Circular Pathways to Sustainability: Asymmetric Impacts of the Circular Economy on the EU’s Capacity Load Factor

Abstract

Amid escalating environmental crises—ranging from biodiversity loss to climate instability—the circular economy has emerged as a promising pathway to align economic growth with ecological limits. The objective of this study is to examine the asymmetric impact of a novel composite circular economy index (CEI)—constructed via entropy weighting—on the load capacity factor (LCF), a holistic sustainability metric, across 27 EU member states over 2010–2023. Employing the method of moments quantile regression (MMQR) and controlling for GDP, foreign direct investment, trade openness, employment, and population growth, the main findings indicate pronounced heterogeneity: positive CEI shocks yield a 1.219 percent increase in LCF at the 90th quantile versus just 0.229 percent at the 10th, revealing a “sustainability premium” for high-performing economies, while negative shocks inflict a −5.253 percent decline at the 90th quantile, exposing their greater vulnerability. Low-LCF countries, by contrast, display relative resilience to downturns, likely due to less entrenched circular systems. Panel Granger causality tests further reveal bidirectional feedback loops between LCF and economic growth, investment, and labor markets, alongside a unidirectional effect from trade openness to enhanced sustainability. These insights carry clear policy implications: high-LCF nations require safeguards against circularity backsliding, whereas low-LCF members need capacity-building to convert latent resilience into sustained gains—together forming a nuanced blueprint for achieving the EU’s 2050 climate-neutrality ambitions.

1. Introduction

The unsustainable and environmentally hazardous exploitation of natural resources to meet societal demands has reached critical levels [1,2,3]. While curbing irresponsible resource use is imperative to safeguard Earth’s ecological equilibrium, these resources remain indispensable for sustaining human well-being and economic systems [4,5]. Scientific evidence underscores that intensive resource consumption degrades ecosystems and diminishes the planet’s load capacity factor (LCF)—a holistic metric measuring the balance between resource utilization and ecological regeneration [6,7]. Industrialized and developing nations alike have historically relied on natural resources to fuel production and development, straining their LCF thresholds. To address this fundamental tension, the circular economy (CE) has emerged as a transformative strategy to harmonize economic activity with planetary boundaries.

The European Union (EU), a global economic leader, merits special attention in this context due to its collective environmental impact and pioneering circular economy policies. The load capacity factor quantifies a region’s ability to sustain resource consumption and waste absorption within regenerative ecological limits [8]. While the EU’s clean energy transition has successfully reduced per capita carbon emissions—exemplified by progressive member states like Germany and Sweden—the bloc’s overall LCF remains under pressure due to high aggregate resource demand. Currently, renewable energy accounts for approximately 22% of the EU’s total energy consumption [9], with ambitious targets set to reach 42.5% by 2030. Waste management across the EU increasingly prioritizes recycling, with an average of 48% of municipal waste recycled or composted in 2022 [10], though rates vary significantly among member states (ranging from Germany’s impressive 68% to Romania’s modest 16%). Despite these advances, persistent challenges in water use efficiency (averaging 150 L per person daily) and waste generation disparities continue to strain the EU’s collective LCF, underscoring the need for harmonized CE policies to enhance sustainability.

At its core, the circular economy integrates “circular” principles with economic systems, seeking to extend product lifespans through reuse, recycling, and closed-loop production methodologies [11,12]. By replacing traditional linear “take–make–dispose” models, CE fosters sustainable growth by reducing waste generation and decoupling resource use from economic output [13]. Its multidisciplinary applications span renewable energy systems, waste management protocols, and infrastructure development [14]. Empirical studies increasingly highlight CE’s potential to improve LCF by enhancing resource efficiency, lowering emissions, and promoting biodiversity conservation [15,16]. EU’s comprehensive sustainability policies, initiated in 2002, demonstrate how effectively implemented circular strategies can decouple economic growth from resource consumption, earning world recognition for reducing emissions and improving energy efficiency outcomes [17].

Building upon this foundation, our study offers several key contributions to the existing literature on circular economy and environmental sustainability assessment. First, it provides the first comprehensive empirical investigation into the impact of circular economy implementation on the LCF within the European Union context. While previous studies have examined circular economy impacts using traditional environmental metrics such as CO₂ emissions (e.g., Tiwari et al. [18] in emerging economies), our research advances this field by employing LCF as a more holistic sustainability measure that captures both environmental degradation and restoration capacity simultaneously. This methodological advancement allows for a more nuanced understanding of circular economy’s net environmental impact beyond single-dimension carbon assessments. Second, given that European economies lead globally in circular economy implementation and sustainability initiatives, examining the nexus between circularity and sustainability outcomes in these contexts provides vital guidance for policymakers regarding CE’s role in fostering sustainable development. This exploration is particularly pertinent considering the EU’s status as the world’s foremost circular economy and its substantial investment in sustainability determinants. Third, we employ a novel circular economy index constructed using a composite measure that integrates circular innovation, trade in recyclables, circular economy investment, and circular material flows through the entropy weighting method. This multidimensional approach captures the complexity of circular economy implementation more effectively than single-indicator measures. Fourth, we utilize the nonparametric method of moments quantile regression (MMQR) to address misspecification bias and capture the effects of circular economy initiatives across the distribution spectrum of ecological footprint outcomes. This methodological approach not only mitigates statistical bias but also highlights differential impacts across lower, middle, and upper quantiles of the LCF distribution. Furthermore, we account for asymmetries through partial sum decompositions of positive and negative changes in independent variables, enabling clear distinction between effects of upward and downward trends in circular economy implementation. This sophisticated approach is particularly appropriate given the significant structural transformations in the EU’s macroeconomy in recent decades, offering more nuanced understanding compared to traditional analytical methods.

Our study investigates how four CE dimensions—renewable energy adoption, circular innovation, recycling systems, and material reuse—impact the EU’s load capacity factor. The MMQR analysis reveals asymmetric effects of CE dynamics across LCF quantiles, demonstrating distinct outcomes for high-LCF versus low-LCF member states. Positive CE developments amplify sustainability gains in high-capacity contexts while negative shocks trigger disproportionate degradation (−5.253% at the 90th quantile). Conversely, low-LCF regions exhibit surprising resilience to CE downturns, reflecting fundamental structural differences in how circular systems are embedded across different economic contexts. These findings offer valuable insights not only for the EU’s circular economy roadmap but also for emerging economies like Malaysia and Pakistan pursuing similar sustainability transitions. While our study focuses on selecting CE indicators within the European context, suggesting opportunities for broader analytical frameworks, the methodological approach and key findings establish an important foundation for future research exploring circularity–sustainability relationships across diverse geographic and economic settings.

The remainder of this paper proceeds as follows: Section 2 reviews relevant literature connecting circular economy principles to sustainability outcomes; Section 3 outlines our methodological approach and data sources; Section 4 presents empirical results of our analysis and discusses policy implications for EU member states and beyond; and Section 5 concludes with recommendations for future research directions.

2. Literature Review

This review synthesizes existing studies on how circular economy (CE) strategies influence environmental sustainability—an aggregate metric encompassing carbon emissions, land use, resource consumption, and ecosystem impacts [19]. By shifting from linear “take–make–dispose” models to closed-loop systems of reuse, remanufacturing, and recycling, CE aims to reduce environmental burdens while supporting sustainable development. The urgency of this transition is underscored by global material extraction trends, which have surged from 36 Gt in 1980 to 72 Gt in 2010, with projections reaching 100 Gt by 2030 under linear systems [20]. Circular approaches such as cradle-to-cradle (C2C) reuse of structural components in construction have demonstrated measurable reductions in ecological impact compared to conventional methods [21]. By closing resource loops and minimizing waste generation, CE techniques enhance material productivity and lower the per-unit ecological footprint of production and consumption [22], although short-term implementation can sometimes incur additional environmental costs. At the operational level, recycling and reuse form the bedrock of CE. For instance, Tiwari et al. [18] employed quantile autoregressive distributed lags (QARDL) and panel PMG models to study emerging economies, revealing that CE practices and stringent climate policies reduce CO₂ emissions, though energy transition and industrialization pressures counteract these gains. Similarly, Song [23] and Yang et al. [24] used augmented mean group (AMG) techniques to analyze top CO₂ emitters, finding municipal waste—a key CE element—positively correlates with emissions, highlighting the need for better waste valorization. In textiles, bio-based recycling technologies mitigate pollution [25], while e-waste recovery extends product lifespans and cuts emissions [26]. CE principles also enhance energy sustainability. Chishti et al. [27] applied quantile VAR techniques to identify CE integration in energy systems as critical for reducing environmental impacts, alongside energy transition policies. Sharma et al. [28] demonstrated waste-to-energy methods (e.g., hydrogen production) can advance decarbonization, while biorefineries converting municipal and agricultural waste into bioenergy exemplify nutrient loop closure [29]. Despite its promise, CE faces systemic challenges. Transition costs may disadvantage workers in linear industries, and rebound effects remain under-researched [30]. Critics argue CE often neglects primary resource extraction impacts and social equity [31,32,33]. For example, Lehmann et al. [34] noted that while CE drives innovation and resource efficiency in Europe, balancing economic growth with environmental goals requires nuanced policy frameworks. National case studies illustrate CE’s variable impacts. Germany, a CE leader, demonstrates how regulatory shifts (e.g., packaging laws) drive adoption but also create compliance challenges [35]. Quantile regressions by Razzaq et al. [36] in the U.S. linked municipal recycling to emission reductions, aligning with CE’s waste-minimization ethos. Conversely, Liu et al. [37] highlighted China’s plastic recycling policies reducing pollution but noted gaps in scalability.

2.1. Critical Perspectives on the Circular Economy Paradigm

While the circular economy offers promising pathways toward sustainability, a growing body of literature highlights significant limitations and challenges that warrant critical examination. These critiques provide essential counterpoints to the often-optimistic CE discourse and reveal important considerations for policy development and implementation.

2.1.1. Rebound Effects in Circular Economy

A fundamental critique of CE centers on rebound effects, which can significantly undermine intended environmental benefits. Castro et al. [38] define circular economy rebound (CER) as occurring “when the eco-efficiency of a productive system is offset by an increase in production or consumption.” Their systematic review identifies three key mechanisms driving circular rebound: initiating mechanisms (which trigger the effect), developer mechanisms (which amplify it), and mitigating mechanisms (which could potentially reduce it). Importantly, these rebound effects differ from those in energy economics, as CE primarily concerns material flows rather than energy efficiency. Zink and Geyer [31] identify two critical mechanisms behind circular rebound: product substitution effects and price effects. When recycled or remanufactured products fail to perfectly substitute for primary products, or when CE activities lower material costs and thereby stimulate increased consumption, environmental benefits can be substantially reduced or even reversed. For instance, improved recycling efficiency might lower material costs, leading to increased production and consumption that offsets the initial environmental gains. Ferrante et al. [39] further expand this analysis by mapping relationships between different dimensions of circular economy rebound effects in manufacturing. Their research reveals that rebound effects manifest across multiple levels (direct, indirect, economy-wide, and transformational) and are influenced by complex interactions between business models, drivers, product lifecycle management, manufacturing ecosystems, and socioeconomic factors. This complexity makes rebound effects particularly challenging to predict and mitigate in CE implementation.

2.1.2. Scalability Limitations and Implementation Barriers

The scalability of circular economic approaches represents another significant challenge. Korhonen et al. [12] identify six fundamental limits to CE implementation, including system boundary problems, physical scale limitations, thermodynamic constraints, and governance challenges. These limitations suggest that while CE may offer benefits in specific contexts, scaling these approaches to address global sustainability challenges faces substantial barriers. Implementation barriers are particularly evident in emerging economies and specific sectors. Kirchherr et al. [40] categorize these barriers into cultural (e.g., limited consumer interest and hesitant company culture), market (e.g., low virgin material prices), regulatory (e.g., limited CE-supportive policy frameworks), and technological constraints (e.g., limited circular design capabilities). Their research indicates that cultural barriers, particularly limited consumer interest and hesitant company cultures, represent the most significant obstacles to CE implementation. Corvellec et al. [32] further highlight structural obstacles to CE implementation across policy, organizational, and consumer levels. Their critique emphasizes that CE often faces resistance due to entrenched economic interests, institutional lock-in, and consumer behaviors that are difficult to change. These structural barriers suggest that CE implementation requires not just technological innovation but fundamental social and economic transformations.

In light of these critiques, our study adopts a comprehensive approach by incorporating a multidimensional CE index that captures various aspects of circularity, including production, consumption, waste management, and secondary material utilization. Furthermore, we employ the LCF as an environmental sustainability metric, which accounts for both environmental degradation and restoration capacity. By analyzing the asymmetric effects of positive and negative CEI shocks across EU member states, our study aims to provide nuanced insights into the complex relationship between CE practices and environmental sustainability, acknowledging potential rebound effects and systemic limitations.

3. Research Design and Data Methodology

3.1. Econometric Model

This study builds upon the theoretical framework advanced by Chen and Pao [41], integrating findings from existing research to explore the determinants of the load capacity factor (LCF). It is hypothesized that LCF is shaped by a variety of factors, including demographic trends, economic performance, and technological progress. Accordingly, the econometric model incorporates the following independent variables into its structure, expressed as:

$${LCF}_{i,t}={f(CEI}_{i,t},{GDP}_{i,t},{FDI}_{i,t}{,TO}_{i,t},{EMP}_{i,t},{POP}_{i,t})$$

(1)

Here, the subscripts i and t refer to specific European Union (EU) countries and time periods, respectively. The variables are defined as follows: LCF denotes the load capacity factor, CEI represents denotes circular economy index, ${GDP}_{i,t}$ denotes the economic growth, ${FDI}_{i,t}$ denotes investment flows, ${TO}_{i,t}$ denotes trade openness, ${EMP}_{i,t}$ denotes employment, ${POP}_{i,t}$ denotes population. To address potential issues of heteroskedasticity, all variables are transformed using natural logarithms. The resulting model is specified as:

$${LCF}_{i,t}={{\beta }_0+{\alpha }_{1}CEI}_{i,t}+{{\alpha }_{2}GDP}_{i,t}+{{\alpha }_{3}FDI}_{i,t}+{{\alpha }_{4}TO}_{i,t}+{{\alpha }_{5}EMP}_{i,t}+{{\alpha }_{6}POP}_{i,t}+{\mu }_{i,t}$$

(2)

In this formulation, the coefficients ${\alpha }_1$ to ${\alpha }_6$ represent elasticities, measuring the percentage change in LCF resulting from a one percent change in each respective independent variable. A key focus of this study is the estimation of ${\alpha }_1$, which quantifies the effect of CEI on LCF. The term $B_0$ serves as the constant, and μ represents the random error term. Equation (1) assumes that changes in CEI symmetrically affect LCF. However, recent scholarships suggest that these effects may be asymmetric, with positive and negative changes producing distinct impacts. To examine this, the study separates CEI into positive and negative components, defined as:

$$\left\{\begin{array}{c}\mathrm{Positive}\mathrm{shocks}:{CEI}_{i,t}^{+}=max(0,{CEI}_{i,t})\\ \\ \mathrm{Negative}\mathrm{shocks}:{CEI}_{i,t}^{-}=min(0,{CEI}_{i,t})\end{array}\right.$$

This leads to an enhanced specification:

$${LCF}_{i,t}={\beta }_0+{\alpha }_{\mathrm{1,1}}{CEI}_{i,t}^{+}+{{\alpha }_{2}GDP}_{i,t}+{{\alpha }_{3}FDI}_{i,t}+{{\alpha }_{4}TO}_{i,t}+{{\alpha }_{5}EMP}_{i,t}+{{\alpha }_{6}POP}_{i,t}+{\mu }_{i,t}$$

(3)

$${LCF}_{i,t}={\beta }_0+{\alpha }_{\mathrm{1,2}}{CEI}_{i,t}^{-}+{{\alpha }_{2}GDP}_{i,t}+{{\alpha }_{3}FDI}_{i,t}+{{\alpha }_{4}TO}_{i,t}+{{\alpha }_{5}EMP}_{i,t}+{{\alpha }_{6}POP}_{i,t}+{\mu }_{i,t}$$

(4)

3.2. Quantile Regression via Method of Moments (MMQR)

Data in environmental economics often exhibits pronounced peaks or heavy tails, posing challenges for traditional analytical methods. Many studies rely on panel data models based on conditional mean regression, yet this approach overlooks the diverse effects of explanatory factors across the distribution of LCF, potentially leading to biased results. To overcome these shortcomings, this study adopts panel quantile regression for estimation.

This technique allows for a detailed analysis of how independent variables influence LCF across its entire conditional distribution within EU countries. Unlike conditional mean regression, quantile regression provides more reliable estimates, as noted by Machado and Santos Silva [42]. Conventional quantile regression methods, however, struggle with weakly associated variables and unobserved heterogeneity across cross-sections. The method of moments quantile regression (MMQR) approach, also proposed by Machado and Santos Silva [42], effectively resolves these issues.

The MMQR differs from location-shifting models by considering both location and scale effects on the conditional distribution of LCF. It excels among nonlinear methods due to its ability to handle asymmetries, endogeneity, and heterogeneity, as highlighted by Bergougui et al. [43,44]. These strengths make the MMQR an ideal choice for this research.

The basic quantile regression model is given by:

$$Q_{y_{i,t}}(\tau |X_{i,t})={\beta }_{\tau }+X_{i,t}^{\prime }{\alpha }_{\tau }$$

(5)

where $0<\tau <1$ and $Q_{y_{i,t}}(\tau |x_{i,t})$ denote the $\tau$^th conditional quantile. The parameter ${\alpha }_{\tau }$ estimates the effect of the independent variables, ${\beta }_{\tau }$ accounts for unobserved influences, and X is the vector of explanatory variables. The coefficients for the τth quantile are estimated as:

$${\widehat{\alpha }}_{\tau }=argmin{\sum }_{i=1}^n{\rho }_{\tau }\left(y_i-{\beta }_{\tau }-X_{i,t}^{\prime }{\alpha }_{\tau }\right)$$

(6)

Here, ${\rho }_{\tau }=\mu (\tau -I(\mu <0))$ is the check function, and $I\left(.\right)$ is an indicator function. Applying this to Equation (3), the quantile regression model becomes:

$$Q_{{LCF}_{i,t}}(\tau |{{\beta }_i,X}_{i,t})={\beta }_i+{\alpha }_{\mathrm{1,1}}{CEI}_{i,t}^{+}+{{\alpha }_{2}GDP}_{i,t}+{{\alpha }_{3}FDI}_{i,t}+{{\alpha }_{4}TO}_{i,t}+{{\alpha }_{5}EMP}_{i,t}+{{\alpha }_{6}POP}_{i,t}+{\mu }_{i,t}$$

(7)

$$Q_{{LCF}_{i,t}}(\tau |{{\beta }_i,X}_{i,t})={\beta }_i+{\alpha }_{\mathrm{1,2}}{CEI}_{i,t}^{-}+{{\alpha }_{2}GDP}_{i,t}+{{\alpha }_{3}FDI}_{i,t}+{{\alpha }_{4}TO}_{i,t}+{{\alpha }_{5}EMP}_{i,t}+{{\alpha }_{6}POP}_{i,t}+{\mu }_{i,t}$$

(8)

The study examines quantiles ranging from 0.10 to 0.90 (i.e., 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90), providing a thorough understanding of how explanatory variables affect LCF across its distribution.

3.3. Data Specification

3.3.1. Sample Selection and Data Sources

This research investigates the relationship between CEI and LCF in Europe from 2010 to 2023. Limited by data availability, the analysis includes 27 European countries over this 14-year period. The timeframe aligns with the initiation of consistent circular economy data collection in 2010. Data was gathered from reputable sources, namely Eurostat and the World Bank.

3.3.2. Dependent Variable

The load capacity factor (LCF) is the central dependent variable in this study, valued for its ability to proxy environmental sustainability (ES). It overcomes deficiencies in metrics like CO₂ emissions, ecological footprint (EF), and environmental performance indices, offering a broader measure of ecological health [45]. CO₂ emissions focus narrowly on energy-related outputs, missing broader environmental dynamics, while ecological footprints emphasize consumption without accounting for Earth’s regenerative capacity (biocapacity). Introduced by Siche et al. [46], the LCF integrates these aspects, defined as:

$$\left(LCF={\displaystyle \frac{\mathrm{B}\mathrm{i}\mathrm{o}\mathrm{c}\mathrm{a}\mathrm{p}\mathrm{a}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y}(\mathrm{B}\mathrm{C})}{\mathrm{E}\mathrm{c}\mathrm{o}\mathrm{l}\mathrm{o}\mathrm{g}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{F}\mathrm{o}\mathrm{o}\mathrm{t}\mathrm{p}\mathrm{r}\mathrm{i}\mathrm{n}\mathrm{t}(\mathrm{E}\mathrm{F})}}\right)$$

BC measures Earth’s ability to regenerate resources and absorb waste, while EF quantifies human resource demand, both in global hectares (gha). LCF values are interpreted as follows:

• LCF > 1: Sustainability, where biocapacity exceeds demand.
• LCF < 1: Unsustainability, indicating ecological strain.
• LCF = 1: A balance point between demand and capacity.

The LCF’s integration of supply and demand makes it a robust indicator of ecological capacity across atmospheric, hydrological, and soil systems. Its relevance to sustainability and circular economy impacts, as noted by Yurtkuran and Pata [47], supports its use here.

3.3.3. Independent Variable

The primary explanatory variable in this analysis is the circular economy index (CEI), designed to capture the multifaceted nature of circular economic practices and their influence on environmental sustainability. Recognizing that circular economy principles encompass complex, interconnected systems, previous research [48] has demonstrated the limitations of unidimensional indicators in adequately representing the comprehensive scope of circularity. To overcome this methodological challenge, we developed a composite index that synthesizes four fundamental dimensions of circular economy performance. The CEI framework incorporates four core dimensions that collectively represent the essential facets of circular economy implementation:

○

Dimension 1: Material System Efficiency

• Material circulation efficiency quantifies the extent to which economies successfully integrate secondary resources into production processes, measuring the transition from linear to circular material flows.

○

Dimension 2: Technological Innovation Capacity

• Innovation in resource recovery captures the technological advancement and research intensity within circular economy sectors, operationalized through patent filings for recycling technologies and secondary material applications.

○

Dimension 3: International Circular Trade Integration

• Recyclable trade flow measures cross-border engagement in circular value chains through the volume of international transactions involving reusable raw materials, reflecting both domestic circular capacity and global market participation.

○

Dimension 4: Economic System Commitment

• Economic commitment to circularity evaluates the financial dedication to circular economy transformation by aggregating private-sector investments and value-added economic outputs derived from circular practices.

The CEI construction employs an entropy-weighting technique to ensure objective assignment of relative importance to each component. This data-driven approach minimizes subjective researcher bias while allowing the inherent information content of each dimension to determine its contribution to the overall index. The entropy method calculates weights based on the degree of variation in each indicator, assigning higher weights to dimensions with greater discriminatory power across countries. This methodological framework aligns with established composite index construction principles proposed in previous studies [49], while incorporating adaptations specific to the institutional and economic contexts of EU member states. The approach ensures both methodological rigor and contextual relevance. The CEI development follows a systematic hierarchical structure:

○

Data standardization: All indicators are normalized to ensure comparability across different units and scales.

○

Entropy weight calculation: Objective weights are computed based on information entropy of each dimension.

○

Index aggregation: Weighted composite scores are calculated for each country and time.

The comprehensive CEI framework offers multiple analytic advantages. By integrating several dimensions of circularity, it faithfully reflects the systemic character of circular economy practices. Its use of entropy-based weights ensures an objective, data-driven assignment of component importance, eliminating researcher bias. Because it applies the same standardized methodology across all EU member states, it supports meaningful cross-country comparisons. The index’s design also accommodates tracking over time, making it possible to monitor countries’ circular economy trajectories longitudinally. Finally, by breaking performance down into distinct yet interconnected dimensions, the CEI yields actionable insights that can guide precisely targeted policy measures.

Figure 1 presents the complete conceptual framework of the CEI, illustrating the hierarchical structure and dimensional relationships that underpin this comprehensive measurement approach.

This robust, empirically grounded index construction enables nuanced analysis of the relationship between circular economy performance and environmental sustainability outcomes, providing a solid foundation for subsequent econometric modeling and policy analysis.

3.3.4. Control Variables

To isolate the specific effects of the CEI on the LCF, this investigation incorporates a comprehensive set of control variables. These variables account for potential confounding factors, as established in contemporary literature. Following [3,5,50,51], the study examines the impact of several socioeconomic variables, each of which may influence the LCF in distinct ways:

• Gross domestic product per capita: Higher GDP per capita often correlates with increased consumption and production, which can elevate the ecological footprint and thereby reduce the LCF. However, wealthier nations may also invest in eco-friendly technologies and sustainable practices, potentially mitigating this effect and improving the LCF [2,52].
• Foreign direct investment (FDI): FDI can enhance the LCF by introducing efficient technologies that reduce environmental impact. Conversely, it may diminish the LCF if it promotes resource extraction or pollution-intensive industries, thereby increasing the ecological footprint [53].
• Trade openness (TO): Greater trade openness can improve the LCF by facilitating access to cleaner technologies and promoting efficient resource use. However, it may also decrease the LCF by stimulating higher levels of consumption and production, which can strain ecological resources [2].
• Employment (EMP): A higher employment rate could lead to greater economic activity and resource utilization, potentially decreasing the LCF. Yet, in economies with strong environmental management, higher employment might be associated with better resource efficiency, thereby supporting an increase in the LCF.
• Population size (POP): Larger populations typically exert greater pressure on natural resources, increasing the ecological footprint and lowering the LCF. Nevertheless, effective resource management or favorable population density patterns can moderate this impact [52].

Each of these control variables interacts with the LCF in context-specific ways, underscoring the need to account for their effects when assessing the influence of the CEI on environmental sustainability (

Table 1. Variables, definitions, and sources.

Variable	Definition	Source
Load Capacity Factor (LCF)	Ratio of a country’s biocapacity to its ecological footprint	Global Footprint Network [19]
Circular Economy Index (CEI)	Composite index integrating circular material use, circular innovation, trade in recyclables, and CE investment	Author’s calculation using Eurostat data [9]
Gross Domestic Product per Capita (GDP)	GDP per capita in current U.S. dollars	World Bank [54]
Foreign Direct Investment (FDI)	Net inflows of FDI as percentage of GDP	World Bank [54]
Employment Rate (EMP)	Employment-to-population ratio (15+, % of total working-age population)	World Bank [54]
Trade Openness (TO)	(Exports + Imports)/GDP × 100	World Bank [54]
Population (POP)	Total population of the country	World Bank [54]

).

4. Empirical Results

4.1. Assessment of Distributional Assumptions

Conventional parametric regression frameworks inherently assume that variables follow a Gaussian distribution. To evaluate the validity of this premise for the dataset under investigation, two complementary diagnostic procedures were implemented:

• Graphical analysis: A quantile–quantile (Q-Q) plot was constructed for the LCF. Under conditions of normality, data points would align closely with a theoretical reference line. As illustrated in Figure 2, pronounced deviations from linearity were observed, signaling marked departures from normality.
• Statistical evaluation: The Shapiro–Wilk test was applied to assess the null hypothesis of normally distributed data. As summarized in

  Table 2. Summary statistics.

  	Mean	Std. Dev.	Min.	Max.	Skew.	Kurt.	Shapiro–Wilk Test	Prob.
  LCF	2.731	2.472	0.532	11.469	2.009	6.358	0.720	0.000
  CEI	5.859	1.535	3.067	9.203	0.213	2.073	0.973	0.000
  GDP	23.486	1.478	20.575	26.440	0.123	2.214	0.977	0.000
  FDI	0.358	0.752	−2.023	3.245	1.424	8.159	0.746	0.000
  EMP	1.703	0.088	1.455	1.888	−0.464	3.074	0.980	0.000
  TO	2.397	0.395	1.712	3.490	0.392	2.838	0.972	0.000
  POP	13.332	1.342	10.497	15.751	−0.108	2.511	0.969	0.000

  , statistically significant results (p < 0.05) for all variables confirmed the presence of non-normal distributions.

These combined findings demonstrate that the dataset violates fundamental assumptions of standard linear regression techniques, which could compromise the validity and precision of parameter estimates. To mitigate these limitations and ensure robust inference, this study employs a panel quantile regression methodology, which relaxes distributional assumptions and accommodates heterogeneous effects across the conditional distribution of the LCF.

4.2. Evaluation of Cross-Sectional Interdependence and Coefficient Heterogeneity

In panel data analysis, the phenomenon of cross-sectional interdependence (CSI) has emerged as a critical consideration, particularly in light of advancements in spatial econometrics [55]. CSI arises when observational units—here, EU member states—exhibit correlated dynamics due to unobserved common shocks or spillover effects, undermining the independence assumption of conventional regression frameworks. Overlooking CSI risks compromise the reliability of first-generation panel models, which assume strict exogeneity and cross-sectional independence [56]. To rigorously address this concern, this study employs two diagnostic procedures: the Breusch–Pagan Lagrange multiplier (LM) test and the Pesaran cross-sectional dependence (CD) test.

As shown in Panel A of

Table 3. Outcomes of CSI and CD.

Panel (A). Outcomes of the CSI Test.
Tests	Breusch–Pagan LM	Prob.	Pesaran CD	Prob.
LCF	2.0 × 105	0.0000	44.179	0.0000
CEI	18,291.80	0.0000	53.673	0.0000
GDP	3.5 × 105	0.0000	20.742	0.0000
FDI	4.2 × 105	0.0000	13.317	0.0000
EMP	72,122.27	0.0000	7.387	0.0000
TO	6420.42	0.0000	63.186	0.0000
POP	4.2 × 105	0.0000	16.805	0.0000
Panel (B). Outcomes of the CD Test.
	Test value		Prob.
Tilde (Delta)	106.713		0.0000
Adjusted tilde (Delta)	109.348		0.0000

, both tests yield statistically significant outcomes (p < 0.01) for all variables, confirming the presence of CSI. This implies that unobserved macroeconomic or environmental shocks in one nation are likely transmitted through interregional economic or ecological linkages, necessitating methodological adjustments to account for such interdependencies.

Prior to model estimation, slope heterogeneity—the variation in regression coefficients across entities—was assessed using the Blomquist–Westerlund [57] diagnostic. This test evaluates whether covariate effects remain consistent across the sampled EU states or vary systematically. Panel B of Table 3 reports the findings: both the delta (Δ) statistic and its adjusted variant reject the null hypothesis of homogeneity at the 1% significance level. This underscores substantial heterogeneity in the relationships between explanatory variables and LCF across jurisdictions, reinforcing the need for estimation techniques that accommodate parameter variability. Collectively, these diagnostics justify the adoption of second-generation panel quantile regression methods, which inherently address CSI and heterogeneous effects.

4.3. Panel Unit Root Test Results

After confirming the presence of cross-sectional dependence, the next step was to examine the stationarity properties of the variables using advanced panel unit root tests that account for such interdependencies. To ensure robust findings, a second-generation test—specifically, the cross-sectionally augmented Dickey–Fuller (CADF) test—was employed. The results, summarized in

Table 4. Second-generation panel unit root analysis.

Variables	CADF—Level I(0)	CADF—Difference I(1)
LCF	−4.855 ***	−6.144 ***
CEI	−5.999 ***	−6.157 ***
GDP	−5.136 ***	−6.190 ***
FDI	−5.450 ***	−6.196 ***
EMP	−2.250 **	−2.206 ***
TO	−1.550	−2.052 ***
POP	−1.721	−2.150 ***

Notes: *** and ** denote rejection of the null hypothesis of unit root (i.e., stationarity) at the 1% and 5%, significance levels, respectively. CADF: cross-sectionally augmented Dickey–Fuller test.

, reveal that CEI and TO are stationary at levels, indicating integration of order zero [I(0)]. In contrast, variables such as LCF, POP, GDP, and EMP become stationary only after first differencing, suggesting they are integrated of order one [I(1)]. These integration properties guided the subsequent analysis, which focused on investigating the presence of a long-run equilibrium relationship among the variables. Establishing cointegration is crucial for modeling long-term dynamics among nonstationary variables, as it helps prevent spurious regression results.

4.4. Panel Cointegration Test Results

To assess the existence of a cointegrated relationship among the variables, three distinct cointegration diagnostics were applied: the Kao [58] residual-based test, the Pedroni [59] panel cointegration framework, and the Westerlund [60] error-correction approach. As detailed in

Table 5. Panel cointegration analysis.

Estimates	Statistic	p-Value
Pedroni test for cointegration
Modified Phillips–Perron t	5.558 ***	0.0000
Phillips–Perron t	4.846 ***	0.0000
Augmented Dickey–Fuller t	−18.266 ***	0.0000
Kao test for cointegration
Modified Dickey–Fuller t	−3.407 ***	0.0003
Dickey–Fuller t	−1.900 **	0.0287
Augmented Dickey–Fuller t	−10.442 ***	0.0000
Unadjusted modified Dickey–Fuller	1.923 **	0.0272
Unadjusted Dickey–Fuller t	0.535	0.2965
Westerlund test for cointegration
Variance ratio	−2.960 ***	0.0015

Note: *** and ** denote statistical significance at the 1% and 5% levels, respectively.

, the results from both conventional (Kao, Pedroni) and advanced (Westerlund) methodologies collectively confirm the presence of a cointegrated equilibrium across the EU examined. This implies that the variables exhibit convergent long-term dynamics, remain bounded over time, and respond to shared macroeconomic or environmental disturbances. The identification of cointegration necessitates the estimation of long-run structural relationships while accounting for potential asymmetries. To address this, the study adopts the MMQR, which accommodates nonlinear adjustments and facilitates a granular examination of heterogeneous effects across the conditional distribution of the LCF.

4.5. Asymmetric MMQR Estimation Results

Following the evaluation of cross-sectional dependence, unit root properties, and slope heterogeneity within the dataset, the MMQR is utilized to capture the heterogeneous effects of an asymmetric CEI across varying levels of the LCF. This approach is adopted due to its ability to provide a more detailed and policy-relevant perspective compared to methods assuming uniform effects across the distribution.

Table 6. Panel quantile estimation analysis.

	Lower Quantile Weaker Sustainability			Middle Quantile Moderate Sustainability			Higher Quantile Stronger Sustainability
	10th	20th	30th	40th	50th	60th	70th	80th	90th
Model I: $Q_{{LCF}_{i,t}}(\tau |{{\beta }_i,X}_{i,t})={\beta }_i+{\alpha }_{\mathrm{1,1}}{CEI}_{i,t}^{+}+{{\alpha }_{2}GDP}_{i,t}+{{\alpha }_{3}FDI}_{i,t}+{{\alpha }_{4}TO}_{i,t}+{{\alpha }_{5}EMP}_{i,t}+{{\alpha }_{6}POP}_{i,t}+{\mu }_{i,t}$
${CEI}^{+}$	0.229 ***	0.314 ***	0.382 ***	0.435 ***	0.517 ***	0.609 ***	0.752 ***	0.967 ***	1.219 ***
	(0.080)	(0.077)	(0.085)	(0.096)	(0.118)	(0.146)	(0.195)	(0.274)	(0.367)
GDP	0.785 ***	0.864 ***	0.928 ***	0.978 ***	1.055 ***	1.141 ***	1.275 ***	1.477 ***	1.713 ***
	(0.044)	(0.042)	(0.046)	(0.052)	(0.064)	(0.080)	(0.106)	(0.149)	(0.200)
FDI	1.349 ***	1.387 ***	1.418 ***	1.442 ***	1.479 ***	1.520 ***	1.585 ***	1.682 ***	1.795 ***
	(0.047)	(0.046)	(0.050)	(0.057)	(0.069)	(0.086)	(0.115)	(0.161)	(0.217)
EMP	−6.840 ***	−6.664 ***	−6.521 ***	−6.411 ***	−6.241 ***	−6.050 ***	−5.752 ***	−5.304 ***	−4.779 ***
	(0.300)	(0.292)	(0.322)	(0.363)	(0.444)	(0.553)	(0.738)	(1.032)	(1.388)
TO	1.201 ***	1.258 ***	1.304 ***	1.340 ***	1.395 ***	1.456 ***	1.553 ***	1.697 ***	1.867 ***
	(0.090)	(0.088)	(0.097)	(0.109)	(0.134)	(0.166)	(0.222)	(0.310)	(0.417)
POP	−0.581 ***	−0.749 ***	−0.886 ***	−0.991 ***	−1.153 ***	−1.336 ***	−1.621 ***	−2.048 ***	−2.550 ***
	(0.054)	(0.051)	(0.056)	(0.063)	(0.077)	(0.096)	(0.128)	(0.180)	(0.241)
Model II: $Q_{{LCF}_{i,t}}(\tau |{{\beta }_i,X}_{i,t})={\beta }_i+{\alpha }_{\mathrm{1,2}}{CEI}_{i,t}^{-}+{{\alpha }_{2}GDP}_{i,t}+{{\alpha }_{3}FDI}_{i,t}+{{\alpha }_{4}TO}_{i,t}+{{\alpha }_{5}EMP}_{i,t}+{{\alpha }_{6}POP}_{i,t}+{\mu }_{i,t}$
${CEI}^{-}$	1.126 ***	0.435 **	0.134	−0.183	−0.734 ***	−1.289 ***	−2.135 ***	−3.517 ***	−5.253 ***
	(0.223)	(0.199)	(0.208)	(0.231)	(0.279)	(0.335)	(0.433)	(0.603)	(0.836)
GDP	0.705 ***	0.832 ***	0.887 ***	0.945 ***	1.047 ***	1.148 ***	1.304 ***	1.557 ***	1.875 ***
	(0.047)	(0.044)	(0.046)	(0.050)	(0.061)	(0.073)	(0.094)	(0.132)	(0.182)
FDI	1.289 ***	1.360 ***	1.390 ***	1.423 ***	1.479 ***	1.535 ***	1.622 ***	1.762 ***	1.939 ***
	(0.053)	(0.050)	(0.053)	(0.058)	(0.069)	(0.083)	(0.108)	(0.150)	(0.207)
EMP	−6.182 ***	−6.181 ***	−6.180 ***	−6.180 ***	−6.179 ***	−6.178 ***	−6.177 ***	−6.175 ***	−6.172 ***
	(0.313)	(0.304)	(0.320)	(0.348)	(0.416)	(0.501)	(0.648)	(0.907)	(1.247)
TO	1.071 ***	1.227 ***	1.295 ***	1.367 ***	1.491 ***	1.616 ***	1.807 ***	2.119 ***	2.510 ***
	(0.099)	(0.094)	(0.099)	(0.108)	(0.130)	(0.156)	(0.202)	(0.282)	(0.388)
POP	−0.538 ***	−0.749 ***	−0.842 ***	−0.939 ***	−1.108 ***	−1.278 ***	−1.537 ***	−1.960 ***	−2.492 ***
	(0.058)	(0.051)	(0.053)	(0.059)	(0.071)	(0.086)	(0.111)	(0.154)	(0.214)

Note. Standard errors in brackets. ** p < 0.05, *** p < 0.01.

presents the MMQR estimates, providing a detailed examination of how both positive and negative shocks in the CEI influence the LCF across various quantiles. Figure 3 and Figure 4 visually depict these effects, illustrating the heterogeneous impact of CEI fluctuations on environmental sustainability metrics throughout the distribution. Furthermore, the influence of control variables is assessed to account for additional factors affecting the LCF, ensuring a thorough and robust understanding of the relationship between CEI shocks and environmental sustainability.

4.5.1. Impact of Positive Shock in CEI on LCF

In Model 1, the estimated coefficients for CEI⁺ exhibit a clear monotonic increasing pattern across the distribution of LCF, ranging from 0.229 at the 10th quantile to 1.219 at the 90th quantile. All coefficients are statistically significant at the 1% level (as indicated by the triple asterisks), demonstrating a robust relationship that persists throughout the conditional distribution of the dependent variable. The standard errors (shown in brackets) generally increase across higher quantiles, from 0.080 at the 10th quantile to 0.367 at the 90th quantile, indicating greater estimation uncertainty at the upper tail of the distribution. The results reveal an important heterogeneous effect: the magnitude of the CEI⁺ coefficient progressively strengthens as we move from lower to higher quantiles of LCF. Specifically, the coefficient at the 90th quantile (1.219) is approximately 5.3 times larger than at the 10th quantile (0.229). This pattern suggests that the elasticity of LCF with respect to positive circular economy developments is substantially more pronounced in regions already exhibiting higher environmental sustainability.

While the findings corroborate the positive CEI–LCF relationship documented in studies such as Chen and Pao [41] (EU), Cai et al. [61] (G20), and Bayar et al. [62] (EU member states), this analysis advances beyond prior work by revealing nonlinear effects masked by conventional mean regression. Earlier research, constrained by conditional mean estimation, reported only average effects (e.g., Musa et al. [63] in Germany). In contrast, the MMQR approach employed here uncovers critical heterogeneity: the CEI⁺ elasticity strengthens significantly as LCF increases, refuting the assumption of uniform marginal effects.

4.5.2. Impact of Negative Shock in CEI on LCF

The panel quantile regression results for the impact of negative shocks in CEI⁻ on LCF reveal statistically significant and asymmetric effects across the conditional distribution of LCF (Table 6 and Figure 4). The coefficients for CEI⁻ exhibit a pronounced negative relationship with LCF, with magnitudes increasing sharply from the 10th quantile (β = −1.126) to the 90th quantile (β = −5.253). This indicates that negative CEI shocks disproportionately degrade environmental sustainability, particularly in EU countries with higher baseline LCF values.

• Lower Quantiles (10th–30th): At the 10th quantile (τ = 0.10), a 1% decrease in CEI (CEI⁻) is associated with a 1.126% decline in LCF, reflecting severe sustainability losses for countries with lower initial LCF values. The effect remains significant but less pronounced at the 40th quantile (β = −0.183), suggesting that negative CEI shocks hinder sustainability even in less vulnerable contexts.
• Middle Quantiles (40th–60th): At the median (τ = 0.50), the coefficient intensifies to −0.734, indicating that CEI⁻ exerts a stronger destabilizing influence on LCF for EU countries with average sustainability levels. This aligns with prior evidence that transitional economies face heightened vulnerability to disruptions in circular practices.
• Higher Quantiles (70th–90th): The effect becomes catastrophic in upper quantiles, with the 90th quantile coefficient reaching −5.253. This implies that negative CEI shocks trigger extreme environmental degradation in high-LCF countries, likely due to their reliance on advanced circular systems (e.g., recycling infrastructure, renewable energy) to maintain sustainability thresholds.

These findings parallel Kakar et al. [49]’s research across 39 European countries, which confirmed that circular economy implementation can dampen biodiversity in Europe. However, while Kakar’s analysis provided only the average effect, this study employs the MMQR procedure to demonstrate that the effect of the CEI increases substantially with rising levels of the LCF. This indicates the absence of a constant effect and highlights the nuanced relationship between circular economy and environmental sustainability across different EU contexts. The results underscore the importance in maintaining a consistent circular economy, particularly for environmental leaders who face disproportionate negative consequences from investment reductions or reversals.

4.5.3. Effect of Control Variables on LCF

The empirical analysis reveals distinct patterns in the influence of control variables across quantiles, underscoring their role in shaping environmental sustainability (LCF) in EU member states. These variables—GDP, FDI, TO, employment, and population—collectively account for confounding factors that could bias estimates of the CEI’s effects. Their coefficients exhibit statistically significant relationships, with directions and magnitudes varying systematically across the LCF distribution.

• GDP demonstrates a positive elasticity with LCF, increasing monotonically from lower to higher quantiles (0.785 to 1.713 in Model I). This suggests that economic growth enhances sustainability more effectively in EU countries with pre-existing high environmental performance [64]. The amplified effect at higher quantiles aligns with the hypothesis that advanced economies leverage institutional frameworks, regulatory standards, and green technologies to decouple growth from ecological degradation [65]. This dynamic likely reflects the EU’s cohesive policy environment, where economic expansion aligns with sustainability targets such as those outlined in the European Green Deal [66].
• FDI exhibits strong positive associations across all quantiles, with coefficients exceeding 1 in both models (e.g., 1.349 to 1.795 in Model I). These results indicate that FDI acts as a conduit for technology transfer, knowledge spillovers, and the adoption of sustainable practices within the EU’s integrated market [67]. The escalating impact at higher quantiles implies that countries with robust environmental governance and innovation ecosystems—such as Germany or Sweden—maximize FDI’s sustainability dividends, consistent with studies emphasizing systemic investments in circular economies [68,69].
• TO displays positive and intensifying effects, particularly at higher quantiles (1.201 to 1.867 in Model I). This reflects the EU’s role as a hub for harmonized environmental standards, where trade facilitates resource efficiency and access to cleaner technologies. The results corroborate literature highlighting trade’s dual role in promoting sustainable practices through competitive pressures and cross-border knowledge diffusion [70].
• Employment displays a consistently strong negative elasticity with LCF across all quantiles (–6.2 to –6.8), indicating that higher employment levels coincide with lower environmental sustainability. While counterintuitive at first glance, several theoretical and empirical factors can help explain this pattern: (i) Labor-intensive, resource-intensive sectors: In many EU regions, high employment rates are driven by manufacturing, construction, and service industries that remain heavily reliant on energy and material inputs. As employment grows in these sectors, production—and hence resource extraction, waste generation, and emissions—also rises, exerting downward pressure on the LCF. (ii) Employment as a Proxy for Economic Activity and Consumption: A high employment rate often correlates with stronger overall economic activity and rising household incomes. Increased incomes tend to boost consumption of goods and services, many of which are resource-intensive, thereby expanding ecological footprints. This “rebound effect” can offset gains from cleaner production techniques. (iii) Structural and sectoral composition: Aggregate employment data mask crucial sectoral differences. For example, jobs in green industries (renewables, recycling) likely exert much smaller negative, or even positive, effects on the LCF than jobs in carbon-intensive sectors. Regions with a high share of “brown” employment may therefore pull down the national LCF when overall employment rises. (iv) Theoretical frameworks: Two complementary theoretical perspectives help explain why higher employment can translate into environmental strain. Under the environmental Kuznets curve and structural-change framework, economies in their early growth phases typically emphasize job creation and industrial expansion, often at the expense of environmental quality; only when employment begins to shift toward high-skill, service-based sectors—characterized by lower resource intensity—does the adverse impact on sustainability begin to subside. The just-transition literature further underscores this dynamic by highlighting the risk of “lock-in” effects: without proactive policies for retraining and reallocating workers, labor markets remain anchored in carbon-intensive industries, impeding the economy’s ability to pivot toward greener, more sustainable employment opportunities.
• Population exerts increasingly negative pressures at higher quantiles (from −0.581 to −2.550 in Model I), signaling that demographic growth exacerbates resource constraints in high-LCF countries. The escalating effect may stem from urbanization, consumption patterns, and infrastructure demands that strain already-optimized systems in sustainability leaders like France or the Netherlands. This aligns with studies emphasizing threshold effects, where population pressures trigger nonlinear declines in ecological resilience.

The inclusion of control variables adheres to best practices in causal inference, mitigating omitted variable bias while isolating the CEI’s effects. Notably: (I) GDP and FDI act as enablers of sustainability, particularly in high-performance economies, validating their role as critical levers in EU policy frameworks. (II) Employment and population reflect systemic challenges, highlighting the need for structural reforms to reconcile socioeconomic priorities with ecological limits. (III) Trade openness reinforces the EU’s strategic advantage in global sustainability governance, though its benefits remain unevenly distributed across member states.

4.6. Robustness Check

To verify our findings, we employed dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS) regressions alongside the MMQR approach. These robust estimation techniques provide additional validation of the long-term relationships between positive and negative shocks in CE and LCF.

Table 7. DOLS and FMOLS regression results.

	DOLS		FMOLS
	Model (1)	Model (2)	Model (1)	Model (2)
${CEI}^{+}$	0.649 ***		2.708 ***
	(0.218)		(0.214)
${CEI}^{-}$		−0.561		−2.004 ***
		(0.341)		(0.333)
GDP	0.451	0.411	1.297 ***	0.481
	(0.433)	(0.454)	(0.424)	(0.462)
FDI	0.120 ***	0.136 ***	0.173 ***	0.239 ***
	(0.040)	(0.042)	(0.039)	(0.042)
EMP	2.698 **	3.374 ***	1.036	3.804 ***
	(1.078)	(1.082)	(1.061)	(1.114)
TO	0.932 *	0.844	3.958 ***	4.150 ***
	(0.503)	(0.521)	(0.401)	(0.431)
POP	3.507 ***	3.521 ***	1.879 **	2.615 ***
	(0.769)	(0.809)	(0.764)	(0.836)

Note. Standard errors in brackets. * p < 0.10, ** p < 0.05, *** p < 0.01.

presents these results.

The results from both DOLS and FMOLS methods corroborate our MMQR findings. Positive shocks in CEI (CEI⁺) demonstrate a consistently positive and statistically significant impact on LCF across both estimation techniques. Conversely, negative shocks in CEI (CEI⁻) show a negative relationship with LCF, though this effect is less pronounced and statistically insignificant in the DOLS model, which aligns with the more modest influence observed in our MMQR analysis.

4.7. Heterogeneous Panel Causality Analysis

The Granger non-causality test results for EU countries, as reported by [71,72], reveal complex causal dynamics between environmental sustainability (proxied by LCF) and key socioeconomic variables. These findings highlight the bidirectional and unidirectional relationships shaping the EU’s sustainability landscape (

Table 8. Juodis, Karavias, and Sarafidis [71] Granger non-causality test results.

Null Hypothesis (H0)	HPJ Wald Test	p-Value	Decision
${CEI}^{+}$ does not Granger-cause LCF	9.8924	0.0017	One-way causality
LCF does not Granger-cause ${CEI}^{+}$	2.2728	0.1317
${CEI}^{-}$ does not Granger-cause LCF	1.2360	0.2662	One-way causality
LCF does not Granger-cause ${CEI}^{-}$	118.4271	0.0000
GDP does not Granger-cause LCF	10.0290	0.0066	Two-way causality
LCF does not Granger-cause GDP	40.9889	0.0000
FDI does not Granger-cause LCF	8.0879	0.0045	Two-way causality
LCF does not Granger-cause FDI	25.1042	0.0000
EMP does not Granger-cause LCF	7.7122	0.0055	Two-way causality
LCF does not Granger-cause EMP	44.0249	0.0000
TO does not Granger-cause LCF	10.0708	0.0015	One-way causality
LCF does not Granger-cause TO	0.1459	0.7025
POP does not Granger-cause LCF	2.9340	0.0867	One-way causality
LCF does not Granger-cause POP	49.4269	0.0000

Note. Two-way causality: Both directions of causality are statistically significant (p-value < 0.05). One-way causality: Only one direction shows statistical significance. No causality: Neither direction is statistically significant.

).

• Two-way causality is observed between LCF and GDP, FDI, and EMP, suggesting a feedback loop between economic activity and environmental outcomes. For instance, GDP growth influences LCF (p = 0.0066), likely through EU-wide investments in green technologies or resource-efficient infrastructure, while improvements in LCF also drive GDP (p = 0.0000). This aligns with the environmental Kuznets curve hypothesis, where economic development initially strains but eventually enhances sustainability through institutional and technological advancements. Similarly, the bidirectional relationship between FDI and LCF underscores how foreign investments facilitate green technology transfer (e.g., renewable energy projects), while strong sustainability performance attracts further FDI, reinforcing the EU’s role as a hub for green finance. The employment–LCF feedback loop reflects tensions between labor markets and sustainability: job growth in carbon-intensive sectors may temporarily harm the LCF, while green transitions (e.g., renewable energy employment) create long-term synergies.
• One-way causality is evident in specific relationships. Trade openness (TO) unidirectionally drives LCF improvements (p = 0.0015), likely due to the EU’s harmonized environmental standards (e.g., circular economy directives) and access to cleaner technologies through integrated markets. Conversely, LCF unidirectionally influences population dynamics (LCF → POP, p = 0.0000), possibly by attracting migration to regions with robust sustainability policies or incentivizing sustainable urbanization patterns under the European Green Deal. Notably, population growth itself (POP → LCF) shows no significant causal impact (p = 0.0867), suggesting advanced EU resource management systems mitigate demographic pressures.

In conclusion, these findings underscore the EU’s unique position as a sustainability laboratory, where integrated governance, economic cohesion, and institutional innovation drive reciprocal relationships between development and environmental health. Policymakers must leverage these feedback loops to accelerate green transitions while addressing regional inequities, ensuring the bloc’s 2050 climate neutrality targets remain achievable.

5. Discussion

This section explores potential explanations for the empirical findings presented in the previous sections, examining how both positive and negative shocks in circular economy implementation affect environmental sustainability across the heterogeneous landscape of EU member states.

5.1. Effects of Positive CEI Shocks on Environmental Sustainability

Possible explanations for the varied effects of positive shocks in circular economy on the LCF distribution across EU countries are as follows: First, the consistently positive and significant coefficients across all quantiles confirm that circular economy initiatives contribute to environmental sustainability regardless of an EU country’s initial ecological status. This finding supports the universal applicability of circular economy principles as environmental policy instruments across the European Union and validates the EU’s Circular Economy Action Plan as an effective framework for improving ecological resilience [73]. Second, the increasing coefficient magnitude across quantiles suggests a cumulative advantage phenomenon within the EU, whereby member states with pre-existing ecological resilience (higher LCF) benefit disproportionately from circular economy advancements. This “sustainability premium” indicates that circular economy investments may yield amplified environmental returns in already-sustainable EU contexts [74], possibly due to complementary effects with existing environmental governance structures, technological infrastructure, or ecological capacities. This may partly explain the divergence in environmental performance observed among EU member states despite shared policy frameworks [75,76]. Third, the relatively smaller coefficients at lower quantiles (EU countries with lower environmental sustainability) suggest that while circular economy initiatives provide benefits, they may be insufficient in isolation to dramatically transform regions facing severe ecological deficits. For these EU member states, complementary policies addressing fundamental ecological challenges may be necessary prerequisites for circular economy measures to achieve their full potential [77,78]. This finding has particular relevance for EU cohesion policy, suggesting that differential approaches may be needed across the Union rather than uniform implementation [79,80].

5.2. Effects of Negative CEI Shocks on Environmental Sustainability

While positive circular economy shocks demonstrate universally beneficial yet heterogeneous effects, the analysis of negative shocks reveals a markedly different and more complex pattern of responses across the EU sustainability spectrum. Moving to the impact of negative shocks in CEI on LCF, which reveal negative effects across the conditional distribution of LCF, this indicates that negative CEI shocks disproportionately degrade environmental sustainability, particularly in EU countries with higher baseline LCF values. The heterogeneous impact of negative circular economy shocks across EU countries reveals several critical insights that fundamentally contrast with the positive shock dynamics previously discussed. First, the sign reversal across quantiles indicates fundamentally different responses to circular economy deterioration based on a country’s existing environmental sustainability status. For EU countries with the lowest environmental sustainability (10^th–30^th quantiles), negative CEI shocks paradoxically associate with improved LCF values. This counterintuitive finding might reflect that these countries have not yet integrated circular economy principles deeply into their economic systems, making reductions in circular activities less impactful or potentially allowing resources to be redirected to more immediate environmental priorities [80,81]. Second, the nonsignificant coefficients in the middle quantiles (40^th–60^th) suggest a transition zone where negative CEI shocks neither significantly improve nor deteriorate environmental sustainability. This may represent a critical threshold in the relationship between circular economy practices and environmental outcomes in moderately sustainable EU economies. Third, the increasingly negative and significant coefficients at higher quantiles (70^th–90^th) demonstrate that more environmentally sustainable EU countries experience severe adverse effects from circular economy deterioration. This suggests that countries with higher LCF values have likely built economic systems that depend heavily on circular principles, making them particularly vulnerable to reductions in circular economy performance [65]. The magnitude of these negative effects increases dramatically with higher levels of environmental sustainability, as evidenced by the coefficient at the 90^th quantile being approximately seven times larger than at the 50^th quantile.

Taken together, these findings carry important policy implications for the EU. High-performing member states must safeguard against backsliding—introducing regulatory “floor” standards or contingency funding to preserve gains—while lower-performing countries should embed circular initiatives within broader environmental reforms, ensuring that foundational ecological deficits are addressed before scaling up circular interventions. By aligning circular economy investments with each country’s existing sustainability baseline, the EU can both maximize environmental returns and guard against systemic vulnerabilities.

The contrasting patterns observed between positive and negative circular economy shocks reveal fundamental asymmetries that carry profound implications for European environmental and economic policy design. These findings point to important policy implications for the European Union’s environmental and economic strategies, particularly in recognizing that the pathways to sustainability improvement and the vulnerabilities to sustainability deterioration operate through distinctly different mechanisms across member states. For countries with higher environmental sustainability, maintaining and advancing circular economy initiatives appears crucial for preserving ecological balance. In these contexts, policies that prevent backsliding on circular economy principles may be as important as those promoting further advancement. Conversely, for EU countries with low environmental sustainability, the results suggest that circular economy initiatives may need to be embedded within broader environmental reforms to achieve desired outcomes. The positive coefficients at lower quantiles indicate that resources might be more effectively directed toward fundamental environmental improvements before extensive circular economy transformations. This pattern of heterogeneous effects across EU member states highlights the importance of tailored policy approaches that account for countries’ existing environmental performance. The findings suggest that the EU’s circular economy framework may need differentiated implementation strategies that recognize these varying relationships between circular economy performance and environmental sustainability outcomes.

6. Conclusions

Contemporary environmental challenges have intensified substantially in recent decades, manifesting through accelerated biodiversity loss, ecosystem degradation, resource depletion, climate instability, and consequent threats to human welfare and development trajectories. The convergence of anthropogenic pressures and planetary boundaries has necessitated transformative approaches to environmental governance and economic systems design. Circular economy principles have emerged as a promising paradigm shift from traditional linear production–consumption models, offering potential pathways toward sustainability. Within this context, this study examines the dynamic relationship between circular economy index (CEI) and load capacity factor (LCF) across the European Union—a region at the forefront of circular economy policy implementation—utilizing the analytical framework of the environmental Kuznets curve (EKC) hypothesis to investigate sustainability transitions in 27 member states during the 2010–2023 period.

This study makes several significant contributions to the literature on environmental sustainability through its empirical analysis of the relationship between CEI and LCF across EU member states. As the first investigation to examine asymmetric CEI effects within the EU context—the world’s largest collective circular economy—this research addresses a critical gap in the scholarly discourse. The quantile-specific analysis provides nuanced insights into how countries with varying sustainability levels respond differentially to circular economy dynamics. The development of a novel composite CEI through entropy weighting methodology represents a methodological advancement. By integrating multiple dimensions including circular material use, circular innovation, trade in recyclables, and circular economy investment, this index transcends traditional unidimensional metrics and offers a more comprehensive analytical framework for assessing circular economy implementation across diverse national contexts. The application of nonparametric MMQR constitutes another important methodological contribution. This approach effectively addresses misspecification bias and captures heterogeneous effects across the conditional distribution of the LCF. By incorporating partial sum decomposition to distinguish between positive and negative CEI shocks, the model provides a more granular analysis than conventional regression methods, particularly appropriate for capturing the effects of structural transformations characterizing EU economies in recent decades.

The empirical findings reveal pronounced asymmetric effects of CEI shocks across the LCF distribution. Positive CEI shocks exhibit amplified benefits in high-LCF countries (1.219% increase at the 90th quantile versus 0.229% at the 10th quantile), suggesting a “sustainability premium” whereby advanced economies leverage existing infrastructure and governance frameworks to maximize circular economy returns. Conversely, negative CEI shocks trigger disproportionate degradation in high-LCF nations (−5.253% at the 90th quantile), highlighting their systemic dependence on established circular practices. Paradoxically, low-LCF countries demonstrate greater short-term resilience to negative shocks, attributable to their less developed circular systems. Economic and demographic factors demonstrate dual roles in the CEI-LCF relationship. GDP, FDI, and trade openness function as enabling mechanisms, with effects intensifying in more sustainable economies—aligning with the EU’s green growth agenda where economic integration facilitates technology transfer and resource efficiency. Employment and population dynamics reveal structural tensions within the sustainability transition, reflecting challenges in sectors tied to carbon-intensive activities and resource pressures in high-LCF regions. Granger causality tests identify significant bidirectional relationships between LCF and GDP, FDI, and employment, illuminating the interconnected nature of economic and environmental systems across the EU. Trade openness unidirectionally enhances the LCF, emphasizing the role of harmonized standards in driving sustainability improvements. Population dynamics are influenced by sustainability outcomes but do not directly degrade them, demonstrating the relatively advanced resource management capabilities of EU nations.

Several targeted policy recommendations emerge from these findings. First, EU policymakers should develop differentiated circular economy strategies that account for member states’ varying sustainability profiles. High-LCF countries require robust safeguards against policy backsliding and implementation failures, including stronger monitoring mechanisms and contingency plans for maintaining circular systems during economic downturns. Conversely, low-LCF states need targeted capacity-building initiatives focused on developing foundational circular infrastructure and skills before pursuing more advanced interventions. Second, the EU should strengthen the integration of circular economy objectives with broader economic policies, particularly those governing trade, investment, and labor markets. The positive synergies identified between economic drivers and sustainability outcomes suggest that properly aligned economic incentives can accelerate circular transitions. This might include preferential trade terms for circular products, investment incentives for circular business models, and targeted workforce development programs for circular economy sectors. Third, the EU’s 2050 climate neutrality ambitions require a more nuanced approach to measuring and managing circular economy progress. The composite circular economy index (CEI) developed in this study offers a multidimensional assessment framework that could inform more sophisticated policy targets and monitoring systems. By moving beyond simplistic metrics to capture the complex interplay of production, consumption, waste management, and secondary resource utilization, policymakers can better track genuine progress toward circular systems and adjust interventions based on member states’ specific positions within the sustainability distribution.

This study has several limitations that warrant acknowledgment. The reliance on aggregated country-level data may obscure important sectoral and regional variations in circular economy implementation and outcomes. Future research should explore these dynamics at more granular levels, potentially incorporating firm-level data and regional case studies to identify best practices and implementation challenges [82]. Additionally, the relatively short time series (2010–2023) limits the ability to assess long-term structural changes and policy impacts, particularly given the recent nature of many circular economy initiatives. Future research should explore the mechanisms underlying the observed “sustainability premium” and the paradoxical resilience of low-performing states, for instance by benchmarking effect sizes against studies in digital green innovation contexts [83]. Qualitative comparative analyses could provide valuable insights into the institutional, cultural, and technological factors that enable some countries to better leverage circular economy initiatives—analogous to work on network fragmentation in digital-green ecosystems [84]. Longitudinal studies tracking the evolution of circular systems over longer timeframes would help clarify whether the observed patterns represent transitional dynamics or more permanent structural features.

The CEI framework developed in this study offers significant potential for application beyond the European Union context, with particular relevance for regions facing distinct sustainability challenges and economic transition pressures. The heterogeneous effects observed across EU member states suggest that similar patterns of differential impacts may exist in other regional contexts, though potentially with different underlying drivers and threshold mechanisms. The MENA (Middle East and North Africa) region presents a compelling context for applying and adapting the CEI framework, given its unique combination of resource constraints, economic diversification imperatives, and climate vulnerabilities. Unlike the EU’s primarily climate-driven sustainability focus, MENA countries face the dual challenge of transitioning away from fossil-fuel dependence while addressing severe water scarcity and growing waste management pressures. The framework could be adapted to incorporate region-specific indicators such as water circularity metrics, renewable energy integration in circular processes, and desert-specific waste-to-energy technologies. Countries like the UAE and Saudi Arabia, with their ambitious economic diversification programs (Vision 2030), may exhibit different “sustainability premium” patterns compared to EU nations, potentially showing how circular economy investments perform under rapid economic transformation conditions. Meanwhile, resource-constrained countries in the region might demonstrate alternative pathways to the low-quantile dynamics observed in the EU, where external resource dependencies rather than internal capacity limitations shape circular economy outcomes.

Caribbean nations offer another valuable comparative context, particularly for understanding how geographic and economic constraints influence circular economy effectiveness. The region’s characteristics—import dependence, limited land area, vulnerability to climate change, and tourism-dependent economies—create fundamentally different conditions from continental European economies. Adapting the CEI framework for Caribbean applications would require incorporating metrics specific to island contexts, such as coastal resilience indicators, tourism-sector circularity measures, and disaster-recovery circular systems. The threshold effects observed in the EU may manifest differently in Caribbean contexts, where resource constraints and external shocks (hurricanes, global economic downturns) could create more volatile sustainability transitions. Small-island developing states might exhibit compressed quantile distributions, where the difference between high and low environmental performers is less pronounced due to shared vulnerabilities, potentially revealing different policy intervention points and circular economy leverage mechanisms.

The methodology could be extended to incorporate alternative environmental metrics that better capture regional sustainability priorities. For MENA applications, indicators such as the water stress index, desalination efficiency ratios, and solar energy circular utilization could provide more relevant environmental outcomes than traditional European-focused metrics. For Caribbean contexts, coastal erosion rates, reef health indicators, and hurricane resilience metrics might serve as more appropriate dependent variables than the load capacity factor used in this study. Furthermore, the framework could integrate region-specific Sustainable Development Goals (SDGs), indicators that reflect local priorities, such as SDG 6 (Clean Water) for MENA or SDG 14 (Life Below Water) for Caribbean applications. Such adaptations would not only test the transferability of the EU-derived patterns but also contribute to a more nuanced understanding of how circular economy principles operate across diverse geographic, economic, and institutional contexts. Finally, expanding the analytical framework to include social sustainability metrics would provide a more comprehensive understanding of circular economy impacts on well-being and equity across diverse populations, particularly important for regions with significant socioeconomic disparities and different labor market structures compared to European economies.