rebound effect , electric vehicle adoption , variable selection
Abstract:
Widespread electric vehicle adoption is considered a major policy goal in order to decarbonize the transport sector. However, potential rebound effects both in terms of vehicle ownership and distance traveled might nullify the environmental edge of electric vehicles. Using
cross-sectional household-level microdata from Germany, we identify rebound effects of electric vehicle adoption on both margins for specific subgroups of electric vehicle owners. As our data is cross-sectional, we resort to
data-driven methods which are not yet commonly used in the economic literature. For the identification of changes in the number of cars owned after electric vehicle adoption, we predict counterfactual car ownership using a supervised learning approach. Furthermore, we investigate the effect of electric vehicle adoption on household mileage based on a genetic matching of households owning electric vehicles to similar owners of conventional cars. For the selection of covariates for matching, we contrast ad hoc variable selection based on the available literature with a
data-driven variable selection method (double LASSO). We cannot verify asignificant increase in the number of cars owned for households with one electric and one conventional vehicle. For the subgroup of households who substitute the electric car for a conventional vehicle, electric vehicle ownership is associated with a significant reduction in annual mileage of -23% of the sample mean. The result indicates a strive for behavior consistent with the environmentally-friendly car choice rather than a rebound effect. Our results are
subgroup-specific and may not generalize to the overall population. Methodologically, we find that data-driven variable selection identifies a refined set of covariates and changes the magnitude of the estimation results substantially. It may thus be considered a useful omplement, especially in settings with limited theoretical or empirical knowledge established.
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