Valuing improvements in the ecological integrity of local and regional waters using the biological condition gradient.

Scientific knowledge related to quantifying the monetized benefits for landscape-wide water quality improvements does not meet current regulatory and benefit-cost analysis needs in the United States. In this study we addressed this knowledge gap by incorporating the Biological Condition Gradient (BCG) as a water quality metric into a stated preference survey capable of estimating the total economic value (use and nonuse) for aquatic ecosystem improvements. The BCG is grounded in ecological principles and generalizable and transferable across space. Moreover, as the BCG translates available data on biological condition into a score on a 6-point scale, it provides a simple metric that can be readily communicated to the public. We applied our BCG-based survey instrument to households across the Upper Mississippi, Ohio, and Tennessee river basins and report values for a range of potential improvements that vary by location, spatial scale, and the scope of the water quality change. We found that people are willing to pay twice as much for an improvement policy that targets their home watershed (defined as a four-digit hydrologic unit) versus a more distant one. We also found that extending the spatial scale of a local policy beyond the home watershed does not generate additional benefits to the household. Finally, our results suggest that nonuse sources of value (e.g., bequest value, intrinsic aesthetic value) are an important component of overall benefits.

The low but uncertain measured benefits of US water quality policy.

US investment to decrease pollution in rivers, lakes, and other surface waters has exceeded $1.9 trillion since 1960, and has also exceeded the cost of most other US environmental initiatives. These investments come both from the 1972 Clean Water Act and the largely voluntary efforts to control pollution from agriculture and urban runoff. This paper reviews the methods and conclusions of about 20 recent evaluations of these policies. Surprisingly, most analyses estimate that these policies' benefits are much smaller than their costs; the benefit-cost ratio from the median study is 0.37. However, existing evidence is limited and undercounts many types of benefits. We conclude that it is unclear whether many of these regulations truly fail a benefit-cost test or whether existing evidence understates their net benefits; we also describe specific questions that when answered would help eliminate this uncertainty.

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.