This bibliometric study investigates the scientific landscape of environmental sustainability within the European Green Deal (EGD) framework. Utilizing data from the Scopus and Science Direct databases, the study aims to map research trends and frontiers, providing a comprehensive overview of the evolving discourse on sustainability. The EGD’s ambitious goal to render the European Union climate-neutral by 2050 is highlighted, showcasing its significant impact across multiple sectors. The study reveals the integration of new methods and broadening research scopes by employing modern bibliometric techniques. Key thematic focuses include the transition to a circular economy and the emphasis on sustainable resource management, underpinning the EGD’s comprehensive approach to combining economic growth with environmental protection. The analysis unveils the critical role of technological innovation and policy reforms in advancing toward a sustainable, competitive, and climate-neutral economy. The research demonstrates the pivotal role of empirical studies in guiding policy formulation and implementation, showing how targeted measures in resource and energy productivity, combined with a decisive shift towards renewable energy, are integral to fostering a sustainable, competitive, and climate-neutral economy. This convergence of findings reinforces the argument that a multifaceted approach, encompassing policy, technology, and innovation, is essential for navigating the challenges of environmental sustainability and climate change, aligning closely with the overarching aims of the EGD.

Bibliometric analysis is a vital methodological tool for mapping scientific knowledge and identifying research trends. However, challenges in structuring, sorting, and interpreting bibliometric results, as well as a lack of standardized workflows, often hinder their practical application, especially for novice researchers. This study introduces a structured methodological framework that improves the design and interpretability of bibliometric analyses. We propose and discuss four key methodological pillars: (i) formulation of precise research questions to guide design and metrics; (ii) the application of artificial intelligence (AI)-based tools for efficient and unbiased article screening; (iii) the normalization of research output data using economic indicators, such as gross domestic product, to improve cross-country comparisons and (iv) the use of dual-tool analysis—specifically VOSviewer and Biblioshiny—to validate bibliometric findings. This integrated framework improves the transparency, clarity and robustness of bibliometric studies. AI-powered article screening leads to faster and more consistent inclusion decisions. Normalizing bibliometric data based on economic output enhances the interpretability of national research performance. Cross-referencing bibliometric models with two complementary tools allows for a more robust depiction of knowledge structures and emerging themes. In conclusion, the introduction of this methodological framework advances the practice of bibliometric analysis, contributing to a more reliable and informative bibliometric approach.

Total Quality Management (TQM) is a holistic approach widely adopted across industries to ensure quality control and management. This document examines TQM practices in the pharmaceutical, food, and nutritional supplement sectors, highlighting their vital role in public health, sustainability, and consumer acceptance. By analyzing the literature and case studies, the article demonstrates how TQM significantly ensures product safety and quality. Real-world examples and empirical evidence showcase the benefits of TQM methodologies, from rigorous quality control to efficient management processes, helping to meet and exceed regulatory standards. The article also underscores TQM’s critical role in addressing sustainability challenges, integrating eco-friendly practices, reducing waste, and optimizing resources. Furthermore, TQM fosters consumer trust and loyalty through transparency, continuous improvement, and responsiveness to feedback, building lasting business–customer relationships. In conclusion, this manuscript illuminates TQM’s multifaceted impact on the pharmaceutical, food, and nutritional supplement sectors, presenting it as a pivotal framework for safeguarding public health, promoting sustainability, and enhancing consumer acceptance in a dynamic global landscape.

This study presents a comprehensive Quantitative Microbial Risk Assessment (QMRA) for Escherichia coli in northeastern Greece’s riverine and deltaic aquatic systems, evaluating potential human health risks from recreational water exposure. The analysis integrates seasonal microbiological monitoring data—E. coli, total coliforms, enterococci, Salmonella spp., Clostridium perfringens (spores and vegetative forms), and physicochemical parameters (e.g., pH, temperature, BOD₅)—across multiple sites. A beta-Poisson dose–response model within a Monte Carlo simulation framework (10,000 iterations) was applied to five exposure scenarios, simulating varying ingestion volumes for different population groups. Median annual infection risks ranged from negligible to high, with several locations (e.g., Mandra River, Konsynthos South, and Delta Evros) surpassing the World Health Organization (WHO)’s benchmark of 10⁻⁴ infections per person per year. A Gradient Boosting Regressor (GBR) model was developed to enhance predictive capacity, demonstrating superior accuracy metrics. Permutation Importance analysis identified enterococci, total coliforms, BOD₅, temperature, pH, and seasons as critical predictors of E. coli concentrations. Additionally, sensitivity analysis highlighted the dominant role of ingestion volume and E. coli levels across all scenarios and sites. These findings support the integration of ML-based tools and probabilistic modelling in water quality risk governance, enabling proactive public health strategies in vulnerable or high-use recreational zones.

Aviation emissions significantly impact air quality, contributing to environmental degradation and public health risks. This study aims to assess the impact of aviation-related emissions on air quality at Alexandroupolis Regional Airport, Greece, and evaluate the role of meteorological factors in pollution dispersion. Using machine learning models, we analyzed emissions data, including CO₂, NOx, CO, HC, SOx, PM_2.5, fuel consumption, and meteorological parameters from 2019–2020. Results indicate that NOx and CO₂ emissions showed the highest correlation with air traffic volume and fuel consumption (R = 0.63 and 0.67, respectively). Bayesian Linear Regression and Linear Regression emerged as the most accurate models, achieving an R² value of 0.96 and 0.97, respectively, for predicting PM_2.5 concentrations. Meteorological factors had a moderate influence, with precipitation negatively correlated with PM_2.5 (−0.03), while temperature and wind speed showed limited effects on emissions. A significant decline in aviation emissions was observed in 2020, with CO₂ emissions decreasing by 28.1%, NOx by 26.5%, and PM_2.5 by 35.4% compared to 2019, reflecting the impact of COVID-19 travel restrictions. Carbon dioxide had the most extensive percentage distribution, accounting for 75.5% of total emissions, followed by fuels, which accounted for 24%, and the remaining pollutants, such as NOx, CO, HC, SOx, and PM_2.5, had more minor impacts. These findings highlight the need for optimized air quality management at regional airports, integrating machine learning for predictive monitoring and supporting policy interventions to mitigate aviation-related pollution.

The implementation of Hazard Analysis and Critical Control Points (HACCP) systems remains a cornerstone of food safety management. However, their effectiveness is influenced by organizational, human, and technological factors. This study investigates professional perceptions of HACCP implementation and explores the extent to which a large language model (LLM)—ChatGPT 4.1—can approximate human judgments in this domain. A structured questionnaire was administered to 90 professionals operating in food service, hospitality, industry, and consultancy. Responses were compared with outputs generated by ChatGPT 4.1 via a standardized multi-persona prompting protocol simulating five professional roles. To enhance response stability and minimize stochastic variation, each question was submitted in independent zero-shot sessions over multiple iterations. Responses were analysed across three thematic dimensions: barriers to HACCP implementation, perceived benefits, and digital readiness. Spearman correlation analysis of human responses revealed a systemic “training–turnover association,” where high staff turnover (r = 0.62, p < 0.01) was significantly associated with difficulties in maintaining continuous training. Statistical benchmarking using one-sample t-tests showed that the tested model generated significantly higher ratings for implementation barriers (p < 0.001) and digital readiness (p < 0.001) than the corresponding human assessments. These findings suggest that while ChatGPT can approximate aggregated professional perceptions in certain areas, notable divergences persist in operational and readiness-related domains. The study contributes methodological insights into human–AI comparative research and highlights opportunities and limitations of AI-supported decision-making in food safety management systems.