GOCART (Gender-Oriented Car Acquisition Relationship Testing) investigates age- and gender-specific patterns in vehicle ownership in Germany using large-scale registry data from the Kraftfahrt-Bundesamt (KBA). While prior research has identified gender differences in mobility behavior and technology adoption, empirical evidence based on comprehensive vehicle registration data remains limited. This project addresses this gap by analyzing registration data as a proxy for ownership structures across age groups, vehicle segments, and CO₂ efficiency classes. The research combines descriptive statistics, chi-square tests of independence, and machine learning techniques. First, ownership distributions are compared with population structures (DESTATIS forecast G1L3W2, 2024), revealing systematic over- and underrepresentation across age and gender groups. Older age groups account for a disproportionately high share of registered vehicles, whereas women and younger individuals are underrepresented relative to their population share Second, Pearson standardized residuals from χ² tests indicate statistically significant deviations from independence between age groups and CO₂ efficiency classes. Higher-efficiency vehicles are more common among younger owners, while lower-efficiency vehicles are more prevalent among older owners Third, an XGBoost machine learning model predicts vehicle attributes from gender. SHAP values quantify the contribution of individual features to the predicted probability. Systematic differences are observed, most prominently for electric vehicles, with male-associated observations contributing less positively than female-associated observations. Importantly, SHAP values describe model behavior and do not imply causal effects Across all age groups, vehicle segment composition differs systematically by gender. Women are more frequently associated with Mini and Small car segments, whereas men are more frequently associated with higher-power, lower-efficiency vehicles The project contributes to the empirical understanding of gendered mobility structures in the context of the transition to low-carbon transport. Findings are based on registered ownership data, which serve as a proxy and do not directly capture individual preferences or usage behavior. Funding: This research was funded by the BMFTR-Project GO Forschung (Gender-Offensive-Forschung), University of Bayreuth; within the funding guideline ‘Gender aspects in focus‘.
Hillenbrand et al. (Mon,) studied this question.