43 citations · 119 across the 7 of their papers we have counts for
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cs.LG2023★ 1 cited
Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents
Felix Wagner, Florian Nachtigall, Lukas Franken +6
Climate change mitigation in urban mobility requires policies reconfiguring urban form to increase accessibility and facilitate low-carbon modes of transport. However, current poli…
physics.ao-ph2023★ 43 cited
Causally-informed deep learning to improve climate models and projections
Fernando Iglesias-Suarez, Pierre Gentine, Breixo Solino-Fernandez +4
Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the r…