Demand for clean-fuel vehicles in California: A discrete-choice stated preference pilot project

Bunch, David S.; Bradley, Mark; Golob, Thomas F.; Kitamura, Ryuichi; Occhiuzzo, Gareth P

doi:10.1016/0965-8564(93)90062-p

Cited by 204 publications

(183 citation statements)

References 10 publications

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“…Whereas Bunch et al (1991) randomly generated the order in which the attributes appeared on the choice set card, we followed a different strategy, as mentioned above, by exposing half the sample to 15 different choice sets with the fuel technologies, "electric", "lpg" and "gasoline", and the other half to 15 different choice sets with the fuel technologies, "hybrid", "lpg" and "gasoline".…”

Section: Experimental Designmentioning

confidence: 99%

“…In the context of studying the potential demand for alternative fuel vehicles, analyses based on stated preference surveys have been carried out by Beggs et al (1981), Hensher (1982) and Calfee (1985), (these are electric vehicles), Bunch et al (1991) and Golob et al (1991). See also Mannering and Train (1985), Train (1980), Brownstone et al (1996), and Brownstone and Train (1999).…”

Section: Introductionmentioning

confidence: 99%

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Potential demand for alternative fuel vehicles

Dagsvik

Wennemo

Wetterwald

et al. 2002

Transportation Research Part B: Methodological

161

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This paper analyzes the potential demand for alternative fuel vehicles. The alternative fuel vehicles we consider are liquid propane gas and electric powered vehicles in addition to a dual-fuel vehicle. The data were obtained from a stated preference survey in which each respondent, in a randomly selected sample, was exposed to 15 experiments. In each experiment the respondents were asked to rank three hypothetical vehicles characterized by specific attributes, according to the respondents' preferences. Several versions of a random utility model are formulated and estimated. They include a model for rank ordered data, and models that allow for different types of correlation in preferences across experiments. Apart from one case the model specifications are estimated from the data on first choices. The most general model turns out to predict aggregate second and third choices rather well. The model is applied to assess the willingness to pay for alternative fuel vehicles.

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Section: Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Potential demand for alternative fuel vehicles

Dagsvik

Wennemo

Wetterwald

et al. 2002

Transportation Research Part B: Methodological

161

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show abstract

“…Discrete choice modeling has been frequently applied to low-emissions vehicles (e.g., Bunch et al, 1993;Ewing and Sarigollu, 2000;Potoglou and Kanaroglou, 2007). Because SP and RP data can have complementary strengths, a growing body of literature demonstrates the potential for ''joint'' modeling techniques (e.g., Swait et al, 1994;Brownstone et al, 2000;Train, 2003;Hensher et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles

Axsen

Mountain

Jaccard

2009

Resource and Energy Economics

237

111

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According to intuition and theories of diffusion, consumer preferences develop along with technological change. However, most economic models designed for policy simulation unrealistically assume static preferences. To improve the behavioral realism of an energy-economy policy model, this study investigates the ''neighbor effect,'' where a new technology becomes more desirable as its adoption becomes more widespread in the market. We measure this effect as a change in aggregated willingness to pay under different levels of technology penetration. Focusing on hybrid-electric vehicles (HEVs), an online survey experiment collected stated preference (SP) data from 535 Canadian and 408 Californian vehicle owners under different hypothetical market conditions. Revealed preference (RP) data was collected from the same respondents by eliciting the year, make and model of recent vehicle purchases from regions with different degrees of HEV popularity: Canada with 0.17% new market share, and California with 3.0% new market share. We compare choice models estimated from RP data only with three joint SP-RP estimation techniques, each assigning a different weight to the influence of SP and RP data in coefficient estimates. Statistically, models allowing more RP influence outperform SP influenced models. However, results suggest that because the RP data in this study is afflicted by multicollinearity, techniques that allow more SP influence in the beta estimates while maintaining RP data for calibrating vehicle

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“…This paper's RP approach involves modeling the characteristics of private individuals who purchased new EEVs, whilst estimating the effect of the congestion tax exemption on marginal demand. The study specifically builds on work undertaken by Bunch et al (1993), Musti and Kockelman (2011), Campbell et al (2012), Graham-Rowe et al (2012) and Ziegler (2012) in Transportation Research Part A: Policy and Practice, in attempting to identify individuals that are most likely to purchase a energy-efficient vehicle. This paper also contributes to the current literature by examining the effectiveness of a tax exemption under revealed preference conditions, and by assessing the total effect of the policy based on key indicators for policy makers, including: vehicle owner home and work locations, commuting patterns, number of children, number of vehicles, age, gender and income.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have investigated the demand for EEVs through stated-preference (SP) surveys across multiple countries, including: Denmark (Mabit and Fosgerau, 2011) Germany (Hackbarth and Madlener, 2013;Ziegler, 2012), Norway (Dagsvik et al, 2002), United Kingdom (Batley et al, 2004), Canada (Ewing and Sarigöllü, 1998), USA (Brownstone et al, 1996;Bunch et al, 1993;Hess et al, 2012;Musti and Kockelman, 2011) and Australia (Beck et al, 2013). Although each of these studies differed in their approach, all involved SP surveys where characteristics were varied among various types of vehicles including EEVs and presented to respondents, who in turn made hypothetical choices about which vehicle they would be most likely to purchase.…”

Section: Introductionmentioning

confidence: 99%

The impact of a congestion pricing exemption on the demand for new energy efficient vehicles in Stockholm

Whitehead

Franklin

Washington

2014

Transportation Research Part A: Policy and Practice

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As governments seek to transition to more efficient vehicle fleets, one strategy has been to incentivize 'green' vehicle choice by exempting some of these vehicles from road user charges. As an example, to stimulate sales of Energy-Efficient Vehicles (EEVs) in Sweden, some of these automobiles were exempted from Stockholm's congestion tax. In this paper the effect this policy had on the demand for new, privately-owned, exempt EEVs is assessed by first estimating a model of vehicle choice and then by applying this model to simulate vehicle alternative market shares under different policy scenarios. The database used to calibrate the model includes owner-specific demographics merged with vehicle registry data for all new private vehicles registered in Stockholm County during 2008. Characteristics of individuals with a higher propensity to purchase an exempt EEV were identified. The most significant factors included intra-cordon residency (positive), distance from home to the CBD (negative), and commuting across the cordon (positive). By calculating vehicle shares from the vehicle choice model and then comparing these estimates to a simulated scenario where the congestion tax exemption was inactive, the exemption was estimated to have substantially increased the share of newly purchased, private, exempt EEVs in Stockholm by 1.8% (+/-0.3%; 95% C.I.) to a total share of 18.8%. This amounts to an estimated 10.7% increase in private, exempt EEV purchases during 2008 i.e. 519 privately owned, exempt EEVs.

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Demand for clean-fuel vehicles in California: A discrete-choice stated preference pilot project

Cited by 204 publications

References 10 publications

Potential demand for alternative fuel vehicles

Potential demand for alternative fuel vehicles

Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles

The impact of a congestion pricing exemption on the demand for new energy efficient vehicles in Stockholm

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