BIO: Christopher Knittel is the William Barton Rogers Professor of Energy Economics in the Sloan School of Management and the Director of the Center for Energy and Environmental Policy Research at the Massachusetts Institute of Technology. He joined the faculty at MIT in 2011, having taught previously at UC Davis and Boston University. Professor Knittel received his B.A. in economics and political science from the California State University, Stanislaus in 1994 (summa cum laude), an M.A. in economics from UC Davis in 1996, and a Ph.D. in economics from UC Berkeley in 1999. His research focuses on environmental economics, industrial organization, and applied econometrics. He is a Research Associate at the National Bureau of Economic Research in the Productivity, Industrial Organization, and Energy and Environmental Economics groups. Professor Knittel is an associate editor of The American Economic Journal — Economic Policy, The Journal of Industrial Economics, Journal of Transportation Economics and Policy, and Journal of Energy Markets. His research has appeared in The American Economic Review, The American Economic Journal, The Review of Economics and Statistics, The Journal of Industrial Economics, The Energy Journal and other academic journals.
Fiona Burlig, UC Berkeley; Christopher Knittel, MIT; David Rapson, UC Davis; Mar Reguant, Northwestern University; and Catherine Wolfram, UC Berkeley
We study the impacts of energy efficiency investments at public K-12 schools in California. Our empirical setting offers two advantages. First, schools provide a rare laboratory to analyze energy efficiency as there are thousands of them, all pursuing very similar economic activities but exposed to different outdoor temperatures and with different existing infrastructures. Second, we make use of high frequency metering data–electricity consumption every fifteen minutes–to develop several approaches to estimating counterfactual energy consumption absent the energy efficiency investments. In particular, we use difference-in-difference approaches with rich sets of fixed effects. We also implement a novel machine-learning approach to predict counterfactual energy consumption at treated schools, and validate the approach with non-treated schools. Using both approaches, we find that the energy efficiency projects in our sample reduce electricity consumption between 2 to 4% on average, which can result in substantial savings to schools. We compare the estimates of the actual energy savings generated by measures to ex ante engineering estimates of savings, and, in ongoing work, compare the costs of installing measures to our estimates of the value of energy saved to come up with measure-specific cost-benefit metrics.