  The recent growth in renewable energy development in the United States has
been accompanied by a simultaneous surge in renewable energy siting ordinances.
These zoning laws play a critical role in dictating the placement of wind and
solar resources that are critical for achieving low-carbon energy futures. In
this context, efficient access to and management of siting ordinance data
becomes imperative. The National Renewable Energy Laboratory (NREL) recently
introduced a public wind and solar siting database to fill this need. This
paper presents a method for harnessing Large Language Models (LLMs) to automate
the extraction of these siting ordinances from legal documents, enabling this
database to maintain accurate up-to-date information in the rapidly changing
energy policy landscape. A novel contribution of this research is the
integration of a decision tree framework with LLMs. Our results show that this
approach is 85 to 90% accurate with outputs that can be used directly in
downstream quantitative modeling. We discuss opportunities to use this work to
support similar large-scale policy research in the energy sector. By unlocking
new efficiencies in the extraction and analysis of legal documents using LLMs,
this study enables a path forward for automated large-scale energy policy
research.
