There is a wealth of recent works that have focused on determinants alone. In this review, we will discuss key factors that have been shown to impact on EV adoption rates. We will also review studies that have focused on lagging regions in terms of their EV adoption.
As with any purchase, consumers often make decisions by considering the dollar price of the item against the expected benefits that can be derived from the product. As cars become an increasingly pricier commodity, there is greater care devoted to these net value assessments. It is clear from the literature that EVs are highly desired as they contribute to a cleaner environment, typically have no congestion charges, operate at lower running costs, benefit from targeted government funding and generally offer a better driving experience (Mustafa et al. 2021; Xiong et al. 2020; Zhang et al. 2021). In the early days of the electric vehicle market, it is generally known that potential consumers were deterred by the EVs high purchase price and lack of charging infrastructure that can support the need for frequent battery charging. However, EV prices have since come down due to lower-production costs, more competition on the supply side and significantly improved charging infrastructure.
Given Kyoto protocol commitments, governments of many industrialised countries have designed subsidy and incentive packages to lower the net cost of acquiring an EV and make adoption a more attractive proposition. Unfortunately, these initiatives tend to be offset by other market-based influences which also have a strong impact on the net price or value of an EV. For example, early studies of Diamond (2009) and Beresteanu and Li (2011) show that petrol prices are a more significant influencing factor in the adoption of hybrid EVs, compared to government incentives. In more recent times, when EV prices have reduced significantly, the increased availability of charging infrastructure has become a major factor in favour of adoption (Salisbury and Toor 2016; Mersky et al. 2016; Hardman et al. 2017).
On the role of consumer characteristics, Potoglou and Kanaroglou (2007) found that in Canada, younger people and those with a university degree are more likely to adopt alternative-fuelled vehicles, and that the demand for high-energy consuming vehicles, such as vans or sport utility vehicles (SUVs), diminished if respondents lived in dense and diversified urban areas. Relatedly, Hidrue et al. (2011) found that being younger, having a higher education (bachelor's degree or above), and higher levels of green awareness increased consumer orientation towards EVs, while income and being a multi-car household did not have a significant impact on being in the EV class. These results are later corroborated in Ritter et al. (2015) which showed that socioeconomic status, such as income and educational level, played a significant role in influencing the consumption of green products generally.
On the role of attitude and preferences in EV adoption, there is growing evidence in the literature showing that the influence of these factors is not just significant but also dynamic. For example, Mau et al. (2008) demonstrated that consumer preferences in choosing between conventional and new technologies can change with market conditions, and that the importance consumers place on certain attributes of a new technology, including that of an electric vehicle, also changes as it gains market share. One study concluded that the environmental aspects are less important for consumers than anticipated, despite the concerns about climate change and renewable energy transition (Anastasiadou and Gavanas 2022). There is also new literature analysing the use of EVs as tools for enhancement of self-image and reputation (Li et al. 2022; Buhmann and Criado 2023).
From a modelling perspective, the two most common approaches to understanding consumer preferences for electric vehicles are discrete choice models or latent class models. Hidrue et al. (2011), Mohamed et al. (2016), Ferguson et al. (2018) and others have pointed out that latent class models are preferable compared to other discrete choice methods, because respondents can be grouped into a range of preference classes based on their attitudes and socioeconomic characteristics. There was a notable scarcity of studies about regions lagging in EV adoption, however – we found only one study that has investigated regional factors. Abotalebi et al. (2019) analysed the low adoption rates in Atlantic Canada by comparing EV outcomes to leading English ‑speaking Canadian provinces, namely British Columbia and Ontario. Using data from a household survey and a latent class random utility model, the study finds that EV driving distance range, maintenance cost, free parking, and access to high occupancy vehicle lanes are not significant attributes in the Atlantic model. With respect to segmentation, the adoption of EVs in the Atlantic model increases with youth, education, and progressive attitudes towards the environment, while income is not a determining factor.
In Australia, publicly available data from the Australia Bureau of Statistics (ABS)4 and the Bureau of Infrastructure and Transport Research Economics (BITRE)5 show that since EVs were introduced nationally, capital cities and coastal cities tend to have a higher proportion of EVs than other regional areas. However, these reports mainly present descriptive tables, and while they present detailed information on EV use and purchase, no systematic modelling was undertaken. This research collects relevant datasets to fill the analytical gap in the understanding of EV adoption rates in Australia. It also provides an opportunity to illustrate the feasibility of the convergence club methodology to analyse trends in this field of study.
Footnotes
[4] Australian Bureau of Statistics, Motor Vehicle Census, Australia, accessed 15 May 2023.
[5] Bureau of Infrastructure, Transport and Research Economics, Australia’s light vehicle fleet - some insights, accessed 15 May 2023.
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