Project

The transportation sector has long been a renegade in efforts to reduce greenhouse gas emissions. Even as total emissions in Europe have decreased over the past decades, those from transportation are on the rise. To buck this trend, Germany's energy transition will therefore require a fundamental change in the mobility behavior of private households. This project investigates how households' mobility responds to changes in the relative costs of alternative transport modes, most notably those caused by changes in energy prices and in the built environment. By estimating household-group-specific fuel price elasticities, we study the heterogeneous impact of higher fuel prices on mobility demand and vehicle ownership of households that differ with respect to (a) wealth, (b) whether they live in rural or urban areas, and (c) driving distance. Each of these dimensions will be studied using innovative statistical methods, including advancements in spatio-temporal models, quantile regression, and machine learning techniques to aid in causal identification and model selection. We will additionally develop new statistical methodologies to investigate the effect of highly volatile fuel prices on kilometers traveled - as was the case following Russia’s attack on the Ukraine - and, more generally, the effect of possible non-stationarity of the analyzed variables on the outcome of the analysis. In particular, we will address spatial dependencies, as well as non-stationarities of energy prices, which may affect valid statistical inference. To account for potential price frictions and selectivity biases, innovative two-stage models will be adapted that incorporate both the discrete decision pertaining to car ownership (or use) and the continuous decision of distance traveled. When estimating the effect of the built environment, multistage models may likewise be required owing to the endogeneity of residential choice: to the extent that people settle in neighborhoods based on their transportation preferences, estimates of the effect of variables such as public transit proximity or local retail density may be biased. In this context, we will employ specific model selection criteria to take account of multistage mobility decisions and heteroskedasticity.


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Project start:
01. October 2024

Project end:
30. June 2028

Project management:
Prof. Dr. Manuel Frondel, Prof. Colin Vance Ph.D., Prof. Dr. Matei Demetrescu (TU Dortmund)

Project partners:
Technische Universität Dortmund, Ruhr-Universität Bochum, Universität Hamburg, Universität Münster, Karlsruher Institut für Technologie, Technische Universität Hamburg, FH Dortmund

Funding:
Deutsche Forschungsgemeinschaft