Abstract: In the first paper I delve into the current rift between the dissidents and supporters of the \"Ecological Kuznets Curve.\" First, by recreating a 2006 study done by Bagliani, Bravo, and Dalmazzone, using a cross-sectional analysis, there does not appear to be strong evidence of an EKC when using the Ecological Footprint as the independent variable. Then, continuing from the Bagliani et. al. analysis, a panel data set is formed and using panel estimation techniques, the evidence is resubmitted. I find that under the panel estimation there is no common EKC realization for a majority of the countries when comparing Ecological Footprint to Per Capita GDP. The second paper looks into the relationship between the diffusion of renewable energy production and the political economy. More specifically, I look at how the different political parties in power effect the penetration of renewable energy into the existing market. There are two novel aspects to the paper: the first being the use of U.S. data to test the analytical model proposed by Johnson and Jacobsson (2002), and second, in order to test the hypothesis put forth by the analytical model, I will map this analytical model into an empirically testable equation. As far as the author\'s knowledge goes, this is the first such analysis. The results show that having a Democratic majority in the Senate will increase both the level and the growth rate of renewable energy. There is also some weaker evidence of a feedback loop between the increasing number of supporting agencies and the spread of renewable energy use. The third paper explores two questions, \'Is the current energy portfolio in California efficient from a cost-risk point of view?\' and, \'What changes are needed to the energy portfolio in order for California to meet its proposed Renewable Portfolio Standards (RPS) requirements?\' After assessing the validity of the Mean-Variance Portfolio (MVP) technique when using illiquid assets, following papers such as Janson (2006) and Awerbuch (2008), I test plant-level data to find correlations between costs (returns), and also any correlations in the error terms due to unobserved shocks. I find that there are significant correlations between both of these factors. Following Krey (2006), I use a Seemingly Unrelated Regression Estimation (SURE) methodology to control for these unobserved shocks which gives a less biased estimate of the true cost risks involved in the energy portfolio. Using the results from the SURE methodology, a cost-risk nexus is constructed and efficient frontiers of energy portfolios are then created. In answering the proposed questions, the results show that relative to the actual energy portfolio used by California, the state could reduce both its portfolio risk and lower its expected portfolio costs by decreasing the use of fossil fuels in its energy production mix, with special attention being paid to coal and natural gas, and by increasing the levels of energy production from renewable resources focusing on geothermal, solar, and wind. With respect to the ability to meet the RPS set in place by California\'s legislature, a substantial increase in geothermal and wind generated power is needed in order to realize either the 20 or 33 percent thresholds set by the renewable portfolio standards for 2020.