In 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.

中文翻译：

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使用因果人工智能量化水排放违规行为和执法影响


在环境治理领域，通过点源监管控制水污染至关重要，因为它可以保护生态系统，保护人类健康，确保法律合规性，履行全球环境责任。根据《清洁水法》，综合合规信息系统监控受国家污染物排放消除系统（NPDES）许可证计划监管的设施的合规和执行状态。这项研究评估了美国境内废水违规的时间和地理趋势，并引入了一种新的指标来量化设施层面的违规趋势。此外，我们利用线性参数方法进行条件平均处理效应 (CATE) 因果分析，以量化 EPA 和州执法行动对拥有 NPDES 许可的设施的污水违规趋势的异质影响。我们的研究揭示了对全国污染物排放趋势、俄亥俄州所有污染物违规类型的区域聚类（Z 得分为 2.15）以及西弗吉尼亚州优先污染物（Z 得分为 3.07）的见解。该趋势指标可识别那些与严重且反复发生的违规行为作斗争的受监管设施。因果模型强调了各州合规性和执法有效性的差异，强调蒙大拿州和马里兰州等州成功遏制了违规趋势。