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Related Experiment Videos

An AI-driven framework for evaluating local and state authorities' permitting processes.

Ranjit R Desai1, Umapriya Renganathan2, Reid Olson3

  • 1Center for Integrated Mobility Sciences, National Laboratory of the Rockies, 15013 Denver West Parkway, Golden, CO, USA. Ranjit.Desai@nlr.gov.

Scientific Reports
|June 16, 2026
PubMed
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Permitting processes for new energy infrastructure in the U.S. are complex and vary by location. This study used an Energy Language Model (ELM) to analyze requirements, finding local documents are often unclear, delaying projects.

Area of Science:

  • Energy Infrastructure Development
  • Public Policy & Administration
  • Computational Linguistics

Background:

  • Increasing demand for U.S. energy infrastructure is hampered by inconsistent and complex local permitting processes.
  • These heterogenous requirements lead to project delays, increased soft costs, and limited developer expansion.
  • Existing analyses lack a systematic, large-scale approach to quantify permitting clarity and effectiveness.

Purpose of the Study:

  • To analyze the variability of local permitting requirements across the U.S.
  • To develop a quantitative method for assessing the clarity and effectiveness of these requirements.
  • To create a scalable framework for identifying and mitigating permitting bottlenecks for energy projects.

Main Methods:

  • Utilized an Energy Language Model (ELM), a specialized large language model (LLM), to systematically collect data from nearly 300 state, county, and city permitting documents.
Keywords:
Authorities having jurisdictionGeneralized permitting processInfrastructureLarge language modelsPermitting processSoft costs

Related Experiment Videos

  • Developed a novel quantitative scoring system to evaluate permitting requirements for clarity and efficiency.
  • Applied the scoring method to electric vehicle supply equipment as an initial use case.

    • Main Results:

    • A structured dataset of permitting requirements was created with approximately 95% accuracy.
    • Local permitting documents were found to be underrepresented compared to state-level guidance.
    • The average local permitting document scored 1.8 out of 5, indicating significant ambiguity in requirements.
    • Half of local permitting requirements were found to be ambiguous, increasing project costs and timelines.

    Conclusions:

    • Local permitting processes present significant challenges to the timely and cost-effective deployment of energy infrastructure.
    • The developed framework, combining LLM data collection and quantitative scoring, offers a scalable solution for evaluating and improving permitting processes.
    • Improvements in permitting clarity and efficiency can accelerate project approvals, reduce costs, and expedite grid connections.