Geodemographic Inequalities in Battery Electric Vehicle Adoption across UK Neighbourhoods

Alex Singleton, Ian Philips and Jillian Anable

No 6q7uf_v1, SocArXiv from Center for Open Science

Abstract: Battery electric vehicle (BEV) adoption is an important contributor to the decarbonisation of road transport in the UK and other OECD countries, yet uptake remains highly uneven between places. This paper examines the geography of privately kept battery electric vehicles across UK small areas using quarterly DVLA vehicle licensing records, linked geodemographically to the 2021/22 UK Output Area Classification. The analysis measures observed private vehicle ownership at neighbourhood scale, enabling within local authority variation to be examined directly. The results show that BEV penetration is strongly right-skewed, with adoption concentrated in affluent professional neighbourhoods, prosperous suburban and rural areas, and dense urban cores with relatively small but rapidly electrifying private fleets. Lower uptake is associated with neighbourhoods characterised by rented tenure, terraced housing and flats, where access to private off-street charging is likely to be more constrained. Partitioning the national time series by geodemographic type reveals that the aggregate adoption curve is composed of distinct trajectories, with some early-adopting neighbourhood types showing signs of slower recent growth while later-adopting groups continue to accelerate. Residuals from the geodemographic baseline are spatially clustered, indicating that neighbourhood composition alone does not explain the geography of adoption. The paper demonstrates the value of combining administrative vehicle records with geodemographic classification to identify where BEV adoption is leading, lagging and diverging from demographic expectation, with implications for a more equitable rollout of charging infrastructure and transport decarbonisation policy.

Date: 2026-08-18
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:6q7uf_v1

DOI: 10.31235/osf.io/6q7uf_v1

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