Predicting European cities’ climate mitigation performance using machine learning

Abstract

Since the Paris Agreement recognized in 2015 cities have pledged climate actions that often exceed the scope and ambition of their national governments’ policies but there is scant evidence of these actions’ outcomes, largely because of the lack of reported emissions data. Here the authors utilize spatially explicit datasets relevant to urban carbon emissions and self-reported emissions data from European cities, and develops a machine-learning approach to predict and explore trends in city-scale mitigation.