Data-driven Rulemaking

Data-Driven Rulemaking is a hybrid rulemaking approach that belongs to the class of regulatory decision-making methodologies, distinguished by its systematic integration of empirical data and quantitative analysis to inform, justify, and refine the formulation and evaluation of rules.

17

Publications

510

Citations

63

Authors

27

Institutions

Publications per year

2017–2026

8

Authors

63

  1. FY
    Fei-Fei Ye

    Fuzhou University, China

    1 publications · 30 citations

    1

    h-index

  2. TA
    Taqdir Ali

    Kyung Hee University, South Korea

    1 publications · 16 citations

    1

    h-index

  3. AD
    AnHai Doan

    University of Wisconsin–Madison, United States

    1 publications · 24 citations

    1

    h-index

  4. CS
    Chong Sun

    Walmart (United States), United States

    1 publications · 24 citations

    1

    h-index

  5. MC
    Malcolm C. Taylor

    Keele University, United Kingdom

    1 publications · 21 citations

    1

    h-index

1–5 of 63

Venues

  1. Sensors

    1 publications · 16 citations

    1

    h-index

  2. Decision Support Systems

    1 publications · 41 citations

    1

    h-index

  3. American Water Works Association

    1 publications · 12 citations

    1

    h-index

  4. IEEE Expert

    1 publications · 21 citations

    1

    h-index

  5. The Science of The Total Environment

    1 publications · 52 citations

    1

    h-index

1–5