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Climate change mitigation in urban mobility requires policies reconfiguringurban form to increase accessibility and facilitate low-carbon modes oftransport. However, current policy research has insufficiently assessed urbanform effects on car travel at three levels: (1) Causality -- Can causality beestablished beyond theoretical and correlation-based analyses? (2)Generalizability -- Do relationships hold across different cities and worldregions? (3) Context specificity -- How do relationships vary acrossneighborhoods of a city? Here, we address all three gaps via causal graphdiscovery and explainable machine learning to detect urban form effects onintra-city car travel, based on mobility data of six cities across threecontinents. We find significant causal effects of urban form on trip emissionsand inter-feature effects, which had been neglected in previous work. Ourresults demonstrate that destination accessibility matters most overall, whilelow density and low connectivity also sharply increase CO$_2$ emissions. Thesegeneral trends are similar across cities but we find idiosyncratic effects thatcan lead to substantially different recommendations. In more monocentriccities, we identify spatial corridors -- about 10--50 km from the city center-- where subcenter-oriented development is more relevant than increased accessto the main center. Our work demonstrates a novel application of machinelearning that enables new research addressing the needs of causality,generalizability, and contextual specificity for scaling evidence-based urbanclimate solutions.

Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents