Progress on SDG 7 achieved by EU countries in relation to the target year 2030: A multidimensional indicator analysis using dynamic relative taxonomy

Abstract

In 2015, 193 UN members adopted the resolution “Transforming our world: the 2030 Agenda for Sustainable Development”, which set out 17 Sustainable Development Goals to be achieved by 2030. The aim of the study is to assess progress towards meeting SDG 7 “Ensure access to affordable, reliable, sustainable and modern energy for all” by individual EU countries in 2010–2021 and to determine their distance in relation to the target set for 2030. Eurostat monitors and assesses progress towards SDG 7 using seven indicators. These indicators were used to create an aggregate index. In order to limit the impact of the compensation effect on the ranking of EU countries, we applied dynamic relative taxonomy with the geometric mean to create an aggregate measure that takes into account target values for the indicators with adjusted data. The study reveals systematic progress towards reaching the EU’s SDG 7 in the period 2010–2021, with differences between individual EU countries clearly decreasing. The smallest distance in relation to the target set for SDG 7 can be observed for Sweden, Denmark, Estonia, and Austria. By far the greatest progress in period 2010–2021 has been achieved by Malta, and significant for Cyprus, Latvia, Belgium, Ireland, and Poland.

Full text

RESEARCH ARTICLE
Progress on SDG 7 achieved by EU countries
in relation to the target year 2030: A
multidimensional indicator analysis using
dynamic relative taxonomy
Marek Walesiak
1
, Grażyna DehnelID
2
*
1Department of Econometrics and Computer Science, Wroclaw University of Economics and Business,
Wrocław, Poland, 2Department of Statistics, PoznańUniversity of Economics and Business, Poznań,
Poland
*grazyna.dehnel@ue.poznan.pl
Abstract
In 2015, 193 UN members adopted the resolution “Transforming our world: the 2030
Agenda for Sustainable Development”, which set out 17 Sustainable Development Goals to
be achieved by 2030. The aim of the study is to assess progress towards meeting SDG 7
“Ensure access to affordable, reliable, sustainable and modern energy for all” by individual
EU countries in 2010–2021 and to determine their distance in relation to the target set for
2030. Eurostat monitors and assesses progress towards SDG 7 using seven indicators.
These indicators were used to create an aggregate index. In order to limit the impact of the
compensation effect on the ranking of EU countries, we applied dynamic relative taxonomy
with the geometric mean to create an aggregate measure that takes into account target val-
ues for the indicators with adjusted data. The study reveals systematic progress towards
reaching the EU’s SDG 7 in the period 2010–2021, with differences between individual EU
countries clearly decreasing. The smallest distance in relation to the target set for SDG 7
can be observed for Sweden, Denmark, Estonia, and Austria. By far the greatest progress
in period 2010–2021 has been achieved by Malta, and significant for Cyprus, Latvia, Bel-
gium, Ireland, and Poland.
1. Introduction
1.1. The characteristics of SDG 7 goal, variables, and target levels
In September 2015, 193 United Nations member states adopted the resolution “Transforming
our world: the 2030 Agenda for Sustainable Development” containing 17 Sustainable Develop-
ment Goals [1]. Goal 7: “Ensure access to affordable, reliable, sustainable and modern energy
for all” includes three core targets, which are to be achieved by 2030 [2, p. 19/35]:
7.1. Ensure universal access to affordable, reliable, and modern energy services.
7.2. Increase substantially the share of renewable energy in the global energy mix.
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OPEN ACCESS
Citation: Walesiak M, Dehnel G (2024) Progress
on SDG 7 achieved by EU countries in relation to
the target year 2030: A multidimensional indicator
analysis using dynamic relative taxonomy. PLoS
ONE 19(2): e0297856. https://doi.org/10.1371/
journal.pone.0297856
Editor: Andre
´Ramalho, FMUP: Universidade do
Porto Faculdade de Medicina, PORTUGAL
Received: July 12, 2023
Accepted: January 10, 2024
Published: February 28, 2024
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0297856
Copyright: ©2024 Walesiak, Dehnel. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The data underlying
the results presented in the study are available
from Eurostat website: “Annual EU SDG indicator

7.3. Double the global rate of improvement in energy efficiency.
7.a. Enhance international cooperation to facilitate access to clean energy research and tech-
nology, including renewable energy, energy efficiency and advanced and cleaner fossil-
fuel technology, and promote investment in energy infrastructure and clean energy
technology.
7.b. Expand infrastructure and upgrade technology for supplying modern and sustainable
energy services for all in developing countries, in particular least developed countries,
small island developing States and landlocked developing countries, in accordance with
their respective programs of support.
Progress towards reaching core targets of SDG 7 is measured by selecting an appropriate set
of indicators. The global indicator framework (United Nations, 2022) contains 248 SDG Indi-
cators for 17 SDGs. Indicators for SDG 7 included in the UN publication and 2030 targets for
OECD countries based on [3] are presented in Table 1.
The main research goal of the study described in this is to assess progress in achieving core
targets of SDG 7 by EU member states in the period 2010–2021 and determining their dis-
tances in relation to the goals set for 2030. Since 2017 Eurostat has published an “Annual EU
SDG indicator review” (https://ec.europa.eu/eurostat/web/sdi/information-on-data), which
contains an updated list of indicators for 17 SDGs (SDGs in the EU context). Seven indicators,
defined in 2017 to monitor progress on SDG 7, have not changed until now. They are system-
atically monitored and assessed by Eurostat. Only three indicators from this list are consistent
or partially consistent with UN Global list indicators (see Table 2).
The following 2030 targets for the seven indicators used to monitor progress on SDG 7
achieved by UE member states have been adopted in the study (see the last column in Table 2):
a. In the case of „Primary energy consumption”, „Final energy consumption” and “Share of
renewable energy in gross final energy consumption”, targets for 2030 are the same as those
set by the European Commission [7]. Since indicators of energy consumption (primary,
final) are expressed in absolute terms (million tonnes of oil equivalent–mtoe), they cannot
be used to compare different EU countries because these quantities are not directly compa-
rable. For this reason, absolute values were replaced with indices representing changes in
primary and final energy consumption in relation to values recorded in 2005 (Index
2005 = 100);
b. If no target value can be found in the 2030 Agenda, it is based on “best performance” among
EU countries for 2015 year. This is defined as:
Table 1. Indicators and targets for SDG 7.
Code Indicators (UN Global list) Unit 2030 targets for OECD
countries
7.1.1 Proportion of population with access to electricity % 100
7.1.2 Proportion of population with primary reliance on clean fuels and technology % 95
7.2.1 Renewable energy share in the total energy consumption % 58.62
7.3.1 Energy intensity measured in terms of primary energy and GDP Megajoules per
US $
NA
7.a.1 International financial flows to developing countries in support of clean energy research and development and
renewable energy production, including in hybrid systems
current US $ NA
7.b.1 Installed renewable energy-generating capacity in developing countries watts per capita NA
Source: Authors’ compilation based on [2] and [3, p. 49].
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review” (https://ec.europa.eu/eurostat/web/sdi/
information-on-data).
Funding: The possibility of funding for publication
within the ’Regional Initiative for Excellence
programme of the Minister of Education and
Science of Poland, years 2019-2023” (grant no.
004/RID/2018/19) ended at the end of 2023.
Currently, the publication can be funded by two
universities: Uniwersytet Ekonomiczny w Poznaniu
(50%) and Uniwersytet Ekonomiczny we
Wrocławiu (50%). The funder does not play any
role in the study design, data collection and
analysis, decision to publish, or preparation of the
manuscript.
Competing interests: The authors have declared
that no competing interests exist.

• the 90th percentile for stimulants–i.e., the level attained in 2015 by the top 10% of EU27
countries. A similar approach was proposed by OECD [3, p. 23] taking into account data
for OECD countries;
• the 10th percentile for destimulants–i.e., the level attained in 2015 by the top 10% of EU27
countries.
The analysis is based on average data for the European Union and for 27 EU countries sepa-
rately from the period 2010–2021. The study includes Croatia, which joined the EU in 2013
and excluded the UK, which left the EU in January 2020. All statistical data come from
Eurostat.
Of the seven indicators listed in Table 2, the biggest number of countries achieved in 2021
the EU target for x7 (Share of population unable to keep home adequately warm). The target
was set at 1.88%, and the group of countries that managed to achieve this target includes Swe-
den, Finland, Slovenia, and Austria. The x7 indicator is strongly associated with low levels of
income in combination with high expenditure on energy and poor building efficiency stan-
dards. Starting from 2012, access to affordable energy for all European Union countries sys-
tematically improved until 2019, just before the outbreak of the pandemic. Countries of
Northern Europe and most of those in Western Europe had the lowest shares of people with-
out affordable access to heating, in contrast to countries of Southern and South-Eastern
Europe, which suffered from the lack of adequate heating. This was mainly due to the poor
energy efficiency of buildings, the lack of adequate heating systems and insulation, which
caused higher heating costs. In addition, the generally lower income levels in these regions
affect housing standards and the ability to pay for fuel [8].
In the case of the x5 indicator (Share of renewable energy in gross final energy consump-
tion), related to energy supply, the target for 2030 (40%) was achieved by Sweden, Finland,
and Latvia. In 2021 the share of renewable energy sources (RES) in gross final energy con-
sumption was equal to 62.6% in Sweden, 43.1% in Finland and 42.1% in Latvia. Such results
were achieved by relying on hydropower and solid biofuels, which are regarded as more
environmentally friendly compared to conventional energy sources. In general, the use of
Table 2. Indicators and targets for SDG 7 for EU countries.
Headline indicators Code in the UN global list Variable type Symbol used in study Unit EU-level
targets for
2030
Primary energy consumption NA D – mtoe 1023 (!)
y1 2005 = 100 68.30
Final energy consumption NA D – mtoe 787 (!)
y2 2005 = 100 75.58
Final energy consumption in households per capita NA D y3 kgoe 319.8 (!!)
Energy productivity 7.3.1 (s) S y4 Euro per kgoe 10.44 (!!)
Share of renewable energy in gross final energy consumption 7.2.1 (i) S y5 % 40 (!)
Energy import dependency NA D y6 % 24.58 (!!)
Share of population unable to keep home adequately warm 7.1.1 (a) D y7 % 1.88 (!!)
(i)–identical, (s)–similar, (a)–alternative indicator.
S–stimulants (where higher values are more preferred), D–destimulants (where lower values are more preferred).
(!)–level set by the European Commission; (!!)–the level attained in 2015 by the top 10% of EU27 countries.
Source: Authors’ compilation based on data from Eurostat [4–6] and the European Commission [7].
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renewable energy in the EU can be said to be steadily growing. This growth is largely due to
the use of wind and solar energy. However, the variation in the share of renewables across
Member States is still very large. This can be attributed, among other things, to differences in
the availability of RES as well as the degree of available financial and regulatory support. Com-
pared to 2005, the share of renewable energy sources in 2021 more than doubled, from 10.2%
to 21.8% of gross final energy consumption. This increase was driven by reductions in invest-
ment costs, the use of more efficient technologies, supply chain improvements and support
schemes for renewables [7]. It is worth noting that the 9.5% increase in the share of renewable
energy in gross final energy consumption in 2019–2021 is also the result of a decline in final
energy consumption during the COVID-19 pandemic, which means that this change was tem-
porary. Once final energy consumption returns to pre-pandemic levels, the share of RES in
gross final energy consumption will most likely fall [5].
The 2030 target for the x6 indicator (Energy import dependency), which is related to energy
supply, was achieved in 2021 by Sweden and Estonia. In 2020, all EU countries were net
importers of energy, with 16 importing more than half of their total energy consumption from
other countries (EU and non-EU countries). Compared to 2005, fuel imports from non-EU
countries slightly decreased from 57.8% to 57.5% of gross available energy. In 2020, the main
non-EU energy suppliers included Russia (43.6% of gas, 28.9% of petroleum products and
53.7% of solid fuel imports), Norway and the United Kingdom (25.4% of gas imports and
16.5% of oil imports), North America (18.8% of solid fuel imports). Imports of fossil fuels still
cover more than half of the EU’s energy demand despite the continuous growth of renewable
energy sources. The stagnation is due to two factors. First, the EU has reduced energy con-
sumption and increased the use of domestic renewables. Second, primary production of fossil
fuels has declined due to the depletion or uneconomical exploitation of domestic sources,
especially in the case of natural gas [5].
In 2021, Spain, Malta and Portugal achieved the 2030 target for the x3 indicator (final
energy consumption in households per capita), which is related to energy consumption.
Households account for about a quarter of final energy consumption. From 2010 to 2015,
household energy consumption in EU countries decreased by 12.7%, and remained at more or
less the same level for the next five years: in 2020, it was only about 0.5% higher than in 2015.
It was not until 2021, that the indicator had increased by 5.6% compared to 2020. Thanks to
improvements in energy efficiency, particularly in space heating, it was possible to balance the
population growth and increases in the number and size of dwellings [5]. In the period from
2010 to 2021, energy consumption per capita in the EU decreased by 7.3%. This decline was
accompanied by a slight downward trend in total household energy consumption offsetting a
1.3% increase in the population (https://ec.europa.eu/eurostat/databrowser/view/demo_gind/
default/table?lang=en).
In 2021, Denmark, Ireland and Luxembourg achieved the 2030 target for the x4 indicator
(energy productivity), related to energy consumption. Since 2000, the EU continuously
increased its energy productivity, reaching EUR 8.55 per kilograms of oil equivalent (kgoe) in
2021. All Member States contributed to this positive trend in an attempt to reach the 2030 tar-
get of 10.44 per kgoe.
As regards the last two indicators, x1 (primary energy consumption) and x2 (final energy
consumption), related to energy consumption, in 2021 the target set for 2030 was only
achieved by Greece. From 2005, primary and final energy consumption in the EU was gener-
ally on a downward trend, which was due to various factors, including a structural shift
towards less energy-intensive industries and improvements in end-use efficiency in the resi-
dential sector. Measures taken during the COVID-19 pandemic and the related restrictions on
public life and economic activity resulted in a significant decrease in energy consumption by
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over 8% in 2020 compared to 2019. Other factors that in the long term contribute to falling
energy consumption include improvements in energy efficiency and the growing use of energy
from renewable sources. Despite the fact that effects of the pandemic can still be felt in the
economy, the recovery observed in 2021 certainly led to higher energy consumption, prevent-
ing EU countries from achieving the 2030 target. Therefore, innovation and additional
improvements in energy efficiency are still required.
1.2. The purpose of the article
Both the European Union as a whole and each of its member states analysed in the study vary
in their progress on SDG 7. To adequately describe this problem, it is necessary to use different
research approaches. This is also confirmed by the existing literature. Three methodological
approaches can be distinguished in this regard:
1. The aggregate measure approach, in which one measures the overall progress on all indica-
tors achieved by individual countries. This approach can be used to create a ranking of
countries according to the value of the aggregate measure of all indicators (diagnostic fea-
tures) representing progress on SDG 7. Because this approach does not take into account
the target values these indicators should achieve by 2030, it cannot be used to determine the
distance of individual EU countries in relation to targets set for 2030. This approach is
applied, among others, by [9–13].
2. The aggregate measure approach which takes into account the target values for the indica-
tors (see Table 2) without adjusting the data. National values of individual indicators are
related to their target values. This approach can also be used to create a ranking of countries
according to the value of the aggregate measure of all indicators (diagnostic features) repre-
senting progress on SDG 7. In addition, it is also possible to calculate how far individual EU
countries are from achieving the goals set for 2030 (EU-level targets for 2030). This kind of
approach was presented, for example, in the work by [3,14].
3. The aggregate measure approach which takes into account the target values for the indica-
tors (see Table 2) with adjusted data. In addition to assessing progress on SDG 7 achieved
by individual EU countries and their distance in relation to the goals set for 2030, the
approach relies on data corrected as follows: when a given country exceeds targets shown in
Table 2 (EU-level targets for 2030), these higher national values for stimulants and lower
national values for destimulants are not included in the analysis but are replaced by target
values set for the entire European Union. This approach was presented, for example, in the
work by [15,16] to assess the implementation of the Europe 2020 Strategy.
The purpose of the following study is to assess progress made by individual EU countries
in 2010–2021 towards achieving SDG 7 and to determine their distance in relation to targets
set for 2030. To overcome the problems with the first and second approach, we propose an
innovative third approach, which accounts for the target values of the indicators and uses
adjusted data. This approach has not been used in this type of research so far and the use of
the geometric mean in the dynamic relative taxonomy method has made it possible to reduce
the impact of the negative compensation effect on the values of aggregate measures. The
results of the study have important implications for individual EU countries. In addition to
showing progress towards the SDG 7 target, they also represent the distance that separates
each country from achieving the 2030 target. The proposed methodology can also be used to
assess progress made by EU countries in the implementation of other SDGs of the 2030
Agenda.
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Composite indicators play an important role in the analysis of socio-economic phenomena.
A number of different approaches to constructing composite indicators have been proposed in
the literature [17, p. 3] depending on the degree of compensation: compensatory, partially
compensatory, and non-compensatory. Studies on multi-criteria decision-making uses of
non-compensatory methods, see [18–20]. An overview of compensatory and partially compen-
satory aggregate measures applied to different types of data can be found in [21,22].
Under the aggregate measure approach, positive and negative deviations from the target
values of individual indicators can accumulate. As a result, countries which have exceeded tar-
gets for some indicators but have failed to achieve those set for the majority of other indicators
can be classified as countries that have made much progress on SDG 7. In other words, the
main weakness of methods based on aggregate measures is their compensatory nature, which
is most strongly manifested in the first and the second approach.
In order to limit the impact of this compensation effect on the ranking of EU countries, the
following were included in the methodology of relative taxonomy (see section 3):
• the third aggregate measure approach which takes into account the 2030 target values for the
indicators (see Table 2) with data adjustment,
• in step 6 and 7 of the procedure, the geometric mean was used in the construction of the
aggregate measure.
2. Literature review
In recent years, energy security has become a basic element of the economic security system.
Recent events have clearly shown that energy policy and its monitoring play a key role in
ensuring stable economic development [23,24]. Without an energy policy, it is difficult to
guarantee the security of energy supply to households and other consumers from all economic
sectors. The Sustainable Development Goals (SDGs) set out by the UN in 2015 are particularly
relevant in the present political and economic context [1,25]. The 17 goals, including 169 tar-
gets, indicate global priorities which comprehensively define sustainable development in terms
of economic, environmental, and social aspects [26]. SDGs are a revised version of the Millen-
nium Development Goals (MDGs) formulated for the period 2000–2015. The operational
period of the MDGs revealed that these goals did not focus enough on some issues related to
sustainable development, such as energy [27,28]. Although access to sustainable energy ser-
vices is one of the fundamental conditions for sustainable development, energy was only
included as a key theme in Agenda 2030 [1].
SDG 7 is to “ensure access to affordable, reliable, sustainable, and modern energy for all”.
This goal includes five targets to be achieved by 2030 (see section 1.1; [29]) and is closely
related to the targets of eight other goals. Many studies on the quality of interlinkages between
SDGs have confirmed that the pursuit of certain targets generates effects that have an impact
on other targets [25,30]. Researchers highlighted positive and negative effects [31–33]. A num-
ber of studies have been undertaken to identify correlations and map relationships between
the SDGs [28,34–40]. Some have focused only on a specific target area such as energy, water
or food and explored its links with other SDGs [41–43]. SDG 7 has been found to be very
strongly correlated with SDG 11 (Sustainable Cities and Communities), SDG 12 (Responsible
Consumption and Production) and SDG 13 (Climate Action).
While studies on interlinkages between SDGs are helpful in evaluation processes, they do
not provide complete information on whether, and to what extent, the SDGs are actually
achieved. To maximize progress on SDGs, it is necessary to assess the importance of the indi-
vidual goals by identifying problems and barriers to achieving them and areas that require
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attention in the future [44]. Undoubtedly, one of the key questions that needs to be answered
is how to measure countries’ performance and evaluate their actions aimed at achieving SDGs.
One of the priorities indicated by [45] for how the scientific community should participate in
this process was to design a way for tracking and assessing progress on each SDG. Given the
wide scope of the 17 goals, different scales (national, regional, global), multi-issue coverage
and ambiguous language, such assessment requires appropriately adjusted statistical tools [10,
46,47]. Even at the level of each country, the position in a particular hierarchy is often relative
and depends on initial assumptions, the selected method, indicators used to create the ranking.
Additionally, some goals (e.g., SDG 7) are more sensitive to methodological choices than oth-
ers (e.g., SDG 16) [14,48,49].
Although the measurement of progress on the SDGs has been the topic of debate among
researchers since the adoption of the 2030 Agenda, the literature concerning possible
approaches is still limited [14,46,47,50]. Since the SDGs and targets cannot be measured
directly, they have been mostly operationalized by a number of indicators [51,52]. 231 indica-
tors were defined by the UN Statistical Commission to monitor and assess global sustainability
[53,54]. The set of SDG indicators defined by the EU comprises around 100 indicators. 31
indicators are used to monitor more than one goal. The indicators have been selected to take
into account the EU context and perspective, availability, country coverage, data freshness and
quality [5,55].
Some authors have proposed approaches focusing on individual indicators. For example,
[56] conducted a detailed assessment of the indicator 6.4.2 (i.e., Level of water stress). Firoiu
et al. [57] used methods of dynamic analysis and prediction tools to assess progress on the
SDGs achieved by Romania. Bidarbakhtnia [58] analysed three methods used by, respectively,
OECD, the Sustainable Development Solutions Network (SDSN) and the United Nations Eco-
nomic and Social Commission for Asia and the Pacific (UNESCAP). All of them measure the
distance of each indicator from the 2030 target. The study conducted by UNESCAP also shows
progress on each indicator since 2000 in proportion to total progress needed for the region to
reach the 2030 target. Giupponi et al. [59] presented an approach for the spatial assessment of
Water Use Efficiency (SDG indicator 6.4.1). Moyer and Hedden [60] used an integrated assess-
ment model to evaluate progress toward target values on nine indicators related to six SDGs
related to human development.
The use of individual indicators without an accurate and scientific follow up on their opera-
tionalization is difficult because of comparability issues, reporting requirements and decision-
making processes [61–63]. Since there are studies indicating that relying exclusively on the
global set of individual indicators leads to questionable results, some researchers argue that
progress on SDGs should be additionally measured by means of composite indicators (e.g. [52,
64]. It is worth adding that in the case of many indicators, it is difficult to conduct analysis and
evaluate the results for a large number of countries without taking into account some form of
index aggregation even if a synthetic measure is hard to construct and causes some loss of
information. Despite limitations, the literature describes some methods to assess progress on
the SDGs, which involve composite indices and aggregated dashboards. Schmidt-Traub et al.
[65] introduced the SDG Index, which synthesizes country-level data for all 17 SDGs taking
into account the upper and lower bounds based on best and worst performing countries. The
SDG Index can be used to estimate the distance that separates each country from achieving the
SDGs. This approach is further developed in the article by [50], who propose a novel approach
combining well-known methods to produce a comprehensive assessment of Australia’s prog-
ress on all SDGs. An attempt to use a composite indicator called ‘SDG achievement index’
(SDG-AI) to measure the SDGs, covering six dimensions of sustainable development (Health,
Education, Services, Employment, Equality and Environment) can be found in [66,67]. The
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SDG-AI can be used in two ways: to highlight differences between countries and to evaluate
the contribution of different dimensions to the final result. Dhaoui [66] assessed progress on
SDGs in MENA (Middle East and North Africa) countries. Rocchi et al. [67] modified the
approach proposed by [66] to make it suitable for the EU context. A methodology for assessing
SDGs on the aggregate level without losing information on single goals was proposed by [68].
Their study proposes several composite indices to assess the performance of EU member
states. The two-stage approach involving Principal Component Analysis was applied to con-
struct goal-based indices, pillar-based indices, and the overall SDG index. The indices were
used to determine where a country currently stands on each of the indicators considered in
the analysis, but they cannot be used to estimate the rate at which a country is moving towards
achieving the SDGs.
Miola and Schiltz [14] compared three main approaches to measuring progress on SDGs at
country level: the SDG Index, the OECD’s distance measure, and progress measures based on
Eurostat’s report. They identified crucial weaknesses in these existing methods and their sensi-
tivity to particular choices made along the way: depending on which indicators and
approaches are applied, countries can receive substantially different relative evaluations. The
authors identify the main methodological challenges that should be addressed when develop-
ing analytical tools to evaluate progress on SDGs.
Cavalli et al. [69] propose a model-based approach to evaluating the sustainability of the EU
regional operational programme (ROP) in terms of SDGs, which is based on a synthetic sus-
tainability index representing the part of ROP resources that contribute to the 2030 Agenda in
relation to the total ROP resources. The usefulness of composite indicators has been demon-
strated by [10], who compared the utility of the Multiple Reference Point Weak-Strong Com-
posite Indicators (MRP-WSCI) and its partially compensatory version (MRP-PCI) for
assessing the sustainability of EU countries according to the framework of the 2030 Agenda.
The approach was used to produce composite indicators with different degrees of compensa-
tion, which constituted the basis for a country ranking.
Different variants of the methodology have already been used to build composite sustain-
ability indicators in relation to SDG 7. Vavrek and Chovancova
´[13] assessed EU countries
using a set of eight energy-related indicators. Indicator weights were determined using the
coefficient of variation from the TOPSIS method. The authors assessed whether a country’s
performance resulted from a single indicator regarded as typical for the positively or negatively
evaluated countries, or from a combination of indicators reflecting general energy issues. Cho-
vancova
´and Vavrek [11] presented a continuation of their research, in which they identified
the best and the worst performing EU countries taking into account a set of indicators.
Cheba and Bąk [12] proposed a synthetic measure based on the TOPSIS method to evaluate
the relationship between SDG 7 and environmental production efficiency, which is a key com-
ponent of the idea of green growth. They found considerable discrepancies between develop-
ment paths of different EU countries despite their efforts to equalize the level of development
in this area.
Dmytro
´w et al. [9] proposed an approach to assessing the EU’s progress on SDG 7 at the
national level using a synthetic measure obtained by applying the method of complex propor-
tional assessment (COPRAS). They produced a ranking of countries in terms of their progress
on SDG 7 by applying the Dynamic Time Warping method. Hierarchical clustering was then
used to determine homogeneous groups of countries.
The rate of progress on SDG 7 achieved by the EU countries was also analysed by [49], who
applied hierarchical cluster analysis to identify hidden associative structures. They also ranked
EU countries in relation to the goals of the 2030 and 2050 Agenda. The ranking was used to
identify clusters of countries sharing similar characteristics regarding their performance on
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Progress on SDG 7 in relation to 2030: A multidimensional indicator analysis using dynamic relative taxonomy
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SDG 7. Cluster analysis was also used by [70,71] to assess energy-related indicators of SDGs.
In both studies, countries’ performance was analysed and compared taking into account their
own conditions and progress on SDG 7 and energy transformation processes taking place in
EU member states.
While the importance of assessing progress towards achieving sustainable development
goals is recognized, the number of articles proposing new methods, especially those represent-
ing a dynamic approach, is still rather limited. In the case of the SDGs, given the large number
of indicators, it is reasonable to opt for composite indicators [10]. Composite indicators are
associated with different degrees of compensation. One can distinguish fully compensatory,
partially compensatory, or non-compensatory indicators (see section 1.2). The recommended
approach is to assess sustainability in relation to a threshold or a target [10,72]. According to
[73], ‘a given indicator does not say anything about sustainability, unless a reference value
such as thresholds is given to it’. Non-compensatory methods can be used to identify weak
points in the global assessment of an object and thus possible areas for improvement. For these
reasons, we propose a novel non-compensatory approach based on principles of dynamic rela-
tive taxonomy applied to the procedure of constructing an aggregate measure. It can account
for reference levels of each indicator and be used to rank countries accordingly showing the
varying distance that separates individual EU countries from the targets set out in Agenda
2030. An additional advantage of this method is that the dynamic approach indicates not only
relations between the objects in specific periods, but also changes in the phenomenon of inter-
est that took place between objects over the entire reference period. Thus, it can be used to
track cross-sectional and longitudinal changes.
3. Using dynamic relative taxonomy to construct a composite index
The classic approach to relative taxonomy was proposed by [74]. Lira [75] developed its posi-
tional version. Both approaches are static which means that relativization given by formula (5)
is conducted separately for each single year of period analysed in the study. Static relative tax-
onomy has been applied, among others, by [76–78].
The following study involves the use of dynamic relative taxonomy, described in [21]. In
the dynamic version, values of the j-th variable in formula (5) is relativised based on a matrix
of data from all periods. This approach was extended by [22] to include robust measures of
central tendency. Geometric mean was used in steps 6 and 7 of the dynamic relative taxonomy
procedure (c.f. [16]):
1. Observations of mvariables for nobjects and T+ 1 periods (2010–2021 and the year 2030,
for which target values are set) are combined into one data matrix:
yijt
h inT∗m¼
y111 y121   y1m1
.
.
..
.
.  .
.
.
yn11 yn21   ynm1
     
y11Ty12T  y1mT
.
.
..
.
.  .
.
.
yn1Tyn2T  ynmT
y11T∗y12T∗  y1mT∗
.
.
..
.
.  .
.
.
yn1T∗yn2T∗  ynmT∗
2
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
4
3
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
5
;ð1Þ
where: i= 1, . . .,n—object number (n= 28: the EU as a whole and 27 EU countries),
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Progress on SDG 7 in relation to 2030: A multidimensional indicator analysis using dynamic relative taxonomy
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j= 1, . . .,m—variable number (m= 7: indicators for SDG 7 –see Table 2),
t= 1, . . .,T,T*—where t= 1, . . .,Trepresents years 2010–2021, and t=T*the year 2030,
for which target values are set–see Table 2.
2. Stimulants and destimulants are identified in the set of variables (both terms were intro-
duced by [79]). Instead of describing variables as stimulants and destimulants, [80] use the
terms ‘positive polarity’ (increasing values of the index correspond to an improvement in
the phenomenon of interest) and ‘negative polarity’ (increasing values of the index corre-
spond to a deterioration in the phenomenon of interest). Hwang and Yoon [81, p. 130] use
the concepts of ‘benefit’ (larger values of a variable are preferred) and ‘cost’ (larger values of
a variable are less preferred).
3. Observations on each variable are replaced with target values if the following conditions are
satisfied (data adjustment):
xijt ¼yijT∗for yijt >yijT∗
yijt for yijt yijT∗
;for stimulants
(ð2Þ
xD
ijt ¼yijT∗for yijt <yijT∗
yijt for yijt yijT∗
;for destimulants
(ð3Þ
y
ijT *
—target values of SDG 7 indicators set for 2030.
For each variable, values higher (for stimulants) or lower (for destimulants) than the targets
are replaced with the values of EU-level targets (target values of SDG 7 indicators set for
2030). This operation can be called one-sided Winsorization of the data (see e.g., [82]).
4. Destimulants Dare converted into stimulants using the ratio transformation:
xijt ¼xD
ijt
 1ð4Þ
5. Values of each j-th variable are relativized according to the following nT*×nT*matrix:
1 . . . xnjT∗=x1j1
.
.
..
.
..
.
.
x1j1=xnj1. . . xnjT∗=xnj1
. . . . . . . . .
x1j1=x1jT . . . xnjT∗=x1jT
.
.
..
.
..
.
.
x1j1=xnjT . . . xnjT ∗=xnjT
x1j1=x1jT∗. . . xnjT∗=x1jT∗
.
.
..
.
..
.
.
x1j1=xnjT∗. . . 1
2
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
4
3
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
5
ð5Þ
As a result of relativization, variable values are dimensionless. When the numerator is not
greater than the denominator, the relativization formula produces values included in the
interval (0; 1], otherwise, values are included in the interval (1; 1).
6. The average similarity of a given relativized observation with respect to other relativized
observations of the j-th variable for each column of matrix (5) is calculated using the
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geometric mean:
zijt
h i¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
QT∗
t¼1Qn
i¼1
x111
xi1t
n:T
r  ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
QT∗
t¼1Qn
i¼1
x1m1
ximt
n:T
r
.
.
..
.
..
.
.
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
QT∗
t¼1Qn
i¼1
xn1T∗
xi1t
n:T
r  ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
QT∗
t¼1Qn
i¼1
xnmT∗
ximt
n:T
r
2
6
6
6
6
6
6
6
4
3
7
7
7
7
7
7
7
5
ð6Þ
The [z
ijt
] matrix is equivalent to a normalised matrix in multivariate statistical analysis.
7. Values of the composite indicator SM
it
are calculated according to the following formula:
SMit ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Ym
j¼1
1
zijt
m
sð7Þ
Values of the composite indicator SM
it
given by (7) can be greater or smaller than 1. The
smaller the value of SM
it
is, the better the position of object irelative to other objects in a
time interval from t= 1 to t=T*. Unlike the static approach, the dynamic approach shows
not only relations between the objects in specific periods, but also changes in the phenome-
non of interest that took place between objects over the entire reference period.
The method of dynamic relative taxonomy is characterised by the following properties:
• it can only be used for variables measured on the ratio scale (their values are positive real
numbers). In other words, it cannot be applied to interval-valued data. This is not a serious
limitation since the majority of variables describing economic phenomena are measured on
the ratio scale;
• composite indicators SM
it
do not have an upper bound, which does not disqualify them as
such;
• the data matric can have missing values (NA), which are excluded in the calculation of the
composite indicators SM
it
;
• the disadvantage of the static approach when calculating the average similarity of a given rel-
ativized observation in relation to other relativized observations has been eliminated (more
details can be found in [21].
4. Results in relation to EU-level targets for 2030
In the first step we analysed changes in SM
it
representing progress on SDG 7 in the EU coun-
tries. Table 3 shows values of SM
it
representing progress on SDG 7 achieved by EU countries
in 2010–2021. The lower the value of SM
it
, the better the position of object irelative to other
objects in each year and over the entire reference period. The dynamic approach reveals not
only relationships between objects in different years but also changes that took place in the
level of a given indicator over the entire reference period.
The last three columns in Table 3 show, respectively, the increment in the composite indi-
cator between 2030 and 2010 (Δ=SM
i2030
−SM
i2010
), the increment in the composite indicator
between 2021and 2010 (Δ1 = SM
i2021
−SM
i2010
) and the distance of each EU country in rela-
tion to the target set for 2030 (Δ2 = SM
i2030
−SM
i2021
).
Countries furthest away from the target at the start of the reference period included Malta
(SM
i2010
= 2.04), Cyprus (SM
i2010
= 1.59), Bulgaria (SM
i2010
= 1.52), Belgium (SM
i2010
= 1.40),
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Lithuania (SM
i2010
= 1.36), Poland (SM
i2010
= 1.36). Countries that were closest to the targets
in 2010 included three Scandinavian–Denmark (SM
i2010
= 0.77), Sweden (SM
i2010
= 0.80), Fin-
land (SM
i2010
= 0.92) and Austria (SM
i2010
= 0.94). Between 2010 and 2021 values of SM
it
fell
on average by nearly 2% each year, which means that the distance of all countries from the tar-
gets kept decreasing. The biggest average annual change (decline) during the reference period
was observed for Malta (4.5%), Latvia (2.8%), Cyprus (2.7%) and Ireland (2.6%). The smallest
average annual change (decline) throughout the reference period was observed for Denmark
(0.4%), Finland (1.1%) and Sweden (1.5%).
The distance of the European Union as a whole in 2021 in relation to the target set for 2030
is Δ1 = –0.4207. Fourteen EU countries were closer to the target in 2021 (see Fig 1). Because of
Table 3. SM
it
values in relation to the EU-level target for 2030, representing progress on SDG 7 achieved from 2010 to 2021, and sorted by values observed in 2021.
No Country Values of the aggregate measure SM
it
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Δ Δ1Δ2
1 Sweden 0.7967 0.7619 0.7448 0.7408 0.7243 0.7087 0.7608 0.7162 0.7304 0.7028 0.7209 0.6775 –0.2481 –0.1192 –0.1289
2 Denmark 0.7661 0.7544 0.7443 0.7827 0.7271 0.7605 0.7289 0.7192 0.7260 0.7537 0.7726 0.7371 –0.2175 –0.0290 –0.1885
3 Estonia 0.9878 0.9439 0.9754 0.9461 0.8596 0.8148 0.9100 0.9080 0.8695 0.8225 0.8146 0.7578 –0.4392 –0.2300 –0.2092
4 Austria 0.9360 0.8791 0.8837 0.8673 0.8647 0.8437 0.8594 0.8535 0.8028 0.8221 0.7751 0.7869 –0.3874 –0.1491 –0.2383
5 Slovenia 1.0198 1.0313 1.0381 0.9729 0.9474 0.9770 0.9637 0.9422 0.9069 0.8445 0.8184 0.7919 –0.4712 –0.2279 –0.2433
6 Finland 0.9174 0.8922 0.8701 0.8540 0.8517 0.8310 0.8417 0.8471 0.8471 0.8313 0.8013 0.8163 –0.3688 –0.1011 –0.2677
7 Netherlands 1.0763 0.9734 1.0043 1.0128 0.9486 1.0427 1.0117 0.9956 0.9767 0.9923 0.8809 0.8980 –0.5277 –0.1783 –0.3494
8 Latvia 1.2351 1.2533 1.2270 1.1998 1.0983 1.0879 1.0340 1.0230 0.9965 0.9924 0.9253 0.9013 –0.6865 –0.3338 –0.3527
9 Germany 1.1174 1.0691 1.0527 1.0897 1.0209 0.9949 0.9895 0.9658 0.9157 0.9048 0.9990 0.9069 –0.5688 –0.2105 –0.3583
10 France 1.0700 1.0675 1.0520 1.0686 0.9876 0.9866 0.9721 0.9627 0.9449 0.9612 0.9077 0.9266 –0.5214 –0.1434 –0.3780
11 Czechia 1.1068 1.1196 1.0851 1.0767 1.0416 1.0252 0.9946 0.9914 0.9601 0.9578 0.9014 0.9367 –0.5582 –0.1701 –0.3881
12 Portugal 1.1676 1.1381 1.1201 1.1055 1.0768 1.0826 1.0516 1.0618 1.0457 1.0307 0.9469 0.9413 –0.6190 –0.2263 –0.3927
13 Croatia 1.0719 1.0864 1.0592 1.0295 0.9812 1.0305 1.0145 1.0120 0.9967 0.9781 0.9306 0.9524 –0.5233 –0.1195 –0.4038
14 Ireland 1.2918 1.2206 1.2210 1.2445 1.1764 1.1966 1.1020 1.0234 1.0360 1.0357 0.9261 0.9659 –0.7432 –0.3259 –0.4173
15 EU 1.1571 1.1282 1.1257 1.1084 1.0564 1.0601 1.0511 1.0386 1.0198 0.9965 0.9584 0.9693 –0.6085 –0.1878 –0.4207
16 Romania 1.0715 1.0371 1.0214 0.9742 0.9370 0.9457 0.9451 0.9387 0.9160 0.9305 0.9294 0.9849 –0.5229 –0.0866 –0.4363
17 Italy 1.1949 1.2326 1.2278 1.1715 1.1069 1.1369 1.1149 1.1031 1.0861 1.0417 0.9467 0.9870 –0.6463 –0.2079 –0.4384
18 Luxembourg 1.2730 1.2470 1.2161 1.1779 1.1119 1.1125 1.0939 1.0816 1.0393 1.0835 1.0308 0.9885 –0.7244 –0.2845 –0.4399
19 Spain 1.0418 1.0092 1.0377 0.9783 1.0139 1.0265 0.9950 0.9795 0.9993 0.9559 0.9294 0.9926 –0.4932 –0.0492 –0.4440
20 Greece 1.2219 1.2669 1.2691 1.1608 1.1836 1.2351 1.2128 1.1738 1.1113 1.0739 1.0472 1.0315 –0.6733 –0.1904 –0.4829
21 Poland 1.3553 1.3026 1.2612 1.1900 1.1316 1.1047 1.1305 1.1614 1.1405 1.0790 1.0124 1.0342 –0.8067 –0.3211 –0.4856
22 Slovakia 1.1522 1.0905 1.0949 1.1059 1.0590 1.0473 1.0369 1.0554 1.0486 1.1207 1.0223 1.0581 –0.6036 –0.0941 –0.5095
23 Belgium 1.4024 1.3483 1.3030 1.2975 1.2070 1.2320 1.2069 1.2249 1.2055 1.1344 1.0649 1.0735 –0.8538 –0.3289 –0.5249
24 Hungary 1.2931 1.2673 1.2534 1.2232 1.1959 1.1882 1.1974 1.1927 1.1541 1.1523 1.0677 1.1215 –0.7445 –0.1716 –0.5729
25 Cyprus 1.5876 1.5589 1.5294 1.4055 1.3668 1.4245 1.3962 1.3885 1.3077 1.3044 1.1956 1.1720 –1.0390 –0.4156 –0.6234
26 Malta 2.0382 1.9016 1.8416 1.7563 1.6676 1.5178 1.3306 1.3475 1.3660 1.3791 1.2420 1.2217 –1.4896 –0.8165 –0.6731
27 Lithuania 1.3615 1.4123 1.3874 1.3060 1.2677 1.2800 1.2989 1.3048 1.3328 1.3014 1.2494 1.2733 –0.8129 –0.0882 –0.7247
28 Bulgaria 1.5183 1.4553 1.4140 1.3405 1.3329 1.3513 1.3470 1.3560 1.2995 1.2671 1.2306 1.2827 –0.9697 –0.2356 –0.7341
mean27 1.1879 1.1600 1.1457 1.1140 1.0699 1.0735 1.0571 1.0493 1.0282 1.0168 0.9663 0.9710
sd27 0.2548 0.2436 0.2307 0.2069 0.1981 0.1909 0.1677 0.1741 0.1695 0.1671 0.1421 0.1535
range27 1.2721 1.1472 1.0973 1.0155 0.9433 0.8091 0.6673 0.6723 0.6400 0.6763 0.5285 0.6052
SM
i2030
= 0.5486 –target value of the aggregate measure (EU 2030) calculated on the basis of the indicators used to measure progress on SDG; Δ=SM
i2030
−SM
i2010
;Δ1
=SM
i2021
−SM
i2010
;Δ2 = SM
i2030
−SM
i2021
;Δ=Δ1 + Δ2; mean27, sd27, and range27 –arithmetic mean, standard deviation and range for the 27 EU countries.
Source: Authors’ calculations using R program [83].
https://doi.org/10.1371/journal.pone.0297856.t003
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changes in the values of the seven indicators that took place over 11 years, the group of coun-
tries that came closest to the 2030 target in 2021, apart from the three Scandinavian countries,
includes Estonia, Austria, and Slovenia. Despite big increases in the values of the composite
indicator, the largest distance in relation to the SDG 7 target in 2021 can be observed for Bul-
garia, Lithuania, Malta, and Cyprus.
Fig 1 provides a graphical representation of SM
it
values in relation to the EU-level target for
2030, representing progress on SDG 7 achieved from 2010 to 2021, and sorted by values
observed in 2021. The horizontal line represents the EU-level SDG 7 target value of the com-
posite indicator SM
i2030
= 0.5486.
The biggest progress (improvement in the value of SM
it
between 2010 and 2021) on
SDG 7 in the period 2010–2021 was made by Malta (Δ1 = −0.8165), followed by Cyprus
(Δ1 = –0.4156), Latvia (Δ1 = –0.3338), Belgium (Δ1 = – 0.3289), Ireland (Δ1 = –0.3259) and
Fig 1. A graphical representation of SM
it
values in relation to the EU-level target for 2030, representing progress on SDG 7 achieved from 2010 to 2021,
and sorted by values observed in 2021. Source: Chart created using R program [83].
https://doi.org/10.1371/journal.pone.0297856.g001
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Poland (Δ1 = –0.3211). The European Union as a whole is making progress towards meet-
ing the SDG 7 goal. The improvement in the value of the aggregate measurer SM
it
in the
period 2010–2021 was Δ1 = −0.1878. In the Eurostat report [84], progress towards the SDG
7 goal was described as moderately favorable.
Compared to 2010, the biggest climb in the ranking for 2021 can be observed for Latvia (by
11 places, from 19th to 8th), Ireland (by 7 places, from 21st to 14th), Netherlands (by 4 places,
from 11th to 7th), Germany (by 4 places, from 13th to 9th), Portugal (by 4 places, from 16th to
12th). Because of small improvement in the value of the composite indicator for Spain (Δ1 =
−0.0492), Romania (Δ1 = −0.0866) and Slovakia (Δ1 = −0.0941), they fell in the ranking,
respectively, by 12 places (from 7th to 19th), by 7 places (from 9th to 16th) and by 8 places
(from 14th to 22nd).
It is worth noting that in 2021 as many as 12 countries represent a similar, average level of
the composite indicator, ranging from 0.898 (Netherlands) to 0.993 (Spain). The degree of var-
iation between the smallest and the biggest values is considerably greater.
Fig 2 includes a line graph showing progress on SDG 7 achieved by the 28 objects (the EU
and the 27 EU countries) between 2010 and 2021.
The reference period includes the time of the COVID-19 pandemic, which evidently
affected deterioration on SDG 7 achieved by Sweden, Denmark, and Germany in 2020 in rela-
tion to the performance observed in 2019. A similar deterioration in performance can be
observed in 2021 in relation to 2020 in the case of 16 countries (Austria, Finland, Netherlands,
France, Czech Republic, Croatia, Ireland, Romania, Italy, Spain, Poland, Slovakia, Belgium,
Hungary, Lithuania, Bulgaria). No negative effects during the pandemic can be observed for
the remaining 8 countries.
The horizontal line at the bottom of the chart represents the EU-level target for 2030:
SM
i2030
= 0.5486. A systematic decrease both the average value of the aggregate measure and
its diversification can be observed in the entire period under study (see Table 3,Fig 2). First of
all, this indicates systematic progress on SDG 7 achieved by the EU countries. Secondly, it
shows that differences between EU countries keep getting smaller, as evidenced by the range
and the standard deviations of the composite indicator (see Table 3). The range of the compos-
ite indicator decreased from RSM
2010 ¼1:2721 in 2010 to RSM
2021 ¼0:6052 in 2021.
5. Conclusions and policy implications
Since September 2015, 193 UN countries have been working to implement the resolution
“Transforming our world: the 2030 Agenda for Sustainable Development” containing 17 Sus-
tainable Development Goals (SDGs). To ensure sustainable performance in the context of the
challenges posed by the 2030 Agenda, each EU country needs to properly assess its progress
towards achieving SDGs. Given the importance of this need, we set out to analyse the perfor-
mance of the EU countries in relation to the core targets covered by SDG 7, measuring prog-
ress by using seven indicators. For three of these indicators the European Commission has set
target values for 2030. For the other four, the target values were based on the “best perfor-
mance” among the EU countries in 2015.
Progress on SDGs is typically measured by means of composite indicators. Three methodo-
logical approaches can be distinguished depending on the degree of compensation: fully com-
pensatory, partially compensatory, and non-compensatory. The third approach is
recommended since in addition to accounting for target values of the indicators, it involves
data adjustment and can be used to identify weak points in the country’s global assessment,
and, consequently, indicate possible areas for improvement (see section 1.2). Because the main
goal of the study was to assess progress on SDG 7 achieved by individual EU countries and to
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determine their distance in relation to the target set for 2030, we decided to use a multidimen-
sional indicator proposed and constructed by applying dynamic relative taxonomy (section 3).
The method used in the analysis represents a non-compensatory approach.
During the reference period (2010–2021), all EU countries made systematic progress
towards achieving SDG 7, although to a different degree and at different rate. Looking at how
many countries managed to achieve the EU targets for 2030, the best result can be observed for
the x7 indicator (Share of population unable to keep home adequately warm), which was
achieved by Sweden, Finland, Slovenia, Austria; followed by the x5 indicator (Share of renew-
able energy in gross final energy consumption)–achieved by Sweden, Finland, Latvia; the x4
indicator (Energy productivity)–achieved by Denmark, Ireland, Luxembourg; for the x3
Fig 2. Changes in progress on SDG 7 achieved by the EU countries between 2010 and 2021 in relation to the EU-
level target for 2030. Source: Chart created using R program [83].
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indicator (Final energy consumption in households per capita)–achieved by Spain, Malta, Por-
tugal; for the x6 indicator (Energy import dependency)–achieved by Sweden, Estonia. Greece
was the only country to achieve the 2030 target level for the remaining two indicators, namely
x1 (Primary energy consumption) and x2 (Final energy consumption).
In 2010, the distance from the 2030 target was the biggest for Malta, Cyprus, Bulgaria, Bel-
gium, Lithuania, Poland, and the smallest for Denmark, Sweden, Finland, and Austria. Because
of changes in the values of the seven indicators that took place over 11 years, the group of
countries that came closest to the 2030 target in 2021, apart from the three Scandinavian coun-
tries, includes Estonia, Austria, and Slovenia. Despite big increases in the values of the com-
posite indicator, Bulgaria, Lithuania, Malta, and Cyprus remained furthest away from the SDG
7 target in 2021.
The trend regarding the implementation of the 2030 Agenda has been influenced by the
COVID-19 pandemic. Many EU countries experienced a slowdown in the progress towards
SDGs. In the case of SDG 7, the distance from the 2030 target in 2020 compared to 2019 had
increased for Sweden, Denmark, and Germany. A similar deterioration in performance can be
observed from 2020 to 2021 in the case of 16 countries (Austria, Finland, Netherlands, France,
Czechia, Croatia, Ireland, Romania, Italy, Spain, Poland, Slovakia, Belgium, Hungary, Lithua-
nia, Bulgaria). It is worth emphasizing that during the pandemic, some improvement could be
observed for certain indicators. Counter-pandemic measures helped to enhance energy effi-
ciency, which is one of the key pillars in achieving SDG 7. The restrictions on public life and
lower economic activity reduced energy consumption from 2019 to 2020 by more than 8%.
The reduction in final energy consumption also resulted in greater energy supply and
increased the share of renewables in gross final energy consumption. The economic recovery
of 2021 and the return to pre-pandemic mobility patterns increased the demand for energy
again. However, consumption remained below pre-pandemic levels as the effects of the pan-
demic continued to shape energy and economic activities. The most obvious negative conse-
quence of the pandemic was the increase in energy consumption by EU households. Given
these short-term trends, it is clear that in order to ensure the EU achieves its goals by 2030,
changes in all indicators should be continuously monitored and assessed. The main motivation
of our study was therefore to propose a methodological approach that could provide informa-
tion to make such monitoring possible.
The novelty of the study consists in applying a non-compensatory approach to show the
varying distance that separates individual EU countries from the targets set out in Agenda
2030. This was possible thanks to the use of the aggregate approach in the methodology of
dynamic relative taxonomy (see section 3) taking into account the target values of the indica-
tors for 2030 with data adjustment (one-sided Winsorization in step 3 –see section 1.2) and
the use of the geometric mean in step 6 and 7 of the procedure of the aggregate measure con-
structing. It is worth noting that the dynamic approach indicates not only relations between
the objects in specific periods, but also changes in the phenomenon of interest that took place
between objects over the entire reference period. In other words, it can be used to tracking
changes from a cross-sectional and longitudinal perspective.
The conducted study has also its limitations. It was not possible to use all SDG 7 indicators
included in Agenda 2030 because required statistical data were not directly comparable (the
first two indicators in Table 2), as explained in section 1.2. The key disadvantage of aggregate
methods, i.e., their compensatory nature (the fact that positive and negative deviations from
the target values of individual indicators can accumulate), was considerably reduced by utilis-
ing the geometric mean and by applying one-sided Winsorization of the data, according to for-
mulas (2) and (3).
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The results of the study contribute to research on energy security, which is currently an
essential element of the economic security system. The proposed approach involving dynamic
relative taxonomy can be used as a tool supporting efforts to monitor progress on SDG 7 as
part of the national energy policy. It is worth emphasizing that progress achieved by particular
countries can be more relevant than their final performance outcomes. In order to properly
assess progress regarding the goals of Agenda 2030, in addition to calculating indices and cre-
ating country rankings for selected years, it is necessary to analyse changes over time using the
dynamic approach.
By design, all 17 SDGs are an integrated set of global priorities and objectives but SDG 7, as
well as SDG 2 (Zero hunger), SDG 3 (Good health and well-being), SDG 14 (Life below water),
are classified as being the most synergistic with other SDGSs. Therefore, the results presented
in this article can be treated as a starting point for policy makers and other stakeholders inter-
ested in identifying the main directions of change, priorities, and strategies in national policies
of sustainable development.
As demonstrated in the study, the proposed methodology can be used not only to assess
progress on SDG 7 but it also provides a relevant contribution to research regarding methods
of measuring national progress on the other SDGs included in Agenda 2030.
Author Contributions
Conceptualization: Marek Walesiak, Grażyna Dehnel.
Data curation: Grażyna Dehnel.
Formal analysis: Marek Walesiak, Grażyna Dehnel.
Investigation: Grażyna Dehnel.
Methodology: Marek Walesiak, Grażyna Dehnel.
Software: Marek Walesiak.
Validation: Marek Walesiak, Grażyna Dehnel.
Visualization: Marek Walesiak.
Writing – original draft: Marek Walesiak, Grażyna Dehnel.
Writing – review & editing: Marek Walesiak, Grażyna Dehnel.
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