Renewable Energy and Electromobility in the EU: Identifying Developmental Synergies Through Cluster Analysis

Abstract

The development of renewable energy in recent years has become an important factor supporting the global shift towards sustainable mobility, particularly in the context of electromobility. This article explores the relationship between the share of renewable energy in the energy mix of the 27 EU countries and the development of electromobility in their area. The objective of the conducted research is to classify the European Union countries into groups that are homogeneous in terms of the level of installed electricity capacity of renewable energy sources, as well as into groups that are homogeneous with respect to the development of electromobility. Cluster analysis was used to achieve this objective. The study will be performed in two main steps. In the first step, clusters of similar European Union countries will be identified in terms of installed electricity capacity and clusters of similar countries in terms of electromobility development. The second step will be based on a comparison of whether the clusters of countries are similar according to the adopted criteria. The cluster analysis method will make it possible to identify groups of countries with similar levels of development in both sectors, which allows the patterns and challenges faced by countries with different development dynamics to be understood.

1. Introduction

The dynamic development of electromobility in European Union countries is one of the most important pillars of the transformation of transport towards sustainable mobility. The growing number of electric vehicles (EVs) brings numerous environmental and economic benefits but also poses new challenges for the European energy sector. The increase in demand for electricity and the need for stable and environmentally friendly electricity production are making renewable energy sources (RES) not only desirable but even necessary to support the development of electromobility [1,2,3]. Integration of renewables and electromobility allows decarbonisation of transport, increased energy independence and more efficient management of the electricity grid [4]. Many EU countries are taking steps to link the two areas, but the scale and pace of implementation of such strategies varies from country to country [5].

The development of electromobility and the share of renewables in the energy mix are proceeding at different speeds in different Member States. In Germany, for example, investment in infrastructure for electric vehicles is going hand in hand with an increase in renewable energy production [6]. In other countries, by contrast, the electrification of transport is progressing much faster than the development of green energy, with the result that electric vehicles are mainly powered by fossil fuel energy [7,8]. An analysis of the degree of integration of these two areas in the European Union makes it possible to assess the effectiveness of national strategies and to identify actions that support more sustainable development [9]. The main objective of this article is to classify European Union Member States into homogeneous groups according to two distinct criteria: the use of energy from renewable sources and the level of development of electromobility. The analysis then compares the similarities between these classifications in order to assess whether progress in the deployment of renewables and electromobility is proceeding in parallel. In addition, the study aims to determine to what extent the two sectors can interact and reinforce each other, thus indicating potential synergies in the broader context of a sustainable energy and transport transition.

This article is divided into the following sections: an introduction (Section 1); a literature review on the use of renewable energy sources and the development of electromobility in EU countries (Section 2); a description of the materials, research methods and research procedure using cluster analysis in Section 3; the main results of the research (Section 4); a discussion of the results obtained (Section 5) and finally a presentation of the main conclusions and recommendations (Section 6).

2. Literature Review

The topic of the use of renewable energy sources in various areas of the economy is very topical and frequently addressed in the scientific literature. It is a very important research area due to the growing need to reduce greenhouse gas emissions and reduce dependence on fossil fuels. No less important research direction is electromobility, which is playing an increasingly important role in transforming the transport sector and reducing its carbon footprint. It poses new challenges for the energy sector [10,11,12], especially in the area of energy demand for power grids [13].

There are many studies in the scientific literature addressing both green energy development and transport electrification, highlighting their importance in the decarbonisation of the economy [14,15]. However, relatively less research has focused on analysing the interdependence of the two areas, particularly at the macroeconomic level, in relation to EU countries, leaving room for further exploration of their effective integration.

A small number of scientific studies are appearing in this area, highlighting the importance of integrating renewable energy sources with the development of electromobility in individual economies, including in terms of supporting the power supply of energy networks [16]. An example is the article by S. Shard et al. [17], which examines the interrelationship between household ownership of an electric vehicle and a PV installation in the US. The analysis conducted in the article is based on the 2018 Whole Traveler Transportation Behavior Study, in which the authors developed an integrated model exploring the interdependencies and interpenetration of EV and PV technologies. The findings show that there is a synergistic effect between the two technologies, which can contribute to the implementation of sustainable solutions in both the energy and transport sectors [17]. Similar conclusions were reached in their study by F. Maurer, C. Rieke, R Schemm and D. Stollenwerk, who emphasise the importance of integrating renewable energy sources and electromobility, especially in the context of the load on urban power grids using Germany as an example. In their view, the increase of PV installations in households and the interest in them with electromobility can lead to more efficient use of energy and relieve the load on the municipal distribution grid in the context of charging electric vehicles [18].

There is also a growing body of research on the wide-ranging integrity of EVs with power grids, the main conclusion of which is the use of smart charging via Vehicle-to-Grid (V2G), which allows significant reductions in CO2 emissions [19,20,21,22]. The analyses conducted also outline the benefits and challenges of using this technology [23,24].

Academic articles and industry reports also discuss somewhat different aspects of the interplay between electromobility development and renewable energy sources, such as the role of energy storage [19,25,26] and the integration of charging infrastructure with RES-based systems [27,28,29].

The studies presented here have high hopes and optimistic development prospects related to the integration of RES and EV, but they are also not without identifying barriers and challenges related to the synergy of these two important areas. According to the researchers, it will require greater cooperation with energy suppliers and significant infrastructure investment [30], with high initial costs and a lack of suitably adapted infrastructure among the main barriers [31].

The reviewed literature underlines the existence of a research gap, mentioned earlier, in the context of studies relating to the areas analysed, i.e., RES and electromobility development in EU countries. The research mainly focuses on individual Member States, specific regions and economies without showing the broader context of the relationships studied. This does not, therefore, provide an opportunity to compare the studied countries with each other and to identify desirable patterns of behaviour.

The subject areas analysed were also studied using clustering methods, which are also used in this article. The aforementioned methods were used to show the interdependence of various factors influencing the development of electromobility, both in a local and regional aspect, to compare the development of electromobility in, for example, one Polish voivodship [32] but also in an international context, examining similarities in the development of this sector in European Union countries. Studies have focused both on a number of factors influencing the development of electromobility [33,34] and on assessing specific areas of electromobility, such as the assessment of EV charging infrastructure, EV availability, battery usage and EV user behaviour and driving style in EU countries [35,36]. Each of the studies discussed here groups the countries analysed based on different indicators of electromobility development, allowing their similarities, differences and the identification of patterns of behaviour and good practice in this area to be identified.

Also, in the case of renewable energy sources, mainly in the area of photovoltaics, clustering methods find their application and allow better identification of behavioural patterns, such as the variability of energy production depending on weather conditions or the location of PV installations [37] and the prediction of the performance of photovoltaic systems [38,39,40,41,42]. Research in this area is also international and aims to show similarities and differences between different regions and countries in Europe. However, they only touch on one of the areas discussed in this article, the use of renewable energy sources.

Despite numerous studies in the area of both electromobility development and the use of renewable energy sources in European Union countries, there is still a research gap regarding the integration of these two issues, especially in the context of the application of clustering methods. As an exploratory method to identify hidden structures and patterns, clustering allows countries with similar profiles to be grouped together in terms of selected indicators without the need to impose predetermined categories. The use of this method makes it possible to uncover non-linear relationships and dependencies between variables that may elude traditional statistical methods.

The advantage of clustering is its flexibility and the possibility of integrating data of a different scale and nature—both quantitative and qualitative. In the context of the subject matter under analysis, this allows for comprehensive coverage of the complex relationships between the level of development of electromobility and the share of RES in the energy mix in the countries studied. The choice of this method is, therefore, justified by the need for a synthetic yet in-depth look at the integration of electromobility and RES.

The analyses to date mainly focus on single aspects, such as the optimisation of electric vehicle charging infrastructure or the modelling of RES energy production, and often, the studies are also regional or focus on a specific Member State. However, the literature lacks a comprehensive approach combining these two areas, especially in terms of identifying similarities and differences between their development in EU countries. There is, therefore, research space for further exploration, particularly in identifying geographical and temporal patterns regarding the relationship between electromobility development and renewable energy availability.

The application of clustering methods in this context has important scientific relevance—it can contribute to a new typology of EU Member States, taking into account their degree of readiness for a sustainable transport and energy transition. Such an approach brings a new quality to climate and energy policy research, making it possible to formulate political, economic and environmental recommendations that are more tailored to real-world circumstances. As a result, this research can play an important role in the design of Community strategies for climate neutrality and contribute to closer cooperation between countries with a similar development profile.

3. Materials and Methods

The objective of the conducted research is to classify the European Union countries into groups that are homogeneous in terms of installed electricity capacity of renewable energy sources, as well as into groups that are homogeneous with respect to the development of electromobility. Subsequently, a comparison was made between the similarity of these two data classifications, and an analysis was carried out to determine whether the development of these two areas progresses in parallel and whether they can mutually influence and stimulate each other.

The following tables were adopted as the source of input data for the grouping procedure:

• Electricity production capacities by main fuel groups and operators [43] in 2022 year from Eurostat.
• European Alternative Fuels Observatory [44].

The data included in the analysis are from 2022, and the analysis was carried out according to the following stages:

• Stage 1. Designating groups of European countries similar to each other in terms of electricity production capacities of renewable energy sources, taking into account the following variables:
  • Installed electricity capacity per 1000 inhabitants—Wind
  • Installed electricity capacity per 1000 inhabitants—Solar photovoltaic
  • Installed electricity capacity per 1000 inhabitants—Hydro
• Stage 2. Designating groups of industries similar to each other in terms of electromobility, taking into account the following variables [45]:
  • Number of charging points per 100 km²
  • Number of passenger cars of alternative fuelled (BEV, PHEV) passenger cars per 1000 inhabitants- vehicles used for the carriage of passengers and comprising not more than eight seats in addition to the driver’s seat.
  • Number of vans alternatively fuelled (BEV, PHEV) per 1000 inhabitants—vehicles used for the carriage of goods and having a maximum mass not exceeding 3.5 tonnes
  • Number of buses of alternative fuelled (BEV, PHEV) per 1000 inhabitants—vehicles used for the carriage of passengers, comprising more than eight seats in addition to the driver’s seat, having a maximum mass not exceeding 5 tonnes and and having a maximum mass exceeding 5 tonnes.
  • Number of trucks of alternatively fuelled (BEV, PHEV) per 1000 inhabitants—vehicles used for the carriage of goods and having a maximum mass exceeding 3.5 tonnes but not exceeding 12 tonnes and having a maximum mass exceeding 12 tonnes.

In order to identify countries similar to each other in terms of renewable sources and electromobility and the features listed in individual stages of the research process, cluster analysis was used. Groups of similar objects will be identified using Euclidean distance. The smaller the distance between countries, the more similar these countries will be to each other. Based on the determined distance and using agglomeration of hierarchical grouping, countries similar to each other will be selected. Detailed steps related to the analysis are described in the following paragraphs.

All calculations were performed with the use of RStudio 2024.12.0 software.

The goal of the analysis is to organise objects into relatively homogeneous groups based on multivariate observations. Objects are grouped in such a way that objects within one cluster have more in common with each other than with objects in other clusters [46]. In the conducted research, a hierarchical method was used, which decomposes a data set of n objects into a hierarchy of groups [47]. This hierarchical decomposition can be represented by a tree structure diagram called a “dendrogram”, whose root node represents the whole dataset, and each leaf node is a single object of the dataset. The clustering results can be obtained by cutting the dendrogram at different levels [48].

The clustering of similar countries in terms of installed electricity capacity of renewable energy sources and then in terms of the development of electromobility was carried out in the following steps:

• Calculating the Euclidean distance between countries—distance matrix dist function from stats library;
• Conducting cluster analysis using agglomeration of hierarchical grouping (Ward’s algorithms), implemented in agnes function from the cluster library (RStudio 2024.12.0 software). Clustering results were presented using dendrograms. Ward’s method minimises the increase in the total within-cluster sum of squared errors [49].
• The number of classes was specified. The silhouette index was adopted to assess the quality. The silhouette index was used to evaluate the quality of the splitting into two, three, four and five clusters. The silhouette index shows which objects lie well in their cluster and which are somewhere between clusters. The average silhouette width provides an assessment of the importance of the clustering and can be used to select the “appropriate” number of clusters [50]. The values of the Silhouette index range from −1 to 1. Positive values indicate that the data units belong to the correct clusters, which indicates good clustering results. The closer to 1, the better the quality of the division of units into clusters. A score of zero suggests overlapping clusters or that the units are close to multiple clusters. Negative values indicate that the units are assigned to incorrect clusters, which indicates poor clustering results. The silhouette index is implemented in the silhouette function in the cluster library.
• In order to assess the similarity of the grouping of EU countries in terms of using energy from renewable sources and in terms of the development of electromobility, the Rand Index and The Adjusted Rand Index were used. The Rand index has a value from 0 to 1, where 0 means that the two data clusters do not agree in any pair of points, and 1 means that the data clusters are exactly the same. The Adjusted Rand Index rescales the index, taking into account that random chance will cause some objects to occupy the same clusters, so the Rand Index will never actually be zero. The rand.index and adj.rand.index function was implemented in the fossil package.

The authors decided to use the traditional clustering technique because this method is easy to implement, has very good visualisation capabilities, and there is no need to set the number of clusters in advance.

4. Results

The cluster analysis of countries similar to each other in terms of installed electricity capacity of renewable energy sources was the main goal of this stage of the analysis. The following variables were included in the analysis:

• Electricity production capacities per 1000 inhabitants—Wind
• Electricity production capacities per 1000 inhabitants—Solar photovoltaic
• Electricity production capacities per 1000 inhabitants—Hydro

Taking into account the variables presented in the analysis, a distance matrix (Euclidean distance) of the similarity of 27 EU countries in terms of the use of renewable energy sources was created, the results of which are presented in Figure 1.

The interpretation of the distance matrix states that the closer the distance value is to zero, the more similar the countries are in terms of the use of renewable energy sources; the further the value is from zero, the more this similarity decreases. On the right side of Figure 1. there is a legend that refers to the distance between countries. The less similar the two countries are to each other, the greater the distance (closer to 2) and the colour at the intersection of these two countries is more purple. The more similar the two countries are to each other, the smaller the distance (closer to 0) and the colour at the intersection of such two countries is more orange.

Based on the distance matrix, cluster analysis was performed, and the EU countries were divided into 2, 3, 4 and 5 clusters. For the cluster analysis, Ward’s algorithm was used, and dendrograms obtained as a result are presented in Figure 2.

• The next step in the analysis was to determine the appropriate number of clusters. The silhouette index was used to assess the quality of the division. As the results in Table 1 show, the best quality was found in the division of UE countries into two clusters. The silhouette index was 0.59, which means that a good structure has been found.

Table 1. The silhouette index for the similarity of UE country’s divisions in terms of renewable energy sources.

	2 Groups	3 Groups	4 Groups	5 Groups
The average silhouette width	0.59	0.41	0.29	0.29

Source: Authors’ elaboration.

Table 1. The silhouette index for the similarity of UE country’s divisions in terms of renewable energy sources.

	2 Groups	3 Groups	4 Groups	5 Groups
The average silhouette width	0.59	0.41	0.29	0.29

Source: Authors’ elaboration.

Figure 3 shows values of the silhouette index aggregated for every cluster and the average silhouette width for division into two clusters.

Table 2. Clusters of EU in terms of similarity of renewable energy sources.

Cluster	Countries
1	Austria, Luxembourg, Sweden
2	Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Malta, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, The Netherlands

Source: Authors’ elaboration.

presents EU countries divided into two clusters.

Based on the data, it can be indicated that the first cluster contains only 3 countries, including Austria, Luxemburg and Sweden. These are the countries that are characterised by the highest use of hydroelectricity, with use significantly exceeding that of other EU countries. The use of the other two renewable energy sources is at a lower level. It should also be noted that regardless of the number of clusters, i.e., the division of EU countries in terms of using energy from renewable sources into two, three, four or five clusters, these three countries always constitute one cluster (Figure 2).

Table 3. Basic statistical measures for the use of renewable energy sources—cluster 1.

Statistical Measures	Hydro	Wind	Solar Photovoltaic
Min	1.569	0.256	0.228
Max	2.061	1.366	0.491
Mean	1.764	0.674	0.381
Median	1.662	0.399	0.422

Source: Authors’ elaboration.

presents basic statistical measures for the use of renewable energy sources for cluster 1.

The second cluster contains 24 other countries (Table 2). The basic statistical measures of this cluster are presented in

Table 4. Basic statistical measures for the use of renewable energy sources—cluster 2.

Statistical Measures	Hydro	Wind	Solar Photovoltaic
Min	0.000	0.000	0.037
Max	0.846	1.206	1.114
Mean	0.301	0.358	0.353
Median	0.320	0.246	0.314

Source: Authors’ elaboration.

. Taking into account the presented results, it should be noted that in the case of cluster 2, the higher use of energy from renewable energy sources concerns Wind and Solar Photovoltaic, while the lower one concerns Hydroelectricity.

The quality of the analysed cluster 2 is good. The silhouette index was 0.6. In the case of dividing the EU countries into a larger number of clusters, the analysed cluster 2 (24 countries) is divided (Figure 2), and cluster 1 (3 countries) remains unchanged. However, in the case of division into three, four and five clusters, the value of the silhouette index, i.e., the quality of the analysed clusters, decreases.

The cluster analysis of countries similar to each other in terms of electromobility was the main goal of this stage of the analysis. The following variables were included in the analysis:

• Number of recharging points per 100 km²
• Number of passenger cars (M1) per 1000 inhabitants
• Number of vans (N1) per 1000 inhabitants
• Number of buses (M2&M3) per 1000 inhabitants
• Number of trucks (N2&N3) per 1000 inhabitants

Taking into account the variables presented in the analysis, a distance matrix (Euclidean distance) of the similarity of EU countries in terms of electromobility was created, the results of which are presented in Figure 4.

The interpretation of the distance matrix states that the closer the distance value is to zero, the more similar the countries are in terms of electromobility; the further the value is from zero, the more this similarity decreases. Based on the distance matrix, cluster analysis was performed, and the EU countries were divided into 2, 3, 4 and 5 clusters. For the cluster analysis, Ward’s algorithm was used, and dendrograms obtained as a result are presented in Figure 5.

The next step in the analysis was to determine the appropriate number of clusters. The silhouette index was used to assess the quality of the division. As the results in

Table 5. The silhouette index for the similarity of UE country’s divisions in terms of electromobility.

	2 Groups	3 Groups	4 Groups	5 Groups
The silhouette index	0.58	0.59	0.59	0.55

Source: Authors’ elaboration.

show, the silhouette index values in the case of division into 2, 3, 4 and 4 clusters are very similar.

In each of the analysed divisions, a cluster of 19 countries with the same structure can be identified. In the case of division into 2 clusters, the cluster structure is shown in

Table 6. Similarity of countries in terms of electromobility.

Cluster	Countries
1	Belgium, Denmark, Germany, France, Luxembourg, Netherlands, Austria, Sweden
2	Bulgaria, Czechia, Estonia, Ireland, Greece, Spain, Croatia, Italy, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Portugal, Romania, Slovenia, Slovakia, Finland

Source: Authors’ elaboration.

.

In the case of division into two clusters, one of the clusters (8 EU countries) is characterised by a lack of similarity. The silhouette index for this cluster was (−0.004), which is shown in Figure 6.

Within this 8-element cluster, we can distinguish 5 countries whose silhouette index has positive values but does not indicate a strong assignment of these countries to a given cluster and three countries whose index has negative values, which indicate that the given countries do not fit into this cluster.

Table 7. Silhouette index for countries belonging to the first cluster, with the EU countries divided into two clusters.

Countries	Silhouette Width
Belgium	−0.4289
Denmark	0.1376
Germany	0.0407
France	−0.2013
Luxembourg	0.1291
Netherlands	0.1817
Austria	−0.1347
Sweden	0.2473

Source: Authors’ elaboration.

presents the discussed results.

It should be noted that the division of countries into 3, 4, or 5 clusters results in the division of the 8-element cluster (cluster 1) into smaller clusters. The second cluster remains unchanged and contains 19 EU countries. In the case of division into 4 and 5 clusters, even single-element groups are created (Figure 5). Each subsequent division does not significantly improve the quality within the clusters.

Countries that belong to cluster 1, apart from Sweden, have the highest number of charging points per 100 km². These countries (including Sweden) are also characterised by the highest number of passenger cars and vans per 1000 inhabitants. Despite these facts, this group is characterised by a large range in the analysed data, which causes a lack of homogeneity within the cluster.

Table 8. Basic statistical measures for electromobility—cluster 1.

Statistical Measures	Passenger_cars	Vans	Buses	Truck	Recharging Points
Min	1.007	0.3810	0.02227	0.003252	5.554
Max	6.498	1.2970	0.53610	0.014052	275.161
Mean	2.571	0.9187	0.11810	0.014947	66.858
Median	2.005	0.9288	0.05576	0.028641	24.314

Source: Authors’ elaboration.

presents basic statistical measures for electromobility for cluster 1.

The second cluster within this division contains 19 EU countries.

Table 9. Basic statistical measures for electromobility—cluster 2.

Statistical Measures	Passenger Cars	Vans	Buses	Truck	Recharging Points
Min	0.02307	0.009347	0.0000956	0.0000000	0.3238
Max	0.99581	0.471841	0.1696366	0.0048503	10.2183
Mean	0.32968	0.137795	0.0266447	0.0012767	3.0537
Median	0.28299	0.079116	0.0149838	0.0007509	1.9313

Source: Authors’ elaboration.

presents basic statistical measures for electromobility for cluster 2.

The silhouette index of this cluster is 0.83, and as well as the indices of individual countries are at a very high level (Figure 6). This indicates a considerable homogeneity of countries within the cluster. It should also be noted that dividing EU countries into 3, 4, and 5 clusters does not change the structure and size of this cluster.

In the last stage of the research, the similarity of clusters created by combining countries similar to each other in terms of renewable energy sources and electromobility was checked. The Rand Index was used for this purpose. The results are presented in

Table 10. Similarity between the division based on renewable energy sources and based on electromobility—Rand Index and Adjusted Rand Index.

Cluster	Rand Index	Adjusted Rand Index
2 clusters	0.687	0.320
3 clusters	0.741	0.480
4 clusters	0.590	0.201
5 clusters	0.587	0.180

Source: Authors’ elaboration.

.

According to the results obtained, the highest similarity of groups can be seen when dividing the analysed countries into tree clusters. The Rand Index was 0.741, and the Adjust Rand Index was 0.48. It should be noted that the quality of the division into three clusters in terms of renewable energy sources was lower than the division into two clusters.

On the other hand, the Rand index in the case of dividing the analysed countries into two clusters was 0.687, and the Adjust Rand Index was 0.320. It should be pointed out that, according to the division into two clusters, there is a large group, 19 countries, which belong to the same cluster when taking into account the installed electricity capacity of renewable energy sources and the development of electromobility. These countries are Bulgaria, Czechia, Estonia, Ireland, Greece, Spain, Croatia, Italy, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Portugal, Romania, Slovenia, Slovakia, and Finland.

5. Discussion

Consequently, the use of renewable energy sources and the development of electromobility are at the same level in these countries. These countries have very similar scores in these two areas studied. Particularly in the case of electromobility, it can be seen that in these 19 countries, the values of the variables are at a lower level than in the 1 cluster into which they were placed: Belgium, Denmark, Germany, France, Luxembourg, Netherlands, Austria, Sweden. As for the 8 countries in cluster 1, it can be clearly indicated that these countries are characterised by better development in terms of electromobility.

In the case of the use of renewable energy sources, the first cluster contains three countries, Austria, Luxembourg, and Sweden, which particularly stand out in terms of the use of energy from water. These countries are classified as highly developed in terms of electromobility development.

The second cluster includes 24 countries, of which 4 countries should be singled out: The Netherlands, Germany, Belgium, and Denmark. These four countries are characterised by the greatest use of solar energy and are classified as highly developed in terms of electromobility development. Within this grouping, Sweden should also be singled out, which ranks first for wind energy and is also classified as highly developed in terms of electromobility.

The clustering analysis carried out identified similarities between groups of countries in terms of the use of renewable energy sources and the development of electromobility. In order to assess the similarity of the divisions, the Rand Index was used, whose highest value (0.538) was obtained for the division into two clusters. This indicates the greatest correspondence between the divisions according to the two aspects analysed, precisely at this number of clusters.

The results obtained show significant differences in the countries’ approaches to the development of electromobility and the use of renewable energy sources. With the division into two clusters applied, which turned out to be the most similar in the context of the two aspects analysed, it can be noted that the countries studied, with similar levels of RES use, also show similar results in terms of electromobility development. At the same time, it is noticeable that there are differences between the clusters, which indicate the existence of groups of countries that are clearly ahead of the others in terms of electromobility, which may be due to various aspects such as more advanced infrastructure, support policies, or access to technology.

Dividing into a larger number of clusters (three, four and five) shows a lower Rand Index, suggesting that dividing the groups further does not significantly improve the consistency of the divisions. The results of the analysis highlight the importance of national energy strategies and policies in shaping both the RES and electromobility sectors. It is worth noting that some countries, such as Sweden and the Netherlands, perform exceptionally well in both aspects analysed, which may be the result of their long-term development strategy in the area of renewable energy use and electrification of transport based on a system of subsidies for both the purchase of renewable energy equipment and electric vehicles.

6. Conclusions

The research carried out and the results obtained fill, to some extent, the research gap identified in the area of comparing and grouping EU countries in terms of their level of development of renewable energy and electromobility. The juxtaposition of the similarities obtained using the clustering method sheds new light on the development directions of these two important areas in EU countries. The results obtained confirm the identified relationship between the development of electromobility and the use of renewable energy sources in EU countries. In both analysed aspects, the identified clusters included similar groups of countries, which confirms the assumptions that the development of these two studied areas go hand in hand and interact and stimulate each other’s development. The identified similarities between groups of Member States also open up new avenues of research in the identification of factors influencing the level of development of the analysed countries, both in the area of the use of renewable energy sources and electromobility. State policy and the level of support for the areas under study, or their geographical location, may prove to be key here. Future research should, therefore, consider a detailed analysis of the factors that influence differences in the development of electromobility and the use of RES in different countries and attempt to assess which of the areas studied and to what extent stimulates the development of the other. Does increasing the share of renewable energy sources in the energy mix influence the faster development of electromobility, or is it the increasing number of electric cars in a given country that mobilises the support of the energy system with renewable energy sources? A limitation of the current study is also the sensitivity of the clustering method to the choice of variables and the lack of causal analysis between sectors. Further research could also verify the sustainability and stability of the resulting clusters. An additional limitation is the assumption of homogeneity within clusters, which may hide important regional and socio-economic differences between countries. Furthermore, the failure to include qualitative variables limits the full understanding of the development context of both sectors. These are very important areas of research that could contribute to the further growth of both sectors and would allow a better understanding of the development directions of each group of countries.