| Over the past two decades, environmental concern has crept to the forefront of the world policy agenda. This concern has manifested itself differently throughout the world. In the United States, this has come in the form of Renewable Portfolio Standards (RPS) which have become one of the primary policy tools which states use to encourage renewable energy generation. The advent of RPS has spurred intense debate at a federal and state level, centering on the economic merits of promoting renewable energy generation. Detractors argue that RPS will raise electricity rates, since generation from renewable sources is typically costlier than energy generated from fossil fuels. At this point, evidence to the relationship between RPS on electricity prices remains unclear.;Researchers have attempted to understand this relationship through a variety of means. The most common being regression based models, which utilize readily available United States Energy Information Agency (US EIA) data, and have uncovered a number of important independent variables which are incorporated into the model in this study. Examples include personal income, state population, and deregulation of an energy market. In addition to empirical studies, the National Renewable Energy Laboratory (NREL) has created complex mathematical models which generate scenario projections based on a number of assumptions. While interesting, these are forward looking tools and as such have not yielded a tremendous amount of insight into the underlying policy mechanics of RPS.;A challenge of addressing this topic which is worth noting is that much of the research available which analyzes the merits of RPS caters to distinct political or private sector agendas. The research gathered for this study is comprehensive, and attempts to avoid studies with any clear political, ideological, or financial motivation. Using the insights from previous researchers this study develops a rigorous fixed effects regression model to understand the relationship between RPS and energy markets. This study utilizes state level panel data from 2000 to 2011 to examine this relationship. The model controls for several factors including the existence of state level RPS, the amount of electricity generation from renewable and non-renewable sources, fuel price mix, and other macroeconomic and demographic indicators. The study compares and contrasts several regressions to illuminate important relationships between RPS design and residential energy prices. The findings of this study have the potential to have far reaching implications for the future of RPS design, and RPS policy.;This paper finds that RPS and more specifically the way a RPS is formulated is related to increased energy prices. Retail energy prices are particularly strongly related to variables having to do with compliance with a specific RPS, while Industrial energy prices are more strongly related to the magnitude of RPS goals. Both of these findings are important and they accentuate the fact that different energy markets are influenced by different drivers. |
