Can spatial indicators fully explain car dependence? Evidence from Lombardy (Italy)

Reducing car dependence is a critical challenge for transport and environmental policy, requiring a thorough understanding of its multidimensional nature. However, existing research often struggles to assess car-dependence’s complexity comprehensively. This paper addresses that gap by applying Sensitivity Analysis (SA) techniques to a rich spatial dataset deployed across the Italian region of Lombardy, which encompasses diverse territories and mobility patterns. The proposed methodology combines moment-independent and variance-based SA methods to better suit observational data and identify key factors shaping car dependence. The resulting SA models show that car dependence cannot be fully explained by numeric variables alone and reveal unexpected causing factors that might point to deeper, underlying patterns. These findings highlight the limitations of purely quantitative approaches in comprehensively capturing the complexity of car dependence, reinforcing the need to complement them with context-based and qualitative approaches. In this way, the study contributes to a more robust understanding of the phenomenon across diverse territorial contexts, supporting more accurate strategies for developing or evaluating policies aimed at reducing car dependence.