1. Introduction
Greenhouse gas emissions associated with climate change and the depletion of fossil fuels have become one of the greatest challenges in the world. Considering these environmental issues, one of the possible solutions is the use of renewable energy sources. Renewable energy, apart from leaving open the possibility of using fossil fuels in the future, contributes significantly to the environmental aspect of sustainability. It is noted that renewable energy sources that use indigenous resources have the potential to provide energy services with zero or near-zero emissions of air pollutants and greenhouse gases [
1]. Renewable energy sources can be considered additional forms of energy alongside existing conventional (fossil) power plants and will become even more important in the future [
2]. Modern or new renewable energy sources (including small hydropower plants or modern biomass plants) emphasise sustainability and environmental preservation. Although investments in modern renewables are significant, they accounted for 12.6% of total final energy consumption in 2020 [
3]. Wind energy also accounts for only a small share of global energy consumption, despite its widespread deployment. Nevertheless, the global penetration of wind energy is increasing, and its installed capacity has grown exponentially, i.e., it has almost quadrupled in the last decade [
4]. In addition, technological innovation and economies of scale have established wind energy as one of the most competitive and resilient energy sources in the world. Compared to other renewable energy sources, wind energy has low maintenance costs. Further, achieving net-zero emissions requires various actions across a wide range of sectors. Wind energy can be one of the cornerstones of green recovery and play an important role in accelerating the global green energy transition [
5]. Currently, the total global wind energy capacity is up to 837 GW and helps the world avoid over 1.2 billion tonnes of CO
2 annually, equivalent to the annual carbon emissions of South America [
6]. Rapid progress in wind turbine installation is expected to continue in the future. Offshore wind energy is characterised by its sustainability and cleanliness and is one of the fastest-growing renewable energy sources in recent years [
7]. It should be noted that offshore wind will be a key driver of the global energy transition towards climate change, with some industry stakeholders calling for an installed offshore wind capacity of over 1400 GW by 2050 [
8]. To achieve this goal, the development of floating offshore wind will be pursued to deploy turbines in deeper waters and unlock up to 10 times more offshore wind resources than are possible with fixed-bottom turbines alone.
In the analysis of renewable energy, the focus is on the economic attributes and the evaluation of social and environmental impacts [
9]. To ensure the development of the wind energy sector, it is necessary to pay attention to the management performance of wind energy companies. Therefore, the performance evaluation of different wind energy companies is crucial for the improvement of the energy sector. In this research, the measurement and assessment of offshore wind energy companies are carried out. It should be noted that the companies operate under the same conditions to evaluate their performance for business improvement. This means that all offshore wind energy companies generate electricity solely by using wind energy. This is one of the main assumptions of the Data Envelopment Analysis (DEA) method used in this study to determine relative efficiency. This nonparametric method has become one of the most widely used approaches to measuring and evaluating different power plants or energy companies. In view of the above, the basic hypothesis of this study is as follows: by evaluating the relative efficiency of offshore wind energy companies in European countries, it is possible to determine a correlation between the results of efficiency between the two observed periods with slight deviations.
This study makes the following specific contributions to previous literature:
Existing offshore wind energy performance studies have focused on the technical and comprehensive characteristics of offshore wind farms. With the exception of one study evaluating capital and operating cost efficiency, the model variables in this study relate to the economic characteristics of offshore wind energy companies, meaning that only allocative (cost) efficiency was analysed.
To the best of the authors’ knowledge, there is only one study to date that incorporates both DEA models (CCR and BCC models) in the evaluation of offshore wind energy companies. This study also applied the two basic DEA models, and in choosing a model to measure and interpret relative efficiency, the BBC model was found to be more appropriate.
Unlike previous research, the paper provides insight into the average number of projections or improvements that can make relatively inefficient offshore wind energy companies relatively efficient.
2. Literature Review
Many studies have been conducted to evaluate the relative efficiency of the power sector using the DEA methodology. In addition to electricity generation, the literature also includes measurements and evaluations of other electric power activities. It refers to electricity transmission [
10], electricity distribution [
11], and the electricity supply industry [
12]. Färe, Grosskopf, and Logan [
13] were the first researchers to use the DEA methodology in electricity generation and evaluated the relative efficiency of electric utilities in the state of Illinois between 1975 and 1979. Since then, many studies have provided a comprehensive review of the application of DEA models in the power industry (e.g., [
14,
15]).
With regard to the evaluation of the relative efficiency of offshore wind energy companies, which is the subject of this study, there has been an increasing number of relevant studies in recent years.
Using the super-efficiency DEA method and the Tobit regression model, Yi-Chia et al. [
16] investigated the factors affecting the environmental performance of offshore wind energy companies based on cross-sectional data from seven locations in Taiwan in 2019. The results suggest that the performance value could be further increased by improving the factors affecting efficiency. These factors included the age of the company, the number of internal committees, the number of shareholders, the amount of research and development, ISO 45,001, government policies, the quality of personnel, and wind speed. Wpd Taiwan Energy Co. had the best environmental performance, while Copenhagen Infrastructure Partners Association had the lowest efficiency score.
Benini and Cattani [
17] measure the long-run capacity of offshore wind farms and estimate the technical efficiency of 26 offshore wind farms over a 13-year period using a fully parametric model and a semiparametric Stochastic Frontier Analysis (SFA) method. This allows production factors to have a nonlinear effect on the amount of electricity generated. Next, the estimated technical efficiency is regressed against the age of the offshore wind farms, taking into account technological change in the wind energy industry, to determine the resilience of technical efficiency to ageing. As explained in the methodology section, this is necessary because technical efficiency does not support technological change in the observed Decision Making Units (DMUs). The results show that technical efficiency ranges from 83% to 98% and does not decrease with age. The results suggest that offshore wind farms can be a long-term solution for the energy transition.
Akbari, Jones, and Treloar [
18] evaluate the relative efficiency of 71 offshore wind farms in 5 northwestern European countries (the United Kingdom, Germany, Denmark, the Netherlands, and Belgium) using the DEA method, a Charnes-Cooper-Rhodes model (CCR model). This is in contrast to this study, where both the CCR and the additional Banker-Charnes-Cooper model (BCC model) are applied. The number of turbines, cost, distance to the coast, and area of wind farms are chosen as inputs, while connectivity to population centres, electricity generated, and water depth are considered outputs. The results of the analysis of the DEA show the following: (I) several offshore wind farms in Germany and Denmark were highlighted as dominant in the selected sample; (II) the average CCR efficiency score of all offshore wind farms is 87%; (III) the efficiency score is not evenly distributed across countries; and (IV) the result of the statistical analysis shows that the median efficiency scores of the wind farms are not statistically different from each other and therefore the wind farms have a relatively high average efficiency score across all countries studied in this analysis. Finally, the study provides offshore wind stakeholders and policymakers with a practical and holistic performance assessment by including economic, environmental, technical, and social inputs and outputs in the analysis.
Ederer [
19] analysed the relative performance of offshore wind farms in terms of costs by using the two basic DEA models (CCR and BCC models) and scale efficiency. The analysis was divided into a static model to evaluate capital cost efficiency, which refers to all one-time expenditures associated with the development and installation until the acquisition of an offshore wind farm, and a dynamic model to examine operating cost efficiency, which refers to all annualised expenditures incurred after the date of acquisition until the decommissioning of an offshore wind farm. For the assessment of capital cost efficiency (specific capital cost is considered as input and installed capacity, distance to shore, and water depth are outputs), 22 observations, i.e., 22 offshore wind farms in selected European countries, were included, and for operating cost efficiency (specific operating cost is the input and installed capacity, distance to the port of operation, energy performance, and availability are outputs), 26 observations, i.e., 7 offshore wind farms, were included. The learning-by-doing rate for capital cost efficiency shows that efficiency increases with accumulated experience. Furthermore, the Tobit regression used in the study shows increasing capital cost efficiency as a function of time and decreasing operating cost efficiency as a function of year of operation.
Additionally, in analysing the offshore wind potential on the Atlantic and Mediterranean coasts of Morocco, Daoudi et al. [
20] listed 14 offshore wind farm sites and classified them based on 6 criteria to prioritise their suitability for a wind farm. The most important criterion is the wind power density calculated with the Weibull distribution function over the period of 2016–2020. Through a dynamic and static study, the application of the new approach based on the DEA method revealed favourable sites with high potential for offshore energy generation.
On the other hand, several studies on onshore wind energy can be found in the literature where relative efficiency is measured and evaluated. Of note is a study in which Maradin, Cerović, and Šegota [
21], using the DEA methodology, analysed the evaluation of the relative efficiency of electricity generation of 78 onshore wind power companies in 12 selected European countries, identifying the factors that improve the efficiency of the companies. To the authors’ knowledge, this was the first study to analyse the comprehensive performance of onshore wind power companies, including their economic and technical characteristics. In addition, the paper provided insight into the modified form of the Cobb-Douglas production function (inputs of capital and fuel were used to produce outputs instead of labour and capital) in evaluating the efficiency of an onshore wind power company. Iglesias, Castellanos, and Seijas [
22] evaluated only the productive efficiency of 57 onshore wind farms in Spain (Galicia region) during the period of 2001–2004 using the frontier methods of the DEA and SFA. The results show no significant changes in the annual efficiency scores for each observed wind farm. Ertek, Tunç, Kurtaraner, and Kebude [
23] present a data-centric analysis of 74 commercial onshore wind turbines from leading manufacturers in the world. Among other methods, they provide benchmarking through the two DEA models to evaluate technical efficiency. Pestana Barros and Sequeira Antunes [
24] evaluate the technical efficiency of 65 onshore wind farms in Portugal during the period of 2004–2008 using stochastic production econometric frontier models, considering ownership and unobserved managerial ability as factors affecting wind farm performance. Starting from the fact that the existing studies only measure the technical (productive) efficiency of onshore wind farms in one country (with the exception of the study by [
21]), this research goes one step further and analyses the other approach, i.e., the allocative efficiency of offshore wind energy companies in nine European countries.
Finally, various studies can be found that address the following offshore issues: (I) a systematic literature review on the methods and theories used in decision-making for offshore wind power investment, followed by the characteristics, applicability of different methods, and discussion of representative literature during the period of 2010–2020 [
7]; (II) the use of policy instruments and deployment of offshore wind power in the North Sea, viz., in Denmark, the United Kingdom, Germany, and the Netherlands between 1990 and 2020 [
25]; (III) the proposed guidelines and policy implications in environmental licencing for offshore wind projects for new markets based on the research cases in the United Kingdom, Germany, Denmark, and Taiwan [
26]; (IV) the analysis of the different approaches of Europe, China, and the United States to the development of the offshore wind energy industry [
27]; (V) a new multi-attribute decision-making model to be applied to the location selection of offshore wind power stations [
28]; (VI) the accuracy of wind speed distribution and compares offshore wind turbine performance predictions in Australia using three international reanalysis datasets: BARRA, ERA5, and MERRA-2 [
29]; (VII) the development of an integrated offshore wind and wave energy system that could be one of the best solutions for the future of the ocean energy sector and the energy transition [
30]. In the study of the development of the onshore wind energy market in the European Union, Germany and Spain are considered the main gross producers of electricity from wind energy in the EU. The study concludes that the cumulative installed wind power capacity will increase in most EU countries, highlighting that the highest growth will be in Croatia in 2022 [
31]. Another study provides a comprehensive overview of the state of wind energy in terms of status, potential, and policy analyses and assessments, as well as recommendations for increasing the installed capacity of wind power [
32]. In addition, there are studies that address climate change and its impact on the dynamic behaviour of an offshore wind turbine [
33]; a review of the technical aspects of wind farm development, including the impact of offshore wind turbines and hybrid energy technologies [
34]; analysing the positive and negative economic effects of renewable energy technologies [
35]; or presenting the advantages and disadvantages of renewable energy sources in general without considering a single type of renewable energy [
36].
4. Empirical Results
The relative efficiency of 47 offshore wind energy companies is evaluated using the input-oriented BCC model, which indicates variable returns to scale.
Table 5 below shows the score and rank of the relative efficiency of 47 DMUs in 2019 and 2020, as well as the 9 countries they belong to.
Table 5 lists all 47 offshore wind energy companies (DMUs) classified into 2 groups: relatively efficient DMUs (
θ* = 1) and relatively inefficient DMUs (
θ* < 1). In assessing relative efficiency based on the 2019 data, 15 DMUs were found to be relatively efficient, while 32 DMUs were found to be relatively inefficient. Based on 2020 data, it can be seen that 7 companies (DMUs) were classified as relatively efficient, while 40 DMUs were classified as relatively inefficient. This can already be seen in
Table 4, which shows the scores for relative efficiency.
As shown in
Table 5, there is no significant relationship between the value of relative efficiency and the country where the offshore wind energy company is located. In other words, the results obtained by the model indicate that the relative efficiency of the offshore wind energy companies is not determined by external factors related to the specific circumstances in each country where the analysed offshore wind energy companies operate but by internal factors in the form of financial variables, i.e., tangible fixed assets, cash and cash equivalents, and current assets.
On the other hand, the results are consistent with the basic hypothesis of this study, which is as follows: by evaluating the relative efficiency of offshore wind energy companies in European countries, it is possible to determine a correlation between the results of efficiency between the two observed periods with slight deviations. More specifically, although there are notable differences in the score results of relative efficiency, the correlation is significant with slight deviations in the ranking of the analysed offshore wind energy companies in the observed years, i.e., in 2019 and 2020. This is confirmed by the fact that several offshore wind energy companies are ranked the same in the efficiency score in the observed years 2019 and 2020. This is true for six companies that are relatively efficient in both 2019 and 2020, as well as for companies that are ranked the same under the following numbers: 33, 34, 37, and 47. In addition, there are a few companies that are ranked nearly equally in 2019 and 2020. For example, Aberdeen Offshore Wind Farm Limited is ranked as 27th most efficient company in 2019 and as 26th most efficient company in the following year 2020.
The assessment of relative efficiency implemented by the DEA method not only provides an estimate of the current level of relative efficiency but is also of great importance in the field of efficiency management of offshore wind energy, as it provides information on how to eliminate relative inefficiency and identifies sources and amounts of inefficiency. Therefore, for a relatively inefficient offshore wind energy company to be able to become relatively efficient, it is necessary to make projections or improvements and “shift” some of the factors to the efficiency frontier. These projections or improvements are the basic objectives of this research, and besides the determination, a solution to the problem of inefficiency is proposed. When applying the input-oriented DEA model, in order to achieve relative efficiency (θ* = 1), it is necessary to reduce the input variables while maintaining the existing output, i.e., the required changes or projections (in percentage) of each variable should be reduced.
To determine the amount of relative inefficiency of offshore wind energy companies in general, significant importance is given to determining the average amounts or average improvements for each observed input in the model (tangible fixed assets, cash and cash equivalents, and current assets). With such average adjustments (the reduction of factors), a possible achievement of relative efficiency at the aggregate size level is suggested. The average percentage improvements for relatively inefficient offshore wind energy companies are shown in
Table 6 below.
Table 6 shows the various amount of projections or average improvements for relatively inefficient offshore wind energy companies to become relatively efficient. Particularly highlighted are the extremely high values in both 2019 and 2020, from 46.19% to 70.14%. Such enormous values of projections indicate that the financial inputs of offshore wind energy companies will radically decrease. Although this research does not show the average improvements for each relatively inefficient offshore wind energy company, the empirical results suggest that about 10 companies (DMUs) have a value of projections above 99% for each input variable. This means that the companies have double resource capacity, which should be halved to become relatively efficient. It should also be noted that the average improvements for each observed input are higher in 2020 than in 2019, which is consistent with the relative efficiency results that show there are fewer relatively efficient offshore wind energy companies in 2020 (7 DMUs) than that in 2019 (15 DMUs). In addition, the average efficiency score in 2020 is lower (0.4542) than that in 2019 (0.546). This can be explained by the fact that the quality of management of offshore wind energy companies should be necessarily higher in 2020 than in the previous year. From the management’s perspective, significant efforts and improvements in all financial variables, i.e., tangible fixed assets, cash and cash equivalent, and current assets, are required to achieve relative efficiency.
To conclude, offshore wind energy companies should, on average, reduce tangible fixed assets by 47.42% in 2019 and 54.78% in 2020, cash and cash equivalents by 58.85% in 2019 and 70.14% in 2020, and variable current assets by 46.19% in 2019 and 63.28% in 2020 to achieve relative efficiency. The economic impact of the proposed reduction of inputs would ensure the “return” to the equilibrium point of the company.
It should be noted that this study does not examine the direct impact of the country and its reforms on the value of relative efficiency. This is indirectly contained in the economic variables (inputs and outputs) of the model.
5. Discussion and Policy Implications
Based on the results of this research, it is quite clear that they imply numerous considerations, especially in the field of managerial decision-making but also in the formation of public policies. The given information related to the necessary adjustments, i.e., amounts of projections and improvements by the relatively inefficient offshore wind energy companies, is of particular importance for all stakeholders involved in the renewable energy sector, especially in the wind energy sector. Ensuring exactly specified projections to eliminate inefficiencies would improve company performance, increase efficiency, and make the best use of resources. By increasing efficiency, more resources would be available for the company to use to further improve its operations and performance or to further stimulate its economic activity. This could also contribute to the growth of the company as a whole. If a company performs functions and activities efficiently, i.e., faster, more cost-effectively, and more competently than its competitors, it can achieve various benefits, such as higher profit levels and greater customer satisfaction.
In addition, the efficiency of the company could have an impact on the competitiveness of other companies in the industry. Therefore, efficiency is important for a company to gain a competitive advantage over its rivals. Consequently, competitiveness ensures market dominance. On the other hand, efficient offshore wind energy companies can contribute to the growth and development of the wind energy industry by ensuring a reliable and sufficient power supply.
Since the world’s first offshore wind farm was installed in Vindeby off the southern coast of Denmark in 1991, wind energy potential has been critical if Europe is to achieve its goals of reducing carbon emissions by at least 55% by 2030 compared to 1990 and becoming carbon neutral by 2050. Europe’s leadership in offshore renewables can be based on the enormous potential offered by the European Union’s seas, from the North Sea and the Baltic Sea to the Mediterranean, from the Atlantic to the Black Sea, as well as the seas around the EU’s outermost regions. The EU Offshore Renewable Energy Strategy envisages an installed capacity of at least 60 GW for offshore wind by 2030, rising to 300 GW by 2050 [
48].