Assessing the Performance of Sustainable Development Goals of EU Countries: Hard and Soft Data Integration
Abstract
:1. Introduction
2. Background Literature
- utilization of equations describing relations [22];
3. Methods
4. Results
4.1. Discussion of Data and Models
4.2. Data Analysis
- SE—the leader, which obtains the high score despite high social awareness and the high GDP per capita;
- FR and IT—with above average GDP per capita and under average agreement;
- RO, BG, and SK—where the score is due to low GDP per capita and low agreement;
- MT—despite a high agreement with average GDP per capita;
- LV—with an average agreement but low GDP per capita.
- with stabile, equal pessimistic, and optimistic results: BG, CY, EL, ES, HR, HU, LT, LV, MT, RO, SE, SI, PT;
- with small, less than 0.2, differences: DE, EE, FR;
- with average, from 0.2 to 0.4, differences: DK, IE, IT, LU, NL, UK;
- with extreme, over 0.4, differences: AT, BE, CZ, FI, PL, SK.
5. Discussion
Author Contributions
Funding
Conflicts of Interest
Abbreviations
AT | Austria |
BE | Belgium |
BG | Bulgaria |
CY | Cyprus |
CZ | Czechia |
DE | Germany |
DK | Denmark |
EE | Estonia |
EL | Greece |
ES | Spain |
FI | Finland |
FR | France |
HR | Croatia |
HU | Hungary |
IE | Ireland |
IT | Italy |
LT | Lithuania |
LV | Latvia |
LU | Luxembourg |
MT | Malta |
NL | The Netherlands |
PT | Portugal |
PL | Poland |
RO | Romania |
SE | Sweden |
SI | Slovenia |
SK | Slovakia |
UK | United Kingdom |
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Author | Methods and Main Indicators | Aims |
---|---|---|
Bigerna, Bollino, and Micheli (2016) [19] | Simulation model and general equilibrium model data: GDP, CO2 emission, RES development targets | To construct a model that identifies and estimates potential costs of non-compliance with RES targets in some counties; to present a simulation |
D’Adamo and Rosa (2016) [20] | Growing steps structure of RE share and average values data: share of RE production and consumption per capita | To assess the EC RES trajectory toward 2020 strategy |
Duscha, Fougeyrollas, Nathani, Pfaff, Ragwitz, Resch, Schade, Breitschopf, and Walz (2016) [21] | Macroeconomic models: NEMESIS and ASTRA with scenario simulation based on PRIMES data: CO2 reduction, consumption and import of fossil fuels, GDP, employment, RE deployment, costs related to RE deployment | To assess whether RES can positively contribute to the objectives of the European energy policy: combating climate change, improving the security of supply and resulting in economic benefits (job creation and economic growth) |
deLlano-Paz, Fernandez, and Soares (2016) [22] | Markowitz portfolio theory data: cost and risk of RES technologies (e.g., production costs investment, GHG emissions, plant accident) from other studies | To analyze the EU policy framework goals, cost, risk, and security issues |
Pacesila, Burcea, and Colesca (2016) [23] | K-means CA data: renewables data (RES share, RES for heating and cooling, transport, energy dependence | To analyze the RE sector in the EU |
Cilinskis, Indzere, and Blumberga (2017) [24] | TOPSIS data: non-ETS emissions of GHG by different sectors | To introduce a methodology based on multi-criteria analysis TOPSIS to policy planning aimed at climate goals using a Latvian example |
Momete (2017) [12] | Composite index of RE development based on aggregation data: production and consumption mix: fossil fuels oil, gas, coal, solid fuels, nuclear energy, and RES shares | To investigate the energy development in terms of RE in the Eastern Bloc of the EU |
Nikolaev and Konidari (2017) [25] | LEAP (Long-range Energy Alternatives Planning) and the MCDM AMS (the combination of three standard MCMs: AHP, MAUT and SMART data: structure of the final energy consumption by fuels, total contribution (capacity, electricity generation) of RES technologies, and policy packages assumptions | To identify the most feasible level of ambition up to 2030 for the Bulgarian RE policy by simulating three scenarios aiming for different RES targets |
Capros, Kannavou, Evangelopoulou, Petropoulos, Siskos, Tasios, Zazias, and DeVita (2018) [26] | PRIMES model that simulates energy consumption and the energy supply system data: GHG emissions, energy efficiency, RES, energy demand, with costs and required investments | To present a set of scenarios quantified by the PRIMES energy systems model |
Cîrstea, Moldovan-Teselios, Cîrstea, Turcu, and Darab (2018) [11] | Index construction based on weighting and aggregation data: RE mix shares, indicators from sustainability dimensions of the energy system (environmental, e.g., quality of the natural environment, renewable energy consumption; institutional, e.g., transparency of policymaking; GDP per capita; social, e.g., availability of latest technology, spending on R&D; economic, e.g., production of RE, inflation rate). The index uses other rankings scores. | To design a composite index: RE Sustainability Index and use it to examine European countries |
Holma, Leskinen, Myllyviita, Manninen, Sokka, Sinkko, and Pasanen (2018) [17] | Qualitative expert judgment methods data: share of energy from RES | To discuss expert assessment of the impacts and risks of RE production in Finland |
Papież, Śmiech, and Frodyma (2018) [15] | LARS (least angle regression), PCA data: the share of RES in RE mix; indicators from 4 dimensions: environmental, e.g., CO2 emissions per capita; security, e.g., energy import; economic, e.g., GDP per capita, energy consumption per capita and per GDP, cost of energy; political (dummy variables) | To identify factors which determine energy policy in EU countries on the basis on the share of RES in the RE mix |
Pietrapertosa, Khokhlov, Salvia, and Cosmi (2018) [16] | Preliminary (yes/no) analysis of the existing initiatives data: an overview of the national adaptation policies: National Adaptation Strategy and National Action Plan | To overview adaptation initiatives undertaken in 11 south-east European countries with reference to the policies and measures promoted by the EU National Adaptation Strategy and National Action plan |
Radulescu, Fedajev, Sinisi, Popescu, and Iacob (2018) [27] | Co-integration tests and OLS panel regression data: Selected Europe 2020 strategy ratios, e.g., GDP, R&D expenses, employment rate, energy consumption, GHG emission etc. | To determine the most important ratios in the “Europe 2020 Strategy” for sustainable and inclusive growth |
Soava, Mehedintu, Sterpu, and Raduteanu (2018) [28] | Linear regression models, panel data techniques data: GDP, energy consumption from RES | To examine the causal relationship between economic growth and RE consumption |
Arbolino, Boffardi, and Ioppolo (2019) [29] | Hierarchical CA, convergence analysis panel data model (regression) data: energy consumption shares of energy consumption for different RES and many variables for CA defining levels of R&D, human capital, and demographic features | To propose an approach for ex-post monitoring of the actions implemented in Italian regions |
Bórawski, Bełdycka-Bórawska, Szymańska, Jankowski, Dubis, and Dunn (2019) [30] | Descriptive statistic and CA data: share of RE in heating cooling and transport | To assess RE market development with regard to biofuels in the EU |
Brożyna, Mentel, Ivanová, and Sorokin (2019) [31] | Hierarchical CA data: indicators of RE | To distinguish countries among the new EU MSs that increased their electrical capacity from RES |
Cirstea, Tiron-Tudor, Nistor, Cirstea, and Fulop (2019) [32] | Multidimensional scaling data reduction method and CA data: energy indicators, e.g., energy imports, energy use, energy production, capacity, the share of RES, GHG | To measure the differences between the countries in the Eastern European region in terms of RE and economic development |
Lindberg and Markard (2019) [6] | Transition pathway (semi-coherent pattern of major changes) analysis data: list of key EU electricity policies and their key industry actors | To assess the EU electricity policy mix supporting different transition pathways |
Lyeonov, Pimonenko, Bilan, Štreimikienė, and Mentel (2019) [33] | Modified OLS data: GDP per capita and GHG emissions, RE consumption, green investment | To analyze the linkages between GDP per capita, GHG and RE in the total final energy consumption and green investments in the EU |
Malinauskaite, Jouhara, Ahmad, Milani, Montorsi, and Venturelli (2019) [5] | Descriptive statistics analysis data: energy consumption trends, sources and sectors, energy savings | To review EU strategies and policies on energy efficiency; to present national case studies for Italy and the UK |
Mikalauskiene, Štreimikis, Mikalauskas, Stankūnienė, and Dapkus (2019) [34] | Descriptive statistics analysis data: GHG emissions and removals by sector, a set of indicators for the assessment of energy intensity, the structure of consumption, dependency, shares of RES in sectors | To assess GHG emission trends and climate change mitigation policies in the fuel combustion sector of Lithuania and Bulgaria |
Neofytou, Karakosta, and Gómez (2019) [18] | Promethee II data: 12 indicators from 4 dimension: environmental impacts, e.g., GHG reduction, energy-saving; social impact, e.g., employment; economic impacts, e.g., GDP; energy systems impacts, e.g., import, intensity | To assess alternative climate and energy policy scenarios and their socioeconomic, environmental, and energy impacts |
Pach-Gurgul and Ulbrych (2019) [35] | Hellwig’s multidimensional comparative analysis data: energy consumption, the share of RE in energy consumption | To empirically verify progress made implementing the provisions of the EU Energy Package by the V4 countries |
Siksnelyte and Zavadskas (2019) [4] | MCDM, TOPSIS data: indicators for monitoring the progress (electricity interconnection, market concentration, electricity prices, retail electricity markets share of RES in final electricity consumption), indicators for the assessment of sustainability: economic (e.g., prices), environmental (e.g., share of RES, distribution losses) security (import) | To monitor the progress of the electricity sector toward EU objectives; assess the sector sustainability |
Siksnelyte, Zavadskas, Bausys, and Streimikiene (2019) [36] | MCDM MULTIMOORA optimization based on Ratio Analysis technique data: indicators for monitoring the progress of energy import dependency and energy security (e.g., import, supplier concentration), indicators for monitoring the progress of decarbonization (e.g., energy consumption, GHG emission), national energy targets and their implementation, set of EISD indicators to comparative assessment of sustainable: social (e.g., affordability of electricity), economic (e.g., energy use and productivity), and environmental (e.g., GHG emissions) | To present the EU energy policy context; to analyze trends in energy development in eight Baltic Sea Region countries |
Arbolino, Boffardi, Simone, and Ioppolo (2020) [13] | Efficiency index based on normalization, weighting, and aggregation and PCA data: indicators from dimension: sectoral trends (e.g., GDP, energy intensity per capita, RE production per capita, energy consumption), interaction with the environment (e.g., CO2 emissions), economic and policy aspects (e.g., Tax, R&D Expenditure) | To propose an approach for achieving increased efficiency energy; to present the test on a sample of 20 Italian provinces |
Fedajev, Stanujkic, Karabašević, Brauers, and Zavadskas (2020) [37] | MCDM MULTIMOORA: The Ratio System, the Reference Point, and the Full Multiplicative Form data: indicators grouped into dimensions: employment, R&D, climate change and energy, education, poverty, and social exclusion: employment rate, GDP expenditure on R&D, GHG, the share of RE, energy consumption per capita, education leavers, attainment of tertiary education, people at risk of poverty | To classify and rank the EU countries according to the progress in the implementation of the EU strategy “Europe 2020” |
Swain and Karimu (2020) [38] | System of equations based on the OLS model data: electricity price, RE demand, non-RE demand, GDP, heating, and cooling | To examine the RE synergy effect on selected SDGs based on electricity prices in EU countries |
Authors | DEA Models | Aims | Variables |
---|---|---|---|
Grochová and Myšková (2016) [41] | Standard DEA BCC model with slacks | To assess the ability of EU countries to achieve the objectives of Strategy 2020 | Input: electricity production from RES, alternative and nuclear energy, fossil fuel energy, adjusted savings of CO2 damage Output: unemployment, GDP per capita |
Iftikhar, He, and Wang (2016) [42] | SBM DEA model with an undesirable output | To analyze energy and CO2 emission efficiency (ECEE) of major economies (including the EU) | Input: labor, capital, energy Output: GDP Undesirable output: CO2 |
Madaleno Moutinho, and Robaina (2016) [43] | Standard CCR and BCC DEA models | To estimate and compare the efficiency | Input: capital, labor, fossil fuel, RE or GDP per labor, GDP per capita, fossil fuel per GDP Output: GDP per GHG |
Sanz, Yñiguez, and Velasco (2016) [44] | DEA with a desirable and undesirable output and MI | To analyze the efficiency of EU countries, focusing on Spain | Input: gross capital formation (GCF), energy consumption, RE consumption, Output: GDP Undesirable output: GHG |
Moutinho, Madaleno, and Robaina (2017) [45] | DEA and regression analysis | To estimate the efficiency | Inputs: labor and capital productivity, the weight of fossil energy and the share of RE in GDP Output: GDP per GHG emissions Regression analysis: environmental tax revenues, resources productivity, and domestic material consumption |
Gökgöz and Güvercin (2018) [46] | super-efficiency DEA and MI | To benchmark the RE performance of countries under different energy regime settings | Input: the deployed renewables Output: an increase in the share of RE in the total electricity generation Undesirable outputs: the imports of coal products, oil products, and natural gas |
Mezősi, Szabó, and Szabó (2018) [47] | DEA | To assess the cost-efficiency of RE support schemes | Input: cost (efficiency indicator based on support in technology price and electricity consumption), LCOE index (cost level for producing electricity from the RES) Output: the share of assessed RE technology compared to the electricity consumption level (generated electricity/electricity consumption) |
Hsieh, Lu, Li, Chiu, and Xu (2019) [48] | SBM dynamic DEA | To measure the environmental efficiency of energy | Input: labor, capital, EC (energy consumption) Output: GDP, sulfur oxide SOx, GCF (gross capital formation) |
Teng, Lu, and Chiu (2019) [49] | DEA type meta-frontier non-radial directional distance function | To evaluate the efficiency of energy and CO2 emission | Input: labor, capital, energy Output: GDP Undesirable output: CO2 |
Zurano-Cervello, Pozo, Mateo-Sanz, Jimenez, and Guillen-Gosalbez (2019) [50] | Lifecycle and DEA | To assess the sustainability level of the power sector | Input: fossil fuel depletion, total land occupation, water depletion, the annualized cost of electricity Output: climate change (GWP100), human toxicity, ozone depletion, total job-years, electricity generated (data from: [51]) |
GDP [B US $] | Population [M] | CO2 in [M T] | Total Public Energy RD&D Budget [M US $] | EU Should Encourage Investments in Energy [%] | ||
---|---|---|---|---|---|---|
Totally Agree | Tend to and Totally Agree | |||||
Avg. | 672.39 | 18.30 | 114.62 | 310.80 | 61.18 | 91.18 |
Std. dev. | 1018.72 | 23.76 | 161.28 | 420.21 | 10.60 | 4.85 |
GDP | CO2 | |
---|---|---|
CO2 | 0.932 | |
Population | 0.962 | 0.946 |
GDP per Capita | GDP per CO2 | EU Should Encourage Investments: Totally Agree | |
---|---|---|---|
GDP/CO2 | 0.680 | ||
RD&D budget per capita | 0.593 | 0.443 | |
EU should encourage investments: | |||
totally agree | 0.248 1 | 0.433 | |
tend to agree and totally agree | 0.213 1 | 0.244 1 | 0.646 |
Country | Ratio DEA only Hard Data | BCC-O | Ratio DEA Model (non-Linear) | Rough BCC DEA Model α = 0.6 | Rough BCC DEA Model α = 0.8 | ||
---|---|---|---|---|---|---|---|
AT Austria | 49.1% | 63.6% | 69.8% | 54.0% | 99.7% | 49.7% | 100.0% |
BE Belgium | 44.9% | 77.3% | 64.0% | 47.7% | 100.0% | 45.6% | 100.0% |
BG Bulgaria | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% |
CY Cyprus | 44.4% | 44.9% | 44.4% | 44.9% | 45.4% | 44.9% | 48.8% |
CZ Czechia | 34.7% | 52.6% | 43.1% | 37.2% | 100.0% | 34.4% | 100.0% |
DE Germany | 41.2% | 45.3% | 43.8% | 42.0% | 53.9% | 41.9% | 100.0% |
DK Denmark | 76.0% | 87.0% | 83.9% | 77.7% | 100.0% | 76.0% | 100.0% |
EE Estonia | 27.9% | 30.8% | 31.0% | 27.6% | 38.7% | 27.6% | 100.0% |
EL Greece | 56.6% | 56.1% | 56.6% | 56.1% | 56.1% | 56.1% | 62.7% |
ES Spain | 61.5% | 62.4% | 61.5% | 62.4% | 62.4% | 62.4% | 65.8% |
FI Finland | 46.5% | 76.9% | 66.9% | 51.0% | 100.0% | 47.2% | 100.0% |
FR France | 77.1% | 100.0% | 100.0% | 82.5% | 100.0% | 78.6% | 100.0% |
HR Croatia | 87.9% | 87.6% | 87.9% | 87.6% | 87.6% | 87.6% | 100.0% |
HU Hungary | 73.5% | 73.3% | 75.6% | 73.3% | 73.3% | 73.3% | 100.0% |
IE Ireland | 66.3% | 88.8% | 94.9% | 68.5% | 100.0% | 66.3% | 100.0% |
IT Italy | 63.8% | 100.0% | 82.7% | 69.6% | 100.0% | 65.0% | 100.0% |
LT Lithuania | 89.3% | 88.7% | 89.3% | 88.7% | 88.7% | 88.7% | 93.1% |
LU Luxembourg | 48.7% | 63.6% | 67.4% | 51.4% | 79.3% | 48.7% | 100.0% |
LV Latvia | 63.5% | 99.7% | 100.0% | 99.7% | 99.7% | 99.7% | 100.0% |
MT Malta | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% |
NL The Netherlands | 40.7% | 53.8% | 58.1% | 45.6% | 77.8% | 40.9% | 100.0% |
PL Poland | 43.2% | 58.5% | 56.7% | 43.1% | 100.0% | 43.1% | 100.0% |
PT Portugal | 67.4% | 74.1% | 76.9% | 66.9% | 73.3% | 66.9% | 100.0% |
RO Romania | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% |
SE Sweden | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% |
SI Slovenia | 50.6% | 50.4% | 50.6% | 50.4% | 50.4% | 50.4% | 54.4% |
SK Slovakia | 57.0% | 100.0% | 100.0% | 56.8% | 100.0% | 56.4% | 100.0% |
UK United Kingdom | 64.7% | 74.2% | 77.6% | 67.5% | 98.4% | 66.0% | 100.0% |
Country | Network Rough BCC DEA Model | |||
---|---|---|---|---|
AT Austria | 82.2% | 98.8% | 44.1% | 44.1% |
BE Belgium | 81.8% | 100.0% | 38.5% | 39.6% |
CZ Czechia | 100.0% | 100.0% | 17.7% | 28.9% |
DE Germany | 77.0% | 91.4% | 35.7% | 35.7% |
DK Denmark | 76.4% | 89.8% | 76.0% | 76.0% |
ES Spain | 77.0% | 82.7% | 39.4% | 39.4% |
FI Finland | 85.5% | 99.4% | 40.7% | 40.7% |
FR France | 83.3% | 99.6% | 62.1% | 62.1% |
HU Hungary | 100.0% | 100.0% | 42.6% | 63.7% |
IE Ireland | 76.8% | 92.8% | 66.3% | 66.3% |
IT Italy | 86.7% | 100.0% | 43.6% | 61.9% |
NL The Netherlands | 80.8% | 96.7% | 39.3% | 39.3% |
PL Poland | 100.0% | 100.0% | 25.1% | 25.1% |
PT Portugal | 100.0% | 100.0% | 34.3% | 36.7% |
SE Sweden | 72.9% | 84.4% | 100.0% | 100.0% |
SK Slovakia | 100.0% | 100.0% | 30.1% | 100.0% |
UK United Kingdom | 80.3% | 95.9% | 51.9% | 51.9% |
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Chodakowska, E.; Nazarko, J. Assessing the Performance of Sustainable Development Goals of EU Countries: Hard and Soft Data Integration. Energies 2020, 13, 3439. https://doi.org/10.3390/en13133439
Chodakowska E, Nazarko J. Assessing the Performance of Sustainable Development Goals of EU Countries: Hard and Soft Data Integration. Energies. 2020; 13(13):3439. https://doi.org/10.3390/en13133439
Chicago/Turabian StyleChodakowska, Ewa, and Joanicjusz Nazarko. 2020. "Assessing the Performance of Sustainable Development Goals of EU Countries: Hard and Soft Data Integration" Energies 13, no. 13: 3439. https://doi.org/10.3390/en13133439