Predicting European cities' climate mitigation performance using machine learning.

Although cities have risen to prominence as climate actors, emissions' data scarcity has been the primary challenge to evaluating their performance. Here we develop a scalable, replicable machine learning approach for evaluating the mitigation performance for nearly all local administrative areas in Europe from 2001-2018. By combining publicly available, spatially explicit environmental and socio-economic data with self-reported emissions data from European cities, we predict annual carbon dioxide emissions to explore trends in city-scale mitigation performance. We find that European cities participating in transnational climate initiatives have likely decreased emissions since 2001, with slightly more than half likely to have achieved their 2020 emissions reduction target. Cities who report emissions data are more likely to have achieved greater reductions than those who fail to report any data. Despite its limitations, our model provides a replicable, scalable starting point for understanding city-level climate emissions mitigation performance. 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. [ABSTRACT FROM AUTHOR]