Master of Science, The Ohio State University, 2022, Environmental Science
US policymakers at the local, state, and federal levels are considering policy mechanisms to promote renewable energy development and ensure a just transition to a clean energy infrastructure. These policies have the potential to both reduce greenhouse gas emissions and create jobs; however, the number of actual jobs created from these policy instruments is often disputed. In this study, I evaluate the direct non-hydroelectric renewable energy employment impacts from eight types of renewable energy policies: (1) subsidy programs; (2) corporate, (3) personal, and (4) other tax incentives; (5) performance-based incentives; (6) industry recruitment/support; (7) renewable portfolio standards; and (8) net metering. Using data from 3,035 US counties from 2001 to 2017, I employ Fixed Effects (FE) regression models controlling for calculated propensity scores, which address the potential selection bias in the model. The results indicate that three of the policy instruments (renewable portfolio standards, industry recruitment/support, and performance-based incentives) have positive and statistically significant impacts on direct non-hydro renewable energy employment at the county level. The policy type with the greatest positive impact was industry recruitment/support. Counties with industry recruitment/support policies present, on average, had 82 more direct non-hydro renewable energy jobs than counties that did not have industry recruitment/support present, holding all else constant. Critically, the results show the importance of addressing selection bias in analyses of renewable energy policy outcomes, as the models run without controlling for propensity scores led to an overestimation of employment impacts.
Committee: Dr. Daniel Gingerich (Advisor); Dr. Jeff Bielicki (Committee Member); Dr. Rob Greenbaum (Committee Member)
Subjects: Energy; Public Policy; Sustainability