Quantifying water effluent violations and enforcement impacts using causal AI

Author:

Wang Yingjie1,Sobien Dan2,Kulkarni Ajay3,Batarseh Feras A.134ORCID

Affiliation:

1. Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington Virginia USA

2. National Security Institute, Virginia Tech Arlington Virginia USA

3. Commonwealth Cyber Initiative, Virginia Tech Arlington Virginia USA

4. Department of Biological Systems Engineering Virginia Tech Arlington Virginia USA

Abstract

AbstractIn the landscape of environmental governance, controlling water pollution through the regulation of point sources is vital as it preserves ecosystems, protects human health, ensures legal compliance, and fulfills global environmental responsibilities. Under the Clean Water Act, the integrated compliance information system monitors the compliance and enforcement status of facilities regulated by the National Pollutant Discharge Elimination System (NPDES) permit program. This study assesses temporal and geographic trends for effluent violations within the United States and introduces a novel metric for quantifying violation trends at the facility level. Furthermore, we utilize a linear parametric approach for Conditional Average Treatment Effect (CATE) causal analysis to quantify the heterogeneous effects of EPA and state enforcement actions on effluent violation trends at facilities with NPDES permits. Our research reveals insights into national pollutant discharge trends, regional clustering of all pollutant violation types in Ohio ( Z‐score of 2.15), and priority pollutants in West Virginia ( Z‐score of 3.07). The trend metric identifies regulated facilities that struggle with severe and recurring violations. The causal model highlights variations in state compliance and enforcement effectiveness, underscoring the successful moderation of violation trends by states such as Montana and Maryland, among others.

Funder

Deloitte

Publisher

Wiley

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