Supporting energy policy research with large language models: A case study in wind energy siting ordinances

• Introduced an automated method using large language models to extract renewable energy siting ordinances from legal documents. • Achieved an accuracy rate of 85% to 90% in ordinance information extraction using a decision tree algorithm powered by large language models. • Significantly reduced the manual labor required to maintain an up-to-date energy siting ordinance database. • Potential to automate similar large-scale policy research across the energy sector. 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.