How are cities pledging net zero? A computational approach to analyzing subnational climate strategies

TL;DR

This paper uses machine learning NLP techniques to analyze 318 city climate action documents, revealing patterns in ambitious net-zero targets and sectoral focus, highlighting energy sectors over others.

Contribution

Introduces a scalable NLP-based method to systematically analyze and compare city climate strategies, addressing heterogeneity and enabling cross-city learning.

Findings

Cities with ambitious targets emphasize quantitative metrics and high-emission sectors.

Energy sectors like buildings, transport, and heating are the primary focus.

Other sectors such as land-use are often underrepresented in plans.

Abstract

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.