  Cities have become primary actors on climate change and are increasingly
setting goals aimed at net-zero emissions. The rapid proliferation of
subnational governments "racing to zero" emissions and articulating their own
climate mitigation plans warrants closer examination to understand how these
actors intend to meet these goals. The scattered, incomplete and heterogeneous
nature of city climate policy documents, however, has made their systemic
analysis challenging. We analyze 318 climate action documents from cities that
have pledged net-zero targets or joined a transnational climate initiative with
this goal using machine learning-based natural language processing (NLP)
techniques. We use these approaches to accomplish two primary goals: 1)
determine text patterns that predict "ambitious" net-zero targets, where we
define an ambitious target as one that encompasses a subnational government's
economy-wide emissions; and 2) perform a sectoral analysis to identify patterns
and trade-offs in climate action themes (i.e., land-use, industry, buildings,
etc.). We find that cities that have defined ambitious climate actions tend to
emphasize quantitative metrics and specific high-emitting sectors in their
plans, supported by mentions of governance and citizen participation. Cities
predominantly emphasize energy-related actions in their plans, particularly in
the buildings, transport and heating sectors, 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.
