The built environment and induced transport CO2 emissions: A double
  machine learning approach to account for residential self-selection
Florian Nachtigall Felix Wagner Peter Berrill Felix Creutzig
Understanding why travel behavior differs between residents of urban centers
and suburbs is key to sustainable urban planning. Especially in light of rapid
urban growth, identifying housing locations that minimize travel demand and
induced CO2 emissions is crucial to mitigate climate change. While the built
environment plays an important role, the precise impact on travel behavior is
obfuscated by residential self-selection. To address this issue, we propose a
double machine learning approach to obtain unbiased, spatially-explicit
estimates of the effect of the built environment on travel-related CO2
emissions for each neighborhood by controlling for residential self-selection.
We examine how socio-demographics and travel-related attitudes moderate the
effect and how it decomposes across the 5Ds of the built environment. Based on
a case study for Berlin and the travel diaries of 32,000 residents, we find
that the built environment causes household travel-related CO2 emissions to
differ by a factor of almost two between central and suburban neighborhoods in
Berlin. To highlight the practical importance for urban climate mitigation, we
evaluate current plans for 64,000 new residential units in terms of total
induced transport CO2 emissions. Our findings underscore the significance of
spatially differentiated compact development to decarbonize the transport
sector.