This study examines how socio-economic and demographic factors influence electric vehicle (EV) adoption in New South Wales (NSW), Australia. Using 2021 Australian Bureau of Statistics (ABS) census data and EV registration records, a cross-sectional analysis was conducted at the postcode level. Ordinary Least Squares (OLS) regression was applied to identify key determinants of EV uptake. The final model demonstrates strong explanatory power (R² = 0.819%). Results indicate that economic status, captured through a combined income and mortgage indicator, is the strongest predictor, associated with an approximately 101.7% increase in EV registrations for each standard deviation increase. Vehicle ownership density also shows a strong positive effect (an 80.9% increase). In contrast, areas with higher reliance on active transport exhibit a statistically significant negative association with EV adoption. Additional factors, including age, education, and occupational composition (managers and professionals), show moderate positive effects. These findings highlight persistent socio-economic disparities in EV uptake and suggest that targeted policy interventions are required to support a more equitable transition to sustainable transport.

Understanding how socio-economic and demographic factors influence electric vehicle (EV) adoption is crucial for designing equitable policies that support the global transition to sustainable transportation. While EV uptake is rising worldwide, adoption patterns often reflect existing social and economic inequalities, with higher-income communities more likely to benefit from emerging technologies. Addressing these disparities is essential to ensure an inclusive transition. This study focuses on Australia, using New South Wales (NSW) as a case study to examine how socio-economic and demographic characteristics shape EV adoption across different postcodes. A cross-sectional analysis was conducted using 2021 data from the Australian Bureau of Statistics (ABS) census and EV registration records. Postcodes were categorised based on EV uptake levels, and Ordinary Least Squares (OLS) regression models were applied to identify key influencing factors. The results show a strong correlation between higher EV adoption rates and areas with greater wealth and population density. In contrast, factors such as marital status, dwelling structure, driving habits, vehicle ownership, and younger age showed no significant association with uptake. These findings highlight the need for targeted policy interventions to bridge socio-economic gaps in EV adoption. Expanding charging infrastructure and providing financial incentives in lower-income areas could promote more inclusive access to EV technology. Moreover, aligning EV adoption strategies with broader environmental and transportation policies can further accelerate Australia’s energy transition. This study emphasises that a more equitable approach to EV adoption not only enhances environmental outcomes but also supports a fair and sustainable transportation future for all communities.