An AI-driven framework for evaluating local and state authorities’ permitting processes
Ranjit R. Desai, Umapriya Renganathan, Reid Olson, Erin Andrews-Sharer, Noah Frey, Alexandria Danner, Grant Buster, Jack Titus, Noel Crisostomo · 2026
Read An AI-driven framework for evaluating local and state authorities’ permitting processes by Ranjit R. Desai, Umapriya Renganathan, Reid Olson, Erin Andrews-Sharer, Noah Frey, Alexandria Danner, Grant Buster, Jack Titus, Noel Crisostomo (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13534710.txt.
Description
Ranjit R Desai and colleagues build in 2026 an AI-driven framework to judge local permitting processes for energy projects across the United States. Varied local rules delay projects and raise soft costs for builders. A language model tuned for energy gathered rules from nearly 300 state, county, and city documents.
Checks put the final database accuracy near 95 percent. A new scoring method rated clarity and efficiency, and average local documents scored low, implying half of builder-facing rules are unclear. A generalised permit path highlights shared steps and fixes towns can make.
Catalog record
| CREATOR | Ranjit R. Desai, Umapriya Renganathan, Reid Olson, Erin Andrews-Sharer, Noah Frey, Alexandria Danner, Grant Buster, Jack Titus, Noel Crisostomo |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 88259 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |