Cleaner production (CP) has evolved from a regulatory obligation into a strategic management approach that supports firms’ transition toward sustainable competitiveness. This study examines how cleaner production practices move beyond compliance-oriented environmental management to become strategic capabilities associated with stronger innovation orientation, operational efficiency, and corporate legitimacy. Drawing on the Natural Resource-Based View (NRBV) and the Porter Hypothesis, this research employs a multiple-case qualitative content analysis of six energy-intensive firms—Enerjisa, Tüpraş, Şişecam, Enel, E.ON, and TotalEnergies—for the 2020–2024 reporting period. The findings identify a three-stage evolutionary trajectory of cleaner production integration: compliance-driven, efficiency-driven, and strategy-driven. Firms that move toward strategic integration tend to exhibit stronger dynamic and organisational capabilities and clearer strategic positioning in sustainability-oriented decision-making. European multinationals demonstrate more holistic and mature integration due to stable policy frameworks and access to sustainable finance, whereas emerging-economy firms primarily leverage cleaner production for efficiency gains and regulatory compliance. The study contributes to theory by conceptualising cleaner production as a dynamic strategic capability rather than a technical or operational tool and by providing comparative qualitative evidence consistent with the innovation-competitiveness mechanisms proposed by the Porter Hypothesis across diverse institutional contexts. In practice, the findings offer actionable insights for managers and policymakers seeking to design regulatory, financial, and organisational enablers that accelerate the transition from compliance to strategy, thereby positioning cleaner production as a central pathway through which firms may build sustainable competitiveness. Given the qualitative and document-based design, the study does not claim causal proof but identifies patterned associations across cases and institutional contexts.
This study developed a sustainability-based governance model for Village-Owned Enterprises (VOEs) to support the acceleration of sustainable development at the village level. Weak governance structures, limited human resource capacity, and insufficient integration of sustainability values continue to constrain VOE effectiveness as drivers of local economic development. Having used the Analytic Hierarchy Process (AHP), this study evaluated five governance criteria: Human resource capacity and quality, transparency and accountability, collaboration and partnership, environmental commitment, as well as community participation and empowerment, across six sustainability-oriented VOEs. The results indicated that human resource capacity and quality constituted the highest priority (weight 0.3333), followed by transparency and accountability (0.2667) and cross-sector collaboration (0.2000). Although environmental commitment and community participation received lower priority weights, evidence from a qualitative study demonstrated that these dimensions played a critical role in strengthening socio-ecological resilience. Empirical cases from Ponggok and Kenteng VOEs showed that water conservation initiatives and waste management innovations were essential to sustaining long-term economic performance. Overall, the findings suggested that effective VOE governance extended beyond administrative functions toward a transformative model that integrated institutional capacity, social legitimacy, and environmental stewardship. This governance framework positions VOEs as socio-ecological actors contributing to the achievement of Sustainable Development Goals (SDGs), thus highlighting sustainability-oriented governance as a strategic prerequisite for resilient village development in the context of green transition.
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.
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.
Increased agricultural production could improve household income but often generates adverse environmental impacts, including soil degradation, rising temperatures, and drought, thereby contributing to climate change. This study aims to optimize income and carbon emissions in the trade of rice, corn, and cattle commodities in the Indonesia–Timor Leste border region and to assess the performance of integrated sustainable trade among farmers, traders, and processing industries. A Multi-Objective Linear Programming (MOLP) model and Partial Least Squares Structural Equation Modeling (PLS-SEM) were employed for analysis. The findings indicated that increased trade activities could improve economic outcomes while maintaining emissions within manageable limits. Farmer income is projected to increase by IDR 5.779 billion per production season, with improved cost efficiency at approximately IDR 64,000 per acre and maximum emissions of 356,561 tons CO₂e. Traders’ income is expected to increase by IDR 8.526 billion, with maximum emissions of 2,443.241 tons CO₂e and average transport costs of IDR 4,600 per kilometer. Carbon emissions at the farm level primarily stem from inefficient use of fertilizer and land burning, while emissions at the trader level are driven by transport capacity and travel distance. Although processing industries have established direct relationships with farmers, most farmers remain dependent on traders for market access. Strengthening the capacity of processing industries in the border region is therefore considered essential for maximizing farmers’ income.
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.
Effective management of freshwater resources in agriculture is essential for ensuring sustainable economic development and environmental resilience, particularly in transitional economies such as Azerbaijan. Over the period 2000–2021, agricultural land area in Azerbaijan exhibited a steady increase, while the sector’s contribution to GDP declined, indicating structural transformation and potential inefficiencies in resource utilization. This study investigates the nonlinear effects of agricultural land use and agricultural value added on freshwater withdrawals using an interpretable machine learning framework. Specifically, Extreme Gradient Boosting (XGBoost) is employed to model complex relationships, while Shapley Additive Explanations (SHAP) quantify feature importance and elucidate threshold and asymmetric effects. The analysis draws on annual country-level data integrating national and international statistics to ensure temporal consistency and comparability. Results indicate that agricultural land area constitutes the dominant driver of freshwater withdrawals, contributing 57% of the model’s predictive gain, whereas agricultural value added accounts for 43%. SHAP dependence plots reveal pronounced nonlinearities: moderate land expansion exacerbates freshwater stress, whereas allocations beyond a critical threshold mitigate pressure, reflecting potential efficiency gains at scale. Agricultural value added exhibits a U-shaped relationship, wherein both low and high productivity levels are associated with increased freshwater use, while intermediate productivity generates the greatest negative impact. The XGBoost model achieves substantial predictive performance (Coefficient of Determination (R²) = 0.78, Root Mean Squared Error (RMSE) = 0.806, Mean Absolute Percentage Error (MAPE) = 0.86%), demonstrating its capacity to capture heterogeneous, nonlinear dynamics that linear models fail to detect. The robustness of the model was further assessed using Leave-One-Out Cross-Validation (LOOCV) to evaluate its out-of-sample predictive performance and mitigate potential overfitting arising from the limited sample size. These findings underscore the necessity of adaptive water management strategies that incorporate scale-dependent effects and productivity heterogeneity. Policies optimizing land allocation and promoting efficient agricultural practices can enhance water-use efficiency while sustaining sectoral output. The study highlights the value of interpretable machine learning in advancing empirical understanding of the water–agriculture nexus under conditions of structural economic change.
Rapid urbanization and land-use transformation have intensified thermal stress in mid-sized cities of Bangladesh; however, spatially explicit environmental screening of heat-related risk remains limited. This study investigates the spatiotemporal dynamics of urban heat risk in Kushtia District from 2010 to 2024 using an environmentally weighted, indicator-based geospatial framework integrating remote sensing and demographic data. Multi-temporal Landsat (Thematic Mapper (TM); Operational Land Imager (OLI); OLI/Thermal Infrared (TIRS)) and WorldPop datasets were employed to derive five environmental indices: Land Surface Temperature (LST), Albedo, Urban Thermal Field Variance Index (UTFVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI), along with Population Density as a proxy indicator of human concentration. A composite Heat Vulnerability Index (HVI) was developed using Principal Component Analysis (PCA) to integrate these environmental and demographic variables into a spatial heat-risk surface. Results indicate a substantial rise in LST (>5 °C), particularly across urban centers such as Kushtia Sadar and Khoksa, alongside a consistent decline in NDVI and NDWI, signifying degradation of green and blue spaces. Correlation analysis revealed strong negative relationships between NDVI–LST and NDWI–LST, underscoring the mitigating role of vegetation and surface moisture. PCA results confirmed that vegetation–moisture interactions dominate environmental variability, while demographic concentration exerts a secondary yet persistent influence. High and very high heat-risk zones expanded from 211.89 km² in 2010 to 424.42 km² in 2024, reflecting intensifying spatial thermal stress. The findings represent an environmentally weighted spatial screening of heat risk rather than a comprehensive socio-ecological vulnerability assessment. The study highlights priority areas for nature-based adaptation strategies, including urban greening, waterbody restoration, and reflective surface planning, to reduce localized heat exposure in rapidly urbanizing regions of Bangladesh.
This paper aims to examine the association of the specific governance indicators, which are the regulatory quality, the rule of law, and government effectiveness, on sustainable development in 60 countries around the world in 2024. This is explained by the key role that the state institutions and the institutional framework may play in enhancing economic, social, and environmental results and in achieving the Sustainable Development Goals (SDGs). The study employed the quantitative approach that is based on 2024 cross-sectional data. The data were obtained from the 2024 SDG Index and the World Bank's Worldwide Governance Indicators (WGI). The Eviews software was used to compute an Ordinary Least Squares (OLS) multiple linear regression model to examine the relationship between the independent variables (regulatory quality, the rule of law and government effectiveness) and the dependent variable (the SDG Index). The results reflected that the rule of law and the efficacy of the government have a positive and substantial effect on sustainable development, but the regulatory quality did not show a direct significant impact. This shows that sustainable development is based on the unity of the institutional framework which consolidates legal, regulatory, and administrative potential to achieve quantitative results.
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