Low-carbon urban planning with machine learning (Ideas Track) Spotlight
Nikola Milojevic-Dupont (Mercator Research Institute on Global Commons and Climate Change (MCC)); Felix Creutzig (Mercator Research Institute on Global Commons and Climate Change (MCC))
Abstract
Widespread climate action is urgently needed, but current solutions do not account enough for local differences. Here, we take the example of cities to point to the potential of machine learning (ML) for generating at scale high-resolution information on energy use and greenhouse gas (GHG) emissions, and make this information actionable for concrete solutions. We map the existing relevant ML literature and articulate ML methods that can make sense of spatial data for climate solutions in cities. Machine learning has the potential to find solutions that are tailored for each settlement, and transfer solutions across the world.