Dr. C. Anna Spurlock is a Research Scientist and a Deputy Department Head in the Sustainable Energy and Environmental Systems Department at Berkeley Lab, where she has worked for almost 10 years. She is an environmental and behavioral economist by training, with a PhD from the Agricultural and Resource Economics Department at UC Berkeley.

In May 2022 Dr. Spurlock completed a one-year Detail as a Justice40 Fellow at the U.S. Department of Energy (DOE). In addition to working on the U.S. Department of Energy Justice40 implementation plan, as part of this role she also served as the representative for the DOE on both the Technical Working Group for Discounting, Equity and Risk Aversion in support of the Interagency Working Group on Social Cost of Greenhouse Gases, and the Distributional Analysis Subgroup in the Office of Management and Budget process for Modernizing Regulatory Review.

Dr. Spurlock has a leadership role in the Berkeley Lab Sustainable Transportation Initiative and is Principal Investigator (PI) for a number of large-scale transportation modeling projects including: the SMART Mobility Consortium work being conducted at Berkeley Lab for the DOE Vehicle Technologies Office Energy Efficient Mobility Systems (EEMS) program; the Geo-Economic Multi-Modal Systems (GEMS) Model being developed for the Federal Highway Administration Office of Transportation Policy Studies; and the BILD-AQ tool being developed for use by the DOE-DOT Joint Office to assess the air quality benefits of electric vehicle charging infrastructure deployment plans, and at what rate those benefits accrue to disadvantaged communities in the context of Justice40. Dr. Spurlock also leads the Economics Research sub-team within the Energy Efficiency Standards Group. This team advises and supports the minimum efficiency standards program, and conducts primary research on the market impacts of minimum efficiency standards policy. In addition to transportation and energy efficiency regulation, Dr. Spurlock has also conducted research using advanced data science and machine learning coupled with rigorous statistical and econometric techniques to generate insights from high resolution smart meters data regarding consumer behavior and decision-making in the context of residential time-based pricing and demand response programs.