Supporting Energy Policy Research with Large Language Models
<|reference_start|>Supporting Energy Policy Research with Large Language Models: 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.<|reference_end|>
@article{buster2024supporting,
    title={Supporting Energy Policy Research with Large Language Models},
    author={Grant Buster, Pavlo Pinchuk, Jacob Barrons, Ryan McKeever, Aaron
  Levine, Anthony Lopez},
    journal={arXiv preprint arXiv:2403.12924},
    year={2024},
    doi={10.1016/j.egyai.2024.100431},
    archivePrefix={arXiv},
    eprint={2403.12924},
    primaryClass={cs.CL}
}