IN-PERSON London. Section A

Abstract

In the face of climate change, ecological degradation, and threats to biodiversity, cities often bear the brunt of responsibility in addressing these complex issues. Comprehensive planning in urban areas can help guide land use planning and address these challenges while setting a long term vision for their city and region. By analyzing policy documents using machine learning, I will advance understanding of how a city’s budget, staff capacity, and population demographics can impact the strength of a city’s environmental policies. This will be traced over two decades of documents to integrate a long-range view and connect to evolving regional policy goals. The presentation of my research to a wide audience at the Livable Cities Conference will facilitate discussion around machine learning as a method of policy analysis as well as the factors that do or do not lead to stronger action to protect natural resources and plan for a changing climate. This will be approached with a regional governance lens to build on theory that supports multi-level and polycentric governance approaches.