The Opportunity Cost Between the Circular Economy and Economic Growth: Clustering the Approaches of European Union Member States

Abstract

The circular economy (CE) framework is increasingly recognized as essential for achieving sustainable development by addressing the challenges of resource depletion, waste generation, and environmental degradation. This study examines the relationship between resource consumption, waste management procedures, and energy efficiency within European Union (EU) member states, leveraging data from 2004 to 2023. Using Pearson correlation analysis, Principal Component Analysis (PCA), and K-means clustering, this study identifies key sustainability performance indicators and classifies EU nations into four distinct clusters based on CE adoption. These findings reveal a strong positive correlation between resource productivity and circular material use, indicating that efficient resource management significantly enhances sustainability performance. Similarly, energy productivity exhibits a moderate correlation with resource efficiency, suggesting that economies optimizing energy consumption also enhance material use efficiency. This study also assesses the role of policy instruments, such as environmental taxation, which show a weak negative correlation with resource productivity. These insights provide actionable recommendations for policymakers to tailor interventions, harmonize sustainability strategies, and address regional disparities to accelerate the transition to a resilient and efficient circular economy model.

1. Introduction

1.1. Theoretical Background

The circular economy (CE) is emerging as a key framework for sustainable development, addressing global challenges such as resource depletion, waste generation, and environmental degradation [1]. In the European Union (EU), the transition towards CE has become a strategic priority, with policies such as the European Green Deal and the Circular Economy Action Plan promoting sustainable growth through resource efficiency, waste reduction, and increased use of secondary raw materials. However, despite these efforts, disparities persist among EU member states in adopting and implementing circular economy practices [2,3].

The circular economy is a key idea that supports the traditional theory of economic growth and ensures macroeconomic sustainability in both the economy and the environment. The goal of green growth principles is to create an environment that is conducive to sustainable economic development [4].

Although there are notable variations among member states, the literature demonstrates that the EU has made great strides in adopting sustainable practices. The advantages of integrated resource management systems that encourage recycling and reuse are highlighted in several studies [3,5,6]. However, studies also point to problems such as the inefficient recycling of plastic waste and the uneven adoption of energy-saving technologies [7,8]. Even though financial tools like environmental taxes can encourage sustainable practices, the degree to which they are accepted and effective varies greatly among EU nations [6]. Fostered by the single market framework, the EU’s economic interdependencies present both opportunities and obstacles to a unified shift to a circular economy. Inequalities in sustainable progress among member states are exacerbated by comparative advantages, such as investments in sustainable practices, access to secondary raw materials, and technological infrastructure [1,6]. The aforementioned discrepancies highlight the necessity of customized policies and international collaboration to guarantee inclusivity in the implementation of circular economy principles [6,9].

Despite extensive policy efforts, the transition to a circular economy is uneven across EU member states. Differences in economic structure, access to technology, and policy implementation create significant disparities in circular economy adoption. While some countries have successfully achieved regulatory alignment, others continue to face challenges in harmonizing their policies with circular economy principles. Understanding these differences is crucial for designing effective policies and strategies tailored to each region’s unique challenges and opportunities. This study aims to bridge this gap by clustering EU countries based on their circular economy performance and evaluating the key factors driving their progress [3,9,10].

This study contributes to the existing literature on circular economy adoption in several key ways. First, while previous research has examined the impact of circular economy policies on sustainability metrics, few studies have conducted a comparative clustering analysis of EU member states based on their circular economy performance. Second, this study integrates multiple indicators (resource productivity, waste management efficiency, and energy consumption) into a unified analytical framework, offering a holistic view of the sustainability transition. Finally, by identifying disparities in circular economy adoption across countries, this research provides valuable insights for policymakers seeking to develop targeted interventions that address specific national challenges.

1.2. Literature Review

Sustainability and resource efficiency are now at the top of the global policy and research agenda due to the mounting economic and environmental challenges of the twenty-first century. With the rise in waste production, material consumption, and ecological deterioration, the idea of the circular economy (CE) is becoming a crucial framework for reshaping economic growth while maintaining environmental responsibility. In recent years, the transition towards a circular economy has gained increasing attention as a key solution to the challenges of resource depletion and environmental degradation. However, despite policy efforts at the European Union level, there is still a significant gap in understanding the trade-offs between circular economy adoption and economic growth. While some studies suggest that circularity enhances long-term sustainability, others argue that it may impose short-term economic constraints. This study aims to bridge this gap by providing a data-driven analysis of how European member states are balancing these priorities. By clustering the performance of countries, we can better understand how different economic structures impact the adoption of circular economy principles [11].

The main objective of this study is to examine the economic and environmental trade-offs in circular economy adoption across EU member states. Specifically, this research aims to (1) identify key sustainability indicators that influence circular economy adoption, (2) classify EU member states into distinct clusters based on circular economy performance, (3) evaluate the impact of circularity on economic growth and waste management efficiency, and (4) assess the role of policy mechanisms, such as environmental taxes and innovation incentives, in accelerating the transition to a circular economy.

Recent studies have provided valuable advancements in circular economy metrics and implementation frameworks. For instance, some studies have focused on the role of GIS-based spatial analysis in optimizing renewable energy integration in urban environments, showcasing a data-driven approach to assessing sustainability potential [7]. Similarly analyses propose a low-carbon urban agriculture framework, demonstrating how urban planning can be adapted to integrate circularity principles efficiently [12]. Furthermore, key barriers to implementing Building-Integrated Photovoltaic (BIPV) technology in high-density urban environments highlight the regulatory and economic challenges associated with transitioning to sustainable energy solutions [13]. These findings align with our study’s focus on identifying structural disparities in circular economy adoption across EU member states, emphasizing the need for region-specific policy intervention.

Some studies have analyzed the development of low-carbon cities, identifying key trends in urban sustainability and the role of governance framework in reducing carbon emissions. These findings emphasize that successful sustainability transitions are often driven by well-structured policy mechanisms, including financial incentives, regulatory enforcement, and urban planning strategies that integrate circular economy principles [14]. Other research explores the implementation of modular agrivoltaics within urban building envelopes, demonstrating how combining photovoltaic technology with vertical farming can enhance resource efficiency and contribute to low-carbon urban development. This approach highlights the potential of integrating renewable energy with agricultural production as part of a broader strategy to optimize building performance and sustainability outcomes [15]. These insights suggest that circular economy performance is influenced not only by macroeconomic factors but also by technological innovation, sector-specific applications, and governance structures that facilitate sustainability adoption.

This study aims to explore the economic and environmental trade-offs in circular economy adoption across EU member states. By applying advanced econometrics techniques, such as Principal Component Analysis (PCA) and clustering, the study identifies distinct groups of countries based on their sustainability performance. The findings provide a comparative perspective on how different EU economies integrate circularity principles into their development strategies, highlighting key challenges and opportunities.

1.3. Research Hypothesis

The greater emphasis on economic objectives as opposed to environmental ones in the discourse on suitability is another point of discussion [16]. By using statistical methods to examine sustainability’s multifaceted aspects, this study seeks to fill these gaps. The study offers a strong framework for benchmarking by organizing nations into clusters according to their sustainability performance and employing PCA to reduce the dimensionality of the data. While economic growth and sustainability goals are often seen as complementary, the interplay between these factors is complex and varies across member states. This study aims to explore these dynamics by examining key sustainability metrics and identifying distinct clusters of countries based on their performance. To achieve this, we formulated the following hypotheses:

H1. 

Hypothesis 1 regarding waste generation: There is a significant relationship between economic efficiency and the reduction in waste generation.

Economic efficiency is often linked to better resource utilization, which should, in theory, reduce waste generation [17]. However, the extent to which this holds depends on the implementation of circular economy principles. In economies where raw materials are designed to be reintegrated multiple times into the supply chain, waste generation is expected to decrease significantly [1,18]. Conversely, in economies that prioritize production volume over sustainability, efficiency gains may not necessarily lead to lower waste levels. By analyzing the relationship between resource productivity, waste reduction and economic performance, this study seeks to assess whether CE adoption plays a moderating role [19].

H2. 

Hypothesis 2 regarding economic–ecologic synergy: There is a distinction between the economic and ecological performance of the member states of the European Union.

While economic growth is a key policy objective, it does not always align with sustainability goals [20]. Green technologies and environmentally friendly production processes often require substantial initial investments, which may not yield immediate economic returns. This leads to disparities among EU nations in the balance they achieve between economic expansion and environmental sustainability [21,22]. Given the heterogeneous economic structure and policy frameworks across the EU, this study hypothesizes that some countries prioritize growth at the expense of sustainability, while others invest heavily in environmental protection at the cost of slower economic expansion.

H3. 

Hypotheses 3 regarding policy influence: There are differences between European Union member countries regarding the extent of their adoption of sustainable development principles compared to economic growth.

EU member states have diverse economic profiles, ranging from highly industrialized nations to economies with significant agricultural or service-sector dominance [8,20]. These structural differences, along with variations in policy stringency and regulatory enforcement, contribute to unequal adoption of circular economy principles. Countries with strong policy incentives, such as environmental taxes and subsidies for renewable energy, are more likely to advance sustainability efforts [1,23]. Conversely, those with weaker policy frameworks or economic constraints may struggle to implement effective CE strategies. This hypothesis examines how policy and economic factors shape sustainability adoption across the EU.

The effectiveness of sustainability policies is often determined by the extent to which governments integrate a comprehensive mix of regulatory tools. Mechanisms such as quotas, market-based incentives, and long-term policy commitments have been shown to accelerate sustainability adoption, particularly in regions where government intervention is strategically aligned with economic development goals. However, relying solely on fiscal incentives without complementary regulatory mechanisms has proven insufficient in driving large-scale adoption of circular economy practices [10]. Given these dynamics, it is expected that countries with well-established policy frameworks and strong institutional enforcement will demonstrate a more advanced integration of circular economy principles. In contrast, nations with weaker policy coordination or fragmented regulatory environments may experience slower adoption rates. The role of government interventions as a key driver of sustainability adoption highlights the necessity of a structural and balanced policy approach that aligns economic incentives with long-term sustainability objectives [24,25].

The remainder of this paper is structured as follows. Section 1 presents the research hypotheses and theoretical background, outlining the key factors influencing circular economy adoption. Section 2 details the methodology, including the data sources, econometric techniques, and clustering approach used in the analysis. Section 3 discusses the empirical results, providing insights into the relationship between economic efficiency, sustainability policies, and waste reduction. Section 4 presents a comparative discussion of the findings and their implications for policy and research. Section 5 addresses the limitations of the study. Finally, Section 6 concludes the study by summarizing the key contributions, limitations, and directions for future research.

2. Materials and Methods

2.1. Data

The Eurostat database was searched for several specific indicators related to the sustainability and environmental aspects of macroeconomic activities to conduct the analysis. To guarantee the rigor of the research, the chosen data had a sufficiently large base of observations. Data from all EU member states between 2004 and 2023 were used, totaling 542 observations. Thus, 11 indicators were used for the analysis, including indicators of consumption and resource use, such as (1) consumption of circular materials and consumption footprint divided into two categories: (2) land use per capita and (3) climate changer per capita; a series of indicators of waste generation: (4) municipal waste per capita and (5) packaging waste per capita—plastic generation; a series of economic and political indicators: (6) resource productivity and (7) environmental taxes as a percentage of total taxes; and indicators of electricity consumption: (8) electricity productivity and (9) the proportion of renewable energy in final consumption. The final category of indicators aimed at governance and taxation activity is represented by (10) government debt as a percentage of GDP, and (11) added value in the environmental sector.

2.2. Variable Selection

A clear indication of how effectively member states use their resources is provided by resource productivity. This variable is essential for evaluating how well national economies adhere to the principles of the circular economy [21]. The percentage of total energy consumption that comes from renewable sources, or renewable energy adoption, also shows progress in decarbonizing the energy sector and demonstrates adherence to EU Green Deal targets [3]. The variables that were chosen played a crucial role in linking statistical findings to actual environmental and economic dynamics, for example, the degree of decoupling between resource use and the circular material use rate. Trade-related variables reveal how vulnerable member states are to changes in the global market and how susceptible they are to green trade policies. The degree to which nations have adopted sustainability strategies at the national level and aligned with broader EU objectives is reflected in indicators of waste management and renewable energy adoption [10,25]. When combined, these factors provide a comprehensive framework for comprehending the clustering results and how they affect the creation of policies.

Resource productivity is a core metric for evaluating how effectively an economy utilizes its material resources. Higher resource productivity reflects the ability to generate economic value with fewer material inputs, thereby reducing dependency on raw materials and minimizing environmental impact. This aligns with the broader principles of sustainability, where economic growth is decoupled from resource depletion. A higher level of resource productivity suggests that industries and national economies have adopted circularity measures, such as improved material efficiency and closed-loop production systems.

Renewable energy adoption represents the transition towards sustainable energy sources and a reduced reliance on fossil fuels. The extent to which renewable energy contributes to total consumption is a key measure of progress in decarbonization efforts. This shift plays a fundamental role in achieving long-term sustainability goals, as energy systems are a major source of emissions and resource depletion. A higher share of renewable energy indicates a structural transformation in energy consumption patterns, supporting the transition towards a low-carbon economy.

The circular material use rate measures the degree to which materials are recovered and reintroduced into the production cycle. This indicator captures the effectiveness of circular economy strategies aimed at reducing reliance on virgin raw materials by maximizing reuse and recycling. A higher circular material use rate signifies stronger material efficiency policies and a well-developed waste valorization infrastructure, both of which contribute to minimizing environmental degradation.

Waste management efficiency, measured as kilograms per capita of municipal waste and packaging waste, serves as a critical measure of how effectively countries manage waste generation. This indicator reflects adherence to sustainability frameworks that prioritize waste reduction and recycling over landfilling and incineration. Lower per capita waste generation, coupled with higher recycling rates, demonstrates the successful implementation of policies promoting sustainable consumption and production patterns.

Environmental taxation is included to assess the role of economic instruments in shaping sustainable behaviors. Environmental taxes are designed to internalize externalities by imposing costs on pollution-intensive activities, thereby encouraging industries and consumers to adopt cleaner practices. A higher percentage of environmental taxation within GDP suggests a regulatory environment that actively promotes sustainable economic activities through financial incentives and disincentives.

Trade vulnerability and exposure to green trade policies are essential for evaluating the impact of global economic dynamics on sustainability transitions. Countries highly dependent on imported raw materials or with weak circular economy policies may face challenges in maintaining a sustainable trade balance. The extent to which trade policies integrate environmental considerations influences the ability of economies to align with circular economy principles while remaining competitive in international markets.

Additionally, variables such as packaging waste generation and pollution footprint were analyzed to account for short-term fluctuations. This method ensures that the analysis reflects long-term sustainability trends rather than temporary shifts caused by policy changes, economic disruptions, or external factors. The use of longitudinal data covering the period from 2004 to 2023 enhances the reliability of this study by reducing the potential for anomalies that could skew the results. By incorporating these variables, the study provides a comprehensive framework for assessing sustainability performance and understanding the economic–environmental trade-offs in circular economy adoption. Each indicator plays a crucial role in evaluating how different economies integrate circularity into their growth models, energy systems, and waste management strategies, ultimately shaping policy recommendations for a more sustainable economic transition.

Table A1. Eurostat’s metrics used in this paper’s analysis.

Abbreviation for Paper	Name of Metrix	Eurostat Code
EE_IN_TOTAL_EE	Share of renewable energy in gross final energy consumption by sector	sdg_07_4
ENV_TAX_IN_TOTAL_TAX	Share of environmental taxes in total tax revenues	sdg_17_50
CF_CLIMATE_CHANGE	Consumption footprint_Climate Change_Per Inhabitant	cei_gsr010
CIRCULAR_MATERIAL_USE	Circular material use rate	ceisrm030
CF_LAND_USE	Consumption footprint_Land Use_Per Inhabitant	cei_gsr010
ENERGY_PROD.	Energy productivity	sdg_07_30
GD_IN_GDP	General government gross debt_% of GDP	sdg_17_40
GVA_IN_ENV_G_S	Gross value added in env. goods and services sector	sdg_12_61
PLASTIC_GEN.	Generation of plastic packaging waste per capita	cei_pc050
RESOURCE_PROD.	Resource productivity	env_ac_rp
WASTE_GENERATION__CAPITA	Waste generation per capita	cei_pc034

Source: elaborated by authors using data provided by Eurostat.

of Appendix A contains a list of the indicators’ correlations with the names used in this work, which can be referred to. This stage’s computations were performed with the econometric program EViews 12. After the data were processed, a set of 542 observations was produced, which was used in the analyses. Three different kinds of analyses were conducted from the perspective of the selected working methods.

2.3. Research Design and Methodological Approach

This study’s clustering procedure ensures that the identified clusters represent actual economic characteristics by fusing statistical robustness with economic logic. The K-means clustering algorithm was the first step in a multi-step process that was supported by the statistical method of Principal Component Analysis (PCA) and the elbow method. By graphing the explained variance (within-cluster sum of squares) against the number of clusters, the elbow method was instrumental in determining the ideal number of clusters. The “elbow point” was determined to be the point at which the benefits of increasing the number of clusters in lowering variance diminished. Interpretability in policy and economic contexts depends on striking a balance between avoiding overfitting and capturing significant group distinctions [3,21].

Although minor, data imbalances were carefully addressed in this study to guarantee the reliability and integrity of the analysis. There were few missing values among the variables used, which covered a wide range of environmental and economic indicators. The circular material use rate, the proportion of renewable energy in gross final energy consumption, the pollution footprint, and waste production of packaging waste were the only variables affected. To fill in these gaps, imputation methods were chosen based on the characteristics of the data as well as the methodological needs of the clustering and econometric procedures. For the aforementioned indicators, time-series data with missing values were processed using imputation techniques.

2.4. Statistical Methods and Imputation Methods

The fundamental assumptions of the statistical models employed in this investigation include stationarity, homoscedasticity, and the lack of multicollinearity. A number of diagnostic tests were carried out to confirm these hypotheses. Several diagnostic tests were carried out to confirm these hypotheses. The sustainability of the data analysis was confirmed. The residuals were homoscedastic across all regression models, as confirmed by the Breusch–Pagan test, which failed to identify heteroscedasticity. The fact that there is no heteroscedasticity indicates that the relationships that have been modeled, like those between GDP, the use of renewable energy, and trade flows, are constant throughout the dataset.

VIF verified that metrics like trade balance, circular material use, and adoption of renewable energy are independent. When multicollinearity is absent, the model’s estimates are more reliable because each variable makes a distinct contribution to the explanation of the observed variations. This study’s statistical models are based on the assumption of the lack of multicollinearity, stationarity, and homoscedasticity. This method reduces issues with multicollinearity and heteroscedasticity by converting the dataset into uncorrelated components by default. The accuracy and interpretability of the clustering and econometrics analyses are improved in this way.

To identify patterns and reduce dimensionality in the second analysis, PCA is crucial for comprehending the relationship between sustainability indicators. This reduces the noise caused by the diversity of data types. Because it offers a strong foundation for dividing nations into uniform working groups—a prerequisite for the subsequent analyses, which involve applying a clustering process—the analysis was required. The two indicators that came out of the Principal Component Analysis method were used as the basis for its execution [16]. Moreover, this method was applied to reduce dimensionality while preserving key sustainability relationships. This method simplifies interpretation by generating independent factors that capture major trends in resource efficiency and waste management. To ensure the dataset was appropriate for PCA, the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity were conducted. The results from

Table 1. KMO and Bartlett’s Test.

Metrix	Unit	Value
Bartlett’s Test of Sphericity	Approx. Chi-square	14.311
	Df	6
	Sig.	0.0236
Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.592

Source: elaborated by authors using SPSS 27.

confirmed that the data met the required conditions for factor extraction, with a moderate KMO value of 0.592 and statistically significant Bartlett’s test results (Chi-square of 14.311 and a p-value of 0.0236). These findings justified the application of PCA to identify key sustainability dimensions while minimizing multicollinearity effects.

Choosing the relevant variables before applying PCA is a crucial step in ensuring the quality and relevance of the analyses. PCA results may be affected by variables that have strong or redundant correlations, leading to the creation of extraneous or complex factors. The process of lowering dimensions and getting data ready for clustering requires PCA. In order to guarantee comparability between variables measured in specific units, Z-scores were computed to standardize the data using methods like PCA and clustering, both of which are components of multivariate analyses. Standardization removes the impact of variation in magnitude and measurement units by converting each indicator’s raw value into a common scale. Formula (1) below is used to calculate the Z-score:

$$Z={\displaystyle \frac{X-\mu }{\sigma }},$$

(1)

where Z is the standardized score, X is the observed value, μ is the mean of the variable, and σ is the standard deviation of the variable. All the analyses processed for the next steps were carried out based on the standardized values of the dataset.

Dendrograms from hierarchical clustering were integrated to further validate the clustering robustness and add another layer of support for the grouping structure. By visualizing the hierarchical relationship between variables, these dendrograms improved the cluster’s interpretive clarity. By placing every nation into a single, discrete group and averaging data from the years under analysis, a homogeneous image was produced. By smoothing out temporal variations in individual indicators, like GPD and trade flow, this method produced a representative snapshot of the traits of each cluster [22,25].

Economic and sustainability dynamics are reflected in the selected indicators, including GDP, trade flows, the use of circular materials, and the adoption of renewable energy. GDP serves as a stand-in for a nation’s ability to implement and maintain green practices since it measures overall economic capacity and resilience. Trade flows shed light on economic interdependence and global integration, particularly in industries that are affected by changes in carbon borders. The adoption of renewable energy and the use of circular materials highlight the shift to sustainability and are consistent with the goals of the EU Green Deal, which include becoming carbon neutral by 2050 and severing the link between economic growth and resource consumption [3,10]. In addition to meeting statistical requirements, this integrated methodological framework made sure that the clustering results provided useful information about the environmental and economic status of EU member states.

To ensure the completeness and reliability of the dataset, an imputation process was applied to handle missing values. Given that some variables, such as packaging waste generation and pollution footprint, had sporadic missing values across certain years, mean imputation was used. This method was applied to variables with limited missing observations to maintain interval consistency without significantly altering variance, as this was the only scenario encountered, and no other imputation methods were needed. This approach ensured that the dataset remained representative for long-term sustainability trends while minimizing distortions in subsequent statistical analysis, such as PCA and clustering.

3. Results

3.1. Pearson Correlation Analysis

As seen in

Table A2. Covariance analysis: ordinary—Pearson correlation matrix.

		EE_IN_TOTAL_EE	ENV_TAX_IN_TOTAL_TAX	CF_CLIMATE_CHANGE	CIRCULAR_MATERIAL_USE	CF_LAND_USE
EE_IN_TOTAL_EE	Pearson Correlation.	1
	Mr. 2-tailed	0.105799
ENV_TAX_IN_TOTAL_TAX	Pearson Correlation.	0.0137	1
	Mr. 2-tailed	0.12046	-----
CF_CLIMATE_CHANGE	Pearson Correlation.	0.005	0.086523	1
	Mr. 2-tailed	−0.050798	0.0441	-----
CIRCULAR_MATERIAL_USE	Pearson Correlation.	0.2377	−0.261061	0.553664	1
	Mr. 2-tailed	0.128263	0.0000	0.0000	-----
CF_LAND_USE	Pearson Correlation.	0.0028	0.078751	0.991981	0.534923	1
	Mr. 2-tailed	−0.002197	0.067	0	0.0000	-----
ENERGY_PROD.	Pearson Correlation.	0.9593	0.070913	0.446167	0.107214	0.429033
	Mr. 2-tailed	−0.088289	0.0991	0.0000	0.0125	0.0000
GD_IN_GDP	Pearson Correlation.	0.0399	−0.052151	0.252761	0.167272	0.224349
	Mr. 2-tailed	−0.017151	0.2255	0.0000	0.0001	0.0000
GVA_IN_ENV_G_S	Pearson Correlation.	0.6903	−0.119861	0.106338	0.159945	0.099586
	Mr. 2-tailed	−0.1629	0.0052	0.0133	0.0002	0.0204
PLASTIC_GEN.	Pearson Correlation.	0.0001	−0.103333	0.016904	0.035012	0.034269
	Mr. 2-tailed	−0.192889	0.0161	0.6946	0.4159	0.4259
RESOURCE_PROD.	Pearson Correlation.	−0.169399	−0.324411	0.296709	0.560876	0.285109
	Mr. 2-tailed	0.0000	0.0000	0.0000	0.0000	0.0000
WASTE_GENERATION_CAPITA	Pearson Correlation.	0.105799	0.080365	−0.025664	−0.096221	−0.00013
	Mr. 2-tailed	0.0000	0.0615	0.551	0.0251	0.9976

Source: elaborated by authors using Eviews.

and

Table A3. Covariance analysis: ordinary—Pearson correlation matrix.

Title 1		ENERGY_PROD.	GD_IN_GDP	GVA_IN_ENV	PLASTIC_GEN.	RESOURCE_PROD.
GD_IN_DGP	Pearson Correlation.	0.24189	1
	Mr. 2-tailed	0	-----
GVA_IN_ENV	Pearson Correlation.	0.131733	0.14176	1
	Mr. 2-tailed	0.0021	0.0009	-----
PLASTIC_GEN.	Pearson Correlation.	0.136394	−0.066182	0.055914	1
	Mr. 2-tailed	0.0015	0.1238	0.1937	-----
RESOURCE_PROD.	Pearson Correlation.	0.498867	0.251681	0.13134	−0.0692	1
	Mr. 2-tailed	0.0000	0.0000	0.0022	0.0176	-----
WASTE_GENERATION_CAPITA	Pearson Correlation.	−0.003393	−0.035109	0.026533	0.037542	−0.021943
	Mr. 2-tailed	0.0472	0.4147	0.5376	0.383	0.0202

Source: elaborated by authors using Eviews.

from Appendix A, the variables measuring various aspects of sustainability showed a strong correlation with one another, according to the Pearson correlation analysis conducted for the years 2004–2023. The findings indicate that policies supporting the circular economy can increase resource efficiency and decrease waste since there are positive correlations between resource productivity and the use of circular materials [18]. This supports the patterns found in recent research emphasizing the benefits of integrated resource management systems, which incorporate material reuse and recycling [26]. Additionally, the analysis found a weak correlation between greenhouse gas emissions and the production of plastic waste, indicating that land use and energy consumption are more closely related to the drivers of climate change. For instance, it has been demonstrated that effective land management and the use of energy-efficient technologies significantly lower global emissions [20]. In this regard, promoting sustainable agriculture methods and prioritizing renewable energy infrastructure can be very important [14]. However, the negative correlations between resource efficiency and the production of plastic waste highlight the necessity of enhancing recycling and reuse procedures. Stricter regulations for selective collection combined with the use of cutting-edge technologies can lower waste and improve the quality of recycled materials [18]. According to recent studies, the circular economy can help significantly lower the demand for primary raw materials, which will support long-term economic sustainability [1,26].

A strong and positive relationship is indicated by the correlation between resource productivity and the rate of circular material utilization, which has a coefficient of 0.56087 and a probability (p-value) of 0.000. According to the coefficient, the implementation of the circular economy practices lessens the need for primary resource extraction while also improving resource use and conserving resources [27]. With a coefficient of 0.498867 and a probability (p-value) of 0.000, indicating a moderately positive correlation, the relationship between energy productivity and resource productivity is also noteworthy. According to the summary, nations that maximize their energy use also typically employ natural resources more effectively through the adoption of efficient industrial policies and renewable energy technologies [18].

With a coefficient of 0.241890 and a probability (p-value) of 0.000, the relationship between gross public debt as a percentage of GDP and energy productivity is weak to moderate. Even in the face of significant public debt, the findings imply that nations with more developed economies are making investments in energy-efficient technologies [1,10]. In addition to improving resource efficiency, these investments—such as green infrastructure and energy-efficient buildings—also strike a balance between sustainability and public spending. Although the analysis is not conclusive, the potential for influence is clear given that energy is a universal product with a substantial weight in all economic activities.

Nevertheless, depending on which conventional green sources it comes from, energy can also be regarded as an environmental factor. As a result, electricity has two distinct dimensions: an economic one that directly affects businesses’ fixed costs and whose cost is reflected in all goods and services offered in an economy, and an ecological one that is pursued when nations invest in lowering their carbon footprint by switching from conventional to more ecologically friendly production methods.

Last but not least, there is a moderately positive correlation between the rate of circular material and climate change, as indicated by the coefficient of 0.553664 and the p-value of 0.000. Through the promotion of material recycling and reuse, the reduction in greenhouse gas emissions, and the optimization of primary resources, the circular economy greatly lessens its effects on the climate [18,26].

Hypothesis 1 is therefore confirmed in several aspects. First, there is a strong correlation between the circularity of materials and the level of resource productivity, which is determined by the ability of resources to be introduced into the supply chain multiple times. As a result, productivity increases with each usage cycle and thus determines the economic efficiency of companies, economic actors, and producers to achieve the objectives aligned with the values of the green economy [28]. In addition, the relationship between the level of taxes generated by activities in this area is inversely proportional to the waste put on the market. This means that, ceteris paribus, the amount of waste decreases the more the amount of tax collection increases. The coercive measure supports green activities.

Figure 1a shows a moderate correlation coefficient (0.498867) and a very high level of significance (p-value of 0.000), indicating a positive relationship between resource productivity and energy productivity. The data indicate a clear interdependence between the two aspects of sustainability, suggesting that more efficient energy use is linked to greater resource use efficiency. Given that the majority of the countries in the sample are at moderate stages of development in terms of the ratio between the two indicators, the distribution of points reveals a high concentration in the low ranges of resource and energy productivity.

There are states that excel at using resources and energy efficiently though, as shown by a few extreme observations at the top of the graph. The correlations highlight this overall pattern, demonstrating that better use of natural resources results from optimizing energy consumption. Studies have shown that by enhancing the interdependence between resource and energy productivity, increased energy efficiency not only lowers costs but also boosts economic competitiveness [20]. From a strategic and policy standpoint, this relationship implies that nations that invest in energy-efficient technologies, like renewable energy sources, also have a tendency to build capacities for sustainable resource use. This convergence may also be facilitated by the complementary effects of technological innovation and economies of scale. These findings emphasize the necessity of enacting comprehensive policies that address resource productivity and energy efficiency in order to maximize the benefits of environmental and economic sustainability.

The negative correlations found between the variables under reanalysis offer important new information about the trade-offs and difficulties economies face as they adopt sustainable practices. Resource productivity and the percentage of environmental taxes in the total tax accumulation showed a moderately negative relationship (−0.324411, p-value of 0.000), indicating that resource productivity is generally lower in nations with higher environmental taxes. The cost of adhering to stringent environmental regulations, which can momentarily lower overall resource efficiency, may be the cause of this phenomenon. Excessive environmental taxes may be a sign of increased non-renewable resource consumption, which lowers resource productivity.

This relationship, though, can also be seen as an indication of the immediate difficulties that must be overcome in order to meet long-term sustainability objectives. With a high significance level (p-value of 0.000) and a weak negative correlation coefficient of −0.192889, Figure 1b shows the inverse relationship between plastic waste generation and the percentage of environmental taxes in total taxes collected. The data show a downward trend, indicating that a slight decrease in the amount of waste produced is linked to an increase in the share of environmental taxes, which are successful in encouraging more sustainable practices, resulting in modifications to the way waste is managed.

The relationship shows the effectiveness of environmental policies and enables the correlation of decisions with the actual outcomes in the economy, even if these changes are not significant. Although the ability to influence is diminished because of the correlation’s limited number of influence factors, it still exists and is significant. The impact of these policies is limited, for instance, by a lack of stringent laws governing single-use plastics or by inadequate recycling infrastructure. A combination of strategies such as laws restricting the use of plastic and public awareness campaigns for improved waste management, is needed to successfully reduce the production of waste. According to recent research, fiscal measures along with financial incentives, such as subsidies for recycling technologies, can boost the effectiveness of policies. Furthermore, the analysis’s findings imply that if high-tax nations do not receive adequate funding for green infrastructure, they may only see modest waste reduction [29]. The broad distribution in Figure 1b indicates that certain economies may have high tax rates but inadequate recycling facilities, which reduces the effects of these fiscal policies with additional regulatory instruments and investments in green infrastructure in order to more effectively reduce the generation of general waste.

The relationship between resource productivity and plastic generation is also weakly negative (−0.0692, p-value of 0.0176), suggesting that nations with higher plastic waste production also typically use resources less effectively. The reliance on non-renewable resources and the failure to implement efficient recycling regulations, which lead to the buildup of plastic waste and restrict resource optimization, explain this phenomenon. Additionally, a weak negative coefficient of −0.003393 (p-value of 0.0472) characterizes the relationship between waste generation per capita and the share of renewable energy in total energy consumption, suggesting that nations with higher energy efficiency tend to produce less waste. According to this correlation, making better use of the resources, regulations, and technologies that maximize energy use also helps to reduce waste production [30]. The need for integrated strategies to support the sustainability transition through complementary and balanced measures is highlighted by these negative relationships, which generally reflect the trade-off between environmental regulations and economic efficiency [31].

The correlation analysis’s insignificant relationship draws attention to variables that are not directly related to one another, thereby highlighting regions with less or no influence. These variables must be identified in order to avoid including data in subsequent analysis that could erroneously alter the outcome and artificially support intricate correlations. An almost zero coefficient and high probability of 0.9593 between energy productivity and renewable energy indicate that these variables are independent of one another. The basic distinctions between the two concepts account for the lack of direct correlation: total energy efficiency represents the decrease in energy losses in consumption processes, whereas green energy represents only the share of energy introduced into the market that comes from renewable resources. As a result, a nation can be energy efficient without optimizing renewable energy production, indicating room for improvement in energy resource utilization. There is a significant lack of correlation between plastic generation and carbon footprint, as evidenced by their correlation coefficient of 0.016904 and a high p-value of 0.6964. The findings show that the production of plastic does not impact the carbon footprint. Although plastic pollution has negative environmental effects, it does not significantly contribute to air pollution. Instead, its impact is more related to land use and environmental degradation.

There is a weak and insignificant correlation between gross value added in the environmental sector and plastic generation, as evidenced by the low coefficient (0.055914) and high p-value (0.1937). The amount of plastic waste generation does not seem to have a significant impact on economic activity. Therefore, economic value can be added through environmentally friendly solutions. This suggests that value is generated through efficiency and resource management, rather than solely relying on traditional raw materials. These data demonstrate a separation between environmental economic activities and plastic reduction issues, indicating room for change. For the remaining indicators, no relevant connection has been shown with the rest of the metrics, suggesting that no significant data were generated by the observations.

The correlation analysis’s findings offer a strong foundation for creating econometric models that examine how waste production, energy efficiency, and climate change affect sustainability and economic performance. These models can be used to investigate causal relationships and create integrated strategies that strike a balance between environmental regulations and economic efficiency. Therefore, to lower the possibility of calculation errors, the following factors have been considered for the additional analysis: (1) resource productivity and (2) electricity; (3) waste generation per capita; (4) the proportion of environmental taxes in the total amount of applied taxes; (5) circularity of materials; (6) climate change indicators; and (7) the amount of public debt. The data selected for the analyses were tested for multicollinearity, autocorrelation, and stationarity. After the analysis, it was found that indicators do not tend to influence each other over time, and the data are non-stationary.

3.2. Principal Component Analysis (PCA) Results

To determine the sustainability of the dataset, the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity were applied. The KMO measure assesses whether the variables exhibit sufficient correlations, while Bartlett’s test verifies whether the correlation matrix is significantly different from an identity matrix, ensuring that factor analysis is appropriate. The suitability of the dataset was confirmed through both tests, as seen in Table 1, which indicated a moderate level of adequacy and significant correlations among variables.

To be more precise, every indicator that was approved by the Pearson correlation and was specified after the correlogram was used in the next analysis because they highlighted the important correlation between environmental factors. However, they were gradually removed for reasons pertaining to the analysis. The contribution of factors that were not considered was not representative of the analysis, and the KMO and Bartlett’s tests produced unsatisfactory results. The data from the deleted items did not contain consistent information and did not contribute significantly to the group separation. Only those indicators, in this case, resource productivity, energy productivity, percentage of environmental tax total contribution, and waste generation per capita, that are substantially weighted in the explanation of the phenomenon have been retained.

Following the confirmation of the dataset’s suitability for PCA, an analysis was conducted to extract key components.

Table 2. Communalities and component matrix.

Indicator	Initially	Extraction	Comp_1	Lower Comp. 1	Upper Comp. 1	Comp_2	Lower Comp. 1	Upper Comp. 1
Resource Productivity	1	0.742	0.861	0.811	0.912	0.033	−0.017	0.083
Energy Productivity	1	0.716	0.820	0.770	0.870	0.210	0.160	0.260
% Env tax in Total Tax	1	0.440	−0.648	−0.698	−0.598	0.140	0.090	0.190
Waste generation Capita	1	0.961	−0.113	−0.163	−0.063	0.974	0.824	1.024

Extraction method: Principal Component Analysis with 2 components extracted. Source: elaborated by authors using SPSS 27.

presents the communalities and component loading, showing how well each variable contributes to the identified principal components. The extraction values indicate the proportion of variance in each variable that is explained by the principal components.

The results in Table 2 show that the first two principal components together explain 71.49% of the total variance, confirming their ability to capture the most relevant sustainability dimensions. Component 1, which explains 46.16% of the variance, is strongly associated with resource productivity (0.861) and energy productivity (0.820). This suggests that Component 1 represents an economic dimension linked to resource efficiency and productivity performance. Component 2, which explains 25.33% of the variance, has the highest loading on waste generation per capita (0.974), indicating that this component primarily captures waste management and environmental sustainability aspects.

Component 1 is strongly associated with resource productivity (0.861) and energy productivity (0.820), indicating that higher economic efficiency is linked to better resource and energy use. Countries that perform well in this dimension tend to prioritize sustainable resource management, technological innovation, and energy efficiency strategies, aligning with circular economy principles. One important strategy for closing gaps in resource and energy efficiency is the adoption of the circular economy [23]. By demonstrating advancements in the integration of resource and energy efficiency technologies, this factor can be viewed as a sign of economic maturity, and dedication to the green economy. According to a sustainable economic performance perspective, nations with high coordination are typically the leaders in the implementation of sustainability policies, highlighting the crucial role that this factor plays in setting nations apart [18,22].

Component 2 is primarily driven by waste generation per capita (0.974), emphasizing its role as a critical factor in sustainability performance. Countries with high scores on this component tend to have stronger recycling infrastructure, circular economy policies, and stricter waste management regulations. Conversely, lower scores may indicate challenges in waste management adoption, necessitating increased investment in circular economy initiatives. The second factor draws attention to the disparities in environmental consciousness and waste management capabilities among nations. It is helpful in determining opportunities for the shift to a circular economy and more effective public policy requirements in waste management. Additionally, it draws attention to the notable differences in waste production between nations, offering insights into the difficulties associated with environmental sustainability [23]. Waste management is a challenge for nations with high scores on this metric, highlighting the need for stronger regulations, and updated infrastructure to support the circular economy.

The economic aspect of resource and energy efficiency is reflected in Component 1, where resource productivity (0.466) and energy productivity (0.444) are important factors. In contrast, the share of environmental tax has a negative contribution (−0.351), indicating that there is a negative correlation between economic efficiency and the share of environmental taxes. With a high contribution (0.961), waste generation per capita dominates Component 2, capturing the ecological aspect of waste management, while the other variables only make minor contributions. Therefore, waste generation disproportionately determines the second component, while resource productivity and energy productivity are crucial for explaining the first component. The environmental taxes indicator has a mixed impact; it is marginal in Component 2 and negative in Component 1. The different roles that variables play in defining the economic and ecological dimensions of the analysis are highlighted by these relationships. Hypothesis 2 is validated in this way. Component 1 is dominated by the production characteristics of resources and the productivity of electricity, which indicates a specific dimension for economic performance.

On the other hand, the component that is driven by the level of waste generated per capita demonstrates a separate factor that is orientated towards environmental aspects, in this case, waste management. In addition, the negative contribution that the environmental tax indicator has in the first component shows that environmental aspects, such as environmental taxes, can exert economic pressure, leading to reductions in efficiency. The clear differentiation of the variables in the two separate dimensions shows that economic and environmental performance function as separate but complementary perspectives. This result highlights the need for balanced policies that take this trade-off into account, seeking to minimize the sacrifice of one category at the expense of the other.

3.3. Cluster Analysis Findings

Instead of using each indicator directly, the clustering analysis was based on the two factors that PCA extracted for a number of basic reasons related to the quality of the analysis. Given the strong correlation between the initial indicators (such as energy and resource productivity), incorporating each one separately into the clustering analysis may result in redundancy that impacts the findings. Rather, the factors are orthogonal, or independent, which ensures a distinct division of the groups. Second, the clusters that are produced can be interpreted more easily and intuitively when the two factors are used. In summary, the decision to employ the two factors rather than the individual indicators was driven by the need to streamline the analysis, eliminate duplication, and produce clusters that are clearer and simpler to understand in terms of critical sustainability dimensions.

To further analyze the differentiation between clusters, an ANOVA was conducted to compare the analyzed values of the extracted principal components across the four identified clusters.

Table 3. Cluster centers and analysis of variance.

Metrix	Center _1	Center _2	Center _3	Center _4	Lower Center_4	Upper Center_4	Mean Sq.	Cluster_df	F_Test	Sig.
REGR factor score 1 for analysis 1	0.74190	1.03091	−1.37153	−0.67391	−0.72391	−0.62391	6.961	3	31.284	0.000
REGR factor score 2 for analysis 1	1.77386	−0.19147	1.98573	−0.58229	−0.63229	−0.53229	7.355	3	42.957	0.000

The F-tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differences among cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted as tests of the hypothesis when the cluster means are equal. Source: elaborated by authors using SPSS 27.

presents the cluster centers, indicating the distinct sustainability profiles of each group. The F-test values, while useful for descriptive purposes, should not be interpreted as inferential statistical tests, as the clustering method maximizes between-group variance by design.

Cluster analysis using the K-means method determined that the optimal number of clusters is four. First, the final centers of the clusters indicate the clear separation of the groups according to the two main dimensions defined by the extracted factors: resource efficiency and waste generation. Cluster 1 shows high values of both factors (0.741904 for Factor 1 and 1.77386 for Factor 2), suggesting high resource efficiency combined with high waste generation. In contrast, Cluster 4 has significantly opposite values for both factors (−0.67391 for Factor 1 and −0.58229 for Factor 2), highlighting poor performance in both resource and waste management. The differentiation between clusters becomes clear and demonstrates the relevance of separation into four distinct groups.

The decision to divide the data into four clusters captures all aspects of the analysis. First of all, clustering in one group does not capture any difference because it merges cases of good economic performance with strong ecological performance which is a goal that has been identified as a possibility, as we showed in the correlation analysis. The two elements do not always move in the same direction, and if they do, they may have completely different intensities. Secondly, dividing the data into two categories would indicate a good economic performance, but a mixed environmental performance. This approach is still limiting and does not allow for capturing nonsense between different performance combinations.

Choosing only two clusters fails to provide enough information for differentiated policies or interventions. Thirdly, three clusters are not an optimal choice because groups with different characteristics, such as those with low performance in both domains, would be merged with other categories with different specifications. This would eliminate the possibility of highlighting countries that would face severe problems and require more detailed policy interventions. In this case, by merging Cluster 4 into Cluster 3, information would be lost between countries with poor economic performance and environmental deficiencies compared to the rest of the economies. Thus, forming four distinct clusters ensured that each possible case was clearly identified.

The results in Table 3 indicate significant differences between clusters in terms of sustainability performance. Cluster 1 exhibits moderate resource efficiency and environmental sustainability (REGR factor score 1 = 0.74190, REGR factor score 2 = 1.77386), suggesting a balanced approach to circular economy implementation. Cluster 2, with the highest factor scores (1.03091 for REGR Factor 1 and −0.19147 for REGR Factor 2), represents economies that prioritize economic sustainability but may face challenges in waste management efficiency. Conversely, Cluster 3 (−1.37153 for REDR Factor 1, 1.98573 for REGR Factor 2) highlights countries with strong waste management policies but lower economic efficiency. Lastly, Cluster 4 (−0.67391 for REGR Factor 1, −0.58339 for REGR Factor 2) represents economies with weaker sustainability performance across both dimensions. The F-test results (31.284 for Factor 1, 42.957 for Factor 2, both <0.001) confirm that these differences are statistically meaningful, though, as noted, these tests should be considered descriptive rather than inferential due to the clustering method used.

In terms of operation, the procedure’s stability was confirmed step by step. After only four iterations, the process became convergent with minimal adjustments to cluster centers in the later stages, indicating a strong and stable group structure. The smallest change in cluster centers observed during the final iteration was 0.000, suggesting that the use of cluster analysis ensures optimal separation without generating unnecessary complexity. The results from the variant analysis endorse this, revealing substantial disparities between the four clusters for both factors examined. The significant F-statistic values (31.284 for Factor 1 and 42.975 for Factor 2) demonstrate that the variation between clusters is significantly greater than the variation within each cluster. The substantial distinction between clusters validates each group as distinct and well defined in relation to the two dimensions of analysis. Furthermore, the distribution of cases within clusters shows a well-balanced and representative distribution. Cluster 4 includes the most observations (13), while Cluster 1 and Cluster 3 contain the least (3 and 2, respectively). This variation in group size reflects the diversity of characteristics among countries or units of analysis while preserving this structure. The choice of separation into four clusters is justified by the distinct cluster centers that reflect obvious differences in the values of the two dimensions, the swift convergence of the k-means algorithm indicating the stability and clarity of the clusters, the significant differences between clusters as revealed by the analysis of variance, and the appropriate distribution of observations that ensures the representativeness of the working groups.

Significant variations in the groups’ performance on the two primary factors were revealed by the analysis from

Table 4. Cluster analysis of variance.

Metrix	Center_1	Mean	Std._Dev.	Std._Error	Lower Bound	Upper Bound	Minimum	Maximum
REGR factor score 1 for analysis 1	Cluster_1	−0.50820	0.60859	0.16879	−0.87597	−0.14043	−1.15124	1.05644
	Cluster_2	1.05786	0.45306	0.15102	0.70960	1.40611	0.46789	2.05928
	Cluster_3	0.07848	0.72483	0.41848	−1.72211	1.87081	−0.69126	0.74797
	Cluster_4	−1.57474	0.67464	0.47704	−7.63617	4.48669	−2.05179	−1.09770
	Total	0.00000	1.00000	0.19245	−0.39558	0.39558	−2.05179	2.05928
REGR factor score 2 for analysis 1	Cluster_1	−0.67595	0.59782	0.07205	−0.83294	−0.51897	−1.17039	−0.13151
	Cluster_2	0.12246	0.82352	0.27450	−0.51055	0.75548	−0.71178	2.19379
	Cluster_3	1.60071	0.43753	0.25255	0.51407	2.68735	1.13114	1.99665
	Cluster_4	0.44154	0.20994	0.85555	−0.9.4293	12.31244	0.58599	2.29710
	Total	0.00000	1.00000	0.19245	−0.39558	0.39558	−1.17039	2.29710

Source: elaborated by authors using SPSS 27.

and

Table 5. Results of cluster analysis.

Metrix	Center_1	Sum of Squares	Df	Mean Square	Lower Mean Square	Upper Mean Square	F	Sig.
REGR factor score 1 for analysis 1	Between Groups	18.407	3	6.136	6.036	6.236	18.587	0.000
	Within Groups	7.593	23	0.330	0.230	0.430	-	-
	Total	26	26	-	-	-	-	-
REGR factor score 2 for analysis 1	Between Groups	17.918	3	5.973	5.873	6.073	16.997	0.000
	Within Groups	8.082	23	0.351	0.251	0.451	-	-
	Total	26	26	-	-	-	-	-

Source: elaborated by authors using SPSS 27.

, as well as the post-analysis performed on the four clusters. Clear differences between clusters were revealed by descriptive statistics. In terms of resource utilization, Cluster 2 had the highest average (1.0578) for Factor 1, followed by Cluster 3 (0.0784) and Cluster 1 (−0.5082). With the lowest average (−1.5744), Cluster 4 indicates low efficiency. These findings showed that clusters differ significantly in their levels of economic development in the production of circular economy strategies [23]. With a negative average (−1.6759) for Factor 2, Cluster 1 performed the best, indicating effective waste management. Cluster 2 (1.1225) and Cluster 4 (1.4415) had the greatest deficiencies in this area, while Cluster 3 had the highest average (1.6001). These findings imply that performance varies not only from an economic point of view but also in terms of waste management policies and the necessary infrastructure for the implementation of sustainable policies [32].

The homogeneity of variance test (Levene) shows that the variances are homogeneous between clusters for both factors, confirming the applicability of the analysis of variance test. The results showed significant differences between clusters, with an F-value of 18.407 for Factor 1 and 16.997 for Factor 2, both having a p-value of 0.000. This clearly highlighted the statistical differences between groups in terms of their performance.

The post hoc (Bonferroni) analysis confirmed the significant differences between the cluster pairs. For Factor 1, Cluster 2 differed significantly from Cluster 1 and Cluster 4, emphasizing the superior resource efficiency in this group. Also, Cluster 4, characterized by the worst performance, differed significantly from all the other clusters. This suggests that economic policy and the degree of integration of sustainable technologies play a key role in the success of resource management. For Factor 1, Cluster 3, which demonstrated the best waste management, differs significantly from all other clusters (p-value < 0.05), while Cluster 4, with the greatest deficiencies, shows significant differences from Cluster 1 and Cluster 2. This underlines the importance of modern recycling, infrastructure, and public education in reducing waste.

3.4. Comparative Analysis of Cluster Performance

Thus, the four clusters are well differentiated. Cluster 2 performs best in the use of resources, demonstrating that sustained economic policies and investment can produce significant positive results. Cluster 1 excels in waste management, highlighting the impact of street regulations and public–private partnerships. In contrast, Cluster 4 requires major improvement in both areas, while Cluster 2 would benefit from stricter policies to reduce waste generation. These differences provide a solid basis for the development of group-specific policies tailored to their needs and challenges. The integration of common policies at the European level could help reduce disparities and accelerate the transition to sustainability.

The dendrogram in Figure 2 underlines the fact that the clusters identified are distinct and separated, according to the characteristics of countries analyzed on the two main dimensions. Cluster 1 represents a mixed category with moderate performance, while Cluster 2 indicates a group of countries that are more economically developed, but with variations in sustainability. Cluster 3 is the clearest example of high performance in both dimensions and Cluster 4 highlights the difficulties of less developed economies. This classification provides a clear basis for making specific recommendations for each group, according to their sustainability, priorities, and needs.

The economies that make up Cluster 1 from

Table 6. The structure of the clusters.

Cluster	Structure
Cluster_I	Slovakia, Slovenia, Hungary, Italy, Lithuania, Croatia, Portugal, Spain, Ireland, Poland, Germany, Denmark, Czech Republic
Cluster_II	France, Cyprus, Greece, Malta, the Netherlands, Belgium, Austria, Latvia, Estonia
Cluster_III	Finland, Sweden, Luxembourg
Cluster_IV	Bulgaria, Romania

Source: elaborated by authors using SPSS.

include Slovakia, Slovenia, Hungary, Italy, Lithuania, Croatia, Portugal, Spain, Ireland, Poland, Germany, Denmark, and the Czech Republic. Thus, this group is made up of economies that are important, but their value in evaluating circularity and circular economy integration in daily operations is limited; these economies also help to improve the adoption of sustainable practices by managing to optimize resources and reduce waste [12]. Countries such as Germany and Denmark currently demonstrate the efficiency of advanced economic models that are supported by technological development and the implementation of industrial eco-friendly practices, which allows them to connect economic sectors for even greater resource efficiencies [9]. These economic models achieve a high score in terms of both ecological outcomes and economic performance, with high financial capacity to support projects as well as the resources that countries possess and manage to the best of their abilities. However, Slovenia and Hungary are among the nations that face significant obstacles, including inadequate infrastructure for ecological processes and financial constraints, resulting from other economic imbalances that are independent of the ecological zone [33]. Although these factors have greatly limited their ability to fully embrace circular economic models, both sets of data show positive results. The economic scale they have at the European level is another significant factor that contributes to the score increase. To improve the collective sustainability process and reduce inequalities in regional areas, it is feasible to implement integrated policies that consider environmental factors, such as public and private partnerships, and a diversified circular economy approach [29]. Countries like Spain and Portugal, which lack a long history of handling such processes, have started projects to contain the waste and minimize resource losses [23,34].

France, Cyprus, Greece, Malta, the Netherlands, Belgium, Austria, Latvia, and Estonia are all part of Cluster 2. In these areas, economic diversification promotes resilience, innovation, and knowledge sharing among related industries, and economic sustainability. Within these economies, communication through the supply chain creates an interdependent economic corridor that makes it easier to share knowledge and best practices, including those pertaining to sustainability [35]. As an economy that has made impressive strides in shifting from a traditional economy to the new forms imposed by the circular economy, France is a leader in waste reduction and resource efficiency. On the other hand, the Netherlands is a prime example of effective waste management and the application of cutting-edge technologies to curb this issue. Malta has a high level of collection and management, despite challenges brought on by its small size and remote location [36]. Circular economy practices in agriculture have a lot of potential to improve resource sufficiency in Greece and Cyprus, but barriers like insufficient funding and regulations stand in the way of development. The inability to secure adequate funding has a greater effect over time in countries with a significant agricultural character because this limitation is industry-specific, and agricultural production operates at a slower rate than non-perishable goods. In these regions, the tax system is one component that does not encompass all activities [23]. Using cutting-edge regulations to maximize natural resources, France and Belgium are still leading the way in waste reduction [37]. The Netherlands is a leader in putting the circular economy into practice, emphasizing waste reduction by utilizing cutting-edge technology to increase resource sustainability. Given the Netherlands’ excellent management of the sustainability sector, there is economic diversification with regard to the economy unit of measurement, also taking sustainability factors into consideration.

Additionally, these geographically grouped nations belong to a category where a correlation between innovations in the field of sustainability and economic performance has been found, with an emphasis on collaboration between the public and private sectors which possess the knowledge required to determine the most efficient trade-off between the two areas. Adequate funding and integrated policies that consider current infrastructure needs are prerequisites for the economic growth of these economies [1,27].

A good example of integration in the circular economy is Cluster 3, which includes Finland, Sweden, and Luxembourg. Waste has been decreased as a result of green technology adoption, and public–private partnerships, supporting long-term economic growth. According to the research, renewable energy is a highly reliable indicator of economic growth and long-term sustainable development, and the nations in this cluster fall into this category. The ecological zone is a bigger concern for these economies than it is for the other ones [38]. Finland and Sweden prioritize resource efficiency through waste reduction and the implementation of creative solution implementation in partnership with the private sector. The technological innovations that these economies produce enable them to gather pertinent data and optimize the entire production chain in accordance with their strategies and goals. Additionally, technology can be used to find potential production optimizations that could have a greater impact and make savings more profitable [39]. Drawing on insights from developed economies, Luxembourg is using its economic flexibility to create efficient and customized solutions despite its size [7]. Sweden and Finland have positioned themselves as global leaders in the shift to sustainability by increasing their use of renewable energy [40]. One of the top growth strategies for these economies is the green economy.

Romania and Bulgaria, two developing nations with sustainable potential but significant structural challenges, formed the last cluster. Inadequate regulations and underdeveloped infrastructure hinder the adoption of the circular economy in these nations [18]. Due to a lack of strategic coordination, Romanian regional initiatives are implemented in a fragmented manner while Bulgaria uses circular materials at a low rate [41]. There is a lack of predictability in the two economies because their legal framework is not well defined in this regard. Other factors that prevent wealth accumulation compared to other EU nations include reliance on imported materials and the lack of advanced recycling technology [19]. To close these gaps, environmental education and funding are essential, particularly given Romania’s encouraging adoption of the circular economy, despite its continued reliance on EU assistance [24,27]. Despite the challenges, both economies have great potential for the circular economy, particularly in the waste sector. However, it is crucial to implement contemporary recycling technologies and build infrastructure for selective waste collection [42].

In conclusion, cluster analysis highlights the need for tailored policies and regional collaboration to support the transition to sustainability. Cluster 3 stands out by applying best-practice models and providing a clear direction for development, while Cluster 4 requires rapid interventions and support in this direction. Cluster 1 continues to benefit from know-how exchanges and infrastructure investment, while Cluster 2 has the potential to make better use of European resources to reduce economic gaps. Collaboration between states and knowledge transfer remain key to accelerating the transition to a circular economy in Europe.

4. Discussion

The findings show strong relationships between resource productivity and the use of circular materials, supporting the literature’s findings about the circular economy’s beneficial effect on waste reduction and economic efficiency. These relationships emphasize how crucial it is to enact laws that promote recycling and reuse in order to maximize the use of natural resources. Additionally, they validate the first hypothesis by showing a strong link between waste generation reduction and economic efficiency.

H1—Hypothesis 1 Regarding Waste Generation

The findings confirm that economic efficiency, particularly measured through resource productivity, plays a crucial role in reducing waste generation. Countries with higher circular material use rates and better waste management infrastructure demonstrate significantly lower waste production. These results align with prior studies that suggest the circular economy enhances economic efficiency by reducing dependency on primary raw materials and optimizing resource flow. However, not all economies exhibit a direct correlation between efficiency and waste reduction, as nations with high industrial output but weak regulatory enforcement still generate substantial waste. This suggests that economic efficiency alone is not sufficient; complementary policies and strict performance mechanisms are necessary to ensure sustainability gains.

H2—Hypothesis 2 regarding economic–ecologic synergy

The results support the hypothesis that there is a measurable trade-off between economic growth and environmental performance, driven by varying levels of sustainability adoption. The Principal Component Analysis (PCA) results show that countries prioritizing economic expansion at the expense of sustainability often exhibit higher waste generation and lower circular economy adoption rates. Conversely, nations that invest heavily in sustainability policies tend to experience slower short-term economic growth but greater long-term resource efficiency. These findings emphasize the need for balanced policy approaches that minimize opportunity costs while promoting green economic transitions.

H3—Hypothesis 3 regarding policy influence:

This study confirms that policy frameworks significantly influence sustainability adoption. Countries with well-developed environmental taxation systems and investments in circular economy infrastructure demonstrate higher sustainability performance. The clustering results indicate that nations with strong regulatory enforcement, financial incentives for green innovation, and well-established sustainability policies are more successful in integrating circular economy principles. However, weaker economies with inconsistent sustainability policies struggle with implementation, highlighting the need for regional policy coordination and cross-border sustainability initiatives.

The findings support the first hypothesis, which states that there is a substantial universal relationship between waste generation and economic efficiency as determined by resource productivity [21]. The idea that economic efficiency encourages waste reduction was validated by lower waste generation rates observed in nations with higher resource productivity. As an illustration of how resource-efficient nations continuously generate less waste, the Pearson correlation analysis revealed a strong negative correlation coefficient between resource productivity and packaging waste [24]. This research emphasizes how crucial it is to incorporate circular economy principles into business plans in order to reap financial and ecological rewards [1].

By identifying different but complementary relationships between ecological and economic performance, the second hypothesis is confirmed. According to PCA results, waste generation per capita was a significant factor affecting the ecological component, whereas economic performance had a negative correlation with environmental tax share and a positive correlation with GDP. Although ecological development can coincide with economic expansion, this trade-off shows that in order to minimize opportunity costs, policies must be carefully balanced. The need for focused interventions that balance economic and ecological approaches without sacrificing long-term sustainability is supported by these findings [25]. The ability of clusters to draw attention to unique trends in trade dependence, economic performance, and sustainability adoption among EU member states accounts for their economic significance. Groups of nations with comparable economic and environmental traits are represented by clusters, which enable customized policy approaches that take advantage of special opportunities and address particular problems. For example, the analysis reveals that certain clusters have a disproportionate dependence on high-carbon goods, which raises concerns about the carbon border adjustment mechanism [6,21]. The need to diversify export structures and embrace more environmentally friendly production practices is evident. Countries in this cluster may need special assistance and may incur additional costs. The priorities are further supported by this study’s econometric findings, which demonstrate strong positive relationships between resource productivity, circular material use, and economic efficiency. The idea that sustainability and economic development can coexist when backed by strong policy frameworks is supported by regions with higher adoption rates of circular practices, which show improved economic performance in addition to decreased waste generation [6,12]. Furthermore, the analysis revealed significant differences between EU member states, with groups of nations that consistently perform better, integrating sustainability indicators into their economic models [21,22,24]. Resource sufficiency and the use of renewable energy, for instance, are important factors in fostering resilient and inclusive economic systems [3,6].

However, the results also highlight systemic issues such as recycling practices across industries in different geographical areas and unequal adoption of energy-efficient technologies. These differences underscore the necessity of specialized interventions that take into account regional infrastructure and economic limitations [25,33]. According to the data, policies that encourage the use of renewable energy sources and circular practices have a multiplicative effect that improves economic and environmental results. This emphasizes the importance of closing implementation gaps by utilizing policy tools such as environmental taxes and green innovation subsidies [29,32]. The econometric evidence reinforces the necessity of harmonized, yet flexible, sustainability policies to accommodate regional diversity. By addressing these structural challenges and fostering collaboration, the EU can achieve its dual goals of economic growth and environmental preservation, ensuring a robust transition for all member states [31]. The results also demonstrate how the circular economy can eventually help lower the demand for primary raw materials, which will support the sustainable green economy. Reuse and recycling-based value chains allow economies to decrease their environmental impact well, fostering innovation, and key industries.

The established link between waste reduction and resource productivity emphasizes how important it is to embrace the concepts of the circular economy to promote both environmental resilience and economic efficiency [43]. This bolsters the idea that, when incorporated into national and regional strategies, sustainability can serve as a lever to improve economic performance [44,45] Furthermore, the importance of sustainable trade practices is highlighted by trade-related indicators. Stronger economic and environmental resilience is demonstrated by nations that use more circular materials and adopt renewable energy, offering a model for striking a balance between ecological demands and trade dependencies. These results support policies that prioritize investments in renewable energy projects, recycling infrastructure, and green technologies, all of which directly reduce related environmental risks while preserving economic competitiveness [23,27,46].

The broader implications of these findings suggest that trade can become a catalyst for long-term ecological and economic objectives through focused investments in sustainability infrastructure. Policymakers ought to consider customizing plans to capitalize on local advantages, lessen reliance on environmentally damaging products in trade, and encourage behaviors that support sustainable development goals. Although this study offers insightful information about the connection between sustainability metrics and economic growth in EU member states, it should be noted that it has a number of limitations. Initially, imputation and averaging techniques were used to address data imbalances; however, the imputed values for variables like the adoption of renewable energy and circular material use may introduce slight biases, especially in countries with sparse data. Second, due to data limitations, variables that could have a substantial impact on the adoption of sustainability, like innovation capacity outcomes and external factors like geopolitical influences, disruptions in global supply chains, or policy changes, were not specifically examined. To offer a more thorough understanding of the factors influencing sustainability, future studies could investigate the inclusion of larger datasets, such as governance metrics or innovation indicators. Furthermore, longitudinal research concentrating on the dynamic effects of outside variables, such as changes in geopolitical and international trade regulation, may strengthen the validity of these conclusions even more. Comparative studies that include non-EU regions may also clarify how applicable these findings are.

5. Limitations of the Study

This study aimed to explore the relationship between economic efficiency, circular economy adoption, and sustainability performance among the EU member states. The results provide empirical insights into the factors driving sustainability transitions and highlight key disparities across different economic contexts. This section discusses the limitations related to all three research hypotheses.

Despite its contributions, this study has several limitations that should be acknowledged. First, this study primarily focuses on macro-level indicators, providing an aggregated perspective on circular economy adoption across EU member states. However, firm-level sustainability strategies, industry-specific regulations, and consumer-driven initiatives are not captured in this analysis. A deeper understanding of circular economy adoption at the microeconomic level could reveal additional insights into the drivers and barriers of sustainable practices. Future research could incorporate business-level case studies, corporate sustainability reports, and survey data to examine how firms and consumers respond to circular economy policies.

Second, the use of clustering techniques effectively identifies patterns in sustainability adoption but does not establish a causal relationship between economic efficiency, policy interventions, and environmental outcomes. While the study highlights correlations and trends, it does not measure the direct impact of specific circular economy policies on economic performance. Future research should explore causal relationships through econometric modeling techniques, such as difference-in-difference analysis or structural equation modeling, to assess how policy changes influence sustainability indicators over time.

Additionally, while this study provides a comparative analysis at the national level, future research should explore regional disparities within countries to assess how subnational policies and infrastructure investments impact sustainability performance. Urban and rural areas often exhibit different levels of circular economy adoption, and these intra-national variations remain underexplored. Expanding the dataset to include non-European countries, such as those in Asia or Latin America, could also provide a broader perspective on global sustainability transitions and reveal how economic development stages influence circular economy adoption. Furthermore, this study does not account for external geopolitical and economic shocks, such as trade disruptions, energy crises, or global supply chain challenges, which can significantly impact sustainability policies and circular economy adoption. Future research could investigate how external macroeconomic factors shape circular economy trends and whether nations with robust circular policies are more resilient to economic downturns.

By addressing these limitations, future studies can provide a more comprehensive understanding of the circular economy’s role in economic transitions, ensuring that policy recommendations remain data-driven, adaptable, and globally relevant.

6. Conclusions

By incorporating the ideas of resource reuse and waste reduction into economic processes, environmental policies are a crucial mechanism for guaranteeing both economic and environmental sustainability. The results of the multi-criteria analyses show notable variations in how member states have adopted the circular economy and sustainability principles. This study identifies significant differences in how the circular economy is implemented across EU nations. According to the indicators examined, waste management and resource efficiency are crucial for lessening the impact on the environment. Both economic and environmental performance have been shown to improve with the implementation of sustainable policies, such as environmental taxes and subsidies for renewable energy. To ensure a more effective transition towards a circular economy across EU member states, a multifaceted policy approach is required. The results of this study highlight several key areas where targeted interventions could accelerate sustainability adoption and economic efficiency. First, environmental policies should be harmonized across the EU while allowing flexibility for national priorities. A coordinated EU-wide circular economy strategy that sets minimum sustainability benchmarks can bridge gaps between high-performing and lagging nations. At the same time, sector-specific policies should be developed to address industry-specific challenges in waste management, energy efficiency, and resource optimization. Second, financial incentives must play a greater role in supporting circular practices. Expanding environmental tax reforms and subsidies can ensure that taxation on polluting activities is paired with financial incentives for businesses investing in waste reduction technologies.

Finally, investments in circular economy infrastructure and green technologies are crucial for long-term sustainability. Countries with higher circular material use and energy efficiency tend to have more advanced infrastructure, and this pattern should be supported through policy. Prioritizing investment in sustainable infrastructure, particularly in nations with weak circular economy adoption, will help scale up waste management and recycling capacity. Taken together, these policy recommendations offer a structured approach to overcoming disparities in circular economy adoption across EU member states, ensuring a balanced transition that aligns economic growth with sustainability goals.

Future research should focus on the long-term impacts of circular economy policies by integrating longitudinal studies that track sustainability transitions over multiple decades. Additionally, comparative analyses between EU and non-EU economies could provide insights into how regional policy frameworks influence circular economy adoption in different regulatory environments. Furthermore, interdisciplinary research incorporating artificial intelligence and digitalization in circular economy modeling could enhance predictive capabilities for sustainability transitions. Finally, expanding the analysis to industry-specific case studies would offer deeper insights into how different economic sectors contribute to, or are constrained by, circular economy strategies.

To ensure a more effective transition towards a circular economy, policymakers should adopt a multi-tiered approach. First, EU-wide sustainability benchmarks should be complemented by national-level flexibility to account for structural differences in economic capacity and resource availability. Second, circular economy incentives must extend beyond taxation by integrating investment schemes for sustainable infrastructure development, particularly in regions with weaker adoption rates. Third, cross-border sustainability initiatives, such as regional circular economy trade agreements, could enhance resource efficiency while reducing regulatory fragmentation. Finally, sustainability monitoring mechanisms should be improved by incorporating real-time data analytics, allowing for more responsive policy adjustments based on economic and environmental performance metrics.