White House rule significantly deregulates wetlands, streams and drinking water, machine learning study finds

The Clean Water Act of 1972 protects “waters of the United States” but does not precisely define which waterways and wetlands are covered by that term, leaving it up to presidential administrations, regulators, and courts to decide. As a result, the exact coverage of the Clean Water Act rules is difficult to estimate.

New research from a team at the University of California, Berkeley, used machine learning to more accurately predict which waterways are protected by law. The analysis found that a 2020 Trump administration rule removed Clean Water Act protections for a quarter of wetlands and a fifth of U.S. waterways, and also deregulated 30%. watersheds that supply drinking water to household taps. The research was published in Science.

“Using machine learning to understand these rules helps decode the DNA of environmental policy,” said author Joseph Shapiro, associate professor of agricultural and resource economics at UC Berkeley. “We can finally understand what the Clean Water Act actually protects.”

Previous analyzes assumed that streams and wetlands sharing certain geophysical characteristics were regulated, without examining data on what was actually regulated – an approach that the Environmental Protection Agency and the Army Corps of Engineers called “very unreliable”.

Researchers trained a machine learning model to predict 150,000 Army Corps jurisdictional decisions. Each Corps decision interprets the Clean Water Act for a site and a rule. The model predicts regulation across the United States under the Trump regime and its predecessor, the Supreme Court’s “Rapanos” ruling, which had previously guided the Corps’ decisions.

The research found that the 2020 rule deregulated 690,000 miles of waterways, more than all the waterways in California, Florida, Illinois, New York, Ohio, Pennsylvania and Texas combined. Wetlands deregulated under the 2020 rule generated more than $250 billion in flood prevention benefits for nearby buildings, according to the study.

“This game of regulatory ping-pong is having staggering effects on environmental protection,” said author Simon Greenhill, a Ph.D. candidate at UC Berkeley.

The study estimates that the model’s predictions could save regulators and developers more than $1 billion per year in permitting costs by providing immediate estimates of how likely a site is to be regulated, rather than to wait months in an uncertain authorization process.

After the data from this study, the 2023 Biden White House rule expanded the jurisdiction of the Clean Water Act and the 2023 Supreme Court Sackett decision subsequently contracted it. Once Sackett is fully implemented, this machine learning methodology will be able to clarify its scope.