MATCHING A STATE’S RENEWABLE ENERGY INCENTIVES WITH THE RISK/RETURN PROFILE OF THE RENEWABLE ENERGY INVESTOR
Martha J. Goodell, The Nelson Institute, University of Wisconsin, Madison, WI, (312) 961-8760,
Overview
Governments have long been engaged in creating policy mechanisms to alter the impact that energy demands have on our environment. Although initially command-and-control based, there is a growing understanding of how businesses and markets can be incentivized to influence substantial change. Through trial-and-error, quantitative and qualitative research, and policy innovation, governments are improving the efficacy of policy initiatives while managing the costs of implementation (Lund 2007).
Where energy policy was typically the domain of federal policy, states in the U.S. have been increasingly passing laws meant to trigger more ambitious change. This is especially true with respect to the states’ efforts to increase renewable energy generation though the use of Renewable Portfolio Standards (RPS) (Rabe 2004). The RPS is intended to stimulate investment in renewable energy generating capacity by mandating renewable energy generation targets at the utility level. The RPS works in concert with a states’ regulatory environment to gradually change the landscape of energy generation and meet the legislated target percentages.
Studies have been prepared over the years evaluating the efficacy of regulations that intend to incentivize renewable energy generation. The assessments vary substantially in the methodology used to evaluate the standards, and much debate still exists as to which regulations are effective. Yet, few studies look at how the combination of a state’s RPS and it’s regulatory environment meets the specific needs of the intended audience - the financing and development entities investing in the renewable energy projects.
Institutional investors fund utility-scale projects that meet their return objectives taking into consideration the amount of risk they are willing to absorb. This balance between risk and return is a basic tenet of Modern Portfolio Theory (Markowitz 1999). The risk/return of a renewable energy project is driven by state policies, federal incentives, and the economics of the project. Therefore, how a state’s policies are written and implemented directly affects the amount of investment in renewable energy projects. This combination of state policy and regulatory environment can be considered its Sphere of Influence (SOI) (left side of Figure 1).
To determine how a state’s SOI influences the funding of renewable energy projects, the components of an SOI must be evaluated for their impact on an investment project’s economic risk and profitability. The understanding of the SOI’s overall risk/return impact can then be compared to a state’s actual observed investments in renewable energy. Not only is this link invaluable to understanding the efficacy of a state’s SOI, an understanding of successful policies is a precursor to the diffusion of renewable energy technologies (figure 1) (Dinica 2006).
Figure 1
Methodology
With the goal of understanding the broader context of the “sustained diffusion of renewable energy”, Valentina Dinica developed a framework and process by which she evaluated three European (feed-in-tariff based) renewable energy support systems (Dinica 2003). Her framework and process for evaluating a support system provides a working model that is specific and comprehensive, yet flexible enough to support the analysis of different policies (such as an RPS).
My research uses Dinica’s framework to evaluate the state SOIs for Illinois, Texas, and California, while adjustments are made to reflect differences between a U.S. and European based energy infrastructure. The research takes qualitative issues and policy components and translates them into a risk/return matrix to allow for a quantitative comparison. For example, Illinois’ Renewable Energy Resources Fund (IRERF - established by the RPS) is seen, by an investor, as a low risk, modest-to-high return component of the state’s SOI. The fund could attract large-scale investors by ensuring a committed resource to purchase renewable energy. My research populates the matrix in Figure 1 with a placeholder noting The Fund’s existence. Since the research includes an analysis of Illinois’ SOI including the regulatory environment, an additional (high risk, low return) placeholder is entered because the governor of Illinois raided the IRERF in its first year.
In addition to tracking the components of a state’s SOI for each year since passage of the RPS, the amount of investment in renewable energy is tracked across time. The populated matrix is compared to the amount of investment in renewable energy for that state given a time lag (given the time frames imbedded in policy components and the long time frames needed for large-scale utility projects). Finally, to ensure the least amount of subjectivity in the research, interviews with decision-makers within large-scale renewable energy developers are part of the analysis.
Results
The results will reveal when a state is able to influence renewable energy investment through its SOI by meeting the risk/return needs of the renewable energy investor. There is a policy aspect to the results in that a correlation between multiple high-return/low-risk policy components and a corresponding increase in investment funds draws a causal link between a state’s SOI and its impact on renewable energy investment. There is also a time link that will be revealed when looking at changes in investment funds across the time-line of policy establishment and amendments.
Conclusions
The results will be valuable in understanding the influence each state has had in increasing investment in renewable energy. The results will also highlight policy components that could be replicated and amended to other state’s RPS or regulatory environment. States that currently don’t have a mandatory RPS will also be able to use the results to more effectively establish or supplement their own renewable energy incentive policy. Conclusions will also include where the researcher’s subjectivity might have affected the analysis. In order to assign a qualitative policy component to a quantitative location on the risk/return matrix some assumptions are made as to how the components of the SOI are analyzed.
References
Dinica, V. (2003). Sustained Diffusion of Renewable Energy. Enschede, The Netherlands, Twente University Press.
Dinica, V. (2006). "Support systems for the diffusion of renewable energy technologies—an investor perspective." Energy Policy 34(4): 461-480.
Lund, P. D. (2007). "Effectiveness of policy measures in transforming the energy system." Energy Policy 35(1): 627-639.
Markowitz, H. M. (1999). "The Early History of Portfolio Theory: 1600-1960." Financial Analysts Journal 55(4).
Rabe, B. (2004). Statehouse and Greenhouse: The Emerging Politics of American Climate Change Policy, Brookings Institute Press.