An AI-based analysis of zoning reforms in US cities

Cities are at the forefront of addressing global sustainability challenges, particularly those exacerbated by climate change. Traditional zoning codes, which often segregate land uses, have been linked to increased vehicular dependence, urban sprawl and social disconnection, undermining broader social and environmental sustainability objectives. This study investigates the adoption and impact of form-based codes (FBCs), which aim to promote sustainable, compact and mixed-use urban forms as a solution to these issues. Using natural language processing techniques, we analyzed zoning documents from over 2,000 United States census-designated places to identify linguistic patterns indicative of FBC principles. Our findings reveal widespread adoption of FBCs across the country, with notable variations within regions. FBCs are associated with higher floor to area ratios, narrower and more consistent street setbacks and smaller plots. We also find that places with FBCs have improved walkability, shorter commutes and a higher share of multifamily housing. Our findings highlight the utility of natural language processing for evaluating zoning codes and underscore the potential benefits of form-based zoning reforms for enhancing urban sustainability.