Identifying the losers in the transport transition: evidence from Germany

Public acceptance of transport transition policies in the European Union is low as they are considered regressive. This paper provides a clear way of identifying the winners and losers of these policies by focusing on transport poverty. A two-step methodology is followed. First, principal component analysis (PCA) unifies the literature by defining the main underlying dimensions of transport poverty. These highlight the importance of a household's spatial matching, self-imposed driving restrictions, and available resources. Second, the use of a latent class model (LCM) makes it possible to classify households according to four profiles in the first-ever transport poverty scale (TPS). 14.7 million German households are classified as transport-poor and car-dependent. These two classes represent the most constrained households in terms of resources (time and money) and options available for transport. The degree of spatial matching plays a key role in defining class assignments. Finally, the application of the TPS quantifies the heterogeneous reaction of each transport poverty class to increases in the cost of driving (e.g., the carbon tax). The car-dependent and the transport-poor are the losers in the transition due to inelastic driving demand. Policies reflecting class-based responses to achieve the goals of the transport transition are presented. Alleviating the burden on the transport-poor and car-dependent could increase the acceptability of policies and accelerate the transport transition.