The ‘neighbor effect’: Simulating dynamics in consumer preferences for new vehicle technologies (original) (raw)
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Resource and Energy Economics, 2009
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 energyeconomy 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, 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 class constraints produce more realistic estimates of willingness to pay. Furthermore, SP influenced coefficient estimates also translate to more realistic behavioral parameters for CIMS, allowing more sensitivity to policy simulations. JEL classification: C5 C9 O3 Q4 R4
Nature Energy
Burgeoning demands for mobility and private vehicle ownership undermine global efforts to reduce energy-related greenhouse gas emissions. Advanced vehicles powered by low-carbon sources of electricity or hydrogen offer an alternative to conventional fossil-fuelled technologies. Yet, despite ambitious pledges and investments by governments and automakers, it is by no means clear that these vehicles will ultimately reach mass-market consumers. Here, we develop state-of-the-art representations of consumer preferences in multiple global energy-economy models, specifically focusing on the non-financial preferences of individuals. We employ these enhanced model formulations to analyse the potential for a low-carbon vehicle revolution up to 2050. Our analysis shows that a diverse set of measures targeting vehicle buyers is necessary to drive widespread adoption of clean technologies. Carbon pricing alone is insufficient to bring low-carbon vehicles to the mass market, though it may have a supporting role in ensuring a decarbonized energy supply.
Energy Economics, 2005
Hybrid energy-economy models combine top-down and bottom-up approaches to explore behaviorally realistic responses to technology-focused policies. This research uses empirically derived discrete choice models to inform key behavioral parameters in CIMS, a hybrid model. The discrete choice models are estimated for vehicle and commuting decisions from a survey of 1150 Canadians. With the choice models integrated into CIMS, we simulate carbon taxes, gasoline vehicle disincentives, and single occupancy vehicle disincentives to show how different policy levers can motivate technological change. We also use the empirical basis for the choice models to portray uncertainty in technological change, costs, and emissions. D
Understanding household preferences for alternative-fuel vehicles technologies
MTI works to provide policy-oriented research for all levels of government and the private sector to foster the development of optimum surface transportation systems. Research areas include: transportation security; planning and policy development; interrelationships among transportation, land use, and the environment; transportation finance; and collaborative labormanagement relations. Certified Research Associates conduct the research. Certification requires an advanced degree, generally a Ph.D., a record of academic publications, and professional references. Research projects culminate in a peer-reviewed publication, available both in hardcopy and on TransWeb, the MTI website (http://transweb.sjsu.edu).
Understanding Household Preferences For Alternative-Fuel Vehicle Technologies
2011
MTI works to provide policy-oriented research for all levels of government and the private sector to foster the development of optimum surface transportation systems. Research areas include: transportation security; planning and policy development; interrelationships among transportation, land use, and the environment; transportation finance; and collaborative labormanagement relations. Certified Research Associates conduct the research. Certification requires an advanced degree, generally a Ph.D., a record of academic publications, and professional references. Research projects culminate in a peer-reviewed publication, available both in hardcopy and on TransWeb, the MTI website (http://transweb.sjsu.edu).
Incorporating Behavioral Effects from Vehicle Choice Models into Bottom-Up Energy Sector Models
2015
Many different types of models are used for evaluating climate-change-related programs and policies, because analysis requirements can vary widely depending on the specific nature of the problem being investigated. Limitations on data and methodology typically ensure that models have various strengths and weaknesses, requiring researchers to make tradeoffs when choosing models. In the case of energy systems, a frequent distinction is between "top down" models (e.g., computable general equilibrium, or CGE models) that address energy systems within the context of the larger economy, versus "bottom up" models (e.g., so-called E4, or "energy/economy/environment/engineering" models), that model the energy system at a much higher level of detail, but simplify the relationship to the rest of the economy. Most attention has been on integrating these two types of models. However, researchers have also been concerned that E4 models, despite their vaunted high level of detail, produce results that are an unrealistic representation of consumer market behavior, calling into question their value for making policy decisions. This is particularly true for household vehicle technology choice, an important sub-sector of the energy system. At the same time, there is a large and well-established literature on modeling household vehicle choice and usage decisions (using discrete and discrete-continuous models). But, the methods and approaches used in this literature differ dramatically from those used in E4 models, and so it has been unclear how to bridge the gap. This paper demonstrates a practical approach for incorporating behavioral effects from vehicle choice models into E4 models. It is based on principles of economic theory that form a common basis for all three types of models (CGE, E4, and vehicle choice/usage models). Derivations are provided that yield a theory-based approach for modifying E4 models that can be used without altering the basic software and modeling infrastructure widely used by many researchers. The approach is illustrated using an empirical application in which the behavioral assumptions from a nested multinomial choice model in an existing modeling system (MA 3 T) are incorporated into a TIMES/MARKAL model.
Electric vehicles adoption: Environmental enthusiast bias in discrete choice models
Transportation Research Part D: Transport and Environment
A Stated Choice (SC) survey, employing a Best-Worst choice design, was administered to 440 households in Perth, Australia as part of a major investigation into consumer preferences and attitudes towards electric vehicles. It was noted that 48 (10.9%) respondents chose EV as their best/most preferred option across all six choice replications. We hypothesise that for most of these respondents their choices reflected their desire to present themselves in a favourable light, with social desirability biasness manifested in nontrading behaviour. There were also 24 (5.5%) respondents who chose EV as their worst/ least preferred option. We hypothesise that for these respondents lack of interest or confidence in the new technology and inertia may have driven their decisions. The paper offers demographic and psychographic profiles of non-traders facilitated by additional items being included in the experiment. While there was little difference between the demographic profiles, the attitudinal scores of the non-traders were significantly higher than for traders, which may indicate social desirability. Non-traders (Best) scored significantly higher on environmental concerns and subjective norms and were more likely to rate their intention to purchase and use an EV higher. Conversely, non-traders (Worst) had the lowest environmental concerns and subjective norms. From a choice modelling perspective, keeping non-traders in the estimation biases the taste parameters and therefore the willingness-to-pay (WTP) measures. However, when incorporating the worst alternatives into the choice models, the 'social desirability' non-traders do appear to be making decisions based on the attributes, which is consistent with the rest of the sample.
Energy Economics, 2008
The paper analyzes how adding alternative fuel passenger cars to the market will affect patterns in demand for passenger cars. We use conjoint analysis and a multiple discrete-continuous choice model to estimate consumer preferences regarding alternative fuel vehicles, and based on the estimates we conduct a simulation to analyze changing rates of ownership and use of variously fueled passenger cars under the effect of the introduction of alternative fuel passenger cars. In addition, we estimate changes in overall fuel consumption and the emission of pollutants. The results show that gasoline-fueled cars will still be most consumers' first choice, but alternative fuel passenger cars will nevertheless compete and offer a substitute for the purchase and use of gasoline-fueled or diesel-fueled cars. Finally, results show that adding alternative fuel cars to the market would effectively lower gasoline and diesel fuel consumption and the emission of pollutants.
Incentives for Alternate Fuel Vehicles: A Large-Scale Stated Preference Experiment
This paper reports on a stated preference (SP) study that was conducted as part of the 2002 California Vehicle Survey of households and businesses. The objective of the SP study was to explore conditions and incentives that might encourage California residents to buy or lease alternate fuel vehicles and to statistically estimate a set of vehicle choice models for use in the California CALCARS vehicle fleet forecasting models. SP data were collected from 2,200 households recruited in in the household portion of the Vehicle Survey. The choice alternatives in the SP experiments included conventional gasoline vehicles, hybrid electric vehicles and diesel vehicles of different size and body style classes. The attributes that were tested included purchase, fuel and maintenance costs, acceleration, gradability and alternative-fuel incentives. Initial estimations assumed a multinomial logit form and focused on the specification of utility functions. Main and interaction effects among the variables were tested and the effects of socio-economic variables on utility values were explored. Once a reasonable set of utility specifications was established, nested logit models of vehicle choice were developed. The model coefficients indicate that fuel cost savings, reductions in vehicle purchase taxes and allowing free parking for alternative-fueled vehicles provide significant purchase incentives for those vehicles. However, the ability of the vehicles to sustain speeds on grades ("gradability") is also a significant factor in purchasers' evaluations of hybrid electric vehicles. The resulting models have been implemented in the CALCARS vehicle fleet forecasting model and are being used by the California Energy Commission (CEC) to analyse strategies for reducing petroleum dependency in the state.
Energy Economics, 2018
Long-term energy systems models have been used extensively in energy planning and climate policy analysis. However, specifically in energy systems optimization models, heterogeneity of consumer preferences for competing energy technologies (e.g., vehicles), has not been adequately represented, leading to behaviorally unrealistic modeling results. This can lead to policy analysis results that are viewed by stakeholders as clearly deficient. This paper shows how heterogeneous consumer behavioral effects can be introduced into these models in the form of perceived disutility costs, to more realistically capture consumer choice in making technology purchase decisions. We developed a novel methodology that incorporates the theory of a classic consumer choice model into a commonly used long-term energy systems modeling framework using a case study of light-duty vehicles. A diverse set of consumer segments (thirty-six) is created to represent observable, identifiable differences in factors such as annual driving distances and attitude towards risks of new technology. Non-monetary or "disutility" costs associated with these factors are introduced to capture the differences in preferences across consumer segments for various technologies. We also create clones within each consumer segment to capture randomly distributed unobservable differences in preferences. We provide and review results for a specific example that includes external factors such as recharging/refueling station availability, battery size of electric vehicles, recharging time and perceived technology risks. Although the example is for light-duty vehicles in the US using a specific modeling system, this approach can be implemented more broadly to model the adoption of consumer technologies in other sectors or regions in similar energy systems modeling frameworks.