Built Environment and Travel: Tackling Non-Linear Residential Self-Selection with Double Machine Learning

Abstract

Understanding how the built environment influences travel is key to low-carbon urban planning. However, previous cross-sectional studies lack a realistic operationalization of residential self-selection that accounts for its non-linear nature, limiting its applicability to urban planning. We propose a double machine learning (DML) approach that accounts for nonlinearities in residential self-selection and captures non-linear moderating effects.Using travel diaries of 32,201 Berlin residents, we estimate the built environment's impact on per capita travel-related CO2 emissions. Our results indicate that neglecting nonlinearities overestimates this impact by 13-18%, inflating the built environment proportion by 13%pt. Age, income, and car ownership also nonlinearly moderate the built environment's effect, with the effect being largest for middle-aged, high-income, car-owning households, a novel finding.Applying the method to urban planning reveals a 43%pt emissions reduction potential for 64,000 planned Berlin housing units, highlighting the need for informed urban planning to effectively mitigate CO2 emissions in cities.

Keywords: urban form, compact development, travel behavior, travel-related CO2 emissions, moderating effects, causal machine learning