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


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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Fig. 1
Fig. 2


  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.



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.


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.


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.


Electric vehicle, includes BEV, PHEV, and HEV


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


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