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Article

Sustainable Energy Development in European Countries 2005–2023: A Dynamic Cluster Analysis Approach

by
Andrzej Sokołowski
1,*,
Małgorzata Markowska
2 and
Danuta Strahl
3
1
Faculty of Management, Media and Technology, Andrzej Frycz Modrzewski Krakow University, 30-705 Krakow, Poland
2
Department of Regional Economy, Wroclaw University of Economics and Business, 53-345 Wrocław, Poland
3
Department of Management, WSB University, 41-300 Dąbrowa Górnicza, Poland
*
Author to whom correspondence should be addressed.
Energies 2026, 19(3), 583; https://doi.org/10.3390/en19030583
Submission received: 17 November 2025 / Revised: 16 January 2026 / Accepted: 21 January 2026 / Published: 23 January 2026
Browse Figures
Review Reports Versions Notes

Abstract

Sustainable energy development is important for social and economic life because energy is used everywhere by everybody. This paper presents a quantitative analysis of the development in Sustainable Development Goal 7 in European countries from 2005 to 2023. SDG 7 is described by seven variables published by Eurostat, covering energy consumption, energy productivity, energy import dependency and energy poverty. Dynamic cluster analysis is the main method used, identifying seven clusters, with membership changing in time. The Composite Energy Index was calculated. Generally, the energy situation described by the variables used has improved, both in individual countries and in Europe as a whole. The best-performing countries are Iceland, Norway and Denmark. In Europe, the average energy productivity measure and share of energy from renewable sources have doubled. The proposed novel approach involves the simultaneous analysis of spatio-temporal data for the whole period, resulting in countries changing their cluster position over time. A new measure of dynamic cluster stability is introduced, and clusters are ordered according to the value of the composite index.

1. Introduction

International discussion on sustainable development started at the 1972 United Nations Conference in Stockholm, and then carried on in Rio de Janeiro (1992), Vienna (1993), Beijing (1995) and Rome (1996). The Millennium Summit (6–8 September 2000) concluded with the Millennium Declaration, in which eight Millennium Development Goals were set out. In 2015, the 2030 Agenda for Sustainable Development was adopted with 17 development goals. The aim of this paper is to analyze development related to the 7th Goal on Energy in European countries for which Eurostat provides almost complete data for the period 2005–2023.
Systematic literature reviews on SDGs (Sustainable Development Goals) can be found in [1,2,3,4,5]. Different approaches have been used in the analysis of level, implementation and distance to goals, such as the Delphi-AHP method [6], machine learning approach [7], comparative cross-national analysis [8], scenario modelling tools [9], data-driven comparative analysis [10], multilayer complex networks [11], and dynamic time warping [12]. Cluster analysis is quite popular in SDG research. The development of SDG goals in 157 countries was analyzed in [13]. Different sets of countries from the European Union, Europe at large, Africa and Asia were clustered using different lists of SDG variables [14,15,16,17,18,19]. All the goals are subject to analysis, along with individual goals like SDG 2.2 [20], SDG 3 [21,22], SDG 6 [23,24,25], SDG 7 [26,27,28,29], SDG 9 [30], SDG 11 [31,32] and SDG 15 [33]. In addition to countries, other territorial units, such as cities [21,25,32,34,35], regions [36,37], groups of countries or regions [19,38,39,40,41,42,43,44] and continents [17,45,46], have also been analyzed.
A review of recent publications on SDGs analyzed using clustering methods is presented in Table 1.
Ward’s agglomerative procedure and k-means were the most popular cluster analysis methods used in research on SDGs. All the publications mentioned above clustered geographical units in a non-dynamic way, considering each year separately. Partitions from different years were compared later. We propose a novel approach: each country in each year is treated as an individual object, and they are clustered simultaneously. The resulting clusters are dynamic, with membership changing over time. The stability is assessed by a newly proposed measure: cluster stability.
The main goal of this paper is to present a dynamic overview of European countries classified in a multidimensional space defined by variables describing SDG 7 performance.
All tables and figures presented in the paper are based on our own calculations.

2. Materials and Methods

Affordable and clean energy is the 7th United Nations Sustainable Development Goal. The Eurostat website (https://ec.europa.eu/eurostat/web/sdi/database/affordable-and-clean-energy (accessed on 10 November 2025)) states that Sustainable Development Goal 7 calls for ensuring universal access to affordable, reliable, and sustainable energy. This includes improving energy efficiency, increasing the share of renewables, and further diversifying the energy mix, while ensuring affordability of energy for all. In the EU context, monitoring SDG 7 looks at the developments in energy consumption, energy supply and access to affordable energy. The monitoring of the EU’s progress towards sustainable development is based on the EU SDG indicator set, which comprises 102 indicators along with the 17 SDGs. Eurostat selects data with the best possible statistical quality and policy relevance.
Eurostat provides seven datasets in Eurostat related to SDG 7. We took the following variables (one from each set), calculated per capita:
  • Primary energy consumption in tons of oil equivalent (TOE) per capita;
  • Final energy consumption in tons of oil equivalent (TOE) per capita;
  • Final energy consumption in households per capita;
  • Energy productivity, purchasing power standard (PPS) per kilogram of oil equivalent;
  • Share of renewable energy in gross final energy consumption;
  • Energy import dependency, total percentage;
  • Population unable to keep home adequately warm, total percentage.
The energy import dependency variable requires further explanation. It is calculated as net imports divided by the gross available energy, expressed in percentages. But it is calculated only for oil and petroleum products, excluding biofuel.
We collected data on 34 European countries, covering 19 years from 2005 to 2023. There are 4522 data cells, 89 (1.97%) of which are missing data in Eurostat data files. We conducted a data imputation procedure using multiple regression models with complete variables used as independent variables and final models obtained by a step-wise backward procedure, eliminating non-significant variables.
Dynamic cluster analysis deals with spatio-temporal objects characterized by a given set of diagnostic variables. In this research, each country in each year is treated as a separate spatio-temporal object (operational taxonomic unit). Thus, we have 646 units (34 countries × 19 years). Variables have been standardized with a classical formula: (value − arithmetic average)/standard deviation. The most controversial step in cluster analysis is the establishment of weights for variables. The best method is to obtain weights externally, such as from some kind of expert panel. The worst method is to calculate weights from the data used for clustering, especially the coefficient of variability. If there is uncertainty, the best way is to treat variables with equal weights, as in this research.
The number of clusters was identified via analysis of the dendrogram obtained from Ward’s [60] agglomerative method with squared Euclidean distance. The final partition has been obtained by the k-means method, introduced by Hugo Steinhaus in 1956 [61]. Clusters were characterized using cluster means and the average value of the composite index. The first five variables were treated as stimulants—the higher, the better—and the last two (import dependency and population unable to keep home warm) were treated as destimulants—the lower, the better. Variables were normalized to a [0;1] interval through dividing the distance by the minimum (in case of stimulants) or the maximum (in case of destimulants) by the range (maximum − minimum). The average of normalized variables gives the value of the composite index measuring the SDG 7 development level.
A dynamic cluster can be described by the binary membership matrix with rows representing objects (countries) and columns representing time units (years). If a country in a given year was present in the cluster, then the specific element in the membership matrix is 1; otherwise, it is zero. The stability of dynamic clusters has been measured by the Dynamic Stability measure [62], defined by the following formula:
D S = ( k − m ) ( m n − r r ) m 2 ( n − 1 ) 2 ∑ t = 1 n m i n c t k ; 1 n 3 ,
where
  • k is the number of 1′s in the membership matrix;
  • m is the number of objects in the cluster;
  • n is the number of time units;
  • rr is the number of row runs in the membership matrix;
  • ct is the number of objects in the cluster, in time t.
The DS takes values from the [0;1] interval—the bigger it is, the more stable the cluster was in a given time span.

3. Results

3.1. Descriptive Statistics

Basic descriptive statistics are presented and discussed in this section. The largest (max) and the smallest (min) values for every second year are reported with country identification, along with the second largest and the second smallest are given to avoid the influence of outliers.
The first variable—primary energy consumption—includes raw energy sources, transformation and distribution losses, non-energy uses and energy used by the energy sector itself. As we can see in Table 2, the highest value in all years was observed in Iceland, and the second highest in Luxembourg. The lowest value was observed in Albania, and the second smallest value was observed first in Turkey, then in North Macedonia since 2013. There is a significant trend in average primary energy consumption, with a decrease of −0.027 per year.
Final energy consumption measures the energy end-use in a country, such as industry, transport, households, services and agriculture, but excludes all non-energy use of energy carriers, consumption of the energy sector itself and losses occurring during transformation and distribution of energy. The observations are the same as for primary consumption, except for the second maximum in 2023, which is Finland (see Table 3).
The average final consumption decreased by −0.012, and the difference between countries measured by standard deviation also decreased (−0.03 per year).
Final energy consumption in households per capita allows for the identification of how much electric energy and heat are consumed in households. Malta is a new country in the second minimum (Table 4). The average of this variable showed a significant negative trend (−2.41 per year), and the same was observed for the standard deviation (−2.71 per year).
Energy productivity measures the amount of economic output (purchasing power standard) that is produced per kilogram of oil equivalent of gross available energy (Table 5). Ireland shows the highest productivity throughout the whole period, while Iceland was the lowest.
The average productivity and standard deviation increased non-linearly and more quickly, resulting in a yearly increase of 0.64 in the average and 0.31 in the standard deviation.
We used the share of renewable energy in gross final energy consumption across all sectors combined (Table 6).
As expected, the highest values were observed in Norway, which increased by 100%. This can happen if a country’s gross renewable electricity production is higher than its gross final electricity consumption. The surplus is often used for exports. There is a significant growth in the average share of renewables: 1.24 percentage points per year. Within the analyzed period, this average share almost doubled. Interestingly, there is no trend in the standard deviation, which means that the development of renewable energy was rather similar across all analyzed countries.
The next variable—energy import dependency—is calculated by dividing the difference between imports and exports by gross available energy. In Norway, exports are and were much higher than imports, so import dependency was negative, almost (−1300) in 2015. Since such outlying values will affect the standardization and normalization process, we replaced the original negative values with zeros (the same operation was performed in [12]). Because of these zeroes, the relation between maximum and minimum cannot be calculated, but we left this row in the table with descriptive statistics (Table 7) to ensure consistency with other tables of this type.
There was high variability among countries with maximum values, something not observed for the other variables. We included 10 years between 2005 and 2023 in the tables, during which there were seven different countries with maximum import dependency: Sweden, Poland, Estonia, Latvia, Finland, Iceland and Slovakia. The average level of import dependency ranged between 86 and 91%.
The last diagnostic variable is the percentage of the population unable to keep their homes adequately warm. The data was self-reported using a survey. All statistics presented in Table 8 show remarkable improvements. The average percentage went down almost two times (−0.76 per year). The same was observed in variability measured by the standard deviation (−0.85 change per year). Generally, “rich” countries (in terms of GDP) had a small share of the population in energy poverty, while “poor” countries had a relatively large share.
Finally, a composite index was calculated using seven normalized variables. The last two of them—import dependency and energy poverty—were treated as destimulants, and the others as stimulants. According to statistics presented in Table 9, the development of SDG 7 has generally improved within the 2005–2023 period. The average composite indicator follows a linear trend, with a trend coefficient of 0.003 per year. Countries were becoming more coherent because the standard deviation decreased linearly by −0.002 per year. The composite index is used to evaluate the development level of spatio-temporal clusters.

3.2. Dynamic Cluster Analysis

Dynamic cluster analysis is looking for homogeneous groups of spatio-temporal objects. Clusters may differ in terms of the average level of variables or their structure, meaning high/low values.
Ward’s agglomerative method was used to cluster 645 spatio-temporal units (countries in years) with the squared Euclidean distance as the criterion function. The obtained dendrogram is presented in Figure 1. In the agglomeration process, the distance identifying subgroups to be merged increases gradually when considering the same group and when subgroups are relatively close to each other. When this method tries to join distant subgroups, a jump in the agglomerative distance is observed. To cut the dendrogram, we observed the changes in the agglomerative distance, looking for the first important increase, and selected the partition before this jump as the final one (see [63]).
The dotted line shows the cutting level, which identified seven dynamic clusters. The final partition was obtained using the k-means method [64]. The average composite index was calculated for each cluster. Clusters were ordered according to the average index value and named from A to G. The average values of the original variables and the composite index are given in Table 10.
Clusters are presented from the best (in terms of the composite indicator) to the worst. There is only one country in Cluster A: Iceland. Its membership matrix (Table 11) is very simple, consisting of only 1 s.
Iceland had the highest average primary, final and household energy consumption and the share of renewable energy. Additionally, it also had the highest average import dependency (as previously mentioned, calculated for oil and petroleum products) and the lowest energy productivity. The special position of Iceland (also shown in tables in Section 3.1) is due to the fact that this country is the world’s leading producer of renewable energy. Geothermal energy dominates space heating (around 90%), while hydropower provides most electricity (around 70%), supplemented by geothermal sources (around 30%). It is clear that Cluster A’s stability is equal to 1.
Cluster B consists of two countries (Table 12). Norway was present for the entire period, and Denmark left the cluster in the last three years. This cluster is characterized by low import dependency and low energy poverty.
Cluster C has the second-highest average primary, final and household energy consumption, but also the second-highest import dependency. Luxembourg, Austria, Finland and Sweden were in this cluster until 2019, and Finland and Luxembourg stayed there until 2023 (Table 13). The Dynamic Stability of this cluster was equal to 0.920.
Cluster D did not exist until 2010 (Table 14). The cluster started in 2011 with Italy and Romania.
Five years later, this group had 8 countries, increasing to 21 in 2023. The cluster stability measure for this group is equal to 0.535. This cluster has the highest average energy productivity. This group’s growth indicates that energy productivity in Europe is reaching a higher level.
Cluster E has just one country, Albania, throughout the whole analyzed period. It had the lowest primary, final and household consumption, and the highest share of people not able to adequately warm their homes.
The second most numerous cluster is Cluster F, consisting of 19 countries, as shown in Table 15.
There are four core countries present in Cluster F over the entire period: Belgium, Czechia, Estonia and Slovakia. Fifteen countries were in this cluster in 2005, decreasing to only five in 2023. The characteristic feature of this group is the second-lowest share of energy from renewable sources. The stability of Cluster F is 0.817.
The lowest average share of renewable energy was observed in the last cluster. It has the second-lowest average primary, final and household energy consumption and energy productivity. This group can be considered the worst performing due to having the lowest average composite index of 0.226. Cluster G (Table 16) has become smaller and smaller as countries advance to better groups. However, three countries stayed in this group until 2023: Bulgaria, Malta and North Macedonia.
Cluster G’s stability is 0.779.

4. Discussion

Sustainable Development Goal 7 is defined as follows: Ensure access to affordable, reliable, sustainable and modern energy for all. The strangest word in this goal is “all”. Nothing can be achieved for all. Who is “all”? Which energy is “modern”, and which is not? It is impossible to ensure affordable access to any good for everybody. Economy and society do not function like this. The declared goal was intended to be reached by 2023. There are five specific targets (https://globalgoals.org/goals/7-affordable-and-clean-energy (accessed on 10 November 2025)):
  • Target 7.1—Universal access to modern energy;
  • Target 7.2—Increase global percentage of renewable energy;
  • Target 7.3—Double the improvement in energy efficiency;
  • Target 7.4—Promote access to research, technology and investments in clean energy;
  • Target 7.5—Expand and upgrade energy services for developing countries.
Due to the lack of a “gold standard”, the choice of variables used to analyze this subject is open for debate. One set of variables is not better than the other; it is just different. Maybe it is beneficial that the same problem can be measured and analyzed using different variables, as long as they are logically connected to the problem. We decided on a set of variables available in Eurostat, presented in the chapter “Affordable and clean energy” (the same as in [12]), depending on the reliability of an official EU institution. Some controversy is connected with primary energy consumption, final energy consumption and household energy consumption. In [12,29], they are treated as destimulants—the lower, the better. It is not only clean energy; it is energy that comes from all available sources. If its consumption is too small, how we can say that we are ensuring affordability of energy for all (according to Target 7.1)? In this paper, we treat these three variables as stimulants—the higher, the better. However, the use of the share of renewable energy as a stimulant and energy poverty as a destimulant is well-validated. In our dataset, primary, final and household energy consumptions are positively correlated with renewable energy and negatively correlated with energy poverty. The type of variable has no influence on the clustering results; it is important only for the calculation of the composite indicator.
Dynamic cluster analysis is a new approach presented in this paper. In the classical method, clustering is performed separately for each year, and changes are analyzed afterwards. Instead, we clustered spatio-temporal objects, and countries can move from one cluster to another. Table 17 presents the summary of such movements.
Clusters are listed from the best to the worst as evaluated by the value of the composite indicator. All movement was towards better clusters, meaning that generally, the development of SDG 7 improved within the period 2005–2023 in individual countries and Europe as a whole. There is a clear linear trend in the average value of the composite indicator (Figure 2a), which, on average, increased by 0.003 per year. There was no autocorrelation of residuals in the average. The forecasts of the average composite indicator are 0.372 for 2025 and 0.375 for 2026, resulting in an improvement of 0.063 since 2005. The standard deviation demonstrates a parabolic trend, with a 7-year autocorrelation of residuals (Figure 2b). This is a good sign for cohesion within European countries. If this trend continues, the standard deviation in 2026 should be equal to 0.087.
According to the average value of the composite index, there are five levels at which our clusters can be positioned. Iceland, as the only country in Cluster A, is definitely the best, with the best values in four out of seven variables. Norway and Denmark are in Cluster B with the lowest import dependency and the second-lowest energy poverty. In an interesting proposal of a composite index for 40 European countries in 2018 [65], based on different variables, normalization methods and weighting systems, Iceland is ranked first, Norway second and Denmark fifth. Sweden, Finland and Austria, which are in Cluster C in this study, were in the Top 10 [65], ranking 3rd, 7th and 10th. The next three clusters (D, E, F) are very close in terms of the composite index level, but differences remain in terms of structure. Cluster D, which is relatively new, is the best in energy productivity. Albania, in Cluster E, is the second best in energy productivity and share of renewable energy, but the worst in the percentage of the population unable to keep their homes warm. Cluster F, with the second-lowest share of renewable energy, became smaller and smaller over the analyzed period.

5. Conclusions

Cluster analysis methods, when applied to spatio-temporal data, with each country in each year treated as a separate object, result in dynamic clusters. The membership matrices show the movement of countries between clusters. Regarding SDG 7 performance, movement always occurred toward better clusters, according to the composite index. The stability of groups can be measured by the newly proposed Dynamic Stability measure. There are five goals within SDG 7. The best performance was observed in Target 7.2 (renewable energy) and Target 7.3 (energy efficiency). The unweighted average share of renewable energy increased from 24.23 in 2005 to 48.04 in 2023. Energy productivity increased from 5.30 in 2005 to 12.03, surpassing Target 7.3′s call to double it.
The next step is to analyze the movement of individual countries from cluster to cluster and changes in individual variables. Logistic regression or classification trees are appropriate statistical tools to identify the causes of advancing to better clusters.

Author Contributions

Conceptualization, A.S., M.M. and D.S.; methodology, A.S. and M.M.; validation, D.S.; investigation, M.M.; resources, M.M.; data curation, A.S.; writing—original draft preparation, A.S. and M.M.; writing—A.S., M.M. and D.S.; visualization, A.S. and M.M.; supervision, A.S. and D.S. All authors have read and agreed to the published version of the manuscript.

Funding

Project no. FESL.10.25-IZ.01-078F/23—“Comprehensive support for the development of the WSB University in line with the needs of the green and digital economy” implemented under the European Funds for Silesia 2021–2027 programme from the Just Transition Fund, Priority X European Funds for Transformation, Measure 10.25 Development of higher education in line with the needs of the green economy.

Data Availability Statement

Data used in the paper is available upon request from the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AHPAnalytical Hierarchy Process
BIRCHBalance Iterative Reducing and Clustering using Hierarchies
DTWDynamic Time Warping
HCHierarchical Clustering
PAMPartition Around Medoids
PCAPrincipal Component Analysis
SDGSustainable Development Goal
TSATrend Surface Analysis

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Energies 19 00583 g001
Figure 1. Ward’s dendrogram of spatio-temporal units (cut-off level as dotted line).
Figure 1. Ward’s dendrogram of spatio-temporal units (cut-off level as dotted line).
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Energies 19 00583 g002
Figure 2. Trends of European composite indicator: (a) average, (b) standard deviation (blue—time series; red—trend models).
Figure 2. Trends of European composite indicator: (a) average, (b) standard deviation (blue—time series; red—trend models).
Energies 19 00583 g002
Table 1. Recent selected publication on SDGs and cluster analysis.
Table 1. Recent selected publication on SDGs and cluster analysis.
Publication Reference/YearSubjectObjectsAnalyzed YearsVariablesMethodsClusters
[14] 201917 SDGs29 countries2012–201612Ward, PCA4
[47] 2021Renewable energy27 countries2009–20193Ward5
[28] 2021SDG 727 countries2000, 201913TSA, HC5, 9, 10, 13, 15
[19] 202217 SDGs110 countries201917k-means5
[12] 2022SDG 730 countries2005, 2009, 20207Ward, DTW8
[48] 202217 SDGs27 countries2015, 202017Ward5
[49] 2022SDG 71553, 1618, 1608, 1589 localities2019, 20209PAM, PCA3
[50] 2023Primary energy consumption38 countries1990, 20218k-means7 and 6
[18] 202316 SDGs45 countries202217Ward4
[51] 2024Sustainable development35 countries20208Ward6
[52] 202417 SDG143 countries202217k-means3 and 4
[53] 2024Renewable energy25 countries202212Ward4
[54] 2025Renewable energy38 countries2018–202213k-means3
[55] 2025“Green” image29 capitals202110k-means4
[56] 2025Sustainable energy30 countries2004–201813DTW, Complete Linkage9
[57] 202517 SDGs170 countries202384BIRCH6
[58] 2025SDG 827 countries202311Ward
k-means		5
[59] 202517 SDGs167 countries202317Ward
k-means		4
This paperSDG 734 countries2005–20237Ward
k-means		7
Table 2. Descriptive statistics of primary energy consumption.
Table 2. Descriptive statistics of primary energy consumption.
Statistic2005200720092011201320152017201920212023
max10.8215.2517.2418.5618.6917.5317.0117.2015.8616.18
countryICICICICICICICICICIC
max 210.259.68.728.747.917.277.197.266.555.61
countryLULULULULULULULULUFI
min0.710.650.690.760.760.740.810.810.800.82
countryALALALALALALALALALAL
min 21.181.361.291.441.321.261.291.351.351.46
countryTRTRTRTRMKMKMKMKMKMK
max − min10.1114.6016.5517.8017.9316.7916.2016.3915.0615.36
max/min15.2423.4624.9924.4224.5923.6921.0021.2319.8319.73
average3.583.723.543.613.493.333.453.383.263.09
median3.183.072.822.832.532.602.792.782.752.58
SD2.162.612.843.023.032.792.702.722.492.49
V (%)60.3670.1580.1183.7086.8783.7078.3780.3576.2580.56
Table 3. Descriptive statistics for final energy consumption.
Table 3. Descriptive statistics for final energy consumption.
Statistic2005200720092011201320152017201920212023
max9.639.048.418.348.869.289.419.018.438.71
countryLULUICICICICICICICIC
max 26.777.618.208.287.607.007.017.086.345.27
countryICICLULULULULULULULU
min0.630.570.640.680.680.680.730.730.720.74
countryALALALALALALALALALAL
min 20.860.910.820.930.890.890.910.951.020.98
countryMKMKMKMKMKMKMKMKMKMK
max − min9.008.477.777.668.188.608.688.287.717.97
max/min15.2915.8613.1412.2613.0313.6512.8912.3411.7111.77
average2.532.552.422.432.382.362.442.432.362.24
median2.172.201.981.981.901.922.012.052.021.90
SD1.731.711.701.691.701.671.671.611.481.41
V (%)68.4567.3170.2169.4871.3070.8168.3166.5162.5863.07
Table 4. Descriptive statistics of energy consumption in households per capita (in kWh).
Table 4. Descriptive statistics of energy consumption in households per capita (in kWh).
Statistic2005200720092011201320152017201920212023
max1130115511781162123311861230125913441175
countryLUICICICICICICICICIC
max 2112910461041945951904104610201076982
countryICLULUFIFIFIFIFIFIFI
min172142156165174180171177195190
countryALALMTMTMTMTALALALAL
min 2179174166172200185195201213206
countryMTMTALALALALMTMTMTMT
max − min9581013102299710591006105910821149985
max/min6.578.137.557.047.096.597.197.116.896.18
average598.15572.53587.12577.32578.65549.29568.35550.26589.76523.18
median654.00597.50622.00614.00595.00561.00563.50545.50589.00521.50
SD250.70239.78246.34228.60245.85224.74236.57228.68240.31210.18
V (%)41.9141.8841.9639.6042.4940.9141.6241.5640.7540.17
Table 5. Descriptive statistics of energy productivity.
Table 5. Descriptive statistics of energy productivity.
Statistics2005200720092011201320152017201920212023
max8.8510.019.2911.1112.1116.8917.7919.6925.9529.25
countryIEIEIEIEIEIEIEIEIEIE
max 27.738.338.979.8410.711.0811.8912.7514.9118.53
countryTRITALALTRDEDERODERO
min2.642.051.711.621.701.992.182.222.360.98
countryICICICICICICICICICIC
min 23.203.554.114.024.215.054.965.325.906.27
countryBGEEMTMTEEBGMTMTFIRS
max − min6.217.967.589.4910.4114.9015.6117.4723.5928.27
max/min3.354.885.436.867.128.498.168.8711.0029.85
average5.306.016.366.767.277.968.118.879.8612.09
median5.075.736.186.697.177.667.968.589.5511.77
SD1.431.661.651.952.042.492.662.863.694.57
V (%)26.9627.6825.9828.7528.0331.3332.7732.1837.4137.80
Table 6. Descriptive statistics for share of renewable energy.
Table 6. Descriptive statistics for share of renewable energy.
Statistics2005200720092011201320152017201920212023
max97.42113.70105.19105.89106.92106.83104.85110.45113.64116.54
countryNOICNONONONONONONONO
max 294.9399.0592.8693.9296.7293.1193.38100.6499.54105.38
countryICNOICICICICICICICAL
min0.000.000.000.451.574.316.857.499.6510.74
countryMTMTMTMTMTMTMTMTMTMT
min 20.020.070.593.455.336.207.519.7613.6616.45
countryCYCYCYCYLULUHUCYHUCZ
max − min97.42113.70105.19105.43105.35102.5298.01102.96103.99105.80
max/min---233.2368.0624.7815.3214.7611.7810.85
average24.2325.3727.4029.1432.1134.6836.1738.8241.7248.04
median16.0116.2118.2922.2426.6329.9030.7832.1735.4341.16
SD25.4427.3225.4424.6724.5424.8825.2526.0625.8226.31
V (%)105.00107.7192.8684.6576.4271.7469.7967.1361.8954.77
Table 7. Descriptive statistics of import dependency.
Table 7. Descriptive statistics of import dependency.
Statistic2005200720092011201320152017201920212023
max103.88105.51106.82101.83109.19114.65115.05129.64104.53105.20
countrySEPLEELVFISEEEEEICSK
max 2102.53105.068106.39101.75104.62109.478104.18106.84101.7102.5
countryBGEEMKIEMTFIMTSELTCY
min0.000.000.000.000.000.000.000.000.000.00
countryDK, NODK, NODK, NODK, NODK, NONODK, NONONONO
min 238.6151.5652.1030.8626.115.3540.2528.3130.3246.00
countryROROROALALDKALALDKAL
max − min103.88105.51106.82101.83109.19114.65115.05129.64104.53105.20
max/min----------
average89.0588.5388.6486.8187.0489.1087.9990.5085.6189.84
median98.6797.7198.3997.0796.8199.5697.0697.6795.5898.01
SD25.3224.8124.9526.2927.2628.7225.5823.5222.7820.39
V (%)28.4328.0228.1530.2831.3232.2329.0825.9926.6022.70
Table 8. Descriptive statistics of the population share unable to keep their homes adequately warm.
Table 8. Descriptive statistics of the population share unable to keep their homes adequately warm.
Statistic2005200720092011201320152017201920212023
max67.3063.3059.7039.8038.3035.0034.4031.7027.8033.00
countryBGBGBGBGBGALALALALMK
max 239.936.835.035.335.031.529.429.326.727.5
countryTRPTALLTALBGBGMKMKAL
min0.500.200.200.700.400.200.300.801.001.20
countryLULULULUSENONONONO. FIIC
min 20.900.600.600.800.600.500.801.001.201.60
countryICICICICICLUMKICICLU
max − min66.8063.1059.5039.1037.9034.8034.1030.9026.8031.80
max/min134.60316.50298.5056.8695.75175.00114.6739.6327.8027.50
average14.9812.6910.7311.0311.339.218.097.457.048.51
median9.008.806.006.157.406.003.754.353.255.50
SD14.9813.6412.5310.8210.509.349.028.327.447.23
V (%)99.96107.50116.7998.1092.68101.44111.60111.75105.6384.97
Table 9. Descriptive statistics of composite index.
Table 9. Descriptive statistics of composite index.
Statistic2005200720092011201320152017201920212023
max0.5980.6340.6520.6600.6800.6680.6760.6880.6670.660
countryNOICICICICICICICICNO
max 20.5600.6020.6080.6110.6160.6050.6070.6100.6280.650
countryICNONONONONONONONOIC
min0.0920.1060.1150.1710.1710.1960.2090.2000.2150.216
countryBGBGBGBGBGBGBGMKMKMK
min 20.1850.1940.1890.1810.1740.2050.2160.2230.2310.234
countryMKMKMKMTBGMTMTMTMTMT
max − min0.5060.5270.5370.4890.5090.4720.4670.4880.4520.445
max/min6.5265.9665.6753.8603.9733.4043.2383.4393.0993.062
average0.3100.3190.3260.3310.3340.3380.3490.3510.3680.369
median0.2880.3020.3030.3100.3160.3210.3300.3310.3540.354
SD0.1090.1100.1090.1060.1080.1020.1020.0990.0970.095
V (%)35.2134.3433.3631.9732.2130.0529.2228.1526.4425.69
Table 10. Averages of clusters.
Table 10. Averages of clusters.
ABCDEFG
Primary energy16.64.35.72.30.73.22.1
Final energy8.53.24.71.80.72.21.4
Households1218.2835.1866.2467.8175.7607.0343.6
Energy productivity1.99.37.511.610.36.76.6
Renewable energy96.977.741.943.082.220.217.6
Import dependency100.22.798.090.839.296.395.0
Energy poverty2.51.91.58.333.15.224.2
Composite index0.6570.5600.4400.3400.3230.3180.226
Table 11. Membership matrix of Cluster A.
Table 11. Membership matrix of Cluster A.
2005200620072008200920102011201220132014201520162017201820192020202120222023
Iceland1111111111111111111
Table 12. Membership matrix of Cluster B.
Table 12. Membership matrix of Cluster B.
2005200620072008200920102011201220132014201520162017201820192020202120222023
Denmark1111111111111110100
Norway1111111111111111111
Table 13. Membership matrix of Cluster C.
Table 13. Membership matrix of Cluster C.
2005200620072008200920102011201220132014201520162017201820192020202120222023
Luxem.1111111111111111111
Austria1111111111111110100
Finland1111111111111111111
Sweden1111111111111111110
Table 14. Membership matrix of Cluster D.
Table 14. Membership matrix of Cluster D.
2005200620072008200920102011201220132014201520162017201820192020202120222023
Denmark0000000000000001011
Germany0000000000000011111
Ireland0000000111111111111
Greece0000000000000001111
Spain0000000111111111111
France0000000000000000011
Croatia0000000001111111111
Italy0000001111111111111
Cyprus0000000000000000111
Latvia0000000000000001111
Lithuan.0000000000000000011
Hungary0000000000000000001
Netherl.0000000000000000011
Austria0000000000000001011
Poland0000000000000000011
Portugal0000000000111111111
Romania0000001111111111111
Slovenia0000000000000000011
Sweden0000000000000000001
Monten.0000000001111111111
Türkiye0000000001111111111
Table 15. Membership matrix of Cluster F.
Table 15. Membership matrix of Cluster F.
2005200620072008200920102011201220132014201520162017201820192020202120222023
Belgium1111111111111111111
Czechia1111111111111111111
Germany1111111111111100000
Estonia1111111111111111111
Ireland1111111000000000000
Greece1111111000000000000
Spain1111111000000000000
France1111111111111111100
Croatia1111111110000000000
Italy1111110000000000000
Latvia0011111111111110000
Hungary1111111111111111110
Malta0000000000001000000
Netherl.1111111111111111100
Poland0000111111111111100
Slovenia1111111111111111100
Slovakia1111111111111111111
Monten.1111111110000000000
Serbia0000000000011111111
Table 16. Membership matrix of Cluster G.
Table 16. Membership matrix of Cluster G.
2005200620072008200920102011201220132014201520162017201820192020202120222023
Bulgaria1111111111111111111
Greece0000000111111110000
Cyprus1111111111111111000
Latvia1100000000000000000
Lithuan.1111111111111111100
Malta1111111111110111111
Poland1111000000000000000
Portugal1111111111000000000
Romania1111110000000000000
N. Maced1111111111111111111
Serbia1111111111100000000
Türkiye1111111110000000000
Table 17. Cluster membership in 2005–2023.
Table 17. Cluster membership in 2005–2023.
2005200620072008200920102011201220132014201520162017201820192020202120222023
IcelandAAAAAAAAAAAAAAAAAAA
NorwayBBBBBBBBBBBBBBBBBBB
DenmarkBBBBBBBBBBBBBBBDBDD
LuxembourgCCCCCCCCCCCCCCCCCCC
FinlandCCCCCCCCCCCCCCCCCCC
SwedenCCCCCCCCCCCCCCCCCCD
AustriaCCCCCCCCCCCCCCCDCDD
ItalyFFFFFFDDDDDDDDDDDDD
RomaniaGGGGGGDDDDDDDDDDDDD
IrelandFFFFFFFDDDDDDDDDDDD
SpainFFFFFFFDDDDDDDDDDDD
CroatiaFFFFFFFFFDDDDDDDDDD
MontenegroFFFFFFFFFDDDDDDDDDD
TürkiyeGGGGGGGGGDDDDDDDDDD
PortugalGGGGGGGGGGDDDDDDDDD
GermanyFFFFFFFFFFFFFFDDDDD
GreeceFFFFFFFGGGGGGGGDDDD
LatviaGGFFFFFFFFFFFFFDDDD
CyprusGGGGGGGGGGGGGGGGDDD
FranceFFFFFFFFFFFFFFFFFDD
NetherlandsFFFFFFFFFFFFFFFFFDD
SloveniaFFFFFFFFFFFFFFFFFDD
LithuaniaGGGGGGGGGGGGGGGGGDD
PolandGGGGFFFFFFFFFFFFFDD
HungaryFFFFFFFFFFFFFFFFFFD
AlbaniaEEEEEEEEEEEEEEEEEEE
BelgiumFFFFFFFFFFFFFFFFFFF
CzechiaFFFFFFFFFFFFFFFFFFF
EstoniaFFFFFFFFFFFFFFFFFFF
SlovakiaFFFFFFFFFFFFFFFFFFF
SerbiaGGGGGGGGGGGFFFFFFFF
MaltaGGGGGGGGGGGGFGGGGGG
BulgariaGGGGGGGGGGGGGGGGGGG
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Sokołowski, A.; Markowska, M.; Strahl, D. Sustainable Energy Development in European Countries 2005–2023: A Dynamic Cluster Analysis Approach. Energies 2026, 19, 583. https://doi.org/10.3390/en19030583

AMA Style

Sokołowski A, Markowska M, Strahl D. Sustainable Energy Development in European Countries 2005–2023: A Dynamic Cluster Analysis Approach. Energies. 2026; 19(3):583. https://doi.org/10.3390/en19030583

Chicago/Turabian Style

Sokołowski, Andrzej, Małgorzata Markowska, and Danuta Strahl. 2026. "Sustainable Energy Development in European Countries 2005–2023: A Dynamic Cluster Analysis Approach" Energies 19, no. 3: 583. https://doi.org/10.3390/en19030583

APA Style

Sokołowski, A., Markowska, M., & Strahl, D. (2026). Sustainable Energy Development in European Countries 2005–2023: A Dynamic Cluster Analysis Approach. Energies, 19(3), 583. https://doi.org/10.3390/en19030583

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