Transitioning to Clean Energy: A Comprehensive Analysis of Renewable Electricity Generation in the EU-27

Abstract

The EU power sector is under increasing pressure due to rising electricity demand and the need to meet decarbonisation targets. Member states have been active in investing in renewables and building capacity to increase their share of renewables in electricity generation. However, it is important to examine what progress each member state has made in the deployment of renewable energy for electricity generation and what factors influence gross electricity generation from renewable energy. In this study, logarithmic mean Divisia index (LMDI) analysis was used to examine the changes in EU-27 countries’ gross electricity generation from renewable energy sources (RES), wind, and solar PV from 2012 to 2021. The results show that the RES deployment per capita effect and the RES share effect were the main positive factors for the total gross electricity generation from RES in the EU. In contrast, the RES capacity productivity effect and the energy intensity effect had negative contributions. Population growth had a positive influence but was less significant than the other factors. The deployment of RES per capita effect was the main factor in the overall growth of gross electricity generation from RES in Northern Europe, Central Western Europe, and Central Eastern Europe, according to comparisons between the regional groups. RES share effect was the main driver in Southern Europe. The decrease in RES capacity productivity was the second most important factor influencing the variation in the amount of energy generated by RES in Northern Europe and Central Western Europe. The results could be used to develop more effective and tailored renewable energy policies that take into account the existing main drivers of RES, wind, and solar energy in each of the EU-27 member states.

1. Introduction

The European Union (EU) has pledged to become a climate-neutral continent by 2050 [1]. In order to achieve these ambitious goals, a set of climate goals for the coming decades has been established and announced. One of the key targets is to reduce greenhouse gas emissions by at least 55% by 2030 compared to the emission levels observed in 1990 [2]. Since the energy sector accounts for more than 75% of the EU’s greenhouse gas emissions [3], achieving these ambitious climate targets will necessitate the proactive implementation of measures focused on decarbonisation and enhancing energy efficiency in the energy sector [4]. To this end, the European Parliament recently declared that the EU’s Renewable Energy Directive will be enhanced, and it is planned to increase the binding renewable energy target from 32% to at least 42.5% by 2030 [5].

As the EU strives for rapid decarbonisation and electrification of its energy system, the EU’s electricity system faces significant challenges. The European Commission projects that the share of renewable energy in electricity generation should reach 55% in 2025 and 72% in 2030 [6], more than doubling its current level of 33% in 2021 [7].

Of the 2785 TWh of net electricity generation in the EU in 2021, one-third came from renewable energy sources. Of all renewable energy sources, wind energy had the highest share of electricity generation, with 13.7%, followed by hydropower with 13.3% and solar energy with 5.8%. Nuclear energy covered 25% of total net electricity generation in the EU in 2021, while almost 42% of electricity was generated from fossil fuels such as natural gas, coal, and oil [7]. Over the past decade, the EU has experienced a significant increase in the generation of renewable energy, which has been largely driven by the expansion of wind energy. The relative importance of hydropower in the total renewable electricity mix has diminished over the past two decades, as hydropower generation has remained relatively stable. Although hydropower accounted for the vast majority (90%) of electricity generated from renewable energy sources in the EU at the beginning of the 21st century, in 2021 it covered approximately 40% of the total renewable power mix [6]. It is expected that the relative significance of wind and solar PV in the total power mix will grow substantially. This projection is based on the European electrical sector’s capacity to effectively incorporate considerable proportions of solar PV and wind power production [8]. The potential of wind and solar energy is still untapped in the EU. The potential varies from country to country, due to the specific characteristics of each country, such as the proportion of agricultural land, the area of the exclusive economic zone and population density. In addition, the wind and photovoltaic potential in the individual countries is determined by the capacities already installed. According to a study by [9], Germany has relatively the lowest development potential due to its large installed capacities, whereas Latvia has enormous development potential due to its low installed capacities [9].

In the future, as smart energy systems continue to evolve, there will be a substantial increase in the electrification of end-use consumption. This shift will be driven by the growing adoption of electrical appliances like heat pumps and cooling systems, as well as efforts to decarbonise transportation by transitioning to vehicles that utilize electricity as an alternative fuel source [10]. The EU’s electricity sector is expected to grow significantly in the coming years [11], as it is expected that electricity demand in the EU will increase by at least 32% by 2050 compared to its current levels [12]. Furthermore, the REPowerEU initiative, which aims to achieve full independence in the EU from Russian energy resources such as natural gas, oil and coal, underlines the urgent need for EU member states to rapidly expand their current renewable energy generation capacities in the coming years [13]. The role of renewable energy use in electricity generation is highlighted in the REPowerEU plan, which has set a target for the share of renewable energy in gross final electricity consumption to reach 69% by 2030 [14], compared to only 37.6% of total electricity demand in the EU from renewable sources in 2021 [15].

To reach these targets a package of policies and regulatory frameworks has been put in place to encourage renewable energy production in the EU. The Renewable Energy Directive, established in 2009, mandated that by 2020, 20% of the EU’s energy consumption must come from renewable sources [16]. The directive outlined various strategies, such as support and collaboration measures, to achieve these objectives. In addition, it confirmed each country’s present national renewable energy goals. Progress towards these objectives was evaluated every 2 years. As part of the “Clean energy for all Europeans” package, the revised Renewable Energy Directive was introduced in 2018 to maintain the EU’s leadership in renewables, with the goal of deriving at least 32% of final energy consumption from renewables by 2030. The directive required EU member states to implement it as national law by June 2021. Three additional amendments to the Renewable Energy Directive were made between July 2021 and March 2023 in response to the “Fit for 55” package and REPowerEU plan, which was introduced in response to the Russian invasion of Ukraine. The amendments continued to increase the overall renewable energy target to 42.5% by 2030 in order to accelerate the phase-out of Russian fossil fuels and encourage the development and deployment of renewable hydrogen [17]. Efforts have been undertaken to establish a shared framework for the advancement of sustainable cross-border energy infrastructure, with the aim of facilitating the deployment of renewable energy across all member states of the European Union. EU Regulation 2022/869 amending Directive 2019/944 establishes EU rules for trans-European energy networks that focus on interconnecting the energy infrastructure of EU countries [18]. The regulation establishes the means by which member states, regulatory authorities, and transmission system operators can work together to establish a fully interconnected internal market for electricity that enhances the incorporation of electricity from renewable sources, free competition, and supply security [19]. The binding legislation emphasises the significance of offshore wind projects while explicitly excluding European Union support for forthcoming natural gas developments. It aims to facilitate the incorporation of renewable energy sources and emerging clean energy technologies into the energy system. Additionally, it seeks to establish connections between regions that are presently disconnected from European energy markets, enhance existing cross-border interconnections, foster collaboration with partner nations, and suggest strategies to streamline and expedite permitting and authorization processes [17].

Following the course set by the EU, member states have indeed been actively investing in renewable energy and installing capacities to increase their share of renewable energy in electricity generation. However, it is important to examine what progress each member state has made in the deployment of renewable energy for electricity generation and what factors influence gross electricity generation from renewable energy. The purpose of this paper is to investigate the primary drivers of change in electricity generation from renewable energy sources (RES) and to determine which factor has had the greatest influence on these changes over the past decade. This paper examines what progress member states have made in increasing the amount of electricity generated from RES and how EU-27 countries compare. This study utilized logarithmic mean Divisia index (LMDI) decomposition analysis to examine changes in EU-27 countries’ gross electricity production from RES, wind, and solar PV from 2012 to 2021. Further analysis was conducted to compare and contrast the differences and similarities between the four groups of regional electricity markets in the European Union: Northern Europe, Central Western Europe, Central Eastern Europe, and Southern Europe. The findings could be used to develop more effective and tailored policies for renewable energy, taking into account the existing primary drivers of RES, wind, and solar PV energy in each of the EU-27 member states.

The structure of the paper is as follows: Section 2 describes previous studies analyzing the primary drivers of RES electricity production and research on LMDI applications; Section 3 presents the methodologies and data used in this study; Section 4 describes the results and discusses the key findings; and Section 5 summarizes the major conclusions of the paper.

2. Literature Review

Different methods and approaches are employed to analyse the primary factors influencing the utilization of renewable energy in electricity generation. A study by [20] investigated the factors affecting the technological innovation of different renewable energy sources (wind, solar, geothermal, ocean, biomass) using a dynamic panel approach. A number of explanatory variables were selected, such as renewable energy tariff, R&D intensity, RES installed capacity, electricity consumption, and electricity price. The findings revealed that electricity consumption was the most important influencing factor for all RES sources and wind energy, while the effect was weaker for solar energy and biomass. R&D intensity proved to be the most influential factor for biomass [20].

In an empirical investigation conducted by [21], the objective was to assess the influence of economic and social factors on the adoption of renewable energy using the random effect of the generalized least squares method [21]. In their study, [22] employed the autoregressive distributed lag (ARDL) bounds testing cointegration approach to examine the primary determinants of renewable energy sources consumption in Malaysia. The researchers analysed the long-term relationships between RES consumption, carbon dioxide emissions, economic growth, trade openness, and foreign direct investment using time series data spanning from 1980 to 2015. The study revealed that the most significant variables contributing to the consumption of renewable electricity were economic growth and foreign direct investment [22].

In addition to quantitative indicators that assess technoeconomic parameters of renewable energy deployment, there are studies that examine the significance of climate policy quality and institutional capacity. For example, a study by [23] employed a Markov-switching equilibrium approach in their investigation of the most influential factors in green energy investment. The authors focused on two primary factors that determine investment in renewable energy: the exploitation of natural resources and the uncertainty of climate policy. The study suggested that there should be robust governance on the use of natural resources, which would encourage resource efficiency, since sustainable natural resource exploitation and a growing proportion of renewable energy are the primary drivers of long-term sustainable development. Moreover, the government should play a crucial role in eliminating uncertainty in climate and energy policy by implementing long-term economic measures. In addition, international organisations such as the World Bank and others should provide effective support for the development of energy policies in developing nations [23]. Moreover, another study emphasized the role of policy strengthening where [24] examined the structural changes connecting economic growth and institutional quality in terms of CO₂ emissions and energy consumption. The study revealed that institutional quality has a significant and positive effect on CO₂ emissions, and that increasing economic growth decreases CO₂ emissions. The study suggests that Pakistan should continue its economic development policies in order to promote the green transition. In addition, the study argues that institutional quality and government efficiency must be considerably enhanced by strengthening the policy and regulatory framework and environmental legislation and reducing corruption in order to transition to a more sustainable economy in the future [24].

Prior research on green growth and energy transition factors in the EU region focused on analysing the factors that influence energy-related CO₂ emission reductions. Using the generalised method of moments (GMM), ref. [25] analysed the impact of GDP, renewable energy growth, energy demand, population, and effective capital on EU-27 CO₂ emissions from 1990 to 2019. The authors determined the “effective capital” indicator, which represents the relationship between the physical machinery used to produce products and energy consumption. The study found that high economic growth that is fuelled by high consumption of fossil fuels has a direct impact on rising carbon emissions. As a result, a rapid transition to renewable energy should be encouraged, as it has been shown to have a significant impact on reducing CO₂ emissions. Effective capital and energy use are observed to increase CO₂ emissions, while capital and population size decrease carbon emissions in the European Union [25]. A study by [26] analysed the impact of renewable energy resources and nuclear energy on the reduction of carbon emissions in 22 EU member states between 1992 and 2019. The study revealed that an increase in renewable energy reduced CO₂ emissions per capita in the European Union by a significant amount. In terms of EU carbon emission reductions, the results of the study also indicated that nuclear power was not significant in any of the studied countries. The study revealed that energy consumption per capita also had an increasing influence on carbon emissions; consequently, the authors recommended that the EU implement more stringent energy efficiency policies [26]. In their study on the primary determinants of CO₂ emissions from electricity generation in the EU, [27] emphasized the need for disaggregating renewable energy types in decomposition studies. Ref. [27] discovered that year-to-year fluctuations in the quantities of hydro energy and nuclear energy impact the overall evaluation of renewable energy expansion in the EU. The EU has witnessed a constant increase in solar and wind energy over the past decade; therefore, there is a need for a more in-depth investigation into the primary factors influencing these increases [27].

There are a few studies investigating the factors influencing the growth of renewable electricity generation in the EU. Most of them focus on the impact of energy policies on the targets of RES. A study conducted by [28] used the pool mean group autoregressive distributive lag model (PMG-ARDL) to investigate how investment in R&D correlates with the expansion of renewable and non-renewable energy resources in the EU. The results show that renewable energy has a bidirectional relationship with R&D expenditure, which means that the economy develops through the use of renewable energy resources, thus driving the future growth of renewable energy resources [28]. A study by [29] investigated the effect of various policy measures on the deployment of renewable energy in power generation in the EU. Different levels of harmonisation and the use of a feed-in tariff, a feed-in premium, or a quota system with a classification scheme had comparable effects on the deployment of renewable energy in electricity generation [29]. In a study conducted by [30], an analysis was performed to assess the influence of nine socioeconomic parameters on the installed capacity of energy derived from renewable sources in 10 European Union (EU) member nations. The parameters included in this study encompassed the installed capacity of electricity derived from renewable sources, population size, inflation rate, GDP per capita, unemployment rate, energy dependence, net imports of electricity, income inequality, total electricity consumption, and electricity prices. The findings indicated that all socioeconomic factors examined had a significant impact on the endogenous variables [30]. The study conducted by [31] examined the advancements made by Northern European nations in the use of renewable energy sources for the purpose of power production. The authors conducted a comparative analysis between the actual progress made in the development of renewable power and the intended future targets. The results of the study revealed disparities in the implementation of various renewable energy power generation technologies. Certain countries demonstrated lower levels of proficiency in adopting newer renewable energy sources, such as wind power, but compensated for this deficiency by excelling in more established technologies, such as biomass. This trend raises concerns, as the utilization of all available technologies will be crucial in achieving the European Union’s decarbonisation objectives [31].

There are also a number of review papers that have attempted to identify the main drivers, barriers, and trends in the deployment of renewable energy in the energy sector. For example, ref. [32] reviewed the main drivers and barriers affecting the deployment of concentrated solar power (CSP) in the EU. The higher added value of CSP was recognised as the main driver, with policy incentives in the form of R&D and deployment support also considered important. High initial investment and policy uncertainty were identified as the main barriers. The importance of R&D expenditure was also addressed in a study by [33], who argued that in the energy sector, policymakers should prioritize increased investment in innovative technologies, expenditure on renewable energy R&D along with improved economic performance, and collaboration on the development of environmental technologies [33]. Another review paper by [34] explored the obstacles hindering the adoption of wind and solar PV technologies in electricity supply systems. The research highlighted several significant barriers, including insufficient financial resources, limited grid capacity, delays in obtaining building permits, resistance from local communities towards wind farm construction, and the absence of a stable institutional framework.

However, review papers fail to estimate and quantify RES development patterns, which can be effectively accomplished by applying decomposition analysis. There exist several decomposition analysis techniques, such as the logarithmic mean Divisia index (LMDI), Laspeyres index, Fisher ideal index [35], and others, but the LMDI approach is one of the most commonly used in energy research because of its ease of application and interpretation of results.

One of the most frequently employed applications of the logarithmic mean Divisia index in the field of energy research relates to the examination of variations in carbon dioxide emissions caused by energy sources. A study by [36] combined the extended Kaya identity with the logarithmic mean Divisia index decomposition method to investigate changes in energy-related carbon emissions in China. The authors demonstrated the practical application of the results of the LMDI decomposition analysis by showing real cases of LMDI application in energy and climate policy, using examples from China, and setting out recommendations for CO₂ reductions [36].

In their study, ref. [37] applied the LMDI technique to investigate the main factors affecting changes in CO₂ emissions in the 23 countries with the highest use of renewable energy, including 12 European countries, with a focus on six main factors: the intensity of carbon trading, the impact of fossil fuel trading, the intensity of fossil fuels, the productivity of renewable energy sources, the effect of electricity financial power, and the impact of financial development. The changes were examined for the period from 1985 to 2011. For European countries, renewable energy productivity effects were found to be the primary contributors to increases in CO₂ emissions, while electricity financial power effects contributed to decreases.

Changes in CO₂ emissions by applying LMDI were also studied by [38], who investigated the causes of the global increase in emissions over the last two decades. The results of the study indicate that the primary driver of CO₂ emissions during the period spanning from 1997 to 2015 was economic growth. The factor of population growth also contributed to the increase in emissions, with a more pronounced effect observed in countries with lower income levels. The authors of this study found that total emissions would have been half as much if there had been no energy efficiency and decarbonisation measures in the past. The authors propose that there should be a more expedited reduction of CO₂ emissions in highly industrialised nations, accompanied by sufficient assistance to developing countries as they transition towards a more environmentally sustainable economy [38].

Another study [39] investigated the factors that influenced the variations in the levels of energy-related CO₂ emissions in four categories (eastern, western, northern, and southern) of 21 European countries in two periods—before (1999–2004) and after (2005–2010) the Kyoto Protocol. The study used LMDI decomposition analysis to decompose CO₂ emission changes into six primary factors: the carbon intensity effect, the energy mix effect, the energy intensity effect, the average renewable capacity productivity effect, the capacity of renewable energy per capita effect, and the population change effect. The study concluded that the consumption of renewable energy was also influenced by country-specific factors such as the scale and structure of the economy.

LMDI was also used to study changes in carbon intensity levels and investigate the key drivers behind these changes. For example, a study by [40] built a model combining index decomposition analysis (IDA) and the LMDI approach to analyse and compare the driving factors of changes in aggregate carbon intensity between different regions of China over a 14-year period. The main decomposition factors used in the analysis were installed capacity mix, thermal capacity factor, and total capacity factor. The results showed that overall carbon intensity decreased after 2015 due to a rebound in carbon intensity in five regions.

In the field of renewable energy, LMDI was used to study renewable energy consumption patterns. A study by [41] examined the primary drivers of wind energy consumption by employing LMDI decomposition for 17 key wind energy-consuming nations, including Denmark, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal, Spain, and Sweden, from the EU. Increasing RES share had the greatest effect on wind energy growth, while decreasing energy intensity reduced wind energy consumption. Based on identified compound annual growth rates for wind energy consumption in the respective countries, the author developed three distinct forecasting scenarios. The study concluded that the use of wind energy will increase in the future if the RES share of the overall energy mix continues to rise, which is highly dependent on robust energy policies aimed at accelerating the decarbonisation of the energy sector [41].

Another study [42] used the extended Kaya identity equation with the LMDI approach to examine the influence of the five key factors affecting renewable energy consumption in 32 BRI countries: structural changes, changes in energy intensity, changes in decarbonisation, changes in carbon emissions, and changes in population distribution and growth. The study shows that energy structure was the main reason for the increase in RES consumption, while the effect of energy intensity reduced the overall consumption of RES [42].

Using the LMDI approach, the study conducted by [43] examined the fluctuations in wind and solar power consumption in China between 2015 and 2018, focusing on six key factors: transmission structure, generation proportion, power self-sufficiency, resource development, resource utilisation, and power consumption. The study found that the electricity mix effect and the resource development effect have played significant roles in the evolution of the wind and solar power consumption rate, but their effects have been diminishing [43].

A study by [44] investigated the evolution of renewable energy generation in the European Union from 2000 to 2020 using the LMDI decomposition analysis method. The study identified four primary factors for analysing variations in renewable energy production: RES share, energy intensity, population, and activity. The study discovered that RES share and energy efficiency had the greatest impact on variations in RES generation over the course of the study period.

A study by [45] explored the spatial-temporal heterogeneity and dynamic evolution of renewable electricity deployment in the EU from 2001 to 2017. LMDI decomposition analysis was applied to investigate changes in electricity generation determined by seven factors: technological structure effect, generation efficiency effect, investment structure effect, investment gap effect, energy structure effect, energy intensity effect, and economic scale effect. The study found that the investment structure effect was the main driver of the penetration of RES, while efficiency improvements reduced renewable electricity generation [45].

LMDI was also applied to investigate the RES innovation trends and efforts in increasing RES efficiency. In their research, [46] employed an extended LMDI approach to explore the effects of nine key factors on patenting in renewable energy technologies, namely the prioritization of specific technologies, the significance of specific technology in relation to all patents, the effectiveness of patents associated with research and development expenditures, the proportion of R&D spending in the gross domestic product, the productivity of renewable energy sources, the share of renewable energy, energy intensity, carbon productivity, and the impact of carbon emissions. The authors concluded that the number of patents for a given renewable energy technology increases, along with other factors, when priority is given to innovation in a specific technology, the efficiency of R&D spending increases, and the share of RES increases [46]. A study by [47] analysed the underperformance of PV-generated electricity in China compared to PV-generated power in the US. The LMDI approach was applied to quantify solar electricity production in China and the US with three main factors: estimated annual peak sun hours, efficiency, and curtailment ratio. The study found that solar system efficiency was the key factor that differentiated China’s PV performance from that of the US. Efficiency was lower because of the lower quality of PV panels, their maintenance and more pollution, which negatively affected the efficiency of PV systems in China [47].

There is no universally accepted method for analysing the RES generation levels and estimating the RES impact factors. However, previous studies have shown that decomposition analysis, and especially the LMDI approach, is widely used in the field of energy research, as it provides results that are easy to interpret and apply for better policymaking.

Based on the literature review, it can be concluded that there has been limited research examining the growth determinants of renewable electricity in the EU over the past 5 years. Prior research in the EU focused primarily on employing an econometric approach and examining the correlations between various variables; however, the majority of this research is out of date. Out of the existing research employing the LMDI approach in the EU, studies are typically limited to applying the Kaya identity approach; however, a number of other factors, such as capital productivity, rate of decarbonisation, and others, should be included. In addition, there is a lack of research on the disaggregation of renewable energy resources, with a focus on wind and solar development, which has seen a significant increase over the past decade. Most research covers the entire aggregated energy sector, with limited attention directed towards an exclusive examination of electricity generation.

This study focuses on the power generation sector rather than the energy sector as a whole to enable a more comprehensive understanding of its role and importance in the pursuit of long-term sustainability, particularly given the significant impacts expected from future electrification and decarbonisation targets. The study develops a novel LMDI decomposition approach, decomposing gross electricity generation from RES into five main factors: RES share effect, energy intensity effect, RES capacity productivity effect, RES deployment per capita effect, and population growth effect. The study examines the main drivers of total gross electricity production by disaggregating renewable energy resources and analysing the dynamics of production changes in wind and solar energy separately. The paper provides a comprehensive analysis of the most recent progress in renewable energy use in electricity generation over the last decade in the context of the EU’s path towards climate neutrality.

3. Methods and Data

The study applied the logarithmic mean Divisia index (LMDI), using an additive approach to decompose the changes in production values of gross electricity from renewable energy sources over the 10-year period from 2012 to 2021. Given that the EU’s long-term climate and energy targets span a period of 10 years, this study specifically chose a 10-year range to examine the extent of changes and progress made towards these established targets. Five main decomposition factors were determined to construct the LMDI for changes in electricity generation from RES over the years: changes in the share of RES in total electricity generation, changes in the energy intensity of electricity generation, changes in RES capacity productivity, changes in the per capita installed capacities of RES, and changes in population growth.

LMDI decomposers were determined based on a combination of the studies by [39,44], which analysed changes in energy-related CO₂ emissions and the development of RES generation in Europe using the LMDI decomposition approach. Equation (1) demonstrates the identity between the LMDI factors that determine the changes in the total amount of energy produced by RES, adapted from [39,44].

$$RES={\sum }_i{RES}_i= {\sum }_i\frac{RES}{EN}\frac{EN}{GDP}\frac{GDP}{RCAP}\frac{RCAP}{POP}POP={\sum }_i{RSH}_i{EI}_i{RPR}_i{RD}_i{POP}_i$$

(1)

where RES denotes gross electricity production from renewables, EN denotes total gross electricity production, GDP denotes gross domestic product, RCAP denotes electricity production capacities for renewables, POP denotes total population, RSH denotes RES share effect, EI denotes energy intensity effect, RPR denotes RES capacity productivity effect, and RD denotes RES deployment per capita effect.

According to the additive decomposition method, variations in RES-produced electricity are further determined by variations in each LMDI decomposer, as shown in Equation (2). The additive approach was chosen instead of the multiplicative approach to allow a more comprehensive representation of the changes in RES in the EU and an uncomplicated interpretation of the results. The additive approach calculates the difference in the amount of change when comparing two time periods [48], whereas the multiplicative approach calculates the ratio of change [49]. The decomposition analysis results in the additive approach are typically expressed in physical units, whereas in the multiplicative approach, the results are expressed as indices [50].

$$∆RES={RES}^T- {RES}^0=∆{RES}_{rsh}+∆{RES}_{ei}+∆{RES}_{rpr}+∆{RES}_{rd}+∆{RES}_{pop}$$

(2)

The changes in the individual LMDI decomposers are calculated using Equations (3)–(7), which were adapted from [51].

$$∆{RES}_{rsh}={\sum }_i\frac{{RES}^T-{RES}^0}{ln{RES}^T-ln{RES}^0}ln\frac{{RSH}_1^T}{{RSH}_1^0}$$

(3)

$$∆{RES}_{ei}={\sum }_i\frac{{RES}^T-{RES}^0}{ln{RES}^T-ln{RES}^0}ln\frac{{EI}_1^T}{{EI}_1^0}$$

(4)

$$∆{RES}_{rpr}={\sum }_i\frac{{RES}^T-{RES}^0}{ln{RES}^T-ln{RES}^0}ln\frac{{RPR}_1^T}{{RPR}_1^0}$$

(5)

$$∆{RES}_{rd}={\sum }_i\frac{{RES}^T-{RES}^0}{ln{RES}^T-ln{RES}^0}ln\frac{{RD}_1^T}{{RD}_1^0}$$

(6)

$$∆{RES}_{pop}={\sum }_i\frac{{RES}^T-{RES}^0}{ln{RES}^T-ln{RES}^0}ln\frac{{POP}_1^T}{{POP}_1^0}$$

(7)

where superscript T stands for indicator value in the future year and 0 for indicator value in the initial year, and subscripts rsh, ei, rpr, ri, pop denote the changes in the share of RES, energy intensity, RES capacity productivity, per capita installed capacities of RES, and population growth, respectively.

All data used for LMDI decomposition analysis construction were retrieved from Eurostat.

Table 1. Description of the data values used in the study and list of data sources.

Notation	Description	Source
RES	Gross electricity production from renewables and biofuels, GWh	[52]
EN	Total gross electricity production, GWh	[52]
GDP	Gross domestic product at market prices, chain linked volumes, MEUR	[53]
RCAP	Electricity production capacities for renewables, MW	[54]
POP	Total population, number of inhabitants	[55]
W	Gross electricity production from wind, GWh	[56]
PV	Gross electricity production from solar photovoltaic, GWh	[56]
WCAP	Electricity production capacities for wind, MW	[54]
PVCAP	Electricity production capacities for solar PV, MW	[54]

summarises the data values used in the study and the list of data sources. All values of the selected variables were collected for all 27 EU countries. The data series covered the period from 2012 to 2021, i.e., the most recent data available at the time this study was conducted.

This study extends the LMDI decomposition analysis to examine how wind and solar PV installations, which have experienced significant growth in the past decade, contribute to the overall increase in renewable energy sources. Equations (8) and (9) are used to examine in more detail the changes in electricity generation from wind and solar PV, adapted from [39,44].

$$W={\sum }_i{W}_i= {\sum }_i\frac{W}{RES}\frac{RES}{EN}\frac{EN}{GDP}\frac{GDP}{WCAP}\frac{WCAP}{POP}POP={\sum }_i{WSH}_i{RSH}_i{EI}_i{WPR}_i{WD}_i{POP}_i$$

(8)

$$PV={\sum }_i{PV}_i= {\sum }_i\frac{PV}{RES}\frac{RES}{EN}\frac{EN}{GDP}\frac{GDP}{PVCAP}\frac{PVCAP}{POP}POP={\sum }_i{PVSH}_i{RSH}_i{EI}_i{PVPR}_i{PVD}_i{POP}_i$$

(9)

where W denotes gross electricity production from wind, PV denotes gross electricity production from solar PV, RES denotes gross electricity production from renewables, EN denotes total gross electricity production, GDP denotes gross domestic product, WCAP denotes electricity production capacities for wind, PVCAP denotes electricity production capacities for solar PV, POP denotes total population, WSH denotes wind share effect, PVSH denotes solar PV share effect, RSH denotes RES share effect, EI denotes energy intensity effect, WPR denotes wind capacity productivity effect, PVPR denotes solar PV capacity productivity effect, WD denotes wind deployment per capita effect, and PVD denotes solar PV deployment per capita effect.

For further decomposition of Equations (8) and (9), the same calculation method is used as for Equations (3)–(7).

Table 2. Description of LMDI decomposition analysis decomposers.

Notation	Factor	Unit	Factor Calculation
RSH	RES share effect	%	Gross electricity production from renewables (GWh)/Total gross electricity production (GWh)
EI	Energy intensity effect	GWh/MEUR	Total gross electricity production (GWh)/Gross domestic product (MEUR)
RPR	RES capacity productivity effect	MEUR/MW	Gross domestic product (MEUR)/Electricity production capacities for renewables (MW)
RD	RES deployment per capita effect	MW/population	Electricity production capacities for renewables (MW)/Population (number)
WSH	Wind share effect	%	Gross electricity production from wind (GWh)/Gross electricity production from renewables (GWh)
WPR	Wind capacity productivity effect	MEUR/MW	Gross domestic product (MEUR)/Electricity production capacities for wind (MW)
WD	Wind deployment per capita effect	MW/population	Electricity production capacities for wind (MW)/Population (number)
PVSH	Solar PV share effect	%	Gross electricity production from solar PV (GWh)/Gross electricity production from renewables (GWh)
PVPR	Solar PV capacity productivity effect	MEUR/MW	Gross domestic product (MEUR)/Electricity production capacities for solar PV (MW)
PVD	Solar PV deployment per capita effect	MW/population	Electricity production capacities for solar PV (MW)/Population (number)

summarizes LMDI decomposition analysis decomposers used in this study to decompose the changes in production values of gross electricity from RES, wind, and solar PV over a 10-year period.

The RES share effect (RSH) represents the extent of decarbonisation of the country’s total electricity production. The energy intensity effect (EI) defines the impact of energy efficiency improvements; a decrease in energy intensity indicates that the overall energy efficiency of electricity production has increased, as fewer energy resources were required to produce a unit of economic output. The RES capacity productivity effect (RPR) defines the changes in economic output generated per installed electricity capacity of renewable energy technologies such as wind turbines, solar PV, hydropower plants, and biofuel stations. The RPR effect outlines the impact on economic growth per installed RES capacity. The wind capacity productivity effect (WPR) describes the changes in generated economic output per installed wind turbine electricity capacities, whereas the solar PV capacity productivity effect (PVPR) describes the changes in generated economic output per installed solar PV electricity capacities. The RES capacity productivity effect was also utilised as one of the LMDI decomposers in a study conducted by [39], which defined the RES capacity productivity factor as the ratio between GDP and renewable capacity. A similar approach was employed in the study by [37], which determined the productivity of electric power from renewable sources—an indicator measuring changes in income or economic value per unit of electric power derived from renewable energy resources— thereby capturing the income effect of electric power generated from renewable energy sources. The RES deployment per capita effect (RD) represents the increase in installed RES capacities per number of inhabitants in the country, thus indicating the rate of green transition in electricity production. The wind share effect (WSH) represents the share of wind in the total amount of electricity produced from renewables. Similarly, the solar PV share effect (PVSH) represents the proportion of solar PV in the total RES mix used for electricity generation.

4. Results and Discussion

4.1. Renewable Energy Sources Profile for Electricity Generation in the EU-27 Countries

Initially, existing energy profiles of EU-27 countries were analyzed to gain a comprehensive understanding of their main characteristics and to facilitate country comparisons. Two key indicators were collected and compared: per capita electricity generation from renewable energy sources and the distribution of different RES in gross electricity generation. This analysis served as a foundation for a more in-depth examination and explanation of the LMDI analysis results, allowing for more accurate interpretations and insights into the data.

Figure 1 shows the per capita RES electricity generation rates for all EU-27 countries in 2012 and 2021. Average per capita electricity generation from RES in the EU-27 increased from 1.9 MWh in 2012 to 2.7 MWh in 2021. Sweden (11.2 MWh), Finland (6.9 MWh) and Austria (6.3 MWh) showed the highest per capita electricity generation from RES in 2021, while Malta (0.5 MWh), Hungary (0.7 MWh), and Poland (0.8 MWh) showed the lowest. It can be observed that the majority of countries increased their renewable electricity generation per capita, with the exception of Latvia and Austria. The highest increases were observed in Malta, Cyprus, Hungary, Greece, Lithuania, and Croatia. The graph shows that the strongest increases were observed in the countries where per capita electricity generation from RES was originally lowest in 2012.

Figure 2 illustrates the share of different renewable energy sources (hydro, wind, solar, biomass, and others) in gross electricity generation in 2020, based on data from [57].

Countries with the highest share of hydropower in gross electricity generation from RES were Slovenia (88%), Latvia (74%), Croatia (70%), with the highest share of wind energy—Ireland (86%), Denmark (69%), Lithuania (55%), with the highest share of solar energy—Malta (98%), Cyprus (51%), Netherlands (27%), Belgium (23%), and with the highest share of biofuels—Estonia (64%), Finland (32%), Hungary (30%).

The most diversified profiles of gross electricity generation from RES were found in the Czech Republic, Luxembourg, Italy, and the Netherlands. In the EU-27, wind energy (36%) and hydropower (33%) were the main sources of gross electricity generation from RES, followed by solar energy (14%), other renewables (8.4%), and solid biofuels (8%).

4.2. LMDI Decomposition Analysis Results

Figure 3 illustrates the results of the LMDI decomposition analysis for EU countries and shows the contribution of each LMDI factor to the changes in gross electricity generation from RES in the period from 2012 to 2021. Figure 3 excludes Malta for more comprehensive representation purposes.

By 2021, the highest increases in gross electricity generation from RES compared to 2012 levels were observed in Malta (926%), the Netherlands (224%), Cyprus (202%), Greece (115%), and Belgium (100%). In terms of absolute increases in gross electricity generation from RES, the leaders were Germany with an increase of 87 TWh, Spain with an increase of 38 TWh, France with an increase of 37 TWh, and the Netherlands with an increase of 28 TWh.

Table 3. LMDI analysis results for changes in gross electricity production from RES from 2012 to 2021, GWh.

	∆RES Share Effect	∆Energy Intensity Effect	∆RES Capacity Productivity Effect	∆RES Deployment per Capita Effect	∆Population Growth Effect	∆Gross Electricity Production from RES
Belgium	7326	2519	−11,005	12,275	718	11,833
Bulgaria	4271	−1452	771	1392	−467	4515
Czechia	3412	−2289	870	933	201	3126
Denmark	9533	−1719	−9029	11,578	897	11,259
Germany	105,078	−36,434	−91,404	102,899	6840	86,978
Estonia	2421	−1609	−2949	3524	15	1402
Ireland	5006	−5456	133	5908	841	6432
Greece	9550	1935	−9609	10,519	−540	11,856
Spain	46,536	−16,000	−24,791	31,357	1287	38,389
France	40,245	−12,168	−43,001	47,612	3840	36,528
Croatia	2595	948	−1824	3981	−495	5205
Italy	28,085	−4493	−19,887	20,857	−333	24,228
Cyprus	468	−57	−382	466	23	518
Latvia	−222	−960	544	522	−276	−392
Lithuania	1410	−583	−408	1397	−181	1635
Luxembourg	1480	−1262	−175	227	329	599
Hungary	3877	−690	−6076	7237	−78	4269
Malta	176	−5	−187	221	33	238
Netherlands	25,370	−15	−38,157	39,938	855	27,991
Austria	2867	−5515	−11,892	12,573	3287	1320
Poland	10,913	−4419	−21,259	28,943	−155	14,023
Portugal	10,596	−689	−6747	10,187	−668	12,678
Romania	11,450	−7629	2055	6750	−1146	11,480
Slovenia	1218	−1248	−148	1254	141	1216
Slovakia	924	−1040	1245	65	67	1262
Finland	8400	−1470	−16,197	18,168	714	9615
Sweden	13,661	−16,177	−11,968	23,254	8573	17,343
EU-27	356,645	−117,979	−321,478	404,033	24,323	345,544

summarizes the LMDI results for the changes in gross electricity generation from RES from 2012 to 2021 in absolute terms, expressed in GWh.

Latvia is the only country that experienced a decline in gross electricity generation from renewable energy sources, by 392 GWh in 2021 when compared to 2012. This decrease can be largely attributed to fluctuations in hydropower generation, which heavily relies on weather conditions. In 2012, Latvia witnessed the second-largest peak in hydropower production over the preceding decade, driven by an exceptional surge in water inflow into the Daugava River, where the main hydropower plants are situated in the country.

Austria showed limited progress in increasing the proportion of gross electricity generation from renewable energy sources when comparing levels in 2012 to those in 2021, with a modest growth of only 2.4%. Similarly to Latvia, Austria experienced record-high hydropower production in 2012. In both countries, hydro energy constitutes a significant portion of the overall electricity production mix, making them more exposed to fluctuations in water inflows caused by changes in weather conditions. This observation suggests that countries with lower levels of RES diversification may be more exposed to significant fluctuations in production quantities. These findings are in line with the results of a study by [27], who argued that annual fluctuations in the amount of hydro energy have a significant impact on the total amount of electricity generated from RES in national power generation plants.

Moderate progress in the relative increase in electricity generation from RES was observed for Sweden (17.6%), Slovakia (21.7%), Italy (25.7%), and Slovenia (26.9%). It is noteworthy that Sweden, Austria, and Latvia emerged as prominent nations with a significant share of renewable energy sources in their electricity generation in the year 2021. This discovery suggests that countries that have already achieved a high level of renewable energy sources penetration in their electricity production have experienced slower advancements in the deployment of RES. In contrast, Denmark, Portugal, and Croatia have demonstrated both an initially higher proportion of renewable energy sources in their electricity generation and significant advancements in increasing this share over time [57].

The results of the LMDI analysis show that several factors contributed to the changes in gross electricity generation from RES for the EU-27 countries. For the EU-27, the main drivers were the RES deployment per capita effect and the RES share effect, which increased gross electricity generation from RES overall, while the negative RES capacity productivity effect and the negative energy intensity effect reduced gross electricity generation from RES. Population growth also contributed to the increase in RES-generated electricity, but the effect was not as significant as for the other factors. In the EU, total gross electricity generation from RES increased by 45.7% during the period from 2012 to 2021. In total, 11 countries reported a change in gross electricity generation from RES below the EU level during this period—Czech Republic (35.5%), Spain (42.4%), France (40.5%), Italy (25.7%), Latvia (−9.5%), Luxembourg (43.7%), Austria (2.4%), Slovenia (26.9%), Slovakia (21.7%), Finland (33.7%), and Sweden (17.6%).

The RES capacity productivity effect contributed negatively to gross electricity generation from RES in most countries except Bulgaria, the Czech Republic, Ireland, Latvia, Romania, and Slovakia. This observation suggests that economic growth is advancing at a faster rate in these countries compared to the growth of installed renewable energy capacities, in comparison to other countries. The RES capacity productivity effect measures the amount of GDP produced per installed capacity. However, if the installed capacities are currently low and not experiencing significant growth, it indicates untapped potential for renewable energy capacity installations in these countries. It is important to note that the RES capacity productivity effect incorporates the speed of economic growth, which in turn also has a direct impact on total electricity demand. If economic growth slows down, this could also indicate a lower total electricity demand and a lower need to generate the amount of electricity required to meet this demand. On the other hand, faster GDP growth has a significant impact on total electricity demand and leads to an increase in electricity generation capacity. A negative RES capacity productivity effect could directly indicate decarbonisation of the electricity sector in countries where economic growth and thus electricity demand tend to be stable or grow only marginally, but the overall sources used for electricity generation are being replaced by renewable energy sources through the gradual abandonment of fossil fuels.

In most countries, population growth had a positive effect and led to an increase in electricity generated from RES, with the exception of 10 countries where the population decreased, namely Bulgaria, Greece, Croatia, Italy, Latvia, Lithuania, Hungary, Poland, Portugal, and Romania. Energy efficiency improvements have reduced energy intensity in most countries and have, therefore, had a negative impact on the amount of electricity generated from RES. However, in Belgium, Greece, and Croatia, the energy intensity of electricity generation increased during this period, contributing to a positive impact on gross electricity generation from RES. For these countries, the growth in gross electricity generation from RES between 2012 and 2021 was well above the aggregate EU growth rate for this period, while GDP growth was significantly below the average growth rate of all EU member states. This explains the overall increase in the energy intensity effect for these countries, indicating that more electricity was generally generated from RES to produce one unit of GDP. In all countries, the RES deployment per capita effect showed an increasing trend and had a positive impact on RES-generated electricity. All EU countries thus showed positive progress in the use of renewable energy per population, indicating a trend towards a green energy transition.

As for RES electricity generated from wind power, the share of wind power in the total RES energy mix has contributed positively to the total electricity generated from wind power in most countries. However, in countries such as Bulgaria, Denmark, Estonia, Spain, Cyprus, Hungary, Portugal, and Slovakia, the share of wind power in the total RES generated electricity decreased and, therefore, had a negative impact on the total gross electricity generated from wind power.

Table 4. LMDI analysis results for changes in gross electricity production from wind, from 2012 to 2021, GWh.

	∆Wind Share Effect	∆RES Share Effect	∆Energy Intensity Effect	∆Wind Capacity Productivity Effect	∆Wind Deployment per Capita Effect	∆Population Growth Effect	∆Gross Electricity Production from Wind
Belgium	4432	2545	1459	−7820	8334	289	9238
Bulgaria	−560	751	−253	223	133	−80	213
Czechia	22	184	−120	−52	141	11	186
Denmark	−1962	6757	−1376	−4739	6480	625	5785
Germany	27,337	45,470	−17,139	−49,855	54,257	2897	62,967
Estonia	−193	845	−551	−1289	1484	4	299
Ireland	598	4006	−4421	−612	5508	686	5765
Greece	1620	4208	615	−6044	6424	−191	6633
Spain	−7210	23,929	−7568	−7637	10,402	674	12,589
France	13,004	9760	−2865	−20,237	21,142	850	21,653
Croatia	1158	199	121	−1445	1767	−67	1733
Italy	4023	3961	−580	−5452	5712	−144	7520
Cyprus	−185	227	−26	31	4	9	61
Latvia	31	5	−38	−4	45	−11	27
Lithuania	195	442	−140	−544	939	−69	822
Luxembourg	191	89	−80	−68	72	33	237
Hungary	−760	637	−175	193	13	−14	−106
Malta	0	0	0	0	0	0	0
Netherlands	−1062	12,674	50	−10,834	11,755	440	13,023
Austria	3997	178	−274	−3649	3712	314	4277
Poland	4732	5198	−2330	−5559	9522	−77	11,487
Portugal	−2569	4700	−301	−1364	2767	−278	2956
Romania	1536	2492	−2038	136	2100	−291	3936
Slovenia	5	1	−1	−1	2	0	6
Slovakia	−1	0	0	2	−2	0	−1
Finland	6270	1348	−17	−6716	7058	69	8012
Sweden	15,321	3349	−2116	−17,090	19,132	1483	20,080
EU-27	69,968	133,955	−40,165	−150,426	178,904	7161	199,398

outlines the LMDI results for changes in gross electricity production from wind from 2012 to 2021.

The increasing share of RES in the total electricity mix has contributed positively to wind energy production in all countries. For wind energy production to increase, the total share of RES in electricity production is a strong incentive. The wind capacity productivity effect had a negative impact on the level of wind power production in almost all countries except Bulgaria, Cyprus, Hungary, and Romania. The wind deployment per capita effect was positive in all countries except Slovakia, where the per capita installation of wind power declined during the study period, suggesting that other renewable energy resources such as hydropower, solar PV and biofuels have taken a more dominant position in the overall RES mix in electricity generation.

In the EU, gross electricity generation from wind power increased by a total of 199 TWh between 2012 and 2021. The largest contributor to the increase in wind power in the EU-27 during this period was the increasing use of wind power per capita. The increasing overall share of RES in electricity generation and the effect of the wind share had a positive impact on wind energy generation. The effect of population growth also made a positive contribution, but the effect was less significant than for the other factors. The effect of wind capacity productivity and the effect of energy intensity had a negative impact on gross electricity generation from wind power.

In absolute terms, the largest increases in gross electricity generation from wind power during the study period were observed in Germany (62.97 TWh), France (21.65 TWh), and Sweden (20.08 TWh). Slovakia was the only country to record a decrease in gross electricity generation from wind power, of 1 GWh in the period from 2012 to 2021. No progress in wind energy generation was observed in Malta. Slovenia, Latvia, and Cyprus all experienced slow progress in wind-generated electricity, with an increase of 6 GWh, 27 GWh, and 61 GWh, respectively. When compared to other Baltic states, Latvia’s progress in expanding wind and solar PV capacities has been notably slow. In contrast, Lithuania and Estonia, which started with lower positions in their renewable energy share in electricity production, have shown proactive efforts in increasing their wind and solar PV capacities. Instead of following a similar path, Latvia has relied more on hydropower plants for its renewable energy generation. This difference in approach has led to differing levels of progress in renewable energy capacity expansion among the Baltic states. The results presented in this study align with the research conducted by [31], which emphasised Latvia’s historical involvement in bioenergy and hydropower development. However, the country has little expertise in wind power deployment and negligible use of solar electricity. According to [31], some nations are seeing lower levels of success in the adoption and implementation of renewable energy sources, including wind energy and solar PV systems. To compensate for this, these countries are placing more emphasis on existing RES technologies, such as hydropower and biomass. However, this overemphasis on incumbent technologies might potentially hinder future RES growth.

Table 5. LMDI analysis results for changes in gross electricity production from solar PV, from 2012 to 2021, GWh.

	∆Solar PV Share Effect	∆RES Share Effect	∆Energy Intensity Effect	∆Solar PV Capacity Productivity Effect	∆Solar PV Deployment per Capita Effect	∆Population Growth Effect	∆Gross Electricity Production from PV
Belgium	1053	1448	540	−2692	2973	148	3470
Bulgaria	−13	668	−238	−154	503	−78	688
Czechia	−500	733	−481	177	197	42	167
Denmark	772	360	−62	−936	1038	34	1205
Germany	6386	19,771	−6860	−19,291	21,630	1324	22,960
Estonia	309	50	−31	−338	363	1	354
Ireland	88	2	−16	−79	94	2	92
Greece	750	2132	591	−3549	3771	−139	3557
Spain	9344	4792	−1081	−12,329	12,851	152	13,729
France	8259	3382	−1093	−10,646	11,092	309	11,305
Croatia	121	6	2	−113	134	−4	147
Italy	1433	5423	−881	−6525	6840	−114	6177
Cyprus	236	184	−25	−374	414	11	447
Latvia	7	1	−1	−7	7	0	7
Lithuania	155	29	−18	−162	189	−4	189
Luxembourg	103	78	−64	−134	138	20	142
Hungary	2494	1084	−8	−2918	3153	−16	3788
Malta	19	161	−2	−181	212	31	239
Netherlands	5102	5820	−81	−10,659	10,967	155	11,305
Austria	2405	35	−94	−2347	2375	70	2445
Poland	3522	106	96	−3515	3734	−9	3933
Portugal	1488	414	−151	−2054	2162	−15	1845
Romania	1465	315	−579	−418	985	−72	1695
Slovenia	244	55	−79	−269	331	8	290
Slovakia	142	75	−88	93	20	6	247
Finland	252	28	4	−266	272	1	292
Sweden	1325	124	−27	−1406	1462	29	1507
EU-27	46,963	47,274	−10,725	−81,093	87,910	1893	92,222

shows the LMDI results for the changes in gross electricity generation from solar PV over the period. Across the EU-27, an increase in gross electricity generation from PV was observed from 2012 to 2021.

The solar PV share effect contributed positively to the total gross electricity generation from solar PV in almost all countries, except in Bulgaria and the Czech Republic, where solar PV share in the total electricity mix decreased, indicating that hydropower, biofuels, and wind (in the Czech Republic) were acquiring a more dominant position in the RES mix for electricity production. The RES share effect contributed positively to gross electricity production from solar PV in all countries, indicating that an increase in the overall share of renewable energy sources had a positive impact on the overall deployment of solar PV in electricity generation.

On the other hand, the solar PV capacity productivity effect had a negative impact on electricity generation from solar PV in all countries except the Czech Republic and Slovakia. This is explained by the fact that in the Czech Republic and Slovakia, over the period from 2012 to 2021, only modest increases in solar PV installed capacities below the EU average growth rate were observed. Negative impacts on the overall changes in gross electricity generation from PV were also due to the energy intensity effect, except in Belgium, Greece, Croatia, Poland, and Finland, which can be explained by the fact that these countries demonstrated the greatest increases in installed solar PV capacities between 2012 and 2021. Solar PV deployment per capita increased in all EU-27 countries and contributed positively to gross electricity production from solar PV.

In absolute terms, Germany (22.9 TWh), Spain (13.7 TWh), France (11.3 TWh), and the Netherlands (11.3 TWh) achieved the largest growth in gross electricity generation from solar PV between 2012 and 2021. In contrast, Latvia (6.8 GWh) and Ireland (92.3 GWh) recorded the slowest progress.

In the EU-27, total gross electricity generation from solar PV increased by 92 TWh. The largest contributors to the changes in electricity generated by PV were the increase in per capita use of PV, the solar PV share effect, and the RES share effect. Population growth also made a positive contribution, while PV capacity productivity and the energy intensity effect had a negative impact on the changes in gross electricity generation from solar PV.

4.3. Differences between the Four Main Regional Groups

The results of the LMDI decomposition analysis were further analysed based on their division into regional groups in order to determine the principal differences and similarities between regions. The countries in this paper were divided into four main regional groups—Northern Europe, Central Western Europe, Central Eastern Europe, and Southern Europe [58]. Figure 4 and

Table 6. Regional electricity markets in the EU, adapted from [58].

Region	EU Countries Included
Northern Europe	Sweden, Finland, Denmark, Estonia, Latvia, and Lithuania
Central Western Europe	Germany, France, Belgium, Austria, the Netherlands, and Luxembourg
Central Eastern Europe	Poland, the Czech Republic, Slovakia, Slovenia, Hungary, and Romania
Southern Europe *	Bulgaria, Croatia, and Greece, Italy and Malta, Spain, and Portugal

* Aggregated results for South-Eastern Europe, Apennine Peninsula, Iberian Peninsula regions.

provide an overview of the countries included in each group.

The division into categories was based on the classification of regional electricity wholesale markets by the European Commission [58]. Southern Europe includes the regions of South-Eastern Europe (Bulgaria, Croatia, and Greece), Apennine Peninsula (Italy and Malta), and Iberian Peninsula (Spain and Portugal). Ireland and Cyprus were excluded from the grouping division due to their lack of alignment with the predetermined criteria for grouping. Originally, Ireland was grouped together with the UK to form the British Isles regional wholesale electricity market. However, in this study, only EU countries were included in the classification of the respective groups.

Figure 5 and

Table 7. LMDI results for changes in gross electricity generation from RES in EU regional electricity markets during 2012–2021, GWh.

	Northern Europe	Central Western Europe	Central Eastern Europe	Southern Europe	EU-27
∆RES share effect	35,203	182,365	31,794	101,809	356,645
∆Energy intensity effect	−22,518	−52,875	−17,316	−19,756	−117,979
∆RES capacity productivity effect	−40,006	−195,634	−23,314	−62,274	−321,478
∆RES deployment per capita effect	58,441	215,524	45,181	78,513	404,033
∆Population growth effect	9742	15,869	−969	−1183	24,323
∆Gross electricity production from RES	40,862	165,249	35,376	97,108	345,544

depict LMDI results for changes in gross electricity production from RES for each of the four regions and the EU-27. The impact of population growth was shown to be positive in Northern Europe and Central Western Europe, whereas it exhibited a negative influence in Central Eastern Europe and Southern Europe.

The primary factor driving the total growth in gross electricity production from renewable energy sources in Northern Europe, Central Western Europe, and Central Eastern Europe was the RES deployment per capita effect. However, the primary influential element in Southern Europe was the RES share effect. The second primary factor that influenced the changes in the amount of electricity produced from RES in Northern Europe and Central Western Europe was the negative effect on RES capacity productivity.

Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10 show the annual changes in gross electricity generation from RES for all regional country groups.

In Northern Europe, there were significant fluctuations in gross electricity production from renewable energy sources during the study period. These fluctuations can be attributed, in part, to the substantial contribution of hydropower in the total RES electricity mix. In fact, hydropower constituted a significant 50% of the total RES electricity generation in this region. The findings indicate that there was a rise in energy intensity during the timeframe spanning from 2019 to 2021. Notably, the results suggest that energy efficiency improvements did not have a major impact on this observed trend. Northern Europe has seen consistent and stable population growth, which has had a positive effect on the amount of electricity produced from RES. This growth can be largely attributed to the increasing population in Scandinavian countries, even though the Baltic states have experienced a decrease in their populations.

Central Western Europe has shown consistent and stable growth in gross electricity production from renewable energy sources, except for a decrease observed during the period from 2020 to 2021. In this region, wind energy plays a significant role, constituting 40% of the total electricity production from renewable sources, which is the largest share compared to other regional groups. This high share of wind energy could explain the smaller fluctuations and the more rapid and constant growth in renewable energy production compared to Northern Europe. Moreover, Central Western Europe has experienced steady but modest population growth, which has had a positive impact on the gross electricity production from renewable sources. Although the overall energy intensity effect was pushing down the amount of renewable energy produced during the study period, there was a positive effect noted in the period from 2019 to 2020. A study conducted by [45] posits that despite a huge increase in the overall installed capacities of renewable energy in Western Europe, there has been no significant improvement in the overall energy efficiency of electricity generation [45].

Central Eastern Europe exhibited notable growth in gross electricity generation from renewable energy sources between 2012 and 2016. However, this growth slowed down and experienced a downturn during the period from 2014 to 2018, only to pick up again from 2018 to 2021. During the past two periods, from 2019 to 2021, the energy intensity effect had a positive impact, indicating that energy efficiency improvements in electricity generation were not effective. The results also revealed a fluctuating RES capacity productivity effect, which means that the productivity of renewable energy sources varied over time. Additionally, there was a minimal negative population growth effect observed during the same period. In terms of the energy mix, Central Eastern Europe has similar shares for hydro energy and wind energy. However, a high share of biofuels helps to compensate for fluctuations in electricity generation driven by hydropower.

In Southern Europe, both hydro and wind energy sources contribute nearly equally to the electricity mix, while solar energy holds the highest share compared to other regions. However, the total gross electricity generation from RES in Southern Europe shows significant fluctuations. The two most substantial drops in gross electricity generation from RES occurred during the periods of 2014–2015 and 2016–2017, both of which were driven by a negative RES share effect. The energy intensity effect was positive during 2019–2020, suggesting that there might not have been sufficient progress in improving energy efficiency during that period.

The aggregated results for the EU-27 indicate a consistent and continuous growth in gross electricity generation from RES. Consistent reductions in energy intensity had a significant negative impact on the quantity of electricity produced from RES in the EU-27, according to the findings. These results align with the findings of [45], who discovered that the energy intensity effect is a significant negative contributor to the quantity of RES electricity generated [45]. However, there was a concerning trend revealed by the positive energy intensity in 2019–2020, suggesting that there might not have been sufficient progress in improving energy efficiency in electricity generation across the EU-27.

Table 8. LMDI results for changes in gross electricity generation from wind in EU regional electricity markets during 2012–2021, GWh.

	Northern Europe	Central Western Europe	Central Eastern Europe	Southern Europe	EU-27
∆Wind share effect	19,662	47,898	5534	−3540	69,968
∆RES share effect	12,746	70,715	8512	37,748	133,955
∆Energy intensity effect	−4238	−18,848	−4665	−7967	−40,165
∆Wind capacity productivity effect	−30,383	−92,463	−5281	−21,718	−150,426
∆Wind deployment per capita effect	35,137	99,272	11,778	27,205	178,904
∆Population growth effect	2101	4821	−371	−86	7161
∆Gross electricity production from wind	35,025	111,395	15,507	31,644	199,398

and

Table 9. LMDI results for changes in gross electricity generation from solar PV in EU regional electricity markets during 2012–2021, GWh.

	Northern Europe	Central Western Europe	Central Eastern Europe	Southern Europe	EU-27
∆Solar PV share effect	2820	23,309	7367	13,143	46,963
∆RES share effect	592	30,535	2367	13,595	47,274
∆Energy intensity effect	−135	−7651	−1139	−1759	−10,725
∆Solar PV capacity productivity effect	−3116	−45,769	−6851	−24,904	−81,093
∆Solar PV deployment per capita effect	3332	49,176	8420	26,474	87,910
∆Population growth effect	61	2027	−42	−166	1893
∆Gross electricity production from solar PV	3553	51,626	10,121	26,382	92,222

depict LMDI results for the changes in gross electricity generation from wind and solar PV, respectively, in the EU regional electricity markets over the period 2012–2021. The findings reveal that the deployment of wind and solar PV per capita effect had the most significant influence on changes in gross electricity generation from these sources across all regional country groups, except for Southern Europe.

Moreover, the impact of the RES share effect was more pronounced in wind-generated electricity compared to LMDI results for solar PV. This suggests that increasing the overall share of renewable energy sources has a stronger effect on the amount of electricity generated from wind. This could be attributed to the fact that wind energy has larger capacity and production capabilities for electricity compared to solar PV.

The comparison of regional groups showed a notable contrast in Southern Europe. While the wind share effect was negative, indicating a decrease in the share of wind energy in the total RES mix for electricity production over the study period, the overall RES share remained positive and dominant in this region. However, what stands out is the swift rise in the solar PV share, which is growing at a faster pace and surpassing wind energy in Southern Europe. This suggests that solar photovoltaic installations are gaining more prominence and becoming the dominant source of renewable energy in the Southern European region.

When compared to other regional country groups, it is evident that the energy intensity effect is somewhat weaker in Northern Europe. This suggests that the implementation of energy efficiency measures in power generation has not resulted in any substantial influence.

The results reveal that in Central Eastern Europe, wind capacity and solar PV capacity productivity effects were notably weaker when compared to the Central Western Europe regional country group. This indicates that capacity productivity experienced a more substantial decrease in Western countries.

The results are consistent with the findings of [39], which decomposed changes in energy-related CO₂ emissions in EU countries by comparing four different groups of countries: The Northern European group (Finland, Denmark, Ireland, United Kingdom, and Sweden); the Southern European group (Italy, Spain, Greece, Slovenia, and Portugal); the Western European group (France, Netherlands, Belgium, Austria, Germany, and Luxembourg); and the Central Eastern European group (Poland, Czech Republic, Hungary, Slovakia, and Estonia). The results of the LMDI analysis showed a negative effect on renewable capacity productivity in Western European countries and a positive effect in Central and Eastern European countries [39].

These findings also align with the study conducted by [37], which revealed a negative resource productivity effect for European countries including Belgium, Denmark, Austria, and Norway for the time period from 1985 until 2011. The study argued that this negative trend could be attributed to the fact that more-developed countries are generally more inclined to invest in renewable energy capacities [37].

5. Conclusions

This study applied the logarithmic mean Divisia index (LMDI) decomposition analysis to examine the primary factors influencing the changes in electricity generated from renewable energy sources in the EU-27 during the decade from 2012 to 2021. The research determined five key factors and thoroughly investigated their respective contributions to the changes in gross electricity production from RES: the RES share effect, energy intensity effect, RES capacity productivity effect, RES deployment per capita effect, and population growth effect.

The aggregated results for the EU-27 countries showed that the main factors contributing positively to the total gross electricity generation from RES were the RES deployment per capita effect and the RES share effect, while the RES capacity productivity effect and energy intensity effect contributed negatively. The population growth effect also contributed positively to the amount of RES-generated electricity, but the effect was not as significant as for the other factors.

In the EU, total gross electricity generation from RES increased by 45.7% during the period from 2012 to 2021. In total, 16 countries reported a change in gross electricity generation from RES above the EU level during this period. The cross-country comparison showed that countries with lower levels of RES diversification may be exposed to greater fluctuations in renewable energy production volumes. Moreover, some countries that have already achieved a high share of RES in their electricity generation (e.g., Latvia, Sweden, Austria) have made slower progress in the deployment of RES over the last decade. Certain countries are encountering challenges in effectively embracing and putting into practice emerging renewable energy sources (RES), notably wind energy and solar photovoltaic (PV) systems. In response, these nations are prioritizing the utilization of well-established RES technologies like hydropower and biomass. However, this heightened focus on established technologies could potentially impede the future advancement of RES.

The study also separately examined the main drivers of change in wind and solar PV electricity generation, driven by six main factors: the wind/solar PV share effect, RES share effect, energy intensity effect, wind/solar PV capacity productivity effect, wind/solar PV deployment per capita effect, and population growth effect.

Between 2012 and 2021, gross electricity generation from wind power in the EU saw a substantial increase, of 199 TWh. The primary driver for this growth was the wind deployment per capita effect. The overall share of RES in electricity generation, the wind share effect, and the population growth effect had a positive impact on wind energy generation, while the wind capacity productivity and energy intensity effects had negative contributions. In absolute terms, the largest increases in gross electricity generation from wind power during the study period were observed in Germany (62.97 TWh), France (21.65 TWh), and Sweden (20.08 TWh).

Similar findings were also observed in the LMDI results for solar PV-generated power. In the EU-27, total gross electricity generation from solar PV increased by 92 TWh. The largest contributors to the changes in electricity generated by solar PV were the solar PV deployment per capita effect, solar PV share effect, and RES share effect, followed by a minor population growth effect. PV capacity productivity and the energy intensity effect negatively impacted the changes in gross electricity generation from solar PV. In absolute terms, Germany (22.9 TWh), Spain (13.7 TWh), France (11.3 TWh), and the Netherlands (11.3 TWh) achieved the largest growth in gross electricity generation from solar PV between 2012 and 2021.

The LMDI results indicated that the impact of the RES share effect was more noticeable in wind-generated electricity when compared to the LMDI results for solar PV. This phenomenon may be attributed to the fact that wind energy exhibits greater capacity and production capabilities in generating electricity in comparison to solar PV systems. This implies that boosting the overall share of RES has a more substantial impact on the amount of electricity generated from wind. In general, the aggregated results for the EU-27 countries showed that an increasing overall share of renewable energy resources positively influences the overall deployment of wind and solar energy in electricity generation.

The deployment of RES per capita effect was the main driver of the total growth in gross electricity production from RES in Northern Europe, Central Western Europe, and Central Eastern Europe, according to comparisons between regional groups. The RES share effect, however, was the main driving force in Southern Europe. The decrease in RES capacity productivity was the second major factor that affected variations in the quantity of energy generated by RES in Northern Europe and Central Western Europe.

A significant divergence in Southern Europe was shown when regional groupings were compared. The overall RES share in this region remained positive and predominant, despite the fact that the wind share impact in Southern Europe was negative, showing a decline in the percentage of wind energy in the entire RES mix for electricity generation throughout the research period. The rapid growth in the solar PV share is outpacing wind energy in Southern Europe. This could be explained by the fact that countries in Southern Europe, such as Spain, Italy, and Greece, have a significant advantage in terms of solar irradiation and, consequently, an abundance of solar resources. These regions receive ample sunlight throughout the year, making solar photovoltaic installations highly productive and efficient.

The findings show that compared to the Central Western Europe regional group, the impact of wind capacity and solar PV capacity productivity was much smaller in Central Eastern Europe. This revealed that capacity productivity fell more precipitously in Western countries, showing that more-developed countries are often more likely to invest in renewable energy facilities. The overall LMDI findings revealed that there is a need for further investments and efforts to fully integrate renewable energy sources and advance sustainable development since economic growth in poorer countries is outpacing increases in their renewable energy capacity.

Further research should investigate the effects of changes in renewable energy capacity productivity on renewable electricity generation in greater depth. This study’s country scenarios demonstrated that a growth rate in electricity capacities from RES that surpasses GDP growth results in a decline in electricity generation from RES, which might seem counterintuitive. Using, for instance, econometric models and larger time series historical data, future research could focus on the economic growth aspect and its effect on the amount of renewable electricity generation. In addition, more in-depth research on the optimal level of economic growth is required to support the development of a sustainable renewable electricity generation infrastructure as global energy demand increases alongside GDP. If the aggregate demand for electricity decreases as a result of slower economic growth, it is not necessary to produce as much electricity. Moreover, as similarly discovered in a study by [59], with weaker or declining economic growth, the economy generates fewer investments that can be attributed to the development of renewable energy generation infrastructure, as the quantity of investments is dependent on economic growth.