How Are the Winds of Climate Risk Shaping the EU's Regional Sustainable Development?

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Cristina Criste, Anastasia Doras (Lisnic), Petru Marin Stefea, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6685377/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study proposes an integrative approach to assess the relationship between climate risk exposure and sustainable development performance in the regional framework of the European Union (EU27), providing a new economic and geopolitical perspective on the green transition. The main aim of the research is to classify European regions according to their climate vulnerability and macroeconomic responsiveness, focusing on the impact of green finance on socio-economic convergence and long-term sustainability (2010-2023). Using advanced analytical techniques, such as heat maps, correlation analysis and cluster analysis, the study identifies distinct patterns and clusters of countries regarding climate resilience and socio-economic performance. The analysis is structured around two significant time frames: 2010, representative of the post-global economic crisis period, and 2023, marked by the consolidation of the European Green Deal, recent energy challenges and global geo-economic pressures. The results highlight clear differences between Western/Northern Europe and Eastern/Southern Europe. In the West and North, there is strong green infrastructure, solid climate policies, and sustainability is well-integrated into economic plans. However, the South and East face more climate risks, rely heavily on polluting industries, and struggle with weak infrastructure and using green funds. The research emphasizes the need for specific public policies and fair resource distribution for the green transition, considering these regional differences. It notes that green finance not only benefits the environment but also strengthens economic and social ties within the EU. Ensuring everyone has equal access to technology, innovation, and green knowledge is crucial for a fair and effective transition across all regions of Europe. climate risk sustainable development green finance cluster analysis European Union Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction This paper aims to examine the sustainable development performance of the European Union (EU27) countries using economic, social, and environmental indicators. Climate change and the shift towards greener practices make it challenging to achieve sustainable development goals with the expanding demands of the environment, economy, and society in EU nations. Heterogeneity among EU economic systems affects how each country copes with climate risks and implements sustainable change, thus highlighting regional differences. These differences widen the gap in sustainable performance, indicating an urgent need for policies that consider each country's unique circumstances. In this evolving scenario, strategies that use data to integrate social inclusion, economic strength, and environmental performance are essential. However, challenges exist in measuring and clustering states by sustainable development performance. EU nations have been grouped based on their performance to achieve the Sustainable Development Goals (SDGs) (Janković Šoja et al. 2016). In contrast, other studies have explored the relationship between research and development and sustainable outcomes (Costantini et al. 2023) and urged countries to prioritise social and environmental impacts (Hojnik 2024). Other studies describe how digital transformation and sustainable development connect (Kolupaieva et al. 2024) and why there is a need for changing management practice in order to reach the SDGs (Popović et al. 2019). Our study aims to fill knowledge gaps with a holistic approach to evaluate sustainable development across 27 EU countries. We propose a region-type classification using social, economic, and environmental elements and explore how these interact with climate risk. We apply heatmaps, correlation matrices, and cluster analysis to analyse data from 2010 and 2023. This helps us to discover regional differences and develop national policies, offering fresh facts and an integrated framework to determine the resilience and sustainability of EU countries toward climate change. Our research addresses the central question: "How do economic and environmental performance differences among EU countries affect their ability to handle climate risks and transition to renewable energy sources?". Using the three pillars of sustainable development: environmental, economic, and social, the purpose of this research is to explore the degree of sustainable development and the impact of climate change across the member states of the EU. Climate change threatens the environment and economic stability in society, which impacts social and economic institutions globally. It creates price fluctuations and market instability due to its influence on GDP growth, inflation, and employment (Acevedo et al., 2020; Andersson et al., 2020). Nevertheless, a healthy economic condition can soften the effect of climate change. Economic factors, such as investment in green infrastructure and technology, or saving to safeguard the environment, establish nations' ability to adapt or mitigate climate change effects (Reinman, 2015; Senapati and Gupta 2012). Additionally, the ability to reduce climate shocks and risks is also associated with the security of an economy, as economies that are subject to these risks are likely to face long-term problems in terms of growth and stability (Piontek et al. 2018). Understanding such linkages is critical in establishing strategies and policies that reduce climate risks while maximising resilience and economic stability. New ideas and technologies are required to help us cope with and adapt to the impacts of climate change; these adaptations allow us to better prepare for the future (Mubarik et al. 2021). To reduce greenhouse gases and ensure that we always have enough food, we must prioritise sustainable agriculture, green buildings, and clean energy such as solar and wind power (Ye et al. 2020). Climate change is causing sea levels to rise, changing precipitation patterns, and extreme weather such as storms and floods, intensifying and becoming more frequent, affecting our economy (Ekins 1997). These problems can cause serious interruptions in key sectors such as industry, infrastructure, tourism, and agriculture (Olaru and Bănacu 2018). In addition, climate change may result in more people being displaced from their homes, cause market instability, and increase political and social tensions, threatening the stability and growth of the global economy (Bucșe et al. 2019; Wurzel and Connelly 2012; Andersson et al. 2020). The world's top priority is achieving the United Nations' sustainable development goals by 2030 (Chaudhry and Hussain 2023). Only sustainable solutions, including green finance, may assist in addressing ongoing ecological catastrophes and energy sustainability issues (Cigu et al. 2020). The economy and the environment have developed collectively due to green finance's role as a pioneer in environmental economic transformation (OECD 2021; Puschmann et al. 2020). The benefits of green financing go beyond environmental protection and can also, in some cases, provide better financial performance than "conventional financing". Therefore, green finance can reduce the risks faced by businesses and society (Galaz et al. 2015), can improve the level of environmental productivity (Lee and Lee 2022), and can reduce carbon emissions (Zhang and Wang 2021), which ultimately affects sustainable development. Massive investments are needed to make the world's economy more circular, with minimal carbon emissions, sensible resource usage, and a healthier environment. There are some significant contributions to this study. First, a comprehensive examination of sustainable development drivers, as the proposed research goes beyond traditional assessments of renewable energy and climate change targets, integrating and analysing broader socio-economic and environmental factors affecting the sustainable development performance of each EU27 nation. Second, longitudinal analysis of progress: comparing the years 2010 (pre-Paris Agreement) and 2023 (post-2030 Agenda and SDG implementation), highlighting progress and challenges in achieving sustainability goals, providing a nuanced understanding of how global policies and commitments have shaped the EU’s sustainability trajectory. Third, innovative methodological approach: using advanced methods, such as heat maps, correlation analysis and cluster analysis, because it allows for a comprehensive and data-driven exploration of the complex relationships between economic, social, and environmental indicators, contributing to revealing the dynamics of sustainable development performance in different regions of the EU. These methods provide a systematic and straightforward way to identify patterns and trends that might not be apparent through conventional qualitative analysis: Heatmaps make it easier to compare different regions or periods by visually depicting the strength of these relationships. Correlation analysis helps to estimate the strength of the relationship between different variables, expressing how strongly economic and environmental variables are interconnected. Cluster analysis, particularly with hierarchical and K-Means techniques, groups EU countries into clusters based on shared socio-economic and environmental characteristics, allowing for a more effective understanding of regional similarities and differences. Our study adds value by grouping EU countries for targeted insights. This study uses hierarchical clustering and K-Means techniques to group EU countries based on socio-economic and environmental characteristics. It identifies key clusters and provides valuable insights into the specific challenges and strategies different regions face in achieving sustainable development, enabling a clear distinction between regions with similar traits. This approach highlights regional disparities that may otherwise go unnoticed when considering the EU and helps policymakers and stakeholders focus their efforts on regions that may be more vulnerable or require more support to reach sustainable development goals (SDGs). The novelty of this study lies in creating the link between climate risk and economic performance because it sheds light on how these two factors interact in shaping the long-term sustainability of EU countries, something that has not been sufficiently explored in previous studies. While much of the existing literature focuses on economic performance or climate risk in isolation, this study analyses how climate risk exposure directly influences economic outcomes, and vice versa. The paper begins with an introductory section that sets the stage for a detailed investigation of the analysed topic. The second section reveals the theoretical context and the current state of knowledge. The third section presents the research data and methodology. Section 4 presents the main results obtained from the analysis, and the fourth section concludes the study and provides policy recommendations and guidance for addressing the impact of climate change on macroeconomic stability. Literature Review The question behind this research is whether EU27 Member States have made great strides or even serve as models of sustainable development for other nations. Using the three economic, social, and environmental indicators pillars, we have examined the relationship and interaction between climate change and the socio-economic system. Falcone (2020) provides evidence that in a world where climate change and environmental degradation have become significant challenges, the implementation of green finance helps mobilise the necessary capital for green projects. The literature highlights the most frequent effects of climate risk on sustainable development, with Cramer et al. (2018) highlighting how climate change exacerbates environmental issues like increasing pollution and biodiversity loss, especially in vulnerable areas like the Mediterranean basin. Byers et al. (2018) claim that populations at risk of poverty, especially those in Asia and Africa, are disproportionately impacted by climate risks such as temperature change and extreme weather events. These areas are more threatened for diverse reasons, and their situation deteriorates to be two to six times worse with the rise of the world's temperatures, exacerbating poverty and inequality and making sustainable development more difficult to achieve. According to Song et al. (2023), climate risk damages economic development and makes it harder for businesses to manage sustainable growth. Strong political and economic institutions can moderate the negative impact of climate risk on productivity, although these effects vary geographically and are smaller in larger countries. As Qin et al. (2023) noted, natural disasters disrupt global supply chains more frequently than geopolitical threats, endangering sustainable development through economic instability. Climate risk also hinders green innovation. Wu (2025) argues that climate risks severely slow down the development of green technology in China, with physical risks lowering market potential and transition risks decreasing foreign investments. Also, abnormal temperatures significantly inhibit green technology innovation among local enterprises, with a more pronounced effect on nonstate-owned and manufacturing enterprises. A study on China's green finance by Lee et al. (2023) finds that green finance speeds up China’s energy transition, but climate risk has a balancing and threshold effect. This suggests that governments must prioritise climate risk when planning frameworks for green finance. According to Tang et al. (2024), climate risk exposure significantly enhances firms' ESG performance, improving environmental practices and reducing carbon emissions, particularly in state-owned and low-polluting businesses. Sustainable Development Theory is central to the analysis, as the study examines the balance between economic growth, environmental resilience, and long-term sustainability objectives across EU regions. Over the years, several papers have been published with a clustering (grouping or ranking) of countries on sustainable development. Among these works, we would like to mention that Çağlar and Gürler (2021) conducted a classification of 110 countries around the world into 5 clusters based on their progress in achieving sustainable development goals using the K-means method. Another paper analysing 117 countries internationally belongs to Linnerud et al. (2021), who used a normative model of six indicators and the six associated thresholds defining a sustainable development space. They used cluster analysis and categorised countries into six groups that deal with similar challenges in closing the sustainable development gap. The period analysed was 2010-2015. This study highlights the importance of reconciling human needs, social justice, and environmental limits. However, the authors emphasise that the analysis is valid only for the period they study; subsequent economic, demographic, political and environmental changes may affect the results mentioned in the research. Popović et al. (2019) performed a European Union-wide clustering for the selected 2016 year, using the Ward method of hierarchical clustering and one-way ANOVA to assess differences between the separated clusters. The analysis resulted in 4 clusters. However, the results were limited by data availability for some indicators of the SDGS. Another paper with a clustering at the EU level is Skvarciany et al. (2020), who analysed the period 2015-2017. The methodology used was the Multicriteria Decision-Making Approach (MCDA) with the Evaluation Based on Distance from Average Solution (EDAS) method for country assessment. Nevertheless, one of the limitations of the paper is that it analyses only 20 countries in the European Union. Noticeably, cluster analyses require a continuous update, any change in economic, social, political or environmental factors, or any other factors taken into account in the cluster analysis have the outcome of changing the results. After an analysis of the literature, it can be seen that the subject is relevant, vast and complex. The works are crucial in understanding how various researchers view sustainable development and how they examine its relationship with climate risk, economic, social, and policy factors, as well as the environment, in addition to the approach of the methodologies and methods used to analyse this relationship. However, by examining the substantial impacts of two different periods on establishing sustainable development policies, this research attempts to analyse the impact of climate change on the socio-economic system. It does this by conducting a study at the level of the European Union's 27 member states. Data and Methodology The main objective of this study was to draw a parallel regarding the performance of sustainable development in different countries of the European Union by classifying these European regions. Furthermore, the research investigates and contextualises the relationship between climate risk and disturbances in the socio-economic landscape by applying correlation. This paper uses a quantitative and visual approach to analyse the interaction between climate risk and economic performance in European Union countries. The research focuses on a mapping technique, namely heatmaps, that allows these relationships across multiple indicators to be visualised, facilitating the rapid identification of regional patterns. Another method applied is correlation analysis, which is used to identify relationships between economic and environmental variables, providing an overview of how economic factors influence climate performance. This is followed by the representation of the most homogeneous regions or clusters of the EU, through the application of Hierarchical Clustering to classify the observations into distinct groups, while minimising the intrusive distance between the elements of a group, which allows countries to be grouped based on the similarity of their socio-economic and climate characteristics. In addition, the K-Means algorithm was used to determine the optimal clusters, confirming and clarifying the structure of the observations identified by hierarchical clustering. Therefore, three different data analysis methods were applied to group the countries and obtain a hierarchy of European countries based on specific indicators, as follows: The analytical approach consisted of the data's graphical representation through heatmaps. A heatmap is considered a technique for visualising data in 2 dimensions, also representing the magnitude of the individual values of a dataset through colours. The variation associated with the colour can be of different hues or intensities depending on the characteristics of the variables associated with each country. This type of graphical representation allows the visualisation and interconnections between different economic, social factors, and climate change variables at the level of the EU27 countries, enabling the rapid identification of patterns and relationships between these variables. Therefore, the heatmap enables the visualisation of the interconnections between various economic and climate change factors at the level of the EU27 countries. This allows for the rapid identification of patterns and relationships between these variables. The correlation analysis examines the relationship of each variable based on the values of the other variables and the degree of dependence between the variables considered. We applied correlation analysis to deepen the interaction between socio-economic and environmental factors. This type of analysis establishes the relationship between variables and determines the form and sign of the relationship. Therefore, the correlation analysis provides a perspective on the interaction between the factors analysed. Cluster analysis is a statistical data processing method, a quantitative form of data grouping based on specific differences and similarities. It encompasses many different algorithms and methods for grouping data and creating data groups. Thus, objects belonging to a group are similar and different from objects belonging to other groups, each belonging to only a particular group. We began the cluster analysis by applying the K-means clustering algorithm using the RStudio software. K-means is the algorithm used to specify and set the cluster centroids for a specified number of clusters. Additionally, this algorithm allows for the precise assignment of a data vector to a single group, where a specified number of groups are used, and the distance between them is calculated using Euclidean distance on specific attributes. In order to identify countries that face comparable challenges in terms of the risk associated with climate change, this analysis is used to group countries based on their shared similarities between economic and climate variables. The present study included related methodological specifications by the methodologies employed in the earlier studies by Chahuán-Jiménez et al. (2025), D'Orazio, P. (2022), Kwon et al. (2018), and Coles et al. (2014). In addition, our study uses a cross-sectional dataset for the EU-27 for two years of analysis, namely 2010 and 2023, in contrast to previous studies. This new angle allows for a comparative analysis of sustainable development performance in EU countries by contrasting the two distinct points over time. Using a Regional Growth and Convergence Model as a guide, to analyse the relationship between environmental factors and the chosen economic and social variables, the study examines the correlation between economic performance and climate risk across the different regions of the EU. To achieve our overarching objective and in alignment with the established methodological procedures, we have formulated the following two hypotheses for testing: H1: There is a statistically significant negative correlation between climate risk indicators and economic and social performance indicators in EU countries H2: Will EU countries be grouped into distinct groups based on the similarity between their exposure to climate risk and their socio-economic characteristics? Will countries with similar levels of climate risk and socio-economic development be grouped? Therefore, the analysis was conducted using the available data from 2010 to 2023, from official sources such as Eurostat and the World Bank and official reports. The dataset was configured based on primary evidence found in the literature. Thus, the paper focuses on constructing a composite indicator using eight indicators extracted using the available data for these periods from 2010 to 2023, using data from official sources such as Eurostat and the World Bank and official reports for 27 countries. The indicators retained in our dataset capture various aspects of the relationship between climate change's effects on the EU's socioeconomic system. The indicators were divided into three pillars associated with sustainable development: environmental, social, and economic, as illustrated in Figure 1. The rationale behind selecting these indicators was primarily based on the following criteria: Environmental Indicators: CO2 emissions, Renewable Energy, Climate Change Performance Index. These indicators provide relevant information about the main driving factors and potential efforts to mitigate climate change risks. Social Indicators: Globalisation Index, Corruption Perception Index, Human Development Index, provide perspectives on societal factors that can influence both vulnerability to climate change and the effectiveness of public policies to address its impact. These indicators reflect the social dimensions of vulnerability and adaptive capacity. Economic Indicators: Total Environmental Taxes, GDP Growth, in this research these indicators are associated with the results of good public administration and public policies that support the framework in which the public sector contributes to economic development. Table 1 provides an overview of the indicators associated with the three levels: environment, social and economic. Table 1 Description of Environmental, Economic and Social Indicators Indicator Abbreviation Official statistics Unit of measurement Link Annual CO2‚ emissions COemis Our World in Data per capita https://ourworldindata.org Renewable energy RE Our World in Data % https://ourworldindata.org Globalisation index GI KOF Score https://kof.ethz.ch Consumer Price Index CPI OECD % https://www.oecd.org Climate Change Performance Index (CCPI) CCPI Official Annual Reports Score https://ccpi.org/ Human Development Index HDI UNDP Score 0-1 https://hdr.undp.org Total Environmental taxes TEX Office for National Statistics % https://www.ons.gov.uk/ GDP growth GDPg Eurostat % annual https://ec.europa.eu/eurostat Source: Authors' own processing Table 2 presents the descriptive statistics of the variables used in the research. The data exhibit high variability, indicating a heterogeneous character of the countries included in the analysis. The sample consisted of 49 observations. Table 2 Statistical description of indicators ANNUAL_CO CLIMATE_C CPI GLOBALISAT RENEWABLE GDP_GROW TOTAL_ENV Mean 7.043155 56.59694 4.523339 83.46857 22.41329 1.181963 2.480204 Median 6.415691 55.6 3.532361 83.68 19.639 1.529106 2.37 Maximum 22.06201 76.67 17.12497 90.38 66.393 7.498512 5.6 Minimum 3.419049 40.41 -1.084636 71.98 2.851 -5.693741 0.87 Std. Dev. 3.316898 8.179248 3.708751 4.36507 13.19962 2.531529 0.872462 Skewness 2.168053 0.043101 1.052768 -0.438541 1.098871 -0.356407 1.271445 Kurtosis 9.890194 2.780982 4.084229 2.534437 4.22228 4.401446 5.486852 Jarque-Bera 135.3147 0.113107 11.45138 2.013126 12.91158 5.047316 25.82856 Probability 0 0.945016 0.003261 0.365473 0.001571 0.080166 0.000002 Sum 345.1146 2773.25 221.6436 4089.96 1098.251 57.91616 121.53 Sum Sq. Dev. 528.0871 3211.205 660.2322 914.5842 8363.042 307.6148 36.5371 Observations 49 49 49 49 49 49 49 Source: Authors' own processing in Eviews 12 Figures 2 and 3 graphically represent the three pillars associated with sustainable development through heatmaps for the years 2010 and 2023, visualising the status of each EU-27 country. According to Figure no.2, the Colour Intensity highlights the relative performance associated with each country according to a given indicator. This type of heat map is used to identify common or similar features and critical values of the dataset. Thus, the indicators grouped in the dendrogram are correlated; the blue colour represents lower values recorded in the dataset, the red colour represents higher values, and the intensity of the colour indicates the magnitude of the value for each pair of indicators by country. At the same time, the dendogram, attached on the left side, groups countries and indicators according to their similarity; countries with similar indicator values are grouped based on 3 clusters. Moreover, the indicators are also grouped. Due to strong environmental policies, public awareness and education, innovation, and technology, the countries with high performance in managing climate challenges include Denmark, Sweden, Estonia, Austria, Latvia, and Finland. These countries have implemented highly advanced policies, such as emission reductions, renewable energy investments and climate change adaptation. However, underperforming countries like Bulgaria, Cyprus, Hungary, Poland, and the Czech Republic face several difficulties, such as limited natural resources, poor environmental infrastructure, and vulnerability to climate change effects. These national differences imply that economic, geographic, and political factors play a significant role in these countries' vulnerability to the threat of climate change, suggesting that national environmental policy and each country's economy should receive careful consideration. Figure 3 highlights a strong economic security associated with robust economic systems in countries such as Sweden, Finland, Denmark, Germany, Austria, Belgium, and the Netherlands. These results can be associated with efficient governance systems, diversified economies, massive research and development allocations, technological innovation, and sustained investments in renewable energy. In contrast, countries like Romania, Bulgaria, Hungary, Poland, and Slovakia have the most deficient values, suggesting possible instabilities in their economic systems, such as vulnerability to external shocks, dependence on risky industries, reliance on polluting industries, and inadequate environmental infrastructure less developed infrastructure, and challenges in implementing reforms. Results To gain a deeper understanding of the interaction between economic factors and climate change performance, we will apply correlation analysis to observe whether the relationships between variables differ from one country to another. The correlation of IP heatmaps for each country, dispersed along the axis, helps visualise these country-specific correlations. This approach also allows for identifying groups of countries with similar correlation patterns, highlighting those that deviate significantly from the general trends. This provides a more in-depth understanding of the complex interaction between economic factors and climate change performance. In order to test and validate the first hypothesis, H1, which presents a correlation between climate risk and economic and social performance in EU countries, we have configured a Scatter plot with histograms. This visualisation incorporates climate risk measurements and socio-economic performance, aligning with the primary objective of our research. Figure 4 presents the correlations between the variables in the upper triangle, and based on the correlation, a linear relationship between the studied variables is demonstrated. The scatter plots are displayed in the lower triangular part, allowing the observation of linear and non-linear relationships between the variables. In addition, a histogram of each variable is represented on the diagonal, illustrating the distribution of the variables and providing additional stochastic information. We constructed a correlation matrix to examine the relationships between the selected variables, where rows represent countries, denoted by codes, and columns represent different indicators. These coefficients quantify the direction and intensity of the linear relationship between pairs of variables, with values ranging from -1 to 1. A coefficient of 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates a lack of correlation. The analysis reveals a robust correlation between HDI and Globalisation index: 0.763, Renewable energy and CO2: -0.422, Climate Change Performance Index vs CPI, %: -0.368; CPI vs GDP growth: -0.210. The most strongly and positively correlated countries are "Spain" and "Bulgaria", with a correlation value of 0.9998371. The most strongly and negatively correlated countries are "Sweden" and "Luxembourg", with a correlation value of -0.8728782. The high positive correlation between "Spain" and "Bulgaria" suggests that these two countries have similar trends in the variables you are analysing. The results suggest that there is a complex relationship between environmental quality, renewable energy consumption, and economic growth (Obradović and Lojanica 2017; Ntanos et al. 2018; Naqvi et al. 2020; Šofranková et al. 2021). In contrast, the strong negative correlation between " Sweden" and "Luxembourg" indicates that these countries exhibit opposing trends. Thus, according to the correlation matrix there is strong evidence that the associated climate risk exposure and macroeconomic factors, and socio-economic variables are interconnected, due to the correlation coefficients of 0.8 or higher, we mention the fact that there is a strong and positive correlation demonstrated p and a weak but positive correlation demonstrated otherwise. The correlation suggests that some countries or indices are highly interrelated. Weak correlations indicated areas where variables are less dependent on each other. Thus, the need to identify the independent factors in our dataset is noted. Although there is a negative correlation between renewable energy and CO2 emissions (-0.422), some states have a weak but positive correlation; this can be explained by specific national circumstances, like dependence on certain polo industries. Therefore, the first research hypothesis (H1), which posits a significant correlation between climate risk, economic performance, and social performance among EU member states, is validated. Figure 5 illustrates how the data points are grouped using a hierarchical clustering algorithm. In 2010 (Figure 5b), a homogeneous hierarchical structure is observed, and in terms of the cohesion of the groups, the variables have approximately similar values, and the differences between the clusters are less pronounced. In contrast, in 2023 (Figure 5a), the observations forming the groups become more distinct, indicating a significant divergence between the states. This inadvertent change in results could be determined by the economic, social, or environmental changes that have modified the characteristics of the already formed groups. The p-values associated with the dendrograms also indicate a degree of statistical significance for the clustering in 2023, suggesting that the clusters formed during this period imply greater robustness than those formed in the previous year. The EU countries have exhibited divergences in terms of sustainable development indicators over the past. decade. To test the second research hypothesis, we used cluster analysis, which allowed us to group and visualise the relative distances of the already formed groups. For the methodological operationalisation, we used the R software. Based on the K-means algorithm, the data grouping process involves establishing the number of "k" clusters and determining the centroid value based on the chosen number of clusters. Figure 6 presents an analysis of the optimal number of clusters, using two distinct methods for determining and validating the coherence of the data groups: the elbow method and the silhouette method. According to the elbow method, the optimal number of clusters is 6 (Fig. 6a), suggesting the creation of four distinct cluster groups. Regarding the silhouette method (Fig. 6 b), two groups can be associated with this method. Based on this information, we can define and group the countries according to their performance in the cluster. Therefore, these tests allow for the observation of the behaviour of countries based on their common characteristics in the face of climate risk while also determining the different and independent behaviour of each cluster from the other clusters. We used the dendrogram to document the hierarchical relationships between countries and clusters. This process provides us with a deep understanding of the different levels of risk of climate change and economic security/macroeconomic conditions associated with each identified cluster and with the individual countries that compose them. This analysis contributes significantly to our ability to evaluate and interpret the complexity and multidimensionality of living conditions within the European Union. Therefore, the Dendrogram visually represents the similarities and differences between the countries or groups studied, using a metric scale ranging from zero to approximately 7.5. As we approach the dendrogram scale's upper end, the clusters' differences become more pronounced, signifying significant group variations. This amplification of distance suggests substantial diversity within the groups, highlighting the distinct particularities of each pillar associated with sustainable development: economic, environmental, and social, highlighting the distinct particularities of each category: Economic, Environmental and Social. Figure 7 presents a visual clustering of EU countries based on climate risk and macroeconomic variables in 2010 and 2023. By 2023, four clusters have emerged, with the red cluster representing the most high-performing countries, including the Netherlands, Germany, Belgium, Luxembourg, Ireland, Finland, Austria, and Estonia. Yellow and green clusters follow this. The most underperforming countries in implementing green finance policies are found in the blue cluster, comprising Hungary, the Czech Republic, and Poland. 2010 a more homogeneous grouping was observed, with only two clusters. The green cluster is composed mainly of Western and Northern European countries known for their sustainable development policies (Włodarczyk et al. 2021), such as Germany, Belgium, France, Ireland, Sweden, Finland, Austria, the Netherlands, Italy, Denmark, and Luxembourg. While some nations in Western and Northern Europe are racing towards a green future, countries in Central and Eastern Europe have had a slower pace of transformation (Bird 2017; Duman and Kasman 2018; Şerbănică and Constantin 2017; Williges et al. 2022). This can be explained in the context of economic difficulties and political uncertainties. Therefore, a common approach to sustainable development in Europe could be inefficient. Hence, we note the need to implement regional strategies tailored to each country's specific characteristics and needs or groups of countries with similar features. This customised approach would reduce gaps between states and promote an inclusive and sustainable transition across the European Union (Iammarino et al., 2018; Steininger et al., 2022; Baicu, 2021). EU countries have an increasing divergence in environmental financing performance and environmental sustainability. The Cluster Plot analysis presents the significant changes recorded in the two analysis periods. In 2010, the data was divided into only two groups, indicating a less heterogeneous separation of observations. The yellow cluster is larger and includes more observations, while the blue cluster is more compact. On the other hand, in 2023, the number of clusters has increased to four, and their distribution is different, indicating a greater diversification of the analysed features. The blue cluster remains relatively stable, but is more sharply delineated, while the yellow cluster has partially separated from the centre of the distribution. Two new clusters have emerged (grey and red), suggesting that the data have become more heterogeneous. The red cluster has a circular distribution, which could indicate a specific pattern in the data, and the grey cluster appears as a separate group, indicating that a particular subset of the observations has clearly distinguished itself from the rest. The Cluster Plots Figure 8 reveals graphically the changes that have occurred in the European landscape over the past decade. In 2010, Figure 8 b, the observations were divided into two distinct clusters, indicating a clear separation of the observations. A dominant yellow cluster, represented by a cloud of yellow points, encompasses a larger number of observations/countries, and a second, blue cluster, while the blue points' dispersion is compact. By 2023, Figure 8a, four clusters have been formed, with a distribution of observations that stands out from the previous year. Thus, a greater diversity of socio-economic and environmental characteristics among the European Member States is noted. Therefore, the blue cluster has maintained its position and leadership stability. In contrast, the yellow cluster has partially fragmented from the centre of the distribution, and this fragmentation has led to the emergence of two new clusters: the grey and the red cluster, which may suggest an increased heterogeneity of the observations. While the red cluster is circular in distribution, indicating a convergence of economic, social, and environmental factors in its structure, as with the blue cluster, the grey cluster differs from these. This difference in the positioning of the grey cluster is based on a combination of unique features specific to each country. A unique approach to European policies for sustainable development is required in response to particular countries' specific circumstances and difficulties, as high-performing countries have managed to maintain their leading edge. In contrast, others remain faced with some of the same restrictions. The gap between EU countries' convergence towards sustainable development has widened over the last ten years. While some high-performing nations have made significant progress, others have declined in their performance on sustainable development (Renou-Maissant et al. 2022; Vié et al. 2018). Every nation has unique circumstances and obstacles to overcome to achieve a sustainable economy, emphasising the necessity of a customised approach to sustainable development policies in Europe. Adaptive policies that consider these regional differences are required to ensure that all EU members develop fairly and sustainably (Ionescu et al. 2021; Řerbănică and Constantin 2017). A clear picture of how the European nations were split up according to socioeconomic and environmental criteria in 2010 and 2023 is provided by Figure 9, which displays the hierarchical clustering under investigation using the K-Means algorithm. Differences in how the countries were grouped into clusters during the two time periods can be seen as a result of the climatic and socioeconomic shifts that took place. Three separate clusters were identified in 2023. Northern and Western European countries, including Sweden, Finland, Denmark, the Netherlands, Luxembourg, and France, form the first cluster. These countries have successfully developed environmental protection mechanisms, significantly improving environmental quality through the control of pollutant emissions. Additionally, these countries are successfully promoting the development of green industries through guaranteed support and encouraging the progress of innovative technology and production (Brunel 2019; Neves et al. 2020; Piłatowska and Geise 2021). These countries invest in developing renewable energy and sustainable mobility (Guyatt et al. 2012; Murdock et al. 2018; Lin and Hao 2020). Therefore, this combination of mechanisms significantly reduces the carbon footprint and enhances resilience to climate change (Fabrizi et al. 2018; Popović et al. 2019). The second cluster (yellow) groups countries from Central and Eastern Europe, so within this cluster we find the following countries: Czechia, Slovakia, Poland, Romania, Lithuania, Latvia, and Bulgaria. These countries, characterised by low renewable energy production, generally are the least economically developed and have the lowest social and environmental conditions in the European Union (Brodny and Tutak 2020; Pakulska 2021; Włodarczyk et al. 2021). This group of countries is typically categorised as the least performing states in the European Union, with high political instability. However, their capacity to absorb European funds represents an opportunity for targeted investments that could act as a lever for green economic development. At the same time, these countries also face a high risk associated with climate change, low energy efficiency, and a slower pace of transition and sustainable economic development compared to the European Union average. Cluster 2 also faces structural obstacles in adapting effective environmental policies, as well as in implementing efficient strategies to mitigate the risks generated by climate change, as the countries making up this cluster are mainly dependent on traditional polluting energy sources (Guo et al. 2020; Mikalauskienė et al. 2019). To reduce disparities among European Union member states, a solution would be to develop and implement a set of tailored policies based on the performance level of each country or by region or grouping of these countries, taking into account the typical characteristics of underperforming states, as well as ensuring sustained long-term support and constant monitoring in the implementation of these policies. This could contribute to an inclusive and equitable green transition across the European Union, thus reducing gaps between European regions. The countries associated with Cluster 3 (grey), Italy, Spain, Portugal, Greece, Hungary, Belgium and Germany, are generally considered developed economies, engaged in a global transition towards a circular economy. Given their large consumer markets and advanced technological capabilities, green investments in these developed economies will likely stimulate domestic economic and social development. However, their high GDP, associated with CO2 emissions, denotes a series of challenges, especially those associated with decoupling economic activity from environmental impact. The year 2010 (Figure 9b) presents a different clustering structure of the European economic and social landscape. The first cluster (blue) includes a mix of Northeastern European countries, Romania, Lithuania, and Latvia. This suggests a degree of similarity in these countries' economic and development characteristics at the time of the analysis. Romania, Lithuania, and Latvia may be included in this cluster due to the economic, social, and environmental development trajectories necessary for EU integration and economic restructuring processes. The second group (yellow) includes countries from Eastern, Central, and Southern Europe, so in the second cluster we find the following countries: Bulgaria, the Czech Republic, Slovakia, Poland, Italy, Portugal, Spain, Greece, Hungary, Estonia, Belgium, Austria, Germany, Greece, Hungary, Poland, Italy, Portugal, Spain, Greece, Hungary, Estonia, Belgium, and Austria. These countries suggest common characteristics such as traditional industries and conventional energy sources. This segmentation, carried out for 2010, highlights the disparities between economic development and energy transition within the European Union. Comparing the two time periods, a clear differentiation trend can be observed among the European Union member states. Northern and Western European nations have maintained their stability, consolidating their leadership positions in globalisation and environmental transformation. In contrast, Central and Eastern European nations have faced a relative stagnation or slowdown due to the economic and political difficulties encountered over the past decade (Gurkov 2015; Kouli and Müller 2023; Pakulska 2021). On the other hand, Central and Eastern European countries have followed a different trajectory, with some of these states adopting the best practices model from Western states. However, some of these states continue to exhibit maladaptations. Therefore, the European Commission's environmental regulations and the EU's aim to move towards a green economy can also be used to explain this reorganisation of country groups. Developing these clusters supports hypothesis H2 (Goi 2022; Secundo et al. 2020) by attesting to notable shifts over time in the composition of the three pillars of sustainable development, upheld by our research: environmental, economic, and social. Furthermore, environmental metrics such as the reduction in CO2 emissions and the increase in the share of renewable energy also have contributed to repositioning countries within these clusters. The dynamics of these clusters can also be attributed to economic stability, which is demonstrated by GDP growth metrics and rising investments in green technologies. Conclusion The differences between the EU27 member states' strategies for reducing the risks related to climate change and economic sustainability policies are examined in this study. Therefore, a comprehensive methodological approach was employed to examine the relationship between sustainable development and climate change. Hierarchical clustering techniques, correlation analysis, and heat maps were all part of this approach. Our primary results draw attention to the differences observed among EU member states. These findings have relevant implications for shedding light on the relationship between economic sustainability and environmental policy in the EU context. Through the comprehensive methodological approach and the scope of the analysis, this study creatively integrates the environmental, economic, and social pillars, contrasting and expanding on earlier research. Our method incorporates numerous elements related to the interplay between the environment, the economy, and society, in contrast to many studies that have only concentrated on specific facets of sustainability, providing a comprehensive understanding sensitive to the multifaceted character of sustainable development. The correlation matrix highlights links between environmental quality, the use of renewable energy, and economic expansion. Meanwhile, the Heatmap dendrograms have revealed economic and environmental performance disparities across the EU member states. Countries with strong economies like Sweden, Finland, and Denmark are better positioned to handle climate risks through renewable energy investments. Conversely, nations such as Romania, Bulgaria, and Poland encounter difficulties stemming from economic vulnerabilities and inadequate environmental infrastructure. To highlight disparities between EU27 member countries, hierarchical clustering was employed to group member states based on a dendrogram reflecting the similarity of climate change risk profiles. The resulting clusters, viewed through the dendrogram, reveal the main EU-level findings regarding climate change risks. Our analysis has highlighted significant differences in how EU27 countries address these risks. Thus, the resulting cluster, visualised through the dendrogram, reports on relevant indicators of climate change risks and presents the main findings at the EU27 level. Our empirical analysis has highlighted significant differences between the EU27 countries regarding how they address the risks associated with climate change. The results attest that countries such as the Netherlands, Germany, Belgium, Luxembourg, Ireland, and Finland have increased resilience to climate risk. Meanwhile, other countries, such as Hungary, the Czech Republic, and Poland, face significant vulnerabilities. These countries with more developed economies have demonstrated the capacity to attract funding and develop and implement high-level research projects, contributing significantly to the global economy and scientific and technological progress. At the same time, we have identified underrepresented countries, such as a series of Eastern European countries. This research complements previous studies, providing new evidence by highlighting the implications that may arise from macroeconomic parameters as a result of climate change at the level of each EU member state. These differences underline the importance of customized mitigation and adaptation strategies to climate change, which must be aligned with the specific economic and social characteristics of each country. The analysis of changes between 2010 and 2023 reveals massive changes in the clustering of EU countries, reflecting the shifting patterns of sustainability, economic development and environmental change. Three groups emerged in 2023, showcasing the ongoing developments in environmental protection and green industries in Northern and Western European nations. Central and Eastern European nations, meanwhile, developed in different ways, some embraced Western best practices, while others lagged behind because of ongoing political and economic issues and reliance on energy sources that harm the environment. This distinction demonstrates the importance of social, economic, and environmental factors in forming the sustainable development landscape of the EU and emphasises the necessity of implementing region-specific, customised policies to support an inclusive green transformation throughout the EU. This is in line with the EU's overarching goals of sustainable, equitable, and smart growth. Based on the findings of the research study, which show that increased awareness of climate change impacts is associated with an increase in the perception of risk, the following set of policy recommendations and guidelines can be developed: i) High-Performing Countries: Remaining at the forefront and pursuing innovation in green technology and sustainable practices should be the top priorities for the high-performing nations. At the EU level, these states could serve as both role models and the primary exporters of green solutions. Their policies should focus on research and development in important fields such as carbon capture and storage, green hydrogen, biofuels, and emerging technologies for the circular economy. Through green bonds and public-private partnerships, they should also promote the private sector's participation in the creation and execution of green projects. ii) Low-Performing Countries: Countries with low performance, often grappling with economic and political difficulties, must target their investments in renewable energy infrastructure, improved energy efficiency, and green transport. Furthermore, financial and technical support could also be considered priority tools for overcoming structural barriers and capacity limitations. It is also recommended that cooperation with countries seen as effective be promoted to facilitate knowledge sharing, as these partnerships will accelerate progress. These countries should adopt a systematic, phase-by-phase strategy for implementing EU regulations, with flexible regulations and adaptation to each country's economic and social conditions. Additionally, the countries must focus on specific objectives, such as increasing rural productivity and income, which could be the key to sustainable development. These countries must focus on a set of concise policy guidelines. They must try harder in some sectors, such as agriculture, which would allow them to assist in closing the gap and to achieve the overall EU objectives of sustainable development. This research has revealed significant disparities among EU member states, disparities that have been substantiated since the risk due to climate is associated with economic and social performance. Strong regulations, technological progress, equitable access to green finance, and massive investment in sustainable infrastructure are the cornerstones of their resilience and rapid recovery from economic and climate shocks. In addition, the implications of our results stress the need for careful interpretation of these results, acknowledging both findings and limitations. From the perspective of all EU27 countries enjoying equal opportunities to know-how and technologies required to cope with climate issues, we must recognise the need to expand partnerships. This policy aims not only to strengthen the connections of the well-positioned countries but also to support those still at the margin of the scientific discussion. These distinctions highlight the need for differentiated measures for climate change mitigation and adaptation, in line with each country's particular economic and social characteristics. Accordingly, scientific research findings indicate that every government in the EU formulates specialised policies and modified strategies to develop an environment that favours green technology innovation and adoption in an effort to adopt technological practices and stimulate economic performance. Developed economies successfully integrate climate change adaptation strategies into economic policies through investments in green technologies. These investments can not only mitigate climate risks but also support long-term stability. Developed economies successfully integrate climate change adaptation strategies into economic policies through investments in green technologies. These investments can not only reduce climate risks but also support long-term stability. Our key findings also support some critical pathways of importance for sustainability. Similarly, our main findings highlight that comparative assessments facilitate the efficient allocation of resources, directing funding and efforts towards programs that have a positive impact with multiple benefits for society. Identifying the most effective regions can be considered a tool for decision-making and improving sustainable development policies, thus allowing a comparison between EU Member States and highlighting the exchange and implementation of best common practices. Our study stands out from other research studies' comparative approach to assessing sustainability efficiency at the regional level within the European Union. This initiative is driven by the recognition that the specific context of each member state influences the implementation and effectiveness of environmental policies. Our research aimed to identify best practices by identifying the most efficient countries and developing strategies to combat the effects of climate change based on the results of our analysis. This dynamic approach confers relevance through its responsiveness to evolving societal and environmental challenges, particularly climate change-associated ones. Therefore, we propose an integrated approach considering the interconnections between economic, social, and environmental policies. This holistic approach underscores economic growth, social inclusion, and environmental sustainability synergies. The limitations of our research are primarily attributable to the availability of data and the scope of the indicators selected for analysis. These limitations may affect our key findings' accuracy and holistic understanding, which are essential for comprehending the complexity of sustainable development and climate change. Another limitation of our study may be attributed to the relatively limited set of pillars included in our research, as several factors can disturb the socio-economic balance, and therefore, not all relevant components that can influence the implementation of sustainable development are considered. Although we capture the main dimensions of sustainable development, this restricted domain may lead to an unrealistic assessment of the determining factors in establishing green policies. Declarations Acknowledgement: This work was supported by a grant from the Romanian Ministry of Research, Innovation and Digitalization, the project with the title „Economics and Policy Options for Climate Change Risk and Global Environmental Governance” (CF 193/28.11.2022, Funding Contract no. 760078/23.05.2023), within Romania's National Recovery and Resilience Plan (PNRR) - Pillar III, Component C9, Investment I8 (PNRR/2022/C9/MCID/I8) - Development of a program to attract highly specialised human resources from abroad in research, development and innovation activities. Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing interest: The authors have no relevant financial or non-financial interests to disclose. Author Contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Cristina Criste and Anastasia Doraș (Lisnic). The first draft of the manuscript was written by Cristina Criste and Anastasia Doraș (Lisnic) and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.” Data availability: The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. References Acevedo S, Mrkaić M, Novta N, Pugacheva E, Topalova P (2020) The Effects of Weather Shocks on Economic Activity: What are the Channels of Impact?. J Macroecon 65:103207. https://doi.org/10.1016/j.jmacro.2020.103207 Andersson M, Morgan J, Baccianti C (2020) Climate Change and the Macro Economy. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3632832 Baicu CG (2021) Towards Better Involvement In Green Banking Practices: Evidence From Romania. Manag Sustain Dev 13:9. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6685377","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485645591,"identity":"f2d9bbd1-cb11-4416-9b34-a0255ee19da1","order_by":0,"name":"Cristina Criste","email":"","orcid":"","institution":"West University of Timisoara: Universitatea de Vest din Timisoara","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Criste","suffix":""},{"id":485645592,"identity":"5397c409-4d70-48da-a6c5-7a1536c7081f","order_by":1,"name":"Anastasia Doras 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number of clusters\u003c/p\u003e\n\u003cp\u003eSource: Authors’ own processing using R software\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6685377/v1/b5d4ae0fa3c904416e471655.png"},{"id":86937287,"identity":"3672f7d0-f7c7-47da-b7f9-3f68cb363e64","added_by":"auto","created_at":"2025-07-17 11:07:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":90618,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram results\u003c/p\u003e\n\u003cp\u003eSource: Authors’ own processing using R software\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6685377/v1/c0fb3cbc413028073dd8e828.png"},{"id":86937289,"identity":"df00ff67-0af3-4614-b584-5d8323387213","added_by":"auto","created_at":"2025-07-17 11:07:14","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":371746,"visible":true,"origin":"","legend":"\u003cp\u003eCluster plot\u003c/p\u003e\n\u003cp\u003eSource: Authors' own processing using R software\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6685377/v1/0acec49b682637041b48261a.png"},{"id":86937276,"identity":"1aefa644-2d88-41c7-a8ba-c6241044f65b","added_by":"auto","created_at":"2025-07-17 11:07:14","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":115446,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical K-Means Clustering\u003c/p\u003e\n\u003cp\u003eSource: Authors’ own processing using R software\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6685377/v1/bb06aaf55b118aaa102e56f2.png"},{"id":92672191,"identity":"f7ccac6a-884d-4fa7-8803-cb36d4587377","added_by":"auto","created_at":"2025-10-02 19:25:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1871152,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6685377/v1/3ef7e24c-2a5e-4dfe-a754-8d7863869824.pdf"}],"financialInterests":"","formattedTitle":"How Are the Winds of Climate Risk Shaping the EU's Regional Sustainable Development?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThis paper aims to examine the sustainable development performance of the European Union (EU27) countries using economic, social, and environmental indicators. \u0026nbsp;Climate change and the shift towards greener practices make it challenging to achieve sustainable development goals with the expanding demands of the environment, economy, and society in EU nations. Heterogeneity among EU economic systems affects how each country copes with climate risks and implements sustainable change, thus highlighting regional differences. These differences widen the gap in sustainable performance, indicating an urgent need for policies that consider each country's unique circumstances. In this evolving scenario, strategies that use data to integrate social inclusion, economic strength, and environmental performance are essential.\u003c/p\u003e\n\u003cp\u003eHowever, challenges exist in measuring and clustering states by sustainable development performance. EU nations have been grouped based on their performance to achieve the Sustainable Development Goals (SDGs) (Janković Šoja et al. 2016). In contrast, other studies have explored the relationship between research and development and sustainable outcomes (Costantini et al. 2023) and urged countries to prioritise social and environmental impacts (Hojnik 2024). Other studies describe how digital transformation and sustainable development connect (Kolupaieva et al. 2024) and why there is a need for changing management practice in order to reach the SDGs (Popović et al. 2019).\u003c/p\u003e\n\u003cp\u003eOur study aims to fill knowledge gaps with a holistic approach to evaluate sustainable development across 27 EU countries. We propose a region-type classification using social, economic, and environmental elements and explore how these interact with climate risk. We apply heatmaps, correlation matrices, and cluster analysis to analyse data from 2010 and 2023. This helps us to discover regional differences and develop national policies, offering fresh facts and an integrated framework to determine the resilience and sustainability of EU countries toward climate change.\u003c/p\u003e\n\u003cp\u003eOur research addresses the central question: \"How do economic and environmental performance differences among EU countries affect their ability to handle climate risks and transition to renewable energy sources?\". Using the three pillars of sustainable development: environmental, economic, and social, the purpose of this research is to explore the degree of sustainable development and the impact of climate change across the member states of the EU. Climate change threatens the environment and economic stability in society, which impacts social and economic institutions globally. It creates price fluctuations and market instability due to its influence on GDP growth, inflation, and employment (Acevedo et al., 2020; Andersson et al., 2020). Nevertheless, a healthy economic condition can soften the effect of climate change. Economic factors, such as investment in green infrastructure and technology, or saving to safeguard the environment, establish nations' ability to adapt or mitigate climate change effects (Reinman, 2015; Senapati and Gupta 2012). Additionally, the ability to reduce climate shocks and risks is also associated with the security of an economy, as economies that are subject to these risks are likely to face long-term problems in terms of growth and stability (Piontek et al. 2018). Understanding such linkages is critical in establishing strategies and policies that reduce climate risks while maximising resilience and economic stability.\u003c/p\u003e\n\u003cp\u003eNew ideas and technologies are required to help us cope with and adapt to the impacts of climate change; these adaptations allow us to better prepare for the future (Mubarik et al. 2021). To reduce greenhouse gases and ensure that we always have enough food, we must prioritise sustainable agriculture, green buildings, and clean energy such as solar and wind power (Ye et al. 2020). Climate change is causing sea levels to rise, changing precipitation patterns, and extreme weather such as storms and floods, intensifying and becoming more frequent, affecting our economy (Ekins 1997). These problems can cause serious interruptions in key sectors such as industry, infrastructure, tourism, and agriculture (Olaru and Bănacu 2018). In addition, climate change may result in more people being displaced from their homes, cause market instability, and increase political and social tensions, threatening the stability and growth of the global economy (Bucșe et al. 2019; Wurzel and Connelly 2012; Andersson et al. 2020).\u003c/p\u003e\n\u003cp\u003eThe world's top priority is achieving the United Nations' sustainable development goals by 2030 (Chaudhry and Hussain 2023). Only sustainable solutions, including green finance, may assist in addressing ongoing ecological catastrophes and energy sustainability issues (Cigu et al. 2020). The economy and the environment have developed collectively due to green finance's role as a pioneer in environmental economic transformation (OECD 2021; Puschmann et al. 2020). The benefits of green financing go beyond environmental protection and can also, in some cases, provide better financial performance than \"conventional financing\". Therefore, green finance can reduce the risks faced by businesses and society (Galaz et al. 2015), can improve the level of environmental productivity (Lee and Lee 2022), and can reduce carbon emissions (Zhang and Wang 2021), which ultimately affects sustainable development. Massive investments are needed to make the world's economy more circular, with minimal carbon emissions, sensible resource usage, and a healthier environment.\u003c/p\u003e\n\u003cp\u003eThere are some significant contributions to this study. First, a comprehensive examination of sustainable development drivers, as the proposed research goes beyond traditional assessments of renewable energy and climate change targets, integrating and analysing broader socio-economic and environmental factors affecting the sustainable development performance of each EU27 nation. Second, longitudinal analysis of progress: comparing the years 2010 (pre-Paris Agreement) and 2023 (post-2030 Agenda and SDG implementation), highlighting progress and challenges in achieving sustainability goals, providing a nuanced understanding of how global policies and commitments have shaped the EU’s sustainability trajectory. Third, innovative methodological approach: using advanced methods, such as heat maps, correlation analysis and cluster analysis, because it allows for a comprehensive and data-driven exploration of the complex relationships between economic, social, and environmental indicators, contributing to revealing the dynamics of sustainable development performance in different regions of the EU. These methods provide a systematic and straightforward way to identify patterns and trends that might not be apparent through conventional qualitative analysis:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eHeatmaps make it easier to compare different regions or periods by visually depicting the strength of these relationships.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"2\" type=\"1\"\u003e\n \u003cli\u003eCorrelation analysis helps to estimate the strength of the relationship between different variables, expressing how strongly economic and environmental variables are interconnected.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003eCluster analysis, particularly with hierarchical and K-Means techniques, groups EU countries into clusters based on shared socio-economic and environmental characteristics, allowing for a more effective understanding of regional similarities and differences.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eOur study adds value by grouping EU countries for targeted insights. This study uses hierarchical clustering and K-Means techniques to group EU countries based on socio-economic and environmental characteristics. It identifies key clusters and provides valuable insights into the specific challenges and strategies different regions face in achieving sustainable development, enabling a clear distinction between regions with similar traits. This approach highlights regional disparities that may otherwise go unnoticed when considering the EU and helps policymakers and stakeholders focus their efforts on regions that may be more vulnerable or require more support to reach sustainable development goals (SDGs).\u003c/p\u003e\n\u003cp\u003eThe novelty of this study lies in creating the link between climate risk and economic performance because it sheds light on how these two factors interact in shaping the long-term sustainability of EU countries, something that has not been sufficiently explored in previous studies. While much of the existing literature focuses on economic performance or climate risk in isolation, this study analyses how climate risk exposure directly influences economic outcomes, and vice versa.\u003c/p\u003e\n\u003cp\u003eThe paper begins with an introductory section that sets the stage for a detailed investigation of the analysed topic. The second section reveals the theoretical context and the current state of knowledge. The third section presents the research data and methodology. Section 4 presents the main results obtained from the analysis, and the fourth section concludes the study and provides policy recommendations and guidance for addressing the impact of climate change on macroeconomic stability.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eThe question behind this research is whether EU27 Member States have made great strides or even serve as models of sustainable development for other nations. Using the three economic, social, and environmental indicators pillars, we have examined the relationship and interaction between climate change and the socio-economic system. \u0026nbsp;Falcone (2020) provides evidence that in a world where climate change and environmental degradation have become significant challenges, the implementation of green finance helps mobilise the necessary capital for green projects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe literature highlights the most frequent effects of climate risk on sustainable development, with Cramer et al. (2018) highlighting how climate change exacerbates environmental issues like increasing pollution and biodiversity loss, especially in vulnerable areas like the Mediterranean basin. Byers et al. (2018) claim that populations at risk of poverty, especially those in Asia and Africa, are disproportionately impacted by climate risks such as temperature change and extreme weather events. These areas are more threatened for diverse reasons, and their situation deteriorates to be two to six times worse with the rise of the world's temperatures, exacerbating poverty and inequality and making sustainable development more difficult to achieve. According to Song et al. (2023), climate risk damages economic development and makes it harder for businesses to manage sustainable growth. Strong political and economic institutions can moderate the negative impact of climate risk on productivity, although these effects vary geographically and are smaller in larger countries. As Qin et al. (2023) noted, natural disasters disrupt global supply chains more frequently than geopolitical threats, endangering sustainable development through economic instability.\u003c/p\u003e\n\u003cp\u003eClimate risk also hinders green innovation. Wu (2025) argues that climate risks severely slow down the development of green technology in China, with physical risks lowering market potential and transition risks decreasing foreign investments. Also, abnormal temperatures significantly inhibit green technology innovation among local enterprises, with a more pronounced effect on nonstate-owned and manufacturing enterprises. A study on China's green finance by Lee et al. (2023) finds that green finance speeds up China’s energy transition, but climate risk has a balancing and threshold effect. This suggests that governments must prioritise climate risk when planning frameworks for green finance. According to Tang et al. (2024), climate risk exposure significantly enhances firms' ESG performance, improving environmental practices and reducing carbon emissions, particularly in state-owned and low-polluting businesses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSustainable Development Theory is central to the analysis, as the study examines the balance between economic growth, environmental resilience, and long-term sustainability objectives across EU regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOver the years, several papers have been published with a clustering (grouping or ranking) of countries on sustainable development. Among these works, we would like to mention that Çağlar and Gürler (2021) conducted a classification of 110 countries around the world into 5 clusters based on their progress in achieving sustainable development goals using the K-means method. Another paper analysing 117 countries internationally belongs to Linnerud et al. (2021), who used a normative model of six indicators and the six associated thresholds defining a sustainable development space. They used cluster analysis and categorised countries into six groups that deal with similar challenges in closing the sustainable development gap. The period analysed was 2010-2015. This study highlights the importance of reconciling human needs, social justice, and environmental limits. However, the authors emphasise that the analysis is valid only for the period they study; subsequent economic, demographic, political and environmental changes may affect the results mentioned in the research. Popović et al. (2019) performed a European Union-wide clustering for the selected 2016 year, using the Ward method of hierarchical clustering and one-way ANOVA to assess differences between the separated clusters. The analysis resulted in 4 clusters. However, the results were limited by data availability for some indicators of the SDGS. Another paper with a clustering at the EU level is Skvarciany et al. (2020), who analysed the period 2015-2017. The methodology used was the Multicriteria Decision-Making Approach (MCDA) with the Evaluation Based on Distance from Average Solution (EDAS) method for country assessment. Nevertheless, one of the limitations of the paper is that it analyses only 20 countries in the European Union.\u003c/p\u003e\n\u003cp\u003eNoticeably, cluster analyses require a continuous update, any change in economic, social, political or environmental factors, or any other factors taken into account in the cluster analysis have the outcome of changing the results.\u003c/p\u003e\n\u003cp\u003eAfter an analysis of the literature, it can be seen that the subject is relevant, vast and complex. The works are crucial in understanding how various researchers view sustainable development and how they examine its relationship with climate risk, economic, social, and policy factors, as well as the environment, in addition to the approach of the methodologies and methods used to analyse this relationship. However, by examining the substantial impacts of two different periods on establishing sustainable development policies, this research attempts to analyse the impact of climate change on the socio-economic system. It does this by conducting a study at the level of the European Union's 27 member states. \u0026nbsp;\u003c/p\u003e"},{"header":"Data and Methodology","content":"\u003cp\u003eThe main objective of this study was to draw a parallel regarding the performance of sustainable development in different countries of the European Union by classifying these European regions. Furthermore, the research investigates and contextualises the relationship between climate risk and disturbances in the socio-economic landscape by applying correlation. This paper uses a quantitative and visual approach to analyse the interaction between climate risk and economic performance in European Union countries. The research focuses on a mapping technique, namely heatmaps, that allows these relationships across multiple indicators to be visualised, facilitating the rapid identification of regional patterns. \u0026nbsp;Another method applied is correlation analysis, which is used to identify relationships between economic and environmental variables, providing an overview of how economic factors influence climate performance. This is followed by the representation of the most homogeneous regions or clusters of the EU, through the application of Hierarchical Clustering to classify the observations into distinct groups, while minimising the intrusive distance between the elements of a group, which allows countries to be grouped based on the similarity of their socio-economic and climate characteristics. In addition, the K-Means algorithm was used to determine the optimal clusters, confirming and clarifying the structure of the observations identified by hierarchical clustering.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherefore, three different data analysis methods were applied to group the countries and obtain a hierarchy of European countries based on specific indicators, as follows:\u003c/p\u003e\n\u003col start=\"1\" type=\"a\"\u003e\n \u003cli\u003eThe analytical approach consisted of the data\u0026apos;s graphical representation through heatmaps. A heatmap is considered a technique for visualising data in 2 dimensions, also representing the magnitude of the individual values of a dataset through colours. The variation associated with the colour can be of different hues or intensities depending on the characteristics of the variables associated with each country. This type of graphical representation allows the visualisation and interconnections between different economic, social factors, and climate change variables at the level of the EU27 countries, enabling the rapid identification of patterns and relationships between these variables. Therefore, the heatmap enables the visualisation of the interconnections between various economic and climate change factors at the level of the EU27 countries. This allows for the rapid identification of patterns and relationships between these variables.\u003c/li\u003e\n \u003cli\u003eThe correlation analysis examines the relationship of each variable based on the values of the other variables and the degree of dependence between the variables considered. We applied correlation analysis to deepen the interaction between socio-economic and environmental factors. This type of analysis establishes the relationship between variables and determines the form and sign of the relationship. Therefore, the correlation analysis provides a perspective on the interaction between the factors analysed.\u003c/li\u003e\n \u003cli\u003eCluster analysis is a statistical data processing method, a quantitative form of data grouping based on specific differences and similarities. It encompasses many different algorithms and methods for grouping data and creating data groups. Thus, objects belonging to a group are similar and different from objects belonging to other groups, each belonging to only a particular group. We began the cluster analysis by applying the K-means clustering algorithm using the RStudio software. K-means is the algorithm used to specify and set the cluster centroids for a specified number of clusters. Additionally, this algorithm allows for the precise assignment of a data vector to a single group, where a specified number of groups are used, and the distance between them is calculated using Euclidean distance on specific attributes. In order to identify countries that face comparable challenges in terms of the risk associated with climate change, this analysis is used to group countries based on their shared similarities between economic and climate variables.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe present study included related methodological specifications by the methodologies employed in the earlier studies by Chahu\u0026aacute;n-Jim\u0026eacute;nez et al. (2025), D\u0026apos;Orazio, P. (2022), Kwon et al. (2018), and Coles et al. (2014). In addition, our study uses a cross-sectional dataset for the EU-27 for two years of analysis, namely 2010 and 2023, in contrast to previous studies. This new angle allows for a comparative analysis of sustainable development performance in EU countries by contrasting the two distinct points over time. Using a Regional Growth and Convergence Model as a guide, to analyse the relationship between environmental factors and the chosen economic and social variables, the study examines the correlation between economic performance and climate risk across the different regions of the EU.\u003c/p\u003e\n\u003cp\u003eTo achieve our overarching objective and in alignment with the established methodological procedures, we have formulated the following two hypotheses for testing:\u003c/p\u003e\n\u003cp\u003eH1: There is a statistically significant negative correlation between climate risk indicators and economic and social performance indicators in EU countries\u003c/p\u003e\n\u003cp\u003eH2: Will EU countries be grouped into distinct groups based on the similarity between their exposure to climate risk and their socio-economic characteristics? Will countries with similar levels of climate risk and socio-economic development be grouped?\u003c/p\u003e\n\u003cp\u003eTherefore, the analysis was conducted using the available data from 2010 to 2023, from official sources such as Eurostat and the World Bank and official reports. The dataset was configured based on primary evidence found in the literature. Thus, the paper focuses on constructing a composite indicator using eight indicators extracted using the available data for these periods from 2010 to 2023, using data from official sources such as Eurostat and the World Bank and official reports for 27 countries.\u003c/p\u003e\n\u003cp\u003eThe indicators retained in our dataset capture various aspects of the relationship between climate change\u0026apos;s effects on the EU\u0026apos;s socioeconomic system. The indicators were divided into three pillars associated with sustainable development: environmental, social, and economic, as illustrated in Figure 1.\u003c/p\u003e\n\u003cp\u003eThe rationale behind selecting these indicators was primarily based on the following criteria: Environmental Indicators: CO2 emissions, Renewable Energy, Climate Change Performance Index. These indicators provide relevant information about the main driving factors and potential efforts to mitigate climate change risks. Social Indicators: Globalisation Index, Corruption Perception Index, Human Development Index, provide perspectives on societal factors that can influence both vulnerability to climate change and the effectiveness of public policies to address its impact. These indicators reflect the social dimensions of vulnerability and adaptive capacity. Economic Indicators: Total Environmental Taxes, GDP Growth, in this research these indicators are associated with the results of good public administration and public policies that support the framework in which the public sector contributes to economic development. Table 1 provides an overview of the indicators associated with the three levels: environment, social and economic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Description of Environmental, Economic and Social Indicators\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOfficial statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnit of measurement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLink\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnnual CO2\u0026sbquo; emissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCOemis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOur World in Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eper capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://ourworldindata.org\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRenewable energy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOur World in Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://ourworldindata.org\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlobalisation index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKOF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://kof.ethz.ch\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConsumer Price Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOECD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://www.oecd.org\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClimate Change Performance Index (CCPI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCCPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOfficial Annual Reports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://ccpi.org/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHuman Development Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUNDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eScore 0-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://hdr.undp.org\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal Environmental taxes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOffice for National Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://www.ons.gov.uk/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP growth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDPg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEurostat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e% annual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttps://ec.europa.eu/eurostat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSource: Authors\u0026apos; own processing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the descriptive statistics of the variables used in the research. The data exhibit high variability, indicating a heterogeneous character of the countries included in the analysis. The sample consisted of 49 observations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Statistical description of indicators\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"801\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eANNUAL_CO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCLIMATE_C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCPI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLOBALISAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eRENEWABLE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP_GROW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOTAL_ENV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.043155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e56.59694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.523339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e83.46857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22.41329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.181963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.480204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.415691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e55.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.532361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e83.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.529106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22.06201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e76.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.12497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e90.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e66.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.498512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.419049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e40.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.084636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e71.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-5.693741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.316898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8.179248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.708751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.36507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13.19962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.531529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.872462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkewness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.168053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.043101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.052768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.438541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.098871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-0.356407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.271445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eKurtosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.890194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.780982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.084229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.534437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.22228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.401446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.486852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eJarque-Bera\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e135.3147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.113107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.45138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.013126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12.91158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.047316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25.82856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eProbability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.945016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.003261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.365473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.001571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.080166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.000002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e345.1146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2773.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e221.6436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4089.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1098.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e57.91616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e121.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum Sq. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e528.0871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3211.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e660.2322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e914.5842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8363.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e307.6148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e36.5371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eObservations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSource: Authors\u0026apos; own processing in Eviews 12\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFigures 2 and 3 graphically represent the three pillars associated with sustainable development through heatmaps for the years 2010 and 2023, visualising the status of each EU-27 country.\u003c/p\u003e\n\u003cp\u003eAccording to Figure no.2, the Colour Intensity highlights the relative performance associated with each country according to a given indicator. This type of heat map is used to identify common or similar features and critical values of the dataset. Thus, the indicators grouped in the dendrogram are correlated; the blue colour represents lower values recorded in the dataset, the red colour represents higher values, and the intensity of the colour indicates the magnitude of the value for each pair of indicators by country. At the same time, the dendogram, attached on the left side, groups countries and indicators according to their similarity; countries with similar indicator values are grouped based on 3 clusters. Moreover, the indicators are also grouped. Due to strong environmental policies, public awareness and education, innovation, and technology, the countries with high performance in managing climate challenges include Denmark, Sweden, Estonia, Austria, Latvia, and Finland. These countries have implemented highly advanced policies, such as emission reductions, renewable energy investments and climate change adaptation. However, underperforming countries like Bulgaria, Cyprus, Hungary, Poland, and the Czech Republic face several difficulties, such as limited natural resources, poor environmental infrastructure, and vulnerability to climate change effects. These national differences imply that economic, geographic, and political factors play a significant role in these countries\u0026apos; vulnerability to the threat of climate change, suggesting that national environmental policy and each country\u0026apos;s economy should receive careful consideration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 3 highlights a strong economic security associated with robust economic systems in countries such as Sweden, Finland, Denmark, Germany, Austria, Belgium, and the Netherlands. These results can be associated with efficient governance systems, diversified economies, massive research and development allocations, technological innovation, and sustained investments in renewable energy. In contrast, countries like Romania, Bulgaria, Hungary, Poland, and Slovakia have the most deficient values, suggesting possible instabilities in their economic systems, such as vulnerability to external shocks, dependence on risky industries, reliance on polluting industries, and inadequate environmental infrastructure less developed infrastructure, and challenges in implementing reforms.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo gain a deeper understanding of the interaction between economic factors and climate change performance, we will apply correlation analysis to observe whether the relationships between variables differ from one country to another. The correlation of IP heatmaps for each country, dispersed along the axis, helps visualise these country-specific correlations. This approach also allows for identifying groups of countries with similar correlation patterns, highlighting those that deviate significantly from the general trends. This provides a more in-depth understanding of the complex interaction between economic factors and climate change performance.\u003c/p\u003e\n\u003cp\u003eIn order to test and validate the first hypothesis, H1, which presents a correlation between climate risk and economic and social performance in EU countries, we have configured a Scatter plot with histograms. This visualisation incorporates climate risk measurements and socio-economic performance, aligning with the primary objective of our research. Figure 4 presents the correlations between the variables in the upper triangle, and based on the correlation, a linear relationship between the studied variables is demonstrated. The scatter plots are displayed in the lower triangular part, allowing the observation of linear and non-linear relationships between the variables. In addition, a histogram of each variable is represented on the diagonal, illustrating the distribution of the variables and providing additional stochastic information.\u003c/p\u003e\n\u003cp\u003eWe constructed a correlation matrix to examine the relationships between the selected variables, where rows represent countries, denoted by codes, and columns represent different indicators. These coefficients quantify the direction and intensity of the linear relationship between pairs of variables, with values ranging from -1 to 1. A coefficient of 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates a lack of correlation. The analysis reveals a robust correlation between HDI and Globalisation index: 0.763, Renewable energy and CO2: -0.422, Climate Change Performance Index vs CPI, %: -0.368; CPI vs GDP growth: -0.210. The most strongly and positively correlated countries are \u0026quot;Spain\u0026quot; and \u0026quot;Bulgaria\u0026quot;, with a correlation value of 0.9998371. The most strongly and negatively correlated countries are \u0026quot;Sweden\u0026quot; and \u0026quot;Luxembourg\u0026quot;, with a correlation value of -0.8728782. The high positive correlation between \u0026quot;Spain\u0026quot; and \u0026quot;Bulgaria\u0026quot; suggests that these two countries have similar trends in the variables you are analysing. The results suggest that there is a complex relationship between environmental quality, renewable energy consumption, and economic growth (Obradović and Lojanica 2017; Ntanos et al. 2018; Naqvi et al. 2020; \u0026Scaron;ofrankov\u0026aacute; et al. 2021). In contrast, the strong negative correlation between \u0026quot; Sweden\u0026quot; and \u0026quot;Luxembourg\u0026quot; indicates that these countries exhibit opposing trends.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThus, according to the correlation matrix there is strong evidence that the associated climate risk exposure and macroeconomic factors, and socio-economic variables are interconnected, due to the correlation coefficients of 0.8 or higher, we mention the fact that there is a strong and positive correlation demonstrated p and a weak but positive correlation demonstrated otherwise. The correlation suggests that some countries or indices are highly interrelated. Weak correlations indicated areas where variables are less dependent on each other. Thus, the need to identify the independent factors in our dataset is noted. Although there is a negative correlation between renewable energy and CO2 emissions (-0.422), some states have a weak but positive correlation; this can be explained by specific national circumstances, like dependence on certain polo industries. Therefore, the first research hypothesis (H1), which posits a significant correlation between climate risk, economic performance, and social performance among EU member states, is validated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 5 illustrates how the data points are grouped using a hierarchical clustering algorithm. In 2010 (Figure 5b), a homogeneous hierarchical structure is observed, and in terms of the cohesion of the groups, the variables have approximately similar values, and the differences between the clusters are less pronounced. In contrast, in 2023 (Figure 5a), the observations forming the groups become more distinct, indicating a significant divergence between the states. This inadvertent change in results could be determined by the economic, social, or environmental changes that have modified the characteristics of the already formed groups.\u003c/p\u003e\n\u003cp\u003eThe p-values associated with the dendrograms also indicate a degree of statistical significance for the clustering in 2023, suggesting that the clusters formed during this period imply greater robustness than those formed in the previous year. The EU countries have exhibited divergences in terms of sustainable development indicators over the past. decade.\u003c/p\u003e\n\u003cp\u003eTo test the second research hypothesis, we used cluster analysis, which allowed us to group and visualise the relative distances of the already formed groups. For the methodological operationalisation, we used the R software. Based on the K-means algorithm, the data grouping process involves establishing the number of \u0026quot;k\u0026quot; clusters and determining the centroid value based on the chosen number of clusters.\u003c/p\u003e\n\u003cp\u003eFigure 6 presents an analysis of the optimal number of clusters, using two distinct methods for determining and validating the coherence of the data groups: the elbow method and the silhouette method. According to the elbow method, the optimal number of clusters is 6 (Fig. 6a), suggesting the creation of four distinct cluster groups. Regarding the silhouette method (Fig. 6 b), two groups can be associated with this method. Based on this information, we can define and group the countries according to their performance in the cluster. Therefore, these tests allow for the observation of the behaviour of countries based on their common characteristics in the face of climate risk while also determining the different and independent behaviour of each cluster from the other clusters.\u003c/p\u003e\n\u003cp\u003eWe used the dendrogram to document the hierarchical relationships between countries and clusters. This process provides us with a deep understanding of the different levels of risk of climate change and economic security/macroeconomic conditions associated with each identified cluster and with the individual countries that compose them. This analysis contributes significantly to our ability to evaluate and interpret the complexity and multidimensionality of living conditions within the European Union.\u003c/p\u003e\n\u003cp\u003eTherefore, the Dendrogram visually represents the similarities and differences between the countries or groups studied, using a metric scale ranging from zero to approximately 7.5. As we approach the dendrogram scale\u0026apos;s upper end, the clusters\u0026apos; differences become more pronounced, signifying significant group variations. This amplification of distance suggests substantial diversity within the groups, highlighting the distinct particularities of each pillar associated with sustainable development: economic, environmental, and social, highlighting the distinct particularities of each category: Economic, Environmental and Social.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 7 presents a visual clustering of EU countries based on climate risk and macroeconomic variables in 2010 and 2023. By 2023, four clusters have emerged, with the red cluster representing the most high-performing countries, including the Netherlands, Germany, Belgium, Luxembourg, Ireland, Finland, Austria, and Estonia. Yellow and green clusters follow this. The most underperforming countries in implementing green finance policies are found in the blue cluster, comprising Hungary, the Czech Republic, and Poland. 2010 a more homogeneous grouping was observed, with only two clusters. The green cluster is composed mainly of Western and Northern European countries known for their sustainable development policies (Włodarczyk et al. 2021), such as Germany, Belgium, France, Ireland, Sweden, Finland, Austria, the Netherlands, Italy, Denmark, and Luxembourg.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile some nations in Western and Northern Europe are racing towards a green future, countries in Central and Eastern Europe have had a slower pace of transformation (Bird 2017; Duman and Kasman 2018; Şerbănică and Constantin 2017; Williges et al. 2022). This can be explained in the context of economic difficulties and political uncertainties. Therefore, a common approach to sustainable development in Europe could be inefficient. Hence, we note the need to implement regional strategies tailored to each country\u0026apos;s specific characteristics and needs or groups of countries with similar features. This customised approach would reduce gaps between states and promote an inclusive and sustainable transition across the European Union (Iammarino et al., 2018; Steininger et al., 2022;\u0026nbsp;Baicu,\u0026nbsp;2021).\u003c/p\u003e\n\u003cp\u003eEU countries have an increasing divergence in environmental financing performance and environmental sustainability.\u003c/p\u003e\n\u003cp\u003eThe Cluster Plot analysis presents the significant changes recorded in the two analysis periods. In 2010, the data was divided into only two groups, indicating a less heterogeneous separation of observations. The yellow cluster is larger and includes more observations, while the blue cluster is more compact. On the other hand, in 2023, the number of clusters has increased to four, and their distribution is different, indicating a greater diversification of the analysed features. The blue cluster remains relatively stable, but is more sharply delineated, while the yellow cluster has partially separated from the centre of the distribution. Two new clusters have emerged (grey and red), suggesting that the data have become more heterogeneous. The red cluster has a circular distribution, which could indicate a specific pattern in the data, and the grey cluster appears as a separate group, indicating that a particular subset of the observations has clearly distinguished itself from the rest.\u003c/p\u003e\n\u003cp\u003eThe Cluster Plots Figure 8 reveals graphically the changes that have occurred in the European landscape over the past decade. In 2010, Figure 8 b, the observations were divided into two distinct clusters, indicating a clear separation of the observations. A dominant yellow cluster, represented by a cloud of yellow points, encompasses a larger number of observations/countries, and a second, blue cluster, while the blue points\u0026apos; dispersion is compact. By 2023, Figure 8a, four clusters have been formed, with a distribution of observations that stands out from the previous year. Thus, a greater diversity of socio-economic and environmental characteristics among the European Member States is noted. Therefore, the blue cluster has maintained its position and leadership stability. In contrast, the yellow cluster has partially fragmented from the centre of the distribution, and this fragmentation has led to the emergence of two new clusters: the grey and the red cluster, which may suggest an increased heterogeneity of the observations. While the red cluster is circular in distribution, indicating a convergence of economic, social, and environmental factors in its structure, as with the blue cluster, the grey cluster differs from these. This difference in the positioning of the grey cluster is based on a combination of unique features specific to each country.\u003c/p\u003e\n\u003cp\u003eA unique approach to European policies for sustainable development is required in response to particular countries\u0026apos; specific circumstances and difficulties, as high-performing countries have managed to maintain their leading edge. In contrast, others remain faced with some of the same restrictions. The gap between EU countries\u0026apos; convergence towards sustainable development has widened over the last ten years. While some high-performing nations have made significant progress, others have declined in their performance on sustainable development (Renou-Maissant et al. 2022; Vi\u0026eacute; et al. 2018).\u003c/p\u003e\n\u003cp\u003eEvery nation has unique circumstances and obstacles to overcome to achieve a sustainable economy, emphasising the necessity of a customised approach to sustainable development policies in Europe. Adaptive policies that consider these regional differences are required to ensure that all EU members develop fairly and sustainably (Ionescu et al. 2021; Řerbănică and Constantin 2017).\u003c/p\u003e\n\u003cp\u003eA clear picture of how the European nations were split up according to socioeconomic and environmental criteria in 2010 and 2023 is provided by Figure 9, which displays the hierarchical clustering under investigation using the K-Means algorithm. Differences in how the countries were grouped into clusters during the two time periods can be seen as a result of the climatic and socioeconomic shifts that took place. Three separate clusters were identified in 2023. Northern and Western European countries, including Sweden, Finland, Denmark, the Netherlands, Luxembourg, and France, form the first cluster. These countries have successfully developed environmental protection mechanisms, significantly improving environmental quality through the control of pollutant emissions. Additionally, these countries are successfully promoting the development of green industries through guaranteed support and encouraging the progress of innovative technology and production (Brunel 2019; Neves et al. 2020; Piłatowska and Geise 2021). These countries invest in developing renewable energy and sustainable mobility (Guyatt et al. 2012; Murdock et al. 2018; Lin and Hao 2020). Therefore, this combination of mechanisms significantly reduces the carbon footprint and enhances resilience to climate change (Fabrizi et al. 2018; Popović et al. 2019).\u003c/p\u003e\n\u003cp\u003eThe second cluster (yellow) groups countries from Central and Eastern Europe, so within this cluster we find the following countries: Czechia, Slovakia, Poland, Romania, Lithuania, Latvia, and Bulgaria. These countries, characterised by low renewable energy production, generally are the least economically developed and have the lowest social and environmental conditions in the European Union (Brodny and Tutak 2020; Pakulska 2021; Włodarczyk et al. 2021). This group of countries is typically categorised as the least performing states in the European Union, with high political instability. However, their capacity to absorb European funds represents an opportunity for targeted investments that could act as a lever for green economic development. At the same time, these countries also face a high risk associated with climate change, low energy efficiency, and a slower pace of transition and sustainable economic development compared to the European Union average. Cluster 2 also faces structural obstacles in adapting effective environmental policies, as well as in implementing efficient strategies to mitigate the risks generated by climate change, as the countries making up this cluster are mainly dependent on traditional polluting energy sources (Guo et al. 2020; Mikalauskienė et al. 2019).\u003c/p\u003e\n\u003cp\u003eTo reduce disparities among European Union member states, a solution would be to develop and implement a set of tailored policies based on the performance level of each country or by region or grouping of these countries, taking into account the typical characteristics of underperforming states, as well as ensuring sustained long-term support and constant monitoring in the implementation of these policies. This could contribute to an inclusive and equitable green transition across the European Union, thus reducing gaps between European regions. The countries associated with Cluster 3 (grey), Italy, Spain, Portugal, Greece, Hungary, Belgium and Germany, are generally considered developed economies, engaged in a global transition towards a circular economy. Given their large consumer markets and advanced technological capabilities, green investments in these developed economies will likely stimulate domestic economic and social development. However, their high GDP, associated with CO2 emissions, denotes a series of challenges, especially those associated with decoupling economic activity from environmental impact.\u003c/p\u003e\n\u003cp\u003eThe year 2010 (Figure 9b) presents a different clustering structure of the European economic and social landscape. The first cluster (blue) includes a mix of Northeastern European countries, Romania, Lithuania, and Latvia. This suggests a degree of similarity in these countries\u0026apos; economic and development characteristics at the time of the analysis. Romania, Lithuania, and Latvia may be included in this cluster due to the economic, social, and environmental development trajectories necessary for EU integration and economic restructuring processes. The second group (yellow) includes countries from Eastern, Central, and Southern Europe, so in the second cluster we find the following countries: Bulgaria, the Czech Republic, Slovakia, Poland, Italy, Portugal, Spain, Greece, Hungary, Estonia, Belgium, Austria, Germany, Greece, Hungary, Poland, Italy, Portugal, Spain, Greece, Hungary, Estonia, Belgium, and Austria. These countries suggest common characteristics such as traditional industries and conventional energy sources. This segmentation, carried out for 2010, highlights the disparities between economic development and energy transition within the European Union.\u003c/p\u003e\n\u003cp\u003eComparing the two time periods, a clear differentiation trend can be observed among the European Union member states. Northern and Western European nations have maintained their stability, consolidating their leadership positions in globalisation and environmental transformation. In contrast, Central and Eastern European nations have faced a relative stagnation or slowdown due to the economic and political difficulties encountered over the past decade (Gurkov 2015; Kouli and M\u0026uuml;ller 2023; Pakulska 2021). On the other hand, Central and Eastern European countries have followed a different trajectory, with some of these states adopting the best practices model from Western states. However, some of these states continue to exhibit maladaptations. Therefore, the European Commission\u0026apos;s environmental regulations and the EU\u0026apos;s aim to move towards a green economy can also be used to explain this reorganisation of country groups.\u003c/p\u003e\n\u003cp\u003eDeveloping these clusters supports hypothesis H2 (Goi 2022; Secundo et al. 2020) by attesting to notable shifts over time in the composition of the three pillars of sustainable development, upheld by our research: environmental, economic, and social.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, environmental metrics such as the reduction in CO2 emissions and the increase in the share of renewable energy also have contributed to repositioning countries within these clusters. The dynamics of these clusters can also be attributed to economic stability, which is demonstrated by GDP growth metrics and rising investments in green technologies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe differences between the EU27 member states' strategies for reducing the risks related to climate change and economic sustainability policies are examined in this study. Therefore, a comprehensive methodological approach was employed to examine the relationship between sustainable development and climate change. Hierarchical clustering techniques, correlation analysis, and heat maps were all part of this approach. Our primary results draw attention to the differences observed among EU member states. These findings have relevant implications for shedding light on the relationship between economic sustainability and environmental policy in the EU context. Through the comprehensive methodological approach and the scope of the analysis, this study creatively integrates the environmental, economic, and social pillars, contrasting and expanding on earlier research. Our method incorporates numerous elements related to the interplay between the environment, the economy, and society, in contrast to many studies that have only concentrated on specific facets of sustainability, providing a comprehensive understanding sensitive to the multifaceted character of sustainable development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe correlation matrix highlights links between environmental quality, the use of renewable energy, and economic expansion. Meanwhile, the Heatmap dendrograms have revealed economic and environmental performance disparities across the EU member states. Countries with strong economies like Sweden, Finland, and Denmark are better positioned to handle climate risks through renewable energy investments. Conversely, nations such as Romania, Bulgaria, and Poland encounter difficulties stemming from economic vulnerabilities and inadequate environmental infrastructure. To highlight disparities between EU27 member countries, hierarchical clustering was employed to group member states based on a dendrogram reflecting the similarity of climate change risk profiles. The resulting clusters, viewed through the dendrogram, reveal the main EU-level findings regarding climate change risks. Our analysis has highlighted significant differences in how EU27 countries address these risks. Thus, the resulting cluster, visualised through the dendrogram, reports on relevant indicators of climate change risks and presents the main findings at the EU27 level. Our empirical analysis has highlighted significant differences between the EU27 countries regarding how they address the risks associated with climate change. The results attest that countries such as the Netherlands, Germany, Belgium, Luxembourg, Ireland, and Finland have increased resilience to climate risk. Meanwhile, other countries, such as Hungary, the Czech Republic, and Poland, face significant vulnerabilities. These countries with more developed economies have demonstrated the capacity to attract funding and develop and implement high-level research projects, contributing significantly to the global economy and scientific and technological progress. At the same time, we have identified underrepresented countries, such as a series of Eastern European countries. This research complements previous studies, providing new evidence by highlighting the implications that may arise from macroeconomic parameters as a result of climate change at the level of each EU member state. These differences underline the importance of customized mitigation and adaptation strategies to climate change, which must be aligned with the specific economic and social characteristics of each country.\u003c/p\u003e\n\u003cp\u003eThe analysis of changes between 2010 and 2023 reveals massive changes in the clustering of EU countries, reflecting the shifting patterns of sustainability, economic development and environmental change. Three groups emerged in 2023, showcasing the ongoing developments in environmental protection and green industries in Northern and Western European nations. Central and Eastern European nations, meanwhile, developed in different ways, some embraced Western best practices, while others lagged behind because of ongoing political and economic issues and reliance on energy sources that harm the environment. This distinction demonstrates the importance of social, economic, and environmental factors in forming the sustainable development landscape of the EU and emphasises the necessity of implementing region-specific, customised policies to support an inclusive green transformation throughout the EU. This is in line with the EU's overarching goals of sustainable, equitable, and smart growth.\u003c/p\u003e\n\u003cp\u003eBased on the findings of the research study, which show that increased awareness of climate change impacts is associated with an increase in the perception of risk, the following set of policy recommendations and guidelines can be developed:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;i) \u003cstrong\u003eHigh-Performing Countries:\u0026nbsp;\u003c/strong\u003eRemaining at the forefront and pursuing innovation in green technology and sustainable practices should be the top priorities for the high-performing nations. At the EU level, these states could serve as both role models and the primary exporters of green solutions. Their policies should focus on research and development in important fields such as carbon capture and storage, green hydrogen, biofuels, and emerging technologies for the circular economy. Through green bonds and public-private partnerships, they should also promote the private sector's participation in the creation and execution of green projects.\u003c/p\u003e\n\u003cp\u003eii) \u003cstrong\u003eLow-Performing Countries:\u0026nbsp;\u003c/strong\u003eCountries with low performance, often grappling with economic and political difficulties, must target their investments in renewable energy infrastructure, improved energy efficiency, and green transport. Furthermore, financial and technical support could also be considered priority tools for overcoming structural barriers and capacity limitations. It is also recommended that cooperation with countries seen as effective be promoted to facilitate knowledge sharing, as these partnerships will accelerate progress. These countries should adopt a systematic, phase-by-phase strategy for implementing EU regulations, with flexible regulations and adaptation to each country's economic and social conditions. Additionally, the countries must focus on specific objectives, such as increasing rural productivity and income, which could be the key to sustainable development. These countries must focus on a set of concise policy guidelines. They must try harder in some sectors, such as agriculture, which would allow them to assist in closing the gap and to achieve the overall EU objectives of sustainable development.\u003c/p\u003e\n\u003cp\u003eThis research has revealed significant disparities among EU member states, disparities that have been substantiated since the risk due to climate is associated with economic and social performance. Strong regulations, technological progress, equitable access to green finance, and massive investment in sustainable infrastructure are the cornerstones of their resilience and rapid recovery from economic and climate shocks. In addition, the implications of our results stress the need for careful interpretation of these results, acknowledging both findings and limitations. From the perspective of all EU27 countries enjoying equal opportunities to know-how and technologies required to cope with climate issues, we must recognise the need to expand partnerships. This policy aims not only to strengthen the connections of the well-positioned countries but also to support those still at the margin of the scientific discussion. These distinctions highlight the need for differentiated measures for climate change mitigation and adaptation, in line with each country's particular economic and social characteristics.\u003c/p\u003e\n\u003cp\u003eAccordingly, scientific research findings indicate that every government in the EU formulates specialised policies and modified strategies to develop an environment that favours green technology innovation and adoption in an effort to adopt technological practices and stimulate economic performance. Developed economies successfully integrate climate change adaptation strategies into economic policies through investments in green technologies. These investments can not only mitigate climate risks but also support long-term stability. Developed economies successfully integrate climate change adaptation strategies into economic policies through investments in green technologies. These investments can not only reduce climate risks but also support long-term stability.\u003c/p\u003e\n\u003cp\u003eOur key findings also support some critical pathways of importance for sustainability. Similarly, our main findings highlight that comparative assessments facilitate the efficient allocation of resources, directing funding and efforts towards programs that have a positive impact with multiple benefits for society. Identifying the most effective regions can be considered a tool for decision-making and improving sustainable development policies, thus allowing a comparison between EU Member States and highlighting the exchange and implementation of best common practices.\u003c/p\u003e\n\u003cp\u003eOur study stands out from other research studies' comparative approach to assessing sustainability efficiency at the regional level within the European Union. This initiative is driven by the recognition that the specific context of each member state influences the implementation and effectiveness of environmental policies. Our research aimed to identify best practices by identifying the most efficient countries and developing strategies to combat the effects of climate change based on the results of our analysis. This dynamic approach confers relevance through its responsiveness to evolving societal and environmental challenges, particularly climate change-associated ones. Therefore, we propose an integrated approach considering the interconnections between economic, social, and environmental policies. This holistic approach underscores economic growth, social inclusion, and environmental sustainability synergies.\u003c/p\u003e\n\u003cp\u003eThe limitations of our research are primarily attributable to the availability of data and the scope of the indicators selected for analysis. These limitations may affect our key findings' accuracy and holistic understanding, which are essential for comprehending the complexity of sustainable development and climate change. Another limitation of our study may be attributed to the relatively limited set of pillars included in our research, as several factors can disturb the socio-economic balance, and therefore, not all relevant components that can influence the implementation of sustainable development are considered. Although we capture the main dimensions of sustainable development, this restricted domain may lead to an unrealistic assessment of the determining factors in establishing green policies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003eThis work was supported by a grant from the Romanian Ministry of Research, Innovation and Digitalization, the project with the title \u0026bdquo;Economics and Policy Options for Climate Change Risk and Global Environmental Governance\u0026rdquo; (CF 193/28.11.2022, Funding Contract no. 760078/23.05.2023), within Romania\u0026apos;s National Recovery and Resilience Plan (PNRR) - Pillar III, Component C9, Investment I8 (PNRR/2022/C9/MCID/I8) - Development of a program to attract highly specialised human resources from abroad in research, development and innovation activities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest:\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Cristina Criste and Anastasia Doraș (Lisnic). The first draft of the manuscript was written by Cristina Criste and Anastasia Doraș (Lisnic) and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcevedo S, Mrkaić M, Novta N, Pugacheva E, Topalova P (2020) The Effects of Weather Shocks on Economic Activity: What are the Channels of Impact?. J Macroecon 65:103207. https://doi.org/10.1016/j.jmacro.2020.103207\u003c/li\u003e\n\u003cli\u003eAndersson M, Morgan J, Baccianti C (2020) Climate Change and the Macro Economy. 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Emerging Markets Finance and Trade, Taylor \u0026amp; Francis Journals 57:3435-3454. https://doi.org/10.1080/1540496X.2019.1695595\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"climate risk, sustainable development, green finance, cluster analysis, European Union","lastPublishedDoi":"10.21203/rs.3.rs-6685377/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6685377/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study proposes an integrative approach to assess the relationship between climate risk exposure and sustainable development performance in the regional framework of the European Union (EU27), providing a new economic and geopolitical perspective on the green transition. The main aim of the research is to classify European regions according to their climate vulnerability and macroeconomic responsiveness, focusing on the impact of green finance on socio-economic convergence and long-term sustainability (2010-2023). Using advanced analytical techniques, such as heat maps, correlation analysis and cluster analysis, the study identifies distinct patterns and clusters of countries regarding climate resilience and socio-economic performance. The analysis is structured around two significant time frames: 2010, representative of the post-global economic crisis period, and 2023, marked by the consolidation of the European Green Deal, recent energy challenges and global geo-economic pressures. The results highlight clear differences between Western/Northern Europe and Eastern/Southern Europe. In the West and North, there is strong green infrastructure, solid climate policies, and sustainability is well-integrated into economic plans. However, the South and East face more climate risks, rely heavily on polluting industries, and struggle with weak infrastructure and using green funds. The research emphasizes the need for specific public policies and fair resource distribution for the green transition, considering these regional differences. It notes that green finance not only benefits the environment but also strengthens economic and social ties within the EU. Ensuring everyone has equal access to technology, innovation, and green knowledge is crucial for a fair and effective transition across all regions of Europe.","manuscriptTitle":"How Are the Winds of Climate Risk Shaping the EU's Regional Sustainable Development?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-17 11:07:09","doi":"10.21203/rs.3.rs-6685377/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1ee64a95-b325-4742-8b0b-704889770e9b","owner":[],"postedDate":"July 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-02T19:17:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-17 11:07:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6685377","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6685377","identity":"rs-6685377","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}