To protect the environment and ensure access to reliable and affordable energy, policymakers need tested approaches that work. The University of Chicago Energy & Environment Lab (E&E Lab) partners with civic and community leaders to identify, rigorously evaluate, and help scale programs and policies. A shared effort of the University of Chicago Urban Labs and EPIC, the E&E Lab uses natural experiments, randomized controlled trials, behavioral economics, and machine learning to generate an evidence base for what works and what doesn’t in energy and environmental policy. In addition, E&E Lab research develops fresh insights into the most important tools that policymakers have to affect environment and energy outcomes: behavior, pricing, and regulation.

Current environmental inspection targeting and pollutant monitoring systems often rely on methods that are decades old and not data driven. The E&E Lab partnered with the U.S. Environmental Protection Agency to develop machine learning models to identifying facilities most likely to be in violation of environmental regulations at a much lower cost than current monitoring techniques. The Lab is developing other machine learning models to   improve inspection targeting for facilities regulated under other federal laws such as the Clean Water Act and the Clean Air Act.

Another project piloted the widespread use of smart meters in the city of Fresno, California to identify its impact on the enforcement of water restrictions. The pilot found that the city can improve water conservation through fines and detailed metering to detect improper water usage, showcasing how testing programs and policies before scaling them can better enhance public wellbeing, address concerns, and find cost-effective solutions.

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