Publication

Cities have become primary actors on climate change and are increasingly setting goals aimed at net-zero emissions, which warrants closer examination to understand how they intend to meet these goals. The incomplete and heterogeneous nature of city climate policy documents, however, has made systemic analysis challenging. We analyze 318 climate action documents from cities with net-zero targets using machine learning-based natural language processing (NLP) techniques. We aim to accomplish two goals: (1) determine text patterns that predict 'ambitious' net-zero targets; and (2) perform a sectoral analysis to identify patterns and trade-offs in climate action themes. We find that cities with ambitious climate actions tend to emphasize quantitative metrics and specific high-emitting sectors in their plans. Cities predominantly emphasize energy-related actions in their plans, but often at the expense of other sectors, including land-use and climate impacts. The method presented in this paper provides a replicable, scalable approach to analyzing climate action plans and a first step towards facilitating cross-city learning.