A computational approach to analyzing climate strategies of cities pledging net zero

This paper analyzes 318 climate action documents from cities with net-zero targets using machine learning-based natural language processing (NLP) techniques. The study aims to identify text patterns predicting 'ambitious' net-zero targets and perform a sectoral analysis to identify patterns and trade-offs in climate action themes. Results show ambitious cities emphasize quantitative metrics and specific high-emitting sectors. Energy-related actions are dominant but often at the expense of other sectors like land-use and climate impacts. The methodology offers a replicable, scalable approach to analyzing climate action plans.

Siddharth Sachdeva, Angel Hsu, Ian French, Elwin Lim