Predicting European cities’ climate mitigation performance using machine learning
Angel Hsu (University of North Carolina at Chapel Hill)
Xuewei Wang (University of North Carolina at Chapel Hill)
Jonas Tan (Yale-NUS College)
Wayne Toh (Yale-NUS College)
Nihit Goyal (TU Delft - Organisation & Governance)
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Abstract
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.
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