Deep Learning Projects Jurisdiction of New and Proposed Clean Water Act Regulation

Simon Greenhill, Brant J Walker and Joseph Shapiro

Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

Abstract: Projecting the effects of proposed policy reforms is challenging because no outcome data exist for regulations that governments have not yet implemented. We propose an ex ante deep learning framework that can project effects of proposed reforms by mapping outcomes observed under past regulations onto the legal criteria of proposed future policies (i.e., by “relabeling”). We apply this framework to study changes in jurisdiction of the US Clean Water Act (CWA). We compare our ex ante deep learning projection of jurisdiction under the Supreme Court’s Sackett decision against widely used projections from domain experts. Ex ante machine learning generates exceptional performance improvements over the leading domain expert model that the US Environmental Protection Agency currently uses, with 65 times more accurate identification of jurisdictional sites. We also develop an ex post deep learning model trained with data after policy implementation. Ex post deep learning performs best. Sackett deregulates one-third of all previously regulated US waters, particularly floodplains and pristine fish habitats, totaling 700,000 deregulated stream miles and 17 million deregulated wetland acres. Deep learning can effectively project consequences of far-reaching regulatory reforms before they are implemented, when projections are both most uncertain and most useful.

Keywords: Social and Behavioral Sciences; Environmental policy; Clean Water Act; machine learning (search for similar items in EconPapers)
Date: 2026-02-09
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