Valentin Marian Antohi

This study examines the structural dimensions associated with research and development investment across European Union countries through an integrated econometric framework encompassing economic performance, efficiency dynamics, environmental...

The transition toward a circular economy requires analytical approaches capable of capturing the interdependence between resource consumption, material circularity, and economic efficiency rather than assessing these processes through isolated indicators. This study develops and empirically evaluates the Consumption–Circularity–Efficiency (CCE) framework as an integrated representation of circular‐economy performance across the EU‐27. A balanced panel of 378 country–year observations covering 27 Member States over 2010–2023 was constructed from 10 harmonized Eurostat indicators. Principal component analysis (PCA) was used to identify the dominant multivariate structure, followed by robustness and alternative‐retention analyses, construction of dimension scores and a composite CCE index, country‐level and temporal assessment, and complementary observed‐variable structural equation modelling (SEM) with country‐clustered robust inference. Three retained PCA components explain 71.44% of total variance and correspond to Consumption, Circularity, and Efficiency, although substantive cross‐loadings show that these dimensions are interconnected rather than empirically isolated. The variance‐weighted CCE index proved highly robust to equal weighting ( r = 0.983, p < 0.001), indicating that comparative positioning is not driven by the aggregation rule. Country‐level results reveal pronounced structural heterogeneity: Similar composite CCE scores arise from markedly different combinations of consumption intensity, circular‐resource performance, and efficiency. Temporal analysis indicates an overall improvement in relative EU‐27 CCE performance between 2010 and 2023, accompanied by non‐linear evolution and persistent divergence in national transition pathways. SEM provides complementary structural corroboration by identifying RMC, resource productivity, and GDP per capita as interconnected interfaces between material throughput, circularity, and economic performance; several relationships operate through significant indirect pathways rather than direct associations alone. Global SEM fit is mixed, supporting structural compatibility rather than complete confirmatory fit or causal interpretation. The study contributes to circular‐economy research by integrating dimensional identification, composite measurement, structural configurations, temporal dynamics, and pathway analysis within a single empirical architecture. The CCE framework therefore provides a configuration‐based diagnostic approach that can support differentiated circular‐economy strategies while preserving the structural information concealed by conventional aggregate rankings.

Introduction: Energy and resource sustainability in the European Union cannot be represented adequately by a single ranking or by isolated relationships between individual indicators. Member States combine different levels of energy use, innovation effort, material throughput, emissions, resource productivity, and waste generation. Such combinations generate structural regimes that can persist even while overall cross-country dispersion declines. This study examines those regimes and their temporal evolution across the EU-27 during 2010–2023. Methods: A balanced panel of 378 Member State-year observations was assembled from Eurostat using six indicators: primary energy consumption, gross domestic expenditure on research and development, raw material consumption, domestic net greenhouse gas emissions, resource productivity, and municipal waste generation. All clustering variables were z -standardized before K-Means estimation. Candidate solutions from k = 2 to k = 10 were evaluated using within-cluster sum of squares, silhouette coefficient, Calinski-Harabasz index, Davies-Bouldin index, Gap Statistic, cluster-size structure, and resampling stability. Robustness was examined through an unstandardized specification, Yeo-Johnson transformation, 1st−99th percentile winsorization, and RobustScaler preprocessing. Country-year mobility, sigma-convergence, and beta-convergence were then analyzed separately from the clustering step. Results: Joint validation supported a four-cluster primary specification rather than the previously imposed three-cluster structure. Cluster 1 represents a lower-intensity resource and innovation profile and contains 59.26% of observations. Cluster 2 combines stronger innovation and resource productivity with higher waste and emissions. Cluster 3 combines very high R&D and material consumption with comparatively low net greenhouse gas emissions. Cluster 4 captures a persistent high-intensity energy, emissions, and resource profile. Standardized and unstandardized partitions showed very low agreement (ARI = 0.135), confirming strong scale distortion in the raw metric space. Winsorization reproduced the baseline partition almost exactly (ARI = 0.981). Only 3 of 351 adjacent-year transitions changed cluster, producing 99.15% persistence. Multivariate RMS distance to the annual EU centroid declined by 14.45% and the beta-convergence coefficient was −0.0358 ( p < 0.001). Resource productivity nevertheless displayed significant divergence, while R&D dispersion showed no significant linear convergence. Discussion: EU energy-resource sustainability is therefore characterized by persistent structural regimes combined with measurable convergence inside the system. Convergence does not occur mainly through widespread movement between clusters. It occurs through declining multivariate distances while most countries remain within stable structural profiles. Results support differentiated policy design, because innovation capacity, material intensity, resource productivity, energy use, and environmental pressure do not improve synchronously. Methodological findings also show that scale correction, explicit k-selection, resampling stability, and panel-aware temporal diagnostics are necessary for defensible sustainability clustering.

Digitalization is reshaping labour markets across the European Union, but its employment implications vary considerably among regions with different economic, technological, innovative and socio‐economic characteristics. This study examines the relationship between digitalization and employment using a balanced panel of 242 EU NUTS2 regions observed between 2011 and 2023, comprising 3146 region‐year observations. The analysis considers regular internet use, employment in high‐technology sectors, R&D expenditure, regional GDP and the at‐risk‐of‐poverty rate. The empirical strategy combines a two‐way fixed‐effects model with NUTS2‐clustered standard errors, a fixed‐effects specification with Driscoll–Kraay standard errors and a two‐step System GMM estimator. The results show that regular internet use is consistently associated with regional employment across all specifications, although its economic magnitude is limited. Regional employment exhibits substantial temporal persistence, whereas the estimated contributions of socio‐economic vulnerability, high‐technology specialization and R&D expenditure vary between the static and dynamic models. These findings indicate that digital adoption alone is insufficient to generate uniform labour‐market improvements, and that regional structural conditions shape the capacity to benefit from technological change. The study contributes to the literature by replacing national averages with harmonized NUTS2 evidence and by distinguishing contemporaneous regional associations from dynamic labour‐market adjustment. From a policy perspective, the results support a differentiated approach that combines selective concentration of frontier technological investment with structured diffusion, workforce development and place‐based capability‐building in less‐developed regions.

Transition towards a circular economy has become a strategic priority for the European Union, although Member States continue to exhibit substantial differences in economic performance, resource efficiency and circular economy implementation. Understanding these structural differences is essential for designing effective policy interventions. This study identifies latent dimensions of circular economy performance and develops a statistically validated structural typology of circular economy transition pathways across the EU‐27. The analysis uses a balanced panel of 378 country‐year observations covering 27 European Union Member States during 2010–2023, based on six harmonised Eurostat indicators. Following logarithmic transformation and Z ‐score standardisation, the analytical framework combines Principal Component Analysis, Horn's Parallel Analysis, K‐means++ clustering, cluster validation criteria (Elbow, Silhouette, Calinski–Harabasz and Davies–Bouldin indices), post hoc statistical tests, and temporal robustness analysis. Two latent dimensions explain 79.53% of total variance, representing economic and resource‐energy performance and circular economy transition intensity. These dimensions identify four statistically distinct country groups, revealing persistent structural differences in the way Member States combine productive efficiency with circular economy implementation. Robustness analyses confirm the stability of both the latent structure and resulting typology across alternative validation procedures and sub‐period estimations. Findings demonstrate that circular economy development follows multiple structural pathways rather than a single convergence pattern. The proposed framework provides an evidence‐based foundation for designing differentiated circular economy policies tailored to distinct groups of Member States while offering a robust approach for analysing structural heterogeneity across the European Union.

The green and digital transitions are essential priorities in the current context of the European Union’s commitment to achieve climate neutrality by 2050 and multiple financial, pandemic, energy crises and international conflicts. The study assesses the relationships between economic, social and environmental variables, using data from 2010–2022 and applying econometric models to analyze the relationships between net greenhouse gas emissions, real Gross Domestic Product (GDP) per capita and factors such as access to digital technology, share of renewable energy, green tax revenues, municipal waste generation and long-term unemployment rate. The results underline the persistent disparities between Member States and the vulnerability of the European Union to external shocks. The novelty of this paper lies in its integrated approach to the relationships between economic, social and environmental variables in the context of the EU’s green and digital transition, using econometric models to analyze the influences between greenhouse gas emissions, real GDP per capita and other determinants. The research contributes to the literature by highlighting essential directions for formulating sustainable public policies capable of meeting both the environmental objectives and the economic needs of the European Union in an increasingly uncertain global landscape. First published online 8 June 2026

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As Europe advances toward the vision of Industry 5.0 (where technological progress is expected to reinforce sustainability, resilience, and human-centric development) understanding the uneven geography of digital transformation becomes increasingly critical. This study proposes a novel analytical framework to examine regional disparities in digitalisation across EU NUTS-2 (European Union Nomenclature of Territorial Units for Statistics) regions between 2012 and 2023. We derive a data-driven representation of digitalisation by combining an autoencoder architecture for latent-feature extraction with deep embedded clustering to uncover structural patterns of technological readiness. To enhance transparency, the latent dimensions are interpreted ex post using explainable-AI techniques (SHAP—SHapley Additive exPlanations), which show that human-capital endowment, R&D (research and development) intensity, high-technology employment, and tertiary education show the strongest overall impact on regional digital advancement, as confirmed by the SHAP importance results (Fig. 19) and structural equation modelling (SEM) loading patterns (Table 6). The resulting clusters reveal a persistent tripartite stratification, suggesting that digitalisation has not yet facilitated broad convergence; instead, it reinforces existing divides by favouring regions with strong knowledge and innovation infrastructures. These findings highlight the need for targeted public policies centred on skills formation, research capacity, and inclusive infrastructure in order to ensure a just and cohesive transition toward a knowledge-based economy. Beyond its empirical contribution, the study provides a replicable methodological pathway for monitoring digital progress and supporting place-based policy design