Search Results (369)

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains. Full article

Highway construction projects face interacting risks that affect time, cost, regulatory compliance, and delivery resilience, all of which are closely linked to sustainable infrastructure development. This study develops a hybrid decision-analytical and machine learning framework for sustainability-oriented risk assessment in highway construction under limited-data conditions. The framework combines (i) the Analytic Hierarchy Process (AHP) and tabular Generative Adversarial Networks (GANs) to structure and stress-test expert judgement, and (ii) Probability-Impact (P-I) scoring with a Bayesian Networks (BNs) to model dependencies and derive posterior weights for probability of occurrence, impact on time, and impact on cost across four headline risk factors: weather-related risks, lack of labour, design-related risks, and permitting/regulatory risks. AHP provides transparent and auditable priorities with consistency checks, while GAN-generated synthetic tables support diagnostics for central tendency (P50) and tail behaviour (P90) under data scarcity. The calibrated P-I scores parameterise BN conditional probability tables, enabling the updating of BN scores; and factor-level decomposition of expected contributions. The framework produces model-ready posterior weights that support early planning, contingency allocation, mitigation prioritization, scenario analysis, and subsequent simulation and optimization studies. In sustainability terms, the proposed approach helps project teams improve climate resilience, strengthen regulatory and environmental preparedness, and reduce inefficient use of time, cost, and project resources in data-constrained settings. The results show that permitting/regulatory risks have the highest contribution to probability of occurrence and time impact, while weather-related risks exert the greatest cost impact. The framework therefore offers a practical tool for supporting more resilient, transparent, and sustainable highway project delivery when large historical datasets or questionnaire surveys are unavailable. Full article

Maintaining consistent quality performance of recycled aggregates is essential for their reliable use in construction applications. This study evaluated whether the regulatory revision permitting concurrent appointment of quality and environmental managers affected post-certification quality test performance within Korea’s recycled aggregate certification system. Extending a previous 2025 audit-based study, this research analyzed 311 certification-application-level follow-up quality test results obtained during the 2023 national post-certification management process. Statistical analyses, including chi-square tests, Fisher’s exact tests, odds ratio comparisons, and subgroup analyses, were conducted according to management structure, personnel change status, and recycled aggregate application type. The results showed that concurrent appointment and personnel changes were not associated with statistically significant deterioration in post-certification quality test performance. In contrast, the recycled aggregate application type showed substantially greater influence on pass/fail outcomes, with relatively higher failure risks observed in concrete and fine aggregate applications requiring stricter quality control conditions. Road construction and asphalt concrete applications generally maintained relatively stable pass rates regardless of management structure or personnel continuity conditions. The subgroup analyses additionally showed that concurrent appointment did not significantly increase failure risk within any recycled aggregate application category. These findings indicate that concurrent appointment did not significantly deteriorate actual post-certification quality performance within the analyzed national certification dataset. Full article

Background: The Vehicle Routing Problem (VRP) is foundational to logistics optimization, yet its alignment with the Triple Bottom Line (TBL) and UN Sustainable Development Goals (SDGs) remains fragmented. This study conducts a strategic bibliometric audit of 301 peer–reviewed publications (1992–2025) to quantify the evolutionary progression and thematic maturity of sustainable routing research. Methods: A four–stage scientometric framework was employed, utilizing Scopus–based data retrieval, longitudinal mapping, and Python 3.14–driven text mining to visualize keyword co–occurrence networks, author collaborations, and regional research clusters. Results: Findings reveal a pronounced “Sustainability Asymmetry,” where 51.5% of studies prioritize economic efficiency, while only 2.6% address the social pillar. Additionally, social sustainability remains an “isolated island” with minimal cross–citation to the research core. Geographic analysis identifies a heavy concentration in China, the USA, and Western Europe, uncovering a critical North–South—collaboration gap. Conclusions: The study proves that while environmental themes reached maturity between 2018 and 2022, social indicators exhibit a significant maturity lag. This quantified social deficit, centered on the neglect of SDG 3 and SDG 10, mandates a fundamental paradigm shift toward a geographically inclusive and socially conscious research agenda to ensure global logistical equity. Full article

This paper presents a methodological framework for anomaly detection in child benefit administration based on Long Short-Term Memory (LSTM) neural networks. The content of this analysis, in general, is situated within the social (S) pillar of the environmental, social, and governance (ESG) accountability framework. We construct a framework applied to 305,338 child allowance claim records from the Fund for Child Protection of Republika Srpska, Bosnia and Herzegovina (February 2017 to December 2025), construct behavioural and demographic features at the applicant and household level, encode sequential claim histories as three-dimensional tensors, and conduct a systematic architecture sweep across six LSTM configurations. The target variable, the guardianship anomaly flag, identifies 172 anomalous records (0.056%) among 305,338 claims, and yields a class weighting ration of approximately 1515:1. Across all six configurations, ROC-AUC values range from 0.706 to 0.870 and PR-AUC from 0.002 to 0.071. The reference configuration (L1_U10_T20_he_normal, ROC-AUC = 0.870) flags 170 applications (0.37% of the test set) for priority manual review at the operational audit threshold of $\tau =0.05$. The highest-risk application identified (anomaly probability 0.935) is characterised by a four-child household with below-poverty declared income, elevated benefit-to-income ratios, home delivery payment method, and a persistent high-risk sequential claim pattern not previously flagged by the Fund’s rule-based administrative system. The results confirm that LSTM-based sequential anomaly detection is a viable and principled complement to rule-based eligibility screening in public social transfer administration. Full article

Across semi-arid and environmentally vulnerable regions, intensifying climate pressures, land degradation, and resource scarcity are placing growing demands on institutions, communities, and land users. However, the knowledge and technical skills required to respond effectively remain uneven and often poorly aligned with local needs. This study presents a comparative skills audit in Kimberley, Upington, and Rietfontein in the Northern Cape, identifying capacity gaps, stakeholder-specific training priorities, and structural barriers in natural resource and sustainable land management. Using questionnaires, semi-structured interviews, participatory site visits, and multi-stakeholder consultations, competencies were assessed across GIS and remote sensing, climate resilience, soil and land restoration, water conservation, sustainable agriculture, and policy literacy. Results show significant disparities in skills proficiency. GIS and remote sensing (0.8) and climate resilience strategies (1.0) were weakest, while policy literacy (1.5) and soil management (2.0) were also limited. Sustainable agriculture (4.0) and water conservation (2.8) showed relatively stronger capacity. Training needs varied by stakeholder, with government prioritizing geospatial tools and governance, and farmers emphasizing climate adaptation and resource management. Key barriers include limited digital infrastructure (83%), insufficient government support (80%), high training costs (78%), and contextual mismatches (50%). Integrated, place-based capacity development is essential to strengthen adaptive governance and long-term resilience. Full article

Outdoor thermal comfort in hot, arid regions critically influences campus open-space use and the sustainability of university campuses, including reduced cooling energy demand and enhanced livability, yet validated integrated assessments remain scarce. This study aims to explore the relationship among microclimate conditions, thermal comfort, and the sustainable use of campus open spaces in a hot, arid region, with the goal of identifying design strategies that enhance both user comfort and environmental sustainability. The study incorporated: (1) a site audit; (2) exploratory RayMan simulations (n = 180, unvalidated) calculating Physiological Equivalent Temperature (PET) across five zones; and (3) a June survey (n = 156, 52% response rate). Physical analysis revealed height-to-width ratios of 0.13–0.30, representing an 80–91% deficit below the 1.5 minimum commonly recommended benchmark for effective shading in the literature. Unvalidated simulations estimated a mean annual PET of 31.2 °C (SD = 4.8 °C), with 17.6% of annual PET values within the comfort range and 65.2% within the hot range. For June, unvalidated simulations estimated 4% of PET values within the comfort range, while 35.5% of respondents reported thermal comfort (mean ASHRAE 1.66, warm range)—a descriptive discrepancy of 31.5 percentage points. Self-reported social factors (friends: 79.8%) ranked higher than shading space selection responses; behavioral observations are required to confirm actual use patterns. Priority interventions from physical analysis and user reports include optimized shade, cool materials (albedo ≥ 0.60), and intentional greening—subject to validation with calibrated measurements. By linking microclimate modification to increased open-space usability and reduced cooling energy demand, this research contributes to sustainable campus planning frameworks. Pending field validation and seasonal surveys, the quantitative thermal comfort estimates should be considered exploratory rather than conclusive. Full article

As global markets navigate the dual transition of digitalization and sustainability, the risk of “digital greenwashing” has emerged as a critical corporate governance challenge. Utilizing a comprehensive dataset of Chinese A-share listed firms from 2018 to 2024—an ideal laboratory characterized by rapid regulatory shifts and unique state-market dynamics that provide highly generalizable insights for other emerging economies—this study empirically investigates whether corporate digital transformation acts as a genuine driver for Environmental, Social, and Governance (ESG) enhancement or merely serves as a symbolic disclosure tool. Fortified by rigorous identification strategies, including Propensity Score Matching and Lewbel heteroskedasticity-based instrumental variable estimations, the results confirm that digitalization serves as an incremental yet statistically significant driver for corporate sustainability. Crucially, mechanism analyses reveal a “full moderation” effect: the positive impact of digitalization on ESG performance is completely activated only in the presence of premium external assurance (e.g., Big 4 audits). Without high-quality IT auditing to act as a credibility enforcer and verify the substance of digital signals, technological adoption alone fails to yield significant ESG improvements. Furthermore, a nuanced structural asymmetry is identified: foundational data infrastructures (Cloud Computing and Big Data) directly enhance quantifiable Environmental and Governance metrics, whereas premium audits are strictly required to activate the “soft,” qualitative Social dimension. Finally, the synergy exhibits distinct boundary conditions. It is heavily concentrated within high-pollution industries where digital transition acts as a regulatory survival imperative rather than mere market expansion, and its reliance on external assurance is fundamentally driven by the market-signaling needs of non-State-Owned Enterprises (non-SOEs) rather than the policy-distorted mandates of SOEs. These findings offer critical theoretical extensions and policy implications for standardizing digital-audit infrastructures globally. Full article

Accurate building performance forecasting is critical for the design and renovation of energy-saving structures, but existing methods face four key challenges: heterogeneous data fusion (sensor streams, design parameters, and environmental sequences), non-stationary physical time series, model interpretability, and sample efficiency (e.g., limited commissioning data). To address these challenges, this paper proposes Fusion-Grounded Forecasting (FGF), which is a framework integrating a gated adaptive fusion layer, deterministic trend-season decomposition, an additive predictor with component decomposition, and Bayesian regularization. This framework is designed for next-hour forecasting broadcast to hourly resolution using hourly sensor data and monthly design parameters. The dataset covers 36 months (approximately 25,920 h). In addition to the combination of existing modules, the novelty lies in the integrated architecture, in which interpretable constraints can adjust the fusion layer in both directions, with decomposition prediction alignment supporting component attributes. The framework is verified on a proprietary 36-month dataset from institutional buildings using standard prediction metrics (MAE, RMSE, MAPE, and directional accuracy) and ablation studies for comparison against 10 baselines: SARIMAX, GPR, LSTM, XGBoost, N-HiTS, Informer, Autoformer, NAM, a physics-informed hybrid, and TFT. FGF achieves a 3.1% MAPE and 92.5% directional accuracy in hourly cooling load forecasting. Ablation confirmed the contribution of each module: removing gated fusion increased the MAPE to 6.8%. Compared with manual feature engineering, the speed of the framework is increased by 1680 times, and the cost is reduced by 99.6%. The explanatory index (counterfactual reliability: 0.95; Stability of functional importance: 0.11) is in compliance with audit requirements. These results indicate that FGF connects descriptive physics with quantitative prediction. However, this study is limited to a single institutional building; transferability to residential, commercial, or industrial buildings requires further verification. While waiting for this verification, FGF has demonstrated its potential as a transparent and efficient tool to build performance models. Full article

Kindergarten toilet design influences children’s autonomy, hygiene behaviours, and psychological well-being. Yet systematic architectural evaluations in conflict-affected and developing regions remain scarce. This study conducts a comparative architectural audit of toilet facilities in ten kindergartens in Erbil, Iraqi Kurdistan, assessing design features associated with child-centred principles. A literature-derived framework comprising four domains (Autonomy and Functionality, Health and Hygiene, Safety and Comfort, Aesthetics and Sustainability) was operationalised through 14 architectural indicators and assessed via a five-point rubric. Data sources included architectural drawings and systematic on-site observations. Overall design feature scores ranged from 3.1 to 4.3 (scale 1–5). While basic safety requirements were universally met, significant deficiencies emerged in inclusive design (accessible fixtures present in 3/10 facilities, 30%), advanced hygiene technologies (sensor-activated fixtures in 2/10, 20%), and aesthetic-environmental quality (mean score 2.4/5). Higher-scoring facilities demonstrated closer classroom-toilet proximity (≤6 m vs. >15 m) and distributed rather than centralised layouts. This study does not measure child outcomes or user experiences; it provides an architectural baseline. Current kindergarten toilet design in Erbil achieves functional adequacy but consistently fails to deliver inclusivity, technological innovation, and spatial quality. Policy revision beyond minimum compliance toward child-centred design standards is warranted, with priority given to accessible fixtures and classroom-adjacent layouts. Full article

This case study aimed to benchmark the healthiness, equity, and environmental sustainability of a large, urban Australian university food environment through two audits conducted in 2022 and 2025. Two cross-sectional audits were completed at a large urban university campus using the Uni-Food tool, which assesses 68 best practice indicators across three components: policy, campus facilities, and food retail outlets. Four assessors independently conducted the audits with excellent inter-rater reliability (Cohen’s Kappa = 0.89). Final scores out of 100 were calculated using weighted domains. Descriptive and inferential statistics were used to compare changes over time. In 2025, the university achieved a score of 52%, up from 48% in 2022, indicating medium compliance with best practice standards. Findings highlight that scores differed modestly but there were persistent gaps in university food policy and practice. Specifically, the policy component remained low (48%), demonstrating strong overall planning but a lack in food retail policy and monitoring systems. The campus component scored moderately (63%), with various nutrition knowledge-building opportunities and environmental sustainability initiatives available but heavy promotion of unhealthy foods at campus events. The food retail component scored lowest overall (36%), especially as there was a lack of adequate nutrition information provided at food outlets. Continued investment in policy development, campus-wide strategies, and food retail innovation is essential to create healthier, more equitable, and environmentally sustainable food environments in tertiary settings. Full article

Sustainable municipal solid waste (MSW) treatment in rapidly urbanizing secondary cities requires evidence-based, district-level prioritization of technologies. We integrate GIS hotspot mapping, Random Forest, and AnyLogic System Dynamics into a decision-support framework and apply it to Shymkent, Kazakhstan (population 1.19 million; ≈301,400 tonnes of MSW in 2025). This is the first application of such a framework to MSW management in a Kazakhstani secondary city and, to our knowledge, the first regional application across Central Asia; the integration concept has prior precedents in Latin American, South Asian, and East Asian metropolitan studies, and the present contribution lies in empirical calibration to a Central Asian upper-middle-income context using 2015–2025 morphological audits, air-quality and soil monitoring, and Sentinel-2 NDVI. Random Forest (n = 80, 9 predictors) achieved R² = 0.976 ± 0.011 under 5-fold cross-validation; a complementary GroupKFold protocol confirms the model is Shymkent-calibrated while the methodology remains transferable. AnyLogic simulation shows an Infrastructure/Waste-to-Energy pathway reduces the 2030 annual landfilled volume to ≈201 kt, environmental risk by 70%, and methane emissions by 86% (≈270 kt CO₂-eq/year) relative to the Inertial baseline. The principal deliverable is a District × Technology × Phase prioritization matrix for sequencing sustainable investment under realistic budget constraints. Full article

Background: Smart-city road and intersection management increasingly aims to smooth bus operations and reduce stop-and-go driving, but cities often lack auditable indicators linking routine fleet data with comparable energy and environmental KPIs. Methods: This study develops a Monitoring–Reporting–Verification (MRV) workflow for daily bus records from a 2024 Polish metropolitan fleet (diesel, compressed natural gas (CNG), hybrid, and battery-electric buses). Records were quality checked, harmonized to MJ/km, aggregated to bus-month observations, and analyzed using a linear mixed-effects model with propulsion technology, season, and activity level as fixed effects and vehicle-level random intercepts. Environmental impacts were then calculated under well-to-wheel (WTW) boundaries using Environmental Footprint 3.1 (EF 3.1) impact categories, Poland’s 2024 electricity mix, and illustrative electricity-mix scenarios through 2050. Results: Relative to diesel, BEV and HEV were associated with lower adjusted energy intensity (ratios 0.272 and 0.681, respectively), whereas the CNG–diesel contrast was directionally higher but statistically inconclusive under the available CNG sample. BEV energy intensity more than doubled in winter in descriptive terms, and vehicle-specific heterogeneity remained high (ICC ≈ 0.61). The BEV climate profile improved under electricity decarbonization, while some EF categories showed mix-dependent trade-offs. The 3–10% traffic-management variants are interpreted as screening assumptions rather than measured ITS effects. Conclusions: Routine bus records can support auditable MRV and preliminary screening of fleet and corridor interventions, but causal traffic-management evaluation requires route-level trajectory, congestion, and before–after data. Full article

Industrial areas, which represent a specific type of urbanised area with an extremely high concentration of material reserves, can be considered key anthropogenic raw material reservoirs in the context of urban mining. Industrial areas, characterised by a high material density and a specific composition of structural systems, show extraordinary potential for providing secondary raw materials with high material and energy value. This increases the need for their systematic evaluation. The aim of the present study was to define the role of the selected industrial area as a strategic node for secondary raw material extraction, to identify the structure and quality of “urban deposits” in the selected location of the Ostrava–Karviná region (CZ), and to provide an analytical framework for its integration into circular planning processes. The methodological approach is based on a combination of pre-demolition audit, material flow mapping, spatial analysis, and structural element characterisation. It is becoming apparent that industrial areas have a high material density and contain significant amounts of recyclable metals, reinforced concrete elements, etc. These stocks are often concentrated in structural systems with predictable geometries, such as serial assembly prefabricated and steel frames, allowing for more accurate estimates of recoverable volumes. The results show that the incorporation of industrial areas into the process of urban mining can significantly reduce the consumption of primary raw materials, mitigate the environmental impacts associated with the extraction of raw materials, and, at the same time, promote the regeneration of industrial areas (or brownfields) through the planned decomposition of structures. The inclusion of urban mining in urban development strategies and the regeneration of industrial sites leads to the prediction that urban mining is one of the key elements for achieving a material-efficient and low-carbon urban environment. Full article

Decarbonizing the building sector requires integrating on-site renewable generation with systematic energy management. Among the most widely adopted alternatives are photovoltaic (PV) systems in buildings; however, they are often implemented as a standalone technological intervention (size–install–estimate savings), without being formally incorporated into an Energy Management System (EnMS) aimed at continuous improvement. In this context, this research addresses this gap through an integrated methodological framework aligned with ISO 50001, in which PV is explicitly included in energy performance management through energy review, the definition of an Energy Baseline (EnB), and the monitoring of Energy Performance Indicators (EnPIs) within the PDCA cycle. The approach articulates the analytical sizing of the PV system based on electricity demand and solar resources; its validation through simulation to ensure operational consistency and a technical, economic, and environmental assessment that translates PV generation into a verifiable reduction in energy imported from the grid and, consequently, into traceable improvements in EnPI under an audit-compatible scheme. The methodology is demonstrated in a multi-family building in Chorrillos, Lima (Peru), where a 14.5 kWp rooftop PV system (25 modules of 580 Wp) is designed to maximize self-consumption during daylight hours. The results show technical performance consistent with the demand profile, economic viability under the conditions of the case, and environmental benefits from replacing grid electricity, along with offsets associated mainly with the manufacture of PV components. The residual gap between the Post-PV EnPIs and the ISO 50001 target confirms that PV integration is a necessary but not sufficient first-cycle action within a comprehensive building decarbonization strategy, with demand-side management and envelope improvements identified as subsequent PDCA cycle priorities. In summary, the central contribution is not the PV sizing itself, but its operational and traceable integration within ISO 50001, making PV a quantifiable, verifiable, and scalable energy improvement action for residential buildings in emerging economies. Full article