Electrifying urban transportation through the adoption of Electric Vehicles (EVs) has great potential to mitigate two global challenges, namely climate change and energy scarcity, and also to improve local air quality and further benefit human health. This paper was focused on the six typical factors potentially influencing the purchase behaviour of EVs in Beijing, China, namely vehicle price, vehicle usage, social influence, environmental awareness, purchase-related policies and usage-related policies. Specifically, this study used the data collected in a paper-based questionnaire survey in Beijing from September, 2015 to March, 2016, covering all of the 16 administrative regions, and tried to quantify the relative importance of the six factors, based on their weights (scores) given by participants. Furthermore, Multinomial Logit (MNL) models and Moran's I (a measure of global spatial autocorrelation) were used to analyse the weights of each factor from statistical and spatial perspectives, respectively. The results suggest that 1) vehicle price and usage tend to be more influential among the six factors, accounting for 32.3% and 28.1% of the importance; 2) Apart from the weight of social influence, the weights of the other five factors are closely associated with sociodemographic characteristics, such as individual income and the level of education; 3) people having similar attitudes towards vehicle usage (Moran's I= 0.10) and purchase restriction (Moran's I= 0.14) tend to live close to each other. This paper concludes with a discussion on applying the empirical findings in policy making and modelling of EV purchase behaviour.

No abstract

Although the impacts of built environment on car ownership and use have been extensively studied, limited evidence has been offered for the role of spatial effects in influencing the interaction between built environment and travel behavior. Ignoring the spatial effects may lead to misunderstanding the role of the built environment and providing inconsistent transportation policies. In response to this, we try to employ a two-step modeling approach to investigate the impacts of built environment on car ownership and use by combining multilevel Bayesian model and conditional autocorrelation (CAR) model to control for spatial autocorrelation. In the two-step model, the predicting car ownership status in the first-step model is used as a mediating variable in the second-step car use model. Taking Changchun as a case study, this paper identifies the presence of spatial effects in influencing the effects of built environment on car ownership and use. Meanwhile, the direct and cascading effects of built environment on car ownership and use are revealed. The results show that the spatial autocorrelation exists in influencing the interaction between built environment and car dependency. The results suggest that it is necessary for urban planners to pay attention to the spatial effects and make targeted policy according to local land use characteristics.