Researching state energy policies one can look back as far as the 1960s when solar-voltaic cells were taking small technological steps in research but were largely seen as gimmicks. Despite this, only within the last 20 years has solar energy become a realistic replacement to nonrenewable sources such as natural gas. Research from 1984, has laid the foundation for early state policy conditions (Sawyer, S 1982). Sawyer notes that state energy plans started to gain popularity over the federal models of the 1970s because they can be finely tuned to a specific region.
Although mainly researching problems of oil shortage, he also notes that solar energy growth is best done at the state level because the federal government’s actions are too broad for such an economically diverse country. This is mirrored in recent research such as from the American Bar Association (Williamson J., & Sayer M. 2012 and Sawyer, S 1982).
One of the most influential sources on solar energy policy was written in Grenoble France (Menanteau P.
, Finon D. & Lamy M., 2003). Cited 782 times since Menanteau et al. compiled data from Western Europe to determine which types of national policy are best used to promote solar growth. They broke countries into two categories, those that pursued subsidies and those with a traditional free-market bidding system. Looking at the relationship of quantity to cost they determined that each model saw exponential cost reduction as quantity increased due to market changes and technological innovation. They concluded that subsidies contributed to a steeper cost-curve (as opposed to a non-intervention free market model) but cost the state significantly more.
The authors recommended pursuing cheaper options with similar effectiveness such as quantity-based Green Certificates (Menanteau P., Finon D. & Lamy M., 2003). This research is foundational because it conceived some of the earliest theoretical models. Similar is size to many European countries, this work acted as a foundation for how states can approach building their energy plans around solar energy. Other researchers have argued that the United States carries a degree of difficulty when attempting to enact feed-in tariffs due to the Federal Power Act of 1935 (Williamson J., & Sayer M. 2012). Additionally Western Europe has shown to implement solar technology at a faster pace per capita than the United States.
Individual States are seen as experimental testing grounds for federal energy policy, making them the primary stage for renewable portfolio standard, also known as RPS (Williamson J., & Sayer M. 2012). Researchers have written theoretical models on what plan each state should pursue in order to most efficiently transform antiquated limited and dirty energy sources to renewables (Jacobson et al. 2015). Showing recent progress in each state paints a picture on which regions are lagging in solar energy policy. 13 states (AK, ID, WY, NE, AR, LA, MS, AL, GA, FL, TN, KY, WV) have no plan or policy for statewide renewable energy policy. Additionally, 8 states (ND, SD, UT, KS, OK, IN, VA, SC) have a voluntary program or guideline, meaning the policies carry little weight in enacting or coercing adoption. The plans also noted that wind is the current favorite in renewable production over solar in terms of volume (Jacobson et al. 2015).
These models take into account recent state policy, items typically passed after the year 2000, and look at both moderate plans and those that aim at complete reliance on renewable energy by 2050. The Department of Energy’s SunShot Initiative also looked at different models of RPS for states, doing so specifically with solar (The United States Department of Energy. 2014). Using a multitude of factors such as sunlight hours, cloud cover, typography and technological improvements, they were able to calculate costs based on specific regions of the United States. This data is valuable for a state drafting a reasonable goal for solar energy that is achievable. Nationally the SunShot calculates that solar can account for 14% of U.S. electricity demand by 2030 and 27% by 2050 if pursued in the majority state.
Looking solely at RPS policy to see if it can effectively lower energy costs overall, a study found that more research is needed to fully understand results (Fischer C. 2010). States only started adding renewable energy to their policy in the 21st century and as stated before, many have yet to do so. The resulting cost and trustworthiness of these decisions have only started in recent years. The study also concluded that it is important to remember that economic cost is not the only goal achieved from RPS policy, but that ecological benefits must be considered even in economic research (Fischer C. 2010). For instance, “Annual U.S. electricity-sector carbon dioxide (CO2) emissions are projected to be significantly lower in the SunShot scenario than in the normal operation scenario: 8%, or 181 million metric tons (MMT), lower in 2030, and 28%, or 760 MMT, lower in 2050” (The United States Department of Energy. 2014).
The SunShot study has done the same types of cost analysis by taking cost, and subtracting revenue to determine how many days a solar plant must operate to become cost effective (The United States Department of Energy. 2014). These numbers can be used to argue that although costs are initially high, states can expect returns on long-term solar loans and that tax incentives do stimulate economic growth. Government is often needed when a technology will become cost effective in 20 years or more (Beck, F., & Mertinot, E. 2004). It also puts hard, reliable cost scenarios that are aggregated from dozens on sources (The United States Department of Energy. 2014). These provide the framework for a state energy budget. Additionally hundreds of sources are compiled to compare different types of solar technology (The United States Department of Energy. 2014). Without actually researching how goals are met and implementing knowledge, poorly designed state solar policy is likely to fail (Fischer, C et Al. 2012).
A 2017 study analyzed the cost models of states with an RPS by using more recent data (Wiser R., et al. 2017). Focusing on a timeline of 2015-2050 the cost-saving benefits of solar energy are predicted to eventually outpace the price of tax incentives and subsidies. Research from the last three years shows that rapidly increasing adoption paired with improvements in solar efficiency is exponentially lowering cost compared to that from merely five years ago (Fischlein M., & Smith T. 2013).
Many times the cost savings from solar energy is not factored into overall models. Fossil fuels are not guaranteed to stay at their current price rate. If cost spikes, solar will become much more competitive. Furthermore, the government saves on health costs associated with pollution; these cost benefits are already heavily considered in areas such as China (Beck, F., & Mertinot, E., 2004). As pollution cost rises in urban areas, projected solar prices decrease. Finally, because solar can be centralized and unrestricted from the distance typical with the traditional energy grid, remote operations are significantly cheaper (Wiser R., et al. 2017).
In general there are a plethora of cost analysis models but even small changes in cost per year can seriously shift the budget of a state planning a 10 or 20 year RPS policy. One can estimate the future cost of solar voltaic cells but only to a certain degree. Also, cost is subject to change based on the adoption rate. The Sunshot initiative takes into account adoption rates for how a model will perform economically (The United States Department of Energy. 2014). Compiling a range of projections from different researchers and aggregating the data, a direct relationship between cost and adoption has been formed (The United States Department of Energy. 2014). Region plays the largest role in larger states with different patterns (Fischlein M., & Smith T., 2013). When developing RPS policies states should take these numbers into account and develop a plan that promotes the greatest adoption rate (Sawyer, S. 1984).
Each state offers much different challenges and a range of outcomes are expected. Currently costs are higher in states implementing renewable portfolio standards. Most policy options have goals of 2030 and 2050, with solar production increasing exponentially by year. Ultimately it is too early to tell if states have implemented effective policy and its effect on statewide energy cost. Overall solar production appears to be growing but according to many set agendas the most aggressive periods of adoption are projected over the next 15 years when solar becomes more efficient. One can estimate the future cost of solar voltaic cells but only to a certain degree; 2030 will be an interesting year because the majority of RPS plans will have been completed.
After doing considerable reading on past and current research, I have noticed a gap on the knowledge of outcomes for RPS policies. Studies tend to focus on theoretical models while doing little to address current progress in states that have implemented plans. This is partially due to how recent solar technologies have increased efficiency at an exponential rate, changing their presence in the market (Jacobson M. et al. 2015). States are implementing policy quickly and the implications are becoming clear only in recent years.
My research will compare a state with a moderate solar RPS policy and a state with an aggressive solar RPS policy to determine if strong incentives work to lower the cost of solar per kilowatt-hour. This measure is a method to quantitatively determine if policy is effective and if theoretical models are accurate. Another gap appears to be in the politics of RPS policy and if solar has enough public approval in the United States to become mainstream. There are indicators that state energy plans could be meant to appease certain parts of a constituency and will be overturned or ignored over the next 10 years when adoption is meant to increase (Wiser R., et al 2017). This is also an important topic, especially in an environment where earth sciences have been given a partisan spin.