Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents
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...
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Main Authors | , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
31.08.2023
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2308.16599 |