U.S. electricity end-use efficiency: policy innovation and potential assessment

Electric end-use efficiency is attracting more and more attentions, but it remains unclear what factors are driving state policy innovations to improve energy efficiency. Controversy also exists over the effectiveness of energy efficiency programs. Several critical problems are facing the policymakers: what factors drive the states taking distinct strategies in policy innovation? Have state policies being able to improve energy efficiency in the past? And, will state policies remain relevant to future efficiency improvements? This dissertation tries to answer these important questions and assumes that policy innovation is relevant to energy efficiency. It first explores the factors that influence the adoption of energy efficiency policies using Internal Determinants models. Results suggest that internal state factors affect policy innovation, including state socioeconomic factors, state fiscal capacity, ideology, and constituent pressure. Policy innovations are found to be correlated with each other. This dissertation also evaluates the impact of policy innovation on energy efficiency by decomposing electricity productivity into activity, structure, and efficiency effects. The findings suggest that financial incentives and building codes have significant impacts on state electricity productivity. Other regulations tend to have mixed effects. In addition, an estimation of the achievable potential of energy efficiency suggests that policies will cost-effectively drive significant electricity savings in the future. Overall, this dissertation offers an in-depth diagnosis of the relationship between policy innovation and energy efficiency. It provides a rigorous statistical analysis covering the most important energy efficiency policies. It represents the first attempt to evaluate policy impact by decomposing electricity productivity. However, the statistical models and energy models are subject to limitations and future research is needed to improve the models.Ph.D