2021/12/14 by Siddharth Sachdeva, Angel Hsu, Sachdeva, Siddharth +5
Economics, Econometrics and Finance · Environmental Science · #Applications (stat.AP) #Climate Change Policy and Economics #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sustainability and Climate Change Governance
paper · pdf · doi:10.48550/arxiv.2112.11207
openalex publication_date 2021/12/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.