Measuring Sustainability in Europe: A Min–Max and TOPSIS-Based Evaluation of SDGs Performance

Measuring Sustainability in Europe: A Min–Max and TOPSIS-Based Evaluation of SDGs Performance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Measuring Sustainability in Europe: A Min–Max and TOPSIS-Based Evaluation of SDGs Performance Idiano D'Adamo, Simone Di Leo, Massimo Gastaldi, Alessandro Paris This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7416080/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Discover Sustainability → Version 1 posted 11 You are reading this latest preprint version Abstract Sustainability is a fundamental global challenge that requires an integrated approach capable of balancing economic, environmental, and social dimensions. In recent years, a wide range of indicators has been proposed in the literature to evaluate progress toward the Sustainable Development Goals (SDGs). To effectively monitor and manage this progress, the application of robust and reliable analytical models is essential. This study employs two established methods - min-max normalization and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) - to assess the performance of European countries based on 76 SDG indicators provided by Eurostat for the year 2022. The analysis shows strong consistency between the two methodologies when all indicators are assigned equal weight. Sweden ranks first in both assessments, followed by Denmark and the Netherlands, with Ireland and Belgium also achieving high scores. In contrast, Greece, Cyprus, and Bulgaria consistently rank at the bottom according to both methods. The study also disaggregates the results by dimension, highlighting Germany’s leading performance in the environmental category, Sweden’s dominance in the social dimension, and its strong performance across all three. Denmark also excels in the social dimension, while the Netherlands stands out in the economic category. Three key recommendations emerge from the analysis: i) strengthen European cohesion policies to reduce disparities in sustainability performance across countries; ii) promote integrated strategies that enhance the interconnections among the various indicators; and iii) invest in improving both the availability and quality of sustainability-related data throughout Europe. Europe min-max method sustainable development goals territorial analysis TOPSIS Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The Sustainable Development Goals (SDGs) are a comprehensive set of 17 interrelated and universally applicable global objectives that were officially adopted in 2015 by all United Nations Member States as a cornerstone of the 2030 Agenda for Sustainable Development. These ambitious goals reflect a global consensus and a shared commitment by the international community to confront and address the most critical and interconnected challenges facing humanity today [ 1 , 2 ]. Various literature reviews have been conducted on this topic [ 3 – 5 ]. Among these pressing issues are poverty [ 6 ], hunger [ 7 ], public health [ 8 ], climate change [ 9 ], environmental degradation [ 10 ], medical waste [ 11 ], smart cities [ 12 ], social inequality [ 13 ], gender discrimination [ 14 ], inclusion [ 15 ], lack of access to quality education [ 16 ] and clean water [ 17 ], improving circular economy models [ 18 ] and the need to foster peace, justice, and strong institutions [ 19 , 20 ]. Particular attention should be paid to the link between sustainability and digitalisation in order to identify the balance between technology and human needs with the support of digital education [ 21 , 22 ]. Rooted in the foundational principle of “leaving no one behind,” the Sustainable Development Goals (SDGs) are designed not only to guide nations toward a more prosperous and equitable future, but also to ensure that the benefits of development are distributed fairly - reaching the most vulnerable and marginalized segments of society [ 23 , 24 ]. The vision promoted by the SDGs is one of holistic development - economically viable, socially inclusive, and environmentally sustainable - thus recognizing the intricate balance and interdependence among economic growth, social well-being, and environmental protection [ 25 ]. The 17 SDGs are a strategic reference point also for cities in monitoring prosperity, people, planet, peace and partnership [ 26 ]. To operationalize this vision, the 17 overarching goals are broken down into 169 specific and actionable targets, each accompanied by a set of measurable indicators that facilitate monitoring and evaluation over time. This detailed structure underscores the complexity of sustainable development while enabling transparency and accountability in the implementation process. The achievement of the SDGs requires the active engagement of a diverse range of actors: not only national and local governments, but also the private sector, civil society organizations, academic and research institutions, local communities, and individual citizens [ 27 ]. Environmental monitoring at the regional level represents a crucial challenge for identifying common gaps and opportunities based on concrete data [ 28 ]. To this end, it is essential to have a robust regional data infrastructure capable of ensuring accurate and timely monitoring, while maintaining a dynamic nature to adapt to relevant social changes [ 29 ]. Customized national roadmaps aim to promote equality in educational opportunities in critical sectors [ 30 ], and within the framework of the European Green Deal, differentiated transition pathways should be provided for European regions that are heavily dependent on carbon-intensive industries; in addition, we must not forget to address the inevitable employment and economic challenges that this entails [ 31 ]. The SDGs represent more than a policy framework - they embody a collective roadmap for global transformation toward a fairer, more inclusive, and more sustainable world for present and future generations. There are notable differences in the emphasis placed on the implementation of the SDGs across various geographical regions [ 32 ]. Identifying methodologies to assess interactions between SDGs and defining the factors that influence synergies is an important challenge in supporting this change [ 33 ]. An approach is also needed that shows why a municipal administration should commit to global sustainability frameworks [ 34 ]; in fact, harmonising local initiatives with global sustainability objectives is essential for achieving coherent and impactful progress toward the SDGs [ 35 ]. Monitoring and evaluating progress toward these goals is essential at every territorial level, from global to local, in order to identify strengths, uncover gaps, and guide strategic interventions. In this context, the use of composite indicators is particularly advantageous, as they allow for the aggregation of diverse, multidimensional data into a single, interpretable metric, facilitating both communication and decision-making [ 36 , 37 ]. Given the multifaceted nature of sustainable development, which encompasses a wide spectrum of social, economic, and environmental dimensions, several multi-criteria decision analysis (MCDA) techniques have proven to be extremely useful tools for managing complexity and supporting evidence-based policy choices. Multi-criteria decision-making methods have been widely employed in various fields and disciplines, including decision problems related to sustainable development issues [ 37 – 42 ]. Multi-criteria analysis plays a crucial role in assessing progress toward the SDGs, especially by distinguishing between two fundamental types of models: compensatory and non-compensatory [ 39 ]. Compensatory models, such as TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), allow for trade-offs among criteria - meaning that a poor score in one area can be offset by a better score in another. In contrast, non-compensatory models, like the min-max method, do not permit such trade-offs; weaknesses in one dimension cannot be masked by strengths in another, thereby highlighting critical deficiencies. In recent years, hybrid approaches have emerged that integrate both compensatory and non-compensatory models [ 2 , 43 ]. These methods aim to capture a more balanced and realistic picture of development, particularly in complex and diverse territorial contexts. However, territorial rankings produced by different models and indicator sets can vary significantly. This variation introduces the risk of overlooking essential interconnections among social, environmental, and economic dimensions of sustainability, which are often deeply intertwined [ 44 ]. Specific analyses for each SDG, based on more precise and context-sensitive indicators, are essential [ 45 ], and territorial differences across Europe highlight the need for a multidimensional and integrated approach to achieving the SDGs [ 46 ]. The present analysis focuses on the European context, which offers a particularly compelling case study due to the continent's ambitious goals for climate neutrality and sustainable development. The topic of SDGs has significantly increased in the literature, as shown by the 14% growth in publications in the period 2020–2024 (partial), with much attention given to SDGs 3 and 13[ 23 ]. Some analyses show that synergies between SDGs are more common than compromises, with SDG 5 having the most connections and SDG 7 showing the fewest [ 47 ]. A sustainability score is used in the period 2018–2020 to assess the 27 EU countries on the basis of 58 indicators proposed by Eurostat [ 48 ], and analysis of the period 2010–2021 shows that the development of these countries cannot be defined as fully sustainable [ 49 ]. The diversity of regional landscapes and policy frameworks in Europe makes it an ideal testing ground for assessing the evolution of SDGs. The gap that emerges from the literature is the lack of an overview of the evolution of SDGs. This work aims to fill this gap by providing an assessment of the 27 European countries in terms of sustainability scores based on Eurostat data for 2022, which examine 76 indicators. The objective is to provide a performance ranking among the various countries, to assess whether or not there is synergy using two different multi-criteria methodologies (min-max and TOPSIS) and to break down the results according to three key dimensions: economic, environmental and social. The implications of this work are therefore also to propose monitoring of countries' performance and any specific regional characteristics that emerge from the integration of the various indicators. Following this introduction, the study is structured as follows: Section 2 proposes a literature analysis on multicriteria decision analysis in sustainability assessment while Section 3 outlines the methodological framework used for the analysis. Section 4 presents the results derived from the application of the two methodologies. Section 5 discusses the findings in depth, exploring their broader implications, and Section 6 concludes the study with final reflections and suggestions for future research. 2. Literature review Sustainability assessment entails the systematic evaluation of environmental, social, and economic dimensions of policies or systems. The concept of sustainability is commonly understood through two main perspectives: weak sustainability and strong sustainability [ 50 , 51 ]. Weak sustainability is based on the idea that natural capital can be substituted by human-made or human capital. In contrast, strong sustainability argues that natural and human capital are complementary and not interchangeable, emphasizing the need to preserve natural resources as they are irreplaceable [ 52 ]. Given the inherently complex and multidimensional nature of sustainability, Multi-Criteria Decision Analysis (MCDA) has emerged as a widely adopted and effective methodological approach [ 53 – 55 ]. MCDA is a valuable technique in decision-making processes due to its ability to synthesize large volumes of data. It allows decision-makers to systematically evaluate and compare various alternatives by using synthesized values derived from a set of predefined criteria. One of the main advantages of MCDA is its simplicity and ease of application. The method is well-documented in the literature and has been widely implemented across a range of fields, including those related to sustainability [ 56 – 58 ]. Several areas of application lend themselves well to this approach [ 59 ]. MCDA comprises a range of techniques such as the Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), TOPSIS, min-max, Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), and ELimination Et Choix Traduisant la REalité (ELECTRE). These methods vary in how they assign weights, normalize criteria, and aggregate results. MCDA is based on the identification and evaluation of appropriate criteria aligned with the decision-making objectives. However, a primary limitation of the method is its static framework; it does not incorporate dynamic aspects or account for direct interactions among variables. Despite this drawback, MCDA remains a robust tool within the field of operations research, particularly for selecting the most suitable alternative among multiple options [ 60 ]. Numerous studies [ 61 , 62 ] have highlighted the adaptability of MCDA in integrating both quantitative and qualitative data, accommodating stakeholder preferences, and addressing context-specific criteria [ 61 , 62 ]. This versatility renders MCDA particularly well-suited for sustainability assessments, which often involve complex trade-offs and inherent uncertainties. Multi-criteria assessment methods are a well-established approach to evaluating countries' sustainability performance [ 63 ] and also to compare economic, environmental and social progress of countries [ 64 ]. The Sustainable Development Index and corresponding dashboard have been proposed as tools to measure countries' progress in achieving the SDGs [ 26 ]. To effectively monitor and support this progress, the application of appropriate statistical and analytical methods is essential [ 65 ]. In this context, several studies have emphasized the advantages of using multi-criteria decision-making methods to assess sustainability performance in relation to the SDGs [ 38 , 66 , 67 ]. Empirical analyses have focused on various geographical levels. Some studies concentrate on European capital cities and their alignment with the SDGs [ 68 ], while others evaluate the overall performance of European countries [ 69 ]. Importantly, the literature underscores that the successful achievement of the 2030 Agenda requires not only the exploitation of synergies among the SDGs but also the management and resolution of potential trade-offs between goals [ 27 ]. Another indicator used in this context is the Sustainability score [ 40 ], calculated as the product of a row vector (representing the values assigned to each criterion for a given country) and a column vector (indicating the weights of the criteria). This calculation yields the SDG Index, a dimensionless measure based on the 17 SDGs proposed by the United Nations. The SDG Index has been applied to assess the sustainability performance of 27 European countries [ 48 ]. In the context of SDG evaluation, the widely cited methodology assigns equal importance to each SDG [ 70 ]. However, the scientific community has not reached full consensus on this approach, with some scholars advocating for differentiated weights and aggregation rules [ 71 – 73 ]. Other approaches explore both compensatory and non-compensatory aggregation rules in sustainability assessments [ 43 ] integrating social welfare considerations and stakeholder participation [ 74 ]. MCDA, while instrumental in analysing sustainability data, faces several methodological and informational challenges within the detailed assessment of SDGs. A significant gap in literature lies in the inconsistency of territorial rankings, often arising from the varied selection of models and indicator sets, which can under-estimate the relations between the social, environmental, and economic dimensions [ 49 ]. To fill this gap, both min-max normalization and TOPSIS are used, providing a cross-validated assessment in evaluating European countries SDG performance. Another important gap in literature concerns the lack of a comprehensive, contemporary overview of SDG evolution and national progress, particularly within the European context, with recent work in this field using 2020 data [ 48 ]. This research fills this gap by delivering an up-to-date assessment using recent Eurostat data across 76 indicators, offering not only a current performance ranking but also an analysis of synergies through multiple multi-criteria perspectives. By directly addressing these gaps, this work promotes the application of MCDA in complex sustainability assessments. 3. Materials and methods This work aims to calculate the Sustainability Score using MCDA. Accordingly, the process can be delineated into three distinct phases: i) establishing appropriate criteria necessary to achieve the intended objective; ii) attributing weights and values to each criterion based on their relative importance; and iii) aggregating the resulting data to compute a comprehensive sustainability score for each alternative. Section 3.1 identifies the selection of criteria and Section 3.2 shows the methodological approach used in this work, based on TOPSIS and min-max. Next, the baseline model (Section 3.3 ) and the alternative model (Section 3.4 ), considering variations in weights indicators assignments, are proposed for evaluating the performance of different European countries. An overview of the methodological framework employed for this work is presented in Figure S1 . 3.1 Criteria Selection The first step in the analysis involves the identification of reliable criteria supported by available data, to minimize subjectivity in the assessment process. To this end, the present study utilizes the Eurostat database, the official statistical office of the European Union, which serves as a widely recognized reference for high-quality, accessible data in academic research [ 44 , 75 , 76 ]. Eurostat provides a structured set of indicators corresponding to each of the 17 Sustainable Development Goals (SDGs). In this work, the criteria will be identified in the indicators provided by Eurostat. All available indicators were considered, excluding any duplicates or those with missing data, resulting in a total of 76 unique indicators, each linked to its respective SDG – Fig. 1 and Table 1 . Notably, the distribution of indicators across SDGs is uneven: for example, seven indicators are associated with SDG 7; six indicators with SDGs 1, 4 and 10 and one indicator with SDGs 6 and 15. This approach ensures the replicability of the results, as it relies on objective data and excludes indicators not due to their perceived unsuitability, but because their data are only available for a limited number of countries in the reference year. Including such indicators would compromise the comparability of the analysis. It is important to acknowledge, however, that the limited availability of indicators constitutes a constraint of this study. Moreover, one of the main challenges in relying on official data is the time lag in their publication, which can result in analyses and policy recommendations becoming outdated [ 77 ]. To mitigate this issue, the most recent data available (from 2022) have been used. Table 1 List of indicators SDG 1 - No Poverty People at risk of poverty or social exclusion Housing cost overburden rate by poverty status People at risk of income poverty after social transfers Severe material and social deprivation rate by age group and sex Persons living in households with very low work intensity, by age group In work at-risk-of-poverty rate SDG2 - Zero Hunger Agricultural factor income per annual work unit AWU source: Eurostat, DG AGRI Area under organic farming Obesity rate by body mass index BMI Ammonia emissions from agriculture source: EEA Government support to agricultural research and development - euro per inhabitant SDG3 - Good Health and Well-Being Share of people with good or very good perceived health by sex Self-reported unmet need for medical examination and care by sex Healthy life years at birth by sex Consumption of antibiotics in the community and hospital sectors – defined daily doses DDD per day SDG4 - Quality Education Tertiary educational attainment by sex Participation in early childhood education by sex children aged and over Adult participation in learning by sex Early leavers from education and training by sex Low achieving -year-olds in reading, mathematics or science Share of individuals having at least basic digital skills, by sex SDG5 - Gender Equality Inactive population due to caring responsibilities by sex Positions held by women in senior management positions source: EIGE Seats held by women in national parliaments and governments source: EIGE -- NAT PARL Gender pays gap in unadjusted form Gender employment gap, by type of employment SDG6 - Clean Water and Sanitation Population connected to at least secondary waste water treatment SDG7 - Affordable and Clean Energy Population unable to keep home adequately warm by poverty status Final energy consumption Energy import dependency by products Share of renewable energy in gross final energy consumption by sector Primary energy consumption Final energy consumption in households per capita Energy productivity SDG8 - Decent Work and Economic Growth Real GDP per capita Young people neither in employment nor in education and training by sex NEET Investment share of GDP by institutional sectors Long-term unemployment rate by sex Employment rate by sex SDG9 - Industry, Innovation and Infrastructure Gross domestic expenditure on R&D by sector R&D personnel by sector Share of buses and trains in inland passenger transport Share of rail and inland waterways in inland freight transport Air emission intensity from industry SDG10 - Reduced Inequality Purchasing power adjusted GDP per capita Income distribution Adjusted gross disposable income of households per capita Relative median at-risk-of-poverty gap Income share of the bottom % of the population Asylum applications by state of procedure SDG11 - Sustainable Cities and Communities Population living in households considering that they suffer from noise, by poverty status Road traffic deaths, by type of roads source: DG MOVE Recycling rate of municipal waste Premature deaths due to exposure to fine particulate matter (PM2.5) SDG12 - Responsible Consumption and Production Circular material use rate Raw material consumption - Tonnes per capita RMC Consumption footprint – Per inhabitant - single weighted score Generation of waste by hazardousness SDG13 - Climate Action Net greenhouse gas emissions source: EEA Population covered by the Covenant of Mayors for Climate & Energy signatories’ source: Covenant of Mayors Net greenhouse gas emissions of the Land use, Land use change and Forestry LULUCF sector - Tonnes per capita Average CO emissions per km from new passenger cars Green bond issuance by type of issuer SDG14 - Life Below Water Surface of the marine protected areas Coastal bathing sites with excellent water quality by location Inland bathing sites with excellent water quality by location SDG15 - Life on Land Surface of the terrestrial protected areas SDG16 - Peace and Justice Strong Institutions Population reporting occurrence of crime, violence or vandalism in their area by poverty status Perceived independence of the justice system source: DG COMM Population with confidence in EU institutions by institution source: DG COMM Corruption Perceptions Index Victims of trafficking in human beings by sex SDG17 - Partnerships to achieve the SDG Official development assistance as share of gross national income source: DG INTPA, OECD High-speed internet coverage, by type of area source: DG CNECT and Eurostat General government gross debt Share of environmental taxes in total tax revenues 3.2 Min-max and TOPSIS methods Multi-Attribute Decision-Making methods can be classified from various perspectives. One key distinction is between non-compensatory and compensatory models [ 39 ]. Notably, these methods have been successfully applied in spatial-level analyses [ 67 ], including the evaluation of territories using TOPSIS [ 78 ] and the min-max method [ 79 ]. Specifically, TOPSIS is a compensatory method enables the comparison of alternatives by calculating their relative closeness to an ideal solution. It allows high values in one indicator to offset low values in another, based on the assumption that strong performance in one area may compensate for weaker outcomes elsewhere. This makes it suitable in contexts where trade-offs are acceptable. Instead, min-max method operates under the premise that each SDG holds intrinsic value and that a shortfall in one area cannot be neutralized by excellence in another. Such approaches emphasize the indivisibility of the goals, reinforcing the idea that sustainable development cannot be truly achieved unless all its dimensions are addressed simultaneously and with equal importance. Min-Max normalization allows for the comparison of criteria expressed in different units by converting them to a common scale. The min-max normalization formula is expressed as: $$\:{x}^{{\prime\:}}ᵢⱼ\:=\frac{\left(xᵢⱼ\:-\text{min}\left(xⱼ\right)\right)}{\left(\text{max}\left(xⱼ\right)-\text{min}\left(xⱼ\right)\right)}$$ where x' i ⱼ is the normalized value for alternative i and criterion j, x i ⱼ is the original value, and min(xⱼ) and max(xⱼ) represent the minimum and maximum values for criterion j across all alternatives. It is valued for its simplicity and ease of implementation, though it can be significantly affected by the presence of outliers [ 80 ]. TOPSIS, which is based on the concept of proximity to the ideal solution, provides an intuitive way to rank alternatives. However, its effectiveness largely depends on the choice of normalization method [ 81 ]. Further insights into these multi-criteria approaches are explored by a number of authors who highlight the various advantages and disadvantages of these approaches [ 82 ]. TOPSIS and the min-max method are prominent in the literature. TOPSIS identifies the optimal alternative as the one closest to the positive ideal solution and farthest from the negative ideal solution across the set of criteria [ 58 ]. The TOPSIS procedure follows five main steps: construct the normalized decision matrix using \(\:rᵢⱼ\:=\:\frac{xᵢⱼ}{\sqrt{\varSigma\:{ᵢ}^{=1}ᵐ\:xᵢ{ⱼ}^{2}}}\) ; calculate the weighted normalized decision matrix as \(\:vᵢⱼ\:=\:wⱼ*\:rᵢⱼ\) , where wⱼ represents the weight of criterion j; determine the positive ideal solution \(\:{A}^{+}=\:\left\{{v}^{1+},\:{v}^{2+},\:\dots\:,\:v{ₙ}^{+}\right\}\) and negative ideal solution \(\:{A}^{-}=\:\left\{{v}^{1-},\:{v}^{2-},\:\dots\:,\:v{ₙ}^{-}\right\}\) ; calculate the Euclidean distances to the ideal solutions using \(\:{D}^{+}ᵢ\:=\:\sqrt{\varSigma\:{ⱼ}^{=1}ⁿ\:{\left(vᵢⱼ\:-\:v{ⱼ}^{+}\right)}^{2}}\) and \(\:{D}^{-}ᵢ\:=\:\sqrt{\varSigma\:{ⱼ}^{=1}ⁿ\:{\left(vᵢⱼ\:-\:v{ⱼ}^{-}\right)}^{2}}\) ; and compute the relative closeness coefficient \(\:Cᵢ\:=\:\frac{{D}^{-}ᵢ}{\left({D}^{+}ᵢ\:+\:{D}^{-}ᵢ\right),\:}\) where values closer to 1 indicate better performance The min-max method, on the other hand, normalizes values within a 0–1 range by assigning 1 to the best performance and 0 to the worst [ 40 ], and is commonly used in international benchmarking reports [ 83 ]. For benefit criteria (higher values are better), the min-max formula applies the standard normalization, while for cost criteria (lower values are better), the formula is modified as $$\:{x}^{{\prime\:}}ᵢⱼ\:=\frac{\left(\text{max}\left(xⱼ\right)-\:xᵢⱼ\right)}{\left(\text{max}\left(xⱼ\right)-\text{min}\left(xⱼ\right)\right)}$$ . To exploit the strengths of both techniques, this study adopts a combined approach that uses both the TOPSIS method (on the original data) and the min-max method. Equal weighting \(\:(wⱼ\:=\frac{1}{n})\) was applied to all criteria to ensure unbiased assessment, where n represents the total number of evaluation criteria. A further approach could be to obtain a composite indicator to classify the territories analysed obtained as the average of min-max and TOPSIS [ 2 ]. 3.3 Baseline model Composite indicators are constructed by aggregating carefully selected sub-indicators, which are normalized and then weighted in order to build a single synthetic indicator [ 84 ]. The main advantage of such indicators lies in the ability to combine diverse information through an appropriate methodology, while also ensuring strong communicative effectiveness. In this context, it becomes essential to define a weighting system, which can be implicit - by assigning equal weight to each element - or explicit, through specific approaches such as expert judgment. Several weighting methods are available [ 85 ]; however, those based on equal weighting are generally preferred due to their simplicity and immediacy [ 86 ]. The composite measure used is the Sustainability Score, a summary indicator that aggregates the various contributions of the SDGs, and the method used is to assign the same weighting to all indicators [ 48 ]. Assigning equal weight to the SDGs ensures fairness, neutrality, and simplicity, avoiding arbitrary hierarchies and facilitating communication and cross-country comparability. The choice of not assigning greater emphasis to specific goals or indicators is supported by several studies [ 87 , 88 ]. Other approaches adopt a weighting system in which the influence of each individual indicator depends on its specific relevance or importance [ 89 ]. Some scholars have argued that multidimensional comparisons should be carried out at the level of individual indicators rather than through aggregated indices [ 90 ]. The practical steps adopted in the analysis are as follows: (i) where data for 2022 are not available, the most recent data from the previous year are used; (ii) where indicators are not expressed in relative terms, values are normalized by dividing them by the number of inhabitants. The analysis is based on a total of 2052 data points, derived from 76 indicators across 27 alternatives (i.e., the EU Member States). The scope of this research is limited to the 27 EU countries, and only indicators for which data are available for all Member States are included, to ensure consistency and comparability across the dataset. These indicators are treated as criteria for the MCDA analysis in this study. Additionally, the indicators are categorized based on the three core dimensions of sustainability, as identified by some authors [ 48 , 91 , 92 ] : Economic dimension - SDGs 7, 8, 9, 11, and 12. Environmental dimension - SDGs 6, 13, 14, and 15. Social dimension - SDGs 1, 2, 3, 4, 5, 10, 16, and 17. 3.4 Alternative model To enhance the robustness of the results obtained, alternative scenarios were examined, with particular attention to variations in indicator values and weight assignments. Regarding the values, it is recognized that they may fluctuate according to different trends. However, since the analysis is based on real data sourced from Eurostat, it is methodologically inappropriate to artificially modify these values. As for the weights, several approaches are possible. The method adopted in this study assigns equal weight to each indicator (Equal Weight among Indicators – EWI). In line with other analyses [ 40 ], an alternative approach could involve assigning equal weight to each SDG (Equal Weight among SDGs – EWG), regardless of the number of indicators associated with each goal. In this case, SDGs with fewer indicators would give greater weight to each one. For example, SDG 4, which includes six indicators, assigns less weight to each compared to SDG 5, which includes five. Another possible method involves assigning equal weight to the three dimensions of sustainable development - economic, social, and environmental - following the Equal Weight among Dimensions of SDGs – EWDG approach. Similarly, this would result in indicators within the most populous dimension receiving less weight. For instance, each of the 41 indicators related to the social dimension would carry less weight than the 25 indicators in the economic dimension. In conclusion, the following weighting models are proposed: EWI - Equal Weight among Indicators. EWG - Equal Weight among SDGs. EWDG - Equal Weight among Dimensions of SDGs. 4. Results The results section presents the ranking of the 27 EU countries based on a baseline scenario in which the two methodological approaches are analysed (section 4.1 ). The data are then broken down into the three dimensions of sustainability (section 4.2 ) and, finally, the European countries are assessed considering alternative scenarios with different proposals on indicators weights (section 4.3 ). In addition, a sensitivity analysis is proposed (section 4.4 ) 4.1 Sustainability score – Baseline context The construction of rankings is often met with various criticisms. Common concerns include the dependency of results on the chosen methodology, the use of outdated indicators, and the potential lack of comprehensiveness in capturing the full scope of assessment criteria. Despite these limitations, the use of quantitative methods remains essential for identifying opportunities to improve existing structures. MCDA offers a key advantage by condensing complex datasets into clear insights, highlighting which countries are achieving specific objectives. As explained above, starting from the absolute data presented in Tables S1-S2, after verifying that all alternatives had a value and applying the approach proposed in section 3.3 , all data were normalized to 0–1 in the min-max approach. The analyses were carried out using two methodological approaches (min-max and TOPSIS) in order to assess whether the performance of these countries may depend on the analysis methodology. Table 2 presents the Sustainability Score calculated using both methods, and based on this score, colour-coded maps are provided in Fig. 2 - where green represents the best-performing countries, and red highlights those with the weakest outcomes. Furthermore, Fig. 3 graphically shows the comparison between European countries in terms of final value (the average value of the min-max (0.560) is smaller than that of TOPSIS (0.606)). Table 2 Sustainability Score of European countries in 2022 according to TOPSIS and min-max methods TOPSIS MIN-MAX AVERAGE Value Ranking Value Ranking Ranking Belgium 0.642 5 0.608 5 4 Bulgaria 0.531 25 0.452 26 25 Czechia 0.651 4 0.601 7 6 Denmark 0.691 2 0.669 2 2 Germany 0.612 14 0.595 9 10 Estonia 0.601 17 0.551 14 17 Ireland 0.640 6 0.621 4 4 Greece 0.522 26 0.446 27 27 Spain 0.583 21 0.506 23 23 France 0.602 16 0.561 13 14 Croatia 0.614 12 0.527 20 18 Italy 0.593 20 0.518 21 21 Cyprus 0.520 27 0.494 24 25 Latvia 0.600 18 0.512 22 19 Lithuania 0.614 10 0.543 17 12 Luxembourg 0.595 19 0.579 11 15 Hungary 0.607 15 0.550 15 15 Malta 0.567 22 0.534 18 19 Netherlands 0.657 3 0.651 3 3 Austria 0.613 13 0.592 10 10 Poland 0.614 11 0.544 16 12 Portugal 0.540 24 0.527 19 22 Romania 0.551 23 0.452 25 24 Slovenia 0.633 8 0.597 8 8 Slovakia 0.638 7 0.569 12 9 Finland 0.625 9 0.607 6 7 Sweden 0.695 1 0.702 1 1 The analysis reveals that, regardless of the method employed, three Northern European countries consistently dominate the top of the ranking. Sweden emerges as the clear leader in both approaches, achieving the highest scores: 0.702 using the min-max method and 0.695 with TOPSIS. Following closely are Denmark (0.669 min-max, 0.691 TOPSIS) and the Netherlands (0.651 min-max, 0.657 TOPSIS). These countries can be classified as top performers, consistently demonstrating high levels of performance across the selected sustainability indicators. This outcome aligns with existing literature, which frequently highlights Sweden, Denmark, and the Netherlands as benchmarks in sustainability and governance [ 47 , 48 ]. When the two methods are combined, equal weight is given to each method in order to produce a single ranking. Sweden, Denmark and the Netherlands, which have the same position, will obviously retain the same place. The first divergence between the two methods emerges from fourth place onwards. In fact, the Czech Republic shows that its performance could be more favourable with a method that takes into account relative proximity to ideal solutions (fourth place in TOPSIS becomes seventh in min-max). Fourth place in min-max is instead occupied by Ireland (which is sixth in TOPSIS). In the single ranking, Ireland is in fourth place together with Belgium (which is fifth in the two separate rankings) and the Czech Republic is in sixth place. However, the most significant differences are for Croatia, Lithuania, but also Slovakia and Poland, which perform better in TOPSIS; Luxembourg, but also Germany and Portugal perform better in min-max. Overall, the TOPSIS method produces consistently higher scores across all countries (Sweden is the exception). This results in a broader distribution, with a value range of 0.256 between the highest and lowest performers, compared to a narrower range of 0.174 in the TOPSIS method. Despite these numerical differences, the ranking order remains relatively stable, particularly at the extremes of the distribution. At the lower end of the scale, Greece records the lowest score in the min-max method (0.446), while Cyprus ranks last in TOPSIS (0.520). Consequently, the combined ranking sees Cyprus (27th in TOPSIS and 24th in min-max) and Bulgaria (25th and 26th in TOPSIS and min-max, respectively) at the bottom of the table, ahead of Greece (26th in TOPSIS and last in min-max). Among Europe’s most populous nations, Germany outperforms France in both methods, indicating a stronger overall sustainability profile. The overall ranking places Germany in 10th place, France in 14th, Italy in 21st and Spain in 23rd . A comparison between the two rankings - both in terms of rank position and performance values -proves to be both useful and insightful (Fig. 4 ). The correlation analysis reveals a relevant coefficient of determination (R squared equal to 0.78 and 0.82), indicating a strong and statistically significant relationship between the results produced by the two methods. This high degree of correlation reinforces the robustness and reliability of the findings. Although some differences in rankings were previously noted, these variations can be considered minor considering the strong alignment observed between the methods. The consistency confirmed by the correlation analysis lends further credibility to the overall evaluation framework. Since the adoption of the 2030 Agenda for Sustainable Development in 2015, the United Nations' 17 SDGs have served as a global framework to tackle the most pressing economic, social, and environmental challenges. One of the key aspects of SDGs is that they are not meant to be pursued in isolation. Instead, they are deeply interconnected, meaning that progress in one area often has ripple effects across others. Understanding these interconnections is essential for anyone involved in policymaking, planning, or implementing sustainable development strategies. These interconnections can be summarized as follows: Synergies Between Goals. In many instances, advancing one goal can significantly support progress in others. Potential Trade-offs. However, not all relationships between SDGs are synergistic. In some cases, progress in one area may come at the expense of another - unless managed carefully. Enabling and Cross-cutting Goals. Some goals, meanwhile, play an enabling role, acting as foundations for progress across multiple sectors. These interconnections suggest that achieving the SDGs requires more than just isolated actions; it calls for a holistic and integrated approach. In particular, there is a growing need for a greater policy coherence, meaning stronger coordination across different sectors and levels of government to manage trade-offs and enhance synergies; moreover an Improved data monitoring is necessary, so that progress on individual SDGs is evaluated in light of its broader impact on the rest of the Agenda; Finally, integrated solutions must be defined, with programs and interventions designed from the outset to contribute to multiple goals simultaneously. In summary, the complexity and interconnectedness of the SDGs should not be seen as an obstacle, but as an opportunity. By recognizing how these goals interact, we can develop smarter, more effective policies that accelerate progress across the entire 2030 Agenda. However, the methods proposed in this study are not able to assess the interdependencies between SDGs, but only between indicators. This study analyses some specific observations through an in-depth and complex analysis of the indicator correlation matrix. However, given the large number of indicators, only a few general considerations can be made (Table S3). The highest positive correlation values are found for the following pairs: 0.96 (SDG08-10 – SDG10-10); 0.92 (SDG01-20 – SDG10-41); 0.89 (SDG01-10 – SDG10-41); 0.88 (SDG01-10 – SDG01-20); 0.84 (SDG02-30 – SDG02-40); 0.83 (SDG01-10 – SDG01-31, SDG07-30 – SDG08-10); 0.81 (SDG16-50 – SDG17-10) and 0.80 (SDG01-10 – SDG10-30, SDG07-30 – SDG10-10, SDG16-40 – SDG16-50). At the same time, the highest negative correlation values are: -0.98 (SDG10-41 – SDG10-51); -0.93 (SDG01-20 – SDG10-50) and − 0.87 (SDG01-10 – SDG10-50). Concentration was then analysed using two levels of analysis at the SDG (Fig. 5 ) and indicator (Figure S4) levels. Figure 5 shows the concentration of individual SDGs, where only SDGs 1, 6 and 7 have a significant concentration (> 0.60). On the other hand, SDGs 13 and 15 have a lower concentration (< 0.50). These data do not show values that differ greatly from one another, highlighting the absence of SDGs in which only one or a few countries predominate or in which almost all countries have a common performance. As regards the individual indicators, it should be noted that 14 of them have a concentration indicator higher than 0.70: SDG09-70 (0.92); SDG10-60 (0.88); SDG02-60 (0.84); SDG07-10 (0.82); SDG01-31, SDG16-70 (0.80); SDG01-50, SDG07-11 (0.78); SDG08-40 (0.77); SDG06-20 (0.76); SDG12-21 (0.74); SDG03-60, SDG12-51 and SDG14-40 (0.73). 4.2 Sustainability score – Economic, environmental and social perspective Sustainability is traditionally understood as encompassing three interdependent dimensions: social, economic, and environmental. In line with the structure presented in the previous section 3.3 , the SDGs are distributed across these dimensions (Table 3 ). While all indicators are assigned equal weight within the analysis, it is important to emphasize that the three dimensions do not hold equal weight overall due to the differing number of indicators that comprise each. Specifically, a significant proportion of the indicators − 41 out of 76 - are associated with the social dimension. This can largely be attributed to the fact that 8 out of the 17 SDGs are primarily social in nature. In comparison, the economic dimension is represented by 25 indicators, whereas the environmental dimension accounts for the fewest, with only 10 indicators. As a direct consequence of this imbalance, countries that perform well in the social - and to a slightly lesser extent, the economic - dimension tend to achieve higher overall scores. In this context, an analysis of top-performing countries provides meaningful insight. Sweden emerges as the leading country in the social dimension of both methodologies; in the min-max, it performs better than TOPSIS in the other two dimensions, ranking first (and fourth) in the economic dimension and second (and third) in the environmental dimension. Denmark consistently ranks high in all three dimensions: it ranks second in the economic and social dimensions using the min-max and TOPSIS methods. In the environmental dimension, it consistently performs better using the min-max method (3rd vs 4th ). The Netherlands also demonstrates strong performance, ranking first and third in the economic dimension, while taking sixth positions in the social and environmental dimension using min-max. Instead, its performance is comparatively weaker using TOPSIS method (12th and 9th, respectively). As regards the countries ranked fourth in the overall ranking, Belgium performs very well in the environmental dimension (first place in TOPSIS) and occupies intermediate positions in the other dimensions (ranging from 7th place with TOPSIS to 15th with min-max in the economic dimension). Ireland ranks third in the social dimension with min-max and in the economic dimension with TOPSIS; however, in both methods, it is very weak in the environmental dimension. Other notable performances include Germany, which tops the environmental dimension under the min-max method (2nd in the TOPSIS). Czechia and Slovenia performs well in the social dimension considering TOPSIS method and Finlandia also for min-max approach. Regarding the economic dimension, Czechia is in fourth place in the min-max method. To better understand the relationship between the two approaches (TOPSIS, min-max), the correlation coefficient (R squared) for the three dimensions is calculated again. There is a strong correlation in the environmental dimension, with 0.81 and 0.83 for performance and ranking respectively. The correlation tends to be less strong, but still present, in the economic dimension (0.63 and 0.76 respectively), while the data emerging from the social dimension are less correlated (0.56 and 0.43 respectively). When aggregated and compared with the overall data, these figures confirm the validity of these methods for capturing the different nuances of sustainability. It should also be noted that the sample sizes vary. Among the lowest-performing countries, some observations emerge for each dimension. From a social perspective, Bulgaria and Greece occupy the bottom positions in both methods; Cyprus and Romania fare worse than them in TOPSIS and min-max respectively. From an environmental perspective, Malta in both methods, Slovenia in TOPSIS and Estonia in min-max stand out for their very weak performance. From an economic perspective, Portugal and Greece in both methods, Bulgaria in TOPSIS and Italy in min-max. Finally, for the analysis of the dimensions, the two methods are aggregated, assigning equal importance to both (Table 4 ). The countries that emerged as leaders stand out in the individual dimensions. In fact, the analysis of the top three positions highlights the following: Sweden leads in the social dimension and ranks second and third in the environmental and economic dimensions, respectively; Denmark leads in the economic dimension, followed by Sweden in the social dimension and occupying fourth place in the environmental dimension; the Netherlands is first in the economic dimension; Finland is third in the social dimension, while Germany and Belgium occupy the top two positions in the environmental dimension. Greece occupies last place in the social dimension, Malta in the environmental dimension, while Portugal and Greece are at the bottom of the economic dimension. As a possible methodological refinement, and to mitigate discrepancies between the two approaches, a potential solution could be to compute an average of the results derived from both TOPSIS and min-max methods [ 2 ]. However, the literature offers various approaches in which a variety of methodologies on sustainability issues can be considered [ 67 , 93 , 94 ]. Table 3 Sustainability Score of European countries in 2022 according to social, environmental and economic dimensions Social dimension Environmental dimension Economic dimension TOPSIS MIN-MAX TOPSIS MIN-MAX TOPSIS MIN-MAX Value Rkg Value Rkg Value Rkg Value Rkg Value Rkg Value Rkg Belgium 0.628 13 0.603 9 0.675 1 0.650 4 0.652 7 0.555 15 Bulgaria 0.547 25 0.428 25 0.445 16 0.551 12 0.527 26 0.480 22 Czechia 0.685 3 0.606 8 0.415 23 0.450 22 0.656 6 0.667 4 Denmark 0.696 2 0.668 2 0.587 4 0.665 3 0.707 2 0.697 2 Germany 0.617 15 0.563 13 0.646 2 0.716 1 0.599 18 0.563 13 Estonia 0.611 17 0.558 15 0.426 21 0.392 27 0.621 13 0.588 9 Ireland 0.642 11 0.653 3 0.380 25 0.431 24 0.692 3 0.634 6 Greece 0.510 26 0.418 26 0.539 8 0.647 5 0.534 25 0.367 27 Spain 0.599 20 0.500 23 0.499 11 0.537 13 0.577 23 0.443 24 France 0.611 18 0.558 14 0.565 6 0.578 7 0.598 20 0.515 19 Croatia 0.650 7 0.543 18 0.420 22 0.462 21 0.611 14 0.495 20 Italy 0.617 16 0.526 20 0.478 13 0.525 16 0.586 21 0.412 26 Cyprus 0.495 27 0.515 22 0.387 24 0.467 19 0.580 22 0.451 23 Latvia 0.619 14 0.499 24 0.427 20 0.419 25 0.609 16 0.558 14 Lithuania 0.643 10 0.552 17 0.511 10 0.533 15 0.601 17 0.487 21 Luxembourg 0.594 21 0.633 5 0.443 18 0.537 14 0.626 10 0.549 16 Hungary 0.609 19 0.516 21 0.582 5 0.565 9 0.610 15 0.593 8 Malta 0.557 23 0.556 16 0.309 27 0.402 26 0.623 12 0.565 12 Netherlands 0.628 12 0.630 6 0.537 9 0.597 6 0.719 1 0.692 3 Austria 0.590 22 0.577 10 0.475 14 0.561 10 0.667 5 0.624 7 Poland 0.654 6 0.566 12 0.439 19 0.431 23 0.599 19 0.536 17 Portugal 0.649 9 0.573 11 0.457 15 0.509 17 0.444 27 0.438 25 Romania 0.551 24 0.416 27 0.498 12 0.559 11 0.561 24 0.518 18 Slovenia 0.672 4 0.620 7 0.375 26 0.463 20 0.648 8 0.584 10 Slovakia 0.649 8 0.533 19 0.558 7 0.574 8 0.640 9 0.581 11 Finland 0.656 5 0.637 4 0.444 17 0.502 18 0.623 11 0.660 5 Sweden 0.731 1 0.706 1 0.642 3 0.707 2 0.668 4 0.766 1 Table 4 Average ranking position by sustainability dimensions Social dimension Environmental dimension Economic dimension Belgium 10 2 10 Bulgaria 25 14 25 Czechia 4 22 5 Denmark 2 4 1 Germany 15 1 16 Estonia 16 25 10 Ireland 6 26 4 Greece 27 5 26 Spain 23 11 23 France 16 5 20 Croatia 11 20 17 Italy 19 15 23 Cyprus 24 20 22 Latvia 20 22 15 Lithuania 13 13 19 Luxembourg 12 16 14 Hungary 22 7 12 Malta 21 27 13 Netherlands 7 8 1 Austria 16 11 6 Poland 7 19 18 Portugal 9 16 26 Romania 26 10 21 Slovenia 4 24 8 Slovakia 13 8 9 Finland 3 18 7 Sweden 1 2 3 4.3 Sustainability score – Alternative contexts The next objective of this work is to provide solidity to the results obtained previously or, in any case, to analyse them further. Section 3.4 showed two different contexts (EWG, EWDG) in addition to the basic context (EWI). Tables 5 – 6 show the sustainability scores and the relative ranking in the three approaches. Here too, a version ranging from green to red has been chosen to better communicate the different performances. The analysis was applied to the min-max method. Table 5 Sustainability scores of European countries in both baseline and alternative contexts (min-max approach) EWI EWG EWDG Belgium 0.608 0.601 0.598 Bulgaria 0.452 0.499 0.519 Czechia 0.601 0.608 0.602 Denmark 0.669 0.657 0.654 Germany 0.595 0.666 0.692 Estonia 0.551 0.552 0.549 Ireland 0.621 0.583 0.561 Greece 0.446 0.514 0.543 Spain 0.506 0.543 0.554 France 0.561 0.594 0.601 Croatia 0.527 0.548 0.550 Italy 0.518 0.520 0.514 Cyprus 0.494 0.529 0.536 Latvia 0.512 0.502 0.501 Lithuania 0.543 0.551 0.549 Luxembourg 0.579 0.621 0.624 Hungary 0.550 0.558 0.566 Malta 0.534 0.518 0.500 Netherlands 0.651 0.664 0.670 Austria 0.592 0.630 0.640 Poland 0.544 0.564 0.567 Portugal 0.527 0.504 0.490 Romania 0.452 0.450 0.458 Slovenia 0.597 0.613 0.612 Slovakia 0.569 0.595 0.610 Finland 0.607 0.591 0.578 Sweden 0.702 0.672 0.664 Table 6 Ranking of European countries in both baseline and alternative contexts (min-max approach) EWI EWG EWDG Belgium 5 9 11 Bulgaria 26 26 22 Czechia 7 8 9 Denmark 2 4 4 Germany 9 2 1 Estonia 14 16 19 Ireland 4 13 15 Greece 27 23 20 Spain 23 19 16 France 13 11 10 Croatia 20 18 17 Italy 21 21 23 Cyprus 24 20 21 Latvia 22 25 24 Lithuania 17 17 18 Luxembourg 11 6 6 Hungary 15 15 14 Malta 18 22 25 Netherlands 3 3 2 Austria 10 5 5 Poland 16 14 13 Portugal 19 24 26 Romania 25 27 27 Slovenia 8 7 7 Slovakia 12 10 8 Finland 6 12 12 Sweden 1 1 3 The results clearly show that Sweden consistently ranks at the top, regardless of the aggregation method used. Similarly, countries previously identified as top performers, such as the Netherlands and Denmark, maintain their strong positions across all approaches. In contrast, Ireland loses its high-performing status under both the EWG and EWDG methods, highlighting its sensitivity to the structure of the weighting scheme. On the other hand, Germany exhibits the opposite trend. While it does not particularly stand out in the EWI method, its performance improves significantly under the EWG and EWDG approaches, suggesting a relative strength in the specific SDGs or dimensions emphasized by these methods. Regarding the lower end of the rankings, Romania consistently occupies one of the bottom positions across all methodologies, indicating a persistently weak performance. Greece and Bulgaria show slight improvements under the EWDG method, and Cyprus performs better in both EWG and EWDG. In contrast, Malta experiences a notable drop in performance under the EWDG method, possibly due to the way indicators are aggregated by dimension. The average scores across methods remain quite similar, ranging from 0.560 under EWI to 0.572 and 0.574 under EWG and EWDG, respectively. However, examining the extremes reveals some key differences: Romania ranks last under EWG and EWDG with scores of 0.450 and 0.458, while Greece falls to the bottom under EWI with 0.446. Sweden maintains its leading position, with scores between 0.702 (EWI) and 0.672 (EWG) and Germany is the first with 0.692 in EWDG. The same analyses are performed using TOPSIS (Tables S5-S6). The application of the TOPSIS method reveals significant changes in the country rankings, confirming trends already observed with the min-max approach. Notably, there is a shift in leadership: Sweden loses its top position to Germany, which ranks first under also EWG method. Among the top-performing countries, the Netherlands is confirmed, while Denmark also maintains a high position in the ranking. Ireland, as previously noted, loses several positions, highlighting how the evaluation of a country may vary depending on the priority assigned to different criteria. Among the low-performing countries, Romania remains in last place. Malta also shows a decrease in its performance, a trend shared by Latvia and Portugal. The main changes concern Croatia and Bulgaria, which have improved, and Malta, which has deteriorated according to the EWG method; on the other hand, Greece in particular, but also Cyprus, have improved, while Lithuania has deteriorated according to the EWDG method. Comparing the average scores obtained through the three approaches, EWI and EWDG show very similar values (0.488 and 0.483), while EWG presents a higher average (0.542). Sweden achieves the highest score in EWI with 0.615, compared to Bulgaria, which scores 0.419. In contrast, when analysing EWG and EWDG, Germany stands out with top scores of 0.679 and 0.796, respectively, while Romania ranks lowest with 0.387 and 0.237. These findings highlight that the robustness of rankings may vary depending on the aggregation method used. Adjusting the weights assigned to indicators can be justified when pursuing specific policy or analytical goals. In the absence of such goals, the EWI method tends to be preferred for three main reasons: i) it is neutral and less subject to arbitrary weighting decisions; it treats all data equally and avoids distortions caused by the structure of the SDGs; it is simple and transparent to implement and interpret. Furthermore, EWI aligns well with the core principles of the SDGs, which aim to foster inclusion and cohesion within civil society, rather than division. That said, when there are targeted objectives - such as emphasizing a particular thematic area or sustainability dimension - alternative weighting approaches like EWG or EWDG may be considered appropriate. However, favouring a specific SDG or sustainability pillar tends to amplify the influence of related indicators, while diminishing the weight of others. This leads to an aggregation outcome that reflects the emphasis placed on certain priorities rather than an unbiased overall performance. Therefore, conducting sensitivity analyses and scenario simulations is essential. These methods allow for a more comprehensive understanding of the effects of methodological choices and provide multiple perspectives, recognizing that stakeholders’ interests may not always align. 4.4. Robustness Analysis In literature, it is well-recognized that MCDA results are highly sensible to the selection of indicators, normalization techniques, and aggregation methods [ 95 ]. This section presents the robustness of the previously presented results using methodological variations and data perturbations. The first analysis employed in this study, considering the EWI case as baseline model, uses min-max normalization combined with the TOPSIS. The selection of the min-max method as a baseline was established on its widespread adoption in international benchmarking reports, including those pertaining to the SDGs, and its simplicity and direct interpretability [ 96 ]. This approach facilitates the clear positioning of each alternative relative to the observed best and worst performances within the dataset. To assess the robustness of the presented rankings, complementary analyses were conducted utilizing alternative normalization methods, specifically scaling and normalization by sum. Specifically, for the scaling operation, for each criterion c , the value x of each alternative is calculated as follows: $$\:{{x}^{c}}_{scaled\:}=\frac{{{x}^{c}}_{original\:}-\:\stackrel{̄}{{x}^{c}}}{{s}^{c}}$$ where \(\:\stackrel{̄}{x}\) is the mean and s the standard deviation. For the normalization by sum, considering C criteria (C = 76 in our case), for each criterion c , the value x of each alternative is calculated as follows: $$\:{{x}^{c}}_{normalized\:}=\frac{{{x}^{c}}_{original\:}}{\sum\:_{i=1}^{C}{{x}^{c}}_{original\:}}$$ The obtained final rankings are presented in Table 7 . Table 7 Sustainability scores and ranking considering TOPSIS (EWI), TOPSIS with scaled data, TOPSIS with normalized data, TOPSIS with the data normalized using the min max approach on the criteria (as data preprocessing procedure) and the min max procedure. Country TOPSIS Rank TOPSIS Scaled Rank TOPSIS Normalized Rank TOPSIS Min Max Rank Min Max Rank Austria 0.613 13 0.579 10 0.613 13 0.512 9 0.592 10 Belgium 0.642 5 0.593 5 0.642 5 0.525 8 0.608 5 Bulgaria 0.531 25 0.485 26 0.531 25 0.419 27 0.452 26 Croatia 0.614 12 0.535 18 0.614 12 0.445 21 0.527 20 Cyprus 0.520 27 0.506 24 0.520 27 0.439 23 0.494 24 Czechia 0.651 4 0.583 6 0.651 4 0.496 11 0.601 7 Denmark 0.691 2 0.640 2 0.691 2 0.577 2 0.669 2 Estonia 0.601 17 0.548 16 0.601 17 0.477 14 0.551 14 Finland 0.625 9 0.579 9 0.625 9 0.530 6 0.607 6 France 0.602 16 0.563 11 0.602 16 0.499 10 0.561 13 Germany 0.612 14 0.581 7 0.612 14 0.525 7 0.595 9 Greece 0.522 26 0.478 27 0.522 26 0.430 25 0.446 27 Hungary 0.607 15 0.551 14 0.607 15 0.452 19 0.55 15 Ireland 0.640 6 0.608 4 0.640 6 0.550 4 0.621 4 Italy 0.593 20 0.532 20 0.593 20 0.471 15 0.518 21 Latvia 0.600 18 0.518 23 0.600 18 0.438 24 0.512 22 Lithuania 0.614 10 0.549 15 0.614 10 0.451 20 0.543 17 Luxembourg 0.595 19 0.561 12 0.595 19 0.533 5 0.579 11 Malta 0.567 22 0.533 19 0.567 22 0.456 17 0.534 18 Netherlands 0.657 3 0.621 3 0.657 3 0.572 3 0.651 3 Poland 0.614 11 0.546 17 0.614 11 0.463 16 0.544 16 Portugal 0.540 24 0.531 21 0.540 24 0.455 18 0.527 19 Romania 0.551 23 0.487 25 0.551 23 0.419 26 0.452 25 Slovakia 0.638 7 0.556 13 0.638 7 0.479 13 0.569 12 Slovenia 0.633 8 0.580 8 0.633 8 0.495 12 0.597 8 Spain 0.583 21 0.523 22 0.583 21 0.443 22 0.506 23 Sweden 0.695 1 0.657 1 0.695 1 0.615 1 0.702 1 A notable variation in Spearman correlation, presented in Fig. 6 , was observed between the rankings derived from these different normalization techniques, highlighting the impact of methodological choices in MCDA studies [ 97 ]. It is important to notice that there is a high Spearman correlation between the Min-Max Rank and the EWI-TOPSIS rank (0.89), indicating a high concordance between the two ranks and high correlation between our baseline approach and the various normalized variations. The differences present in the scores are not arbitrary but are attributable to the distinct mathematical properties of each method. Normalization and scaling methods exhibit extreme sensitivity to outliers, which can disproportionately compress other values and distort the final ranking, an undesirable characteristic for an analysis focused on cohesion and relative performance within the EU context. These results indicate that, while TOPSIS is theoretically susceptible to rank reversal when alternatives are introduced or removed [ 98 ], its strong agreement with the various results offers a practical validation of the rankings. To provide a more robust ranking, and to analyze the possible different weight scenarios (according to statistical distributions), we analyzed 3 different weight scenarios, beside the EWI approach. Specifically, we rely on Casual weight (Casual scenario), Normal distribution weight (Normal scenario) and exponential decreasing weight (Decreasing scenario). In Fig. 7 we present the spearman correlation matrix between the different weight scenarios, while in Table S7 the weight per criterion is reported. The baseline results have a high correlation with the normal distribution weight scheme (0.98) and casual weight (0.83) while have a moderate correlation with decreasing weight scheme (0.65). These results suggest that alternative weight can change the results, but not in a significant way, except when a specific proportion of the weight is concentrated in small part of the criterion. It is important to stress that these results can highlight one of the main problems that policymakers face in the SDGs evaluation process: the policy priorities. Even if the TOPSIS is sensitive to the weight of the criterion, different policymakers can have various priorities and strategy. From a methodological standpoint, the robustness analysis demonstrates that the baseline ranking is largely invariant to plausible changes in weighting schemes, thereby reinforcing the internal consistency of the composite indicator. The lower correlation observed under the exponential decreasing scenario indicates that distortions arise only when the weighting structure systematically privileges a restricted subset of indicators, a situation that mirrors extreme policy prioritization rather than statistical instability. This finding has two implications: first, it confirms that the composite results are primarily driven by the underlying performance profiles of EU Member States rather than artefacts of methodological specification; second, it highlights that the role of weights is less about altering empirical rankings and more about reflecting normative preferences embedded in policymaking. Consequently, the empirical evidence suggests that TOPSIS, when combined with transparent weighting procedures, offers a technically robust and policy-relevant tool for SDG monitoring in the EU context, balancing statistical rigor with the need to accommodate heterogeneity in national priorities. 5. Discussion The themes of the SDGs and sustainability are intrinsically and deeply interconnected, forming the cornerstone of contemporary global development discourse [ 5 ]. This connection has been increasingly recognized within the academic community, as evidenced by a marked rise in both the number of publications and citation rates on the subject in recent years [ 23 , 42 ] showing differences at the individual country level [ 9 ]. The growing recognition of synergies between the SDGs is becoming a cornerstone of sustainable development strategies, as emphasized by recent research [ 99 ]. A particular emphasis is placed on the active involvement of future generations, notably through the innovative framework of living labs [ 100 ]. Within this evolving landscape, the European context has emerged as especially relevant, with increasing efforts to integrate sustainability into cultural, social, and institutional practices [ 101 ]. Europe is thus called to embrace a more humane and fraternal approach, rooted in the principle of pragmatic sustainability - a concept that emphasizes realistic, context-sensitive, and action-oriented solutions to complex challenges [ 102 ]. This vision naturally extends to the broader notion of sustainable communities, promoting locally grounded yet globally interconnected models of development. At the heart of this approach lies a clear set of priorities: broadening stakeholder participation, empowering and trusting younger generations, and advancing practical, evidence-based solutions that transcend ideological divisions. In this context, fostering strong international cooperation is not only desirable, but essential, to address shared challenges and to ensure the successful implementation of the 2030 Agenda. In this regard, collaboration among networks is crucial to maximizing the impact of their sustainability initiatives and fostering more cohesive, large-scale transformations [ 103 ]. In this context, Europe has committed to an ambitious and transformative agenda through the European Green Deal, aiming to position itself as a global leader in the transition toward sustainability [ 31 ]. Among the analytical tools available to support this transition, multi-criteria analysis has emerged as a valuable approach. It assists policymakers in identifying targeted, effective strategies [ 104 ] and in monitoring the progress of SDG-related indicators [ 30 ]. Addressing the complex and interconnected challenges of sustainability requires an integrated and systemic approach [ 105 ], capable of capturing the multidimensional nature of sustainable development. This study contributes to a growing body of research that seeks to synthesize the vast array of available data into meaningful insights, despite existing limitations related to data availability, quality, and the extent to which all dimensions of sustainability are adequately covered. Similarly, future directions may involve sensitivity analyses to be conducted on weights for multicriteria methods [ 106 , 107 ]. Some analyses have pointed out that certain SDGs may, at times, be in tension with one another, highlighting the need for strategic compromises in policy implementation. However, recognizing these potential trade-offs does not detract from the imperative of adopting rigorous and effective climate policies [ 108 ]. A balanced approach involves designing policy packages that not only address environmental goals but also ensure social equity - for instance, by allocating revenues from carbon taxes to support low-income households [ 109 ], or by incentivizing the development and diffusion of low-carbon technologies [ 110 ]. Empirical analyses based on European data reveal that, overall, synergies between SDGs tend to outweigh trade-offs. Notably, Poland exhibits the highest proportion of synergies, while Italy shows the highest proportion of trade-offs, suggesting that national contexts significantly influence the alignment - or friction - between sustainability objectives [ 27 ]. Europe is steadily progressing toward sustainability, both in terms of the overall performance across its multiple dimensions [ 111 ] and in relation to specific SDGs [ 112 ]. However, a clear divide persists among EU countries, closely linked to their levels of economic development. Western and Northern European nations consistently outperform their Eastern and Southern counterparts in sustainability metrics [ 113 ]. In particular, Northern European countries such as Sweden and Denmark frequently rank among the top performers across multiple SDGs, with the Netherlands also demonstrating high levels of achievement [ 47 ]. Swedish cities and regions often rank at the top in sustainability indices [ 114 ]. This pattern is further supported by additional studies, which consistently highlight strong performance from Northern countries, contrasted with weaker outcomes in countries such as Romania, Bulgaria, and Greece [ 66 ]. Similar findings are reported in broader comparative analyses, where Denmark, Finland, and Sweden occupy leading positions, while the lowest rankings are attributed to Bulgaria, Cyprus, Croatia, Greece, Romania, and Hungary [ 115 ]. Sweden, in particular, maintains a leading position not only in aggregate sustainability performance but also across a wide range of individual SDGs [ 116 ]. Another study suggests that Denmark, Sweden and the Netherlands occupy the top positions, while Romania and Bulgaria occupy the bottom positions [ 117 ]. A disaggregated analysis by sustainability dimensions offers further insights. In the social dimension, Denmark and Sweden lead the rankings; in the economic dimension, Sweden and Denmark again take top positions; and in the environmental dimension, Austria and Sweden stand out [ 49 ]. These results underscore the importance of considering both geographical and thematic variations in assessing the progress of European countries toward the SDGs. In addition, a comparative analysis is conducted with a previous study that utilized the same dataset, albeit referring to data from the year 2020 [ 48 ]. To ensure consistency and comparability between the two analyses, the min–max normalization method is adopted in both cases. The findings of the current study reveal a notable shift in the rankings: Denmark ascends to second place, effectively swapping positions with the Netherlands. When examining the top ten countries, several changes in ranking are observed. Finland and Germany each drop two positions, while Luxembourg and France fall by three places. Conversely, Ireland improves its position by two places, Slovenia by three, and the Czechia records the most significant upward movement among the top countries, gaining five positions. Slovakia also registers a notable improvement. In contrast, Italy and Portugal experience the most substantial declines, falling six and four places respectively. At the lower end of the ranking, Greece moves to the last position, replacing Romania, while Bulgaria remains third from the bottom, indicating a persistent lag in sustainability performance. An analysis of individual sustainability dimensions provides further insights. In the economic dimension, the top three positions remain unchanged, with Sweden, the Netherlands, and Denmark leading the ranking. In the environmental dimension, Germany surpasses Sweden to claim the top position, while Denmark retains third place. In the social dimension, Sweden maintains its leading position, but Denmark and Ireland now occupy the second and third spots, replacing the Netherlands and Luxembourg. These findings highlight both progress and regression among EU countries over time and emphasize the value of longitudinal analysis in tracking national sustainability performance across multiple dimensions. Finally, another useful comparison is with the Sustainable Development Report 2025. The data in this report was collected at the end of 2024 and includes 111 indicators, of which approximately 70% come from official statistics (mainly European Commission services) and 30% from unofficial sources (NGOs, academia). Northern European countries perform very well, with Finland ranking first, followed by Denmark, Sweden and Austria. Bulgaria and Cyprus occupy the bottom positions [ 118 ]. The implications that emerge from the analysis are manifold. First, there is a clear need for a systemic and interconnected approach that can both enhance synergies among sustainable development objectives and effectively manage potential trade-offs. Second, while the European Green Deal positions Europe as a global leader in sustainability, this role can only be fully realized if the disparities between Member States are addressed. Third, analyses conducted at the European level must also be replicated at the national level, where differences in performance should be evaluated through the lens of country-specific policies. Fourth, the use of quantitative methods such as MCDA proves to be essential, provided that the underlying assumptions are clearly specified and the results are transparently presented. Finally, the availability of up-to-date data is crucial for informing strategic choices that are both effective and efficient. 6. Conclusions The analysis evaluates the sustainability performance of EU countries using two distinct tools: a compensatory technique (TOPSIS) and a non-compensatory technique (min–max). Despite methodological differences, the rankings are consistent in a context where all indicators have the same weight, with Sweden, Denmark and the Netherlands consistently emerging as top performers. Sweden stands out for its excellence in the social dimension, ranks second in the environmental dimension, which is led by Germany, and third in the economic dimension, in which Denmark and the Netherlands occupy the first position. Conversely, Greece, Cyprus and Bulgaria occupy the last places in the ranking. These results paint a picture of a fragmented Europe. Nordic countries consistently lead in sustainability, demonstrating a balanced approach that integrates social welfare, environmental stewardship, and economic resilience. In contrast, Eastern and Southern European countries continue to face challenges in reaching similar levels of sustainability. This underscores the need for differentiated and targeted strategies at the EU level to reduce these disparities and foster a more cohesive and sustainable Union. Public funding must be effectively allocated to avoid reinforcing existing divides. Based on the findings, three key recommendations are proposed: Refocus EU cohesion policies to explicitly aim at narrowing the sustainability gap between high- and low-performing Member States. Promote integrated strategies that recognize and leverage the interconnections among social, economic, and environmental dimensions. Enhance data availability and quality to improve the robustness of comparative assessments and inform evidence-based policymaking. Nonetheless, this study has some limitations. First, the lack of updated data restricts the timeliness and accuracy of the recommendations. Second, the indicator set could be expanded and diversified to better reflect the full scope of sustainability. Thirdly, it could be conducted at the group of countries level in order to carry out more in-depth analyses at the individual country level. A methodological limitation of this study is the reliance on TOPSIS and min-max methods for rank creation. While these methods were central in the presented work, the cross-validation of these ranks using different multi-criteria methods was beyond the scope of this article. This validation is reserved for future research. In fact, future research could also explore the application of probabilistic approaches, such as Monte Carlo simulation, particularly if incorporating uncertain data distributions or alternative model parameters becomes a primary focus to have more robust result. Similarly, it is essential that new studies assess the interdependencies between the various SDGs, for which, however, it is essential to have more information available (the set of 76 indicators should be increased) and it is possible to integrate the data with new analysis methodologies, providing relevant sensitivity analyses. However, this work provides food for thought, is limited to two analytical methodologies, suggests that the approach in which all indicators have the same relevance, but also identifies further directions for future research. For Europe to progress, its approach must be pragmatic, inclusive, and grounded in cooperation - supported by analytical tools capable of capturing the complexity of sustainable development. Declarations Ethics approval and consent to participate : This article does not contain any studies with human participants performed by any of the authors. Consent to Publish declaration : not applicable. Clinical trial number not applicable. Competing interests : The authors declare no competing interests. Funding: The authors received no specific funding for this work. Author Contribution All authors (Idiano D'Adamo, Simone Di Leo, Massimo Gastaldi, Alessandro Paris) contributed equally to the paper. Acknowledgements The present study was conducted within the framework of the PEACE (‘Protecting the Environment: Advances in Circular Economy’) project, funded by the ‘Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale (PRIN)’, Investimento M4.C2.1.1-D.D. 104.02- 02-2022, 2022ZFBMA4 under the European Union – Next Generation EU. The manuscript reflects the views and opinions of the authors, only, who bear full responsibility for them. Data availability: Data are available from the authors upon reasonable request. References Leal Filho W, Dibbern T, Pimenta Dinis MA, Coggo Cristofoletti E, Mbah MF, Mishra A, et al. The added value of partnerships in implementing the UN sustainable development goals. J Clean Prod. 2024;438:140794. 10.1016/j.jclepro.2024.140794 . D’Adamo I, Gastaldi M, Uricchio AF. A multiple criteria analysis approach for assessing regional and territorial progress toward achieving the Sustainable Development Goals in Italy. 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Sustain Energy Technol Assessments. 2021;47:101380. 10.1016/j.seta.2021.101380 . Moreno J, Campagnolo L, Boitier B, Nikas A, Koasidis K, Gambhir A, et al. The impacts of decarbonization pathways on Sustainable Development Goals in the European Union. Commun Earth Environ. 2024;5:136. 10.1038/s43247-024-01309-7 . Fragkos P, Fragkiadakis K, Sovacool B, Paroussos L, Vrontisi Z, Charalampidis I. Equity implications of climate policy: Assessing the social and distributional impacts of emission reduction targets in the European Union. Energy. 2021;237:121591. 10.1016/j.energy.2021.121591 . Corradini M, Costantini V, Markandya A, Paglialunga E, Sforna G. A dynamic assessment of instrument interaction and timing alternatives in the EU low-carbon policy mix design. Energy Policy. 2018;120:73–84. 10.1016/j.enpol.2018.04.068 . Gracia-de-Rentería P, Ferrer-Pérez H, Drabik D. Sustainable development goals in the European Union and its regions: Are we moving forward in economic, social, and environmental dimensions? Sustain Dev. 2023;31:3540–52. 10.1002/sd.2609 . Grzebyk M, Stec M, Hejdukova P. Implementation of sustainable development goal 8 in European Union countries–A measurement concept and a multivariate comparative analysis. Sustain Dev. 2023;31:2758–69. 10.1002/sd.2545 . Sylvie CJ-PE-T, Mathieu O, Claire P. The interlinkages between the SDG indicators and the differentiation between EU countries: It is (mainly) the economy! Stat J IAOS. 2020;36:455–70. 10.3233/SJI-190507 . Shmelev SE, Shmeleva IA. Smart and sustainable benchmarking of cities and regions in Europe: The application of multicriteria assessment. Cities. 2025;156:105533. 10.1016/j.cities.2024.105533 . Rocchi L, Ricciolini E, Massei G, Paolotti L, Boggia A. Towards the 2030 Agenda: Measuring the Progress of the European Union Countries through the SDGs Achievement Index. Sustainability. 2022;14:3563. 10.3390/su14063563 . Carrillo M. Measuring Progress towards Sustainability in the European Union within the 2030 Agenda Framework. Mathematics 2022;10:2095. 10.3390/math10122095 Resce G, Schiltz F. Sustainable Development in Europe: A Multicriteria Decision Analysis. Rev Income Wealth. 2021;67:509–29. 10.1111/roiw.12475 . Lafortune G, Fuller G. Europe Sustainable Development Report 2025: SDG Priorities for the New EU Leadership. 2025. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Discover Sustainability → Version 1 posted Editorial decision: Revision requested 08 Sep, 2025 Reviews received at journal 07 Sep, 2025 Reviews received at journal 05 Sep, 2025 Reviews received at journal 04 Sep, 2025 Reviewers agreed at journal 31 Aug, 2025 Reviewers agreed at journal 30 Aug, 2025 Reviewers agreed at journal 28 Aug, 2025 Reviewers invited by journal 28 Aug, 2025 Editor assigned by journal 22 Aug, 2025 Submission checks completed at journal 22 Aug, 2025 First submitted to journal 20 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Rome","correspondingAuthor":false,"prefix":"","firstName":"Simone","middleName":"","lastName":"Di Leo","suffix":""},{"id":508682065,"identity":"8a183b27-7e0a-4fec-a139-0890167633cb","order_by":2,"name":"Massimo Gastaldi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYLCCBDirgJmHH0gdALHZiNDC2MBgwMwj2QDTglcPkhYGgwMwPg4tuu3HH394mGPDIO9+/PmDHwbWMsY3cg8e5qmxZuCTb8CqxexMjplE4rY0BsMzOYaNPQbpPGY38hIO8xxLx+kwswM5bAyJ2w4zGDbkMDbwGBwGaskxODiD7TBuLeefP/4A1tL//GHjH6AW4xkgLf/waLmRYCAB0iIvkWDYDLLFQCLH4MDHNnxa3oD9AlT5xnC2DNAvEmfeALX0pfOwsSXgcFj6448/t9nIyfenP/j4psLanr89x/hDwjdrOfnmA9itgQIeAzR5Zh686kEAPRKYCeoYBaNgFIyCEQMAGZldXLhRvz0AAAAASUVORK5CYII=","orcid":"","institution":"University of L'Aquila","correspondingAuthor":true,"prefix":"","firstName":"Massimo","middleName":"","lastName":"Gastaldi","suffix":""},{"id":508682066,"identity":"af7d153b-33c5-45b7-b2ba-c546b7133bd9","order_by":3,"name":"Alessandro Paris","email":"","orcid":"","institution":"ESSEC Business School","correspondingAuthor":false,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Paris","suffix":""}],"badges":[],"createdAt":"2025-08-20 09:53:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7416080/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7416080/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43621-025-02129-1","type":"published","date":"2025-11-14T15:58:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90593960,"identity":"c73572b8-ada7-4976-82ab-8c2b15ca8f14","added_by":"auto","created_at":"2025-09-04 13:17:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35751,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of SDGs indicators\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7416080/v1/cc619c1d1fd5de531e1b5659.png"},{"id":90595005,"identity":"5eb80971-0baa-483f-b103-b5df37749549","added_by":"auto","created_at":"2025-09-04 13:25:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":571548,"visible":true,"origin":"","legend":"\u003cp\u003eSustainability score in 2022 – A comparison between TOPSIS and min-max methods\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7416080/v1/42ea8579876aeab84ee2ed0d.png"},{"id":90593959,"identity":"71d4c655-f207-40aa-9b7d-cbce3e0e9d4c","added_by":"auto","created_at":"2025-09-04 13:17:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72720,"visible":true,"origin":"","legend":"\u003cp\u003eSustainability score in 2022 (horizontal lines represent the average values)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7416080/v1/c57366cb4f7dbc4a520112b4.png"},{"id":90593963,"identity":"b64791fb-d7ac-4ad0-a5a1-2b51471c15f4","added_by":"auto","created_at":"2025-09-04 13:17:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":178078,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the two methods TOPSIS and min-max\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7416080/v1/64a558885d5d80974af71b22.png"},{"id":96105419,"identity":"4ea0ee6b-8a61-4c7c-9ebc-43df82eecf89","added_by":"auto","created_at":"2025-11-17 16:11:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3301823,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7416080/v1/4e16e545-e65a-4f40-8754-8f583aa773cd.pdf"},{"id":90595442,"identity":"f36bfc6f-f3e4-4939-a126-1ad276b70c21","added_by":"auto","created_at":"2025-09-04 13:33:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":134935,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7416080/v1/a6f3e96aba156fa8c0682ab9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Measuring Sustainability in Europe: A Min–Max and TOPSIS-Based Evaluation of SDGs Performance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Sustainable Development Goals (SDGs) are a comprehensive set of 17 interrelated and universally applicable global objectives that were officially adopted in 2015 by all United Nations Member States as a cornerstone of the 2030 Agenda for Sustainable Development. These ambitious goals reflect a global consensus and a shared commitment by the international community to confront and address the most critical and interconnected challenges facing humanity today [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Various literature reviews have been conducted on this topic [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among these pressing issues are poverty [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], hunger [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], public health [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], climate change [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], environmental degradation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], medical waste [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], smart cities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], social inequality [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], gender discrimination [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], inclusion [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], lack of access to quality education [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and clean water [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], improving circular economy models [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and the need to foster peace, justice, and strong institutions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Particular attention should be paid to the link between sustainability and digitalisation in order to identify the balance between technology and human needs with the support of digital education [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRooted in the foundational principle of \u0026ldquo;leaving no one behind,\u0026rdquo; the Sustainable Development Goals (SDGs) are designed not only to guide nations toward a more prosperous and equitable future, but also to ensure that the benefits of development are distributed fairly - reaching the most vulnerable and marginalized segments of society [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The vision promoted by the SDGs is one of holistic development - economically viable, socially inclusive, and environmentally sustainable - thus recognizing the intricate balance and interdependence among economic growth, social well-being, and environmental protection [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The 17 SDGs are a strategic reference point also for cities in monitoring prosperity, people, planet, peace and partnership [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo operationalize this vision, the 17 overarching goals are broken down into 169 specific and actionable targets, each accompanied by a set of measurable indicators that facilitate monitoring and evaluation over time. This detailed structure underscores the complexity of sustainable development while enabling transparency and accountability in the implementation process. The achievement of the SDGs requires the active engagement of a diverse range of actors: not only national and local governments, but also the private sector, civil society organizations, academic and research institutions, local communities, and individual citizens [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEnvironmental monitoring at the regional level represents a crucial challenge for identifying common gaps and opportunities based on concrete data [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. To this end, it is essential to have a robust regional data infrastructure capable of ensuring accurate and timely monitoring, while maintaining a dynamic nature to adapt to relevant social changes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Customized national roadmaps aim to promote equality in educational opportunities in critical sectors [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and within the framework of the European Green Deal, differentiated transition pathways should be provided for European regions that are heavily dependent on carbon-intensive industries; in addition, we must not forget to address the inevitable employment and economic challenges that this entails [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe SDGs represent more than a policy framework - they embody a collective roadmap for global transformation toward a fairer, more inclusive, and more sustainable world for present and future generations. There are notable differences in the emphasis placed on the implementation of the SDGs across various geographical regions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Identifying methodologies to assess interactions between SDGs and defining the factors that influence synergies is an important challenge in supporting this change [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. An approach is also needed that shows why a municipal administration should commit to global sustainability frameworks [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; in fact, harmonising local initiatives with global sustainability objectives is essential for achieving coherent and impactful progress toward the SDGs [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMonitoring and evaluating progress toward these goals is essential at every territorial level, from global to local, in order to identify strengths, uncover gaps, and guide strategic interventions. In this context, the use of composite indicators is particularly advantageous, as they allow for the aggregation of diverse, multidimensional data into a single, interpretable metric, facilitating both communication and decision-making [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Given the multifaceted nature of sustainable development, which encompasses a wide spectrum of social, economic, and environmental dimensions, several multi-criteria decision analysis (MCDA) techniques have proven to be extremely useful tools for managing complexity and supporting evidence-based policy choices. Multi-criteria decision-making methods have been widely employed in various fields and disciplines, including decision problems related to sustainable development issues [\u003cspan additionalcitationids=\"CR38 CR39 CR40 CR41\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMulti-criteria analysis plays a crucial role in assessing progress toward the SDGs, especially by distinguishing between two fundamental types of models: compensatory and non-compensatory [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Compensatory models, such as TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), allow for trade-offs among criteria - meaning that a poor score in one area can be offset by a better score in another. In contrast, non-compensatory models, like the min-max method, do not permit such trade-offs; weaknesses in one dimension cannot be masked by strengths in another, thereby highlighting critical deficiencies.\u003c/p\u003e\u003cp\u003eIn recent years, hybrid approaches have emerged that integrate both compensatory and non-compensatory models [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These methods aim to capture a more balanced and realistic picture of development, particularly in complex and diverse territorial contexts. However, territorial rankings produced by different models and indicator sets can vary significantly. This variation introduces the risk of overlooking essential interconnections among social, environmental, and economic dimensions of sustainability, which are often deeply intertwined [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Specific analyses for each SDG, based on more precise and context-sensitive indicators, are essential [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and territorial differences across Europe highlight the need for a multidimensional and integrated approach to achieving the SDGs [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe present analysis focuses on the European context, which offers a particularly compelling case study due to the continent's ambitious goals for climate neutrality and sustainable development. The topic of SDGs has significantly increased in the literature, as shown by the 14% growth in publications in the period 2020\u0026ndash;2024 (partial), with much attention given to SDGs 3 and 13[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Some analyses show that synergies between SDGs are more common than compromises, with SDG 5 having the most connections and SDG 7 showing the fewest [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. A sustainability score is used in the period 2018\u0026ndash;2020 to assess the 27 EU countries on the basis of 58 indicators proposed by Eurostat [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], and analysis of the period 2010\u0026ndash;2021 shows that the development of these countries cannot be defined as fully sustainable [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The diversity of regional landscapes and policy frameworks in Europe makes it an ideal testing ground for assessing the evolution of SDGs. The gap that emerges from the literature is the lack of an overview of the evolution of SDGs.\u003c/p\u003e\u003cp\u003eThis work aims to fill this gap by providing an assessment of the 27 European countries in terms of sustainability scores based on Eurostat data for 2022, which examine 76 indicators. The objective is to provide a performance ranking among the various countries, to assess whether or not there is synergy using two different multi-criteria methodologies (min-max and TOPSIS) and to break down the results according to three key dimensions: economic, environmental and social. The implications of this work are therefore also to propose monitoring of countries' performance and any specific regional characteristics that emerge from the integration of the various indicators.\u003c/p\u003e\u003cp\u003eFollowing this introduction, the study is structured as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e proposes a literature analysis on multicriteria decision analysis in sustainability assessment while Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e outlines the methodological framework used for the analysis. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results derived from the application of the two methodologies. Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e5\u003c/span\u003e discusses the findings in depth, exploring their broader implications, and Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e6\u003c/span\u003e concludes the study with final reflections and suggestions for future research.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eSustainability assessment entails the systematic evaluation of environmental, social, and economic dimensions of policies or systems. The concept of sustainability is commonly understood through two main perspectives: weak sustainability and strong sustainability [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWeak sustainability is based on the idea that natural capital can be substituted by human-made or human capital. In contrast, strong sustainability argues that natural and human capital are complementary and not interchangeable, emphasizing the need to preserve natural resources as they are irreplaceable [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Given the inherently complex and multidimensional nature of sustainability, Multi-Criteria Decision Analysis (MCDA) has emerged as a widely adopted and effective methodological approach [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMCDA is a valuable technique in decision-making processes due to its ability to synthesize large volumes of data. It allows decision-makers to systematically evaluate and compare various alternatives by using synthesized values derived from a set of predefined criteria. One of the main advantages of MCDA is its simplicity and ease of application. The method is well-documented in the literature and has been widely implemented across a range of fields, including those related to sustainability [\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Several areas of application lend themselves well to this approach [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMCDA comprises a range of techniques such as the Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), TOPSIS, min-max, Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), and ELimination Et Choix Traduisant la REalit\u0026eacute; (ELECTRE). These methods vary in how they assign weights, normalize criteria, and aggregate results.\u003c/p\u003e\u003cp\u003eMCDA is based on the identification and evaluation of appropriate criteria aligned with the decision-making objectives. However, a primary limitation of the method is its static framework; it does not incorporate dynamic aspects or account for direct interactions among variables. Despite this drawback, MCDA remains a robust tool within the field of operations research, particularly for selecting the most suitable alternative among multiple options [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Numerous studies [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] have highlighted the adaptability of MCDA in integrating both quantitative and qualitative data, accommodating stakeholder preferences, and addressing context-specific criteria [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. This versatility renders MCDA particularly well-suited for sustainability assessments, which often involve complex trade-offs and inherent uncertainties. Multi-criteria assessment methods are a well-established approach to evaluating countries' sustainability performance [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] and also to compare economic, environmental and social progress of countries [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Sustainable Development Index and corresponding dashboard have been proposed as tools to measure countries' progress in achieving the SDGs [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. To effectively monitor and support this progress, the application of appropriate statistical and analytical methods is essential [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In this context, several studies have emphasized the advantages of using multi-criteria decision-making methods to assess sustainability performance in relation to the SDGs [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Empirical analyses have focused on various geographical levels. Some studies concentrate on European capital cities and their alignment with the SDGs [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], while others evaluate the overall performance of European countries [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Importantly, the literature underscores that the successful achievement of the 2030 Agenda requires not only the exploitation of synergies among the SDGs but also the management and resolution of potential trade-offs between goals [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Another indicator used in this context is the Sustainability score [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], calculated as the product of a row vector (representing the values assigned to each criterion for a given country) and a column vector (indicating the weights of the criteria). This calculation yields the SDG Index, a dimensionless measure based on the 17 SDGs proposed by the United Nations. The SDG Index has been applied to assess the sustainability performance of 27 European countries [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the context of SDG evaluation, the widely cited methodology assigns equal importance to each SDG [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. However, the scientific community has not reached full consensus on this approach, with some scholars advocating for differentiated weights and aggregation rules [\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Other approaches explore both compensatory and non-compensatory aggregation rules in sustainability assessments [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] integrating social welfare considerations and stakeholder participation [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMCDA, while instrumental in analysing sustainability data, faces several methodological and informational challenges within the detailed assessment of SDGs. A significant gap in literature lies in the inconsistency of territorial rankings, often arising from the varied selection of models and indicator sets, which can under-estimate the relations between the social, environmental, and economic dimensions [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. To fill this gap, both min-max normalization and TOPSIS are used, providing a cross-validated assessment in evaluating European countries SDG performance. Another important gap in literature concerns the lack of a comprehensive, contemporary overview of SDG evolution and national progress, particularly within the European context, with recent work in this field using 2020 data [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This research fills this gap by delivering an up-to-date assessment using recent Eurostat data across 76 indicators, offering not only a current performance ranking but also an analysis of synergies through multiple multi-criteria perspectives. By directly addressing these gaps, this work promotes the application of MCDA in complex sustainability assessments.\u003c/p\u003e"},{"header":"3. Materials and methods","content":"\u003cp\u003eThis work aims to calculate the Sustainability Score using MCDA. Accordingly, the process can be delineated into three distinct phases: i) establishing appropriate criteria necessary to achieve the intended objective; ii) attributing weights and values to each criterion based on their relative importance; and iii) aggregating the resulting data to compute a comprehensive sustainability score for each alternative. Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e identifies the selection of criteria and Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e shows the methodological approach used in this work, based on TOPSIS and min-max. Next, the baseline model (Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e) and the alternative model (Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e), considering variations in weights indicators assignments, are proposed for evaluating the performance of different European countries. An overview of the methodological framework employed for this work is presented in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Criteria Selection\u003c/h2\u003e\u003cp\u003eThe first step in the analysis involves the identification of reliable criteria supported by available data, to minimize subjectivity in the assessment process. To this end, the present study utilizes the Eurostat database, the official statistical office of the European Union, which serves as a widely recognized reference for high-quality, accessible data in academic research [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Eurostat provides a structured set of indicators corresponding to each of the 17 Sustainable Development Goals (SDGs).\u003c/p\u003e\u003cp\u003eIn this work, the criteria will be identified in the indicators provided by Eurostat. All available indicators were considered, excluding any duplicates or those with missing data, resulting in a total of 76 unique indicators, each linked to its respective SDG \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Notably, the distribution of indicators across SDGs is uneven: for example, seven indicators are associated with SDG 7; six indicators with SDGs 1, 4 and 10 and one indicator with SDGs 6 and 15. This approach ensures the replicability of the results, as it relies on objective data and excludes indicators not due to their perceived unsuitability, but because their data are only available for a limited number of countries in the reference year. Including such indicators would compromise the comparability of the analysis. It is important to acknowledge, however, that the limited availability of indicators constitutes a constraint of this study. Moreover, one of the main challenges in relying on official data is the time lag in their publication, which can result in analyses and policy recommendations becoming outdated [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. To mitigate this issue, the most recent data available (from 2022) have been used.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of indicators\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"1\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDG 1 - No Poverty\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeople at risk of poverty or social exclusion\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousing cost overburden rate by poverty status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeople at risk of income poverty after social transfers\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere material and social deprivation rate by age group and sex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePersons living in households with very low work intensity, by age group\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIn work at-risk-of-poverty rate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG2 - Zero Hunger\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural factor income per annual work unit AWU source: Eurostat, DG AGRI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea under organic farming\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity rate by body mass index BMI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmmonia emissions from agriculture source: EEA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment support to agricultural research and development - euro per inhabitant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG3 - Good Health and Well-Being\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShare of people with good or very good perceived health by sex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-reported unmet need for medical examination and care by sex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthy life years at birth by sex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsumption of antibiotics in the community and hospital sectors \u0026ndash; defined daily doses DDD per day\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG4 - Quality Education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary educational attainment by sex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParticipation in early childhood education by sex children aged and over\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdult participation in learning by sex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEarly leavers from education and training by sex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow achieving -year-olds in reading, mathematics or science\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShare of individuals having at least basic digital skills, by sex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG5 - Gender Equality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInactive population due to caring responsibilities by sex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositions held by women in senior management positions source: EIGE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeats held by women in national parliaments and governments source: EIGE -- NAT PARL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender pays gap in unadjusted form\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender employment gap, by type of employment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG6 - Clean Water and Sanitation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation connected to at least secondary waste water treatment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG7 - Affordable and Clean Energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation unable to keep home adequately warm by poverty status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinal energy consumption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy import dependency by products\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShare of renewable energy in gross final energy consumption by sector\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary energy consumption\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinal energy consumption in households per capita\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy productivity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG8 - Decent Work and Economic Growth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReal GDP per capita\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYoung people neither in employment nor in education and training by sex NEET\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInvestment share of GDP by institutional sectors\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLong-term unemployment rate by sex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment rate by sex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG9 - Industry, Innovation and Infrastructure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGross domestic expenditure on R\u0026amp;D by sector\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026amp;D personnel by sector\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShare of buses and trains in inland passenger transport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShare of rail and inland waterways in inland freight transport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir emission intensity from industry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG10 - Reduced Inequality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePurchasing power adjusted GDP per capita\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome distribution\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted gross disposable income of households per capita\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelative median at-risk-of-poverty gap\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome share of the bottom % of the population\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsylum applications by state of procedure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG11 - Sustainable Cities and Communities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation living in households considering that they suffer from noise, by poverty status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoad traffic deaths, by type of roads source: DG MOVE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecycling rate of municipal waste\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremature deaths due to exposure to fine particulate matter (PM2.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG12 - Responsible Consumption and Production\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCircular material use rate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRaw material consumption - Tonnes per capita RMC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsumption footprint \u0026ndash; Per inhabitant - single weighted score\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneration of waste by hazardousness\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG13 - Climate Action\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNet greenhouse gas emissions source: EEA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation covered by the Covenant of Mayors for Climate \u0026amp; Energy signatories\u0026rsquo; source: Covenant of Mayors\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNet greenhouse gas emissions of the Land use, Land use change and Forestry LULUCF sector - Tonnes per capita\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage CO emissions per km from new passenger cars\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen bond issuance by type of issuer\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG14 - Life Below Water\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurface of the marine protected areas\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoastal bathing sites with excellent water quality by location\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInland bathing sites with excellent water quality by location\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG15 - Life on Land\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurface of the terrestrial protected areas\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG16 - Peace and Justice Strong Institutions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation reporting occurrence of crime, violence or vandalism in their area by poverty status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived independence of the justice system source: DG COMM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation with confidence in EU institutions by institution source: DG COMM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorruption Perceptions Index\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVictims of trafficking in human beings by sex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSDG17 - Partnerships to achieve the SDG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOfficial development assistance as share of gross national income source: DG INTPA, OECD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-speed internet coverage, by type of area source: DG CNECT and Eurostat\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral government gross debt\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShare of environmental taxes in total tax revenues\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Min-max and TOPSIS methods\u003c/h2\u003e\u003cp\u003eMulti-Attribute Decision-Making methods can be classified from various perspectives. One key distinction is between non-compensatory and compensatory models [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Notably, these methods have been successfully applied in spatial-level analyses [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], including the evaluation of territories using TOPSIS [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e] and the min-max method [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Specifically, TOPSIS is a compensatory method enables the comparison of alternatives by calculating their relative closeness to an ideal solution. It allows high values in one indicator to offset low values in another, based on the assumption that strong performance in one area may compensate for weaker outcomes elsewhere. This makes it suitable in contexts where trade-offs are acceptable. Instead, min-max method operates under the premise that each SDG holds intrinsic value and that a shortfall in one area cannot be neutralized by excellence in another. Such approaches emphasize the indivisibility of the goals, reinforcing the idea that sustainable development cannot be truly achieved unless all its dimensions are addressed simultaneously and with equal importance.\u003c/p\u003e\u003cp\u003eMin-Max normalization allows for the comparison of criteria expressed in different units by converting them to a common scale. The min-max normalization formula is expressed as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{x}^{{\\prime\\:}}ᵢⱼ\\:=\\frac{\\left(xᵢⱼ\\:-\\text{min}\\left(xⱼ\\right)\\right)}{\\left(\\text{max}\\left(xⱼ\\right)-\\text{min}\\left(xⱼ\\right)\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere x'\u003csub\u003ei\u003c/sub\u003eⱼ is the normalized value for alternative i and criterion j, x\u003csub\u003ei\u003c/sub\u003eⱼ is the original value, and min(xⱼ) and max(xⱼ) represent the minimum and maximum values for criterion j across all alternatives. It is valued for its simplicity and ease of implementation, though it can be significantly affected by the presence of outliers [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. TOPSIS, which is based on the concept of proximity to the ideal solution, provides an intuitive way to rank alternatives. However, its effectiveness largely depends on the choice of normalization method [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Further insights into these multi-criteria approaches are explored by a number of authors who highlight the various advantages and disadvantages of these approaches [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTOPSIS and the min-max method are prominent in the literature. TOPSIS identifies the optimal alternative as the one closest to the positive ideal solution and farthest from the negative ideal solution across the set of criteria [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The TOPSIS procedure follows five main steps:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003econstruct the normalized decision matrix using \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:rᵢⱼ\\:=\\:\\frac{xᵢⱼ}{\\sqrt{\\varSigma\\:{ᵢ}^{=1}ᵐ\\:xᵢ{ⱼ}^{2}}}\\)\u003c/span\u003e\u003c/span\u003e;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ecalculate the weighted normalized decision matrix as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:vᵢⱼ\\:=\\:wⱼ*\\:rᵢⱼ\\)\u003c/span\u003e\u003c/span\u003e, where wⱼ represents the weight of criterion j;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003edetermine the positive ideal solution \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}^{+}=\\:\\left\\{{v}^{1+},\\:{v}^{2+},\\:\\dots\\:,\\:v{ₙ}^{+}\\right\\}\\)\u003c/span\u003e\u003c/span\u003e and negative ideal solution \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}^{-}=\\:\\left\\{{v}^{1-},\\:{v}^{2-},\\:\\dots\\:,\\:v{ₙ}^{-}\\right\\}\\)\u003c/span\u003e\u003c/span\u003e;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ecalculate the Euclidean distances to the ideal solutions using \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{D}^{+}ᵢ\\:=\\:\\sqrt{\\varSigma\\:{ⱼ}^{=1}ⁿ\\:{\\left(vᵢⱼ\\:-\\:v{ⱼ}^{+}\\right)}^{2}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{D}^{-}ᵢ\\:=\\:\\sqrt{\\varSigma\\:{ⱼ}^{=1}ⁿ\\:{\\left(vᵢⱼ\\:-\\:v{ⱼ}^{-}\\right)}^{2}}\\)\u003c/span\u003e\u003c/span\u003e; and\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ecompute the relative closeness coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Cᵢ\\:=\\:\\frac{{D}^{-}ᵢ}{\\left({D}^{+}ᵢ\\:+\\:{D}^{-}ᵢ\\right),\\:}\\)\u003c/span\u003e\u003c/span\u003e where values closer to 1 indicate better performance\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe min-max method, on the other hand, normalizes values within a 0\u0026ndash;1 range by assigning 1 to the best performance and 0 to the worst [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and is commonly used in international benchmarking reports [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. For benefit criteria (higher values are better), the min-max formula applies the standard normalization, while for cost criteria (lower values are better), the formula is modified as\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{x}^{{\\prime\\:}}ᵢⱼ\\:=\\frac{\\left(\\text{max}\\left(xⱼ\\right)-\\:xᵢⱼ\\right)}{\\left(\\text{max}\\left(xⱼ\\right)-\\text{min}\\left(xⱼ\\right)\\right)}$$\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e\u003cp\u003eTo exploit the strengths of both techniques, this study adopts a combined approach that uses both the TOPSIS method (on the original data) and the min-max method. Equal weighting \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(wⱼ\\:=\\frac{1}{n})\\)\u003c/span\u003e\u003c/span\u003e was applied to all criteria to ensure unbiased assessment, where n represents the total number of evaluation criteria. A further approach could be to obtain a composite indicator to classify the territories analysed obtained as the average of min-max and TOPSIS [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Baseline model\u003c/h2\u003e\u003cp\u003eComposite indicators are constructed by aggregating carefully selected sub-indicators, which are normalized and then weighted in order to build a single synthetic indicator [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. The main advantage of such indicators lies in the ability to combine diverse information through an appropriate methodology, while also ensuring strong communicative effectiveness. In this context, it becomes essential to define a weighting system, which can be implicit - by assigning equal weight to each element - or explicit, through specific approaches such as expert judgment. Several weighting methods are available [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]; however, those based on equal weighting are generally preferred due to their simplicity and immediacy [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe composite measure used is the Sustainability Score, a summary indicator that aggregates the various contributions of the SDGs, and the method used is to assign the same weighting to all indicators [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Assigning equal weight to the SDGs ensures fairness, neutrality, and simplicity, avoiding arbitrary hierarchies and facilitating communication and cross-country comparability. The choice of not assigning greater emphasis to specific goals or indicators is supported by several studies [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Other approaches adopt a weighting system in which the influence of each individual indicator depends on its specific relevance or importance [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Some scholars have argued that multidimensional comparisons should be carried out at the level of individual indicators rather than through aggregated indices [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe practical steps adopted in the analysis are as follows: (i) where data for 2022 are not available, the most recent data from the previous year are used; (ii) where indicators are not expressed in relative terms, values are normalized by dividing them by the number of inhabitants. The analysis is based on a total of 2052 data points, derived from 76 indicators across 27 alternatives (i.e., the EU Member States). The scope of this research is limited to the 27 EU countries, and only indicators for which data are available for all Member States are included, to ensure consistency and comparability across the dataset.\u003c/p\u003e\u003cp\u003eThese indicators are treated as criteria for the MCDA analysis in this study. Additionally, the indicators are categorized based on the three core dimensions of sustainability, as identified by some authors [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e] :\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eEconomic dimension - SDGs 7, 8, 9, 11, and 12.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEnvironmental dimension - SDGs 6, 13, 14, and 15.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSocial dimension - SDGs 1, 2, 3, 4, 5, 10, 16, and 17.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Alternative model\u003c/h2\u003e\u003cp\u003eTo enhance the robustness of the results obtained, alternative scenarios were examined, with particular attention to variations in indicator values and weight assignments. Regarding the values, it is recognized that they may fluctuate according to different trends. However, since the analysis is based on real data sourced from Eurostat, it is methodologically inappropriate to artificially modify these values. As for the weights, several approaches are possible. The method adopted in this study assigns equal weight to each indicator (Equal Weight among Indicators \u0026ndash; EWI). In line with other analyses [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], an alternative approach could involve assigning equal weight to each SDG (Equal Weight among SDGs \u0026ndash; EWG), regardless of the number of indicators associated with each goal. In this case, SDGs with fewer indicators would give greater weight to each one. For example, SDG 4, which includes six indicators, assigns less weight to each compared to SDG 5, which includes five.\u003c/p\u003e\u003cp\u003eAnother possible method involves assigning equal weight to the three dimensions of sustainable development - economic, social, and environmental - following the Equal Weight among Dimensions of SDGs \u0026ndash; EWDG approach. Similarly, this would result in indicators within the most populous dimension receiving less weight. For instance, each of the 41 indicators related to the social dimension would carry less weight than the 25 indicators in the economic dimension.\u003c/p\u003e\u003cp\u003eIn conclusion, the following weighting models are proposed:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eEWI - Equal Weight among Indicators.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEWG - Equal Weight among SDGs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEWDG - Equal Weight among Dimensions of SDGs.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe results section presents the ranking of the 27 EU countries based on a baseline scenario in which the two methodological approaches are analysed (section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e). The data are then broken down into the three dimensions of sustainability (section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e) and, finally, the European countries are assessed considering alternative scenarios with different proposals on indicators weights (section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4.3\u003c/span\u003e). In addition, a sensitivity analysis is proposed (section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4.4\u003c/span\u003e)\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Sustainability score \u0026ndash; Baseline context\u003c/h2\u003e\u003cp\u003eThe construction of rankings is often met with various criticisms. Common concerns include the dependency of results on the chosen methodology, the use of outdated indicators, and the potential lack of comprehensiveness in capturing the full scope of assessment criteria. Despite these limitations, the use of quantitative methods remains essential for identifying opportunities to improve existing structures. MCDA offers a key advantage by condensing complex datasets into clear insights, highlighting which countries are achieving specific objectives. As explained above, starting from the absolute data presented in Tables S1-S2, after verifying that all alternatives had a value and applying the approach proposed in section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e, all data were normalized to 0\u0026ndash;1 in the min-max approach. The analyses were carried out using two methodological approaches (min-max and TOPSIS) in order to assess whether the performance of these countries may depend on the analysis methodology. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the Sustainability Score calculated using both methods, and based on this score, colour-coded maps are provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e - where green represents the best-performing countries, and red highlights those with the weakest outcomes. Furthermore, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e graphically shows the comparison between European countries in terms of final value (the average value of the min-max (0.560) is smaller than that of TOPSIS (0.606)).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSustainability Score of European countries in 2022 according to TOPSIS and min-max methods\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTOPSIS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMIN-MAX\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAVERAGE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRanking\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRanking\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRanking\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBelgium\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBulgaria\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCzechia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDenmark\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGermany\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEstonia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIreland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGreece\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpain\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFrance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCroatia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eItaly\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCyprus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLatvia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLithuania\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLuxembourg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHungary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMalta\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNetherlands\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAustria\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePoland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePortugal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRomania\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSlovenia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSlovakia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFinland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSweden\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe analysis reveals that, regardless of the method employed, three Northern European countries consistently dominate the top of the ranking. Sweden emerges as the clear leader in both approaches, achieving the highest scores: 0.702 using the min-max method and 0.695 with TOPSIS. Following closely are Denmark (0.669 min-max, 0.691 TOPSIS) and the Netherlands (0.651 min-max, 0.657 TOPSIS). These countries can be classified as top performers, consistently demonstrating high levels of performance across the selected sustainability indicators. This outcome aligns with existing literature, which frequently highlights Sweden, Denmark, and the Netherlands as benchmarks in sustainability and governance [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhen the two methods are combined, equal weight is given to each method in order to produce a single ranking. Sweden, Denmark and the Netherlands, which have the same position, will obviously retain the same place. The first divergence between the two methods emerges from fourth place onwards. In fact, the Czech Republic shows that its performance could be more favourable with a method that takes into account relative proximity to ideal solutions (fourth place in TOPSIS becomes seventh in min-max). Fourth place in min-max is instead occupied by Ireland (which is sixth in TOPSIS). In the single ranking, Ireland is in fourth place together with Belgium (which is fifth in the two separate rankings) and the Czech Republic is in sixth place. However, the most significant differences are for Croatia, Lithuania, but also Slovakia and Poland, which perform better in TOPSIS; Luxembourg, but also Germany and Portugal perform better in min-max.\u003c/p\u003e\u003cp\u003eOverall, the TOPSIS method produces consistently higher scores across all countries (Sweden is the exception). This results in a broader distribution, with a value range of 0.256 between the highest and lowest performers, compared to a narrower range of 0.174 in the TOPSIS method. Despite these numerical differences, the ranking order remains relatively stable, particularly at the extremes of the distribution.\u003c/p\u003e\u003cp\u003eAt the lower end of the scale, Greece records the lowest score in the min-max method (0.446), while Cyprus ranks last in TOPSIS (0.520). Consequently, the combined ranking sees Cyprus (27th in TOPSIS and 24th in min-max) and Bulgaria (25th and 26th in TOPSIS and min-max, respectively) at the bottom of the table, ahead of Greece (26th in TOPSIS and last in min-max). Among Europe\u0026rsquo;s most populous nations, Germany outperforms France in both methods, indicating a stronger overall sustainability profile. The overall ranking places Germany in 10th place, France in 14th, Italy in 21st and Spain in 23rd .\u003c/p\u003e\u003cp\u003eA comparison between the two rankings - both in terms of rank position and performance values -proves to be both useful and insightful (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The correlation analysis reveals a relevant coefficient of determination (R squared equal to 0.78 and 0.82), indicating a strong and statistically significant relationship between the results produced by the two methods. This high degree of correlation reinforces the robustness and reliability of the findings. Although some differences in rankings were previously noted, these variations can be considered minor considering the strong alignment observed between the methods. The consistency confirmed by the correlation analysis lends further credibility to the overall evaluation framework.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSince the adoption of the 2030 Agenda for Sustainable Development in 2015, the United Nations' 17 SDGs have served as a global framework to tackle the most pressing economic, social, and environmental challenges. One of the key aspects of SDGs is that they are not meant to be pursued in isolation. Instead, they are deeply interconnected, meaning that progress in one area often has ripple effects across others. Understanding these interconnections is essential for anyone involved in policymaking, planning, or implementing sustainable development strategies. These interconnections can be summarized as follows:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSynergies Between Goals. In many instances, advancing one goal can significantly support progress in others.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePotential Trade-offs. However, not all relationships between SDGs are synergistic. In some cases, progress in one area may come at the expense of another - unless managed carefully.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEnabling and Cross-cutting Goals. Some goals, meanwhile, play an enabling role, acting as foundations for progress across multiple sectors.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese interconnections suggest that achieving the SDGs requires more than just isolated actions; it calls for a holistic and integrated approach. In particular, there is a growing need for a greater policy coherence, meaning stronger coordination across different sectors and levels of government to manage trade-offs and enhance synergies; moreover an Improved data monitoring is necessary, so that progress on individual SDGs is evaluated in light of its broader impact on the rest of the Agenda; Finally, integrated solutions must be defined, with programs and interventions designed from the outset to contribute to multiple goals simultaneously. In summary, the complexity and interconnectedness of the SDGs should not be seen as an obstacle, but as an opportunity. By recognizing how these goals interact, we can develop smarter, more effective policies that accelerate progress across the entire 2030 Agenda.\u003c/p\u003e\u003cp\u003eHowever, the methods proposed in this study are not able to assess the interdependencies between SDGs, but only between indicators. This study analyses some specific observations through an in-depth and complex analysis of the indicator correlation matrix. However, given the large number of indicators, only a few general considerations can be made (Table S3). The highest positive correlation values are found for the following pairs: 0.96 (SDG08-10 \u0026ndash; SDG10-10); 0.92 (SDG01-20 \u0026ndash; SDG10-41); 0.89 (SDG01-10 \u0026ndash; SDG10-41); 0.88 (SDG01-10 \u0026ndash; SDG01-20); 0.84 (SDG02-30 \u0026ndash; SDG02-40); 0.83 (SDG01-10 \u0026ndash; SDG01-31, SDG07-30 \u0026ndash; SDG08-10); 0.81 (SDG16-50 \u0026ndash; SDG17-10) and 0.80 (SDG01-10 \u0026ndash; SDG10-30, SDG07-30 \u0026ndash; SDG10-10, SDG16-40 \u0026ndash; SDG16-50). At the same time, the highest negative correlation values are: -0.98 (SDG10-41 \u0026ndash; SDG10-51); -0.93 (SDG01-20 \u0026ndash; SDG10-50) and \u0026minus;\u0026thinsp;0.87 (SDG01-10 \u0026ndash; SDG10-50).\u003c/p\u003e\u003cp\u003eConcentration was then analysed using two levels of analysis at the SDG (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and indicator (Figure S4) levels. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the concentration of individual SDGs, where only SDGs 1, 6 and 7 have a significant concentration (\u0026gt;\u0026thinsp;0.60). On the other hand, SDGs 13 and 15 have a lower concentration (\u0026lt;\u0026thinsp;0.50). These data do not show values that differ greatly from one another, highlighting the absence of SDGs in which only one or a few countries predominate or in which almost all countries have a common performance. As regards the individual indicators, it should be noted that 14 of them have a concentration indicator higher than 0.70: SDG09-70 (0.92); SDG10-60 (0.88); SDG02-60 (0.84); SDG07-10 (0.82); SDG01-31, SDG16-70 (0.80); SDG01-50, SDG07-11 (0.78); SDG08-40 (0.77); SDG06-20 (0.76); SDG12-21 (0.74); SDG03-60, SDG12-51 and SDG14-40 (0.73).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Sustainability score \u0026ndash; Economic, environmental and social perspective\u003c/h2\u003e\u003cp\u003eSustainability is traditionally understood as encompassing three interdependent dimensions: social, economic, and environmental. In line with the structure presented in the previous section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e, the SDGs are distributed across these dimensions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). While all indicators are assigned equal weight within the analysis, it is important to emphasize that the three dimensions do not hold equal weight overall due to the differing number of indicators that comprise each. Specifically, a significant proportion of the indicators \u0026minus;\u0026thinsp;41 out of 76 - are associated with the social dimension. This can largely be attributed to the fact that 8 out of the 17 SDGs are primarily social in nature. In comparison, the economic dimension is represented by 25 indicators, whereas the environmental dimension accounts for the fewest, with only 10 indicators.\u003c/p\u003e\u003cp\u003eAs a direct consequence of this imbalance, countries that perform well in the social - and to a slightly lesser extent, the economic - dimension tend to achieve higher overall scores. In this context, an analysis of top-performing countries provides meaningful insight. Sweden emerges as the leading country in the social dimension of both methodologies; in the min-max, it performs better than TOPSIS in the other two dimensions, ranking first (and fourth) in the economic dimension and second (and third) in the environmental dimension. Denmark consistently ranks high in all three dimensions: it ranks second in the economic and social dimensions using the min-max and TOPSIS methods. In the environmental dimension, it consistently performs better using the min-max method (3rd vs 4th ). The Netherlands also demonstrates strong performance, ranking first and third in the economic dimension, while taking sixth positions in the social and environmental dimension using min-max. Instead, its performance is comparatively weaker using TOPSIS method (12th and 9th, respectively). As regards the countries ranked fourth in the overall ranking, Belgium performs very well in the environmental dimension (first place in TOPSIS) and occupies intermediate positions in the other dimensions (ranging from 7th place with TOPSIS to 15th with min-max in the economic dimension). Ireland ranks third in the social dimension with min-max and in the economic dimension with TOPSIS; however, in both methods, it is very weak in the environmental dimension.\u003c/p\u003e\u003cp\u003eOther notable performances include Germany, which tops the environmental dimension under the min-max method (2nd in the TOPSIS). Czechia and Slovenia performs well in the social dimension considering TOPSIS method and Finlandia also for min-max approach. Regarding the economic dimension, Czechia is in fourth place in the min-max method.\u003c/p\u003e\u003cp\u003eTo better understand the relationship between the two approaches (TOPSIS, min-max), the correlation coefficient (R squared) for the three dimensions is calculated again. There is a strong correlation in the environmental dimension, with 0.81 and 0.83 for performance and ranking respectively. The correlation tends to be less strong, but still present, in the economic dimension (0.63 and 0.76 respectively), while the data emerging from the social dimension are less correlated (0.56 and 0.43 respectively). When aggregated and compared with the overall data, these figures confirm the validity of these methods for capturing the different nuances of sustainability. It should also be noted that the sample sizes vary.\u003c/p\u003e\u003cp\u003eAmong the lowest-performing countries, some observations emerge for each dimension. From a social perspective, Bulgaria and Greece occupy the bottom positions in both methods; Cyprus and Romania fare worse than them in TOPSIS and min-max respectively. From an environmental perspective, Malta in both methods, Slovenia in TOPSIS and Estonia in min-max stand out for their very weak performance. From an economic perspective, Portugal and Greece in both methods, Bulgaria in TOPSIS and Italy in min-max.\u003c/p\u003e\u003cp\u003eFinally, for the analysis of the dimensions, the two methods are aggregated, assigning equal importance to both (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The countries that emerged as leaders stand out in the individual dimensions. In fact, the analysis of the top three positions highlights the following: Sweden leads in the social dimension and ranks second and third in the environmental and economic dimensions, respectively; Denmark leads in the economic dimension, followed by Sweden in the social dimension and occupying fourth place in the environmental dimension; the Netherlands is first in the economic dimension; Finland is third in the social dimension, while Germany and Belgium occupy the top two positions in the environmental dimension. Greece occupies last place in the social dimension, Malta in the environmental dimension, while Portugal and Greece are at the bottom of the economic dimension.\u003c/p\u003e\u003cp\u003eAs a possible methodological refinement, and to mitigate discrepancies between the two approaches, a potential solution could be to compute an average of the results derived from both TOPSIS and min-max methods [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the literature offers various approaches in which a variety of methodologies on sustainability issues can be considered [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSustainability Score of European countries in 2022 according to social, environmental and economic dimensions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eSocial dimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eEnvironmental dimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e\u003cp\u003eEconomic dimension\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTOPSIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMIN-MAX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" 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colname=\"c4\"\u003e\u003cp\u003e0.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" 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colname=\"c8\"\u003e\u003cp\u003e0.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDenmark\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGermany\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c13\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEstonia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c7\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNetherlands\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAustria\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePoland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePortugal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRomania\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSlovenia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSlovakia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFinland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSweden\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAverage ranking position by sustainability dimensions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial\u003c/p\u003e\u003cp\u003edimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEnvironmental dimension\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEconomic\u003c/p\u003e\u003cp\u003edimension\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBelgium\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBulgaria\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCzechia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDenmark\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGermany\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEstonia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIreland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGreece\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpain\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFrance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCroatia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eItaly\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCyprus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLatvia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLithuania\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLuxembourg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHungary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMalta\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNetherlands\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAustria\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePoland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePortugal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRomania\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSlovenia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSlovakia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFinland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSweden\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Sustainability score \u0026ndash; Alternative contexts\u003c/h2\u003e\u003cp\u003eThe next objective of this work is to provide solidity to the results obtained previously or, in any case, to analyse them further. Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e showed two different contexts (EWG, EWDG) in addition to the basic context (EWI). Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e show the sustainability scores and the relative ranking in the three approaches. Here too, a version ranging from green to red has been chosen to better communicate the different performances. The analysis was applied to the min-max method.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSustainability scores of European countries in both baseline and alternative contexts (min-max approach)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEWI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEWG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEWDG\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBelgium\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.598\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBulgaria\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.519\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCzechia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDenmark\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGermany\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEstonia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.552\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIreland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGreece\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpain\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.554\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFrance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCroatia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eItaly\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.514\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCyprus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.536\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLatvia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLithuania\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLuxembourg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHungary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMalta\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNetherlands\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAustria\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePoland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePortugal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.490\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRomania\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSlovenia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSlovakia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.610\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFinland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSweden\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRanking of European countries in both baseline and alternative contexts (min-max approach)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEWI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEWG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEWDG\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBelgium\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBulgaria\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCzechia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDenmark\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGermany\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEstonia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIreland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGreece\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpain\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFrance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCroatia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eItaly\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCyprus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLatvia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLithuania\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLuxembourg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHungary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMalta\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNetherlands\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAustria\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePoland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePortugal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRomania\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSlovenia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSlovakia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFinland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSweden\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results clearly show that Sweden consistently ranks at the top, regardless of the aggregation method used. Similarly, countries previously identified as top performers, such as the Netherlands and Denmark, maintain their strong positions across all approaches. In contrast, Ireland loses its high-performing status under both the EWG and EWDG methods, highlighting its sensitivity to the structure of the weighting scheme. On the other hand, Germany exhibits the opposite trend. While it does not particularly stand out in the EWI method, its performance improves significantly under the EWG and EWDG approaches, suggesting a relative strength in the specific SDGs or dimensions emphasized by these methods. Regarding the lower end of the rankings, Romania consistently occupies one of the bottom positions across all methodologies, indicating a persistently weak performance. Greece and Bulgaria show slight improvements under the EWDG method, and Cyprus performs better in both EWG and EWDG. In contrast, Malta experiences a notable drop in performance under the EWDG method, possibly due to the way indicators are aggregated by dimension.\u003c/p\u003e\u003cp\u003eThe average scores across methods remain quite similar, ranging from 0.560 under EWI to 0.572 and 0.574 under EWG and EWDG, respectively. However, examining the extremes reveals some key differences: Romania ranks last under EWG and EWDG with scores of 0.450 and 0.458, while Greece falls to the bottom under EWI with 0.446. Sweden maintains its leading position, with scores between 0.702 (EWI) and 0.672 (EWG) and Germany is the first with 0.692 in EWDG.\u003c/p\u003e\u003cp\u003eThe same analyses are performed using TOPSIS (Tables S5-S6). The application of the TOPSIS method reveals significant changes in the country rankings, confirming trends already observed with the min-max approach. Notably, there is a shift in leadership: Sweden loses its top position to Germany, which ranks first under also EWG method. Among the top-performing countries, the Netherlands is confirmed, while Denmark also maintains a high position in the ranking.\u003c/p\u003e\u003cp\u003eIreland, as previously noted, loses several positions, highlighting how the evaluation of a country may vary depending on the priority assigned to different criteria. Among the low-performing countries, Romania remains in last place. Malta also shows a decrease in its performance, a trend shared by Latvia and Portugal. The main changes concern Croatia and Bulgaria, which have improved, and Malta, which has deteriorated according to the EWG method; on the other hand, Greece in particular, but also Cyprus, have improved, while Lithuania has deteriorated according to the EWDG method.\u003c/p\u003e\u003cp\u003eComparing the average scores obtained through the three approaches, EWI and EWDG show very similar values (0.488 and 0.483), while EWG presents a higher average (0.542). Sweden achieves the highest score in EWI with 0.615, compared to Bulgaria, which scores 0.419. In contrast, when analysing EWG and EWDG, Germany stands out with top scores of 0.679 and 0.796, respectively, while Romania ranks lowest with 0.387 and 0.237.\u003c/p\u003e\u003cp\u003eThese findings highlight that the robustness of rankings may vary depending on the aggregation method used. Adjusting the weights assigned to indicators can be justified when pursuing specific policy or analytical goals. In the absence of such goals, the EWI method tends to be preferred for three main reasons: i) it is neutral and less subject to arbitrary weighting decisions;\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eit treats all data equally and avoids distortions caused by the structure of the SDGs;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eit is simple and transparent to implement and interpret. Furthermore, EWI aligns well with the core principles of the SDGs, which aim to foster inclusion and cohesion within civil society, rather than division.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThat said, when there are targeted objectives - such as emphasizing a particular thematic area or sustainability dimension - alternative weighting approaches like EWG or EWDG may be considered appropriate. However, favouring a specific SDG or sustainability pillar tends to amplify the influence of related indicators, while diminishing the weight of others. This leads to an aggregation outcome that reflects the emphasis placed on certain priorities rather than an unbiased overall performance.\u003c/p\u003e\u003cp\u003eTherefore, conducting sensitivity analyses and scenario simulations is essential. These methods allow for a more comprehensive understanding of the effects of methodological choices and provide multiple perspectives, recognizing that stakeholders\u0026rsquo; interests may not always align.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Robustness Analysis\u003c/h2\u003e\u003cp\u003eIn literature, it is well-recognized that MCDA results are highly sensible to the selection of indicators, normalization techniques, and aggregation methods [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. This section presents the robustness of the previously presented results using methodological variations and data perturbations.\u003c/p\u003e\u003cp\u003eThe first analysis employed in this study, considering the EWI case as baseline model, uses min-max normalization combined with the TOPSIS. The selection of the min-max method as a baseline was established on its widespread adoption in international benchmarking reports, including those pertaining to the SDGs, and its simplicity and direct interpretability [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. This approach facilitates the clear positioning of each alternative relative to the observed best and worst performances within the dataset. To assess the robustness of the presented rankings, complementary analyses were conducted utilizing alternative normalization methods, specifically scaling and normalization by sum. Specifically, for the scaling operation, for each criterion \u003cem\u003ec\u003c/em\u003e, the value x of each alternative is calculated as follows:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{{x}^{c}}_{scaled\\:}=\\frac{{{x}^{c}}_{original\\:}-\\:\\stackrel{̄}{{x}^{c}}}{{s}^{c}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̄}{x}\\)\u003c/span\u003e\u003c/span\u003e is the mean and s the standard deviation. For the normalization by sum, considering C criteria (C\u0026thinsp;=\u0026thinsp;76 in our case), for each criterion \u003cem\u003ec\u003c/em\u003e, the value x of each alternative is calculated as follows:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{{x}^{c}}_{normalized\\:}=\\frac{{{x}^{c}}_{original\\:}}{\\sum\\:_{i=1}^{C}{{x}^{c}}_{original\\:}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe obtained final rankings are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSustainability scores and ranking considering TOPSIS (EWI), TOPSIS with scaled data, TOPSIS with normalized data, TOPSIS with the data normalized using the min max approach on the criteria (as data preprocessing procedure) and the min max procedure.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTOPSIS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTOPSIS Scaled\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTOPSIS\u003c/p\u003e\u003cp\u003eNormalized\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTOPSIS\u003c/p\u003e\u003cp\u003eMin Max\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eMin Max\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAustria\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBelgium\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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colname=\"c11\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBulgaria\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCroatia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCyprus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCzechia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDenmark\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEstonia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFinland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFrance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGermany\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGreece\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHungary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIreland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eItaly\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLatvia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLithuania\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLuxembourg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMalta\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNetherlands\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePoland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePortugal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRomania\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSlovakia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSlovenia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpain\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSweden\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA notable variation in Spearman correlation, presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, was observed between the rankings derived from these different normalization techniques, highlighting the impact of methodological choices in MCDA studies [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. It is important to notice that there is a high Spearman correlation between the Min-Max Rank and the EWI-TOPSIS rank (0.89), indicating a high concordance between the two ranks and high correlation between our baseline approach and the various normalized variations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe differences present in the scores are not arbitrary but are attributable to the distinct mathematical properties of each method. Normalization and scaling methods exhibit extreme sensitivity to outliers, which can disproportionately compress other values and distort the final ranking, an undesirable characteristic for an analysis focused on cohesion and relative performance within the EU context.\u003c/p\u003e\u003cp\u003eThese results indicate that, while TOPSIS is theoretically susceptible to rank reversal when alternatives are introduced or removed [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e], its strong agreement with the various results offers a practical validation of the rankings. To provide a more robust ranking, and to analyze the possible different weight scenarios (according to statistical distributions), we analyzed 3 different weight scenarios, beside the EWI approach. Specifically, we rely on Casual weight (Casual scenario), Normal distribution weight (Normal scenario) and exponential decreasing weight (Decreasing scenario). In Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e we present the spearman correlation matrix between the different weight scenarios, while in Table S7 the weight per criterion is reported. The baseline results have a high correlation with the normal distribution weight scheme (0.98) and casual weight (0.83) while have a moderate correlation with decreasing weight scheme (0.65). These results suggest that alternative weight can change the results, but not in a significant way, except when a specific proportion of the weight is concentrated in small part of the criterion. It is important to stress that these results can highlight one of the main problems that policymakers face in the SDGs evaluation process: the policy priorities. Even if the TOPSIS is sensitive to the weight of the criterion, different policymakers can have various priorities and strategy. From a methodological standpoint, the robustness analysis demonstrates that the baseline ranking is largely invariant to plausible changes in weighting schemes, thereby reinforcing the internal consistency of the composite indicator.\u003c/p\u003e\u003cp\u003eThe lower correlation observed under the exponential decreasing scenario indicates that distortions arise only when the weighting structure systematically privileges a restricted subset of indicators, a situation that mirrors extreme policy prioritization rather than statistical instability.\u003c/p\u003e\u003cp\u003eThis finding has two implications: first, it confirms that the composite results are primarily driven by the underlying performance profiles of EU Member States rather than artefacts of methodological specification; second, it highlights that the role of weights is less about altering empirical rankings and more about reflecting normative preferences embedded in policymaking.\u003c/p\u003e\u003cp\u003eConsequently, the empirical evidence suggests that TOPSIS, when combined with transparent weighting procedures, offers a technically robust and policy-relevant tool for SDG monitoring in the EU context, balancing statistical rigor with the need to accommodate heterogeneity in national priorities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe themes of the SDGs and sustainability are intrinsically and deeply interconnected, forming the cornerstone of contemporary global development discourse [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This connection has been increasingly recognized within the academic community, as evidenced by a marked rise in both the number of publications and citation rates on the subject in recent years [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] showing differences at the individual country level [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The growing recognition of synergies between the SDGs is becoming a cornerstone of sustainable development strategies, as emphasized by recent research [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. A particular emphasis is placed on the active involvement of future generations, notably through the innovative framework of living labs [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. Within this evolving landscape, the European context has emerged as especially relevant, with increasing efforts to integrate sustainability into cultural, social, and institutional practices [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEurope is thus called to embrace a more humane and fraternal approach, rooted in the principle of pragmatic sustainability - a concept that emphasizes realistic, context-sensitive, and action-oriented solutions to complex challenges [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. This vision naturally extends to the broader notion of sustainable communities, promoting locally grounded yet globally interconnected models of development. At the heart of this approach lies a clear set of priorities: broadening stakeholder participation, empowering and trusting younger generations, and advancing practical, evidence-based solutions that transcend ideological divisions. In this context, fostering strong international cooperation is not only desirable, but essential, to address shared challenges and to ensure the successful implementation of the 2030 Agenda. In this regard, collaboration among networks is crucial to maximizing the impact of their sustainability initiatives and fostering more cohesive, large-scale transformations [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this context, Europe has committed to an ambitious and transformative agenda through the European Green Deal, aiming to position itself as a global leader in the transition toward sustainability [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Among the analytical tools available to support this transition, multi-criteria analysis has emerged as a valuable approach. It assists policymakers in identifying targeted, effective strategies [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e] and in monitoring the progress of SDG-related indicators [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Addressing the complex and interconnected challenges of sustainability requires an integrated and systemic approach [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e], capable of capturing the multidimensional nature of sustainable development. This study contributes to a growing body of research that seeks to synthesize the vast array of available data into meaningful insights, despite existing limitations related to data availability, quality, and the extent to which all dimensions of sustainability are adequately covered. Similarly, future directions may involve sensitivity analyses to be conducted on weights for multicriteria methods [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSome analyses have pointed out that certain SDGs may, at times, be in tension with one another, highlighting the need for strategic compromises in policy implementation. However, recognizing these potential trade-offs does not detract from the imperative of adopting rigorous and effective climate policies [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e]. A balanced approach involves designing policy packages that not only address environmental goals but also ensure social equity - for instance, by allocating revenues from carbon taxes to support low-income households [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e], or by incentivizing the development and diffusion of low-carbon technologies [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. Empirical analyses based on European data reveal that, overall, synergies between SDGs tend to outweigh trade-offs. Notably, Poland exhibits the highest proportion of synergies, while Italy shows the highest proportion of trade-offs, suggesting that national contexts significantly influence the alignment - or friction - between sustainability objectives [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEurope is steadily progressing toward sustainability, both in terms of the overall performance across its multiple dimensions [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e] and in relation to specific SDGs [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. However, a clear divide persists among EU countries, closely linked to their levels of economic development. Western and Northern European nations consistently outperform their Eastern and Southern counterparts in sustainability metrics [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e]. In particular, Northern European countries such as Sweden and Denmark frequently rank among the top performers across multiple SDGs, with the Netherlands also demonstrating high levels of achievement [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Swedish cities and regions often rank at the top in sustainability indices [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis pattern is further supported by additional studies, which consistently highlight strong performance from Northern countries, contrasted with weaker outcomes in countries such as Romania, Bulgaria, and Greece [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Similar findings are reported in broader comparative analyses, where Denmark, Finland, and Sweden occupy leading positions, while the lowest rankings are attributed to Bulgaria, Cyprus, Croatia, Greece, Romania, and Hungary [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. Sweden, in particular, maintains a leading position not only in aggregate sustainability performance but also across a wide range of individual SDGs [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e]. Another study suggests that Denmark, Sweden and the Netherlands occupy the top positions, while Romania and Bulgaria occupy the bottom positions [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA disaggregated analysis by sustainability dimensions offers further insights. In the social dimension, Denmark and Sweden lead the rankings; in the economic dimension, Sweden and Denmark again take top positions; and in the environmental dimension, Austria and Sweden stand out [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. These results underscore the importance of considering both geographical and thematic variations in assessing the progress of European countries toward the SDGs.\u003c/p\u003e\u003cp\u003eIn addition, a comparative analysis is conducted with a previous study that utilized the same dataset, albeit referring to data from the year 2020 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. To ensure consistency and comparability between the two analyses, the min\u0026ndash;max normalization method is adopted in both cases. The findings of the current study reveal a notable shift in the rankings: Denmark ascends to second place, effectively swapping positions with the Netherlands. When examining the top ten countries, several changes in ranking are observed. Finland and Germany each drop two positions, while Luxembourg and France fall by three places. Conversely, Ireland improves its position by two places, Slovenia by three, and the Czechia records the most significant upward movement among the top countries, gaining five positions. Slovakia also registers a notable improvement. In contrast, Italy and Portugal experience the most substantial declines, falling six and four places respectively. At the lower end of the ranking, Greece moves to the last position, replacing Romania, while Bulgaria remains third from the bottom, indicating a persistent lag in sustainability performance. An analysis of individual sustainability dimensions provides further insights. In the economic dimension, the top three positions remain unchanged, with Sweden, the Netherlands, and Denmark leading the ranking. In the environmental dimension, Germany surpasses Sweden to claim the top position, while Denmark retains third place. In the social dimension, Sweden maintains its leading position, but Denmark and Ireland now occupy the second and third spots, replacing the Netherlands and Luxembourg. These findings highlight both progress and regression among EU countries over time and emphasize the value of longitudinal analysis in tracking national sustainability performance across multiple dimensions.\u003c/p\u003e\u003cp\u003eFinally, another useful comparison is with the Sustainable Development Report 2025. The data in this report was collected at the end of 2024 and includes 111 indicators, of which approximately 70% come from official statistics (mainly European Commission services) and 30% from unofficial sources (NGOs, academia). Northern European countries perform very well, with Finland ranking first, followed by Denmark, Sweden and Austria. Bulgaria and Cyprus occupy the bottom positions [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe implications that emerge from the analysis are manifold. First, there is a clear need for a systemic and interconnected approach that can both enhance synergies among sustainable development objectives and effectively manage potential trade-offs. Second, while the European Green Deal positions Europe as a global leader in sustainability, this role can only be fully realized if the disparities between Member States are addressed. Third, analyses conducted at the European level must also be replicated at the national level, where differences in performance should be evaluated through the lens of country-specific policies. Fourth, the use of quantitative methods such as MCDA proves to be essential, provided that the underlying assumptions are clearly specified and the results are transparently presented. Finally, the availability of up-to-date data is crucial for informing strategic choices that are both effective and efficient.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThe analysis evaluates the sustainability performance of EU countries using two distinct tools: a compensatory technique (TOPSIS) and a non-compensatory technique (min\u0026ndash;max). Despite methodological differences, the rankings are consistent in a context where all indicators have the same weight, with Sweden, Denmark and the Netherlands consistently emerging as top performers. Sweden stands out for its excellence in the social dimension, ranks second in the environmental dimension, which is led by Germany, and third in the economic dimension, in which Denmark and the Netherlands occupy the first position. Conversely, Greece, Cyprus and Bulgaria occupy the last places in the ranking.\u003c/p\u003e\u003cp\u003eThese results paint a picture of a fragmented Europe. Nordic countries consistently lead in sustainability, demonstrating a balanced approach that integrates social welfare, environmental stewardship, and economic resilience. In contrast, Eastern and Southern European countries continue to face challenges in reaching similar levels of sustainability. This underscores the need for differentiated and targeted strategies at the EU level to reduce these disparities and foster a more cohesive and sustainable Union. Public funding must be effectively allocated to avoid reinforcing existing divides.\u003c/p\u003e\u003cp\u003eBased on the findings, three key recommendations are proposed:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eRefocus EU cohesion policies to explicitly aim at narrowing the sustainability gap between high- and low-performing Member States.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePromote integrated strategies that recognize and leverage the interconnections among social, economic, and environmental dimensions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEnhance data availability and quality to improve the robustness of comparative assessments and inform evidence-based policymaking.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eNonetheless, this study has some limitations. First, the lack of updated data restricts the timeliness and accuracy of the recommendations. Second, the indicator set could be expanded and diversified to better reflect the full scope of sustainability. Thirdly, it could be conducted at the group of countries level in order to carry out more in-depth analyses at the individual country level. A methodological limitation of this study is the reliance on TOPSIS and min-max methods for rank creation. While these methods were central in the presented work, the cross-validation of these ranks using different multi-criteria methods was beyond the scope of this article. This validation is reserved for future research. In fact, future research could also explore the application of probabilistic approaches, such as Monte Carlo simulation, particularly if incorporating uncertain data distributions or alternative model parameters becomes a primary focus to have more robust result. Similarly, it is essential that new studies assess the interdependencies between the various SDGs, for which, however, it is essential to have more information available (the set of 76 indicators should be increased) and it is possible to integrate the data with new analysis methodologies, providing relevant sensitivity analyses. However, this work provides food for thought, is limited to two analytical methodologies, suggests that the approach in which all indicators have the same relevance, but also identifies further directions for future research.\u003c/p\u003e\u003cp\u003eFor Europe to progress, its approach must be pragmatic, inclusive, and grounded in cooperation - supported by analytical tools capable of capturing the complexity of sustainable development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cb\u003eEthics approval and consent to participate\u003c/b\u003e:\u003c/strong\u003e\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e: not applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003cp\u003enot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003e\u003cb\u003eCompeting interests\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors (Idiano D'Adamo, Simone Di Leo, Massimo Gastaldi, Alessandro Paris) contributed equally to the paper.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe present study was conducted within the framework of the PEACE (\u0026lsquo;Protecting the Environment: Advances in Circular Economy\u0026rsquo;) project, funded by the \u0026lsquo;Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale (PRIN)\u0026rsquo;, Investimento M4.C2.1.1-D.D. 104.02- 02-2022, 2022ZFBMA4 under the European Union \u0026ndash; Next Generation EU. 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In recent years, a wide range of indicators has been proposed in the literature to evaluate progress toward the Sustainable Development Goals (SDGs). To effectively monitor and manage this progress, the application of robust and reliable analytical models is essential. This study employs two established methods - min-max normalization and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) - to assess the performance of European countries based on 76 SDG indicators provided by Eurostat for the year 2022. The analysis shows strong consistency between the two methodologies when all indicators are assigned equal weight. Sweden ranks first in both assessments, followed by Denmark and the Netherlands, with Ireland and Belgium also achieving high scores. In contrast, Greece, Cyprus, and Bulgaria consistently rank at the bottom according to both methods. The study also disaggregates the results by dimension, highlighting Germany\u0026rsquo;s leading performance in the environmental category, Sweden\u0026rsquo;s dominance in the social dimension, and its strong performance across all three. Denmark also excels in the social dimension, while the Netherlands stands out in the economic category.\u003c/p\u003e\u003cp\u003eThree key recommendations emerge from the analysis: i) strengthen European cohesion policies to reduce disparities in sustainability performance across countries; ii) promote integrated strategies that enhance the interconnections among the various indicators; and iii) invest in improving both the availability and quality of sustainability-related data throughout Europe.\u003c/p\u003e","manuscriptTitle":"Measuring Sustainability in Europe: A Min–Max and TOPSIS-Based Evaluation of SDGs Performance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 13:17:22","doi":"10.21203/rs.3.rs-7416080/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-08T06:41:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-07T17:49:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-05T18:35:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-04T12:00:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192226933506534818252250148909591056849","date":"2025-08-31T19:58:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40446057300655886110389336669915152474","date":"2025-08-30T09:00:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33567351536983848428683052554351047042","date":"2025-08-28T17:10:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-28T08:44:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-22T13:35:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-22T13:33:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-08-20T09:51:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2bfe198d-826a-4d80-9609-5fdc8af28f10","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:08:05+00:00","versionOfRecord":{"articleIdentity":"rs-7416080","link":"https://doi.org/10.1007/s43621-025-02129-1","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2025-11-14 15:58:05","publishedOnDateReadable":"November 14th, 2025"},"versionCreatedAt":"2025-09-04 13:17:22","video":"","vorDoi":"10.1007/s43621-025-02129-1","vorDoiUrl":"https://doi.org/10.1007/s43621-025-02129-1","workflowStages":[]},"version":"v1","identity":"rs-7416080","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7416080","identity":"rs-7416080","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}