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

Ruggles, Thomas; Yoon, Hong-Jun; Bhattacharyya, 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

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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:

;

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Publication Date:: Thu Jul 03 04:00:00 UTC 2025

Other Number(s):: 1

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), Renewable Power Office. Water Power Technologies Office

OSTI Identifier:: 2570983

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

Citation Formats


                    Ruggles, Thomas, Yoon, Hong-Jun, Bhattacharyya, 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, Thomas, Yoon, Hong-Jun, Bhattacharyya, 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, Thomas, Yoon, Hong-Jun, Bhattacharyya, 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/2570983. Pub date:Thu Jul 03 04:00:00 UTC 2025

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@article{osti_2570983,

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

  author       = {Ruggles, Thomas and Yoon, Hong-Jun and Bhattacharyya, 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         = {Thu Jul 03 04:00:00 UTC 2025},

  month        = {Thu Jul 03 04:00:00 UTC 2025}

}

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

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