University of National and World Economy

Sustainable finance has become a central issue across many sectors, including higher education, where universities must balance fiscal responsibility with environmental, social and governance (ESG) imperatives. In this paper, sustainable finance is defined as the systematic integration of ESG criteria into budgeting, investment, procurement and asset management decisions within academic institutions. At the same time, students increasingly demand that universities translate sustainability pledges into ethical and transparent financial practices. The aim of the study is to assess students’ familiarity with sustainable finance concepts and to identify the main educational barriers limiting competence development in this field. A quantitative Computer-Assisted Web Interview (CAWI) survey was conducted among students in Austria, Bulgaria, Poland, Slovenia and the Netherlands. Findings reveal strong student interest in ESG and sustainable finance topics, yet significant shortcomings remain. Respondents observed that ESG content is rarely integrated into curricula, that opportunities for interdisciplinary and practical learning are limited, and that qualified teaching staff are insufficient. These gaps reduce student engagement and represent a missed opportunity for universities to use education as a driver of sustainability. The paper concludes with recommendations to enrich curricula, foster experiential and interdisciplinary learning, and strengthen faculty expertise in order to better align higher education practices with the principles of sustainable finance.

Sustainable manufacturing systems require intelligent methods to balance economic performance with environmental responsibility. This research presents a digital twin-fuzzy multi-objective optimization framework for simultaneously managing cost, energy consumption, and waste in sustainable manufacturing. In this framework, fuzzy logic is used to model data uncertainty, a digital twin is used to obtain real-time data from the manufacturing process, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to generate a Pareto front and analyze the relationships between economic and environmental objectives. The proposed model was tested in 10 simulated scenarios based on digital twin data. The results showed that the proposed framework maintained the service level above 95%, reduced the total cost by 14% and the amount of waste by 18% compared to the baseline. Pareto front analysis also showed that although there is a relative conflict between economic and environmental objectives, this conflict is controllable. Also, sensitivity analysis revealed that energy ceiling and machinery efficiency have the greatest impact on the sustainability and profitability of the system. Overall, the proposed framework provides a reliable, quantitative decision-making tool for managers and policymakers on the path to green and sustainable production.

Background: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. Methods: In this study, an integrated decision support framework is presented that combines multi-objective fuzzy modeling and an adaptive digital twin to simultaneously manage logistics costs, product quality degradation, and service time compliance under operational uncertainty. Key uncertain parameters are modeled using triangular fuzzy numbers, and the digital twin dynamically updates the decision parameters based on operational information. The proposed framework is evaluated using real industrial data and comprehensive computational experiments. Results: The results show that the proposed approach is able to produce stable and balanced solutions, provides near-optimal performance in benchmark cases, and is highly robust to demand fluctuations and temperature deviations. Digital twin activation significantly improves the convergence behavior and stability of the solutions. Conclusions: The proposed framework provides a reliable and practical tool for adaptive planning of cold chain distribution in Agri-Biotech industries and effectively reduces the gap between advanced optimization models and real-world operational requirements.

Contemporary European societies face overlapping societal challenges—ecological degradation, immigration pressures, and widening economic inequality—which generate a pervasive climate of uncertainty affecting citizens’ perceptions of their own life conditions. This study investigates how social pessimism, conceptualised as a multidimensional orientation reflecting perceived threats across environmental, migratory, and distributive domains, relates to subjective financial insecurity at the individual level. Drawing on harmonised cross-national data from the CRONOS-II panel (N = 8993), covering eleven European countries, we construct a composite pessimism index and analyse its association with perceived financial strain using multivariate and multilevel regression models. Results demonstrate that individuals who express greater societal pessimism report significantly higher levels of financial insecurity, even after controlling for income, education, employment status, and country-level heterogeneity. This relationship is moderated by socioeconomic position; specifically, the pessimism–insecurity link is strongest among lower-income and less-educated groups, suggesting that material precarity and anticipatory anxiety compound one another. Cross-national analysis reveals substantial variation in effect magnitude, with the strongest associations observed in Hungary, Portugal, and the Czech Republic, and the weakest in Slovenia and Iceland. These findings contribute to the interdisciplinary understanding of how macro-level societal concerns permeate individual wellbeing, demonstrating that subjective economic vulnerability is shaped not only by objective circumstances but also by the broader socio-political climate in which citizens interpret their life situations. The results underscore the need for policies that address both material conditions and the affective dimensions of societal uncertainty in order to strengthen social cohesion and reduce perceived economic risk. Theoretically, we frame social pessimism as a formative composite capturing perceived threat to societal stability, offering an integrative perspective on how structurally distinct societal concerns converge to shape economic subjectivities.

This article examines labor market dynamics in Bulgaria, Italy, and the United Kingdom by integrating demographic pressures, wage and labor cost adjustment, redistribution mechanisms, inequality outcomes, and digital readiness into a single comparative framework. This study first applies hierarchical clustering to a harmonized EU country panel for 2017–2024, using GDP per capita in PPS, average annual wage, and unemployment rate to position the three countries within the European convergence space and income–labor cost groupings. The results show that Bulgaria belongs to a low-income, fast-converging group, with nominal wages and hourly labor costs more than doubling, strong real-wage growth from a low base, and an improving price level index. At the same time, unemployment fell to below the EU average, yet income inequality remains persistently high. Italy represents a high-income but slow-growing labor market, in which real wages have declined, and labor costs per hour remain above the EU mean with a significant non-wage component. Unemployment remains relatively elevated, indicating divergence in workers’ purchasing power despite high income levels. The UK has labor costs in the mature high-income range, low unemployment, and the lowest tax wedge for low-wage workers, but with relatively high and volatile inequality. This study shows that wage dynamics, labor cost composition, and tax–benefit structures jointly mediate the translation of macroeconomic performance into household outcomes, generating distinct policy trade-offs across the three labor market configurations. Digital indicators further suggest that income level is not a sufficient predictor of digital engagement and that the observed aggregate labor market trends do not indicate a sharp employment contraction contemporaneous with the diffusion of technical innovations, such as generative AI.