Research Proposal – Barriers to Electric Vehicle Adoption in Regional Australia

Introduction

Australia has committed to an electric transition of its light vehicle fleet through the National Electric Vehicle Strategy (DCCEEW 2023) and, from 2025, the New Vehicle Efficiency Standard, which sets declining carbon dioxide targets across supplier fleets and is expected to expand the range of battery electric models offered locally (DITRDCA 2024). Consumer response has been rapid but narrow. Battery electric vehicles accounted for roughly seven per cent of new light vehicle sales nationally in 2024, yet still represented under one per cent of the 21.7 million vehicles on the road at the most recent Motor Vehicle Census (ABS 2024; Electric Vehicle Council 2024).

That national picture conceals a pronounced geographic divide. Uptake is concentrated in inner metropolitan postcodes of Sydney, Melbourne, Brisbane and Canberra, while the 28 per cent of Australians living in areas classified as inner regional, outer regional, remote or very remote under the Australian Statistical Geography Standard register a small fraction of new electric vehicles (ABS 2023). This proposal outlines a twelve-month sequential mixed methods study of the behavioural, economic and infrastructural barriers that suppress battery electric vehicle adoption outside major cities, and of how those barriers vary with remoteness.

Background

Uptake trends and the metropolitan-regional gap

Registration data indicate that the electric fleet is expanding from a small base at differing speeds across the country. Electric Vehicle Council (2024) reporting places national new-vehicle share in the high single digits, but postcode-level analysis consistently shows outer regional and remote areas trailing capital cities by a factor of three or more. The gap is not simply a lag effect of income or vehicle age. Regional households buy more new vehicles per capita than metropolitan households, drive substantially greater annual distances and are therefore the group with the most to gain from lower per-kilometre energy costs, which makes their comparatively low uptake a genuine puzzle rather than a predictable diffusion delay.

Charging infrastructure and network capacity

Regional charging provision has improved through co-funded programs such as the Australian Renewable Energy Agency’s investment in highway fast-charging corridors, yet coverage remains corridor-shaped rather than areal (ARENA 2023). Fast chargers cluster along major national highways, leaving substantial inland and coastal areas beyond a comfortable return trip of the nearest reliable charger. Provision is also constrained by the distribution network: many regional townships are supplied by long single-phase feeders with limited hosting capacity, and the Australian Energy Market Operator has identified transport electrification as a material driver of future demand that will require coordinated network and charging investment (AEMO 2024). Charger reliability, queueing at single-bay sites and the absence of a viable second option within range compound the problem in ways that metropolitan charging studies do not capture.

Range, towing and vehicle task fit

Regional driving profiles differ qualitatively, not only in distance. Trips between service centres frequently exceed 200 kilometres in one direction, a large proportion of the fleet is composed of dual-cab utilities and large sport utility vehicles, and towing is routine rather than occasional: caravans, boats, horse floats, stock trailers and plant. Towing typically reduces effective battery range by 40 to 50 per cent, and gravel or unsealed surfaces further increase consumption. Battery electric models capable of sustained towing have only recently entered the Australian market, and at price points well above equivalent diesel utilities. Vehicle task fit therefore functions as a distinct barrier that is largely absent from urban adoption research.

Problem Statement

Policy instruments currently in place assume that price signals and model availability will diffuse electric vehicles evenly once supply improves. If, however, regional non-adoption is driven principally by infrastructure sufficiency, range and towing adequacy rather than by cost or attitude, then supply-side measures such as the New Vehicle Efficiency Standard will widen rather than close the geographic gap, and the transport emissions reduction embedded in national targets will be delivered disproportionately by metropolitan households. There is at present no Australian study that measures perceived barriers against objectively mapped charger coverage across remoteness strata, so the relative weight of these explanations is unknown.

Aim and Research Questions

The aim of this study is to identify and rank the barriers to battery electric vehicle adoption among drivers in regional and remote Australia, and to establish how those barriers vary with remoteness and with measured charging infrastructure coverage. Three research questions follow.

1. RQ1: Which factors most strongly predict battery electric vehicle adoption intention among regional and remote Australian drivers, and do these predictors differ across remoteness areas?
2. RQ2: How do range adequacy, towing requirements and perceived total cost of ownership interact with charging availability in shaping that intention?
3. RQ3: To what extent does objectively measured charger coverage correspond with drivers’ perceptions of infrastructure sufficiency, and where are the largest mismatches located?

Literature Review

Technology acceptance

The dominant behavioural framework in adoption research is the Unified Theory of Acceptance and Use of Technology, which identifies performance expectancy, effort expectancy, social influence and facilitating conditions as the principal antecedents of behavioural intention, later extended for consumer contexts (Venkatesh, Thong & Xu 2012). The framework transfers to vehicle purchase with one important qualification: a car is a durable, high-cost, infrequently replaced asset, so facilitating conditions are not merely enabling but potentially decisive. Applications of the model to electric vehicles have generally been conducted in dense European or North American settings where charging is ambient, which limits their external validity for Australian conditions.

Range anxiety

Range anxiety is the most heavily researched psychological barrier. Franke and Krems (2013) demonstrated that stated range preferences substantially exceed actual daily requirements, and that experienced users recalibrate their comfortable range downward over time. Noel et al. (2019) argue that range anxiety is partly a socially reproduced narrative rather than a purely experiential phenomenon. Both positions were developed in contexts where the gap between required and available range is modest. In outer regional and remote Australia the gap may be real rather than perceptual, which makes disentangling justified range assessment from anxiety an empirical question this study can address by pairing self-report with mapped coverage.

Total cost of ownership

Economic analyses generally find that battery electric vehicles reach cost parity over a typical ownership period through lower energy and maintenance costs, particularly at high annual distances. Broadbent, Drozdzewski and Metternicht (2018) show, however, that consumers respond to upfront purchase price and to visible incentives far more strongly than to modelled lifetime cost, and that policy design must account for this asymmetry. Regional conditions cut both ways: high annual kilometres improve the running-cost case, while thin used-vehicle markets, longer replacement cycles, limited local servicing and uncertainty about battery resale value weaken it.

Infrastructure sufficiency

Hardman et al. (2018) synthesise consumer interactions with charging infrastructure and conclude that home charging availability is the strongest single enabler, with public fast charging functioning primarily as insurance for occasional long trips. High rates of detached dwellings with off-street parking should therefore advantage regional households. That advantage is offset if inter-town public charging is sparse or unreliable, because the insurance function fails precisely where trip distances are longest. Sufficiency is thus better conceptualised as a spatial property of a driver’s actual travel network than as a simple count of chargers within a region.

Research gap

The literature offers well-validated constructs but almost no evidence from low-density, long-distance, towing-intensive settings, and no Australian study that tests perceived barriers against measured coverage across the full remoteness gradient. This study addresses that gap.

Conceptual Framework

Figure 1 illustrates the conceptual model. Four constructs are drawn from technology acceptance theory and three from the regional transport literature: charging access, range adequacy, and towing and task fit. Perceived total cost of ownership advantage is modelled as an economic predictor. Remoteness area and towing requirement are modelled as moderators, on the expectation that infrastructure and task-fit paths strengthen as remoteness increases while attitudinal paths weaken.

Hypotheses H1 to H4 and H7 propose positive paths to adoption intention from performance expectancy, effort expectancy, social influence, charging access and perceived cost of ownership advantage. H5 and H6 propose positive paths from range adequacy and towing and task fit, both of which are expected to be stronger in outer regional and remote strata than in inner regional areas.

Research Design and Methodology

Overall design

The study adopts a sequential explanatory mixed methods design in which a quantitative phase establishes the relative weight of barriers and a subsequent qualitative phase explains the mechanisms behind the strongest and the most counterintuitive results (Creswell & Plano Clark 2018). A parallel spatial component supplies an objective measure of charging coverage that is joined to survey responses by postcode, allowing perception to be tested against provision.

Phase 1: survey of regional drivers

A cross-sectional online and paper questionnaire will be administered to licensed drivers aged 18 and over whose principal residence falls outside the major cities remoteness category. Sampling will be stratified by the four non-metropolitan remoteness classes of the Australian Statistical Geography Standard, with quotas of approximately 180 inner regional, 180 outer regional and 90 remote or very remote respondents (ABS 2023). Recruitment will proceed through local government newsletters, agricultural show attendance, regional motoring organisation membership lists and geographically targeted social media advertising. Minimum sample size was determined for a proportion estimate at 95 per cent confidence with a five per cent margin of error:

n = (1.96² × 0.5 × 0.5) / 0.05² = 0.9604 / 0.0025 = 384.16, rounded to 385.

Inflating this figure by 15 per cent to allow for incomplete responses gives 385 × 1.15 = 442.75, so a target of 450 completed questionnaires has been adopted. This sample also satisfies the requirement for multi-group comparison across remoteness strata.

Table 1 sets out the constructs, their operational definitions and the sources of the measurement items. All items use five-point Likert response formats, and range adequacy items are reverse coded so that low scores indicate high range anxiety.

Table 1: Constructs, measurement items and sources for the Phase 1 questionnaire

Phase 2: focus groups

Four focus groups of 7 to 9 participants each will be convened, one in each remoteness stratum and one composed exclusively of drivers who tow regularly for work or recreation. Participants will be recruited purposively from survey respondents who consented to further contact, sampled to include both high and low intention scores. Discussion will probe the reasoning behind survey patterns, the trip types that respondents believe an electric vehicle could not perform, and reactions to specific policy options such as depot charging, township fast chargers and purchase concessions.

Phase 3: spatial analysis of charger coverage

Public charger locations, connector types and rated power will be compiled from national charging registries and program reporting, then mapped against the road network and settlement pattern (ARENA 2023). Two indices will be constructed for each respondent postcode: distance to the nearest fast charger of at least 50 kilowatts, and the proportion of a 250 kilometre travel radius that lies within a return trip of a charger at 80 per cent usable battery capacity. Distribution network hosting constraints identified in system planning material will be noted as a supply-side qualifier (AEMO 2024).

Analysis plan

Survey data will be analysed using partial least squares structural equation modelling, which suits prediction-oriented models with formatively influenced constructs and modest sample sizes, with 5,000 bootstrap subsamples for significance testing. Measurement quality will be assessed through composite reliability, average variance extracted and heterotrait-monotrait ratios before the structural model is interpreted. Multi-group analysis will test whether path coefficients differ significantly across remoteness strata, addressing RQ1, while the mapped coverage indices will be entered as an objective predictor alongside perceived charging access to address RQ3. Focus group transcripts will be analysed thematically using an initial coding frame derived from the conceptual model and open codes for unanticipated barriers. Integration will occur through a joint display in which each quantitative path result is set beside the qualitative evidence that confirms, explains or contradicts it.

Ethical Considerations

The study will be submitted for approval by the university Human Research Ethics Committee and conducted in accordance with the National Statement on Ethical Conduct in Human Research (NHMRC 2023). Participation will be voluntary and based on written informed consent, with a plain-language statement explaining that responses will be reported only in aggregate. Because small regional communities carry a real risk of reidentification, postcodes will be aggregated to statistical area level 3 before any output is published, and focus group transcripts will be de-identified at transcription with community names removed. Data will be stored on encrypted university servers for five years. Participation carries minimal risk, although focus group discussion of vehicle costs may touch on household financial stress, so participants will be advised that they may decline any question or withdraw without consequence.

Project Timeline

Table 2 presents the twelve-month schedule. Phases overlap deliberately so that spatial analysis is complete before survey fieldwork closes, allowing coverage indices to inform focus group prompts.

Table 2: Twelve-month project timeline by phase, activity and week

Policy Significance

The findings will inform three live Australian policy questions. First, whether the New Vehicle Efficiency Standard requires complementary regional measures to avoid a two-speed transition in which supply-side obligations deliver emissions reductions predominantly in capital cities (DITRDCA 2024). Second, where the next tranche of co-funded charging investment should be directed, since a mapped perception-provision gap identifies communities where either infrastructure or information is the binding constraint (ARENA 2023). Third, how state and territory implementation of the National Electric Vehicle Strategy should treat towing-intensive vehicle segments, which the current model mix serves poorly (DCCEEW 2023). Ranking barriers rather than merely cataloguing them allows scarce public funds to be allocated to the constraint that actually binds, and the coverage indices developed here are reusable for network planning as demand grows (AEMO 2024).

Limitations

Three limitations are acknowledged. The cross-sectional design measures intention rather than purchase, and the intention-behaviour gap is well documented in vehicle research, so results will be interpreted as evidence about barrier salience rather than as a sales forecast. Recruitment through motoring organisations, local government channels and social media will under-represent drivers with low digital engagement and those not affiliated with community organisations, a bias likely to understate barriers; offering a paper response option and attending regional shows partially mitigates it. Finally, the charging landscape is changing quickly, so coverage indices constructed in the first half of the project will be recalculated at week 40 and any material change reported alongside the primary results.

Conclusion

Australia’s electric vehicle transition is proceeding fastest where distances are shortest and charging is densest, and slowest where fuel costs are highest and annual travel is greatest. This proposal sets out a study designed to explain that inversion by testing behavioural, economic, task-fit and infrastructure explanations against one another, and by comparing what regional drivers believe about charging provision with what the network actually supplies. The combination of a stratified survey of 450 drivers, four targeted focus groups and postcode-level coverage mapping offers an evidence base capable of directing regional charging investment and complementary policy to the barriers that matter most.

References

Australian Bureau of Statistics 2023, Australian Statistical Geography Standard (ASGS) Edition 3: remoteness areas, ABS, Canberra.

Australian Bureau of Statistics 2024, Motor vehicle census, Australia, ABS, Canberra.

Australian Energy Market Operator 2024, Integrated system plan for the National Electricity Market, AEMO, Melbourne.

Australian Renewable Energy Agency 2023, Driving the nation: regional and highway charging infrastructure, ARENA, Canberra.

Broadbent, GH, Drozdzewski, D & Metternicht, G 2018, ‘Electric vehicle adoption: an analysis of best practice and pitfalls for policy making from experiences of Europe and the US’, Geography Compass, vol. 12, no. 2, pp. 1-14.

Creswell, JW & Plano Clark, VL 2018, Designing and conducting mixed methods research, 3rd edn, Sage, Thousand Oaks.

Department of Climate Change, Energy, the Environment and Water 2023, National electric vehicle strategy, Commonwealth of Australia, Canberra.

Department of Infrastructure, Transport, Regional Development, Communications and the Arts 2024, New vehicle efficiency standard: impact analysis, Commonwealth of Australia, Canberra.

Electric Vehicle Council 2024, State of electric vehicles, Electric Vehicle Council, Sydney.

Franke, T & Krems, JF 2013, ‘What drives range preferences in electric vehicle users?’, Transport Policy, vol. 30, pp. 56-62.

Hardman, S, Jenn, A, Tal, G, Axsen, J, Beard, G, Daina, N, Figenbaum, E, Jakobsson, N, Jochem, P, Kinnear, N, Plotz, P, Pontes, J, Refa, N, Sprei, F, Turrentine, T & Witkamp, B 2018, ‘A review of consumer preferences of and interactions with electric vehicle charging infrastructure’, Transportation Research Part D: Transport and Environment, vol. 62, pp. 508-523.

National Health and Medical Research Council 2023, National statement on ethical conduct in human research, NHMRC, Canberra.

Noel, L, Zarazua de Rubens, G, Sovacool, BK & Kester, J 2019, ‘Fear and loathing of electric vehicles: the reactionary rhetoric of range anxiety’, Energy Research and Social Science, vol. 48, pp. 96-107.

Venkatesh, V, Thong, JYL & Xu, X 2012, ‘Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology’, MIS Quarterly, vol. 36, no. 1, pp. 157-178.