Degree Year

2012

Document Type

Thesis - Open Access

Degree Name

Bachelor of Arts

Department

Environmental Studies

Advisor(s)

Jordan Suter

Committee Member(s)

Harlan Wilson
John Petersen

Keywords

Ohio, Alternative Energy Portfolio Standard, Renewable Portfolio Standard, National Renewable Electricity Standard, Renewable energy, Unemployment

Abstract

I performed quantitative analyses and qualitative interpretation of energy policy data, energy production and consumption data, and political data. I collected data on state Renewable Portfolio Standards from the Database for State Incentives for Renewable Energy (DSIRE), energy production and consumption data for the 50 states and Washington D.C. from the Energy Information Agency (EIA), and 1992 presidential election data from the internet. I identify relationships that exist between these different types of variables, and where Ohio fits in the national context of existing energy patterns and policies. There are several conclusions found in the literature that are independently tested with the data I have collected. I hypothesize that enactment of Renewable Portfolio Standard (RPS) policies and geographic location in the United States are not robust indicators of the proportion of energy generation in states that comes from renewable sources, and that the strength of RPS policies is not based upon location (Carley 2009). Furthermore, I predict that states that are politically left leaning have larger proportions of their energy generation coming from renewable sources and have stronger RPS policies (Carley 2009). Finally, I postulate that Ohio's energy policy will be weaker relative to some policies based upon descriptive statistics of the RPS policies. Tests utilized include correlations, T-tests, and multiple linear regressions for geographic variables.

I also performed a spatial analysis of renewable energy potential and unemployment rates in the state of Ohio. I found National Renewable Energy Laboratory maps of average wind speed, solar radiation, biomass yield, and a Bureau of Labor Statistics map of unemployment rates at the county level. I calculated correlation coefficients between unemployment rate and renewable resource abundance according to a 24-section grid I overlaid on the state. No positive statistically significant results occurred, with the highest unemployment in the Southeast and greatest wind potential in the Northwest. There was insufficient variation of solar radiation across the state to perform a meaningful correlation.

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