CLUSTER ANALYSIS OF EUROPEAN UNION MEMBER STATES PERFORMANCE IN TERMS OF SDG INDICATORS

Abstract

The implementation of Sustainable Development Goals (SDGs) and related targets, adopted by all UN Member States in 2015, can be monitored at various levels using global, regional or national SDG indicators. The present paper deals with progress of EU Member States towards sustainable development using data from the EU SDG indicator set that was developed by the European Commission due to policy relevance and statistical quality of indicators. The aim of the paper is to categorize EU Member States into broader groups based on similar performance in selected SDG indicators. To reach the aim of this paper, cluster analysis with Eurostat data from 2015 and 2020 is employed. The results show that the best-performing groups of countries in terms of progress towards SDGs are cluster 1, consisting of the Benelux countries, France, Germany and Denmark, and cluster 5 made up of Austria, Finland, Sweden and Slovenia. On the contrary, the worst performance in selected SDG indicators was shown by cluster 2, which comprises Romania and Bulgaria, followed by cluster 3 consisting of Greece, Spain, Italy, Cyprus, Malta, Ireland, as well as the Visegrad countries that joined this cluster in 2020. The results also indicate that more advanced EU economies, especially Western and Northern European countries, tend to achieve better results in most of SDG indicators as compared to less developed Central and Eastern as well as Southern European countries.

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CLUSTER ANALYSIS OF EUROPEAN UNION MEMBER STATES
PERFORMANCE IN TERMS OF SDG INDICATORS
Peter Jančovič
Faculty of International Relations, University of Economics in Bratislava, Dolnozemská cesta 1/b,
852 35 Bratislava 5, Slovak Republic, e-mail: peter.jancovic@euba.sk
Abstract: The implementation of Sustainable Development Goals (SDGs) and related
targets, adopted by all UN Member States in 2015, can be monitored at various levels using
global, regional or national SDG indicators. The present paper deals with progress of EU
Member States towards sustainable development using data from the EU SDG indicator set
that was developed by the European Commission due to policy relevance and statistical
quality of indicators. The aim of the paper is to categorize EU Member States into broader
groups based on similar performance in selected SDG indicators. To reach the aim of this
paper, cluster analysis with Eurostat data from 2015 and 2020 is employed. The results
show that the best-performing groups of countries in terms of progress towards SDGs are
cluster 1, consisting of the Benelux countries, France, Germany and Denmark, and cluster
5 made up of Austria, Finland, Sweden and Slovenia. On the contrary, the worst
performance in selected SDG indicators was shown by cluster 2, which comprises Romania
and Bulgaria, followed by cluster 3 consisting of Greece, Spain, Italy, Cyprus, Malta,
Ireland, as well as the Visegrad countries that joined this cluster in 2020. The results also
indicate that more advanced EU economies, especially Western and Northern European
countries, tend to achieve better results in most of SDG indicators as compared to less
developed Central and Eastern as well as Southern European countries.
Keywords: Sustainable Development Goals, European Union, cluster analysis, EU SDG
indicator set, progress towards SDGs
JEL: F63, I30, Q01
Introduction
Sustainable development is defined by the United Nations as “development that meets
the needs of the present generations without compromising the ability of future generations to
meet their own needs”
1
. In general terms, sustainable development has the three main
dimensions, such as economic, social and environmental dimension. In 2015, all United Nations
Member States adopted the 2030 Agenda for Sustainable Development (2030 Agenda) that is
represented by 17 Sustainable Development Goals, also known as the Global Goals, with 169
associated targets which are integrated and indivisible.
2
The SDGs and related targets are being
monitored through a set of global, regional as well as national indicators. The international
community should achieve 17 SDGs by 2030.
3
The implementation of SDGs and related targets
requires the mobilization of available resources, as well as the engagement of many
stakeholders, such as governments, private sector, civil society, the UN system and other actors.
In this regard, the European Union (EU) and its Member States play an important role.
The European Union, in coordination with the Member States, has fully committed itself
to delivering on the 2030 Agenda and its implementation through its external and internal
1
UNITED NATIONS (2022): Sustainable Development Goals.
2
UNITED NATIONS (2022): Department of Economic and Social Affairs. Sustainable Development.
3
It is important to note that the Sustainable Development Goals are not legally binding.

252
policies, as outlined for instance in European Commission’s Communication Next steps for a
sustainable European future – European action for sustainability (2016), The European
Consensus on Development (2017), The European Green Deal (2019) and Commission Staff
Working Document Delivering on the UN’s Sustainable Development Goals – A
Comprehensive Approach (2020).
4
Monitoring of SDG implementation is carried out at the
global, regional, national, local and thematic levels. To monitor the implementation of SDGs at
the regional level, the European Commission has developed a set of sustainable development
indicators which have been selected for their policy relevance for the EU as well as for their
statistical quality.
5
The present paper deals with the current progress of EU Member States
towards the SDGs compared to 2015, as a reference year when the 2030 Agenda was adopted.
In this context, the aim of this paper is to categorize European Union Member States into
broader groups according to the similarities and dissimilarities between them in terms of
progress towards the Sustainable Development Goals. The Member States of the EU are
classified into relatively homogeneous groups based on selected SDG indicators provided by
Eurostat. In terms of methodology, the agglomerative hierarchical cluster analysis technique is
used.
This paper is organized as follows. Section 1 provides a brief overview of literature on
the use of cluster analysis to categorize selected countries into similar groups based on their
progress towards SDGs. Section 2 presents methodology and data applied in cluster analysis.
In section 3, the results of hierarchical cluster analysis are presented. Last section concludes the
present paper with the main findings.
1 Literature Review
In a few studies, researchers used cluster analysis to classify selected countries into
relatively homogeneous groups in terms of the implementation of the Sustainable Development
Goals. Çağlar and Grler used non-hierarchical cluster analysis technique, applying K-means
method, to classify 110 worldwide countries in terms of SDGs progress.
6
They further
examined each cluster based on socioeconomic and politico-cultural structure of the countries.
The authors identified five clusters within which countries with more advanced socioeconomic
and politico-cultural structure tend to have better SDGs progress.
7
Çağlar and Grler found that
most of EU countries belong to the best-performing cluster in terms of achieving the SDGs,
except for Austria, Czechia, Hungary, Luxembourg and Slovakia that were not included in the
analysis, as well as Bulgaria, Croatia and Romania that belong to the cluster with relatively
lower mean SDG index. Researchers such as Adamišin et al.,
8
Allievi et al.,
9
Drastichová,
10
4
EUROPEAN COMMISSION (2022): Sustainable Development in the European Union. Overview of progress
towards the SDGs in an EU context, p. 3.
5
EUROPEAN COMMISSION (2022): Monitoring and reporting the SDGs in an EU context.
6
ÇAĞLAR, M. – GÜRLER, C. (2022): Sustainable Development Goals: A cluster analysis of worldwide
countries.
7
Ibid.
8
ADAMIŠIN, P. et al. (2015): Cluster analysis of Central and Southeast Europe via selected indicators of
sustainable development.
9
ALLIEVI, F. et al. (2011): Grouping and ranking the EU-27 countries by their sustainability performance
measured by the Eurostat sustainability indicators.
10
DRASTICHOVÁ, M. (2020): Cluster Analysis of Sustainable Development Goal Indicators in the European
Union.

253
Drastichová and Filzmoser,
11
Huttmanová,
12
Petrov et al.
13
used cluster analysis to classify
European countries into broader groups based on their performance in terms of selected
sustainable development indicators. Allievi et al. employed hierarchical agglomerative
clustering technique to categorize 27 Member States of the EU into similar groups based on
their sustainability performance in economic, social and environmental dimension measured
with selected indicators provided by Eurostat.
14
Huttmanová evaluated the management of
sustainable development in 28 European Union countries through selected nine indicators from
2014, using hierarchical cluster analysis.
15
She found that more developed EU countries, such
as Germany, France, Italy, United Kingdom and Spain, achieve better results in the field of
sustainability. Huttmanová concluded that the European Union does not create a homogeneous
group of countries in terms of sustainable development.
16
Petrov et al. employed 15 multi-metric sustainable development indicators to classify
10 Southeast European countries into distinctive clusters.
17
They used hierarchical clustering
method and squared Euclidean distance as a measure of the similarity or differences between
the clusters. Petrov et al. identified three clusters, whereas the two of them consisted of EU
Member States and the third one was made up of candidate and potential candidate countries.
Drastichová applied hierarchical cluster analysis to classify 28 EU Member States and Norway
into similar groups of countries according to their performance in selected nine SDG indicators,
using data from 2007 and 2016.
18
The author employed Ward’s method of clustering together
with squared Euclidean distance. Using data from 2016, Drastichová identified four clusters –
cluster 1 consists of more advanced EU countries, such as Benelux countries, Austria, Germany,
France and the UK, cluster 2 contains Baltic, Southeast and Southern countries, cluster 3 is
made up of the Visegrad countries, Slovenia, Malta and Ireland, and cluster 4 consists of the
four developed Northern countries.
19
Drastichová concluded that cluster 4 represents the most
sustainable group of countries, while cluster 2 has the worst performance in sustainable
development. These results are supported by the findings of Drastichová and Filzmoser who
also argued that the most developed EU countries (Northern countries, Benelux countries,
Germany, France, Austria, the UK) have the best performance in sustainable development,
while relatively least developed EU countries, such as Bulgaria and Romania, tend to have the
worst performance in the SDGs.
20
The European Union has published several reports focused on the implementation and
monitoring of achievements in SDGs. The most recent report titled Sustainable development in
the European Union – 2022 edition shows that the pace of achieving individual Sustainable
Development Goals varies within the EU.
21
Over the past five years, the EU has made the most
11
DRASTICHOVÁ, M. – FILZMOSER, P. (2019): Assessment of Sustainable Development Using Cluster
Analysis and Principal Component Analysis.
12
HUTTMANOVÁ, E. (2016): Sustainable Development and Sustainability Management in the European
Union Countries.
13
PETROV, V. et al. (2018): Assessing sustainability of the Southeast European economies.
14
ALLIEVI, F. et al. (2011): Grouping and ranking the EU-27 countries by their sustainability performance
measured by the Eurostat sustainability indicators.
15
HUTTMANOVÁ, E. (2016): Sustainable Development and Sustainability Management in the European
Union.
16
Ibid.
17
PETROV, V. et al. (2018): Assessing sustainability of the Southeast European economies.
18
DRASTICHOVÁ, M. (2020): Cluster Analysis of Sustainable Development Goal Indicators in the European
Union.
19
Ibid.
20
DRASTICHOVÁ, M. – FILZMOSER, P. (2019): Assessment of Sustainable Development Using Cluster
Analysis and Principal Component Analysis.
21
EUROSTAT (2022): Sustainable development in the European Union – 2022 edition.

254
significant progress towards five SDGs – 16 (Peace, justice and strong institutions),
1 (No poverty
22
), 8 (Decent work and economic growth), 7 (Affordable and clean energy) and
9 (Industry, innovation and infrastructure). The EU has also achieved good results in the
implementation of SDGs 3 (Good health and well-being), 14 (Life below water) and 5 (Gender
equality). On the other hand, progress towards the rest of the SDGs was considerably slower.
23
The report also provides an overview of the progress of individual EU Member States towards
the SDGs, based on the EU SDG indicator set. The study emphasises the importance of
considering natural conditions and historical developments of individual EU Member States
when analysing their status and progress towards the SDGs.
24
2 Methodology and Data
The aim of this paper is to categorize European Union Member States into broader
groups according to the similarities and dissimilarities between them in terms of progress
towards the Sustainable Development Goals. The purpose of classifying the Member States of
the EU into relatively homogeneous groups is to analyze the implementation of the SDGs in a
wider (international) context and identify the specifics of groups of EU countries. To classify
EU Member States into similar groups based on progress towards SDG indicators, cluster
analysis and Eurostat data from 2015 and 2020 were applied.
The cluster analysis can be defined as a set of techniques that classifies cases (variables
or observations) into groups (clusters) which are relatively homogeneous within themselves
and relatively heterogeneous between each other.
25
Cluster analysis algorithms can be divided
into hierarchical and non-hierarchical, while the former one is subdivided into agglomerative
and divisive clustering algorithms.
26
In this paper, we use agglomerative hierarchical clustering
technique that allows us to determine an appropriate number of clusters during the clustering
process, as well as obtain a dendrogram as the output of cluster analysis. At the beginning of
agglomerative hierarchical clustering, each case forms its own individual cluster, and
subsequently similar cases are merged together until every case is grouped into one single
cluster.
27
Regarding the method of clustering, we use Ward’s method where the dissimilarity
between two clusters is defined to be the loss of information from joining the two clusters. Loss
of information is found by measuring the increase in the error sum of squares, or the sum of
squared deviations of each pattern from the centroid for the cluster.
28
This method is appropriate
for quantitative variables. We employ the squared Euclidean distance as a measure of distance
or similarity between the cases, which is the most commonly used distance metric in terms of
Ward’s method of clustering.
The set of observations includes 27 EU Member States: Belgium (BE), Bulgaria (BG),
Czechia (CZ), Denmark (DK), Germany (DE), Estonia (EE), Ireland (IE), Greece (EL), Spain
(ES), France (FR), Croatia (HR), Italy (IT), Cyprus (CY), Latvia (LV), Lithuania (LT),
Luxembourg (LU), Hungary (HU), Malta (MT), Netherlands (NL), Austria (AT), Poland (PL),
Portugal (PT), Romania (RO), Slovenia (SI), Slovakia (SK), Finland (FI) and Sweden (SE).
22
The latest available data on reducing poverty and social exclusion come from 2019 and therefore do not reflect
the COVID-19 pandemic’s impacts.
23
EUROSTAT (2022): Sustainable development in the European Union – 2022 edition.
24
Ibid.
25
YIM, O. – RAMDEEN, K. T. (2015): Hierarchical Cluster Analysis: Comparison of Three Linkage Measures
and Application to Psychological Data.
26
HANSEN, P. – JAUMARD, B. (1997): Cluster analysis and mathematical programming.
27
YIM, O. – RAMDEEN, K. T. (2015): Hierarchical Cluster Analysis: Comparison of Three Linkage Measures
and Application to Psychological Data.
28
KING, R. S. (2015): Cluster Analysis and Data Mining. An Introduction, p. 44.

255
The European Commission has created a set of SDG indicators in accordance with their
policy relevance for the European Union and their statistical quality. The purpose of the EU
SDG indicator set is to monitor progress towards the achievement of the SDGs in an EU
context.
29
The EU set of SDG indicators is in line with the United Nations list of global
indicators, but it is not completely identical.
30
The indicator set consists of 101 indicators that
are structured along 17 SDGs, whereas 31 of them are multi-purpose indicators (MPIs)
31
which
are used to monitor more than one goal.
32
The data are reviewed annually and reported in
Eurostat. The data used for cluster analysis come from Eurostat’s EU SDG indicator set. A set
of SDG indicators which are employed in cluster analysis can be found in Table 1. Selected
indicators are structured along the 17 SDGs and contain a brief description of the variable.
Indicators related to SDG 14 (Conserve and sustainably use the oceans, seas and marine
resources for sustainable development) were excluded from the analysis, since they are not
relevant to five landlocked EU Member States.
Table 1: Overview of SDGs and corresponding indicators used for cluster analysis
No
Goal
Selected indicator and its description
1
End poverty in all its forms
everywhere
SDG_1: People at risk of poverty or social exclusion (% of total
population). This indicator corresponds to the sum of persons who are
at risk of poverty after social transfers, severely materially deprived or
living in households with very low work intensity.
2
End hunger, achieve food
security and improved nutrition
and promote sustainable
agriculture
SDG_2: Agricultural factor income per annual work unit (AWU)
(Chain linked volumes (2010), euro per annual work unit – AWU). This
indicator measures the income generated by farming and it is a partial
labour productivity measure in agriculture.
3
Ensure healthy lives and promote
well-being for all at all ages
SDG_3: Healthy life years (HLY) at birth (in years). This indicator
measures the number of remaining years that a person of specific age
is expected to live without any severe or moderate health problems.
4
Ensure inclusive and equitable
quality education and promote
lifelong learning opportunities
for all
SDG_4: Tertiary educational attainment (% of the population aged 25-
34). This indicator measures the share of the population aged 25-34
who have successfully completed tertiary studies (e.g., university,
higher technical institutions etc.).
5
Achieve gender equality and
empower all women and girls
SDG_5: Gender employment gap (in percentage points). This indicator
measures the difference between the employment rates of men and
women aged 20 to 64. The employment rate is calculated by dividing
the number of persons aged 20 to 64 in employment by the total
population of the same age group.
6
Ensure availability and
sustainable management of water
and sanitation for all
SDG_6: Population having neither a bath, nor a shower, nor indoor
flushing toilet in their household (% of the total population).
7
Ensure access to affordable,
reliable, sustainable and modern
energy for all
SDG_7: Share of renewable energy in gross final energy consumption
(in %). This indicator measures the share of renewable energy
consumption in gross final energy consumption, i.e., energy used by
end-consumers plus grid losses and self-consumption of power plants.
8
Promote sustained, inclusive and
sustainable economic growth,
full and productive employment
and decent work for all
SDG_8: Employment rate (% of the total population). This indicator
measures the share of the population aged 20 to 64 which is employed.
Employed persons are defined as all persons who, during a reference
29
EUROPEAN COMMISSION (2022): Monitoring and reporting the SDGs in an EU context.
30
EUROSTAT (2022): Sustainable development in the European Union. Monitoring report on progress towards
the SDGs in an EU context, p. 19.
31
MPIs include, inter alia, indicators such as people at risk of poverty or social exclusion (SDGs 1,10), tertiary
educational attainment (SDGs 4,5,9), share of renewable energy in gross final energy consumption (SDGs 7,13)
and employment rate (SDGs 8,10).
32
EUROPEAN COMMISSION (2022): Sustainable Development in the European Union. Overview of progress
towards the SDGs in an EU context.

256
week, worked at least one hour for pay or profit or were temporarily
absent from such work.
9
Build resilient infrastructure,
promote inclusive and
sustainable industrialization and
foster innovation
SDG_9: Gross domestic expenditure on R&D (% of GDP). This
indicator measures combined public and private investment in R&D,
which comprises creative work undertaken on a systematic basis in
order to increase the stock of knowledge to devise new applications.
10
Reduce inequality within and
among countries
SDG_10: Income distribution (quintile share ratio). The indicator is
calculated as the ratio of total income received by the 20% of the
population with the highest income (the top quintile) to that received
by the 20% of the population with the lowest income (the bottom
quintile). The higher this ratio, the bigger the income inequality.
11
Make cities and human
settlements inclusive, safe,
resilient and sustainable
SDG_11: Recycling rate of municipal waste (% of total municipal
waste). This indicator measures the tonnage recycled from municipal
waste divided by total municipal waste arising.
12
Ensure sustainable consumption
and production patterns
SDG_12: Circular material use (CMU) rate (% of total material use).
The indicator measures the share of material recovered and fed back
into the economy – thus saving extraction of primary raw materials –
in overall material use. CMU is the ratio of the circular use of materials
to the overall material use.
13
Take urgent action to combat
climate change and its impacts
SDG_13: Net greenhouse gas emissions (index, 1990=100). The
indicator measures total national emissions from all sectors of the
greenhouse gases emission inventories including international aviation
and indirect CO2. The indicator is presented as net emissions including
land use, land use change and forestry.
14
Conserve and sustainably use the
oceans, seas and marine
resources for sustainable
development
–
15
Protect, restore and promote
sustainable use of terrestrial
ecosystems, sustainably manage
forests, combat desertification,
and halt and reverse land
degradation and halt biodiversity
loss
SDG_15: Share of forest area (% of the total land area). This indicator
measures the proportion of forest ecosystems in comparison to the total
land area. The wider FAO definition of forest (all forest area FAO) is
employed, i.e., forests together with other wooded land.
16
Promote peaceful and inclusive
societies for sustainable
development, provide access to
justice for all and build effective,
accountable and inclusive
institutions at all levels
SDG_16: Corruption Perceptions Index (CPI). This indicator is a
composite index based on a combination of surveys and assessments of
corruption from 13 different sources and scores and ranks countries
based on how corrupt a country’s public sector is perceived to be, with
a score of 0 representing a very high level of corruption and a score of
100 representing a very clean country. The CPI is published by
Transparency International.
17
Strengthen the means of
implementation and revitalize the
Global Partnership for
Sustainable Development
SDG_17: Official development assistance (ODA) (% of GNI). ODA
consists of grants or loans that are undertaken by the official sector with
the objective of promoting economic development and welfare in
recipient countries.
Source: Author’s own elaboration based on EUROSTAT (2022): SDGs by goals.
We use data from 2015, as a reference year when the SDGs have been adopted by the
United Nations, and 2020 as the most recent year for which all data are available, except for the
indicator share of forest area (SDG 15), for which the latest data are from 2018. The values of
all variables are standardized in order to avoid the problems caused by scale differences. The
cluster analysis is performed in the statistical software SPSS Statistics.

257
4 Results and Discussion
The results of cluster analysis for the years 2020 and 2015 are shown through the
dendrogram (Figure 1). In both years, five clusters can be determined at a similar distance in
the dendrogram. Using data from 2020, cluster 1 consists of six Western European countries,
such as the Benelux countries
33
, Germany, France and Denmark. Cluster 2 contains only
Bulgaria and Romania. Cluster 3 is made up of the Visegrad Group countries
34
, five Southern
European countries, such as Greece, Spain, Italy, Cyprus and Malta, as well as Ireland. Cluster
4 consists of the Baltic states
35
, Croatia and Portugal. Finally, cluster 5 is composed of two
Northern European states, such as Finland and Sweden, as well as Austria and Slovenia. As
compared to 2015, clusters 1, 2 and 5 remained unchanged. The composition of clusters 3 and
4 has changed slightly in the period under review. The Visegrad Group countries belonged to
cluster 4 in 2015, but they shifted into cluster 3 in 2020. Therefore, there were no significant
changes in the composition of clusters between 2015 and 2020.
Figure 1: The results of cluster analysis – dendrogram, using data
from 2020 (left side) and 2015 (right side)
Note: Cluster membership in 2020 (dendrogram on the left): cluster 1 – BE, FR, NL, DK, DE, LU, cluster 2 – BG,
RO, cluster 3 – EL, ES, IT, HU, PL, CZ, SK, IE, CY, MT, cluster 4 – HR, PT, LV, LT, EE and cluster 5 – AT, FI,
SI, SE. Cluster membership in 2015 (dendrogram on the right): cluster 1 – BE, FR, NL, LU, DK, DE, cluster 2 –
BG, RO, cluster 3 – IE, CY, EL, ES, IT, MT, cluster 4 – EE, LV, LT, HR, PT, HU, PL, CZ, SK and cluster 5 –
AT, SI, FI, SE.
Source: Author’s own elaboration using SPSS Statistics.
Table 2 reports the average values of SDG indicators within each of the five clusters, as
well as the overall average of all 27 EU Member States. In 2020, cluster 1 has, on average, the
best performance in indicators related to the SDGs 2, 4, 6, 11, 12, 16 and 17. Thereby, cluster
1 has comparatively the best results in the fields of labour productivity in agriculture, tertiary
school attainment, share of people living in households with basic sanitary facilities, recycling
33
Belgium, Netherlands and Luxembourg.
34
Czechia, Hungary, Poland and Slovakia.
35
Estonia, Latvia and Lithuania.

258
rate of municipal waste, circular use of materials, perception of corruption in the public sector
and the ratio of ODA to GNI (Table 2). On the contrary, cluster 1 as a whole shows the worst
performance in terms of the share of forest area (SDG 15) in both years. Between 2015 and
2020, cluster 1 made a progress towards all SDG indicators, except for the indicators related to
SDGs 10 and 16. Overall, almost all values of SDG indicators, apart from the indicators used
for SDGs 7 and 15, were above the EU-27 average in 2020.
Cluster 2, consisting only of Bulgaria and Romania, shows relatively poor performance
in sustainable development policies. In 2020, cluster 2 had the worst results in 11 (SDGs 1, 2,
4, 6, 8, 9, 10, 11, 12, 16 and 17) out of 16 SDG indicators used in the cluster analysis. On the
contrary, cluster 2 achieved the best results regarding net greenhouse gas emissions index (SDG
13) as compared with other clusters. In line with European Climate Law, the EU’s target is to
reduce net greenhouse gas emissions by at least 55% until 2030 compared to 1990.
36
Romania
has already achieved this target, with the value of net greenhouse gas emissions index equals to
34.6 in 2020, and Bulgaria is close to achieve the target with the present value of index 49.3.
37
Between 2015 and 2020, the values of 13 out of 16 SDG indicators improved. However, this
group of countries remained below the EU-27 average in terms of almost all SDG indicators,
with the exception of indicators used for SDGs 3, 13 and 15 (Table 2).
In 2020, cluster 3 was the best performing group of EU countries in terms of indicator
related to SDG 3 – healthy life years at birth (HLY), which focuses on the quality of life spent
in a healthy state.
38
This was mainly due to Southern European countries that tend to achieve
higher HLY values. On the other hand, cluster 3 had the worst performance in sustainable
development indicators such as gender employment gap (SDG 5), share of renewable energy in
gross final energy consumption (SDG 7) and net greenhouse gas emissions index (SDG 13).
Cluster 3 reached the values better than the overall EU-27 average only in five indicators out
of sixteen in 2020. In 2015, cluster 3 consisted of the same EU Member States, i.e. Southern
European countries and Ireland, less the Visegrad countries (see Figure 1). Between 2015 and
2020, this cluster improved the score of 12 out of 16 SDG indicators.
Cluster 4 showed the best results in indicator related to SDG 5 (gender employment gap)
and the worst results in SDG 3 (healthy life years at birth) in 2020. Overall, the values of 6 out
of 16 SDG indicators were above the EU-27 average in 2020. Between 2015 – 2020, the average
values of 14 out of 16 SDG indicators improved, except for indicators used for SDGs 3 and 10
(Table 2). It is important to note that the Visegrad Group countries left cluster 4 between 2015
and 2020. In 2015, cluster 4, including the Visegrad countries, had better results in the SDGs
as compared to cluster 3. In 2020, cluster 3, where the Visegrad countries shifted to, showed
slightly worse results in implementation of the SDGs as compared to cluster 4. The results of
cluster analysis indicate that the Visegrad group countries moved from better performing cluster
in 2015 (cluster 4) towards cluster with relatively worse sustainable development performance
in 2020 (cluster 3), whereas the other EU countries remained unchanged within each cluster.
Cluster 5 had, on average, the best performance in indicators related to the SDGs 1, 7,
8, 9, 10 and 15 in 2020 (Table 2). This cluster is the best performing group of countries in terms
of the number of people at risk of poverty or social exclusion, share of renewable energy,
employment rate, investment in R&D, income distribution and the share of forest area. With
the exception of Slovenia, cluster 5 countries have already achieved the EU’s aim of increasing
the share of renewable energy sources in gross final energy consumption to at least 32% by
36
EUROSTAT (2022): Sustainable development in the European Union. Monitoring report on progress towards
the SDGs in an EU context, p. 369.
37
EUROSTAT (2022): SDGs by goals.
38
EUROSTAT (2022): Sustainable development in the European Union. Monitoring report on progress towards
the SDGs in an EU context, p. 70.

259
2030.
39
Following cluster 1, cluster 5 achieved the second-best results in sustainable
development policies. In 2020, almost all values of SDG indicators, apart from the indicators
used for SDGs 2 and 12, were above the EU-27 average. Between 2015 and 2020, cluster 5
recorded positive progress in 12 out of 16 SDG indicators.
Table 2: Average values of indicators within clusters and the EU-27 average
Source: Author’s own elaboration based on data obtained from EUROSTAT (2022): SDGs by goals.
To sum up, clusters 1 (Benelux countries, France, Denmark and Germany) and 5
(Austria, Finland, Sweden and Slovenia) show the best results in terms of progress towards
achieving the SDGs as compared to other groups of EU Member States. These clusters
performed best regarding average values of several selected SDG indicators in both years. On
the contrary, cluster 2, consisting of Romania and Bulgaria, has, on average, the worst
performance in most of SDG indicators (11 out of 16 indicators) that were analysed in 2015 as
well as 2020. In both years, the second-worst performing group of EU countries was cluster 3,
consisting of Southern European countries (Greece, Spain, Italy, Cyprus, Malta) and Ireland in
2015 that were joined by the Visegrad Group countries in 2020.
In the dendrogram, we can observe some regional and historical specifics of EU Member
States regarding the implementation of the SDGs. For instance, the Visegrad Group countries,
Western European countries, Baltic states and two eastern Balkan countries (Bulgaria and
39
Ibid., p. 369.

260
Romania) are closest to each other in terms of progress towards sustainable development, as
can be seen in Figure 1. In general, more advanced economies of the EU, especially Western
and Northern European states, tend to achieve better results in most of SDG indicators as
compared to less developed Central and Eastern as well as Southern European countries. This
is supported by the findings of some other authors, such as Drastichová and Filzmoser.
40
The
results of cluster analysis also indicate that European Union Member States do not create a
homogenous group of countries regarding progress towards sustainable development. This is
supported by many other researchers, such as Huttmanová, who reached the same conclusion,
even though she used data from 2014.
41
Conclusion
The aim of this paper was to categorize 27 EU Member States into broader groups
according to the similarities and dissimilarities between them in terms of progress towards the
SDGs. Using agglomerative hierarchical cluster analysis, all EU Member States were classified
into relatively homogeneous groups based on 16 selected SDG indicators provided by Eurostat.
Furthermore, we compared progress of identified clusters towards sustainable development
between two years – 2015 as the reference year when the 2030 Agenda and the SDGs have been
adopted, and 2020 as the most recent year for which relevant data are available.
In both years, five clusters were identified at a similar distance. The best-performing
groups of EU countries in terms of sustainable development policies were cluster 1, consisting
of the Benelux countries, France, Denmark and Germany, as well as cluster 5 made up of
Austria, Finland, Sweden and Slovenia. On the contrary, the worst performance in average
values of SDG indicators was shown by cluster 2, comprising Romania and Bulgaria, followed
by cluster 3 consisting of Greece, Spain, Italy, Cyprus, Malta, Ireland in 2015 and the Visegrad
Group countries that joined this cluster in 2020. Except for the Visegrad countries’ shift from
cluster 4 in 2015 into cluster 3 in 2020, there were no changes in the composition of clusters.
Therefore, the groups of EU countries remain relatively homogeneous in terms of their
performance in sustainable development policies.
The results of cluster analysis indicate that more advanced EU economies, especially
Western and Northern European countries, tend to achieve better results in most of SDG
indicators as compared to less developed Central and Eastern as well as Southern European
countries. Furthermore, we can observe some regional specifics regarding the implementation
of the SDGs, such as the Visegrad Group countries, Benelux countries, Baltic states, two eastern
Balkan countries (Bulgaria and Romania) and Southern European countries, which are close to
each other in terms of progress towards the SDGs. In general, the average performance of
clusters in terms of selected SDG indicators improved between 2015 and 2020, even though
EU economies were negatively affected by the COVID-19 pandemic in 2020. To sum up,
European Union Member States do not create a homogenous group of countries regarding
progress towards sustainable development. The pace of achieving the SDGs varies not only
among individual EU Member States, but also among identified groups of countries.
References:
1. ADAMIŠIN, P. et al. (2015): Cluster analysis of Central and Southeast Europe via
selected indicators of sustainable development. [online]. 2015. [Cited 4. 11. 2022.]
Available online: https://www.elibrary.ru/item.asp?id=43045309.
40
DRASTICHOVÁ, M. – FILZMOSER, P. (2019): Assessment of Sustainable Development Using Cluster
Analysis and Principal Component Analysis.
41
HUTTMANOVÁ, E. (2016): Sustainable Development and Sustainability Management in the European
Union Countries.

261
2. ALLIEVI, F. et al. (2011): Grouping and ranking the EU-27 countries by their
sustainability performance measured by the Eurostat sustainability indicators. [online].
2011. [Cited 2. 11. 2022.] Available online: https://www.utupub.fi/bitstream/handle/
10024/147572/eBook_2011-15.pdf.
3. ÇAĞLAR, M. – GÜRLER, C. (2022): Sustainable Development Goals: A cluster
analysis of worldwide countries. In: Environment, Development and Sustainability,
2022, Vol. 24, No. 6, pp. 8593-8624.
4. DRASTICHOVÁ, M. – FILZMOSER, P. (2019): Assessment of Sustainable
Development Using Cluster Analysis and Principal Component Analysis. In: Problems
of Sustainable Development, 2019, Vol. 14, No. 2, pp. 7-24.
5. DRASTICHOVÁ, M. (2020): Cluster Analysis of Sustainable Development Goal
Indicators in the European Union. In: BILGIN, M. H. et al. (eds.): Eurasian Economic
Perspectives, Cham: Springer, 2020, pp. 99-124. ISBN 978-3-030-35040-6.
6. EUROPEAN COMMISSION (2022): Monitoring and reporting the SDGs in an EU
context. [online]. 2022. [Cited 9. 11. 2022.] Available online: https://ec.europa.eu/info/
strategy/international-strategies/sustainable-development-goals/monitoring-and-
reporting-sdgs-eu-context_en.
7. EUROPEAN COMMISSION (2022): Sustainable Development in the European Union.
Overview of progress towards the SDGs in an EU context. [online]. 2022. [Cited 7. 11.
2022.] Available online: https://ec.europa.eu/eurostat/web/products-catalogues/-/KS-
06-22-017.
8. EUROSTAT (2022): SDGs by goals. [online]. 2022. [Cited 4. 11. 2022.] Available
online: https://ec.europa.eu/eurostat/web/sdi/database.
9. EUROSTAT (2022): Sustainable development in the European Union – 2022 edition.
[online]. 2022. [Cited 15. 11. 2022.] Available online: https://ec.europa.eu/eurostat/
en/web/products-flagship-publications/-/ks-09-22-019.
10. EUROSTAT (2022): Sustainable development in the European Union. Monitoring
report on progress towards the SDGs in an EU context. [online]. 2022. [Cited 10. 11.
2022.] Available online: https://ec.europa.eu/eurostat/web/sdi/publications.
11. HANSEN, P. – JAUMARD, B. (1997): Cluster analysis and mathematical
programming. In: Mathematical Programming, 1997, Vol. 79, No. 1, pp. 191-215.
12. HUTTMANOVÁ, E. (2016): Sustainable Development and Sustainability Management
in the European Union Countries. In: European Journal of Sustainable Development,
2016, Vol. 5, No. 4, pp. 475-482.
13. KING, R. S. (2015): Cluster Analysis and Data Mining. An Introduction. Dulles:
Mercury Learning and Information, 2015. ISBN 978-1-938549-38-0.
14. PETROV, V. et al. (2018): Assessing sustainability of the Southeast European
economies. In: Ekonomika Poljoprivrede, 2018, Vo. 65, No. 2, pp. 519-529.
15. UNITED NATIONS (2022): Department of Economic and Social Affairs. Sustainable
Development. [online]. 2022. [Cited 6. 11. 2022.] Available online: https://sdgs.un.org
/2030agenda.
16. UNITED NATIONS (2022): Sustainable Development Goals. [online]. 2022. [Cited 6.
11. 2022.] Available online: https://www.un.or-g/sustainabledevelopment/
development-agenda/.
17. YIM, O. – RAMDEEN, K. T. (2015): Hierarchical Cluster Analysis: Comparison of
Three Linkage Measures and Application to Psychological Data. In: The Quantitative
Methods for Psychology, 2015, Vol. 11, No. 1, pp. 8-21.

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Contact:
Ing. Peter Jančovič, PhD.
Faculty of International Relations
University of Economics in Bratislava
Dolnozemská cesta 1/b
852 35 Bratislava 5
Slovak Republic
e-mail: peter.jancovic@euba.sk