Sustainable Energy Sources and Financial Development Nexus—Perspective of European Union Countries in 2013–2021

Abstract

The focus of this paper is the relationship between sustainable energy sources and financial development. The main research hypothesis assumes a positive link between these areas, with inevitable differences across countries and business sectors. The following research questions were asked: Is the impact of financial development on sustainable energy resources the same in different EU countries advanced in green transition processes? How is transition towards renewable energy sources progressing in different economic sectors? Does financial development influence sectoral transition in particular countries? This study uses the TOPSIS method and 25 variables for EU countries from 2013 to 2021. Key findings reveal that the link between sustainable energy sources and financial development varies across EU countries, country size affects energy autonomy, and the transition also differs by business sector. Surprisingly, higher financial development correlates with less progress in sustainable energy initiatives. The results of our research may be useful for government decision-makers in the process of designing and controlling the country’s transition to sustainable energy. The original contribution of the study is expressed in its the diagnosis of the relationship between financial development and sustainable energy sources, while most studies have focused on the relationship between the energy market and financial development.

1. Introduction

The problem of energy efficiency and financial development is one of the research interests that are growing in importance in the conditions of energy transition. Research results on the relationship between energy efficiency and financial development could be more coherent. Mills et al. [1] report financial development’s vital and significant impact on energy efficiency. The authors point out that the effect of financial development on energy efficiency is different for every country; especially countries at higher financial development levels stand out, where it is insignificant. Shah et al. [2] confirm the positive effect between financial development and energy efficiency in their research results. Latif et al. [3] report a positive relationship between financial development and CO₂ emissions, which energy efficiency reduces. In his research, Sadorsky [4] confirms the positive link between energy consumption and financial development. The transition towards a green economy is based on digitalization, as many entities have decided to introduce a green office philosophy with, among others, electronic document workflows and increased number of electronic processes in the office. At the same time, research indicates that “overall energy consumption increases due to ICT” [5]. It is also proved that financial development could contribute to higher energy consumption and energy intensity [6,7]. Canh et al. [6] indicate that financial development results in increasing production energy intensity “except for the negative effects of financial institutions’ efficiency”. Due to the varied results of research on energy efficiency and financial development, in particular by the type of countries covered, the data have been so far inconclusive. The purpose of the article is to examine the relationship between sustainable energy sources and financial development. In particular, the following research questions were asked: In the case of countries advanced in green transition processes, is the impact of financial development on sustainable energy resources the same in different EU countries? How is transformation towards renewable energy sources progressing in individual economic sectors? Does financial development influence sectoral transition in particular countries? The main research hypothesis assumes a positive link between sustainable energy sources and financial development, with inevitable differences across countries and business sectors. The original research approach presented in the article is reflected in the assessment of the relationship between the process of transitioning towards renewable energy sources in individual European Union countries and their financial development. Such a process-based approach has not been used so far; previous research analyzed energy efficiency or energy consumption, while the research presented in the article offers a broader process-based and sectoral view.

The structure of the paper is as follows. After the introduction, Section 2 is devoted to the literature review, followed by the methodology and data section. Section 4 and Section 5 present the results and discussion and the final section summarizes the findings.

2. Literature Review

2.1. Sustainable Energy

In the literature and economic practice, the terms “renewable energy” and “sustainable energy” are often used interchangeably. These concepts, despite many common areas (many of the sustainable energy sources are also renewable), feature some differences. The term renewable energy refers to these types of energy that use unlimited resources. According to the United Nations definition, renewable energy “is energy derived from natural sources that are replenished at a higher rate than they are consumed” [8]. In other words, renewable energy comes from sources that regenerate naturally at a rate that meets energy demand. Renewable energy include biomass, geothermal, hydro, solar, and wind energy.

The term “sustainable energy” refers to the concept of sustainable development, especially the framing of this concept as contained in the Brundtland Commission report [9]. It refers to intergenerational solidarity, which consists of “meeting the needs of the present without compromising the ability of future generations to meet their own needs”. Teste et al. [10] defined it as “a dynamic harmony between the equitable availability of energy-intensive goods and services to all people and the preservation of the earth for future generations”. Sustainable energy therefore refers to energy sources that can be used without depleting natural resources or significantly negatively impacting the environment [11]. It includes solar energy, storage systems, wind energy, geothermal energy, ocean energy, hydroelectric energy, hydrogen energy, and biomass energy [12]. The sustainable energy paradigm is based on a moderate and robust principle of sustainability, allowing for parity between the pillars of sustainability (social, economic, and environmental) [13]. It is related to climate justice [14] and energy justice [14,15], but also energy security [16,17].

Sustainable energy is an important element in the process of implementing sustainable development [18,19,20,21] and achieving the sustainable development goals [22,23,24,25], including the transition to a low- and ultimately zero-emission economy [26,27,28]. In the literature, the concept of sustainable energy development is centered around this issue.

The growing interest in sustainable energy and its increasing use brings many benefits. First of all, they contribute to reducing carbon footprint [29,30] and dependence on fossil fuels, reduce energy costs in the long term [31,32], and are an element of the process of enterprises building their competitive advantage [33,34]. An inexhaustible source of energy, sustainable energy has a positive impact on the level of energy security of market entities and countries [35].

However, the transition to sustainable energy is a difficult process and requires that a wide range of conditions be met; these can be broadly divided into two groups: technical requirements (e.g., obtaining sustainable energy resources, using beneficial energy carriers, increasing the efficiency of energy systems, mitigating the life-cycle impact of energy systems on the environment) and nontechnical requirements (e.g., social acceptance, lifestyle change) [36].

The complexity of the issue at hand makes the implementation of sustainable development or energy for sustainable development a challenge at the macroeconomic and microeconomic levels. A transition plan to sustainable energy sources can be developed at the international and national levels based on local conditions and resources [37] and at the level of individual economic entities [38]. The transitioning of individual countries towards sustainable energy is conditioned primarily by the availability of natural resources, but the related policy implemented at the state and international (EU) level also has a significant impact. The literature presents research on the process of implementing renewable energy in individual countries. An example is the study by Potrč et al. [39], which shows that in the case of the European Union, wind farms turned out to be the most promising solution for the rapid expansion of electricity production from renewable energy sources. At the same time, the importance of solar photovoltaics (PV) is growing and, according to scientists’ estimates, it will reach a 43% share in the production of electricity from renewable energy sources in 2050. Brodny and Tutak [40] classified European Union countries according to the similarity of the structure and volume of renewable energy production in relation to the GDP levels. Research shows that Croatia, Latvia, Austria, and Sweden are the countries with the highest production of hydro energy. Austria and Sweden are also the countries that generated the most renewable energy per capita. At the bottom of the list in this regard were Poland, France, Malta, and Belgium.

In the process of achieving or transitioning towards sustainable energy, its measurement is extremely important, as it enables monitoring and management. In the literature, one can find many relevant indicators, both those proposed by international organizations, included in statistical databases, and those proposed by individual authors in relation to member states of international organizations and developed and developing countries (Table 1).

Gunnarsdóttir et al. [45] point out that the lack of methodological transparency in the selection and use of indicators for evaluation is a challenge for scientists because the validity and reliability of indicators depend on the transparency of the methods used.

2.2. Financial Development Index

Due to its functions (allocation, mobilization, and redistribution), the financial sector plays an important role in stimulating social and economic changes. It is created by financial institutions, markets, and financial instruments along with applicable legal regulations that enable transactions. As defined by the World Bank [46], “financial sector development therefore occurs when financial instruments, markets and intermediaries mitigate the effects of information, enforcement and transaction costs and therefore do a correspondingly better job of providing the key functions of the financial sector to the economy”. In practice, financial development (FD) therefore leads to an increase in the volume of financial services provided by banks and other financial intermediaries and the number of transactions carried out on capital markets [47], but also contributes to improving the efficiency and stability of financial markets and increasing/facilitating access to financial markets for individuals and companies [48].

The analysis of the literature on the subject has shown that there is no single universal measure of financial development. When measuring the level of financial development, some authors use indicators based on data from the banking sector; e.g., King and Leavine [49] considered the ratio of commercial banks’ assets to total assets (the sum of assets of commercial banks and the central bank) as a measure of financial development, while Dermirgüç-Kunt and Maksimovic [50] used the ratio of banking sector assets to GDP. Dalloshi and Badivuku-Pantina [51] note that when financial development indicators are to be based on data from the banking sector, they should take into account all aspects of the development of this sector, i.e., its depth, access, and efficiency.

When creating measures of financial development, some researchers also take into account, in addition to data from the banking sector, variables relating to capital markets; e.g., Beck and Levine [52] measured the development of the financial sector on the basis of the general activities of financial intermediaries and capital markets, combining the value of loans made to the private sector by financial intermediaries in relation to GDP with the volume of turnover on capital markets. In turn, Paun et al. [53] used variables such as commercial bank branches (per 100,000 adults), domestic credit provided by the financial sector (% of GDP), domestic credit to the private sector by banks, market capitalization of listed domestic companies to GDP, net foreign assets to GDP and stocks traded, total value (% of GDP).

A measure of the level of financial development that also takes into account the development of financial institutions and the capital market in terms of the term of their depth (size and liquidity), access (ability of individuals and companies to access financial service), efficiency (ability of institution to provide financial service at low cost and with sustainable revenues, and the level of activity of capital market is the Financial Development Index (FDI), which was developed by the International Monetary Funds (Table 2). It is “a relative ranking of countries in terms of the depth, access and efficiency of their financial institutions and market financial” [54].

The financial development and its relationships with other variables are a frequent direction of scientific research. The relationship between financial development and economic growth is analyzed particularly closely, but the results obtained are not conclusive. A positive relationship between financial and economic development has been indicated by Pal and Mahalik [55], Tian et al. [56], Giri et al. [57], Mtar and Belazreg [58], and Hossin [59]. Financial development is important for economic growth because financial instruments drive business activity [60]. It promotes economic growth through capital accumulation [61] and technological progress, mobilization, accumulation, and allocation of savings [62], creation and dissemination of investment information [46], and financial innovations [63].

The literature also includes studies whose results indicate a small or negative impact of financial development on economic growth. A negative correlation between economic growth and financial development in both the long and short term was revealed by Nabi et al. [64], who examined panel data for N-11 countries (South Korea, Mexico, Bangladesh, Egypt, Indonesia, Iran, Nigeria, Pakistan, Philippines, Turkey, and Vietnam) over the period 2000–2018. Similar conclusions were published by Khoury et al. [65], who analyzed the relationship between financial development and economic growth in the Middle East and Central Asia (MECA) countries from 2008 to 2018. Their results indicated that financial development does not necessarily contribute to economic growth.

Čižo et al. [66] and Botev et al. [67] noted a bidirectional, nonlinear relationship between the variables in question. Tariq et al. [68], examining the relationship between economic growth and financial development in Pakistan from 1980 to 2017, found that economic growth responds positively to financial development when the level of financial development exceeds the threshold value of 0.151. Below this value, financial development negatively affects economic growth.

An important direction of research is the analysis of the relationship between financial development and sustainable development. Ji et al. [69] showed that financial development contributes to achieving one of the most important sustainable development goals, which is reducing the intensity of carbon dioxide emissions, and this occurs through industrial modernization and the deployment of technological innovations. The positive impact of financial development on reducing greenhouse gas emissions and promoting sustainable development was also confirmed in the studies by Shobande and Ogbeifun [70] and Khan and Ozturk [71]. Similar results were obtained by Yudaruddin et al. [72], who showed a negative relationship between financial development and greenhouse gas emissions. The study used greenhouse gas emissions data from the Indonesian Central Statistical Office for the period 2000–2019.

Qin et al. [73], based on their research, showed that in addition to financial development, increasing the share of renewable energy also contributes to reducing greenhouse gas emissions. Musa et al. [74] also confirmed that environmental performance is positively correlated with financial development and renewable energy. Ma et al. [75] showed that improving access to, depth, and efficiency of financial institutions, as well as improving the availability, depth, and efficiency of financial markets, can dramatically reduce the energy intensity of developing countries. Therefore, it is important to support the transition to energy-efficient technologies and increase the use of renewable energy sources. Liu et al. [76], based on panel data on G7 economies from 2000 to 2020, showed that green investments and financial development stimulate renewable energy transformations [77]. Financial development favors the demand for renewable energy [78,79].

The literature includes studies analyzing the relationship between financial development and renewable energy consumption. The vast majority of these present results confirming the positive impact of financial development on renewable energy consumption [80,81,82]. However, there are also studies indicating that the correlation between these variables is not always positive. For example, research by Sun et al. [83] showed that financial development has a significantly positive impact on renewable energy consumption from a macroeconomic (global) perspective and in developed economies, but this is not confirmed in developing economies.

The literature also contains studies devoted to analyzing the relationship between financial development and individual types of sustainable energy. Doğan et al. [84] analyzed the relationship between wind and geothermal energy consumption and financial development based on data from 10 countries (Germany, Iceland, Italy, Japan, Mexico, New Zealand, Portugal, Turkey, and the United States of America). The research showed that geothermal energy consumption had a positive impact on financial development, while there is no such impact in the case of wind energy. Ullah et al. [85] focused on diagnosing the relationship between hydroelectric power generation, financial development, and economic growth using, for example, data from 10 countries with the highest hydroelectric energy efficiency, in the years 1990–2020. The results confirmed a bidirectional relationship between all three factors. In turn, research conducted by Zeren and Hizarci [86] did not confirm the relationship between hydropower energy consumption (HEC) and financial development (FD). The research included data from “newly industrialized countries” in the time period between 1979 and 2020. Zeren, F., & Hizarci [87] showed a positive relationship between biomass energy consumption and financial development. Similar results were achieved by Kevser et al. [88], who studied the relationship between biomass energy consumption (BEC), economic growth (EG), and financial development (FD). Their results confirmed the existence of a bidirectional positive relationship between biomass consumption and financial development.

However, there is no research in the literature that would analyze the relationship between sustainable energy as a whole and financial development. Our study fills this research gap. For the purposes of this study, a synthetic sustainable energy indicator was constructed based on 25 variables, which were divided into stimulants and destimulants. This allowed for the classification of European Union countries in terms of sustainable energy and a comparison of the transition process of individual countries towards sustainable energy. The relationship between the synthetic sustainable energy indicator and financial development was then analyzed. The study used a process-based approach, unlike previously used research methods based on energy efficiency or energy consumption.

3. Statistical Materials and Methods

The basis for the empirical analyzes presented in the work is a group of indicators related to sustainable energy (SE) and the financial development indicator (FDI). The study was conducted for the 27 European Union countries in the years 2013–2021. Due to the adopted study period, the United Kingdom, which left the EU in 2021, was not included among the EU countries. The analysis included 25 diagnostic features (indicators), which concerned, among others, energy efficiency, primary and final energy consumption, renewable energy sources, dependence on energy imports, and greenhouse gas emissions, as well as energy consumption in various sectors of the economy. The selection of these features was determined by their availability for all the studied facilities (EU countries). The statistical data used in the work were taken from Eurostat databases [42].

For each of the analyzed indicators (diagnostic features) from the SE group, its impact on the analyzed phenomenon is indicated by qualifying it to a set of features that stimulate development in an area (symbol S) or destimulate this development (symbol D):

• X_1S—Energy efficiency;

• X_2D—Primary energy consumption;

• X_3D—Final energy consumption;

• X_4D—Final energy consumption in households;

• X_5S—Energy productivity;

• X_6S—Renewable energy sources %;

• X_7S—Renewable energy sources in transport %;

• X_8S—Renewable energy sources in electricity %;

• X_9S—Renewable energy sources in heating and cooling %;

• X_10D—Total % of energy imports;

• X_11D—Energy import dependency—Solid fossil fuels;

• X_12D—Energy import dependency—Petroleum products;

• X_13D—Energy import dependency—Natural gas;

• X_14D—Population unable to keep home adequately warm;

• X_15S—Energy productivity Euro per KGOE;

• X_16D—Net greenhouse gas emissions per capita;

• X_17D—Net greenhouse gas emissions of the land use, land use change and forestry sector;

• X_18D—Gross available energy by product;

• X_19D—Final energy consumption by product;

• X_20D—Final consumption—Industry sector;

• X_21D—Final consumption—Transport sector;

• X_22D—Final consumption—Commercial and public services;

• X_23D—Final consumption—Households;

• X_24D—Final energy consumption in industry—Solid fossil fuels;

• X_25S—Final energy consumption in industry—Renewables and biofuels;

• (see Appendix A for the list of indicators with full descriptions and units of measure).

It should be noted that most of the indicators (68%) are destimulants, i.e., features that negatively affect the phenomenon at hand. On the basis of the listed indicators, a synthetic variable was determined for each of the examined years, by which it will be possible to classify EU countries in terms of the level of sustainable energy. For this purpose, the TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) is used—one of the methods of multivariate statistical analysis. This method was developed as a tool supporting the decision-making process in complex situations when many different criteria must be taken into account. Therefore, it is included in the group of multicriteria decision-making methods, marked in the literature as MADM (multiple-attribute decision making) [89,90,91,92,93]. The main idea of the TOPSIS method is to evaluate decision variants based on measuring the distance of these variants from two reference points—the ideal solution (PIS—positive ideal solution) and the anti-ideal solution (NIS—negative ideal solution). The most advantageous decision-making variant is the one with the smallest distance from PIS and the largest distance from NIS. This method can also be used to create rankings of objects, which is performed in this article. Its procedure takes place in seven steps:

Step 1. Determining the matrix:

(1)$X=\left[x_{ij}\right]$

where

• i—object number (i = 1, 2, …, n);

• j—diagnostic feature number (j = 1, 2, …, m);

• $x_{ij}$—the value of the j-th diagnostic feature for the i-th object.

Step 2. Normalization of diagnostic features:

(2)$z_{ij}=\frac{x_{ij}}{\sqrt{{\sum }_{i=1}^n{x}_{ij}^2}}$

where

• $z_{ij}$—the value of the j-th normalized diagnostic feature for the i-th object.

Step 3. Weighting of the standardized diagnostic features, resulting in a matrix:

(3)$V=\left[v_{ij}\right]=\left[{w_{j}z}_{ij}\right]$

for

(4)$\sum _{j=1}^m{w}_j=1$

where

• $w_j$—weight of the j-th diagnostic feature.

Step 4. For each normalized weighted diagnostic feature, two reference points are determined from the matrix (3), which determine the coordinates of the positive ideal solution and the negative ideal solution—the pattern and the antipattern, respectively:

(5)$v_j^{+}=\left\{\begin{array}{c}\underset{i}{\max }{v}_{ij} \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{s}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\\ \underset{i}{\min }{v}_{ij} \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{d}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\end{array}\right.$

(6)$v_j^{-}=\left\{\begin{array}{c}\underset{i}{\min }{v}_{ij} \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{s}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\\ \underset{i}{\max }{v}_{ij} \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{d}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\end{array}\right.$

where

• $v_j^{+}$—j-th coordinate of positive ideal solution;

• $v_j^{-}$—j-th coordinate of negative ideal solution.

Step 5. For all objects, their Euclidean distances from the pattern and antipattern are calculated, respectively:

(7)$d_i^{+}=\sqrt{\sum _{j=1}^m{\left(v_{ij}-v_j^{+}\right)}^2}$

(8)$d_i^{-}=\sqrt{\sum _{j=1}^m{\left(v_{ij}-v_j^{-}\right)}^2}$

where

• $d_i^{+}$—Euclidean distance of the i-th object from the positive ideal solution;

• $d_i^{-}$—Euclidean distance of the i-th object from the negative ideal solution.

Step 6. The value of the aggregate variable denoting the relative proximity of the i-th object to the positive ideal solution is determined as the quotient:

(9)$R_i=\frac{d_i^{-}}{d_i^{-}+d_i^{+}}$

where

• $0\le R_i\le 1$.

The preferred object has the smallest distance from the pattern and at the same time the largest distance from the antipattern, i.e., it has the highest value of the R_i coefficient.

Step 7. Linear ordering of objects is performed due to the nonincreasing value of the aggregate variable (9).

Based on the obtained aggregate measure, objects are divided into groups characterized by a similar situation with regard to sustainable energy. The so-called three-median method involves determining the median of the value of the measure and then dividing the group of objects into those for which the value of the measure exceeds the median and is not greater than it [94].

The main advantages of the TOPSIS method are its simplicity, comprehensibility, and the guarantee of easy interpretation of results [91]. Some limitations of the method and problems associated with its application are also pointed out in the literature. These are related to the nature and scale of data measurement and the implementation of computational algorithms. A study by Filipowicz-Chomko [95] showed that the adopted normalization technique influences the evaluation and, consequently, the final ranking of objects for both the TOPSIS algorithm with the Euclidean distance applied and the Mahalanobis distance. A similar view is taken by other authors [96,97,98]. Furthermore, to a large extent, the impact of the normalization technique on the final ranking depends on the initial dataset (e.g., type and number of data).

4. Results

High levels of the coefficient of variation (Vs) and the asymmetry coefficient (A) of diagnostic characteristics, for all examined years, indicate large disparities between the studied countries in terms of sustainable energy. Table 3 presents selected descriptive parameters of the indicators adopted for the study in 2021. As shown in the table, we are dealing with strong differentiation for all the examined features. The coefficient of variation exceeds 30%, and for 10 indicators its value is higher than 100%. The exception is the X_12D indicator (energy import dependency—petroleum products), in the case of which the relative measure of diversity is 17.73%. The measures of asymmetry are included, and what is particularly important is that in the case of most features (84%), there is a right-sided asymmetry, which means that for most EU countries the feature values are below the EU average, which is negative in the case of stimulants, and positive in the case of destimulants. The highest right-side asymmetry (2.90) is characterized by the feature X_7S (renewable energy sources in transport %). This is a very unfavorable situation in the case of a stimulant, indicating that for most EU countries, this indicator is below the average. This applies to 17 member states (63%), with the lowest value of this indicator found in Ireland, where its value was approximately 50% lower than the EU average. The most favorable situation was in Sweden, where the value of the indicator was three times higher than the average value for all countries.

An unfavorable situation was also found for the indicator related to energy import dependency (X_12D), which was characterized by the lowest left-sided asymmetry (−2.41). Due to the nature of this indicator (destimulant), its low value is desirable. Meanwhile, for 12 EU countries, its value exceeded the EU average. The most difficult situation in this respect was in Malta (the indicator was higher than the average for EU countries by 76%), and the best for Estonia, where the indicator was almost 40 times lower than the EU level.

A detailed analysis of the remaining features showed an unfavorable distribution of six stimulant indicators (X₅—energy productivity; X₆—renewable energy sources %; X₈—renewable energy sources in electricity %; X₂₅—final energy consumption in industry—renewables and biofuels) and five destimulant indicators (X₁₀—total % of energy imports; X₁₁—energy import dependency—solid fossil fuels; X₁₃—energy import dependency—natural gas; X₁₇—net greenhouse gas emissions of the land use, land use change and forestry sector; X₁₈—gross available energy by product).

Table 4 shows the classification of EU countries based on the value of the synthetic measure calculated, taking into account the features given in Section 2 in the years 2013–2021. Noteworthy, the situation of the countries is quite stable in the period under study. For most of them, no significant changes were observed in the positions occupied in the rankings in individual years. In 2021, compared to 2013, the change by one position was found for eight countries, five of which (Croatia, Hungary, Lithuania, Romania, Slovakia) saw a decrease in the ranking, and the remaining four (Greece, Estonia, Ireland, Portugal) saw an improvement. Sweden is the continuous leader in each of the analyzed years. This country is characterized by the highest level of share of renewable energy in gross final energy consumption by sector (X₆–X₉) and the lowest net greenhouse gas emissions (X₁₆) among EU countries. The position of the countries at the bottom of the ranking is also stable in the period under review. These include Germany, France, Italy, and Poland. Germany, which ranks last, is characterized by the highest level of energy efficiency (X_1S), but at the same time energy consumption in this country (X₂–X₄) exceeds the EU average, and indicators related to share of renewable energy in gross final energy consumption by sector (X₆, X₇, X₉) are below the average value for all EU countries.

The rest of the study focused on the analysis of the financial development index (FDI). The analysis of descriptive parameters indicates that we are dealing with a fairly stable average value of this indicator in the examined period and its strong differentiation (Table 5). The lowest FDI level was in Lithuania and the highest in Spain and France. The coefficient of variation for this indicator ranged from 32.44% in 2013 to 36.40% in 2021. In the analyzed years, the FDI distribution was characterized by weak or moderate left-sided asymmetry, which means that most EU countries had a value for this indicator above average.

Table 6 shows the correlation coefficients between the FDI measure and the designated synthetic measures R_i in the years 2013–2021. The values of the correlation measure indicate the existence of a weak negative relationship.

5. Discussion

The subject of our study was the analysis of the relationship between financial development and a group of indicators related to sustainable energy in individual European Union countries in the years 2013–2021. The research shows that there are large disparities in sustainable energy utilization between the countries studied. The values of variables are below the EU average for most member state countries, which is a positive phenomenon in the case of variables identified as destimulants, but negative in the case of stimulants. The lowest value of the indicator in terms of renewable energy sources in transport was achieved by Ireland. Our results are consistent with the results of the study by Brodny et al. [99], who assessed the level of development of renewable energy in EU countries in 2008–2018. Ireland was in the class of countries with the lowest level of renewable energy development in both 2008 and 2018. In terms of renewable energy share, Sweden achieved the best result in our study. Similar conclusions were published in the study by Tutak et. al. [100]. Energy consumed in Sweden comes mainly from renewable energy sources. Sweden aims at generating 100% of its electricity from renewable energy sources by 2040 (European Commission). For this purpose, Sweden has been investing huge funds in the search for alternative energy sources for several decades [101] and allocates significant funds in relation to GDP (%) to research and development in this area.

A study of the impact of financial sector development indicators and access to financial institutions on primary energy consumption in European Union countries in 1996–2017 was conducted by Bayar et al. [102]. The research results showed a positive impact of the financial indicator on primary energy consumption in Bulgaria, Croatia, the Czech Republic, Hungary, and Slovenia, and private loans also had a positive impact on primary energy consumption in Bulgaria, the Czech Republic, Estonia, Hungary, Lithuania, Poland, and Slovakia. Access by financial institutions negatively correlated with primary energy consumption in Croatia, Estonia, Hungary, Poland, and Romania.

Our study found no positive relationship between financial development and increasing demand for sustainable energy sources. Different results were obtained by Samour et al. [103], Usman et al. [104], Musa et al. [74], and Shahbaz et al. [79], who showed in independent research that financial development has a positive impact on the demand for renewable energy.

In terms of the energy import dependency indicator (X_12D), Estonia had the most favorable situation and Malta the worst. The obtained results are consistent with the results of Holmgren et al. [105], Streimikiene [106], and Rokicki and Perkowska [107], who showed that Estonia is one of the most energy-independent countries in the EU-27. Malta’s energy dependence was confirmed in research by Carfora et al. [108].

As part of our study, we analyzed the level of the FDI ratio in individual countries. The results showed that most countries had a value above the EU average. The highest value of the indicator was observed for Spain and France. The abovementioned results are consistent with the research results of Shahbaz et al. [109] and Zhu et al. [110].

In our conducted study, the European Union countries were classified in terms of the level of sustainable energy based on the TOPSIS method. This method was also used by Tutak et al. [100] to rank the countries of the European Community in terms of sustainable energy development. The research results showed that the EU leaders in the field of sustainable energy development are Sweden, Finland, and Austria.

6. Conclusions

The problem of the role of renewable energy sources in green economic transformation is often analyzed and researched in the literature. The studies cover different countries and have different contexts. One of the aspects of the study is the relationship between financial development and renewable energy sources or, more broadly, the energy market. The purpose of the article is to examine the relationship between sustainable energy sources and financial development. In particular, the following research questions were asked: In the case of countries advanced in green transition processes (countries leading the charge on renewable energy), is the impact of financial development on sustainable energy resources the same in different EU countries? How is transition towards renewable energy sources progressing in individual economic sectors? Does financial development influence sectoral transitioning in particular countries? An innovative approach to the study consists of developing an aggregated measure of development for sustainable energy sources in individual European Union countries in 2013–2021. The TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) was used to classify EU countries in terms of the level of sustainable energy level. The research found that the pace and advancement of the transition process towards renewable energy sources differ in individual countries; there are leading countries that have invested in the green transition of the energy market and have achieved significant effects, e.g., Sweden and Germany, and countries that are lagging behind in this process, in descending order, are Croatia, Hungary, Lithuania, Romania, and Slovakia.

The TOPSIS method ensures the multidimensionality of the survey, allowing not only for the ranking of individual countries, but also the determination of the distance of each country from the abstract ideal country with the most favorable values of individual indicators. Of course, one should be aware of the limitations of the method, especially taking into account the initial set of diagnostic characteristics and the way they are normalized. For this reason, it seems important to carry out further systematic research using the described method for successive periods and different methods of normalization, which will enable the analysis of the dynamics and direction of sustainable energy concept implementation in EU countries.

The results of the study in the field of sustainable energy sources and financial development, depending on the examined variable from the sustainable energy resources group, indicate positive (Bulgaria, Croatia, the Czech Republic, Lithuania, Poland, Hungary, and Slovenia) or negative (Croatia, Estonia, Hungary, Poland, and Romania) relationships between sustainable energy sources and FDI. The study of the impact of FDI on sector transformation in renewable energy sources also showed significant differences between countries; for example, Ireland’s transport sector ranks the worst, while Sweden tops the rankings. The key findings are as follows:

• The link between sustainable energy sources and financial development differs among EU countries;

• Scandinavian countries are the leaders in transitioning towards sustainable energy sources;

• The size of the country matters in energy autonomy;

• The transition towards sustainable energy sources differs not only on a country level but also on a business sector level.

The results of our research may be useful for government decision-makers in the process of designing and controlling the country’s transition to sustainable energy.

In particular, important implications for governments and policymakers from the conducted research are that the policy of developing the financial and energy markets should be made in parallel and in concert because these policies are interlinked. In particular, it is recommended that decision-makers responsible for financial development and the development of the energy market coordinate and join their efforts in designing and introducing financial innovations and eco-innovations in the energy market. Policymakers, in particular, should create advisory teams responsible for preparing development strategies for sustainable energy markets and sustainable financial development, including representatives of financial markets, energy markets, governments, scientists, and other stakeholders. The development of such strategies should result from joint discussions on the shape of sustainable energy markets and their financing sources.

In the research process, a significant limitation came from the need to ensure comprehensive access to data and their comparability. In turn, future research can be focused on examining the impact of individual financial instruments (e.g., loans) and financial markets (e.g., banking, capital) on the development of sustainable energy resources.

Author Contributions

Conceptualization, M.Z., I.B. and A.S.; methodology, I.B.; software, I.B.; validation I.B.; formal analysis, M.Z., I.B. and A.S.; investigation, M.Z., I.B. and A.S.; resources, A.S.; data curation, I.B.; writing—original draft preparation, M.Z., I.B. and A.S.; writing—review and editing, M.Z., I.B. and A.S.; visualization, M.Z., I.B. and A.S.; supervision, M.Z., I.B. and A.S.; project administration, M.Z.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

These data were derived from the following resources available in the public domain: Eurostat (https://ec.europa.eu/eurostat/web/main/data/database, accessed on 20 May 2024), International Monetary Fund (https://data.imf.org/?sk=f8032e80-b36c-43b1-ac26-493c5b1cd33b&sid=1481126573525, accessed on 20 May 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Tables

Table 1

Selected indicators for sustainable energy.

Source	Indicators Used for Sustainable Energy Measurement
International Atomic Energy Agency (IAEA) in cooperation with the United Nations Department of Economic and Social Affairs (UNDESA), the International Energy Agency (IEA) and the European Environment Agency (EEA) [41]	Energy indicators for sustainable development (EISD).
Eurostat [42]	Energy productivity, electricity price by type of user, market share of the largest generator in the electricity market, gross available energy by product, total energy supply by product, final energy consumption by product, final energy consumption by sector, final energy consumption in households by type of fuel, final energy consumption in industry by type of fuel, primary energy consumption, share of renewable energy in gross final energy consumption by sector, energy import dependency by products, population unable to keep home adequately warm by poverty status.
Ligus and Peternek [43]	Sustainable Energy Development Aggregated Index composed of 47 indicators, which are grouped into eight categories and twenty one subcategories. These categories are then grouped into three dimensions of society, economy, and environment. The social dimension considers energy equity (accessibility, affordability, disparities) and health categories. The economic dimension considers competitive energy market (market structure, prices, and efficiency), energy security (dependency and depletion of local energy resources), and energy consumption patterns (energy use and intensity) categories. The environmental dimension is based on environmental pressure (GHG emissions, air quality, waste generation) and resource pressure (share of renewables in energy production, combined heat and power, energy recovery) and energy taxes categories.
Saraswat and Digalwar [44]	767 indicators were used to measure sustainable energy, and then they selected 26 indicators grouped into six categories: economic, technical, social, environmental, political, and flexible.

Table 2

Structure of Financial Development Index.

Financial Development (FD)	Financial Institutions (FI)	Depth—compiles data on bank credit to the private sector as a percentage of GDP, pension fund assets as a percentage of GDP, mutual fund assets as a percentage of GDP, and life and nonlife insurance premiums as a percentage of GDP.
		Access—compiles data on the number of bank branches per 100,000 adults and the number of ATMs per 100,000 adults.
		Efficiency—compiles data on banking sector net interest margin, spread between lending and deposits, noninterest income as a proportion of total income, overhead costs relative to total assets, return on assets, and return on equity.
	Financial Markets (FM)	Depth—compiles data on stock market capitalization as a percentage of GDP, stocks traded as a percentage of GDP, international government debt securities as a percentage of GDP, and total debt securities of financial and nonfinancial corporations as a percentage of GDP.
		Access—compiles data on the proportion of market capitalization not accounted for by the top 10 largest companies and the total number of debt issuers (including domestic and external, nonfinancial, and financial corporations) per 100,000 adults.
		Efficiency—compiles data on the ratio of stock market turnover (stocks traded to capitalization).

Source: International Monetary Funds https://data.imf.org/?sk=f8032e80-b36c-43b1-ac26-493c5b1cd33b&sid=1480712464593 (accessed on 1 July 2024).

Table 3

Selected descriptive parameters for indicators related to sustainable energy in 2021.

Feature	Average	S(x)	Vs	A
X 1	35.83	48.34	134.92	2.31
X 2	3.04	1.11	36.55	1.56
X 3	2.34	1.03	44.12	2.47
X 4	596.30	185.87	31.17	0.10
X 5	9.99	3.56	35.59	2.62
X 6	24.80	12.09	48.77	1.35
X 7	9.38	4.80	51.20	2.90
X 8	35.69	18.74	52.49	0.72
X 9	31.17	16.76	53.75	0.51
X 10	55.21	22.09	40.01	−0.16
X 11	61.88	40.55	65.53	−0.46
X 12	88.91	15.77	17.73	−2.41
X 13	80.46	28.34	35.22	−1.53
X 14	7.37	6.72	91.24	1.29
X 15	8.08	4.50	55.72	2.25
X 16	7.77	3.35	43.07	0.63
X 17	−8517.95	15,234.84	178.86	−1.55
X 18	54,160.82	72,864.26	134.53	2.19
X 19	34,770.73	46,570.90	133.94	2.25
X 20	9023.40	12,066.44	133.72	2.55
X 21	10,074.90	13,330.53	132.31	1.93
X 22	4791.59	7035.20	146.82	2.47
X 23	9635.82	13,465.83	139.75	2.43
X 24	402.82	691.02	171.54	2.88
X 25	861.23	1214.69	141.04	2.06

Where S(x)—standard deviation; Vs—coefficient of variation; A—asymmetry coefficient.

Table 4

Rankings of EU countries in terms of sustainable energy in 2013–2021.

Country	2013		2014		2015		2016		2017		2018		2019		2020		2021
	${\mathit{R}}_{\mathit{i}}$		${\mathit{R}}_{\mathit{i}}$		${\mathit{R}}_{\mathit{i}}$		${\mathit{R}}_{\mathit{i}}$		${\mathit{R}}_{\mathit{i}}$		${\mathit{R}}_{\mathit{i}}$		${\mathit{R}}_{\mathit{i}}$		${\mathit{R}}_{\mathit{i}}$		${\mathit{R}}_{\mathit{i}}$
Austria	0.598	1	0.589	1	0.594	1	0.590	1	0.595	1	0.581	1	0.582	1	0.595	1	0.570	1
Belgium	0.529	4	0.528	4	0.526	4	0.530	4	0.544	4	0.523	4	0.533	4	0.552	4	0.524	4
Bulgaria	0.554	3	0.548	3	0.549	3	0.548	3	0.557	3	0.542	3	0.552	3	0.567	3	0.537	3
Croatia	0.583	1	0.579	2	0.577	2	0.571	2	0.579	2	0.569	2	0.579	2	0.586	2	0.564	2
Cyprus	0.547	3	0.542	3	0.544	3	0.550	3	0.527	4	0.542	3	0.546	4	0.563	3	0.545	3
Czechia	0.550	3	0.548	3	0.549	3	0.549	3	0.558	3	0.538	3	0.547	3	0.564	3	0.529	3
Denmark	0.604	1	0.599	1	0.602	1	0.601	1	0.615	1	0.599	1	0.597	1	0.611	1	0.602	1
Estonia	0.554	3	0.548	3	0.564	2	0.552	3	0.561	3	0.553	2	0.566	2	0.542	4	0.566	2
Finland	0.601	1	0.609	1	0.614	1	0.590	1	0.615	1	0.591	1	0.600	1	0.608	1	0.591	1
France	0.466	4	0.465	4	0.468	4	0.470	4	0.484	4	0.461	4	0.470	4	0.496	4	0.452	4
Germany	0.402	4	0.397	4	0.391	4	0.392	4	0.414	4	0.402	4	0.404	4	0.416	4	0.390	4
Greece	0.552	3	0.544	3	0.546	3	0.542	3	0.561	3	0.545	3	0.559	2	0.575	2	0.547	2
Hungary	0.565	2	0.557	2	0.561	2	0.560	2	0.567	2	0.552	2	0.557	3	0.576	2	0.546	3
Ireland	0.547	3	0.547	3	0.554	3	0.560	2	0.578	2	0.564	2	0.577	2	0.592	2	0.561	2
Italy	0.480	4	0.473	4	0.481	4	0.481	4	0.495	4	0.465	4	0.479	4	0.512	4	0.468	4
Latvia	0.578	2	0.581	1	0.579	2	0.576	2	0.586	2	0.576	1	0.582	1	0.593	1	0.571	1
Lithuania	0.557	2	0.553	2	0.553	3	0.549	3	0.557	3	0.540	3	0.547	3	0.563	3	0.540	3
Luxembourg	0.511	4	0.512	4	0.520	4	0.521	4	0.533	4	0.517	4	0.524	4	0.545	4	0.522	4
Malta	0.556	2	0.555	2	0.561	2	0.565	2	0.569	2	0.555	2	0.564	2	0.578	2	0.556	2
Netherlands	0.550	3	0.548	3	0.539	3	0.537	4	0.551	3	0.537	3	0.548	3	0.563	3	0.534	3
Poland	0.506	4	0.498	4	0.504	4	0.498	4	0.512	4	0.480	4	0.493	4	0.522	4	0.471	4
Portugal	0.574	2	0.573	2	0.581	1	0.580	1	0.586	1	0.574	2	0.581	2	0.605	1	0.579	1
Romania	0.591	1	0.584	1	0.584	1	0.583	1	0.596	1	0.576	1	0.582	1	0.590	2	0.560	2
Slovakia	0.558	2	0.557	2	0.562	2	0.559	2	0.564	2	0.550	3	0.556	3	0.572	3	0.546	3
Slovenia	0.576	2	0.567	2	0.567	2	0.563	2	0.576	2	0.564	2	0.578	2	0.592	2	0.570	2
Spain	0.542	4	0.530	4	0.537	4	0.546	3	0.559	3	0.536	4	0.550	3	0.574	3	0.526	4
Sweden	0.647	1	0.643	1	0.647	1	0.650	1	0.658	1	0.647	1	0.655	1	0.659	1	0.641	1

Table 5

Selected descriptive parameters for the FDI indicator in 2013–2021.

Year	Average	Min	Max	Vs	A
2013	0.559	0.220	0.844	32.441	−0.343
2014	0.553	0.215	0.878	34.816	−0.260
2015	0.552	0.213	0.877	34.818	−0.199
2016	0.547	0.212	0.870	34.452	−0.183
2017	0.558	0.219	0.901	34.701	−0.204
2018	0.546	0.212	0.847	34.496	−0.217
2019	0.535	0.200	0.815	35.905	−0.238
2020	0.534	0.207	0.827	35.859	−0.215
2021	0.528	0.197	0.815	36.395	−0.235

Where Vs—coefficient of variation; A—asymmetry coefficient.

Table 6

Correlation coefficients between the FDI measure and the synthetic measure R_i.

Year	Pearson
2013	−0.2036
2014	−0.2199
2015	−0.2427
2016	−0.1792
2017	−0.1587
2018	−0.1961
2019	−0.1707
2020	−0.1381
2021	−0.2008

Appendix A

List of variables with full description and units:

• X_1S—Energy efficiency (final energy consumption, Mt of oil equivalent)

• X_2D—Primary energy consumption (tons of oil equivalent (TOE) per capita)

• X_3D—Final energy consumption (tons of oil equivalent (TOE) per capita)

• X_4D—Final energy consumption in households per capita (kg of oil equivalent)

• X_5S—Energy productivity (purchasing power standard (PPS) per kilogram of oil equivalent)

• X_6S—Share of renewable energy in gross final energy consumption by sector—Renewable energy sources %

• X_7S—Share of renewable energy in gross final energy consumption by sector—Renewable energy sources in transport %

• X_8S—Share of renewable energy in gross final energy consumption by sector—Renewable energy sources in electricity %

• X_9S—Share of renewable energy in gross final energy consumption by sector—Renewable energy sources in heating and cooling %

• X_10D—Energy import dependency by products—Total % of imports in total gross available energy

• X_11D—Energy import dependency by products—Solid fossil fuels (% of imports in total gross available energy

• X_12D—Energy import dependency by products—Oil and petroleum products (excluding biofuel portion) (% of imports in total gross available energy)

• X_13D—Energy import dependency by products—Natural gas (% of imports in total gross available energy)

• X_14D—Population unable to keep home adequately warm by poverty status % of population

• X_15S—Energy productivity EUR per kilogram of oil equivalent (KGOE)

• X_16D—Net greenhouse gas emissions (tons per capita)

• X_17D—Net greenhouse gas emissions of the land use, land use change and forestry (LULUCF) sector (thousand tons)

• X_18D—Gross available energy by product—Total thousand tons of oil equivalent

• X_19D—Final energy consumption by product—Total (thousand tons of oil equivalent)

• X_20D—Final consumption—Industry sector—Energy use (thousand tons of oil equivalent)

• X_21D—Final consumption—Transport sector—Energy use (thousand tons of oil equivalent)

• X_22D—Final consumption—Other sectors—Commercial and public services—Energy use (thousand tons of oil equivalent)

• X_23D—Final consumption—Other sectors—Households—Energy use (thousand tons of oil equivalent)

• X_24D—Final energy consumption in industry by type of fuel—Solid fossil fuels (thousand tons of oil equivalent)

• X_25S—Final energy consumption in industry by type of fuel—Renewables and biofuels (thousand tons of oil equivalent)

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