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With an increasing interest for electric vehicles (EV) and the associated charging needs, understanding how public charging infrastructure networks impact the adoption of such vehicles is central for planners and policymakers. While most studies are conducted at the regional or national scales, urban contexts present specificities for the deployment of these networks, given the scarcity of private parking spaces and the strong competition for land. Using data from a survey (n = 642) conducted in Montreal, Canada, this work investigates the influence of public charging infrastructure accessibility on EV adoption in an urban context. Three measures of accessibility are analyzed: 1) objective (number of charging stations near home), 2) perceived (perceptions about the current public charging network), and 3) prospective (expectations on the public charging network in five years). The findings reveal the importance of perceived and prospective accessibility measures as opposed to objective ones, and thus demonstrate that the deployment of public charging networks is an issue tied by perceptions. We also find that non-EV owners underestimate their accessibility to public chargers, thereby illustrating how perceptions may vary across individuals. The results suggest that, in addition to expanding the public charging network, efforts aiming at better informing citizens on the presence and the locations of these charging facilities could contribute to increasing EV adoption. This study highlights the importance of perceptions and expectations and provides a nuanced understanding of how integrating accessibility-based policies into electrification plans might speed up EV adoption in metropolitan areas. [ABSTRACT FROM AUTHOR]

In this paper, we aim to examine people's EV purchase intention in Hong Kong, an Asian compact city, and how the influential factors are different from the Western context, for which a low-density Nordic context, Denmark, was chosen. To achieve this, we conducted a survey in Hong Kong to understand the effects of several key factors, including subjective norms, personal norms, and perceptions and knowledge about EVs. Structural equation modelling was employed and multi-group analysis was conducted. Our findings show that EV purchase intentions in Hong Kong are highly value-driven with the assimilation to social expectations directly and strongly encouraging individuals' EV acceptance, which is different from the European case where the adoption desire is built upon the assessment of usage difficulty. In addition, range anxiety poses a direct deterrence to EV adoption even in the compact city. Availability of charging opportunities, be it the public ones or home-based ones, plays a critical role in an individual's decision of EV adoption in the high-density city. Our study reveals a strong disparity in the pathways towards EV adoption between the Asian and the Nordic contexts, thereby illustrating the necessity to differentiate e-mobility promotion policies in compact cities from low-density cities. • We analyse the influential factors of EV purchase intention in Hong Kong & Denmark. • Attitude, moral obligation and perceived ease of use affect EV adoption. • EV purchase intentions are highly value-driven in Hong Kong. • Range anxiety poses a deterrence to EV adoption even in the compact city (HK). • Availability of charging facilities is key to EV adoption. [ABSTRACT FROM AUTHOR]

• We propose an innovative methodology for optimal deployment of public EV chargers. • A generalised ordered probit model is used to estimate EV purchase intention. • We derive parameters of the LA model based on survey, public data and interview. • Existing charging network should be substantially expanded, especially in suburbs. • Adding chargers at existing stations is more economical than building new stations. The optimal deployment of public charging infrastructure is critical to the popularisation of electric vehicles (EVs) in high-density cities. Existing studies on public EV charging facilities have rarely integrated government policy and spatial constraints into their optimisation algorithms. To address this research gap, we proposed a contextualised EV charger optimisation model that incorporates carefully derived supply-and-demand constraints and tested it in the case of Hong Kong. From the supply side, we studied the latest planning guidelines and conducted a spatial analysis of potential charging sites. From the demand side, we conducted a questionnaire survey with local residents, estimated their EV purchase intention using a generalised ordered probit model, and then projected the usage demand for public chargers. These supply-and-demand constraints were subsequently incorporated into a location-allocation model to minimise both charging demand shortfall and travel time to charging facilities. We also conducted sensitivity analyses with varying budget, charging demand and facility service radius. Based on our results, we made several key recommendations regarding the spatial planning of public EV charging facilities in our high-density context: (1) the existing charging network should be substantially expanded to meet the projected demand; (2) the charging network should be expanded beyond the central business district and the urban core into other urban neighbourhoods and suburbs; and (3) installing more chargers at existing charging stations is more economical than building new stations. Our research provides an important reference for the spatial planning and deployment strategies for public EV charging infrastructure in high-density cities. [ABSTRACT FROM AUTHOR]

In this paper, we present a case study on planning the locations of public electric vehicle (EV) charging stations in Beijing, China. Our objectives are to incorporate the local constraints of supply and demand on public EV charging stations into facility location models and to compare the optimal locations from three different location models. On the supply side, we analyse the institutional and spatial constraints in public charging infrastructure construction to select the potential sites. On the demand side, interviews with stakeholders are conducted and the ranking-type Delphi method is used when estimating the EV demand with aggregate data from municipal statistical yearbooks and the national census. With the estimated EV demand, we compare three classic facility location models – the set covering model, the maximal covering location model, and the p -median model – and we aim to provide policy-makers with a comprehensive analysis to better understand the effectiveness of these traditional models for locating EV charging facilities. Our results show that the p -median solutions are more effective than the other two models in the sense that the charging stations are closer to the communities with higher EV demand, and, therefore, the majority of EV users have more convenient access to the charging facilities. From the experiments of comparing only the p -median and the maximal covering location models, our results suggest that (1) the p -median model outperforms the maximal covering location model in terms of satisfying the other’s objective, and (2) when the number of charging stations to be built is large, or when minor change is required, the solutions to both models are more stable as p increases. [ABSTRACT FROM AUTHOR]