Risks and cost of capital for onshore wind energy investments in EU countries

Abstract

The national binding targets, set for renewable energy deployment at European Union (EU), call for extended clean energy investments. Renewable energy projects require high up-front expenditures including, in many cases, considerable financing costs. The main objective of this paper is to elaborate and apply a methodology that allows assessing the most important risk categories related to renewable energy investments. The cornerstone of this approach is the weighted average cost of capital, which has been extracted for new onshore wind projects in EU-28 member states based on diverse methods e.g. Capital Asset Pricing Model. Moreover, to validate the model results, a series of interviews with renewable energy project developers and financers across the EU has been conducted. The results show that, following the country risk, policy-related risks exert the highest impact on the cost of capital. Moreover, there are significant discrepancies between different geographical regions and market deployment levels. To support policy makers' decision on effective risk-reducing policy designs, the assessment could also be extended to other renewable energy technologies.

Keywords

Investment risks cost of capital WACC renewable energy onshore wind EU

Introduction

In the context of the Directive 2009/28/EC, a common policy framework for the promotion of renewable energy (RE) has been established and mandatory national RE targets have been set for 2020.¹ Within this framework, considerable investments in RE projects are necessary to achieve these ambitious targets and policies play a critical role in boosting these investments.

This paper aims at developing investment risk profiles for onshore wind investments applicable to all European Union (EU) member states. The risk profiles are beneficial to both policy makers and private investors; getting insight into policy-induced risks enables policy makers to adopt the appropriate policy actions, thus reducing risks and costs related to RE investments, while creditors or debtors are effectively informed about investment risks of RE projects in EU countries. These risks, as highlighted by Doukas et al.,² have to be addressed through the adoption of an effective and sustainable energy policy.

The weighted average cost of capital (WACC) is considered an appropriate tool for assessing investment risks and quantifying the respective cost of capital. Based on the approach stated in the literature,^3,4 the WACC constitutes an effective indicator for quantifying the overall cost of capital and, therefore, is used as an adequate measure for the selection of the appropriate discount rate utilized in the financial evaluation of RE investments.

For completeness of this analysis, a series of interviews with equity providers, project developers, bankers, energy consultants and analysts, and representatives of non-governmental organizations (NGOs) in the energy sector has been conducted. The interview process is used to validate the model outcomes regarding the identification and assessment of risks and the quantification of the WACC for onshore wind energy investments in EU countries.

This paper encompasses an Introduction, which is followed by five sections. Section Literature review presents a literature review regarding the risk categories and the cost of capital of RE investments. Section Methodology is devoted to the comprehensive analysis of the proposed methodology, presenting the applied model for the quantification of the cost of capital, along with the procedure followed during the interviews, for onshore wind investments across EU countries. Section Results presents the outcomes extracted from the proposed methodological framework. Section Discussion includes detailed discussion and comparisons between the different RE risk elements and the values of the cost of capital observed in several EU countries. Finally, Section Conclusions provides conclusions and suggests fields for further research.

Literature review

Risks of RE investments

Project risk is an uncertain event or condition that, if it occurs, has an effect on at least one project objective.⁵ Several studies and scientific papers have addressed risks that are related to investments in the RE sector.^6–10

In a study assessing the impacts of policies on RE deployment,¹¹ a description of the potential risks regarding RE in Europe is provided from the perception of both producers and buyers of green energy. Justice⁸ has identified four different risk categories, namely country and financial, policy and regulatory, technical and project-specific, and market-related risks.

In the report of Waissbein et al.,⁶ an assessment of large-scale onshore wind projects in developing countries, including Mongolia, South Africa, Kenya and Panama, is incorporated providing a detailed risk categorization, including also social acceptance and grid access risks, among others. This report, as well as DB Climate Change Advisors,¹² elaborates a detailed illustration of the different risk categories. The study of IRENA¹³ identifies several risk categories and notes that higher risks result in higher expected rate of return and, thus, greater cost of capital for the specific investments. A detailed presentation of the risk categories selected in current methodological approach is incorporated in Section Model.

Cost of capital

The cost of capital constitutes a critical component in the investment decision making and the company's valuation process by investors.¹⁴ The cost of capital is considered as the expenses and interests to be paid in order to raise all necessary funds for the financing of potential investments and, thus, represents the internal rate of return that makes equal the current stock price to the present value of the expected future cash flow.¹⁵ In this context, it represents the opportunity cost or, equivalently, the specific rate of return that a capital supplier requires as compensation for investing capital.¹⁴

WACC

The WACC is utilized in order to measure the mean cost of capital of investments.¹⁶ In general, the total capital of a company or a project may consist of both debt and equity capital. The WACC is the summation of the cost of every capital element multiplied by its proportional share.¹⁷ The following mathematical formula presents the WACC indicator¹⁸

WACC = \frac{E}{E + D} * CoE + \frac{D}{E + D} * CoD * (1 - CTR)

(1)

where

WACC: weighted average cost of capital

CoE: cost of equity

E: market value of equity

CoD: cost of debt

D: market value of debt

CTR: corporate tax rate

In order to calculate the WACC, the required return by both equity and debt providers is needed as well as the ratio among the respective capital shares. These elements are discussed in the following subsections.

According to IRENA,¹³ the WACC for RE investments is assumed to be 7.5% for Organisation for Economic Co-operation and Development (OECD) countries and China, and 10% for the other countries. Nevertheless, the use of a unique mean WACC value for quite different renewable technologies in several countries is considered not adequate in terms of risk portfolios.

Cost of equity

The cost of equity illustrates the minimum required rate of return that equity investors expect from their investments. It also constitutes an adequate index for quantifying the level of risk of specific investment alternatives. In particular, greater values of the cost of equity reflect a higher level of risk and, thus, investment decisions differ, as they depend on the different risk perception of several investors.

Based on the capital asset pricing model (CAPM), the cost of equity is calculated by using the following formula^19,20

CoE = RfR + Beta * MRP

(2)

where

CoE: cost of equity

RfR: risk-free rate

Beta: beta coefficient

MRP: market risk premium

The CAPM is considered the most accepted and widely utilized method in financial market decisions for the calculation of the cost of equity^21–27 despite the fact that members of the academic community have been critical of this methodology, as Donovan and Nuñez²⁸ and Da et al.²³ state.

Risk-free rate

The rate earned on riskless investments is considered a risk-free rate.²⁹ Regarding the quantification of the risk-free rate, alternative options are followed in the scientific literature. In particular, some of the most commonly used indices are the interest rate of US treasury zero coupon bonds or German government bonds as well as the London Interbank Offered Rate (LIBOR), the zero Swap (Libor) curve or the Overnight Indexed Swap (OIS). It is a common practice to link risk-free rates directly to government bond interest rates, as they offer the most appropriate observable proxy for a riskless asset.^22,30 (Current experiences have shown that government bonds cannot be considered always as risk-free rates e.g. Greece threat of insolvency. However, in the European context the German government bonds that have shown partly even negative rates during the Euro crisis could be considered as close to risk-free up to now.)

According to Deloitte,³¹ two different approaches are identified for the estimation of the cost of equity; the conditional and the unconditional one. Based on the conditional methodology, the country risk is included in the market risk premium and the risk-free rate constitutes the “real” risk-free rate (e.g. German government bond yield). On the contrary, the unconditional methodology assumes the country risk to be incorporated into the risk-free rate and, thus, the risk-free rate is represented by the interest rate of the national long-term government bonds.

Market risk premium

The Market risk premium is the return of the market portfolio in excess of the risk-free rate. There is no consensus on the quantification of the market risk premium, despite the fact that broad international literature exists on this topic. The two most utilized methodologies are survey-based approaches³² and estimation-based methods on historical data.^33,34

The most cited sources identify a wide spread in equity risk premium estimates,^35–37 due to variation in estimation of input parameters, mainly the risk-free rate, market portfolio, extent of period and frequency of estimation.

Beta

Beta constitutes a measure of risk arising from the exposure of an investment to the general market movements and expresses the sensitivity of an investment return compared with that of the entire market. Thus, beta is considered a measure of the co-movement of shares' returns with the market portfolio returns. Beta values higher than 1 indicate that the stock is more volatile than the market. In this context, the returns of a company are regressed on the returns of the market portfolio. The most influential parameters in the quantification of betas, through regression, are the frequency of data, the time period and the assumption for the market portfolio.

Statistical regression is considered the most widely applied way to estimate the CAPM beta of a share. According to the studies of Welch²⁴ and Graham and Harvey,²⁶ the CAPM has received great attention by finance professors and respondents as the 75% and 73.5% of the interviewed experts, respectively, recommend the use of this valuation model for corporate capital budgeting. Nevertheless, some weaknesses of the applied methodology cannot be ignored. One such weakness, particularly relevant to the case of power generation technologies, is that certain power technologies, such as offshore wind energy, may become more mature and less risky over time, whilst others, like nuclear power, may become riskier, due to special events (e.g. Fukushima incident), and, as a result, the historical values of expected return may not be suitable for the quantification of the beta coefficients. Nevertheless, onshore wind energy is considered a mature and less risky RE technology,³⁸ leading to the mitigation of this concern.

Cost of debt

Cost of debt is the total amount of interest paid by a firm or an entity in order to borrow capital. The debt providers generally require higher returns for financing more risky investments or companies, which, mostly, results in higher values of the cost of debt. The cost of debt can be quantified by summing a risk-free rate and a risk premium so as to incorporate the perceived risks.

The calculation of the cost of debt for the WACC indicator is implemented on an after-tax basis, as interest payments are generally tax-deductible expenses.

A range of different values for debt margin/cost of debt is observed in existing literature. The average cost of debt, at a pre-tax level, for the year 2011/2012 was 5.7% and 4.6% for the Eurozone and Switzerland, respectively, according to KPMG.³⁹ In this report, it is also stated that the average cost of debt applied to German companies that took part in the respective survey was 5.7%. In addition, the average cost of debt before corporate taxes is 4.7% for the case of “Energy & Natural Resources,” compared to the overall average of 5.6% for all examined sectors of activity. According to Deloitte,⁴⁰ a representative rate of 4.5–6% has been introduced, based on the installation plan and the specific project risk. Nevertheless, these rates cannot be used at nominal values as they mainly depend on the business cycle and the policies followed by central banks, such as the European Central Bank (ECB).

Debt-to-equity ratio

Capital structure refers to the amount of debt and equity that a company or a project is using for its funding. The shares of debt and equity capital depend on the level of the average debt-to-equity ratio for the relevant sector and on the firm's strategy.

Debt financing is considered a more aggressive strategy due to the potentiality of higher profits, but also increases the risk of bankruptcy, due to inability to service debt. In case of investments in capital intensive industries, the capital leverage is usually high. In addition, debt capital is considered less expensive than equity as it imposes lower risk to investors.

Literature indicates that the debt-to-equity structure in a RE project in the EU was equal to 80:20 during the period before the 2008 financial recession, for instance for the case of onshore wind projects considered in the study of Boccard⁴¹ and Mazars.⁴² Nevertheless, the share of debt capital showed a decrease to 70%, during the post-financial crisis period. According to Mazars,⁴² onshore wind energy projects in the United Kingdom, previously financed at a debt share of 80% or higher, had a slightly decreased debt financing ratio of approximately 75% in 2009 and 2012. Klessmann et al.³ also recorded that the capital structure has been altered after the start of the financial recession, from a ratio of 80:20 to 70:30.

According to the report of Schwabe et al.,⁴³ the debt-to-equity ratio for onshore wind energy investments in several countries of Europe for the year 2008 is presented in Table 1.

Table 1.

Debt-to-equity ratio by country in 2008.

Country	Debt-to-equity ratio
Denmark	80:20
Germany	70:30
Netherlands	80:20
Spain	80:20
Sweden	87:13
Switzerland	70:30

Source: Schwabe et al.⁴³

In Table 2, the debt-to-equity ratio is presented for the main RE technologies and for the third quarter of 2013 in Germany.

Table 2.

Debt-to-equity ratio for renewable energy (RE) technologies in Germany (3rd Quarter, 2013).

Technology	Debt-to-equity ratio
Onshore Wind	70:30
Offshore Wind	60:40
PV	80:20

Source: Fraunhofer ISE.⁴⁴

In addition, Knápek and Vašícek⁴⁵ assume a debt-to-equity ratio equal to 60:40 for a typical wind energy project in the Czech Republic. At the same level was the proportion of debt for onshore wind projects in Greece. Based on a reference of YPEKA,⁴⁶ debt capital represented 60% of the total funding of onshore wind projects in the country. According to EWEA,⁴⁷ the finance of RE projects via debt capital has strong correlation with the trend of existing macroeconomic factors and current liquidity limitations in the banking market, as these reduced banking activities have been the result of the financial crisis in EU.³⁹

Methodology

Model

For the purpose of the present analysis, a theoretical model has been created in order to provide an estimation of the relevant risks and the cost of capital for investments in the RE sector in each EU country. Aim of this methodology is to estimate the WACC values for onshore wind energy investments, investigate the effect of different risk elements on the respective cost of capital, in each EU country, and provide also a comparison among different EU countries. The proposed methodology has been applied for a typical large-scale onshore wind project in each of the EU-28 member states and validated data have been used, as necessary conditions for gaining reliable extracted outcomes.^48,49

In the context of the current study, potential risk elements that can influence future outcomes and, thus, the investors' decisions about whether or not to invest in RE projects are identified and categorized. Nine risk categories, covering a wide range of potential effects on investments, have been identified, namely: country risk, social acceptance, administrative, RE financing, technical and management, grid access, policy design, market design and regulatory, and sudden policy-change risks.

Similarly to the study of IRENA,¹³ three phases of RE projects development have been recognized, namely the planning, construction and operation phases. Social acceptance and administrative risks are present at the planning stage, technical and management risks occur during the construction and operation phases and policy and market design and regulatory risks exclusively prevail in the operation period of a RE project. The country risk, RE financing, grid access and sudden policy-change risks exist throughout all project phases.

In order to define the risks affecting investments in the RE sector at member state level, the risk database of eclareon was used, as well as several risk indicators documented in the scientific literature.^50,51 Based on these sources, Table 3 is developed, showing the risk categories that are present during the different RE project phases.

Table 3.

Phases and risk categories of renewable energy (RE) investments.

Risk category	Planning	Construction	Operation
Country risk	✓	✓	✓
Social acceptance	✓
Administrative	✓
RE financing	✓	✓	✓
Technical & management		✓	✓
Grid access	✓	✓	✓
Policy design			✓
Market design & regulatory			✓
Sudden policy change	✓	✓	✓

Country risk comprises a wide range of factors affecting the profitability of investments in a specific country. The most important influential factors are political stability, level of corruption, economic development, legal system and exchange rate fluctuations. Another critical risk category related to the absence of social awareness and community acceptability of investments in the RE sector is the social acceptance risk. To obtain the necessary permits and follow the respective administrative procedures, a considerable administrative lead time may occur, as the issuing licenses period may vary from 2 to 154 months as reported by EWEA.⁵² Such delays lead to unexpected costs and additional administratively induced project risks.

The high capital intensity of RE investments requires large amounts of equity capital, financial leverage or public financing. An under-developed and weak national financial sector may tighten up capital scarcity and financial risks. Missing technical and managerial experience with RE projects results in high technical and management risks, for instance during construction, as this risk element may be significant for technologies with long construction periods. Grid access risks encompass all risk elements concerning the connection of a RE project to the electricity network.

The effect of special policy designs on risks has been analyzed by Couture and Gagnon.⁵³ They focus on the design of feed-in schemes like tariffs and premiums. According to them, fixed-price policies can facilitate the mitigation of investment risks due to lower price risks, while premium policies expose renewable electricity generators to price and, hence, to additional investment risks. Similarly, Dinica⁵⁴ states that the impact of different policy designs on risks can be captured through changes in revenues or expenditures. In this study, the effect of several policy elements on price, demand and contract risks is recognized. In Giebel and Breitschopf,⁵⁵ the impact of different policy designs on the cost of capital is captured by means of cash-flow analysis, as well.

As the design of a policy could significantly reduce investors' risk exposure by ensuring certainty in revenues and expenditures, it is crucial when analyzing risks. Therefore, policy design is not per se a risk but refers to the uncertainties arising from low effectiveness of current policy in reducing risks arising from uncertain revenues due to unanticipated changes of prices and market sales.

Uncertainties related to governmental energy strategy and power market liberation are included in the market design and regulatory risks. At last, retroactive and abrupt change in the RE strategy and the existing support mechanisms are linked to the sudden policy-change risk.

The financial evaluation of a project requires the selection of a representative WACC, either corporate or project-related. The cost of capital of an entire company is not necessarily identical to the cost of capital of a project within this company as the project risk may differ from the risk of the overall company due to differences between the debt-to-equity ratio of the project and the firm. Thus, the estimation of the WACC for a project may be assumed as undertaken for a company established for this specific project, having the same debt-to-equity ratio as it. The WACC thus estimated will reflect the risk of the project.

For the purpose of the present study, the unconditional approach has been followed for the quantification of the cost of equity and the national long-term government bond yields have been selected for all EU member states for the year 2013⁶⁷ (ECB).

In the context of our analysis, the market risk premium values obtained from surveys conducted by Fernandez et al.⁵⁷ are selected and the latest available numerical values are used for each EU country.

A representative beta value is extracted from the use of comparable listed companies' returns via the method of peer-review analysis. First, the beta of every company is estimated and then the obtained values are averaged. The average beta factor thus obtained is adjusted for the financial leverage of the project. The sample used in this study is structured by 52 RE companies in Europe that are either pure play actors (companies that have, or are very close to having, single business focus) or that achieve at least 50% of their revenues in the RE sector.

In order to extract the final beta values a six-step procedure has been followed (see Figure 1).

Figure 1.

Steps for the calculation of beta.

At the first stage, a representative sample of 52 firms in European stock markets and in RE indices has been collected. The indices considered are RENIXX World, ALTEX Global, Ardour Global Alternative Energy IndexSM, DAXglobal Alternative Energy Index, Italian Renewable Energy Index and ISE Global Wind Energy. Specifically, the Altex Global index includes only pure play companies, i.e. companies that have or are very close to having single business focus. The rest of the indices include companies that achieve half of their revenues in the RE industry, the majority of which operate in the wind and solar energy segments.

At the second phase, regression betas have been extracted for all companies via daily and monthly return observations for different time periods (5–3–1 years and 6–3 months for daily observations, and 5–4–3–2 years for monthly observations). Moreover, the values of betas have been statistically evaluated to test their explanatory value (R²) and statistical significance (t-statistic and p value) and the index used for market proxy is the MSCI ALL CAP. Daily and monthly prices of the stocks and the index as well as debt-to-equity ratio and market capitalization of every company were collected from the Thomson Reuters Database.

At the following step, the entire set of statistically significant betas is averaged and unlevered by utilizing Hamada's equation⁵⁸

β L = β U * [1 + (1 - CTR) * (\frac{D}{E})]

(3)

where

β_L: levered beta

β_U: unlevered beta

CTR: corporate tax rate

D: market value of debt

E: market value of equity

The debt-to-equity ratio selected is the average ratio of the companies used as suggested by Damodaran.⁵⁹

The fourth stage of the betas calculation incorporates the selection of nine different beta values based on literature and also on statistical evaluation procedures. Particularly, monthly returns are preferred over daily returns, in order to avoid the illiquidity problem that would underestimate our beta. Furthermore, the R² of daily returns is lower than that of monthly returns, indicating lower explanatory power. Monthly data during the past 5 and 4 years are chosen due to lower standard error in contrast to the 3 and 2 years data. Moreover, results corresponding to longer periods are more statistically significant and, subsequently, decrease substantially the standard error.

At the fifth step, the two unlevered betas are finally re-levered again to the selected debt-to-equity ratio, which is 70% debt and 30% equity capital. Different beta values are obtained as the corporate tax rate varies among EU countries.

At the final phase, the beta values are estimated again by using a sub-sample of the original to cross-check our results, which includes companies operating exclusively in the wind and solar sectors. No significant difference from the result obtained by using the full sample has been recorded.

In the context of this section, cost of debt values have been quantified for the case of onshore wind investments among all EU member states. For the purposes of this analysis, we have implemented two different approaches to conduct these calculations. The former is grounded on a report compiled by Eurelectric⁶⁰ and the latter is based on a study of Bloomberg.⁶¹

Eurelectric⁶⁰ provided the following mathematical formula

CoD = EuropeanRfR + CDS + PS

(4)

where

CoD: cost of debt

European RfR: risk-free rate at EU-level

CDS: 10-year credit default spread of the examined country

PS: renewable energy project spread

Based on this methodology, the debt risk premium is estimated on the ground of the average annual 10-year credit default swap (CDS) quotations of the respective companies plus a relevant project spread.

In the context of our calculations and according to the Eurelectric approach, the average 10-year German government bond is used as the representative European risk-free rate. For the year 2013, this is equal to 1.57%. In addition, the average annual 10-year CDS for each EU member state and the project spreads, equal to 3% for onshore wind projects, have been included.

BNEF⁶¹ proposes another calculation model based on the formula

CoD = TS + CR + PS

(5)

where

CoD: cost of debt

TS: term swap interest rate

CR: country risk spread

PS: renewable energy project spread

As “Term Swap Interest Rate,” the fixed payment exchange for a floating payment that is linked to an interest rate (mostly LIBOR) is defined. The “Country Risk Spread” is equal to the surplus between the average 10-year national government bond interest rate and the respective German interest rate. At last, the “Project Spread” is the risk element related to the RE project risk which is inherent into the calculation of the total cost of debt. This indicator is the risk premium charged on loans by bank borrowers,⁶² which exceeds 3% for wind energy technology, based on the study of Mazars.⁴² For the case of a 138 MW wind energy project in Italy, in 2010, a debt ratio equal to 78% and margins on the loans in the range 2.6–2.9%⁶³ are considered. The report of Clean Energy Pipeline⁶⁴ records that onshore wind projects are financed at an average of 3.2% above LIBOR, at a European level.

Within the framework of our calculations, a Term Swap equal to 2.68% is selected as risk-free rate. In addition, the difference between the average 10-year national government bond interest rate and the average 10-year German government bond interest rate has been considered as country risk. At last, a project spread of 3% was assumed for onshore wind projects.⁴²

According to another study,⁶⁵ the cost of debt is equal to the sum of the nominal risk-free rate and a debt margin. As risk-free rate Eurelectic uses the 40-day average of the 10-year common wealth government bond yield whilst the BNEF uses the 10-year average yield of the same bond. For the quantification of the debt margin two different approaches are available, utilizing existing market data and long-term averages, respectively. Regarding the debt margin, these two approaches use the 7-year Bloomberg fair value curve and the 10-year average of the 7-year Bloomberg fair, respectively. At last, an allowance of 12.5-basis points for debt-raising costs is added to the cost of debt.

In conclusion, based on the two approaches presented above, the cost of debt consists of three main elements, namely, the risk-free rate, the country risk and the RE project spread. The risk-free rate is represented by the German government bond in the first and by the swap curve in the second approach. In addition, two different approximations for the country premium were taken into account. In the former the country premium is considered to be equal to the respective credit default swap, while in the latter it is taken to be the difference between the national and the German 10-year government bond yields. The RE project spread is the same in both methodologies. These two approaches lead to a range for the cost of debt rather than a single value. In the context of current study, and based on discussions with financial experts, the values of the Eurelectric approach are chosen as input for the estimation of WACC for each EU country and the assumed capital structure is 70% debt and 30% equity capital for onshore wind energy investments.

Interview procedure

In order to validate the accuracy of the theoretical model developed and the extracted numerical outcomes for each individual EU member state, a set of interviews with more than 80 project developers, equity providers and representatives from utilities and the financial sector (e.g. banks, insurance companies) in 26 of the EU-28 member states has been conducted. The interviews took place between October and December 2014. The main objectives of the interview process were to check whether the identified risk categories are covering all risks related to RE projects, to validate important model assumptions, to evaluate the modeled risk profile in order to confirm its accuracy, to assess the effectiveness of current policy on reducing investments risks and to explore future actions through which this effectiveness can be improved.

In a first step, potential interviewees in EU member states were identified from the authors' broad expert network and were subsequently contacted by email and by phone, giving these experts a short introduction to the study. Provided that they were interested in participating in the project, the interview partners then received the questionnaire with the individual model results for their country by email and were called for a telephone interview at an agreed time.

Each interview focused on the extracted results for the respective member state. The first part of the questionnaire showed a ranking of previously identified risk categories based on the risk database of eclareon. The national experts stated their subjective perception on which risk category is the most important in their member state based on their experience from previous onshore wind projects. The aggregated results are shown in Figure 3 (see Section Interview results).

In addition, the interviewees were asked to give assessments on whether the values used in the model regarding the WACC for onshore wind power projects (absolute WACC, Cost of Equity, Cost of Debt and the ratio between debt and equity capital) corresponded with the interviewees' practical experience from their RE markets. In case the model results deviated from perceived reality, the experts were asked to give their own assumptions regarding the above-mentioned financial parameters. In some cases, the interviewees were able to provide exact numbers from onshore wind projects they had implemented in recent years; in other cases, they were not able (or not willing) to share these figures with the authors. The experts were then asked to give a more general assessment, i.e. if the figures were “more than 2% higher” or “not more than 1% lower.” Based on these answers, an average number or an approximate range for the WACC, Cost of Equity, Cost of Debt and the ratio between debt and equity capital was extracted for each member state (see Section Results). In some interviews, the experts also provided an additional assessment regarding these financial parameters for offshore wind or solar photovoltaic (PV) projects. In the context of this study, only the extracted outcomes for onshore wind projects are included and analyzed.

Nevertheless, the research suffered from several barriers. In general, it was found that there is by no means one wind onshore project that can represent all projects. Factors such as the size of the project, the size and financial situation of the project company, the location of the project have a strong impact on the financial parameters of any single project.

The access to information through the interviews differed considerably depending on the relevant market. In some markets, especially those markets that witnessed a boom-and-bust-phase in the past, such as Bulgaria and Czech Republic, or generally under-developed onshore wind markets, such as Slovakia, there is a general lack of wind onshore projects. For that reason, project developers made assumptions based on their experiences from past projects.

Another hurdle is the fact that many parameters are constantly changing because of floating interest rates and ongoing changes of the existing policy designs. In this context, future research on this specific element will expand the effectiveness of existing analysis.

Within the framework of this study, mitigation of these challenges by conducting more interviews and by sharing the aggregated results with our interviewees to receive additional input took place. For that reason, the extracted results have been presented on several occasions, ranging from bilateral meetings with national financers and small groups of experts to large national and European meetings.

Following the conclusion of the interviews, the country risk profiles and the respective cost of capital levels were updated with the feedback and information received from the experts interviewed about onshore wind energy projects.

Results

Model results

Based on the theoretical model conducted, country risk profiles have been created to reflect RE investments risks in all EU member states. The country profiles present both the outcomes of the financial parameters and the RE risk categories for each EU member state. In order to validate these results, these country profiles were shared with financial experts during the interviews.

Interview results

A comparison of model estimations and results obtained from all the interviews is provided so as to detect over- or under-estimations. Specifically, during the interviews, interviewees were asked to provide feedback on the extracted results of the model regarding investment in onshore wind energy farms. In addition, a further approximation of the final results was implemented according to the comments received within these interviews.

Regarding the risk categorization and the respective risk elements identified, Figure 2 presents an overview on how market actors rank the aforementioned risks, except from the country risk, to onshore wind power projects in 24 member states of EU-28.

Figure 2.

Perception of risk categories across EU Member States (except from HR, IE, LU, MT).

This specific Figure graphically illustrates that, on average, policy design is the most critical factor regarding risk exposure of investors in onshore wind power projects across the EU. The design of a policy affects the degree of price and volume (balancing) risks. As a result, the design of the support scheme is an important factor for stable investment conditions. Accordingly, several interviewed experts referred to the policy design as being “the rules of the game”, meaning that the policy design is the most important factor influencing the RE investment environment.

Following the policy design risk, a group of risk categories, namely administrative issues, market design and grid access, is stated at approximately equal level of importance. Interviewees especially indicated that difficulties are being faced in obtaining grid access for RE projects, with a prospect of becoming more critical in most countries in the upcoming decades.

Figure 3 illustrates a comparison of the top-ranked risks across EU member states.

Specifically, this Figure presents the top-ranked risk category in terms of importance, in each EU member state. Based on this graph, policy design is ranked first in more EU member states than the other risk categories, individually, followed by administrative and market & regulatory risks. This map presents a relatively uniform geographical distribution of these three top-ranked risk elements across the European Union.

Figure 3.

Top ranked risk categories across the EU-28 countries (interview results for onshore wind energy).

Another segmentation of the EU-28 countries is implemented based on the existing RE market development. Specifically, the wind power development, for both onshore and offshore wind technologies, was used as a criterion, in terms of installed capacities,⁶⁶ and related to the share of wind energy in the overall electricity consumption and to growth over the last three years (2011–14), resulting in a member states' ranking according to the maturity of their wind power sector. Specifically, scores from 0 to 10 were given for the following criteria: the proportion of wind power in total electricity consumption; the mean annual wind power capacity increase in the period from 2011 to 2014; and the total installed capacity of wind power plants. According to this ranking, the member states were clustered into three market groups, namely nascent markets (0–3 points), emerging markets (4–5 points) and mature markets (6–10 points).

Figure 4 depicts the perception of different risk categories allocated to these three market-related groups.

Figure 4.

Comparing EU-28 member states according to wind market development.

Figure 5.

WACC estimations for onshore wind energy plants – approximation based on interviews.

This graph above demonstrates that there is a difference in risk occurrence and perceived severity based on the market development in a country. In particular, sudden policy change risks score much higher in nascent markets than in emerging and mature ones. The highly perceived risk of sudden policy changes may be a critical reason why some nascent markets fail to be upgraded into emerging markets; the high risk of changing policy design might discourage project developers and equity providers to invest in wind energy projects.

The final estimations of the financial indicators, based on the interview outcomes, are included in Table 4.

Table 4.

Financial indicators' estimations for onshore wind energy projects – approximation based on interviews.

Country	WACC	CoE	CoD	D/E ratio
Austria	6.5%	8–10%	4.5–5.5%	75:25
Belgium	5–6%	10.8%	5–5.5%	80:20
Bulgaria	10%	12–13%	7.5–8%	50:50
Croatia	12%	–	–	70:30
Cyprus	8–12%	15%	4.5–9%	70:30
Czech Republic	8%	12%	6.5–7.5%	70:30
Denmark	5–6.5%	10–11.2%	4.5–5.5%	80:20
Estonia	6.4–13%	15–20%	4.5–4.7%	65:35
Finland	6–7%	12–15%	3–5%	75:25
France	5.7%	10.5–11.5%	5.7%	80:20
Germany	3.5–4.5%	6–9%	1.8–3.2%	80:20
Greece	12%	14–16%	8.5–12.5%	55:45
Hungary	11.3%	14–15%	8–10%	65:35
Ireland	9%	11–12%	6.5–7.5%	70:30
Italy	7–9%	10–13%	8–10%	65:35
Latvia	9.3%	16.1%	6%	70:30
Lithuania	8–9%	16.6%	6%	70:30
Luxembourg	–	–	–	–
Malta	–	–	–	–
Netherlands	6–6.7%	13.7–14.2%	4.7–6.3%	70:30
Poland	8.7–10%	14–14.5%	6.1–8.1%	70:30
Portugal	7.5–8.5%	12–13%	6%	60:40
Romania	11.1%	16–18%	7–10%	50:50
Slovakia	8.1%	13.6%	6–7.3%	70:30
Slovenia	11%	17.4%	8.2–9.9%	75:25
Spain	10%	13–15%	9–10%	70:30
Sweden	7.4–9%	10–12%	4.5–6%	60:40
United Kingdom	6.5%	7–15%	5–5.5%	75:25

WACC: weighted average cost of capital; CoE: cost of equity; CoD: cost of debt; D/E ratio: debt-to-equity ratio.

Figure 5 presents the WACC approximations based on the interviewees' responses. A first element to highlight is the great gap of WACC values among EU member states. Germany has the lowest WACC in the EU-28, with values of 3.5–4.5% for onshore wind power plants, while several countries, where circumstances are less favorable, have WACC values that are three times Germany's rate or even higher. This considerable difference can be explained by the fact that, in all components of the WACC calculation, the respective values are much lower for Germany. An important factor to be considered in this regard is the fierce competition among banks in Germany, which significantly reduces the cost of debt.

Moreover, the level of WACC highly depends on the alternative ways that the financial closure is structured. In particular, the participation in the project funding of financial institutions such as the European Investment Bank (EIB) or the KfW Development Bank, which may provide long-term capital financing on more favorable terms, especially the equity or the junior loan part, can significantly lower the WACC.

Discussion

According to the results of the present study, risks related to policy design and support schemes are considered as the most critical elements towards the extended deployment of RE investments. The policy-related risk, apart from the country risk, is ranked as the most critical risk component that directly affects the competitiveness of RE technologies in comparison with conventional ones. This outcome is in line with the reference of Oxera Consulting,⁶⁷ where it is stated that the policy risk, or the mitigation of risks through policy, is of paramount importance for the case of high capital and intermittent RE technologies, such as wind power. In addition, administrative, market design and grid access risk components are characterized as more important than the other risk categories. The sudden policy design risk also ranked high as several retroactive policy actions took place in some countries of the EU (e.g. Czech Republic, Bulgaria, Slovenia, Spain and Greece).

Considerable differences among investment risk categories are identified based on the regional and the RE market development levels. Countries in south EU face higher financing risks, while in Northwestern EU countries the financing risk is less important. Moreover, RE markets with a similar development status also show similar administrative risk levels.

Based on the extracted outcomes, the cost of equity shows a general tendency of lower values in interviews' values than in the model outcomes. The cost of equity for onshore wind projects ranges between 6.0% in Germany and above 15% in countries like Estonia, Greece, Latvia, Lithuania, Romania and Slovenia. Member states in Western Europe generally show lower values, typically between 7% and 15%, compared to several Eastern countries, which have cost of equity of at least 16%. Some interviewees explained that the cost of equity decreased when the RE boom collapsed in their market. During the boom, the cost of equity was much higher due to interest in higher profit margins.

In addition, the cost of debt values are substantially lower than assumed by the model in the majority of markets, against the background of very low interest rates and an abundance of capital, which had not been foreseen in the model calculations. Interviewees' results show that the cost of debt varies between 1.8% in Germany and up to 12.5% in Greece. German experts explained that the main reason for the low cost of debt is the existing high overcapacities in the capital market and, hence, the strong competition in the lending business in Germany. Many banks consider wind energy projects as secure investments and underbid each other. Apart from Germany, low cost of debt also occurs in Northern member states, with a maximum of 7.5%, while the Southern EU countries report values equal to or higher than 6%. According to investors, the main parameters affecting the cost of debt are the general country risk, the specific renewable investment risks and the existing competition among sectors.

Furthermore, the conditions for financing onshore wind projects differ a lot among EU member states. In particular, mature energy markets, like Germany and Denmark, receive a debt ratio of at least 80%, as it emerged from the performed interviews in 2014. This large share of debt capital constitutes a high incentive for investments in RE projects as the lower values of the cost of debt, compared to the cost of equity, contribute to higher profit margins for project developers. In contrast, onshore wind projects in Southeastern EU countries are financed with up to 50% equity capital share. This drives up the costs for financing onshore wind energy power plants and often makes financing of such projects impossible. A debt capital share below 70% (ranging from 50% to 65%) is identified in about one-third of the EU-28 countries, which illustrates the perceived risks for onshore wind investments in many EU member states.

Conclusions

As optimistic clean energy targets have already been established for 2020 and beyond at EU level, the deployment of significant investments on new power plants of RE is deemed more necessary than ever. Nevertheless, the current adverse economic conditions have hampered the implementation of these investments during the previous years. RE projects are considered as capital intensive investments which are characterized by high capital costs and are influenced by several risk categories, mainly country- and policy-related ones. In the context of the performed study, the most critical risk components related to RE investments are identified and assessed, and the respective average cost of capital is quantified. In general, low capital and financial costs lead to increased renewable development.

Within this framework, the indicator of the WACC has been considered as the cornerstone of this analysis. WACC plays a significant role in the financial evaluation of RE projects, as it constitutes a critical parameter in the discount of the expected cash flows of a project. Higher values of WACC result in increased expenses of tax payers and energy consumers.

Except from the literature review implemented and the methodological framework structured, a series of interviews with energy experts and representatives from the RE and financial sectors was conducted in order to validate our methodology and its extracted results. Differences between EU countries, according to inherent characteristics of the respective markets, have been analyzed and discussed.

Specifically, the risk categories related to policy issues have been identified as the most critical components of risk. This is grounded on the fact that the selection of the appropriate support scheme and its effective design may result into risk mitigation, contrary to retroactive and sudden policy changes which may lead to the increase of the respective investment risks. Moreover, low capital costs may drive the development of RE projects and overcome potential barriers and obstacles related to less favorable natural conditions, i.e. mean wind speed and solar irradiation. Germany has become the leader in the RE across all EU countries mainly due to the low capital costs observed and despite the fact that less favorable natural resources exist compared to the Mediterranean countries (e.g. Greece, Portugal and Spain).

The combination of the approaches used stresses the validity and, hence, the significance of the results. However, the study's outcomes can be improved by examining companies operating solely in the onshore wind energy sector of Europe for the extraction of more accurate values for the beta factor and the cost of equity that are specific to this sector.

A critical element for further research constitutes the design of smart policy measures and actions in order to remove the non-economic barriers, mitigate the respective risks and, finally, lead to the reduction of the financial cost of the respective RE investments. In this context, it is considered critical to investigate the extent by which the abovementioned financial parameters are altered when policy-related elements change (e.g. selection of exact support mechanism). Lastly, the quantification of the cost of capital for other, less mature, RE investments, like offshore wind or ocean energy technologies, and an in-depth geographical focus on assessing the cost of capital of RE investments at a regional level, per country, are also potential fields for further research.

Footnotes

Funding

This current paper was primarily based on the research implemented within the context of the ongoing project “DIA-CORE Policy dialogue on the assessment and convergence of RES policies in EU Member States” (project number: IEE/12/833/SI2.645735), financially supported by the Intelligence Energy Europe program. The content of the paper is the sole responsibility of its authors and does not necessarily reflect the views of the EC.

Dimitrios Angelopoulos is an Electrical and Computer Engineer of the National Technical University of Athens (NTUA) and PhD candidate in Decision Support Systems laboratory (EPU-NTUA). His domains of expertise incorporate energy policy and planning, electricity markets and prices and energy investments. He has gained experience in financial engineering and operational research, supporting decisions in real world management problems, and participated as a researcher in research projects related to RES, climate policy and sustainable energy development.

Robert Brückmann is Head of the Policy Department at the consulting company eclareon. He is a specialist for the analysis of political and legal frameworks for the deployment and use of renewable energies. His focus is on support schemes, non-technical barriers, integration of renewable energy and investment risks. Robert Brückmann is a fully qualified lawyer. He also holds a Master of Law degree in European law (University of Helsinki).

Filip Jirouš studied Eastern European Sciences at Freie Universität Berlin. During his studies, he worked as a freelancer for the German Parliament's visitors' service and the Prague-based business consulting company Czech Trade Office. Mr. Jirouš was working as a research assistant at eclareon company, analysing the legal framework conditions as well as questions concerning grid connection, operation and development for RES. He was also actively involved in the RES integration and RES Legal projects.

Inga Konstantinavičiutė, PhD, is a senior research associate in Laboratory of Energy Systems Research at Lithuanian Energy Institute. She has experience in studies related to analysis of energy balances, energy demand forecasting, environmental issues in energy policy development, evaluation and assessments of energy policy instruments. She has been involved in international studies dealing with assessment of renewable support schemes. She is responsible for national greenhouse gas emissions inventory preparation for energy sector.

Paul Noothout works as a consultant at Ecofys. He has ample experience in the fields of renewable energy and low-carbon technologies as well as studying technological development and innovation systems. Over the past years he worked on a broad variety of energy topics and projects, including evaluations of policy (effectiveness, risks and improvements), energy finance, scenario studies, technology development and energy system analysis. Paul holds a master's degree in Innovation Studies with a specialisation to energy.

Dr. John Psarras is a Professor in the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA) and Director of the Decision Support Systems laboratory (EPU-NTUA). His scientific background covers capacity building, technology transfer and training in the fields of energy policy and planning, renewable energy and energy savings, decision support systems analysis and sustainable energy technologies. He has published more than 150 papers in scientific journals and international conferences.

Lucie Tesnière has a background in political science and history and a Joint Masters in European Studies from Charles University (Prague), Humboldt University (Berlin) and Bath University (Bath). Her expertise encompasses EU energy policies with a focus on EU renewable energy policies. Prior to joining Ecofys, Mrs. Tesnière was Senior Policy Advisor for the European Renewable Energy Council (EREC) where she was representing the European renewable energy sector to EU institutions.

Barbara Breitschopf works as senior researcher at the Fraunhofer ISI. As economist, her principal interests are in energy economics, especially in assessing impacts of (renewable) energy policies on costs and benefits, competiveness of industries, technological changes, financing costs and risks. Her studies have been assigned by German Ministries as well as by the European Commission. In addition she lectures at the Karlshochschule and the Karlsruhe Institute for Technology, both in Germany.

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