The built environment and induced transport CO2 emissions: A double machine learning approach to account for residential self-selection
This addresses urban planning and climate change mitigation by providing spatially-explicit estimates to inform sustainable development, though it is incremental as it applies a known method to a specific domain.
The study tackled the problem of measuring the built environment's impact on travel-related CO2 emissions by controlling for residential self-selection, finding that emissions differ by almost a factor of two between central and suburban neighborhoods in Berlin. It evaluated plans for 64,000 new residential units to highlight practical implications for urban climate mitigation.
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.