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Demand for Green Refueling Infrastructure

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Abstract

Despite increasing public investment in charging infrastructure for plug-in electric vehicles (PEVs), policymakers know little about drivers’ preferences for publicly-accessible charging stations. Using data from an innovative choice experiment, we estimate demand for PEV charging stations, characterizing willingness to pay for access to types of locations as well as driver tradeoffs between refueling duration and costs. Prospective PEV drivers are willing to pay the actual variable cost of recharging at public charging stations and are willing to pay to cover significant fixed costs at select locations. Not surprisingly, many prospective drivers reveal a positive willingness to accept to wait while refueling, but this varies greatly across latent classes.

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Notes

  1. PEVs include both battery electric vehicles (BEVs) and plug-in hybrid vehicles (PHEVs). BEVs are powered exclusively by electricity from the on-board battery, while PHEVs are powered by both electricity and gasoline, having both a battery and gasoline engine on board.

  2. There are three types of charging infrastructure that support plug-in electric vehicles, each of which offers progressively faster charging times. Level 1 charging involves 110V charging from standard building electrical outlets. Level 2 offers 220V and 240V charging from dedicated chargers. Fast charging offers much higher voltage and is often comparable to gasoline refueling in terms of wait times.

  3. “U.S. Department of Transportation Names Six Interstate Routes as ‘Corridors of the Future’ to Help Fight Traffic Congestion.” Federal Highway Administration, U.S. Department of Transportation. September 10, 2007. http://www.fhwa.dot.gov/pressroom/dot0795.cfm.

  4. “Corridor: Interstate 5 (I-5)- Washington to California.” Corridors of the Future Fact Sheet. Federal Highway Administration, U.S. Department of Transportation. http://www.fhwa.dot.gov/pressroom/fsi5.cfm.

  5. See http://www.greentechmedia.com/articles/read/NRG-Settlement-Funds-Californias-Electric-Expressway-EV-Charger-Network.

  6. The CPUC approved SCE to install 1500 at the cost of up to $22 million, SDG&E to install 3,500 at the costs of up to $45 million and PG&E to install 7,500 at a cost of up to $160 million. See https://www.csis.org/analysis/utility-involvement-electric-vehicle-charging-infrastructure-california-vanguard.

  7. See Fig. 5 in the “California statewide plug-in electric vehicle infrastructure” by NREL, 2014. This program was authorized by Assembly Bill 118 (Nùñez, Chapter 750, Statutes of 2007) instructing California Energy Commission to develop and deploy alternative and renewable fuels and advanced transportation technologies to help attain the state’s climate change policies.

  8. For more details on this module, see Sheldon et al. (2017).

  9. The charging choice experiments were only administered to respondents who chose some sort of PEV (approximately 85% of the respondents) at least once in the previous choice experiment. Respondents who never chose a PEV were not administered the charging choice experiments, as they were unlikely to view these choices as relevant or realistic.

  10. For more details see Scarpa and Rose (2008).

  11. The weighted California Household Travel Survey, relative to our weighted sample, exhibits modestly fewer upper middle households ($75–100k; 15% compared to 23%) and greater upper income households (>$150K; 21% compared to 12%). With respect to age, it exhibits a lower number of 18–24 year olds (2% compared to 16%), modestly greater 55–64 years olds (28% compared to 14%) and greater 65+ year olds (19% compared to 10%). With respect to education, it contains fewer households with less than a high school diploma (3% compared to 7%), fewer with a high school degree (11% compared to 25%) and greater with graduated degrees (26% compared to 13%). Finally, with respect to home ownership, it has modestly greater households that own their homes (77% compared to 62%).

  12. For example, the multinomial logit estimated in Table 2 assumes that if an alternative such as grocery store charging were not available, the probability that had been assigned to choosing the grocery store would be equally split amongst the remaining alternatives. In contrast, the mixed logit estimated in Table 2 would allocate the probability according to the parameter distributions and the covariance between preferences for remaining alternatives and for grocery store charging.

  13. While it is possible to make the mean or variance of a mixed logit parameter a function of observed covariates, we ran into frequently incurred problem that such models with more than one or two covariates tend to be numerically unstable and would not converge to a well-defined maximum value. Individual taste parameters can be identified as detailed in Revelt and Train (1999), although the authors caution that estimating a mixed logit as a function of observed covariates such as demographics is ‘more direct and more accessible to hypothesis testing than estimating a mixed logit without these characteristics, calculating expected tastes, and then doing cluster and other analyses on the expected tastes.’

  14. Multinomial choice questions involving private goods are not necessarily incentive compatible even when the survey itself is consequential. This is because a respondent must balance an increase in the likelihood that the good is made available for purchase against an increase in the price charged if offered. As such, estimates can potentially be biased in either direction. Because our estimates are lower than fuel cost per mile for almost all gasoline vehicles and in the same general range as home charging costs, if a bias exist it is likely to be in a downward direction.

  15. Our focus is on demand by the early and mid-market PEV adopters who are in our sample. Respondents who never chose electric vehicles were not administered the charging modules. Marginal willingness to pay for public charging is likely higher for respondents with a higher probability of owning a PEV. Since respondents who never chose electric vehicles were not administered the charging modules, the aggregate demand for charging at non-home stations in the longer-term future is likely underestimated here, to the extent households represented by these respondents later buy electric vehicles as their attributes including price and range improve and gasoline prices increase as California’s actions to reduce carbon dioxide and other air pollutants become increasingly stringent.

  16. In California, PEV drivers are currently able to access HOV lanes free of charge. Research indicates this policy has had a significant positive impact on PEV sales in the state (Sheldon and DeShazo 2017). Our results indicate that development of public charging infrastructure could be equally if not more successful in promoting PEV adoption in California.

  17. Because having to recharge one’s vehicle battery is an unfamiliar act for the respondents, the survey includes over 8 screens describing how recharging works as well as what the private costs and benefits of doing so are. We pre-check respondent knowledge of, and ability to distinguish, PHEVs from BEVs. We do not present this choice scenario to the 38 respondents who fail this pre-check. In this choice experiment, we place the respondent in PHEVs not only because it enables us to better a critical policy issue (increasing share of PHEV miles driven using electricity) but also because PHEVs more closely resemble ICEs than would a BEV. Importantly, PHEVs are not range limited while offering the driver flexibility to decide whether to recharge or not. Our pretesting revealed that by this point in the survey, respondents could conceptualize the time-cost trade-off that choosing the recharge represented for them.

  18. At the time the survey was conducted, public charging stations were not common, which is why refueling at the gas station was likely to be perceived by respondents as a status quo.

  19. The CAIC, introduced by Bozdogan (1987), is an extension to the traditional AIC that make it “asymptotically consistent and penalize overparameterization more stringently to pick only the simplest of the ‘true’ models.”

  20. BIC is slightly smaller with fewer covariates, but this requires removing from the control set variables with highly significant coefficients.

  21. There has been little work on the strategic incentives faced by a respondent asked a multinomial forced choice question without a no purchase alternative. Because making an alternative available with a price higher than the respondent is willing to pay can provide no additional utility, any bias in the willingness to pay estimates would appear to be downward. Similar to all multinomial choice questions, more complex patterns of strategic behavior across different types of alternatives cannot be ruled out as these depend on perceptions of how many alternatives will be supplied and the beliefs of other people, with convergence to truthful preference revelation occurring when only one of the alternatives will not be supplied and/or uninformative priors about the beliefs of other people are held by the respondent.

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Authors and Affiliations

  1. Department of Economics, University of South Carolina, 1014 Greene St., Columbia, SC, 29208, USA

    Tamara L. Sheldon

  2. Luskin School of Public Affairs, University of California, 337 Charles E Young Dr E, Los Angeles, CA, 90095, USA

    J. R. DeShazo

  3. Department of Economics, University of California, 9500 Gilman Dr., La Jolla, San Diego, CA, 92093, USA

    Richard T. Carson

Authors
  1. Tamara L. Sheldon
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  2. J. R. DeShazo
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  3. Richard T. Carson
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Correspondence to Tamara L. Sheldon.

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Appendix

Appendix

See Tables  8 and 9.

Table 8 UCLA new car buyer survey population\(^\dagger\)
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Table 9 Definition of variables
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Sheldon, T.L., DeShazo, J.R. & Carson, R.T. Demand for Green Refueling Infrastructure. Environ Resource Econ 74, 131–157 (2019). https://doi.org/10.1007/s10640-018-00312-9

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