Evaluation of Renewable Energy Sources Sector Development in the European Union

Abstract

The global energy landscape is transforming, driven by the urgent need to address climate change, reduce dependency on fossil fuels, and promote sustainable economic growth. Renewable energy sources (RESs) have emerged as a cornerstone of this transition, offering environmental benefits and significant potential to catalyze economic development. By harnessing inexhaustible natural resources, such as solar, wind, hydro, and biomass, renewable energy systems provide a pathway to achieving energy security, fostering innovation, and generating new economic opportunities. In this article, the economic effect on the RES sector development was examined. The authors defined the set from seven indicators: real GDP growth, unemployment rate, inflation rate, exports of goods and services, government debt, foreign direct investments, and labor cost index, which allowed them to evaluate the EU countries’ economic situation and rank the countries by economic stability level. The results, which were obtained using a multi-criteria evaluation method, show that the EU countries whose economies are the strongest according to the evaluated macroeconomic indicators are Luxembourg, Malta, Estonia, and Ireland. The countries with the lowest scores are Greece, Italy, and Spain. Seeking to evaluate the development level of the RES sector in all ranked EU countries, the analysis of RES sector development during the 2012–2022 period, using these RES indicators—share of renewable energy in gross final energy consumption by sector—in general, in transport, in electricity, and in heating and cooling, was carried out and, through a different multi-criteria method, the countries were ranked by RES development. After the analysis was carried out, it could be stated that the economic situation stability in the country does not directly affect the growth of the RES sector development, and the two rankings by different indicators are heavily uncorrelated. RES sector development can be affected by many other circumstances. RES development is still stagnating in some countries, despite macroeconomic stability, for several reasons: institutional and political barriers, differences in the availability of finance, infrastructure limitations, and technological and human resource shortages.

1. Introduction

The world is currently facing a variety of complex challenges that impact energy consumption and production. One of the most pressing issues is the growing global population, which is expected to reach nearly 10 billion by 2050. With more people on the planet, energy demand will increase exponentially. Simultaneously, fossil fuel reserves are dwindling. The extraction of coal, oil, and natural gas—once relatively abundant—is becoming more difficult, expensive, and environmentally destructive. The depletion of these resources, paired with the increasing pollution caused by their consumption, threatens the well-being of ecosystems and the quality of life for people worldwide.

Fossil fuels are a major source of greenhouse gas emissions, which contribute to climate change [1]. Carbon dioxide, methane, and nitrous oxide, the primary culprits of global warming, are released into the atmosphere when fossil fuels are burned for energy production. This has led to rising global temperatures, more frequent and severe weather events, and disruptions to ecosystems and agricultural production.

The allocation of the European Union (hereinafter—EU) budget and funds for the development of the renewable energy sources market is closely related to the initiative’s support for modernizing the European energy system and phasing out our dependency on Russian fossil fuels. Also, the continuing threat of climate change and its impact on human health and well-being have dramatically raised the need for renewable energy sources. The main energy policy objectives of many countries worldwide are to ensure the security of fuel and energy supply, improve economic competitiveness, well-being, increase energy efficiency, and foster a transition toward a more circular economy [2,3,4,5,6,7].

To achieve these objectives, the key goal for every country is to increase the use of renewable energy sources. The ways to reach this aim may be different, starting with encouraging socially responsible investing [8], diminishing energy demand [9,10], and revising governance of the energy sector by ensuring fair competition. In this context, excessive regulation might slow down the transition process [11]. Furthermore, some studies suggest that reducing the prevalence of state-owned companies in the industry may accelerate the transition [12,13], although opposite findings have also been reported [14].

Economic development, a multifaceted process encompassing increases in income, employment, and societal well-being, is intricately linked to energy consumption [15,16,17]. Also, economic growth is linked to the increase in mobility, because an increase in energy consumption is needed to provide more transportation and connection capabilities, fueling economic growth [18,19,20]. Traditional energy systems, reliant on fossil fuels, have long been associated with economic growth because fossil fuels are used for growth as they are cheap to provide energy, but, nowadays, traditional energy systems are increasingly viewed as unsustainable due to their environmental and geopolitical implications. In contrast, renewable energy systems offer a cleaner, more resilient alternative, capable of driving growth while mitigating environmental degradation and reducing vulnerability to volatile energy markets.

The relationship between RES and economic development is complex and dynamic. Studies have shown that renewable energy can enhance economic productivity by creating green jobs, stimulating technological innovation, and reducing energy import dependence [21,22]. However, the economic impact of renewable energy varies across regions and depends on factors such as policy infrastructure readiness and policies [23,24].

While developed economies often reap immediate benefits due to their advanced technological and financial capabilities, developing regions may face initial challenges in transitioning to renewables but stand to gain significantly in the long run.

The main objective of this article is to evaluate the RES sector development in the context of the EU and to highlight the absence of correlation between RES and the economic strength of the country, evaluated through seven indicators, i.e., real GDP growth, unemployment rate, inflation rate, exports of goods and services, government debt, foreign direct investments and labor cost index. These indicators are widely used to assess the overall economic condition [25,26,27,28,29,30]. All data are obtained from Eurostat, and the analyzed period is 2012–2022, because these data were available in all countries.

In this article, the following methods were used: multi-criteria evaluation, Simple Additive Weighting method (hereinafter—SAW), and correlation analysis.

The article finds that the strongest economies among EU countries are Luxembourg, Malta, Estonia, and Ireland, and the weakest are Greece, Italy, and Spain. This was obtained through multi-criteria evaluation, using the Simple Additive Weighting method. However, it was demonstrated via correlation analysis that RES growth is independent of the economic solidity of the country. This finding constitutes a novelty of this work as it was never previously highlighted in other studies.

The rest of this article is structured as follows: a literature review, where other authors works on the topic of RES development are analyzed, is conducted in Section 2, in Section 3; the methods are illustrated, the case study is described in Section 4 and the results are described in Section 5; in Section 6, the discussion is carried out and in Section 7 the conclusions are provided.

2. Literature Review

2.1. Examination Level of Renewable Energy Sources

Renewable energy, by definition, refers to energy derived from natural sources that are naturally replenished at a rate faster than they are consumed. These sources—such as sunlight, wind, geothermal heat, and hydropower—are constantly regenerated through natural processes and are virtually inexhaustible on human timescales [31]. Unlike fossil fuels, which are finite and depleting, renewable energy offers a sustainable alternative that can meet the increasing global demand for energy while addressing environmental concerns. As such, renewable energy plays a crucial role in the quest for sustainable development, which aims to balance human development with environmental preservation.

The alarming consequences of climate change highlight the pressing need to transition toward renewable energy sources (RESs) that are clean, combustion-free, and emit no harmful pollutants into the atmosphere [32,33]. In today’s context, the demand for sustainable energy solutions is more critical than ever. As the energy sector transforms, governments, businesses, and individuals are increasingly recognizing the necessity of shifting away from fossil fuels toward renewables as a foundation for sustainable progress. In this evolving landscape, sustainability accounting—that is, the practice of measuring, analyzing, and reporting a company’s social and environmental impacts—is gaining prominence in tracking and supporting this transition [34,35,36].

The growing significance of RES underscores their central role in tackling global challenges such as energy security, climate change mitigation, and long-term economic resilience [37,38]. A broad body of research investigates the causal links between renewable energy consumption and economic development, highlighting the strategic potential of renewables to drive both environmental and financial sustainability [1,39,40,41,42,43].

Technologies like solar panels, wind turbines, and hydropower systems are increasingly seen as practical, scalable alternatives to fossil fuel-based energy. As these technologies continue to advance in efficiency and affordability, they offer substantial promise for reducing global greenhouse gas emissions and advancing sustainable development goals [44]. In addition, the decentralized nature of many renewable systems makes them especially advantageous for remote or underserved communities, providing local and resilient energy solutions.

In addition to reducing emissions, renewable energy technologies can help mitigate the effects of climate change by promoting energy efficiency and reducing reliance on imported fuels [45]. By harnessing local, renewable energy resources, countries can reduce their dependence on fossil fuel imports, which are subject to price volatility and geopolitical tensions. This decentralization of energy production can enhance energy security and reduce the risk of energy shortages during times of crisis.

A key challenge in the widespread adoption of renewable energy is its intermittency [46]. Unlike fossil fuels, which provide a steady and reliable supply of energy, renewable energy sources such as wind and solar are dependent on weather conditions. Solar panels generate electricity only when the sun is shining, and wind turbines only operate when the wind is blowing. As such, energy storage and harvesting technologies are critical for ensuring a stable and reliable energy supply.

Energy storage technologies, such as batteries and pumped hydro storage, can store excess energy generated during periods of high renewable energy production and release it when demand is high or when renewable sources are not available [47,48]. These storage systems, which can be realized based on modules recycled from the automotive industry, help smooth out the fluctuations in renewable energy generation and ensure that energy is available when needed [49]. It is also interesting to point out that energy storage technologies can be used to support the grid if it reaches its limits only sometimes during the day, useful for delaying costly upgrades to the grid [50].

Additionally, advances in energy harvesting technologies, which capture and store small amounts of energy from everyday activities, are opening up new possibilities for decentralized energy generation [51,52]. These technologies include energy harvesters integrated into shock absorbers [53], wearable devices [54], pavements [55], and tidal energy systems [56], offering diverse opportunities for sustainable and location-specific energy solutions.

As renewable energy becomes increasingly central to sustainable development, advancements in energy storage and harvesting technologies are critical to ensuring its reliability, scalability, and affordability worldwide. According to [15], energy storage plays a pivotal role in optimizing renewable energy systems by enabling a more stable and flexible energy supply, which is essential for integrating intermittent sources like solar and wind into the grid. This is particularly important in regions where renewable energy generation is plentiful, but electricity demand varies significantly throughout the day or across seasons.

Renewable energy also plays an important role in reducing the cost of energy imports [57]. Countries that rely heavily on fossil fuel imports can benefit from renewable energy systems that harness local resources, reducing their dependence on foreign energy sources. In the meantime, countries in the Middle East and North Africa, which are heavily dependent on oil and gas exports, are increasingly investing in solar and wind energy to diversify their energy mix and create new economic opportunities.

Although renewable energy systems often require a substantial upfront investment, the long-term benefits outweigh the initial costs. As technology improves and economies of scale take effect, renewable energy costs continue to fall. Renewable energy is already cheaper in many regions than fossil fuel-based power generation, and this price difference is expected to widen in the coming years. This makes renewable energy not only an environmentally sustainable choice but also an economically advantageous one.

In addition to solar and wind, modern biomass and hydrogen are expected to play a crucial role in the global energy transition. Biomass, which includes organic materials such as wood, agricultural residues, and waste, is already being used to generate electricity and heat [58,59].

By 2050, biomass is expected to meet 16% of the world’s final energy demand [60]. Hydrogen, which can be produced using renewable electricity, will also become an important energy carrier, accounting for 14% of global energy demand by mid-century [61]. The transition to renewable energy will require significant investments in infrastructure, research and development, and policy support. Governments, businesses, and individuals must work together to accelerate the adoption of renewable energy technologies and ensure that the transition is fair and equitable for all. By doing so, they can help build a sustainable, low-carbon energy future that supports economic growth, environmental protection, and social well-being for generations to come [62,63,64,65].

2.2. Renewable Energy Consumption in Relation to Economic Growth

The relationship between RES and economic growth in different regions has been examined by many authors, and most of them are devoted to measuring the relationship between renewable energy consumption (hereinafter—REC) and economic growth. A nonlinear relationship was observed between renewable energy consumption and economic growth in OECD countries [1]. Urbanization levels, non-renewable energy intensity, and per capita income were key threshold variables. The findings suggest that increasing renewable energy consumption has a positive effect on economic growth, but the magnitude of this impact depends on the thresholds set by these variables. It was focused on the relationship between REC, economic growth, and financial development in China over the period 1997–2017 [23]. The key finding of this study is that long-run economic growth stimulates REC, but financial development negatively impacts it. The Black Sea and Balkan countries were examined in another study, and a long-term balance relationship between renewable energy consumption and economic growth was found, and different hypotheses were supported: the Growth hypothesis, the Feedback hypothesis, and the Neutrality hypothesis [40]. The Growth hypothesis assumes that renewable energy consumption causes economic growth. In other words, increases in renewable energy use lead to improvements in economic performance. The Feedback hypothesis suggests a bidirectional causal relationship between renewable energy consumption and economic growth, meaning that not only does energy consumption influence growth, but economic growth also drives energy consumption. The Neutrality hypothesis posits that there is no causal relationship between renewable energy consumption and economic growth, indicating that changes in one do not significantly affect the other [40]. The findings varied by country: The Growth hypothesis was supported in Bulgaria, Greece, Macedonia, Russia, and Ukraine; the Feedback hypothesis was supported in Albania, Georgia, and Romania; the Neutrality hypothesis was supported in Turkey.

Further study was performed on G7 Countries, where the data from 1980 to 2014 were examined, and the asymmetric relationships between total, renewable, and non-renewable energy consumption and economic growth using causality methods were investigated [66]. The main results revealed that asymmetric effects exist, with increases and decreases in energy consumption having differing impacts on economic growth. Both renewable and non-renewable energy positively influence economic growth in the long term, but the magnitude varies across countries and energy types. Also, the differences between traditional (EU13) and new EU member states were highlighted [41]. Long-term positive effects of renewable energy consumption on economic growth were evident in both groups. However, in the short term, new members experienced negative impacts, while traditional members showed no significant short-term effects. The role of fossil fuel prices in determining renewable energy consumption was analyzed [42]. Results showed that economic growth and fossil fuel prices significantly influenced renewable energy consumption in seven European countries. However, no causality from renewable energy to economic output was observed. The relationship between renewable energy consumption and economic growth across 38 countries from 1990 to 2018 was studied in another study [67]. A positive long-term relationship between renewable energy consumption and economic growth was observed in 58% of the countries studied. The research highlights the importance of international cooperation in boosting renewable energy investment to achieve low-carbon growth. Although renewable energy positively impacts many economies, a subset of countries shows a negative or insignificant relationship due to factors such as reliance on non-renewable energy or inadequate investment in renewable technologies.

Another part of the research is devoted to renewable energy consumption and CO₂ emissions. The relationship between renewable and non-renewable energy consumption, CO₂ emissions, and economic growth in Thailand (1971–2013) was explored [68]. Key findings include economic growth dependence—Thailand’s economic growth heavily relies on energy consumption, especially from non-renewable sources. This dependency poses environmental risks due to increased CO₂ emissions, and renewable energy consumption is still at a very low level. The environmental impacts of different energy sources on CO₂ emissions within the G7 nations were also examined. Utilizing methodologies such as quantile regression (QR), generalized method of moments (GMMs), and fixed and random effects models, this study reveals that electricity generated from coal and gas significantly increases CO₂ emissions, with coal having a more pronounced effect. Conversely, hydroelectric and renewable energy sources are associated with reductions in CO₂ emissions across all models. The research underscores the importance of transitioning from fossil fuels to renewable and hydroelectric energy to mitigate environmental degradation.

Beyond environmental benefits, the expansion of renewable energy infrastructure serves as a powerful engine for economic growth and sustainable job creation. In many regions, the renewable energy sector has generated millions of employment opportunities across research, manufacturing, installation, and maintenance [69,70]. Investments in renewable energy not only stimulate new markets and industries but also reduce long-term operational costs—thanks to the free and abundant nature of resources like wind and sunlight once systems are in place. Investment in renewable energy technologies has surged over the past decade, with global investments reaching USD 619.1 billion in 2023 [71]. This investment is driven by both the need to combat climate change and the potential for economic growth. Renewable energy is now recognized as a key driver of job creation, technological innovation, and long-term energy security. Renewable energy is also playing an increasingly important role in agricultural energy development. According to a study by [72], renewable energy sources are being integrated into agricultural energy systems, both for autonomous energy supply and as part of the broader electricity grid. This is particularly significant in rural areas where access to reliable electricity can be limited. Solar energy, for example, can power irrigation systems, heating for greenhouses, and refrigeration for food storage. Wind and biogas can also be harnessed to generate electricity or heat for agricultural operations.

Various economic factors and their impact on RES development were analyzed by other researchers [30,73,74]. The relationship between renewable energy consumption and various economic factors, such as gross domestic product (hereinafter—GDP), foreign direct investment (hereinafter—FDI), and CO₂ emissions was discussed as the most likely factors affecting renewable energy consumption in highly developed countries are CO₂ per capita emissions, terms of trade, GDP, FDI, crude oil price, and energy consumption from alternative sources. The same study highlighted that the most important factors in medium-developed countries are CO₂ per capita, labor force, forest area, and gas and coal consumption. In low-developed countries, REC consumption is driven by CO₂ per capita emissions, terms of trade, GDP, foreign direct investment, crude oil price, and energy consumption from alternative sources. Another study examined how economic and environmental factors influenced REC across 94 countries from 1995 to 2019. Using heterogeneous panel data fixed-effects estimation techniques with robust Driscoll–Kraay standard errors, the research identifies several key findings: higher carbon intensity significantly reduces REC globally, with a more pronounced effect in low-income and very high-income countries; an increase in GDP per capita promotes REC, particularly when it exceeds the threshold of USD 5000 and investment in R&D substantially boosts REC in very high-income countries [75].

3. Materials and Methods

For the evaluation of country attractiveness for the development of the RES sector and the impact of the country’s economic situation on RES sector development, several methods were selected. The methodology process is presented in Figure 1.

Firstly, a multi-criteria evaluation method, SAW, which reflects the basic idea of multi-criteria methods as it combines indicator values and weights into one size, the criteria of the method [76], was used for the measurement of country attractiveness for the development of this analyzed sector. The SAW method also allows for scenario analysis, where policymakers can explore the potential outcomes of adjusting specific criteria or priorities, thus enabling more flexible and dynamic decision-making. By using this approach, countries can better identify the most effective strategies for accelerating RES development based on their unique circumstances and resource availability. Firstly, using the SAW method, the primary data matrix is created (

Table 1. Initial data for multi-criteria analysis.

Indicators	The Importance of Indicators (Weight)	Values of Indicators
		1	2	…	j	…	n
X1	w1	x11	x21	…	xij	…	x1n
X2	w2	x21	x22	…	x2j	…	x2n
…	…	…	…	…	…	…	…
XI	wi	xi1	xi2	…	xij	…	xin
…	…	…	…	…	…	…	…
Xm	wm	xm1	xm2	…	xmj	…	xmn

Source: compiled by the authors.

).

Then, the normalization procedure of selected indicator values is carried out, and, according to the indicators’ influence on the main criteria, they are maximized and minimized. Minimization and maximization of the indicators is carried out according to the following equations [77]:

$${\overline{x}}_{ijk}={\displaystyle \frac{\underset{j}{min}{x}_{ijk}}{x_{ijk}}},$$

(1)

$${\overline{x}}_{ijk}={\displaystyle \frac{x_{ijk}}{\underset{j}{max}{x}_{ijk}}},$$

(2)

where $\underset{j}{max}{x}_{ijk}$ is the largest ith indicator in the kth year, $\underset{j}{\mathit{min}{x}_{ijk}}$ is the lowest ith indicator in the kth year, i refers to the indicator, j to the country, and k to the year.

The best values are the highest values of maximized indicators and the lowest values of minimized indicators.

In case of minimisation, the lowest value of the analyzed indicator for a specific country in the concrete year is found; so, this value is valued as the best and is equalized to 1, and then all other values of this indicator for all other countries are minimized based on the lowest value found using Equation (1). Then, the same procedure is repeated, evaluating the same indicators in the following years. This means that minimization is conducted indicator by indicator and year by year. In the case of maximisation, the same procedure is performed, only with the highest value of the indicator using Equation (2).

After the normalization procedure is ended, the weighted sum calculation is carried out. The sum S_jk is the sum of all normalized indicators for the country j in year k, with a final sum of all years k for the country j, leading to the overall value of country j, which is then used to rank economic strengths using the equation [78]:

$$S_{jk}={\sum }_{i=1}^m{w}_i{\overline{x}}_{ijk},$$

(3)

where $w_i$ is the weight of the i-th indicator, ${\overline{x}}_{ijk}$ is the normalized value of the i-th indicator for the j-th object in year k, and m is the total number of indicators.

To obtain the overall value for object j across all years, sum over k:

$$S_{j }={\sum }_{k=1}^T{S}_{jk}={\sum }_{k=1}^T{\sum }_{i=1}^m{w}_i{\overline{x}}_{ijk,}$$

(4)

where $T$ is the total number of years considered.

The best j-th corresponds to the highest value of S_jk. Comparative variants are arranged in descending order.

Practically, for all quantitative multi-criteria methods, positive values of the indicators are used. However, in practice, some indicators may be negative; in this paper, the real GDP growth and inflation rate can have positive and negative values. So, these data must be moved up to positive values according to the following equation [77,78]:

$${\overline{x}}_{ij}=x_{ij}+b_i(j=1,...,n)$$

(5)

where b_i is the i-th variable displacement constant.

The size b_i must match the condition presented in Equation (6).

$${ b}_i=\left|\underset{j}{min}{x}_{ij}\right|$$

(6)

The movement does not change the distance between values x_ij with any constant b_i. However, the ratio of values x_ij depends on the value b_i.

In order to additionally evaluate the impact of individual indicators on the development of the RES, the correlation coefficient (r) was calculated for each country’s macroeconomic indicator and the share of renewable energy in gross final energy consumption by sector (energy balance—renewable energy sources) indicator. Correlation is a statistic that measures the degree to which two variables move relative to each other. Correlation indicates the strength of the relationship between two variables and is expressed numerically as a correlation coefficient. Values of the correlation coefficient range between −1 and 1. It is also important to note that there is no linear relationship between variables when the correlation coefficient is zero or close to zero. The following Equation (7) is used to calculate the correlation:

$$r={\displaystyle \frac{{\sum }_{i=1}^n(x_i-\overline{x})(y_i-\overline{y})}{\sqrt{{\sum }_{i=1}^n{(x_i-\overline{x})}^2{\sum }_{i=1}^n{(y_i-\overline{y})}^2}}}$$

(7)

where r is the correlation coefficient, $\overline{y}$ is the average of the actual values of the dependent variable y, and $\overline{x}$ is the average of the values of the independent variable x.

Table 2. Scale of correlation coefficient values.

Interpretation of the Correlation Coefficient (r)—Significance	Direct Dependency	Inverse Dependency
No relation	0.00
Very weak relation	from 0.01 to 0.19	from −0.01 to −0.19
Weak relation	from 0.20 to 0.39	from −0.20 to −0.39
Medium relation	from 0.40 to 0.69	from −0.40 to −0.69
Strong relation	from 0.70 to 0.89	from −0.70 to −0.89
Very strong relation	from 0.90 to 1.00	from −0.90 to −1.00

Source: compiled by the authors.

shows the scale of the correlation closeness indicators. The scale indicates the strength of the relationship between variables. The closer the correlation coefficient is to one, the stronger the effect of the independent variables on the dependent variable, and vice versa. If the correlation coefficient of the variables is zero, there is no statistically significant relationship between the variables. It is also important to mention that correlation only shows whether there is a linear relationship between variables [30].

The significance of the correlation coefficient is tested using the Student’s t test. Statistical hypotheses need to be formulated before testing the t criterion:

H₀. 

The correlation coefficient is equal to zero.

H₁. 

The correlation coefficient is not equal to zero.

The t criterion is used to test the correctness of the hypothesis H₀ and is calculated according to Equation (8):

$$t=r\sqrt{{\displaystyle \frac{n-2}{1-r^2}}};$$

(8)

where r is the calculated value of the correlation coefficient;

n is the sample size.

The significance level α is selected. The hypothesis H₀ will be rejected if the absolute value of t is greater than the critical value of t α/2 (n − 2) for the Student distribution with (n − 2) degrees of freedom. The sample selected for the research is equal to 7, so the degrees of freedom are equal to 5. The level of significance chosen is α = 0.05.

Also, after the calculation of correlation coefficients and the evaluation of their statistical importance, the coefficient of determination ($R^2$) of the regression relationship for the indicators with the strongest correlation is calculated. This is the most important characteristic of a linear regression model for fitting data. The coefficient of determination indicates the proportion of the total variation in one factor that can be explained by the variation in the values of the other factor. The determinacy of a regression relationship indicates the extent to which the regression explains, in relative terms, the scatter of the actual values of the dependent variable around the mean. When $R^2 = 1$, it indicates that the regression explains all the scatter in the actual values of the dependent variable about the mean, while $R^2 = 0$ indicates that the regression does not explain any of the scatter in the actual values of the dependent variable about the mean [30]. The equation for the determination coefficient calculation (9):

$$R^2={\displaystyle \frac{{\mathsf{\Sigma }(\widehat{y_{i }}-\overline{y})}^2}{{\mathsf{\Sigma }(y_i-\overline{y})}^2}},$$

(9)

where $R^2$ is the determinant of the relationship;

${\mathsf{\Sigma }(\widehat{y_{i }}-\overline{y})}^2$ is the sum of the squares of the deviations of the $y_i$ values from the mean calculated from the regression equation;

${\mathsf{\Sigma }(y_i-\overline{y})}^2-y_i$ is the sum of the squares of the deviations of the values from the mean.

4. Case Study

For the analysis, the authors selected the European Union countries (27 countries) and seven macroeconomic indicators—real GDP growth, unemployment rate, inflation rate, exports of goods and services, government debt, foreign direct investments, and labor cost index. In order to apply the SAW method for the evaluation of countries, additionally, the significance and the direction of the selected indicators (their weights) should be determined (

Table 3. The identification of the direction of the analyzed indicators.

Indicator	Direction of the Indicator	wi
Real GDP annual growth rate, % (GDP)	Max	0.178
Inflation rate (IR)	Min	0.138
Exports of goods and services, % of GDP (EX)	Max	0.126
Government debt, % of GDP (GD)	Min	0.144
Unemployment rate (UR)	Min	0.148
Foreign direct investment, % of GDP (FDI)	Max	0.134
Labor cost index (LCI)	Max	0.132

Source: compiled by the authors.

). Based on the authors’ previous research [16,79], the importance of these indicators (their weights) was set. The most important indicator in the model is real GDP growth, whose weight is 0.178, and the least important is exports of goods and services, whose weight is 0.126.

Looking at the changes in analyzed macroeconomic indicators, it was observed that Ireland and Malta had the highest GDP growth ratios in the past twelve years. In comparison, the lowest GDP ratio was in Greece and Cyprus. In the 2012–2018 period and in 2021, the lowest unemployment rate was in Germany. Therefore, the normalized value of Germany in this period was the highest (equal to one). During the analyzed period, the lowest inflation rates were fixed in countries such as Denmark, Belgium, Ireland, and France.

The highest exports of goods and services ratio was fixed in Luxembourg and rose from 171.2 to 212.5% of GDP during the analyzed period. When a country‘s export ratio as a percentage of GDP exceeds 100 percent, it means that the value of the country‘s exported goods and services is greater than the value of the country‘s entire GDP generated in a year. This is characteristic of small, open economies. The lowest ratios were in Spain, France, and Italy, and, on average, rose by only 4–7%. Estonia had the lowest government debt ratio during the 2012–2022 period. In comparison, the highest ratios in the European Union were recorded in Greece (average—194.8%), Italy (154.4%), Portugal (138.22%), Belgium (122.14%), and Cyprus (101.96%). Luxembourg had the highest ratios of inward foreign direct investment stocks in % of GDP during all analyzed period. The highest increases in hourly wage costs for the whole economy were recorded in Romania, Lithuania, Latvia, and Estonia, when analyzing data year by year, and varied, on average, by from 16 to 30%.

After the evaluation of the EU countries’ economic strength, the statistical historical data of the RES sector development was also analyzed in order to evaluate and see if more developed countries with stronger economies have a more developed RES sector.

The development of the RES sector in all these countries was evaluated using the following indicators:

-

Share of renewable energy in gross final energy consumption by sector (energy balance—renewable energy sources)—ξ_tot;

-

Share of renewable energy in gross final energy consumption by sector (energy balance—renewable energy sources in transport)—ξ_transp;

-

Share of renewable energy in gross final energy consumption by sector (energy balance—renewable energy sources in electricity)—ξ_elect;

-

Share of renewable energy in gross final energy consumption by sector (energy balance—renewable energy sources in heating and cooling)—ξ_heat&cool.

It should be noted that, at the time of the study preparation, the data of these indicators were published until 2022, so the analyzed period of these data is 2012–2022.

The highest positive change of share of renewable energy in gross final energy consumption during the 2012–2022 period was in Sweden (16.60%), Denmark (16.14%), and Estonia (12.89%), and the lowest change of share of renewable energy in gross final energy consumption was in Croatia, Slovenia, Bulgaria, Romania, and Austria (Figure 2). The average highest share of consumption of the RES during the analyzed period was observed in Sweden (55.22%), Finland (40.65%), Latvia (39.41%), Denmark (33.18%), and Estonia (30.23%).

Looking at the dynamics of the change in the share of renewable energy in the gross final energy consumption by sector in the transport sector (Figure 3), it is observed that, during analyzed period, the growth of the share of the RES in the transport sector was the highest in Finland (the growth from 1.01 to 18.83%) and Sweden (the growth from 6.29 to 29.16%). The decrease in the share of the RES in the transport sector is observed in Poland and Latvia, despite the fact that these countries can be affected most in the current geopolitical situation, and energy independence is the key goal for every EU country.

A slightly different situation exists in terms of with RES development in electricity—the most significant changes during the analyzed period are observed in countries with mediocre macroeconomic indicators, such as Denmark, Germany, and the Netherlands, and the country with the weakest macroeconomic indicators, Greece, had an average growth during the analyzed period of about 30–40% (Figure 4). Countries, such as Slovakia, Czechia, and Bulgaria, with the lowest growth indicators, should review their RES energy strategy in order to increase the share of RES in gross final energy consumption.

From the perspective of renewable energy share growth in the heating and cooling sector, the highest growth is observed in Malta (24.58% growth during the analyzed period), Estonia (22.22%), Lithuania (17.01%), and Denmark (16.91%) (Figure 5). Despite the fact that Austria, for example, had a decrease in renewable energy in gross final energy consumption in heating and cooling, the share of consumption remains stable and is around 33% during the analyzed period. Meanwhile, the average highest share of consumption in heating and cooling is found in Sweden (64.31%), Latvia (54.16%), Finland (53.51%), and Estonia (52.43%).

The analysis that was carried out showed that RES sector growth is observed in all countries, independent of the strength of the country’s economy.

5. Results

In order to carry out the evaluation of RES sector development in EU countries, the authors have chosen to analyze the 2012–2022 period data (Statistical data were extracted from the Eurostat database. Source: https://ec.europa.eu/eurostat/en/web/main/data/database, access on 19 March 2025). It was assumed that, in the analyzed period, all countries had experienced a range of business cycle phases. Thus, it is appropriate to examine such intervals of data for such kinds of evaluations. This is a remarkably important step, as the solutions must be efficient not just in a specific period of the past but also in extremely different situations, varying from the phases of the global economic recovery to the economic recession stages. All 27 EU countries were chosen for analysis. Seven macroeconomic indicators were selected for the evaluation of the EU countries’ economic situation from 2012 to 2022 (see Section 4).

The calculated S values, using Equation (3), for all countries are presented in Figure 6. According to the calculated results of S values, the countries were ranked for the analyzed period.

In order to assess the impact of a country’s economic situation on RES development, correlation coefficients, using 2012–2022 period data, were calculated between separate macroeconomic indicators and the changes in the share of renewable energy in gross final energy consumption (

Table 4. The correlation coefficients between the share of renewable energy in gross final energy consumption indicator and countries’ macroeconomic indicators.

Country	ξtot and GDP	ξtot and UR	xi,tot and IR	ξtot and EX	ξtot and GD	ξtot and FDI	ξtot and LCI
Belgium	0.178945	−0.78136	0.556673	0.736457	−0.04044	−0.38402	0.440805
Bulgaria	−0.2053	−0.63951	0.073879	−0.27499	0.450717	−0.41845	0.42664
Czechia	0.018643	−0.74946	0.578084	−0.61955	−0.42121	0.810677	0.25956
Denmark	0.536203	−0.95391	0.532525	0.732078	−0.85987	0.441097	0.715226
Germany	−0.15774	−0.73063	0.617228	0.281674	−0.61174	0.769289	0.15125
Estonia	0.079026	−0.67582	0.651603	0.047316	0.753661	0.633809	0.194686
Ireland	0.191203	−0.81699	0.226954	0.888588	−0.80735	0.629915	0.838107
Greece	0.423085	−0.9459	0.489036	0.760159	0.806473	0.930583	0.691515
Spain	0.074905	−0.82018	0.492335	0.432488	0.685991	0.940699	0.716483
France	0.040153	−0.9606	0.53521	0.400048	0.596199	0.962489	0.6033
Croatia	0.111751	−0.4928	−0.19775	0.039886	0.639463	0.749906	−0.11564
Italy	0.035905	−0.68901	0.099696	0.50352	0.795497	0.921638	0.401107
Cyprus	0.566431	−0.85151	0.473154	0.936812	0.581126	0.19701	0.65166
Latvia	−0.3896	−0.84167	0.581898	0.601461	0.604484	0.73202	0.352944
Lithuania	−0.18261	−0.75218	0.60991	0.26263	−0.31957	0.684619	0.260898
Luxembourg	−0.07937	−0.38085	0.575552	0.881396	0.689122	−0.41011	0.198002
Hungary	−0.32127	0.758548	0.197943	0.499285	0.345171	0.546313	−0.39697
Malta	−0.05271	−0.83262	0.323491	0.823767	−0.25061	−0.69337	−0.24311
Netherlands	0.215162	−0.83705	0.593275	0.639623	−0.51793	−0.89351	0.548618
Austria	−0.51456	0.365564	−0.04693	−0.15514	0.302834	−0.46651	0.356507
Poland	0.170149	−0.81168	0.66111	0.82431	−0.28527	−0.72867	0.834036
Portugal	0.361936	−0.86017	0.250392	0.474293	−0.9321	0.91711	0.733339
Romania	0.034455	−0.11515	−0.41911	0.57315	0.183016	−0.16734	−0.01202
Slovenia	−0.04526	−0.39872	0.333497	0.314767	0.129209	0.473597	0.319756
Slovakia	−0.16472	−0.78121	0.493058	−0.11609	0.71548	−0.43499	0.697905
Finland	0.317531	−0.45103	0.380201	0.590575	0.502173	−0.40307	0.035247
Sweden	0.124696	0.453445	0.775246	0.791967	−0.50922	0.819158	0.255235

Source: compiled by the authors.

).

The information presented in Table 4 shows that the analyzed indicators do not affect RES development directly, because relations exist only between separately analyzed indicators. It can be seen that only a medium strength relation between real GDP growth and RES sector development is observed in Denmark (r = 0.536203) and Cyprus (r = 0.566431). In the meantime, an inverse strong relation between the unemployment rate and RES sector development can be observed in many EU countries; such a relation can be evaluated positively because the development of the RES sector probably leads to a decrease in the unemployment rate in the EU countries. Also, a strong relation is observed between RES sector development and the indicator of exports of goods and services—in Belgium (r = 0.736457), Denmark (r = 0.732078), Cyprus (r = 0.936812), Luxembourg (0.881396), Malta (r = 0.823767). Moreover, a strong relation is observed between the share of renewable energy in gross final energy consumption and foreign direct investment in such countries as Greece, Spain, France, Italy, and Portugal, where correlation coefficients are about 0.9, and a medium strength relation exists in Sweden (r = 0.819158), Czechia (r = 0.810677), and Croatia (r = 0.749906). The medium strength relations are determined between the share of renewable energy in the gross final energy consumption and the labor cost index in Ireland (r = 0.838107), Denmark (r = 0.715226), Poland (0.834036), Portugal (r = 0.733339), and Spain (r = 0.716483). While, in all other countries, only weak relations between these indicators are observed.

As can be seen from

Table 5. The significance of the correlation coefficients, calculated using the Student’s t-test.

Country	ξ,tot and GDP	ξtot and UR	ξtot and IR	ξ,tot and EX	ξ,tot and GD	ξ,tot and FDI	ξ,tot and LCI
Belgium	0.3150	2.1685	1.1606	1.8856	0.0701	0.9300	1.0981
Bulgaria	0.3633	1.4408	0.1283	0.4953	0.8745	1.0302	1.0548
Czechia	0.0322	1.9607	1.2270	1.3670	0.8043	3.0961	0.6010
Denmark	1.1002	5.5058	1.0897	1.8613	2.9173	1.0990	2.2883
Germany	0.2766	1.8534	1.3587	0.5084	1.3394	2.6924	0.3421
Estonia	0.1373	1.5881	1.4878	0.0820	1.9860	1.8323	0.4438
Ireland	0.3373	2.4539	0.4036	3.3552	2.3698	1.8136	3.4355
Greece	0.8087	5.0493	0.9710	2.0264	2.3624	5.6841	2.1406
Spain	0.1301	2.4831	0.9797	0.8308	1.6329	6.2005	2.2966
France	0.0696	5.9860	1.0974	0.7560	1.2862	7.9323	1.6915
Croatia	0.1947	0.9809	0.3494	0.0691	1.4406	2.5347	0.2603
Italy	0.0622	1.6466	0.1735	1.0094	2.2738	5.3108	0.9791
Cyprus	1.1904	2.8127	0.9302	4.6382	1.2368	0.4493	1.9211
Latvia	0.7327	2.6997	1.2393	1.3039	1.3143	2.4026	0.8435
Lithuania	0.3217	1.9770	1.3330	0.4714	0.5841	2.1002	0.6043
Luxembourg	0.1379	0.7134	1.2190	3.2317	1.6471	1.0055	0.4517
Hungary	0.5876	2.0162	0.3497	0.9980	0.6370	1.4585	0.9671
Malta	0.0914	2.6039	0.5921	2.5167	0.4483	2.1516	0.5604
Netherlands	0.3816	2.6498	1.2765	1.4412	1.0486	4.4493	1.4672
Austria	1.0394	0.6802	0.0813	0.2720	0.5503	1.1793	0.8532
Poland	0.2990	2.4068	1.5261	2.5218	0.5155	2.3791	3.3803
Portugal	0.6724	2.9212	0.4479	0.9331	0.1621	5.1444	2.4120
Romania	0.0597	0.2007	0.7995	1.2114	0.3224	0.3795	0.0269
Slovenia	0.0784	0.7530	0.6127	0.5743	0.2256	1.2024	0.7546
Slovakia	0.2892	2.1675	0.9816	0.2024	1.7738	1.0802	2.1790
Finland	0.5799	0.8752	0.7119	1.2675	1.0058	0.9848	0.0789
Sweden	0.2176	0.8811	2.1257	2.2466	1.0248	3.1935	0.5903

Source: compiled by the authors.

, the criteria t values of four variables out of seven analyzed only for specific countries satisfy the necessary condition for stochastic dependence, i.e., the estimated values of these indicators are all greater than the value of t(n − 2), which is equal to 2.5705. If this condition is met, the correlation coefficient is statistically significant, and a stochastic relationship between the X and Y variables exists. Therefore, it can be concluded that there is a significant correlation between the share of renewable energy in gross final energy consumption and unemployment rate in Denmark, France and Greece, between the share of renewable energy in gross final energy consumption and exports of goods and services, % of GDP in Ireland, Cyprus and Luxembourg, between the share of renewable energy in gross final energy consumption and foreign direct investment in Czechia, Greece, Spain, France, Italy, Netherlands, Portugal and Sweden, between the share of renewable energy in gross final energy consumption and labor cost index in Ireland and Poland. Other relationships are not statistically significant. When statistically significant indicators are selected, the determination coefficient ($R^2$) is calculated.

The calculated $R^2$ values for the indicators with significant correlation are near 1, which means that the regression explains all the scatter in the actual values of the dependent variable about the mean. The coefficient of determination of the regression relationship $R^2$ for Denmark is equal to 0.9078 and shows that changes in the share of renewable energy in the gross final energy consumption account for about 90% of the change in unemployment (

Table 6. The calculated $R^2$ for the indicators that satisfy the necessary condition for stochastic dependence.

Country	ξtot and UR	ξ,tot and EX	ξtot and FDI	ξ,tot and LCI
Czechia	-	-	0.9962	-
Denmark	0.9078	-	-	-
Germany	-	-	0.9881	-
Ireland	-	0.9731	-	0.7727
Greece	0.8856	-	0.9794	-
Spain	-	-	0.9976	-
France	0.9419	-	0.9955	-
Italy	-	-	0.9972	-
Cyprus	0.6992	0.9703	-	-
Luxembourg	-	0.7769	-	-
Malta	0.663
Netherlands	0.6241	-	0.7616	-
Poland	-	-	-	0.9099
Portugal	0.8232	-	0.9972	-
Sweden	-	-	0.9964	-

Source: compiled by the authors.

). A very similar situation is with Greece. Also, a similar situation is with the indicator of exports of goods and services for Ireland, Cyprus, and Luxembourg. After carrying out the analysis, it was observed that the significance of correlations is high in the cases of Greece, Germany, Czechia, Spain, France, Italy, Portugal, and Sweden, and this shows that changes in the share of renewable energy in gross final energy consumption for about 90% affect the change in foreign direct investment. In the case of the RES impact on LCI, only a few cases were determined—Ireland, with 77%, and Poland, with 90%.

Taking into account geopolitical changes in the world and, especially, the war between Russia and Ukraine, which also has a direct impact on EU countries, firstly, energy independence from Russia is very important. Looking at the changes in per capita energy consumption from renewables (in kW) and the share of electricity generated by renewables (%) from the angle of the overall EU situation, it can be observed that per capita energy consumption from renewables (in kW) in the European Union countries increased during the period of 2012–2022 about 59%, while, at the same time, the share of electricity generated by renewables (%) also increased by about 55%, evaluating the average of all EU countries.

Also, it should be noted that the correlation analysis carried out between separate macroeconomic indicators and the indicator of the share of renewable energy in the gross final energy consumption showed that the analyzed indicators do not affect the RES sector development in the country. Only in a few countries, such as Denmark, Ireland, Greece, France, Cyprus, Luxembourg, and Poland, was a significant correlation observed between the share of renewable energy in gross final energy consumption and the indicator of exports of goods and services, unemployment rate, or labor cost index. However, these may be accidental relationships that have not proven the direct macroeconomic indicator impact on the RES sector development. However, more significant relations were determined between the share of renewable energy in gross final energy consumption and the indicator of foreign direct investment in many EU countries, which shows the direct relation between these regarding the development of RES.

6. Discussion

The analysis reveals only a weak interplay between countries’ economic stability and renewable energy growth. Only in a few countries was a statistically significant correlation found between a few separate macroeconomic indicators, such as unemployment rate, exports of goods and services, labor cost index, and RES change during the analyzed period. However, economic development using renewable resources is becoming increasingly important on a global scale as the world faces increasing environmental challenges and growing energy needs. The use of renewable energy sources, such as solar, wind, geothermal, and biomass resources, is one of the main strategies for achieving sustainable growth and reducing dependence on fossil fuels. The advantages of renewable energy in economic development are not only environmental, but also economic and social.

First of all, the use of renewable energy sources helps reduce carbon dioxide emissions and other greenhouse gases, which are the main cause of climate change [67,80]. Climate change can lead to serious consequences, such as extreme weather, droughts, floods, and rising temperatures. The use of renewable energy sources, which do not cause direct emissions, contributes to reducing these problems. This not only helps to mitigate climate change but also contributes to the preservation of biodiversity, as pollution that can harm ecosystems is reduced.

The use of renewable resources has economic benefits. One of them is greater energy independence [81].

Traditional energy sources such as oil, gas, and coal are often imported, and their price depends on global market fluctuations. The use of renewable sources such as solar and wind allows countries to reduce their dependence on imported energy resources and create energy security. This is particularly important in reducing geopolitical dependence on other countries and ensuring stability in the field of energy supply.

The renewable energy sector also promotes economic diversification and innovation. Solar and wind energy technologies, energy storage systems, and smart grids require highly qualified specialists and engineering solutions, therefore contributing to the creation of high-value-added jobs [15,82,83,84]. This not only promotes technological development but also provides opportunities for countries’ economies to become more competitive [85]. Investments in renewable energy also help develop related industries such as construction, manufacturing, and scientific research.

In addition, the use of renewable sources has a social impact [33,86,87]. It encourages community involvement and provides opportunities for local initiatives. For example, small wind turbines or biomass power plants can be installed in regions that previously relied on expensive and polluting energy sources. This contributes to social well-being and reduces poverty, especially in rural areas.

The authors‘ decision to use the SAW method was based on the fact that, as demonstrated by [88], when applying the SAW method, a higher tolerance level to significant changes in criterion weights occurs. Sensitivity analysis is recommended when using more than one multi-criteria evaluation method in research. The usage of such methods allows the ranking of EU countries by the level of their economic stability. The EU’s drive for energy independence and sustainability presents both challenges and opportunities. Addressing these requires a multi-faceted approach that combines economic support, technological innovation, and cohesive policy frameworks to unlock the full potential of RESs for a sustainable future [89]. The findings underscore significant regional differences in RES development: Northern European countries (e.g., Sweden, Denmark, and Finland) lead in RES adoption due to favorable policy frameworks, high public awareness, and advanced technological capacities; Eastern and Southern European countries (e.g., Bulgaria, Greece, and Slovakia) exhibit slower growth due to economic challenges and limited infrastructure. The EU must prioritize cohesive strategies to bridge these gaps, including financial support for lagging regions and cross-country collaboration.

Government subsidies for renewable energy innovation (RES) are often allocated to promote green transformation, technological development, and sustainable economic growth [90,91]. However, the mechanism of subsidy distribution and their availability can lead to the so-called brokerage effect, when intermediaries appear between the state and the final beneficiaries of support—most often large companies, research institutes, or innovation centers. In this case, these intermediaries act as gateways through which subsidies reach only certain organizations or initiatives, often ignoring smaller market participants, small and medium-sized enterprises (SMEs), or new start-ups.

Such a brokerage mechanism, although seemingly helping to allocate resources more efficiently, can hinder the absorption of technological capabilities on a wider scale. This is especially relevant in the context of RES, where technological diversity, experimentation, and flexible adaptation are necessary conditions for the development and implementation of innovative solutions. When subsidies only reach well-established structures, smaller market participants are deprived of the opportunity to experiment, implement advanced technologies, or develop alternative solutions, which are often riskier but potentially transformative.

In addition, such a situation strengthens dependence on existing technology supply chains and knowledge centers, hindering the development of local capabilities and the regional diffusion of innovations [92]. Blocking the absorption of technological capabilities not only reduces competitiveness in the RES sector but also limits the overall ability of the country to transition to a sustainable energy system in the long term. This is especially dangerous for smaller countries or regions, which risk becoming only consumers of technologies but not their creators or developers.

Therefore, when designing subsidy mechanisms for the RES sector, it is necessary to take into account possible intermediary effects and strive for the widest possible accessibility in order to promote the formation of technological capabilities at all levels of the innovation ecosystem—from startups to scientific institutions.

Based on the research conducted, areas that could be explored further have emerged: investigating the impact of specific policies and incentives on RES growth in different EU countries or analyzing the long-term economic impacts of RES adoption, particularly in developing regions.

7. Conclusions

• The literature analysis showed that increasing global population and dwindling fossil fuel reserves are cited as primary drivers of renewable energy adoption, and fossil fuels are linked to environmental degradation, including greenhouse gas emissions and climate change, necessitating a shift to RES. RES contributes to sustainable development by providing clean and inexhaustible energy. Developed countries often benefit from immediate economic gains, while developing countries face challenges in transitioning to RES but hold long-term potential for growth. The RES sector development in the EU countries can be affected by many factors—infrastructural, political, and economic. The financial effect on the RES sector development was examined in this article.
• Various multi-criteria decision-making methods and assessment techniques are utilized across different scientific research fields. For the assessment of the economic stability of the EU countries, seven macroeconomic indicators were selected, and a multi-criteria decision-making SAW method was applied for rating these countries. Based on the evaluated macroeconomic indicators, the EU countries with the strongest economies are Luxembourg, Malta, Estonia, and Ireland, while the weakest are Greece, Italy, and Spain.
• Correlation analysis between individual macroeconomic indicators and the share of renewable energy in final energy consumption in the general context shows that, in most countries, the indicators do not have a significant impact on the development of the RES sector. Only in some countries—for example, Denmark, Ireland, Greece, France, Cyprus, Luxembourg, and Poland—was a statistically significant relationship established between a few separate indicators, such as unemployment rate, exports of goods and services, and labor cost index. However, more significant relations were determined between the share of renewable energy in gross final energy consumption and the indicator of foreign direct investment in many EU countries, which shows the direct relation between these two indicators. This suggests that, although specific macroeconomic factors are not directly responsible for the development of RES, growth in the sector is determined by broader country conditions and the general context of the economic, political, and institutional environment.
• RES development is still stagnating in some countries, despite macroeconomic stability, for several reasons: institutional and political barriers (if the economy is stable, political uncertainty, regulatory inefficiency or uncoordinated energy policies can hinder RES projects), differences in the availability of finance (under favorable macro conditions, the cost of capital, investors’ risk assessment and the availability of financial instruments differ between countries), infrastructure limitations (RES require appropriate infrastructure (e.g., grids, storage solutions), the development of which can be slow), and technological and human resource shortages (some countries lack the technological capabilities and skilled workforce needed for RES development). In order to better understand the differences in RES development between countries, future research should include indicators reflecting institutional and political variability, assess social and cultural factors that determine public attitudes towards RES, analyze the attitudes of individual investors or regions towards RES, and consider the integration of geographical potential to account for the heterogeneity of natural resources.