  Climate change mitigation in urban mobility requires policies reconfiguring
urban form to increase accessibility and facilitate low-carbon modes of
transport. However, current policy research has insufficiently assessed urban
form effects on car travel at three levels: (1) Causality -- Can causality be
established beyond theoretical and correlation-based analyses? (2)
Generalizability -- Do relationships hold across different cities and world
regions? (3) Context specificity -- How do relationships vary across
neighborhoods of a city? Here, we address all three gaps via causal graph
discovery and explainable machine learning to detect urban form effects on
intra-city car travel, based on mobility data of six cities across three
continents. We find significant causal effects of urban form on trip emissions
and inter-feature effects, which had been neglected in previous work. Our
results demonstrate that destination accessibility matters most overall, while
low density and low connectivity also sharply increase CO$_2$ emissions. These
general trends are similar across cities but we find idiosyncratic effects that
can lead to substantially different recommendations. In more monocentric
cities, we identify spatial corridors -- about 10--50 km from the city center
-- where subcenter-oriented development is more relevant than increased access
to the main center. Our work demonstrates a novel application of machine
learning that enables new research addressing the needs of causality,
generalizability, and contextual specificity for scaling evidence-based urban
climate solutions.
