This study applies economic modeling to assess the importance of variables thought to benefit or hinder the clean energy transition in all 50 states and one district in the United States. The study will discuss existing theories and literature behind the drivers of renewable energy generation. Linear and logit regression techniques are used to evaluate four different models. The results find that an increase in state population, renewable energy incentives, and average peak sun hours increases renewable energy generation, while an increase in the amount of protected land and people registered as Democrats decrease renewable energy generation. They also find that an increase in state population, the amount of protected land, and people registered as Democrats increase per capita electric vehicle registrations. The study shows that any state can excel in the energy transition given beneficial geographical attributes, increased clean energy incentives, and political accord.