Analyzing policy support instruments and regulatory risk factors for wind energy deployment—A developers' perspective
Highlights
► Paper suggests conjoint analysis as scenario tool for estimating potential effects of specific policy measures. ► It provides a quantitative, empirical dataset of 119 onshore wind energy developers' preferences. ► Results suggest that the aspects “Legal security“ and “Remuneration“ are important attributes. ► Cluster analyses yields slightly different preferences for developers from EU and US.
Introduction
Although the overall share of renewable energy in global electric power supply is still low, there is significant growth in some subsectors. A relevant indicator for this trend is the investment flow in new power stations. In 2008, global power generation investment in renewable power technologies exceeded investment in fossil-fueled technologies for the first time (Hohler et al., 2009). Among the various renewable energy sources, wind energy is one of the most prominent: in 2009, it accounted for 64% in the EU and 90% in the US of all new renewable generating capacity and for 39% of all new US and EU electric generation capacity (Bloem et al., 2010, EWEA, 2010a, Wiser and Bolinger, 2010). While the cost of wind energy has come close to grid parity in locations with consistently strong winds, its growth has been, and still is, largely driven by policy incentives.
Among the most prominent instruments to support wind energy and other renewable energy technologies are those that subsidize revenues from electricity sales, for instance feed-in tariffs and renewable obligation certificates. However, attractive return levels alone are not sufficient to effectively drive the diffusion of wind energy. The OECD and the IEA find in a report on the wind energy policies effectiveness in OECD and BRICS1 countries (OECD/IEA, 2008) that “[b]eyond some minimum threshold level, higher remuneration levels do not necessarily lead to greater levels of policy effectiveness” (OECD/IEA, 2008, p. 17). They argue that high non-economic barriers are one reason for this result. Therefore, financial support schemes need to be complemented by policy measures that address non-economic barriers to deployment (OECD/IEA, 2008, EWEA, 2010b, Valentine, 2010).
The question is which deployment barriers are most relevant and how policies can help to remove them. According to the IPCC, “[b]arrier removal includes correcting market failures directly or reducing the transactions costs in the public and private sectors by, for example, improving institutional capacity, reducing risk and uncertainty, facilitating market transactions, and enforcing regulatory policies” (IPCC-WGIII, 2007, p. 77). A substantial body of literature has identified a broad variety of such barriers and development risks for wind energy. They include administrative hurdles such as lengthy, ill-structured authorization and permitting procedures (OECD/IEA, 2008, EWEA, 2010b); nontransparent and costly procedures for grid connection (RETD, 2006, Swider et al., 2008, EWEA, 2010b); strict environmental regulations (EWEA, 2010b); support policy instability with sudden policy changes and stop-and-go situations (Wiser and Pickle, 1998, Meyer, 2007, Barradale, 2010); and lack of social acceptance (Jobert et al., 2007, Nadaï, 2007, Wüstenhagen et al., 2007).
To understand how these factors influence decision making on a company level and how the resulting risks for deployment can best be mitigated, it is useful to study them from an investors' perspective. Such a perspective was adopted by academics as early as the nineties. Wiser et al. (1997) identify perceived resource and technology risks and high support policy risk as the main hurdles that renewable energy project developers face in obtaining financing. Langniss (1996) categorized different types of investors and recognized that the type of support and taxation scheme has implications on which type of investor will be attracted. Dinica (2006) proposes to adopt an investor perspective and shows conceptually how developers evaluate the ratio of risks and profitability associated with renewable energy projects. Bürer and Wüstenhagen (2009) emphasize that the understanding of investor perceptions may provide policy-makers with the opportunity to leverage private investment to reach renewable energy targets. All these studies provide valuable insights for a better understanding of the drivers of development and diffusion of new wind energy capacity. The applied methodologies have been either conceptual analyses or qualitative case studies. The maturing of markets with an ever growing number of companies engaged in wind energy development now allows this paper to employ a large scale quantitative empirical analysis. The paper largely builds on the findings of existing literature and intends to add to them in three ways. First, it employs a relatively novel methodology to energy policy research: multivariate adaptive choice-based conjoint analysis. The paper suggests that conjoint analysis could be a helpful scenario tool for estimating potential effects of specific policy measures on wind energy project developers' investment behavior. Second, the paper provides a quantitative, empirical dataset of developers' preferences. The preference data quantifies how much value specific measures provide to developers. Third, it gives some indication of how these findings differ among different groups of developers and across different regions within the two most mature wind energy markets: the EU and the US.
The paper is structured as follows. The next section specifies the method by introducing conjoint analysis and explaining the experimental design as well as the data analysis approach. Section 3 describes the study sample. Section 4 presents and discusses the results. It includes a breakdown of developers' preferences for the studied attributes and levels, simulations of the effects of policy measures on developers' preferences in specific investment environments and an analysis of preference differences between different subgroups of project developers. Finally, section five concludes by highlighting main findings, outlining policy recommendations, indicating the limitations of this study and making suggestions for further research.
Section snippets
Conjoint analysis
Conjoint analysis methods are based on work done in the sixties by the mathematical psychologists and statisticians Luce and Turkey (1964) and were introduced into marketing research in the early 1970s (Green and Srinivasan, 1990, Orme, 2007b). The key characteristic of conjoint analysis is that respondents evaluate product profiles composed of multiple conjoined elements (attributes). The main objective is to mimic real decision-making processes as closely as possible. The respondents'
Data collection and sample
The wind energy project developers included in this study are experts who work or have worked for companies engaged in the project development business. These include
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highly specialized, typically small firms whose exclusive business focus is the development of renewable energy projects. Due to the lack of capital or financing, they often sell the project during or after the development process;
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vertically integrated, typically larger firms, who plan, build, own, and operate renewable energy
Results and discussion
The root likelihood (RLH) indicates the goodness of fit of the HB model. In our model, it amounts to 0.751 indicating a good fit. The RLH for each individual is the geometric mean of the probabilities of the different choices made by the individual. The probabilities are calculated using the posterior means of an individual's part-worth utilities in the MNL model (Wonder et al., 2008). The RLH of the model is the arithmetic average of all the individual RLH values (the upper level normal
Summary and implications for policy-makers and project developers
The objective of this paper was to examine project developers' policy preferences to enhance the deployment of wind energy. This study builds on the findings of existing literature and substantiates them by employing a novel research methodology, providing an empirical dataset of wind developers' preferences and making the impact of various policy factors on developers' utility measureable. It also suggests that conjoint analysis could be a helpful tool for the estimation of potential effects
Acknowledgments
The authors would like to thank the anonymous reviewers for their insightful and constructive comments.
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