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

Abstract

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

Private household demand for vehicles on alternative fuels and drive trains: a review

“…Second, research from the 80s and 90s mainly focused on the potential demand for battery EVs [13,14,24,27,38,39,54,55,79,90], while studies from the late 90s and 2000s rather address a mix of AFVs including EVs, LPG, CNG or methanol [21][22][23]33,34,42,81,87]. In line with technological developments, recent research also concentrates on HEVs [3,10,26,29,31,32,45,49,53,72], PHEVs [ 9 , 11], bi ofuels [51,68,69,80,89,91], hydrogen [2,66,74,76,86] and HFCVs [46,61,62,65]. Finally, most literature has been published in America.…”

“…Recently, multiple discretecontinuous extreme value (MDCEV) models have been introduced that deal with the existence of multiple-vehicle households, where households own and use multiple vehicles for satisfying their travel needs [16,17]. Other recent progresses in CM aim to improve realism, by for instance adding a nochoice option [31], or by customising attribute levels based on respondent's current vehicle choices [10,31,62]. In CV, value elicitation is whole-product based by asking respondents to express their maximum willingness to pay (WTP) for a given improvement of a public good provision level (e.g., cleaning up a lake) or for public goods aspects of a market good (e.g., eco-labeled goods) [44,63].…”

“…Finally, RP data may often produce strong correlations between the variables (multicollinearity) and may evoke difficulties in measuring the vehicle attributes [1]. Because SP and RP have complementary strengths, a growing body of literature applies joint SP-RP modeling techniques (e.g., [10,22]). …”

“…Except for Brownstone et al [22] and Axsen et al [10], all studies, listed in Table 5, applied SP surveys with a predominance of CM, used for the identification of unique attributes of AFVs and their effects on vehicle purchase. All reviewed studies from the 80s and 90s were performed in America and focused on battery EVs or on a mix of AFVs including either EVs, LPG, CNG or methanol.…”

“…Recent studies also apply CM to capture the dynamics in consumer preferences for new technologies [10,31,62]. These studies reveal that conventional vehicles become less desirable with increases in AFV adoption, given equal monetary costs (i.e., neighbour effect) [10,31,62].…”