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

Simon Greenhill, Brant J. Walker and Joseph Shapiro

No 34947, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C45 D61 H11 H23 K32 Q25 Q53 Q58 R11 (search for similar items in EconPapers)
Date: 2026-03
New Economics Papers: this item is included in nep-big, nep-ppm and nep-reg
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Working Paper: Deep Learning Projects Jurisdiction of New and Proposed Clean Water Act Regulation (2026)
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