Automotive industry report 2017/2018. Polski Zwiazek Przemyslu Motoryzacyjnego. http://www.pzpm.org.pl/en/Automotive-market/Reports/PZPM-Automotive-Industry-Report-2017-2018. September 2017.

  58. Qian, L., & Soopramanien, D. (2011). Heterogeneous consumer preferences for alternative fuel cars in China. Transportation Research Part D: Transport and Environment, 16(8), 607–613.

  59. Rasouli, S., & Timmermans, H. (2016). Influence of social networks on latent choice of electric cars: a mixed logit specification using experimental design data. Networks and Spatial Economics, 16(1), 99–130.

    MathSciNet  Article  Google Scholar 

  60. Revelt, D., & Train, K. (1998). Mixed logit with repeated choices: households’ choices of appliance efficiency level. Review of Economics and Statistics, 80(4), 647–657.

    Article  Google Scholar 

  61. Rose, J. M., Bliemer, M. C. J., Hensher, D. A., & Collins, A. T. (2008). Designing efficient stated choice experiments in the presence of reference alternatives. Transportation Research Part B: Methodological, 42(4), 395–406.

    Article  Google Scholar 

  62. Sándor, Z., & Wedel, M. (2001). Designing conjoint choice experiments using managers’ prior beliefs. Journal of Marketing Research, 38(4), 430–444.

    Article  Google Scholar 

  63. Scarpa, R., & Rose, J. M. (2008). Design efficiency for non-market valuation with choice modelling: how to measure it, what to report and why. Australian Journal of Agricultural and Resource Economics, 52(3), 253–282.

    Article  Google Scholar 

  64. Scarpa, R., Thiene, M., & Train, K. (2008). Utility in willingness to pay space: a tool to address confounding random scale effects in destination choice to the Alps. American Journal of Agricultural Economics, 90(4), 994–1010.

    Article  Google Scholar 

  65. Ščasný, M., Zvěřinová, I., & Czajkowski, M. (2015). Report on determinants and barriers of purchase of low carbon vehicles, including WTP estimates for specific attributes of passenger vehicles in Poland. Deliverable 8.1. of project “Development of an Evaluation Framework for the Introduction of Electromobility” (DEFINE) funded by Era-Net Transport Transnational Call Electromobility+.

  66. Train, K., & Weeks, M. (2005). Discrete choice models in preference space and willingness-to-pay space. In R. Scarpa & A. Alberini (Eds.), Applications of simulation methods in environmental and resource economics (Vol. 6, pp. 1–16). Berlin/Heidelberg: Springer-Verlag.

    Google Scholar 

  67. Valeri, E., & Cherchi, E. (2016). Does habitual behavior affect the choice of alternative fuel vehicles? International Journal of Sustainable Transportation, 10(9), 825–835.

    Article  Google Scholar 

  68. Valeri, E., & Danielis, R. (2015). Simulating the market penetration of cars with alternative fuel power train technologies in Italy. Transport Policy, 37, 44–56.

    Article  Google Scholar 

  69. Xing, J., Leard, B., & Li, S. (2018). What does an electric vehicle replace? Paper presented at the 5th World Congress of Environmental and Resource Economists (WCERE), Gothenburg, June 25–29, 2018. Retrieved from https://www.jianweixing.com/research.html on 5 July 2018.

  70. Zhang, X., Wang, K., Hao, Y., Jing-Li, F., & Wei, Y. M. (2013). The impact of government policy on preference for NEVs: the evidence from China. Energy Policy, 61, 382–393.

    Article  Google Scholar 

  71. Ziegler, A. (2012). Individual characteristics and stated preferences for alternative energy sources and propulsion technologies in vehicles: a discrete choice analysis for Germany. Transportation Research Part A: Policy and Practice, 46(8), 1372–1385.

    Google Scholar 

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Acknowledgments

This research has been supported by the Czech Science Foundation (GA15-23815S; Ščasný), Charles University (PRIMUS/17/HUM/16; Zvěřinová), and the National Science Centre of Poland (Sonata 10, 2015/19/D/HS4/01972; Czajkowski). Data collection and preliminary analysis were financed by the Polish NCBiR (Centre for Research and Development), within the framework of the project “Development of an Evaluation Framework for the Introduction of Electromobility – DEFINE” provided to the Center for Social and Economic Research (CASE Poland). This article is a part of research presented at the ECOCEP Conference on Economic Modelling for Climate-Energy Policy (FP7-PEOPLE-2013-IRSES, No. 609642) and secondments funded by the H2020-MSCA-RISE under GA 681228. This support is acknowledged. The views expressed here are those of the authors and not necessarily those of our institutions. Responsibility for any errors remains with the authors.

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Ščasný, M., Zvěřinová, I. & Czajkowski, M. Electric, plug-in hybrid, hybrid, or conventional? Polish consumers’ preferences for electric vehicles. Energy Efficiency 11, 2181–2201 (2018). https://doi.org/10.1007/s12053-018-9754-1

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Keywords

  • Battery electric vehicles
  • Hybrid vehicles
  • Discrete choice experiments
  • Willingness to pay
  • Driving range
  • Fast-mode charging infrastructure
  • Recharging time
  • Incentives