With a clear political influence spearheading the fight against climate change, this paper investigates renewable energy policies in U.S. states from 2000 to 2018, utilizing panel data and OLS regression analysis to pinpoint the most effective renewable energy policies. Policy data in each state comes from DSIRE, a database of state incentives for renewables & efficiency. Specific policies examined in this paper include Sales Tax Incentives, Grant Programs, Loan Programs, Renewable Portfolio Standards, Energy Standards for Public Buildings, Building Energy Codes, and Solar/Wind Access Policies. Controls for CO2 emission analysis include total state GDP, transportation GDP, manufacturing GDP, utilities GDP, number of registered vehicles, and population. All GDP controls are lagged to avoid endogeneity with CO2 emissions. Employment analysis includes sex and race as controls. Both dependent variables are run with state and year fixed effects. Contrary to existing literature, results vary depending upon the high-level subsamples in the analysis: High Emission Group, Low Emission Group, High Population Group, Low Population Group, Red States, and Blue States. Most policies examined have opposite effects in their subsample counterparts. For example, an RPS Policy increased emissions in Red States by 2.1% but decreased emissions by 3.4% in Blue States. However, a Grant or Loan Policy has positive impacts on employment across all subsamples. Overall, the results discussed in this paper give insight into how popular policies can be effective when implemented in the right situation. These findings indicate that policy-makers should make decisions on a case-by-case basis to reach their desired goals.