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.

翻译：美国可再生能源的近期发展伴随着可再生能源选址法令的同步激增。这些分区法规在决定风能和太阳能资源布局方面起着关键作用，而这些资源对于实现低碳能源未来至关重要。在此背景下，高效获取和管理选址法令数据变得刻不容缓。美国国家可再生能源实验室（NREL）近期推出了一个公共风能与太阳能选址数据库以满足这一需求。本文提出了一种利用大型语言模型（LLMs）从法律文件中自动提取这些选址法令的方法，使该数据库能够在快速变化的能源政策环境中保持准确的最新信息。本研究的一个创新贡献是将决策树框架与LLMs相结合。结果表明，该方法的准确率达到85%至90%，其输出可直接用于下游定量建模。我们探讨了利用此项工作支持能源领域类似大规模政策研究的机遇。通过利用LLMs解锁法律文件提取与分析的新效率，本研究为实现自动化大规模能源政策研究开辟了道路。