Supporting Energy Policy Research with Large Language Models
Grant Buster Pavlo Pinchuk Jacob Barrons Ryan McKeever Aaron Levine Anthony Lopez
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.