Search Results (1,149)

This study explicitly assesses how crop and livestock production, along with real labor productivity, affect greenhouse gas emissions in agriculture across the European Union (EU), considering both per capita and total emissions. Using annual Eurostat data for EU Member States from 2008 to 2024, the research applies multiple regression models and a multivariate General Linear Model (GLM) to evaluate structural relationships, complemented by Holt exponential smoothing and ARIMA models to analyze temporal dynamics and generate forecasts. The empirical results indicate that crop and livestock production have a statistically significant positive effect on emissions, while real labor productivity has a significant negative impact. The models explain over 92% of the variation in total emissions and over 95% of the variation in per capita emissions, confirming strong explanatory power. Forecasts show continued growth in agricultural output but a declining trend in per capita emissions, primarily driven by productivity improvements. These findings demonstrate that improvements in labor efficiency and technological progress can partially offset the environmental pressures associated with increased agricultural production. The study concludes that achieving climate-neutral agriculture in the EU is feasible through sustained productivity gains and innovation-driven transformation. Full article

The transformation of healthcare systems presents a key challenge for EU Member States, as they face growing population needs, resource pressures, and ongoing health disparities among communities. Using a systems-thinking approach provides a suitable framework for analyzing how healthcare resources, behavioral and social health determinants, and overall population outcomes are interconnected. This study aims to explore these relationships from a systemic viewpoint within health communities across the European Union. The research employs a cross-sectional approach, utilizing aggregated data from all 27 EU Member States. It involves descriptive and factor analyses to create composite indices of healthcare resources, positive health determinants, and health outcomes. Furthermore, it uses a univariate Generalized Linear Model (GLM) and multilayer perceptron neural networks to model nonlinear relationships among variables. Cluster analysis is also conducted to classify Member States into different health performance typologies. The results emphasize connections between healthcare system resources and population health outcomes, illustrating how positive determinants impact health status and highlighting structural differences across European communities. The results indicate relevant associations between healthcare system resources and population health outcomes, whereas the analysis of positive health determinants suggests more complex, partly nonlinear patterns. The findings also highlight structural differences across European health communities. Full article

The transition toward a circular economy has made industrial symbiosis an important approach for improving resource efficiency and reducing environmental impact, especially for small- and medium-sized enterprises (SMEs). However, the extent to which SMEs can adopt these practices differs across countries. This study aims to explore the readiness of SMEs for industrial symbiosis in Türkiye, Jordan, Portugal, and Croatia, and to propose a digital model that can support this transition. The research is based on a qualitative, literature-driven comparative analysis examining institutional structures, technological capacity, sectoral characteristics, and collaboration networks in each country. The findings indicate that, despite contextual differences, all four countries face similar challenges, such as limited data sharing, insufficient digital infrastructure, and weak inter-firm cooperation. While EU member states demonstrate more developed policy frameworks, implementation gaps remain evident across cases. Building on these insights, the study introduces the Digital Recycling and Material Network (DREAM) model, a digital platform that connects waste-generating firms, recycling companies, and businesses that use secondary raw materials. The model enables real-time data sharing and supports sustainability-oriented matching mechanisms. Overall, the study suggests that digital platforms like DREAM can play a key role in strengthening industrial symbiosis practices and supporting SMEs in their transition toward circular production systems. Full article

Quantitative sustainability assessments in the EU rarely differentiate between the roles of governments, businesses, and the population, making it difficult to empirically test theories of socio-technical transitions, stakeholder governance, and convergence/club convergence. To address this gap, the study constructs four stakeholder-specific indices: the Government Sustainability Index (GSI), Environmental Sustainability Index (ESI), Population Sustainability Index (PSI), and Business Sustainability Index (BSI) alongside a Composite Sustainability Index (CSI). The indices are built from harmonised Eurostat, European Environment Agency, and SDG Index data using min–max normalisation, covering all 27 EU Member States over the period of 2015–2024 (270 country–year observations). The empirical analysis applies K-means clustering, compound annual growth rates (CAGRs), and correlation analysis, complemented by a robustness module testing alternative weighting schemes, z-score normalisation, and ±10% indicator perturbations. The results identify four relatively stable sustainability tiers with limited inter-tier mobility, an S-curve-type relationship between initial performance levels and subsequent growth, a consistent hierarchy of stakeholder response speeds (ESI > GSI > PSI), and a structural slowdown after 2019. These patterns are robust across alternative specifications and imply that EU sustainability transitions follow multiple, tier-structured trajectories shaped by institutional lock-in rather than converging toward a single equilibrium. The framework offers a basis for tier-differentiated and stakeholder-sensitive policy strategies. Full article

Although Smart City transitions are typically assessed using technological and financial indicators, the underlying structural correlates remain insufficiently explored. This study examines how different forms of capital within the Quintuple Helix model—natural, social, intellectual, economic and institutional (governance)—are associated with a country’s position in the Global Smart Cities Index and the Eco-Innovation Index. The methodology is based on data from 26 EU Member States. Correlation analysis was used to identify key factors of city performance, cluster analysis was applied to categorize countries/capitals based on their capital profiles and the impact of Smart Cities and eco-innovation. This study identifies three distinct clusters of EU countries/capitals, ranging from leaders to economies in transition. The results show that intellectual capital and institutional governance are the most significant correlates of Smart City success. In addition, governance emerged as a primary association of eco-innovation. These results provide a roadmap for lagging regions to optimize their Quintuple Helix synergies to achieve higher smart city rankings and environmental sustainability. Full article

Organic farming has gained increasing relevance worldwide due to its environmental benefits and its prominent role in sustainable food systems; however, the persistence of organic farms remains uneven across regions, particularly within the European Union. While the number of organic farms has grown overall in the EU, significant exits from organic production highlight the need to better understand the factors shaping farm survival, especially in newer Member States, where organic conversion and maintenance support schemes are often implemented through area-based CAP payments. This study aims to identify the structural and contextual determinants of short-term organic farm persistence in Hungary within a broader European context. Using farm-level data for the period 2020–2023, including Standard Output (SO) indicators, we applied a combined modelling framework based on Logistic Regression, Decision Trees, and Random Forest algorithms to assess the relative importance of economic, structural, and regional variables. The results show that organic farm persistence is primarily driven by structural characteristics such as farm size, economic scale, degree of conversion to organic farming and regional embeddedness, while production specialization and organizational features play a secondary, conditional role. The convergence of results across modelling approaches indicates that survival is shaped by hierarchical structural constraints rather than isolated management decisions. Our findings suggest that policy measures aiming to stabilize and expand the organic sector should move beyond uniform incentives, such as area-based payments, and should place greater emphasis on the structural conditions of farms and region-specific support mechanisms. Full article

The transformation of transport is necessary not only for climate change mitigation, but also for increasing competitiveness, developing modern technologies in transport, and improving the well-being and quality of life of the population. This article discusses the current state of the transformation of transport and infrastructure to low/zero emission within EU member states and, in particular, their smart cities. This article discusses the challenges, modern technologies, disadvantaged groups and overall concept of transformation with the aim of designing the most effective strategy for transport transformation in the EU, potentially at the smart cities level. The potential relationship between the position of EU member states in the Climate Change Performance Index (CCPI) ranking and different environmental and non-environmental indicators in the EU is identified and analyzed. Regression and ordered logit models are calculated. The results show that only minimum indicators are not correlated, and greenhouse gas emission (GHG), urbanization rate in the EU member state and the ratio of private car ownership to public transport usage have a significant impact on the potential transformation of transportation and a country’s ranking in the CCPI. The odds ratio for urbanization rate is 3.18 (+1 means better ranking 24 times greater) and 4.68 for the ratio of private car ownership to public transport usage (+1 means better ranking 108 times greater). The discussion of the article defines research trends aimed at improving the level of transport transformation and challenges related to successful transformation. Full article

The transition from cash to digital payment instruments is reshaping retail commerce across Europe unevenly, with Central and Eastern European (CEE) countries exhibiting both some of the fastest growth and some of the lowest baseline levels in online shopping participation. This study examines whether the development of digital payment infrastructure proxied by the share of individuals using internet banking (NetBank) is associated with e-commerce adoption across eleven CEE EU member states over the period 2014–2023, yielding a balanced panel of 110 country-year observations. Drawing on harmonised data from Eurostat, the World Bank, and the ITU, we estimate a two-way fixed-effects model with kernel-robust standard errors and a dynamic specification with a lagged dependent variable. The results indicate that a one-standard-deviation improvement in internet banking penetration is associated with a 6.2 percentage point increase in the share of online shoppers once country and year fixed effects are controlled for, a finding that is precisely estimated under kernel standard errors (p < 0.001). Income-group heterogeneity analysis suggests that this association may be substantially larger in lower-income CEE countries (β = 6.9, p = 0.006) compared to higher-income ones (β = 2.3, p = 0.554), consistent with the hypothesis that payment infrastructure improvements generate the highest marginal returns where baseline access is lowest. Romania, despite recording the steepest absolute growth in online shopping in the EU over the sample period (+33 percentage points), remains persistently below the CEE median, illustrating how payment infrastructure constraints can slow convergence even during periods of rapid digitisation. The findings should be interpreted as robust conditional associations rather than causal effects, given the limitations of macro-panel identification. Full article

This study investigates the convergence of energy intensity in the member states of the European Union (EU) between 1993 and 2021. To assess convergence, sigma and stochastic (beta) convergence analyses are performed using conventional, Fourier transform, and quantile-based unit root tests. Unlike previous studies, this work combines Fourier transform and quantile approaches to capture both gradual structural breaks and distributional differences between EU countries. The results show that convergence was strong between 1993 and 2009 but weakened after 2009, indicating increasing structural divergence between member states. The results also show that the convergence behavior is not uniform, suggesting that linear methods alone provide an incomplete picture. These findings imply that a single EU policy may not be sufficient and that a sustainable energy transition requires coordinated yet flexible strategies that take national differences into account. All empirical analyses were performed using R (version 4.3.2) and Stata (version 18). Full article

In recent years, several states have introduced restrictive measures regarding children’s use of digital devices in schools, shifting policy focus from digital literacy development towards prohibition and regulation. This study employs a comparative policy analysis to examine the regulation of ICT device use across EU member states, followed by a Hungarian case study focusing on a ministerial decree that restricted students’ access to digital devices. The social and educational implications are explored through an empirical survey-based study conducted among parents of children aged 6–18. The findings indicate that the regulation’s legitimacy is based on a general normative conviction rather than direct experience. The study reveals that a top-down policy, lacking broad social consensus and student participation, tends to function as a lex imperfecta (imperfect law) in practice, which in turn fosters a “hidden curriculum” of rule circumvention among students. We argue that such policies, by undermining the perceived legitimacy of rules, may unintentionally damage students’ long-term legal socialization and respect for norms. This suggests that effective regulation requires participatory approaches to build legitimacy, rather than relying solely on prohibition. Full article

Variations in government effectiveness across EU Member States are reflected in significant differences in socio-economic, fiscal and migration outcomes, which are rarely analysed within a single integrated framework. The analysis combines a structured bibliometric review with an empirical investigation based on EU-27 panel data covering the period 2015–2024, using co-occurrence and co-citation networks generated with VOSviewer to anchor the analytical framework in the literature. Government effectiveness, as measured by the Worldwide Governance Indicators, is analysed in relation to the risk of poverty and social exclusion (AROPE), GDP per capita, employment, social expenditure and migration dynamics. The results show that higher levels of institutional effectiveness are consistently associated with reduced social vulnerability, improved labour market performance and higher income levels in Member States. In contrast, the relationship with migration appears weaker and less robust across different econometric specifications. The interaction between government effectiveness and social spending also suggests that higher institutional quality can enhance the effectiveness of social policies in reducing vulnerability. Overall, the findings highlight the role of institutional quality as a conditioning factor of socio-economic outcomes, while indicating that the estimated relationships should be interpreted as conditional associations rather than strict causal effects. Full article

This study investigated the performance and dynamics of progress for renewable energy resources (RES) using data from the International Renewable Energy Agency (IRENA). The main objective was to recognize and interpret patterns of dynamic clustering among countries based on the temporal evolution of total installed renewable energy (RE) capacity. The study covers 27 European Union (EU) states and 15 RCEP countries (including China, Japan, South Korea, Australia, and ASEAN members) over the period from 2015 to 2024. Unlike static cross-sectional classification, the proposed clustering approach captures longitudinal trajectories of total installed RES capacity. Moreover, 2020 was considered a structural reference point (the energy shock, COVID-19, and the intensification of climate policies), marking a clear acceleration in renewable energy expansion. This scale allows differentiation between energy systems that experienced a post-2020 acceleration in transformation and those that either maintained earlier growth trends or experienced deceleration. A hierarchical clustering using Ward’s scheme was applied, which identified three distinct dynamic clusters: (1) moderate growth with transformation constraints; (2) sustained expansion and accelerated structural development; and (3) stagnation reflecting minimal structural change. To further segregate states within clusters, the Compound Annual Growth Rate (CAGR) has been employed, presenting a dual classification: (1) installed capacity levels, and (2) reflecting the momentum of renewable energy growth. An Acceleration indicator has been constructed to capture shifts in development dynamics leading up to 2020 and beyond. By measuring changes in RES capacity growth from 2015 to 2019, and from 2020 to 2024, this index allows us to determine whether the RES transition accelerated after the energy shock and the ramp-up of climate policies. According to the findings, the patterns of RE development are largely defined by investment capacity, economic scale, and institutional maturity, with continental or regional belonging playing a secondary role. Full article

Indirect treatment comparisons (ITCs) are essential in the context of joint clinical assessments (JCAs) under Regulation (European Union [EU]) 2021/2282, bridging evidence gaps where head-to-head data are lacking and enabling assessment across diverse national patient, intervention, comparator, and outcome (PICO) requirements. This paper critically reviews the EU Health Technology Assessment Coordination Group’s (HTACG) guidelines on direct and indirect comparisons, with particular focus on ITCs. While the guidelines promote transparency and rigorous evaluation of assumptions, they adopt a restrictive stance on assumption violations, the use of unanchored comparisons, and population-adjusted methods such as matching-adjusted indirect comparisons (MAIC) and simulated treatment comparisons (STC). The guidance shows limited support for Bayesian methods and undervalues meta-regression in favor of subgroup analyses. Operational implications for health technology developers (HTDs) are substantial, including new requirements for dual systematic reviews, multiple network structures, and shifted null hypothesis testing. Moreover, the guidelines effectively dissuade the use of non-randomized comparisons in rare or rapidly evolving indications and may inadvertently hinder access to effective treatments. Emerging practices such as external control arms (ECA) or target trial emulation are underdeveloped. Notably, there is no indication that the guidelines are grounded in systematic methodological validation studies. As JCAs evolve, greater methodological flexibility, empirical grounding, and clear operational guidance will be essential. Refining the guidelines along these principles would enhance their practical utility, mitigate intrinsic assessment variability, support consistent assessments across Member States (MS), and ultimately improve patient access to innovative therapies. Full article

The revised EU Urban Wastewater Treatment Directive (UWWTD, EU 2024/3019) expands the scope of the previous directive (Council Directive 91/271/EEC, 1991) by explicitly including combined sewer systems, stormwater discharges, and overflow events while promoting energy neutrality and reducing greenhouse gas (GHG) emissions across urban wastewater systems. Although the Directive establishes energy accountability at the system level, it does not define how energy performance in wastewater collection networks should be structured, assessed, or benchmarked, resulting in a significant implementation gap. This paper presents a novel, regulatory-aligned, data-driven framework to organise, analyse, and interpret energy-relevant information in support of UWWTD requirements, with specific focus on wastewater collection networks. Using Portuguese regulator datasets, supplemented with published sources, existing metrics are reorganised into energy-significant dimensions that differentiate structural, excess-driven, operational, and renewable-related components of energy use. The preliminary findings show that available datasets already support a screening-level diagnosis of specific energy intensity, pumping-related energy shares, inflow-driven excess volumes, and associated GHG emissions. However, important gaps remain regarding subsystem disaggregation, hydraulic normalisation, and measurement granularity. The study restructures existing information into a novel audit-compatible framework, proposes additional metrics and measurement requirements, and identifies measures to facilitate UWWTD implementation. Although developed for the Portuguese context, the framework offers a scalable pathway for integrating wastewater collection networks into energy neutrality governance across European Member States. Full article

This research paper examines the spatial and time variation of demographic dependency in Europe in a 30-year horizon of the evolution of the demographic dividend regarding the economic dependency ratio (ADR1). We used the Curve Fit Forecast tool to estimate the trends of ADR1 in each of the EU Member States using data on Eurostat projections and a sophisticated geostatistical analysis tool developed in ArcGIS Pro 3.2.2. The findings indicate that the dependency in all countries has increased significantly in a statistically significant manner as the Gompertz function has appeared as the best curve in a third of the cases. It is an S-shaped asymptotic behaviour of this function that effectively describes the nonlinear patterns of acceleration and saturation of demographic ageing. As indicated in the analysis, the European regions are increasingly moving apart, with the southern and eastern nations such as Romania demonstrating the most alarming decline in ADR1. These trends highlight the need to reform labour market policies and social protection mechanisms to an ageing population. The paper combines the curve-fitting, descriptive statistics (median, skewness, interquartile range (IQR)) with time clustering (value, correlation, and Fourier) to provide an effective, replicable approach to early warning and policy prioritisation. Overall, the results highlight the importance of integrating predictive spatial modelling and demographic economics to support anticipatory and evidence-based policy decisions. The proposed approach proves to be a robust and transferable framework, applicable to a wide range of socio-economic phenomena characterised by inertia and structural change. Future research should extend the analysis to subnational levels, incorporate additional explanatory variables, and develop scenario-based simulations, including multivariate Gompertz-type models, to further enhance both predictive accuracy and policy relevance in the context of emerging structural labour scarcity. Full article