Improving Private Well Testing Programs: Experimental Evidence from Iowa.

Approximately 23 million U.S. households rely on private wells for drinking water. This study first summarizes drinking water behaviors and perceptions from a large-scale survey of households that rely on private wells in Iowa. Few households test as frequently as recommended by public health experts. Around 40% of households do not regularly test, treat, or avoid their drinking water, suggesting pollution exposure may be widespread among this population. Next, we utilize a randomized control trial to study how nitrate test strips and information about a free, comprehensive water quality testing program influence households' behaviors and perceptions. The intervention significantly increased testing, including high-quality follow-up testing, but had limited statistically detectable impacts on other behaviors and perceptions. Households' willingness to pay for nitrate test kits and testing information exceeds program costs, suggesting that the intervention was welfare-enhancing.

Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.

We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the Army Corps of Engineers, each deciding regulation for one water resource. Under a 2006 Supreme Court ruling, the Clean Water Act protects two-thirds of US streams and more than half of wetlands; under a 2020 White House rule, it protects less than half of streams and a fourth of wetlands, implying deregulation of 690,000 stream miles, 35 million wetland acres, and 30% of waters around drinking-water sources. Our framework can support permitting, policy design, and use of machine learning in regulatory implementation problems.