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 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 Energy & Environment 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. Past and ongoing projects include: a field test with the U.S. Environmental Protection Agency that has shown machine learning can be used to significantly increase detection of rule violations at much lower cost than current monitoring techniques; and an experiment with the city of Fresno, California that has demonstrated the city can improve water conservation through fines and detailed metering to detect improper water usage.