This study examined the mediating role of policy in the relationship between improvement in construction waste management and effectiveness of construction and demolition waste (C&DW) management in Malang City, Indonesia. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) with data from 48 construction companies, this research investigated how policy frameworks translated improvement initiatives into sustainable waste management outcomes. Improvement in construction waste management was measured by four phases (planning, design, construction, and operation), policy through four instruments (regulation, incentive, reward-sanction, and standard), and C&DW management through triple bottom line dimensions (economic, social, and environmental). Results revealed that improvement in construction waste management significantly influenced policy formulation (β = 0.761, p < .001, f² = 1.374), and that policy substantially affected the effectiveness of C&DW management (β = 0.692, p < .001, f² = 0.950). However, the direct effect of improvement in C&DW management was not significant (β = 0.240, p = .249), indicating full mediation through policy (β = 0.526, p < .001, υ = 0.077). The model explained 78.8% variance in C&DW management with strong predictive relevance (Q² = 0.711). These findings demonstrated that improvement efforts should be channeled through robust policy frameworks to achieve systemic waste management transformation, thus highlighting the critical role of integrated policy instruments in translating operational improvements into sustainable outcomes in developing urban contexts.

The aim of the submitted scientific article is to identify significant predictors of the sense of safety at the university and to compare their differences between the male and female populations of respondents. The empirical research was conducted in 2024 at Alexander Dubček University of Trenčín, with the participation of 358 respondents. Data were collected through an online questionnaire containing statements rated on a 5-point Likert scale, examining factors associated with informational influences, subjective concerns, and reflection on the Prague incident of December 2023. Descriptive statistics, correlation analysis, and multiple linear regression methods were used for analytical processing at a significance level of α = 0.05. The results showed that among women in this sample, the sense of safety was significantly associated with concerns about potential threats (X3) and the role of the incident at the Faculty of Arts of Charles University in Prague (X5). Among men in this sample, only factor X5 was confirmed as a key determinant, with its association being stronger than in women. Correlation analysis also indicated different patterns of perception—female respondents in this sample showed a higher sensitivity to subjective concerns, whereas male respondents in this sample appeared to respond more strongly to external security events. These findings confirm the importance of gender differences in shaping the sense of safety in the academic environment and highlight the need for targeted communication and security measures on the part of universities.

Despite increasing attention to inclusive education, sustainability transitions under the Industry 5.0 paradigm remain constrained by the limited integration of socio-cultural barriers affecting marginalised groups. This study examines how harmful cultural practices (HCPs) and socio-economic conditions are associated with education access among 467 ethnic minority youth engaged in agri-startups, contributing theoretically by linking social norms and human capital perspectives within sustainability transition frameworks. A mixed-methods approach combining regression analysis and the Analytic Hierarchy Process (AHP) is employed to capture both statistical relationships and priority-setting mechanisms. The regression results indicate that early marriage (-0.171), gender bias (-0.199), and restrictive religious norms (-0.127) are associated with lower education access, while household income shows a significant positive association (0.722); the model is statistically significant with acceptable explanatory power (R² = 0.315; Adjusted R² = 0.280). The AHP results, based on expert evaluation, demonstrate consistent judgments (CR = 8.120%) and identify personal factors (0.341), particularly HCP awareness, as the highest priority, followed by socio-economic conditions (0.310), education and skills (0.212) and cultural–community factors (0.141). These findings suggest that although economic capacity is a dominant enabling factor, individual agency and behavioural change are critical for reducing harmful practices and improving access to education. However, education access reflects both actual and perceived opportunities due to infrastructural constraints in remote areas. Although the study is limited to data from marginalised youth in agri-startups, the findings highlight that education functions as a key enabling condition linking cultural transformation and livelihood improvement, offering policy-relevant insights for designing inclusive, human-centred education systems to support future-oriented Industry 5.0 transitions.

This study aims to evaluate and rank regional agricultural technology competitiveness in East Java, Indonesia, using a structured multi-criteria decision-making approach. Specifically, it addresses four key objectives: (1) to apply the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method to assess and rank regional competitiveness across multiple technological dimensions; (2) to examine whether agricultural technology adoption levels differ significantly across regions using one-way Analysis of Variance (ANOVA); (3) to evaluate the sensitivity and robustness of the ranking results under alternative weighting scenarios through sensitivity analysis and rank correlation measures (Spearman’s ρ and Kendall’s τ); and (4) to derive policy-relevant and system-oriented implications for enhancing competitiveness and reducing regional disparities. The study employs a quantitative approach based on primary survey data collected from 210 farmers across seven regions in East Java. Four key dimensions are considered, namely environmental, irrigation, marketing, and production technologies. The PROMETHEE method is used to generate regional rankings, while ANOVA is applied to test for statistically significant differences in technology adoption. Robustness is further assessed through systematic weight variations and rank correlation analysis. The results reveal substantial regional disparities in relative technological competitiveness, with leading regions demonstrating more balanced, integrated adoption across multiple technological dimensions. ANOVA results confirm that differences in technology adoption across regions are statistically significant (p < 0.01), thereby providing complementary statistical evidence for inter-regional variation in the underlying technology adoption indicators used in the PROMETHEE analysis. The robustness analysis shows that the ranking results are highly stable across most weighting scenarios, with only minor variations observed when marketing-related criteria are emphasized. This study contributes methodologically by integrating multi-criteria decision-making with statistical validation and robustness testing in a unified framework. From a policy perspective, the findings highlight the importance of strengthening market access, improving technological integration, and implementing region-specific interventions to enhance agricultural competitiveness and reduce disparities.

Coastlines host dense human activity that concentrates combustion and elevates carbon monoxide (CO) and nitrogen dioxide (NO₂) burdens. Yet complex coastal meteorology often limits ground monitoring. This study addresses this gap with a multi-year, dual-pollutant, jurisdiction-scale analysis using a transparent Sentinel-5P column-burden workflow. This work employs the workflow on Canada’s Nova Scotia (NS), a cool and relatively stable North Atlantic coast, and the US state of Louisiana (LA), a warm-humid Gulf coast with one of the densest refining hubs, providing two contrasting coastal domains. The present analysis evaluates 2019–2024 tropospheric column CO and NO₂, applies uniform quality-assured screening, generates time series composites at native resolution, classifies spatial fields with Jenks Natural Breaks, and examines temporal trends. Columns are compared with inventories and ground networks as consistency checks. Six-year means highlight persistent contrasts: NS’s column CO is slightly higher than LA’s (0.0338 vs. 0.0321 mol m⁻²), and NS’s NO₂ is ≈ 2.5× LA’s (6.09×10⁻⁵ vs. 2.39×10⁻⁵ mol m⁻²). In NS, NO₂ peaks in summer, while CO reaches its highest seasonal mean in spring; in LA, NO₂ peaks in winter and CO peaks in spring. Recurring hotspots appear over Halifax-Dartmouth and North Sydney, and along the Baton Rouge-New Orleans corridor and northern parishes. These patterns may reflect a combined influence of coastal setting, seasonal atmospheric structure, and local activity, although direct meteorological attribution was not performed. By integrating satellite archives with ground networks, the study provides a reproducible, auditable approach that translates seasonal column dynamics into jurisdiction-ready evidence for evaluation calendars and corridor-focused siting, improving the timing and targeting of coastal air-quality management, and supporting United Nations Sustainable Development Goals (SDGs) 3 and 11.

An Environmental, Social, and Governance (ESG) report is an essential information source for evaluating a company’s performance in sustainability practices. Organizations structure their environmental impacts, social responsibilities, and governance practices within a defined framework. This standardization is provided by the Global Reporting Initiative (GRI), which constitutes an internationally recognized guideline for sustainability reporting. Traditional reporting workflows are time-consuming for organizations and prone to data-entry errors, which limits the reliability of disclosed information. In this context, leveraging the capabilities of Large Language Models (LLMs) offers significant time and resource savings. This study uses the Llama-3.1-8B-Instruct model under two scenarios, Retrieval-Augmented Generation (RAG) and Low-Rank Adaptation (LoRA) fine-tuning, to analyze 30 food-sector ESG reports and produce ESG summaries, SWOT analyses, and GRI-aligned recommendations. The two approaches are evaluated on a stratified hold-out set of 6 unseen test reports (24 reports used for training) under a fair, matched-budget setup in which RAG retrieves the target report at inference. On four quality metrics, LoRA achieved higher mean scores than RAG; however, statistically significant differences were observed in only 4 of the 12 task–metric comparisons. Token usage was comparable, whereas RAG was substantially faster at inference. Rather than favoring one approach over the other, these findings reveal a trade-off between output quality and computational efficiency: LoRA yields quality gains on specific metrics, whereas RAG is substantially more efficient at inference. Given the limited size of the held-out test set, these results should be interpreted with caution.

University students, as a key youth consumer demographic, will play a vital role in shaping sustainable purchasing behavior in the future. This study aims to uncover the factors influencing students’ intention to minimize food waste at universities using the extended norm activation theory. An online survey of 664 students examined intentions to reduce food waste on campus. Of these, 245 students used online food delivery (OFD), while 419 engaged in in-canteen dining (IC). To evaluate the empirical data, this study utilized a partial least squares structural equation model and executed measurement invariance testing within the composite model. The empirical results demonstrate that the activation of personal norms is driven by awareness of consequence and the ascription of responsibility, which consequently has a direct impact on the intention to reduce food waste. Personal norms also indirectly influence the intention to minimize food waste. Students who purchased meals only reported weaker personal norms and lower intention to reduce food waste than those who ate in the canteen. However, the OFD group showed greater awareness of consequence, which supported their efforts to reduce food waste, compared with the IC group. Overall, this study provides further insight into the psychological mechanisms underlying sustainable food consumption among university students.