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Willing to Pay? Spatial Heterogeneity of e-Vehicle Charging Preferences in Germany

Author

Listed:
  • Wolff, Stefanie

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

  • Madlener, Reinhard

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

Abstract

In this paper, we spatially map the willingness to pay for e-vehicle charging options according to the availability of public charging spots. We combine a Discrete Choice Experiment on charging preferences with a data set of public charging spots. Our results show spatial heterogeneity, i.e. respondents’ choices depend on the quantity of public charging spots available to them. Non-availability of public charging spots in the vicinity has a larger effect on the choice probability than 1, 2, or 3 charging spots have. This could be evidence for charging infrastructure awareness. For the charging locations, we find marked spatial heterogeneity in the willingness to pay subject to the number of available public charging spots. The interaction of charging location with the number of public charging spots reveals a strong preference for charging at home rather than at work or charging on the road. However, with every additional public charging spot, respondents are more likely to charge away from home. This holds until the number of charging spots has reached a tipping point at which respondents become indifferent between home and work charging. When the tipping point is exceeded, respondents rather charge at work than at home. Thus, with increasing numbers of charging spots, public chargers near home are less relevant than those near work. Eventually, public chargers away from home become more attractive. Also, with increasing numbers of charging spots our results reveal a fivefold greater willingness to pay for reducing waiting time (for a charging spot to become available) than for accelerating charging speed. Thus, charging point operators could surcharge by implementing a booking scheme than by implementing fast-charging. From the findings, we derive further implications for charging infrastructure policy, business models, and infrastructure planning, e.g. regarding the expected break-even points for rolling out charging infrastructure and the provision of green energy.

Suggested Citation

  • Wolff, Stefanie & Madlener, Reinhard, 2020. "Willing to Pay? Spatial Heterogeneity of e-Vehicle Charging Preferences in Germany," FCN Working Papers 9/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    • Handle: RePEc:ris:fcnwpa:2020_009
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    References listed on IDEAS

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    Cited by:

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    2. Fabianek, Paul & Glensk, Barbara & Madlener, Reinhard, 2021. "A Sequential Real Options Analysis for Renewable Power-to-Hydrogen Plants in Germany and California," FCN Working Papers 1/2021, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    3. Liu, Xueying & Madlener, Reinhard, 2021. "Economic Benefits of Direct Current Technology for Private Households and Peer-to-Peer Trading in Germany," FCN Working Papers 7/2021, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    4. Madlener, Reinhard & Sheykhha, Siamak & Briglauer, Wolfgang, 2022. "The electricity- and CO2-saving potentials offered by regulation of European video-streaming services," Energy Policy, Elsevier, vol. 161(C).
    5. Walter, Antonia & Held, Maximilian & Pareschi, Giacomo & Pengg, Hermann & Madlener, Reinhard, 2020. "Decarbonizing the European Automobile Fleet: Impacts of 1.5 °C-compliant Climate Policies in Germany and Norway," FCN Working Papers 18/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).

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    More about this item

    Keywords

    Electric mobility charging behavior; Charging spot awareness; Discrete Choice Experiment; Econometric modeling; Willingness to pay; Germany;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • M38 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Government Policy and Regulation
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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