Evidence on local climate policies achieving emission reduction targets by 2030

Abstract

Local governments play a crucial role in combating climate change. They directly engage with citizens, impact their daily lives, and implement local policies to meet mitigation goals. This paper focuses on identifying specific policy themes that significantly contribute to achieving 2030 mitigation targets, thereby supporting local governments in developing effective climate action plans. We developed an innovative machine learning methodology to extract policy topics and evaluate their impact on meeting committed mitigation targets. This approach includes a new stopping criterion for Structural Topic Modeling. We applied this methodology to a sample of 744 Global Covenant of Mayors signatories, each committed to reducing a percentage of their baseline emissions by 2030. Our findings reveal that policies addressing building integration and transport modal shift, among others, show a strong positive correlation with the likelihood of meeting emissions reduction targets. By leveraging machine learning techniques, our methodology effectively categorizes diverse individual policies into more cohesive topics, facilitating knowledge sharing among committed cities and enhancing the overall effectiveness of climate action strategies.