Comprehensive Database of Environmental Mitigations Extracted from FERC-Licensed Hydropower Projects using Artificial Intelligence Techniques, 1998-2023

Ruggles, Tom; Yoon, Hong-Jun; Bhattacharya, Arjun; Singh, Debjani

doi:10.21951/Environmental_MitigationsAI/2570983

Title: Comprehensive Database of Environmental Mitigations Extracted from FERC-Licensed Hydropower Projects using Artificial Intelligence Techniques, 1998-2023

Dataset
Other Related Research

Abstract

This dataset provides a comprehensive inventory of environmental mitigation measures required by Federal Energy Regulatory Commission (FERC) licensed hydropower facilities from 461 licenses that were issued from 1998 to 2023. These licenses constitute 446 of the 1015 FERC projects that were active at the end of 2023. 17,612 mentions of environmental mitigations were identified and categorized in 128 unique categories. Mitigations were identified using a Natural Language Processing (NLP) approach, specifically with a Bidirectional Encoder Representations from Transformer (BERT) model. Model-derived results were then reviewed and updated by a subject matter expert as needed. This dataset introduces important enhancements to previous efforts to inventory environmental mitigations, such as including associated license text for each mitigation, tracking the number of instances a mitigation was identified within a license, and providing improved location information. These enhancements significantly expand the dataset’s utility, offering greater analytical capabilities and ensuring reproducibility. The dataset is downloadable as a zip file containing the metadata and dataset files.

Authors:

;

; Bhattacharya, Arjun;

ORNL
Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville

Publication Date:: Sun Jun 01 04:00:00 UTC 2025

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE

OSTI Identifier:: 2575249

DOI:: https://doi.org/10.21951/Environmental_MitigationsAI/2570983

Citation Formats


                    Ruggles, Tom, Yoon, Hong-Jun, Bhattacharya, Arjun, and Singh, Debjani. Comprehensive Database of Environmental Mitigations Extracted from FERC-Licensed Hydropower Projects using Artificial Intelligence Techniques, 1998-2023.  United States: N. p., 2025. 
        Web.  doi:10.21951/Environmental_MitigationsAI/2570983.

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                    Ruggles, Tom, Yoon, Hong-Jun, Bhattacharya, Arjun, & Singh, Debjani. Comprehensive Database of Environmental Mitigations Extracted from FERC-Licensed Hydropower Projects using Artificial Intelligence Techniques, 1998-2023.  United States.  doi:https://doi.org/10.21951/Environmental_MitigationsAI/2570983

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                    Ruggles, Tom, Yoon, Hong-Jun, Bhattacharya, Arjun, and Singh, Debjani. 2025.  
"Comprehensive Database of Environmental Mitigations Extracted from FERC-Licensed Hydropower Projects using Artificial Intelligence Techniques, 1998-2023".  United States.  doi:https://doi.org/10.21951/Environmental_MitigationsAI/2570983.  https://www.osti.gov/servlets/purl/2575249. Pub date:Sun Jun 01 04:00:00 UTC 2025

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@article{osti_2575249,

  title        = {Comprehensive Database of Environmental Mitigations Extracted from FERC-Licensed Hydropower Projects using Artificial Intelligence Techniques, 1998-2023},

  author       = {Ruggles, Tom and Yoon, Hong-Jun and Bhattacharya, Arjun and Singh, Debjani},

  abstractNote = {This dataset provides a comprehensive inventory of environmental mitigation measures required by Federal Energy Regulatory Commission (FERC) licensed hydropower facilities from 461 licenses that were issued from 1998 to 2023. These licenses constitute 446 of the 1015 FERC projects that were active at the end of 2023. 17,612 mentions of environmental mitigations were identified and categorized in 128 unique categories. Mitigations were identified using a Natural Language Processing (NLP) approach, specifically with a Bidirectional Encoder Representations from Transformer (BERT) model. Model-derived results were then reviewed and updated by a subject matter expert as needed. This dataset introduces important enhancements to previous efforts to inventory environmental mitigations, such as including associated license text for each mitigation, tracking the number of instances a mitigation was identified within a license, and providing improved location information. These enhancements significantly expand the dataset’s utility, offering greater analytical capabilities and ensuring reproducibility. The dataset is downloadable as a zip file containing the metadata and dataset files.},

  doi          = {10.21951/Environmental_MitigationsAI/2570983},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sun Jun 01 04:00:00 UTC 2025},

  month        = {Sun Jun 01 04:00:00 UTC 2025}

}

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DOI: https://doi.org/10.21951/Environmental_MitigationsAI/2570983

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