The Achievements of Climate Change and Energy Policy in the European Union

Abstract

The European transition to green economy is based on long-term strategies. The Europe 2020 Strategy was launched in 2010 in order to promote smart, sustainable, and inclusive growth in the region. Later, the ambitions regarding this strategy were followed by the Green Deal and Sustainable Development Goals. Now, the effort of countries towards the green economy is even more relevant than ever because of the context of today’s global energy crisis and economic uncertainty due to many challenges such as the COVID-19 pandemic or Russia invasion of Ukraine. This paper seeks to assess the countries’ achievements in seeking climate change and energy targets of the Europe 2020 Strategy by applying the innovative multi-criteria decision-making (MCDM) technique. The kernel-based comprehensive assessment (KerCA) method was applied for the calculations of the progress achieved and countries’ rankings. It allows to evaluate countries’ achievements and compare them using the mathematical models. The analysis of the national target implementation in the countries allows to determine the compliance of countries with their commitments and liability towards other countries and the European Union (EU). An analysis of the implementation of the strategy can serve as a guide to further develop climate change and energy policy in the region. Furthermore, the added value of the article is systematic overview of scientific studies proposing different methodologies for the analysis of target implementation at the whole region level. The novelty of this paper also relies on the approach, which considers not only the level of achievement of the target, but also assesses the excess.

1. Introduction

The Europe 2020 Strategy was launched by the European Commission in 2010 in order to promote smart, sustainable, and inclusive growth in the region. This 10-year strategy was followed by the Green Deal [1] and Sustainable Development Goals [2]; whose ambitions are a continuation of this expired strategy. Now, the effort of countries towards the green economy are even more relevant than ever because of the context of today’s global energy crisis and economic uncertainty due to many challenges such as the COVID-19 pandemic or Russia invasion of Ukraine. The scientific studies showed that the COVID-19 pandemic caused a lot of significant changes in the energy sector regarding consumption and demand of energy and its patterns [3,4,5], development of renewable energy [6], or greenhouse gas (GHG) emissions and countries’ possibilities to achieve the reduction of emissions objectives [7,8].

The negative impact of the COVID-19 pandemic became a large problem in the second half of 2021. The energy prices started to increase significantly. To alleviate the burden on energy consumers, a toolbox of measures [9] for the European Union (EU) countries has been introduced. Even more energy prices rose sharply following the Russian invasion of Ukraine. After two weeks (on 8 March 2022), the European Commission presented a communication on how to attain energy independence from Russian fossil fuels before 2030 in Europe [10]. In principle, even in the face of these global crises, European energy policy keeps the same direction. This is the development of renewable energy resources, increasing energy efficiency, and reducing climate emissions along with the search for new energy suppliers.

The 10-year Europe 2020 Strategy set targets for climate change and energy for the EU region as a whole and for each country individually. Numerous attempts to predict the implementation level of the strategy can be found in the scientific literature. There are also studies that evaluated the results of intermediate periods. However, a full assessment of the achievements of the strategy’s implementation has not been found in the literature.

This paper seeks to evaluate the countries’ achievements in seeking to implement the climate change and energy targets of the Europe 2020 Strategy. The analysis of the national target implementation among countries allows to determine the compliance of countries with their commitments and responsibility towards other countries and the EU as a strategic actor in the region. A full analysis of the implementation of the strategy can serve as a guide to further develop climate change and energy policy in the region. Furthermore, the added value of the article is systematic overview of scientific studies proposing different methodologies for the analysis of progress made or to monitor target implementation at the whole region level. The novelty of this paper relies on approach, which takes into account not only the level of achievement of the target, but also assesses the excess. Additionally, an innovative multi-criterion decision-making (MCDM) technique for country ranking was applied.

The article is divided into several subsections. The literature review of scientific studies dealing with Europe 2020 Strategy is presented in the second section. The third section presents an overview of climate change and energy policy in the EU. The fourth section provides an overview of the countries’ achievements in implementing Europe 2020 Strategy targets regarding climate change and energy priority. The methodology of the assessment and country ranking is presented in the fifth section. The sixth section is dedicated to analysis of the results, and finally the conclusions of the research are presented.

2. Literature Review

In order to review the previous studies, using different methodologies and proposing new ones, which evaluate countries’ achievements by implementing climate change and energy targets of the Europe 2020 Strategy, the literature review was performed using the Web of Science Core collection database on topic “Europe 2020” in the Energy Fuels, Environmental Sciences, and Environmental Studies categories. The proposed different methodologies by various authors can also serve as a tool for evaluating, forecasting, and modeling the goals of future energy policy strategies and scenarios for their implementation. The variety of methods applied for the assessment of implementation of the Europe 2020 Strategy objectives are summarized in Figure 1.

There are many studies in the scientific literature that seek to determine whether one or another country will succeed in achieving the climate change and energy targets set out in the Europe 2020 Strategy. In order to determine how successfully a country can meet the climate and energy targets, different prediction methods were applied. For example, Komusanac et al. [11] used EnergyPLAN model for the analysis of 69 scenarios of wind and solar PV development in Croatia. Additionally, the authors applied the MCDM technique multi-attribute value theory (MAVT) for the sensitivity analysis of the created scenarios. Furmankiewicz et al. [12] analyzed goals, actions, and indicators related to climate change and renewable energy in Polish local development strategies. The authors performed content analysis of local development strategies created in 2014–2015. According to the results, the interest of Polish rural-policy actors in achieving climate and energy targets of the Europe 2020 Strategy is very low. The results showed that only 9% of all local development strategies planned investments in renewable energy.

Additionally, different sustainability assessment methodologies were proposed for the development of renewable energy. For example, Kryk [13] evaluated the energy sustainability in the EU. The developed framework considers environmental and social responsibility issues, social justice, and the implementation of policy priorities. Bartolini et al. [14] measured environmental and socio-economic impacts of farm biogas in Italy. The authors introduced a model, which can serve as support tool for the development of biogas installation. The proposed approach considers traditional sustainability dimensions at the regional level. Garcia-Alvarez et al. [15] developed an Environmental and Resource Pressure Aggregated Index, which measures both the pressure on the environment and resource of the European energy policy. The proposed index was applied for the case study of all EU member states in order to assess the pressure of the targets of the Europe 2020 Strategy. The proposed methodology can be easily applied to the analysis of various energy policy areas. Moreno et al. [16] created the Resource Efficiency Capacity Index for the measurement of resource efficiency among EU countries by implementing the EU energy policy targets. Serbanica and Constantin [17] analyzed sustainability issues in the Central and Eastern European countries in the context of European Energy policy.

Other studies focused on assessing the impact of the implementation of the strategy’s goals. For example, Paun and Paun [18] applied least squares methodology and regression analysis in order to determine how the development of renewable energy affected energy prices in Romania. Andreas et al. [19] analyzed the drivers and consequences of renewable energy development in Bulgaria. The authors sought to identify the main trends of this expansion in poorly developed EU countries.

Some of the studies created indices for the measurement of the progress made but applied the created methodology for the data analysis before the Europe 2020 Strategy was launched. For example, Pasimeni [20] introduced the Europe 2020 index, Hudrlikova [21] created the Composite Indicator for the assessment of level of strategy implementation, Colak and Ege [22] calculated indices not only for the whole strategy, but also for each priority, Balezentis et al. [23] proposed an indicator system and MCDM technique for country comparisons in achieving targets. Balezentis and Balezentis [24] proposed a framework for a strategic management of implementation of Europe 2020 Strategy objectives. Brauers et al. [25] applied the MCDM technique to assess the economic readiness of the EU member states to implement the Europe 2020 Strategy targets.

After analysis of the literature, the scientific studies were grouped into two categories: studies evaluating only climate change and energy achievements and studies evaluating implementation of all targets. The first category of studies is provided in

Table 1. The results summary of studies ranking climate change and energy achievements among countries.

Source	Subject of the Assessment	Period of the Assessment	Countries under Assessment	Method Applied	Best-Performing Countries *	Worst-Performing Countries *
[26]	Forecast—implementation of the targets	2008–2014	All EU member states	Mathematical model (average value)	LU, IE, NL	SE, FI, AT, LV
[27]	Forecast—implementation of the targets	2005–2012	All EU member states	Regression analysis	BG, LV, LT, RO	EE, MT, NL, BE
[28]	Forecast—implementation of the targets	2017–2018	Czech Republic and Slovakia	Statistical linear model—Auto Regressive Integrated Moving Average (ARIMA)	-	-
[29]	Efficiency of target implementation	2014–2018	Two groups: old (15) and new EU members (13)	DEA and the Malmquist index	CY, LV, MT	AT, HR, FI, FR, DE, GR, IT, LT, PT, RO, ES, UK
[30]	Implementation of the targets	2010–2019	All EU member states	Taxonomic and zero-unitarization method	SE, DK. RO	BE, CY, LU
[31]	Implementation of the targets	2010–2018	All EU member states	Index construction (Green Economy Index)	AT, SE, UK, DK, FR, NL	BG, RO, HU

* Abbreviations of the EU member states are provided in Appendix A.

by highlighting the methodology and results of the rankings.

D’Adamo and Rosa [26] used a common mathematical model to predict future renewable energy trends in EU countries. The model relies on calculation of three indicators, which are: primary renewable energy production; gross final renewable energy consumption; and share of renewable energy in gross final energy consumption.

Liobikiene and Butkus [27] sought to evaluate possibilities of EU member states to achieve the targets of the Europe 2020 Strategy and The Paris Agreement. The authors carried out regression analysis in the period of 2005–2012 to forecast the likelihood of countries achieving national targets of energy efficiency, renewable energy, and GHG emissions. The results of the study showed that it is crucial to achieve energy efficiency and renewable energy targets of the Europe 2020 Strategy for the implementation of The Paris Agreement goals for GHG emissions reduction.

Brozyna et al. [28] applied a statistical linear model ARIMA for the forecast of Europe 2020 targets implementation in Slovakia and the Czech Republic. The authors highlighted the importance of stakeholders and policymakers for the development of sustainable national energy policy and implementation of climate change and energy targets of set by the EU. According to the prognosis of the study, the countries under analysis would not reach climate and energy targets. In fact, both countries reached renewable energy and GHG emissions targets. The Czech Republic also achieved energy efficiency targets, only one final energy efficiency target was not reached by Slovakia.

Guzowska and Kryk [29] sought to evaluate the efficiency of the Europe 2020 Strategy climate and energy targets implementation in the period of 2014–2018. The authors performed DEA and analyzed changes over time by grouping countries into the old (15) and new members (13) of the EU. The results showed that only seven member states reached efficiency in the process of climate and energy targets implementation during the four years period under analysis. Additionally, it was determined that the new EU countries were distinguished as having higher efficiency in implementing these goals than the old EU members.

Kryk and Guzowska [30] applied a taxonomic and zero-unitarization method for the evaluation of progress made by EU member states in reaching climate change and energy objectives of the Europe 2020 Strategy. The authors created a set of 15 indicators and considered not only general targets, but also those that are related or part of main targets. For example, for the implementation of the renewable energy target in total energy consumption, the share of renewables in transport, electricity, heating and cooling, and share in gross final energy consumption were counted. The indicator reflecting the percentage of the population unable to heat their home adequately, percentage of energy import of the country, energy productivity in EUR, and by purchasing power standard were included in the assessment of energy efficiency improvements.

In order to measure European countries’ achievements towards the idea of a green economy, Kasztelan [31] introduced the Green Economy Index. The index considers 27 indicators and evaluates the state of the green economy implementation among EU countries in 2018. As the base year, 2010 was chosen. According to the results, the average value of the Green Economy Index decreased in the period under analysis and is interpreted as a slowdown in the process.

The studies grouped in the second category are dedicated to the evaluation of implementation of all targets among countries. The summary of studies ranking the implementation of all Europe 2020 Strategy targets among countries is presented in

Table 2. The results summary of studies ranking the implementation of all Europe 2020 Strategy targets among countries.

Source	Period of the Assessment	Countries under Assessment	Method Applied	Best-Performing Countries *	Worst-Performing Countries *
[32]	2004–2013	Two groups: old (15) and new EU members (13)	Taxonomic and zero-unitarization method	-	-
[33]	2004, 2010, 2015	Two groups: old (15) and new EU members (13)	The linear ordering method	AT, DK, SE, FI	RO, GK, BG, ES, MT
[34]	2009–2014	Two groups: old (15) and new EU members (13)	Zero-unitarization method	SE, FI, DK, AT	RO, BG, DK, ES, GR
[35]	2016	All EU member states	MCDM—Multi-Objective Optimization on the basis of Ratio Analysis (MULTIMOORA) method, the Shannon Entropy Index	SE, DK, AT	BE, BG, ES, IT, CY, LU, MT, NL, RO
[36]	2010–2019	All EU member states	Index construction	DK, SE, AT, FI	MT, RO, BG
[37]	2010–2018	All EU member states	Index construction (EU2020 index)	SE, FI, DE	ES, IT, BG, RO

* Abbreviations of the EU member states are provided in Appendix A.

:

Balcerzak [32] analyzed structural diversity between old and new EU countries in implementing Europe 2020 Strategy targets. The author applied the taxonomic and zero-unitarization method and performed an analysis in the period of 2004–2013. Significant differences among these two groups of countries were found. Stec and Grzebyk [34] applied the same method for the measurement of target implementation level in the period 2009–2014.

Fura et al. [33] performed an analysis of achievements of European countries in three time intervals (2004, 2010, 2015) using application of the linear ordering method. The authors created a synthetic indicator, which considered 16 indicators reflecting Europe 2020 Strategy priorities. Significant disparities among old and new EU member states were found.

Fedajev et al. [35] applied the MCDM MULTIMOORA technique and Shannon Entropy Index for the measurement of the progress of implementation of Europe 2020 targets among countries in 2016. The results of the assessment showed that the biggest disparities among countries were regarding the development of renewable energy. The differences among countries in investments in research and development were also relatively high.

Walesiak et al. [36] measured the implementation of all nine Europe 2020 targets among countries in the period of 2010–2019. The authors calculated a composite indicator, which considers all nine targets. The measurement was based on distance of each member state in relation to the both the EU-level objectives and national-level strategy objectives. The study showed that the improvements in targets were observed in each year among countries. Additionally, the disparities existing between EU countries were reduced significantly. In spite of that, at the end of 2019 there was no country that had achieved all EU-level objectives.

Becker et al. [37] proposed the EU2020 index for the measurement of Europe 2020 Strategy target implementation among member states. The index considers all eight targets of the strategy and was applied for the measurement of the implementation in 2018 at the NUTS 2 level in the EU.

As seen from the results of the previous studies, the best- and the worst-performing countries in the context of both implementation climate change/energy or whole Europe 2020 Strategy priorities, differs among assessments. This is due to the different evaluation periods and the different methodologies applied. However, general trends can be identified. The best performing countries in the whole strategy are the Nordic countries and Austria in the different evaluation periods. Meanwhile, the new EU member states were most often identified as the worst-implementing countries. Bulgaria and Romania were at the end of the rankings in all performed studies. Meanwhile, the results for measuring climate change and energy target implementation differs. The Nordic countries no longer dominate here. This is due to the very high targets set for these countries. It is also worth mentioning that even before the strategy was launched, the Nordic countries had exceptionally high climate change and energy indicators.

Multi-criteria analysis is a popular tool for solving energy policy issues, because of its universality and wide selection of different approaches. The scientific achievements of MCDM techniques allows to evaluate contradictory effects of different issues and to rank alternatives according to the criteria selected. Numerous studies were performed and various MCDM techniques were applied for the issues of energy policy, energy planning, sustainable energy development, climate change, renewable energy development, and other areas. In the scientific literature, there are a lot of review articles the overview the studies using different application areas. For example, Mardani et al. [38] and Kaya et al. [39] reviewed different MCDM methods applied for energy policy and decision-making problems. Bohra and Anvari-Moghaddam [40] provided a comprehensive review on applications of MCDM approaches in power and energy systems. The studies by Pohekar and Ramachandran [41] and Siksnelyte et al. [42] reviewed the studies dealing with sustainable energy development issues using application of the MCDM approaches. Kumar et al. [43] and Campos-Guzman [44] reviewed the application of different MCDM techniques for sustainable renewable energy development. Kaya et al. [45] performed a comprehensive review of the literature regarding the methodologies and applications of fuzzy MCDM in the field of energy. The application of MCMD methods to the selection of renewable energy technologies in the household sector was reviewed in the study of Siksnelyte et al. [46]. The analysis of MCDM techniques for the selection of insulation materials in buildings was performed by Siksnelyte-Butkiene et al. [47].

3. European Climate Change and Energy Policy

Being the world leader in climate change mitigation, the EU has established the target of becoming a climate-neutral society by 2050. For achieving this target, the European Commission in the summer of 2021, implemented a series of legislative proposals for achieving climate neutrality by 2050, involving the intermediate target of 55% net GHG emission reduction by 2030. These most important climate change mitigation proposals include effort sharing regulation for the EU emission trading scheme, transport and land use legislation for implementing EU climate change mitigation goals under the European Green Deal [1].

The Regulation on the Governance of the Energy Union established common rules for planning, reporting, and monitoring in national energy and climate plans (NECPs) of EU Member States in line with the ambition cycles under the Paris Agreement. These plans address the following main pillars of the Energy Union: decarbonization including GHG decrease and use of renewable energy resources; energy security; energy efficiency; and internal energy market and research, innovation, and competitiveness. EU countries were obliged to prepare their 2021–2030 plans by the end of 2019. In addition, for EU member states it is necessary to develop national long-term strategies in order to guarantee uniformity between climate strategies and their NECPs.

The 27 plans were submitted and assessed by the European Commission in order to define how EU members are meeting the first stage of changeover climate neutrality and what their plans and goals are for the 2021–2030 crosswise five pillars. The performed assessment of climate plans showed the share of renewable energy in final energy consumption will be in range between 33.1 and 33.7% by 2030 by surpassing the EU renewable energy target of 32% in 2030. In addition, the recently founded renewable energy financing mechanism allows member states to minimize the cost of renewable energy support, and to help them to achieve national and EU 2030 and 2050 targets. The renewable energy financing mechanism combined with other EU instruments such as the Connecting Europe Facility (CEF) or InvestEU programme can provide fast penetration of renewables. The InvestEU Programme was developed based on the effective model of the Investment Plan for Europe, mobilizing more than EUR 500 billion in the period 2015–2020. In 2020–2025, more than EUR 372 bill of investments are foreseen, applying the EU budget guarantee.

The analysis of energy efficiency pillar in NECPs showed that a foreseen reduction in primary energy consumption of 29.7% and final energy consumption of 29.4% will be achieved by 2030 in the EU. There is a gap matching the EU 2030 target of 32.5% reduction in energy (2.8 % for primary and 3.1 % for final energy) consumption. It is necessary to emphasize that the European Commission legislation stressed the main role of energy efficiency in transition to a carbon neutral society; however, the EU countries are reluctant to apply this principle in their NECPs though energy efficiency is important for achieving all targets.

Based on analysis of NECPs, the economy wide GHG emissions, including the EU Emissions Trading System (ETS), are expected to be 41% below 1990 levels, exceeding the EU 2030 40% reduction target. In order to achieve GHG reductions, the sectoral and cross-sectoral measures are included in NECPs. Many EU member states are planning to use carbon pricing, like Germany that has approved national GHG emissions trading legislation covering fossil fuel CO₂ emissions from transport and building sectors which are not included in the EU ETS.

Energy security is closely linked with promotion of renewables and energy efficiency, causing a lower energy import dependency of the country. Several EU member states including Lithuania, Malta, France, and Portugal in their NECPs highlighted energy efficiency and renewable energy sources as the main drivers of energy security.

The assessment of the energy market in terms of flexibility throughout smart grids, energy storage facilities, and demand-side response showed that their NECPs countries encounter some problems in developing energy interconnections between EU countries. Electricity interconnections and well-developed smart local grids are a key driver of decarbonization, market integration, security of supply, etc. Additionally, some EU members face problems in energy retail market liberalization, like Lithuania, which is still not fully prepared for retail market liberalization and face many challenges. However, not liberalized energy markets have negative impacts for customers and industries, hinder a positive recovery from the pandemic, and the transition towards climate neutrality.

Research and Development (R&D) are key enablers of low carbon energy transition. In NCPs, the decrease in the share of R&D expenditures in national budgets of EU countries can be noticed for clean energy technologies. Most NECPs do not have ambitious plans to increase the share of R&D for clean energy technologies in their GDPs.

Though all member states understood the importance of regional cooperation, the full potential of regional cooperation has not been fully realized. Just very few EU countries planed policies and measures on how to plan better renewable energy development and energy efficiency measures in cooperation with other EU member states.

4. An overview of Country Achievements in Implementing Climate Change and Energy Policy Goals

Although the EU is a single region with common climate change and energy policy objectives, the national efforts and achievements of each EU member state vary. The disparities between member states are related to economical, geographical, resource availability, social, or historical aspects. Additionally, the different targets for each country are established according to each country’s potential to implement them. The national Europe 2020 Strategy targets for the priority of climate change and energy policy are presented in

Table 3. Climate change and energy policy national targets of the Europe 2020 Strategy and its implementation among countries in 2020. Source: [48].

	GHG Emissions in ESD Sectors, Mtoe CO2e		Final Energy Consumption, Mtoe		Primary Energy Consumption, Mtoe		Share of Renewables. %
	Target	Result	Target	Result	Target	Result	Target	Result
Belgium	66.55	68.25	33.29 *	32.5	43.88	43.7	13.000	13
Bulgaria	27.91	26.54	9.54	8.6	17.19	16.9	23.319	16
Czech Republic	64.29	67.20	24.48	25.3	37.47	39.6	17.303	13
Denmark	29.83	32.06	13.15	14.4	15.32	17.4	31.648	30
Germany	416.99	410.91	201.66	194.3	262.49	276.6	19.312	18
Estonia	5.96	6.02	2.78	2.8	4.31	6.5	30.069	25
Ireland	44.27	37.65	11.18	11.7	13.43	13.9	16.160	16
Greece	41.8	60.05	14.33	18.4	19.68	24.7	21.749	18
Spain	179.34	212.39	73.76	80.1	105.03	119.8	21.220	20
France	308.37	342.48	130.23	131.4	208.36	219.9	19.109	23
Croatia	15.84	19.32	6.47	7.0	7.76	11.15	31.023	20
Italy	253.94	291.01	102.74	124.0	132.32	158.0	20.359	17
Cyprus	4.06	3.98	1.57	1.8	2.20	2.2	16.879	13
Latvia	8.44	9.99	3.86	4.5	4.26	5.4	42.132	40
Lithuania	13.89	15.24	5.31	4.3	6.23	6.5	26.773	23
Luxembourg	7.86	8.12	3.81	4.2	3.94	4.5	11.699	11
Hungary	44.64	52.83	18.01	14.4	23.89	24.1	13.850	13
Malta	1.31	1.17	0.54	0.5	0.74	0.7	10.714	10
Netherlands	90.36	107.36	45.52	52.2	58.38	60.7	13.999	14
Austria	46.57	47.75	26.07	25.1	29.73	31.5	36.545	34
Poland	201.82	205.18	71.00	71.6	96.53	96.4	16.102	15
Portugal	39.01	49.08	15.02	17.4	19.54	22.5	33.982	31
Romania	77.64	89.81	23.53	30.3	30.92	43.0	24.478	24
Slovenia	9.9	12.31	4.39	5.1	6.13	7.3	25.000	25
Slovakia	19.52	25.95	10.34	9.0	15.15	16.4	17.345	14
Finland	28.51	28.51	23.28	26.7	29.80	35.9	43.802	38
Sweden	30.54	36.08	30.93	30.3	41.82	43.4	60.124	49

* Targets that countries did not achieve are marked in red.

.

The EU overachieved all climate change and energy targets set in the Europe 2020 Strategy. However, 14 countries have not met at least one of the four climate change and energy priority targets. The level of implemented targets varies and will be assessed in this article by applying a multi-criteria technique. The biggest challenge for countries was to meet their national energy efficiency targets. Nine countries did not achieve the final energy consumption target and four did not reach the target for primary energy consumption. Additionally, five countries did not reach the target for GHG emissions reduction. The best implemented have been national targets for the development of renewable energy. Theoretically, two countries, France and the Netherlands, did not achieve this goal. However, it would be fairer to say that only France did not reach the target, as the distance between the Netherlands’ reached value and the target is very short and may be achieved due to statistical deviations.

Looking at the EU region as a whole, all climate change and energy targets of the Europe 2020 Strategy were implemented. The goal for RES in final energy consumption was implemented at 2.1% above the target and reached 22.1% in 2020 (Figure 2).

The EU energy efficiency target was also reached. The EU set a goal for primary energy consumption not to exceed more than 1483 Mtoe and for final energy consumption not to exceed more than 1086 Mtoe. It should be noted that after the withdrawal of the United Kingdom, the targets were adjusted to the situation of 27 countries as follows: no more than 1312 Mtoe of primary energy consumption and no more than 959 Mtoe of final energy consumption. The statistical data shows that the primary energy consumption target was exceeded by 5.8% (Figure 3), while the target for final energy consumption was exceeded by 5.4% (Figure 4).

The EU target for GHG emissions reduction was fully implemented before the deadline. The overall decrease in GHG emissions was 34% compared to the 1990 level. The biggest decrease in emissions was detected in 2020. The key driver for that significant decrease can be identified as the global COVID-19 pandemic and lockdowns.

5. Methods

The measurement of climate change and energy target implementation of the Europe 2020 Strategy and the country rankings with reference to achievements made is based on an innovative MCDM approach. Balezentis [49] proposed the kernel-based comprehensive assessment (KerCA) method that resembles the PROMETHEE family in the sense of the pairwise comparisons of the alternatives involved in the assessment. The KerCA technique can be described as a comprehensive MCDM approach, which is capable of ensuring robust optimization with minimum arbitrary assumptions. The applied technique combines the virtues of different types of aggregation measures. The major departure points from the previous formulations of the PROMETHEE are (i) the use of the least squares cross validation for tuning the bandwidth parameter and (ii) the use of multiple value aggregation schemes when constructing the utility scores. The utility scores serve as the composite indicators for ranking the alternatives. The KerCA method proceeds as follows:

(1)

The $m$ alternatives are defined in terms of $n$ criteria and indexes $i=1,2,\dots ,m$ and $j=1,2,\dots ,n$ keep track of these. The criteria set is decomposed into the two subsets, namely $B$ for benefit criteria that achieve maximum values for the most desirable alternative, and $C$ for cost criteria that achieve the lowest values for the most desirable alternative. Additionally, the weight vector $(w_1,w_2,\dots ,w_n)$ is established that indicates the relative importance of each criterion $j$ and ${\displaystyle \sum _{j=1}^n{w}_j}=1$ is assumed.

(2)

The least squares cross validation [50]. Let the resulting bandwidths be arranged into vector $(h_1,h_2,\dots ,h_n)$.

(3)

The degree of superiority of alternative $i$ against alternative $k=1,2,\dots ,m$ is then measures by exploiting the standard normal cdf, $\Phi (\cdot )$, as follows

$$\begin{array}{l}{p}_{ij}=\frac{1}{m}{\displaystyle \sum _{k=1}^m\Phi \left(\frac{x_{ij}-x_{kj}}{h_j}\right)},j\in B,\\ p_{ij}=1-\frac{1}{m}{\displaystyle \sum _{k=1}^m\Phi \left(\frac{x_{ij}-x_{kj}}{h_j}\right)},j\in C\end{array}$$

(1)

Note that the standard normal cdf takes the value of 0.5 at the zero point. In this case, the alternatives are said to be equally performing in terms of criterion $j$. In case alternative $i$ outperforms alternative $k$ (ignoring the direction of optimization), the standard normal cdf exceeds 0.5. Then, the values are aggregated across the alternatives against which the comparisons are made. Note that an adjustment is needed for the cost criteria.

(4)

The resulting performance indicators are aggregated across the criteria for each of the alternatives under consideration. The aggregation proceeds in four manners to ensure robustness. Specifically, the additive, multiplicative, reference point for lower bounds, and reference point upper bounds approaches as implemented as follows:

$$v_i^A={\displaystyle \sum _{j=1}^n{w}_j{p}_{ij}},i=1,2,\dots ,m.$$

(2)

$$v_i^M={\displaystyle \prod _{j=1}^n{\left(p_{ij}\right)}^{w_j}},i=1,2,\dots ,m.$$

(3)

$$v_i^{\min }=\underset{j=1,2,\dots ,n}{\min }{p}_{ij},i=1,2,\dots ,m.$$

(4)

$$v_i^{\max }=\underset{j=1,2,\dots ,n}{\max }{p}_{ij},i=1,2,\dots ,m.$$

(5)

(5)

The aggregate performance values are further normalized with respect to their maximum values. The importance of the aggregate performance values obtained using different principles is represented by the weight vector $\left({\lambda }_A,{\lambda }_M,{\lambda }_{\min },{\lambda }_{\max }\right)$. Note that the aforementioned weights add up to unity. Thus, the utility of each alternative is defined as

$$u_i={\displaystyle \sum _{\xi \in \left\{A,M,\min ,\max \right\}}^{}{\lambda }_{\xi }\frac{v_i^{\xi }}{\underset{i=1,2,\dots ,m}{\max }{v}_i^{\xi }}},i=1,2,\dots ,m.$$

(6)

(6)

The alternative with the highest value of $u_i$ is the best-performing one.

6. Results

The research considers the EU member states and their progress (as opposed to the observed levels) towards implementation of their low-carbon economy. To measure the progress, the observed values are compared to the target ones. Therefore, successful achievement of the targets appears as the major objective of the economic and energy policy rather than adjustment of the indicator values themselves.

The four indicators are considered. First, the GHG emissions in the Effort Sharing Decision (ESD) sectors (Mtoe CO₂e) measure the environmental pressures generated by the economies. Second, final energy consumption (Mtoe) indicates the energy requirements of the economy. Third, primary energy consumption (Mtoe) indicates the energy input in the economy that is further processed via energy conversion. Fourth, the share of renewables (in %) measures the extent of the penetration of the renewables in the energy system. The ratios of the observed to target values are considered as the decision variables. The first three indicators need to be minimized to identify the optimal solution, whereas the last one is to be maximized. The indicators used and data from countries are provided in Table 3.

The criteria may have different importance in the MCDM. The present case considers three groups of criteria: the first indicator measures environmental pressures, the second and third ones represent energy efficiency and conservation, and the fourth criterion indicates to which extent energy-mix has become cleaner. Thus, the four weighting schemes to assign each of the three groups with different importance were created. The basic scenario is Balanced, where each of the three dimensions discussed above are assigned with equal importance. Then, the focus on each of the three dimensions with the remaining two being assigned with lower weights were considered. The discussed weighting schemes are outlined in

Table 4. Weights defining different assessment scenarios for low-carbon economy.

Scenario	GHG Emissions in ESD Sectors, Mtoe CO2e	Final Energy Consumption, Mtoe	Primary Energy Consumption, Mtoe	Share of Renewables, %
Balanced	0.33	0.17	0.17	0.33
Environment	0.5	0.125	0.125	0.25
Energy	0.25	0.25	0.25	0.25
Renewables	0.25	0.125	0.125	0.5

Note: rounding errors are present.

.

The KerCA method was applied to the data and the resulting rankings are provided in

Table 5. Results of the KerCA analysis for the EU countries.

Country	Balanced		Environmental		Energy		Renewables
	Utility	Rank	Utility	Rank	Utility	Rank	Utility	Rank
Belgium	0.425	25	0.430	25	0.428	25	0.395	25
Bulgaria	0.600	18	0.457	22	0.576	18	0.640	12
The Czech Republic	0.722	9	0.597	15	0.735	8	0.728	4
Denmark	0.676	11	0.671	11	0.681	12	0.615	17
Germany	0.491	22	0.442	24	0.504	20	0.485	21
Estonia	0.674	12	0.562	16	0.768	7	0.663	10
Ireland	0.357	26	0.318	26	0.412	26	0.358	27
Greece	0.992	1	1.000	1	0.992	1	0.866	2
Spain	0.756	8	0.766	8	0.723	10	0.634	14
France	0.480	23	0.503	18	0.466	22	0.410	24
Croatia	0.927	2	0.857	2	0.959	2	0.909	1
Italy	0.836	4	0.819	5	0.906	3	0.797	3
Cyprus	0.624	17	0.494	19	0.628	17	0.639	13
Latvia	0.825	5	0.829	4	0.793	6	0.696	7
Lithuania	0.495	21	0.487	20	0.464	24	0.504	19
Luxembourg	0.653	15	0.634	14	0.677	13	0.618	16
Hungary	0.502	20	0.534	17	0.466	23	0.425	23
Malta	0.350	27	0.283	27	0.340	27	0.370	26
The Netherlands	0.648	16	0.667	12	0.668	14	0.518	18
Austria	0.507	19	0.487	21	0.521	19	0.497	20
Poland	0.466	24	0.442	23	0.470	21	0.462	22
Portugal	0.837	3	0.849	3	0.811	4	0.700	6
Romania	0.776	7	0.775	7	0.808	5	0.711	5
Slovenia	0.782	6	0.798	6	0.728	9	0.667	9
Slovakia	0.668	14	0.693	9	0.635	16	0.624	15
Finland	0.708	10	0.656	13	0.709	11	0.684	8
Sweden	0.669	13	0.677	10	0.662	15	0.662	11

. As one can note, the utility scores based on different raking schemes are highly correlated (

Table 6. Correlation coefficients for utility scores.

	Balanced	Environment	Energy
Balanced
Environment	0.96
Energy	0.98	0.92
Renewables	0.95	0.85	0.96

). The lowest correlation coefficient is 0.85 for the Renewables and environment-oriented scenarios. Indeed, the GHG emission partially depends on the share of the renewables in the energy-mix; however, it seems that some other factors exist that play a role in the utility assessment. Anyway, the correlation is substantially high. The rank correlation (

Table 7. Correlation coefficients for ranks.

	Balanced	Environment	Energy
Balanced
Environment	0.95
Energy	0.97	0.89
Renewables	0.93	0.82	0.94

) is also high. For the aforementioned pair of scenarios (Renewables–Energy), the lowest value is observed (0.82).

The ranking of the countries shows certain quantitative variations across the scenarios, yet the qualitative conclusions persist. Regarding the best-performing countries, one can identify Greece, Croatia, Italy, Portugal, and Romania. These countries appear at the top of the ranking irrespectively of the weighting imposed. The use of the balanced environment-oriented approach puts Romania down by two places if compared to the other scenarios, indicating its relatively low success in achieving the GHG emission mitigation. However, these ranks are based on the ratios of the observed situation to the commitments rather than other measures. Therefore, countries with relatively low industrial development levels appear at the top of the ranking.

The worst-performing countries (in the sense of the climate-related objectives’ implementation) are Malta, Belgium, Ireland, France, and Poland. France ascends by several places if the environment-oriented weighting is applied. These countries include both developed and developing economies. Therefore, implementation of the climate-related policies may be more difficult to follow for more developed economies, yet the same can be observed for such countries such as Malta and Poland. Therefore, it is important to assess the progress towards energy- and climate-related commitments and ensure that the countries may exchange the best practices in order to further the mutual learning and climate change mitigation.

The ranking rendered by the KerCA approach was verified by employing to well-known MCDM methods, namely Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Hwang and Yoon [51] provided a description of these approaches. The data for 2020 were selected to check the robustness of the ranking by applying different MCDM methods. The results suggest that the KerCA-based ranking is similar to those rendered by the SAW (rank correlation of 0.95) and TOPSIS (rank correlation of 0.96). Thus, the KerCA approach allows for a more comprehensive aggregation while resulting in a reasonable ranking.

7. Conclusions

The context of today’s global energy crisis and economic uncertainty due to the COVID-19 pandemic and Russia invasion of Ukraine increased the importance of decarbonizing the economy as fast as possible in many countries and regions, especially in the EU. The paper evaluated countries’ achievements in implementing climate change and energy targets of the Strategy Europe 2020.

The EU overachieved its climate change and energy targets set in Europe 2020 Strategy. However, 14 countries have not met at least one of the targets. The biggest challenge for member states was to meet their national energy efficiency targets. Nine countries did not achieve final energy consumption target and four did not reach the target for primary energy consumption. Additionally, five countries did not reach the target for GHG emissions reduction. The best achieved are national targets for the development of renewable energy.

The measurement of the targets’ implementation levels allows to monitor the progress and to rank countries’ effort and fulfillment of commitments in seeking sustainable, smart, and inclusive growth. This study not only considered the level of achievement of the target, but also assessed the excess. This approach allows to assess the efforts of countries and the acceleration of the strategy to achieve climate change and energy goals at the individual level. The analysis of implementation of the objectives reflects not only the effort of countries, but also can serve as a supporting tool for further development of environmental and energy policy.

The assessment of implementation of the climate change and energy targets of the Strategy Europe 2020 and the countries’ rankings was based on the innovative KerCA approach. The use of the least squares cross-validation allowed to scale the data without arbitrary assumptions. In addition, the KerCA method allowed aggregating the data on countries’ performances in seeking the energy targets from a comprehensive perspective as several aggregation principles were applied. The implementation was measured by comparing reached values to the target ones. Additionally, the sensitivity analysis was performed by creating several weighting schemes. Despite that, the ranking of the countries shows certain quantitative variations across the scenarios, yet the qualitative conclusions persist. The best-performing countries in the context of climate change and energy priority implementation can be identified as Greece, Croatia, Italy, Portugal, and Romania. While, the lowest achievements showed Malta, Belgium, Ireland, France, and Poland. Comparing the obtained results with previous studies, several predictions can be found that have identified the positions of some countries quite accurately. However, overall, the use of past data for long-range predictions was not verified.

The analysis of NECPs of these countries showed that these countries (Greece, Croatia, Italy, Portugal, and Romania) had a large potential for GHG emission reduction, including use of renewables and energy efficiency improvements and implemented effective policies and measures to reduce GHG emissions, promote use of renewables and energy efficiency, also having a positive impact on security of energy supply. The countries lagging behind, such as France, Belgium, Ireland, and Poland, are facing more difficulties in implementing climate and energy targets due to lower GHG emission reduction potentials and other circumstances linked to fossil fuel dependency (coal in Poland, oil and natural gas in Belgium) and France (nuclear, oil and gas). The energy transition of these countries was experiencing significant delays as their economy is dominated by fossil fuels, notably in transport.

The added value of the paper is a systematic overview of scientific literature proposing different methodologies for the analysis and monitoring of target implementation at the whole region level. The methods applied for the assessment of implementation of Strategy Europe 2020 objectives can be grouped into six categories: statistical analysis, forecasting models, MCDM techniques, DEA, taxonomic and zero-unitarization method, and composite indicator construction. The overview of different methodologies by various authors can serve as a tool for further research.

The performed research also has limitations. The study did not assess the country’s output, which directly determines the effort required to meet climate change and energy goals. It was assumed that volumes of the production of countries were accurately forecasted when setting national targets. However, further research that includes a comprehensive assessment of countries’ capacities to meet climate change and energy targets is necessary.