Vehicle Purchase Decisions Research Articles

Food Inflation and Passenger Vehicle Purchases in South Africa: Cointegration, Short Run Dynamics and Causality

Food inflation in South Africa has been observed to be a major source of underlying inflationary pressures in the economy due to its persistence beyond that of other commodities. In this regard, this study investigated whether an increase in the prices of food products has a significant effect on passenger vehicle purchases in South Africa. The Phillip-Perron (PP) test, showed vehicle purchases to be stationary in level while food inflation was stationary in the first difference. Using secondary time series data, the Johansen cointegration test revealed that the variables in the vehicle purchase function were cointegrated in the long run. The vector error correction model showed a long-run relationship, albeit insignificant, between food inflation and vehicle purchases. There was no evidence of a short run relationship between the two variables. The Granger causality test revealed no causal effect between the variables, regardless of the direction of the test. The study concluded that an increase in the prices of food products will not play a considerable role in consumers’ vehicle purchase decisions in South Africa. These results have policy implications for the motor and related industries.

Food Inflation and Passenger Vehicle Purchases in South Africa: Cointegration, Short Run Dynamics and Causality

Food inflation in South Africa has been observed to be a major source of underlying inflationary pressures in the economy due to its persistence beyond that of other commodities. In this regard, this study investigated whether an increase in the prices of food products has a significant effect on passenger vehicle purchases in South Africa. The Phillip-Perron (PP) test, showed vehicle purchases to be stationary in level while food inflation was stationary in the first difference. Using secondary time series data, the Johansen cointegration test revealed that the variables in the vehicle purchase function were cointegrated in the long run. The vector error correction model showed a long-run relationship, albeit insignificant, between food inflation and vehicle purchases. There was no evidence of a short run relationship between the two variables. The Granger causality test revealed no causal effect between the variables, regardless of the direction of the test. The study concluded that an increase in the prices of food products will not play a considerable role in consumers’ vehicle purchase decisions in South Africa. These results have policy implications for the motor and related industries.

Fuel cycle emissions and life cycle costs of alternative fuel vehicle policy options for the City of Houston municipal fleet

Municipal fleet vehicle purchase decisions provide a direct opportunity for cities to reduce emissions of greenhouse gases (GHG) and air pollutants. However, cities typically lack comprehensive data on total life cycle impacts of various conventional and alternative fueled vehicles (AFV) considered for fleet purchase. The City of Houston, Texas, has been a leader in incorporating hybrid electric (HEV), plug-in hybrid electric (PHEV), and battery electric (BEV) vehicles into its fleet, but has yet to adopt any natural gas-powered light-duty vehicles. The City is considering additional AFV purchases but lacks systematic analysis of emissions and costs. Using City of Houston data, we calculate total fuel cycle GHG and air pollutant emissions of additional conventional gasoline vehicles, HEVs, PHEVs, BEVs, and compressed natural gas (CNG) vehicles to the City's fleet. Analyses are conducted with the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (GREET) model. Levelized cost per kilometer is calculated for each vehicle option, incorporating initial purchase price minus residual value, plus fuel and maintenance costs. Results show that HEVs can achieve 36% lower GHG emissions with a levelized cost nearly equal to a conventional sedan. BEVs and PHEVs provide further emissions reductions, but at levelized costs 32% and 50% higher than HEVs, respectively. CNG sedans and trucks provide 11% emissions reductions, but at 25% and 63% higher levelized costs, respectively. While the results presented here are specific to conditions and vehicle options currently faced by one city, the methods deployed here are broadly applicable to informing fleet purchase decisions.

Supporting the adoption of electric vehicles in urban road freight transport – A multi-criteria analysis of policy measures in Germany

Policies in Germany to support electric vehicles, which are free of exhaust emissions, mostly focus on urban road passenger transport. However, road freight vehicles are a main source of the traffic air pollutants and noise emissions in cities. Available vehicle types, tour planning and purchase decisions in urban road freight transport differ from the passenger transport segment. The political and scientific literature lacks a comprehensive discussion of specific policy measures to support electric urban road freight vehicles. This article contributes to the existing body of knowledge, by undertaking a multi-criteria analysis of policy measures to support battery electric freight vehicles based on the rating by two stakeholder groups, “policymakers” and “freight electric vehicle users”. These stakeholders rate 23 policy measures as suggested in the literature or which are implemented in European countries. In comparing and ranking the rating results of the groups, we find that the discordance between the groups can be large and offers noticeable insight and room for future research and practice. Although financial support of electric vehicles is often named in the literature as the primary measure to overcome the total cost of ownership gap of freight electric vehicles, the current study shows that the effect of special legal measures and supporting the setup of company-charging infrastructure are underestimated by the policymakers. Recommendable policy options – beyond several fiscal measures – are to request emission-free vehicles in municipal tenders, to allow drivers with a class B license to drive freight EVs over 3.5tons, or to implement a city toll on the long-term. The practicability of other policy measures depends on the local implementation goals of the municipality. Hence, a transparent debate on the aim of supporting electric freight mobility is as necessary as choosing measures targeted at the freight transportation segment.

Improving the behavioral realism of global integrated assessment models: An application to consumers’ vehicle choices

A large body of transport sector-focused research recognizes the complexity of human behavior in relation to mobility. Yet, global integrated assessment models (IAMs), which are widely used to evaluate the costs, potentials, and consequences of different greenhouse gas emission trajectories over the medium-to-long term, typically represent behavior and the end use of energy as a simple rational choice between available alternatives, even though abundant empirical evidence shows that real-world decision making is more complex and less routinely rational. This paper demonstrates the value of incorporating certain features of consumer behavior in IAMs, focusing on light-duty vehicle (LDV) purchase decisions. An innovative model formulation is developed to represent heterogeneous consumer groups with varying preferences for vehicle novelty, range, refueling/recharging availability, and variety. The formulation is then implemented in the transport module of MESSAGE-Transport, a global IAM, although it also has the generic flexibility to be applied in energy-economy models with varying set-ups. Comparison of conventional and ‘behaviorally-realistic’ model runs with respect to vehicle purchase decisions shows that consumer preferences may slow down the transition to alternative fuel (low-carbon) vehicles. Consequently, stronger price-based incentives and/or non-price based measures may be needed to transform the global fleet of passenger vehicles, at least in the initial market phases of novel alternatives. Otherwise, the mitigation burden borne by other transport sub-sectors and other energy sectors could be higher than previously estimated. More generally, capturing behavioral features of energy consumers in global IAMs increases their usefulness to policy makers by allowing a more realistic assessment of a more diverse suite of policies.

Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users

Wider deployment of alternative fuel vehicles (AFVs) can help with increasing energy security and transitioning to clean vehicles. Ideally, adopters of AFVs are able to maintain the same level of mobility as users of conventional vehicles while reducing energy use and emissions. Greater knowledge of AFV benefits can support consumers’ vehicle purchase and use choices. The Environmental Protection Agency’s fuel economy ratings are a key source of potential benefits of using AFVs. However, the ratings are based on pre-designed and fixed driving cycles applied in laboratory conditions, neglecting the attributes of drivers and vehicle types. While the EPA ratings using pre-designed and fixed driving cycles may be unbiased they are not necessarily precise, owning to large variations in real-life driving. Thus, to better predict fuel economy for individual consumers targeting specific types of vehicles, it is important to find driving cycles that can better represent consumers’ real-world driving practices instead of using pre-designed standard driving cycles. This paper presents a methodology for customizing driving cycles to provide convincing fuel economy predictions that are based on drivers’ characteristics and contemporary real-world driving, along with validation efforts. The methodology takes into account current micro-driving practices in terms of maintaining speed, acceleration, braking, idling, etc., on trips. Specifically, using a large-scale driving data collected by in-vehicle Global Positioning System as part of a travel survey, a micro-trips (building block) library for California drivers is created using 54million seconds of vehicle trajectories on more than 60,000 trips, made by 3000 drivers. To generate customized driving cycles, a new tool, known as Case Based System for Driving Cycle Design, is developed. These customized cycles can predict fuel economy more precisely for conventional vehicles vis-à-vis AFVs. This is based on a consumer’s similarity in terms of their own and geographical characteristics, with a sample of micro-trips from the case library. The AFV driving cycles, created from real-world driving data, show significant differences from conventional driving cycles currently in use. This further highlights the need to enhance current fuel economy estimations by using customized driving cycles, helping consumers make more informed vehicle purchase and use decisions.

Longitudinal Vehicle Transaction Model: Assessment of Lead and Lagged Effects of Longer-Term Changes and Life-Cycle Events

This paper presents a dynamic vehicle transaction model developed with data from a retrospective survey conducted in Halifax, Canada. The study investigated four types of vehicle transaction decisions, including first-time vehicle purchase, vehicle acquisition, vehicle disposal, and vehicle trade. A panel-based latent segment logit (PLSL) model was developed to account for repeated transaction decisions and to capture unobserved heterogeneity among the sample households. The study took a life-oriented approach by examining the lead and lagged effects of longer-term changes and life-cycle events. The PLSL model was estimated for two latent segments, and the model results suggest that profound heterogeneity exists, as evident in parametric values of the two segments. The life-cycle event represented by the birth of a child might trigger vehicle acquisition in Segment 1. In contrast, that life-cycle event might deter an acquisition decision in Segment 2. The model confirmed historical deposition effects of key life events. For instance, a 2-year lagged effect on vehicle acquisition was seen with the birth of a child. Moreover, the model results revealed that first-time vehicle purchase behavior was considerably different from the decision to acquire a vehicle. For example, for the longer-term change represented by the addition of a job, significant heterogeneity across the two segments for a first-time vehicle purchase decision was seen and a 3-year lagged effect was confirmed. A positive relationship for the decision to acquire a vehicle in both segments was found for the same variable, and a 1-year lagged effect was confirmed. Finally, the study provided important behavioral insights for targeting specific groups of the population to promote sustainable travel behavior.

From e-bike to car: A study on factors influencing motorization of e-bike users across China

Household car ownership has risen dramatically in China over the past decade. At the same time a disruptive transportation technology emerged, the electric bike (e-bike). Most studies investigating motorization in China focus on macro-level economic indicators like GDP, with few focusing on household, city-level, environmental, or geographic indicators, and none in the context of high e-bike ownership. This study examines household vehicle purchase decisions across 59 cities in China with broad geographic, environmental, and socio-economic characteristics. We focus on a subset of households who own e-bikes and rely on a telephone survey from an industry customer database. From these responses, we estimate two three-level hierarchical choice models to assess attributes that contribute to (1) recent car purchases and (2) the intention to buy a car in the near future. The results show that the models are dominated by household characteristics including household income, household size, household vehicle ownership, number of licensed drivers and duration of car ownership. Some geographic, environmental and socio-economic factors have significant influences on car purchase decisions. Only two city-level transportation variable have an effect – higher taxi density and higher bus density reducing car purchase. Cold weather, population density gross domestic product per capita positively influence car purchase, while urbanization rate reduces car purchase. Because of supply heterogeneity in the data set, described by publicly available urban transportation data, this is the first study that can include geographic and urban infrastructure differences that influence purchase choice and suggests potential region-specific policy approaches to managing car purchase may be necessary.

Modelling consumer preferences for electric vehicles in Portugal: an exploratory study

Purpose – The purpose of this paper is to understand consumer preferences concerning electric vehicles (EV) in Portugal, based on comparisons with other vehicles with different powertrains. Design/methodology/approach – The analysis incorporated two survey-based approaches: choice-based conjoint analysis (CBC) and multicriteria decision analysis (MCDA) based. The survey interviewed 252 respondents. The criteria characterizing each vehicle are purchase price, range, fuel consumption and CO2 emissions. Another criterion was added to verify the potential of EV privileges to influence consumer preferences. A sensitivity analysis on the influence of purchase price and fuel price in the global utility of the vehicles was performed. Findings – The results showed that monetary criteria are those influencing vehicle purchase decisions the most, whereas the existence of privileges for EV owners has little relevance. EV are chosen by the consumer only if their price decreases or if gasoline and diesel prices increase sharply. The position of PHEV in the rankings makes the promotion of this type of vehicle an interesting path to exploit as potential intermediates to the diffusion of EV. Practical implications – The results underline the need of improving technical barriers of EV that are responsible for consumers’ relevant concerns and that a price subsidy could eventually be effective to increase EV sales at its current market price. Originality/value – This study compares a wide range of vehicles (conventional, hybrid and electric), addresses the Portuguese market and proposes an MCDA-based approach to obtain preference information, which is compared with a CBC approach.

PLEASE DON'T REGULATE MY INTERNALITIES.

PLEASE DON'T REGULATE MY INTERNALITIES.

Trailer fleet planning for industrial gas distribution

For industrial gas providers, fleet planning is important to their financial and operational performance. This article considers long-term vehicle purchase decisions, medium-term vehicle relocation decisions, and short-term rental decisions that are useful for increasing flexibility to meet time-varying demand. A mixed-integer programming model is developed to minimize total distribution costs and fleet investment costs over multiple time periods and multiple depots. To solve the industrial-sized problem efficiently, a two-phase approach is proposed. In Phase I, routes are generated to capture the characteristics of typical gas delivery operations. A reduced model is solved to select routes for meeting customer demands, estimate distribution costs, and determine the preferred fleet size. Phase II addresses the trailer purchase, relocation, and rental decisions based on the outputs of Phase I. The numerical studies, conducted using a data set from a leading industrial gas company, demonstrate the effectiveness and efficiency of the decomposition approach. Different routing algorithms are compared to evaluate the impact of candidate routes. When compared with the integrated optimization model, the two-phase approach obtains quality solutions within reasonable computational time.

Conspicuous conservation: The Prius halo and willingness to pay for environmental bona fides

This paper develops a theory of conspicuous conservation, a phenomenon related to conspicuous consumption in which individuals seek status through displays of austerity amid growing concern about environmental protection. We identify a statistically and economically significant conspicuous conservation effect in vehicle purchase decisions and estimate a mean willingness to pay for the green signal provided by the distinctively designed Toyota Prius in the range of $430–4200 depending upon the owner's location. Results are related to the growing literature on green markets and suggest that policy should target less conspicuous conservation investments that will be under-provided relative to those that confer a status benefit.

Conditional-logit Bayes estimators for consumer valuation of electric vehicle driving range

Range anxiety – consumers’ concerns about limited driving range – is generally considered an important barrier to the adoption of electric vehicles. If consumers cannot overcome these fears it is unlikely that they will consider purchasing an electric car. Hence, a successful introduction of low emission vehicles in the market requires a full understanding of consumer valuation of driving range. By analyzing experimental data on vehicle purchase decisions in California, I derive and study the statistical behavior of Bayes estimates that summarize consumer concerns toward limited driving range. These estimates are superior to marginal utilities as parameters of interest in a discrete demand model of vehicle choice. One of the empirical results is the posterior distribution of the willingness to pay for electric vehicles with improved batteries offering better driving range. Credible intervals for this willingness to pay, as well as both parametric and nonparametric heterogeneity distributions, are also analyzed.

Developing green fleet management strategies: Repair/retrofit/replacement decisions under environmental regulation

The considerable cost of maintaining large fleets has generated interest in cost minimization strategies. With many related decisions, numerous constraints, and significant sources of uncertainty (e.g. vehicle breakdowns), fleet managers face complex dynamic optimization problems. Existing methodologies frequently make simplifying assumptions or fail to converge quickly for large problems. This paper presents an approximate dynamic programming approach for making vehicle purchase, resale, and retrofit decisions in a fleet setting with stochastic vehicle breakdowns. Value iteration is informed by dual variables from linear programs, as well as other bounds on vehicle shadow prices. Sample problems are based on a government fleet seeking to comply with emissions regulation. The model predicts the expected cost of compliance, the rules the fleet manager will use in deciding how to comply, and the regulation’s impact on the value of vehicles in the fleet. Stricter regulation lowers the value of some vehicle categories while raising the value of others. Such insights can help guide regulators, as well as the fleet managers they oversee. The methodologies developed could be applied more broadly to general multi-asset replacement problems, many of which have similar structures.

Constraints to green vehicle ownership: A focus group study

We consider constraints that prevent people with environmental concerns from buying “green” vehicles that are smaller, more fuel-efficient, and less polluting by using a series of focus group discussions. We find that the features of vehicles currently on the market, family and work responsibilities, residential choices, and routines and preferences all act as constraints. Serious misunderstandings about the environmental impacts of owning and using vehicles also were noted, making it difficult for many to accurately assess their alternatives. For some, environmental concerns are unlikely to influence future vehicle purchase decisions, even if constraints were removed altogether; other priorities have taken and will take precedence over the environmental impacts of their choices.

Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian hybrid choice model

In this article we develop, implement and apply a Markov chain Monte Carlo (MCMC) Gibbs sampler for Bayesian estimation of a hybrid choice model (HCM), using stated data on both vehicle purchase decisions and environmental concerns. Our study has two main contributions. The first is the feasibility of the Bayesian estimator we derive. Whereas classical estimation of HCMs is fairly complex, we show that the Bayesian approach for HCMs is methodologically easier to implement than simulated maximum likelihood because the inclusion of latent variables translates into adding independent ordinary regressions; we also find that, using the Bayesian estimates, forecasting and deriving confidence intervals for willingness to pay measures is straightforward. The second is the capacity of HCMs to adapt to practical situations. Our empirical results coincide with a priori expectations, namely that environmentally-conscious consumers are willing to pay more for low-emission vehicles. The model outperforms standard discrete choice models because it not only incorporates pro-environmental preferences but also provides tools to build a profile of environmentally-conscious consumers.

Reducing transit fleet emissions through vehicle retrofits, replacements, and usage changes over multiple time periods

Bus transit is often promoted as a green form of transportation, but surprisingly little research has been done on how to run transit systems in a green manner. Both vehicle task assignment and purchase models are generally constructed to minimize financial costs. Integrating vehicle task assignment with purchase decisions is made challenging by the different time scales involved. An integer programming approach is used to combine vehicle purchase, retrofit and aggregated task assignment decisions. The formulation is designed to operate in sequence with traditional vehicle task assignment models, to add emissions and long term financial cost elements to the objective, while maintaining computational tractability and feasible input data requirements. In a case study, a transit agency saves money in the long term by using stimulus money to buy CNG infrastructure instead of purchasing only new buses. Carbon prices up to $400/(ton CO 2 equivalent) do not change vehicle purchase decisions, but higher carbon prices can cause more diesel hybrid purchases, at a high marginal cost. Although the motivation and numerical case study are from the US transit industry, the model is formulated to be widely applicable to green fleet management in multiple contexts.

How Important is Vehicle Safety in the New Vehicle Purchase/Lease Process for Fleet Vehicles?

Objective. Despite the potential benefits that fleet vehicle purchase decisions could have on road safety, the role that vehicle safety plays in fleet managers' purchase decisions is poorly understood. Methods. In this study, fleet managers from Sweden and Spain completed a questionnaire regarding the importance of vehicle safety in the new vehicle purchase/lease process and the importance that is placed on safety options/features relative to other convenience and comfort features. Results. The findings of the current study suggest that vehicle safety is generally not the primary consideration in the vehicle purchase process and is consistently outranked by factors such as price and dependability/reliability. For example, when asked to indicate the vehicle factors that are included in their company's criteria for purchasing/leasing a new vehicle, fleet managers from both Sweden and Spain were more likely to list the vehicle's price, reliability, running costs, size, and fuel consumption than the vehicle's safety (defined as the vehicle's EuroNCAP rating/other safety reports). In addition, the findings of this study suggest that the importance of vehicle safety did not differ across the two countries. For example, there was no significant difference in the proportion of fleet managers who indicated that EuroNCAP ratings were part of their official policy across the two countries. Conclusion. The findings highlighted the need to educate fleet managers about vehicle safety in the new vehicle purchase/lease process. In addition, vehicle safety information, such as EuroNCAP results or other crash test results need to be promoted more widely and effectively so that they play a more prominent role in their new vehicle choices.

Factors affecting consumer assessment of eco-labeled vehicles

A statewide sample of Maine registered vehicle owners is used to examine factors that affect their assessments of eco-labeled conventionally fueled passenger vehicles. The study focuses on developing an empirical and theoretical framework with which to model vehicle choice decisions under eco-labeled conditions. Particular attention is paid to how eco-information may affect the two-stage vehicle purchase process. The study builds upon environmental economic and psychology literature in examining the role of personal characteristics such as perceived effectiveness of consumer purchase decisions and perceptions of the eco-labeled products as factors in the vehicle purchase decision. It was found that environmental attributes of an eco-labeled passenger vehicle are significant in the purchase decision. The eco-information is considered in the vehicle purchase decision, but is generally not considered at the class-level decision.

An Assessment of Corporate Average Fuel Economy (CAFE)

At current gasoline prices in the United States consumers do not pay for the full consequences of their vehicle purchase or use decisions; consequently, the financial incentive for consumers to purchase a fuel-efficient vehicle is small. In theory, regulations like Corporate Average Fuel Economy (CAFE) can play a role in reducing oil consumption. An evaluation of the effectiveness and economic impacts of CAFE involves multiple issues, including potential impacts on travel, safety, industry financial condition and structure, and use of alternative fuels. Critics have overstated some problems linked to CAFE´, including the possibility that improvements in fuel efficiency regulations have been offset by increases in travel and potential impacts of vehicle downsizing on safety. However, under current gasoline prices, innovations to improve fuel efficiency, which are economically viable in Japan and Europe, are unattractive in the United States. Given current economic conditions, automobile firm opposition to CAFE is rational. The actual impact of CAFE has been and is likely to remain small until US gasoline taxes are increased.