Search Results (2,020)

Sustainability has become a strategic priority in logistics and supply chain management, yet empirical evidence from emerging economies remains scarce, particularly in the Middle East and North Africa (MENA) region. This study investigates how green logistics practices (GLPs) and social performance (SP) influence the organizational performance (OP) of logistics service providers (LSPs) in Morocco, a Euro-Mediterranean hub undergoing rapid transformation. Based on survey data from 210 managers, analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM), the results show that GLPs have a significant positive effect on both SP (β = 0.494, p < 0.001) and OP (β = 0.263, p < 0.001), while SP strongly enhances OP (β = 0.379, p < 0.001). Together, GLPs and SP explain 31.1% of the variance in OP and 24.4% in SP, confirming their complementarity. The findings demonstrate that social sustainability acts as a key pathway through which environmental practices translate into improved competitiveness. By highlighting the interdependence between environmental and social dimensions, this study contributes to the Triple Bottom Line and Resource-Based View frameworks and provides actionable insights for policymakers and managers aiming to align logistics operations with the Sustainable Development Goals (SDGs 8, 9 and 12). Full article

Integrating blockchain technology into the marketing, communication, and logistics processes of sustainable supply chains faces multidimensional barriers. This study uses a mixed-methods approach to identify, prioritize, and analyze the expert-perceived influence relationships among these barriers. Semi-structured interviews with 14 managers and experts were thematically analyzed in MAXQDA to derive the barrier criteria, weighted and structurally analyzed using the Best-Worst Method (BWM) and Fuzzy DEMATEL with judgments from 11 domain experts, re-derived from the raw questionnaire data. BWM identifies Legal and Regulatory Barriers (C1, w = 0.2225) and Economic Barriers (C5, w = 0.2110) as most critical, jointly accounting for 43.4% of the total weight; nine of eleven experts’ solutions met the CR < 0.10 threshold; the remaining two (E4 and E9) were retained rather than adjusted. Fuzzy DEMATEL classifies Economic, Legal, and Trust barriers as cause-group factors and Technological, Institutional, and Knowledge barriers as effect-group factors, with Economic Barriers showing the strongest net driving influence (D − R = +0.705). The two highest-priority barriers are also the two most upstream, and sensitivity analysis confirms the top and bottom ranks are stable. Interventions targeting cause-group barriers may generate broader system-level effects across effect-group barriers, an expectation grounded in expert-perceived influence rather than demonstrated causal evidence. Full article

Construction supply chains are pivotal to circular economy transitions but remain structurally fragmented, limiting the scalability of resource-efficient solutions. At the same time, digital technologies and life cycle assessment are often deployed in isolation, constraining their ability to enable system-level circularity. Using a theory-building literature synthesis of 141 publications across circular economy, sustainable supply chain management, digitalization, and life cycle sustainability assessment, this study develops an integrated conceptual framework that explains how circular performance may be shaped by AI-enabled decision capabilities, lifecycle sustainability constraints operationalized through PESI-LCA, and system-level alignment conceptualized through DCAM. AI is conceptualized as a dynamic capability for prediction and optimization, while PESI-LCA is positioned as an operationalized LCSA-based constraint system that embeds environmental, social, and economic criteria into decision architectures. DCAM defines the alignment conditions required across digital infrastructure, circular strategies, business models, and institutional enablers. The framework advances a non-additive logic: circular outcomes depend on how sustainability constraints shape AI-driven decision-making and how alignment enables coordinated implementation across supply chains. A key theoretical contribution is the identification of structural distortion as a failure mode in which digital optimization reinforces linear resource flows. The study advances sustainable supply chain theory and offers testable propositions and governance implications for scaling circular construction systems. Full article

Driven by the escalating worldwide consumption of animal-sourced commodities, the livestock industry faces intensified constraints regarding its ecological consequences, encompassing large-scale deforestation, elevated greenhouse gas emissions, progressive soil deterioration, and the suboptimal management of hydrological resources. In response to these challenges, the concept of sustainable livestock husbandry integrates production practices with environmental management strategies designed to conserve biodiversity, optimize resource use, and reduce emissions. The objective of this review is to emphasize the significance of sustainable cervid farming as an integral element of contemporary food supply chains, comprehensively addressing its environmental, production, and nutritional dimensions. A comprehensive assessment of current scholarly articles from Scopus, Web of Science, and Google Scholar was performed to explore topics concerning eco-conscious livestock practices, non-conventional farming models, organic rearing, animal well-being, and the evolution of deer breeding. Particular attention was paid to the role of permanent grasslands in venison production, soil protection, carbon sequestration, and biodiversity preservation. The nutritional value of red deer and fallow deer meat and its significance in sustainable food systems are also outlined. As indicated by the analysis of available research findings, extensive cervid farming based on the use of permanent grasslands and local feed resources can reduce environmental pressures through support of landscape conservation, preservation of ecosystem functions, and efficient utilization of biomass that is inedible for humans. Venison is shown to be a valuable source of high-quality protein, minerals, and essential fatty acids, which meets growing consumer demands for high-quality food. The literature review has confirmed that properly managed cervid farming can be an important element of sustainable food production systems combining production goals with environmental protection and animal welfare. It also emphasizes the need for further research into the environmental, economic, and social aspects of this branch of animal production. Full article

Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green finance for sustainable transitions has created a rare policy window in which AI-optimized biofuel production on degraded lands can simultaneously serve multiple imperatives. This study presents a comprehensive secondary data analysis of AI-based optimization frameworks for deploying biofuel production systems on degraded lands, integrating an explicit green finance dimension that has been largely absent from prior synthesis literature. Drawing on 152 peer-reviewed studies and authoritative datasets from FAO, IEA, IRENA, UNCCD, the Green Climate Fund (GCF), and the World Bank, we analyze machine learning, deep learning, reinforcement learning, and hybrid AI architectures applied to feedstock selection, soil remediation, yield prediction, supply-chain logistics, and green finance risk-return optimization. Based on evidence synthesized from 152 studies and supporting geospatial and scenario analyses, results indicate that AI-optimized systems can recover 75–94% of prime-land yields, achieve carbon sequestration rates of 2.1–6.8 t CO₂e ha⁻¹ yr⁻¹, central estimate ≈ 7–9 Gt CO₂e yr⁻¹ at 35% adoption with moderate exclusions, and generate projected internal rates of return ranging from 8–22%, depending on feedstock type, regional conditions, and financing assumptions. Yield-recovery and carbon-sequestration ranges are drawn from synthesis of the reviewed literature; IRR, financial-leverage, and market-expansion figures are author-constructed scenario projections based on this evidence, not independently observed outcomes. Green bonds, Article 6 carbon credits, GCF concessional finance, and blended finance structures are identified as the most impactful instruments, collectively projected, under scenario-based modeling, to reduce composite project risk scores by 30–45% and expand the investable universe of degraded-land biofuel projects by an estimated 340% relative to a no-AI, no-green-finance baseline; these figures represent author-constructed scenario estimates rather than direct empirical findings. We develop the AI-Biofuel-Land Restoration-Green Finance (ABLR-GF) conceptual framework (not yet empirically validated through field pilots or simulation) with explicit green finance routing pathways and identify critical policy enablers for global deployment. This study advances the evidence base for policy-makers, investors, researchers, and development practitioners working at the intersection of artificial intelligence, bioenergy, green finance, and sustainable land management. Full article

Customer customization and high-mix, low-volume production have increased operational complexity and sustainability pressures in global apparel supply chains. Although artificial intelligence (AI) improves visibility, forecasting, and decision support, limited research explains how AI-generated intelligence is translated into coordinated organizational action in labor-intensive manufacturing. This study examines how strategic flexibility, manufacturing flexibility, AI-enabled operational intelligence, and human expertise interact in the enactment of Human-Centered Supply Chain Agility (HCSA) in customized apparel manufacturing. An exploratory qualitative design was employed using semi-structured interviews with 13 senior managers and executives from apparel manufacturing firms operating in Guatemala, Honduras, Nicaragua, and El Salvador. Reflexive thematic analysis identified seven interrelated themes: customer customization and operational uncertainty, strategic flexibility, manufacturing flexibility, AI-enabled operational intelligence, human expertise, AI–human complementarity, and coordinated organizational response. Participants reported varying levels of engagement with AI-assisted and broader digital systems. Where AI-supported tools were used, they primarily provided enhanced visibility and analytical support, while experienced managers remained responsible for contextual interpretation, priority setting, exception handling, and implementation decisions. Participants associated these digitally supported and human-centered processes with more efficient resource use, reduced operational waste, organizational resilience, and more responsible managerial decision-making. Rather than proposing a new theory, this study offers an empirically grounded, process-oriented interpretation of how established agility capabilities are enacted in AI-enabled customized apparel manufacturing. Full article

Sustainable apparel supply chain research is dominated by the fashion sector, while the specific operational, technical, institutional, logistics, and security requirements of military apparel supply chains remain insufficiently integrated into existing conceptual frameworks. This study aims to develop a conceptual model for sustainable military apparel supply chain management. A narrative literature review was used to identify source determinants derived from fashion supply chain research, followed by a sectoral analysis of military apparel supply chains. The determinants were then adapted using an adaptation matrix based on four transformation rules: functional, actor-related, restrictive, and extending transformation. The analysis identified six adaptive conditions defining the admissibility of sustainable solutions and nine target determinants grouped into four management pillars. These elements were integrated into the House of Sustainable Military Apparel Supply Chain Management (HoSMASC), in which the adaptive conditions form a qualifying foundation and the target determinants form pillars subject to graded assessment. A preliminary assessment matrix was also developed, combining a binary, conjunctive evaluation of the foundation with a 0–2 assessment of the determinants within the pillars. The model provides a structured framework for preliminary diagnosis and design support but remains conceptual and requires empirical validation through expert assessment and case studies. Full article

Food-safety systems increasingly need to manage large detection files and externally generated analytical results across multiple organizations while linking these records to batch-level control actions. This study proposes a blockchain-based dual-track mechanism for trusted circulation of food-safety detection data and batch-level risk control, using aflatoxin B1 (AFB1) as the empirical case. High-dimensional files are stored in InterPlanetary File System (IPFS) and anchored on-chain by content identifiers (CIDs); three authorized oracles use two-of-three matching of detection values and evidence hashes before contract-based state determination. A stage–device–operation inverted index identifies associated batches, while signed second-track confirmations drive GREEN/YELLOW/RED state transitions. Experiments on a four-node Quorum Byzantine Fault Tolerance (QBFT) network used 57 independent HyperPistachio samples with 86.625-mebibyte (MiB) band-interleaved-by-line (BIL) files. Real-file access was successfully completed, single-oracle failures were tolerated when two consistent oracle reports remained, and associated-batch query latency increased only from 13.06 to 16.78 ms as fanout rose from 1 to 40. A 115.15 min sustained run maintained consistent states across all four nodes. The study manages externally supplied AFB1 results rather than evaluating analytical AFB1 detection accuracy, and its experimental validation is limited to the AFB1 case. Full article

The textile, apparel, and fashion (TAF) industry generates significant environmental burdens across its entire supply chain. As the global textile recycling market expands, a systematic framework for classifying exhibitors and assessing their sustainability-related characteristics is increasingly needed. This study analyzes exhibitors at the 2026 Textiles Recycling Expo USA, the first specialized textile recycling exhibition in North America, to develop an exploratory sustainability assessment framework. Using qualitative content analysis, 78 exhibiting companies were categorized into four functional groups: (1) Hard-tech Infrastructure, (2) Chemical & Material Innovation, (3) Logistics & Waste Management, and (4) Knowledge & Support Services. Based on a review of sustainability assessment literature in the TAF industry, a four-dimensional framework was developed, encompassing Material Renewability, Process Sustainability, End-of-Life Options, and Technical & Digital Attributes. For a preliminary pilot application, 11 companies were purposively selected and evaluated by eight experts using defined key performance indicators (KPIs). The total scores ranged from 10.3 to 16.3 out of 20, with ESO RECYCLING Società Benefit receiving the highest overall score (16.3), followed by MARGASA (15.8). Across the evaluated companies, Technical & Digital Attributes generally showed relatively lower scores than the other dimensions, indicating comparatively limited publicly evidenced digital traceability capabilities. These findings demonstrate the preliminary applicability of the proposed framework for characterizing heterogeneous exhibitors while highlighting the need for further refinement and validation using larger and more diverse samples. Full article

Digital technologies are widely expected to reshape supply chains, yet the conditions under which they deliver operational and sustainability-related value in practice remain insufficiently understood. This study examines how digital technologies influence supply chain operations and performance, where performance is operationalized through demand forecasting accuracy, cost, lead time, visibility, and inter-organizational collaboration, and asks how these technologies can support more resilient, resource-efficient, and sustainable supply chain operations. The study adopts an exploratory qualitative design based on semi-structured interviews with eight supply chain professionals at manager level or above, drawn from the aluminum, elevator, railway, food, and consumer goods industries in Austria, Slovakia, and the Czech Republic, and conducted between March and June 2025. A structured literature review complements the interview evidence. Within this exploratory sample, Artificial Intelligence and Data Analytics were the most widely adopted technologies (five of eight participants each), followed by the Internet of Things (four of eight) and Automation (five of eight), while no participant reported active Blockchain deployment. Reported benefits concentrated on forecasting accuracy, operational efficiency, visibility, and collaboration, whereas high implementation costs, legacy system integration, skill shortages, and regulatory uncertainty formed the principal barriers. The central finding is a conditional relationship between adoption and competitiveness: internal operational gains did not automatically translate into competitive advantage among the professionals interviewed, but appeared to require strategic alignment, cross-functional integration, and performance measurement. Because digitally enabled forecasting, inventory positioning, and resource optimization also reduce waste and improve resource utilization, the findings link digital supply chain transformation to sustainable development objectives. This exploratory study of eight participants therefore provides practitioner-level propositions and a research agenda for digital and sustainable supply chain transformation rather than statistically generalizable findings. Full article

Reliable and sustainable Yellow River water allocation in Henan Province, China, requires the coordinated use of heterogeneous evidence, sequential allocation calculations, operational constraints, and ecological-management requirements. Existing approaches often lack explicit coordination among demand forecasting, supply calibration, intake-gate allocation, and constraint-triggered feedback. This study proposes a constraint-gated multi-agent framework to support regional Yellow River water allocation. The framework integrates multi-source evidence retrieval with a sequential demand–supply–intake computational chain and sustainability-oriented constraint validation. Regional demand is predicted using a quota-prior-guided neural network (QPG-NN) that incorporates quota-derived demand information as both an input feature and a soft consistency constraint. Supply is calibrated using historical trends and demonstrated supply capacity, while intake-gate allocation combines historical trends, recent operational inertia, and regional supply adjustment. Ecological-water satisfaction, regional supply reliability, and sectoral satisfaction balance are embedded as auditable screening criteria. Experiments using 2015–2023 data from 13 prefecture-level regions in the Henan Yellow River supply area showed that QPG-NN achieved a mean RMSE of 0.736 × 10⁸ m³ and an R² of 0.976. Supply-calibration MAPE decreased from 10.05% to 5.76%, while intake-allocation WAPE decreased from 15.75% to 3.8%. The proposed framework obtained the highest mean expert score of 4.52 among the evaluated methods. In four synthetic drought scenarios, all cases triggered supply recalculation and passed a second constraint-validation evaluation after one automatic recalculation cycle. These results support the framework’s ability to coordinate traceable numerical computation, constraint-triggered feedback, and sustainability-oriented screening within an auditable water-allocation workflow. Full article

In the aviation sector, spare parts management plays a critical role in mitigating aircraft downtime caused by stockouts while supporting the efficient and sustainable use of resources in maintenance operations. It involves a range of inventory control methodologies combined with spare parts demand forecasting, which is particularly challenging due to the stochastic nature of component failures. Beyond ensuring operational readiness, effective spare parts management can contribute to sustainability by reducing excess inventory, minimizing waste from obsolete components, and optimizing storage and transportation requirements within Maintenance, Repair, and Overhaul (MRO) systems. Accurate demand forecasting has the potential to enhance both supply chain resilience and environmental performance, as it may contribute to the mitigation of overproduction, stockouts, unnecessary and emergency transportation, and resource waste. This study provides a systematic review of recent approaches to spare parts forecasting and infers their contribution to sustainability in the aviation industry. A systematic review was conducted, including the identification, screening, and analysis of studies published between 2010 and 2025. The review of 15 selected studies highlights the growing relevance of aligning demand forecasting with spare parts management, enabling more efficient inventory decisions that improve aircraft availability while supporting resource efficiency and sustainability objectives in MRO operations. This study consolidates current knowledge on sustainable spare parts management practices in aviation and identifies key areas for future research in aviation supply chains. Full article

Electronic waste (e-waste) is among the fastest-growing global waste streams, with 62 million tonnes generated in 2022 and only 22.3% formally recycled, while generation is projected to reach 82 million tonnes by 2030. Fragmented record-keeping, weak chain-of-custody controls, and limited transparency enable illegal dumping, fraudulent recycling claims, and loss of recoverable materials. This paper presents a narrative literature review of blockchain and distributed ledger technologies applied to electronics and e-waste management. Following a structured search and relevance screening of major scientific databases, publisher platforms and grey literature (last search was run on 15 July 2026), 110 sources published between 2017 and 2026 were thematically synthesized across four domains: technological developments, sustainability and circular-economy implications, traceability and compliance mechanisms, and e-waste management applications. Blockchain delivers value primarily through an auditable chain-of-custody built on three pillars: standardized item and batch identities, standardized lifecycle event schemas, and licensed-actor attestations supported by off-chain evidence anchored on-chain. Recurring design patterns include permissioned consortium networks, hybrid storage, and role-based access control. Persistent barriers include data-input integrity, scalability, governance, and cost. Blockchain immutability protects stored records but cannot correct falsified inputs, making trusted data capture and governance the decisive factors for reliability. Adoption should be evaluated against measurable indicators, such as traceability completeness, audit-time reduction, fraud rates, and verified material recovery, rather than assumed benefits. Full article

Steel production contributes significantly to global resource consumption and accounts for nearly 8% of worldwide CO₂ emissions. As the construction industry moves toward decarbonization, the reuse of structural steel has emerged as a highly impactful strategy for reducing embodied carbon and extending material life cycles. However, most end-of-life structures are still demolished using conventional methods that prioritize recycling for scrap value rather than recovering reusable steel components, resulting in the loss of high-quality material and reduced environmental and economic benefits. Despite growing interest in circular construction, systematic analyses of real-world structural steel reuse projects remain limited. This restricts the understanding of the practical conditions required for scalable implementation. Moreover, there is a lack of structured approaches for identifying the factors influencing deconstruction and large-scale structural steel reuse across reverse supply chains. This study addresses these gaps by developing a framework of factors affecting efficient deconstruction and structural steel reuse, identified through a thematic analysis of the literature and structured around structure attributes, business attributes, value chain activities, project management, and regulations. Fifteen case studies were analyzed: two from North America, twelve from Europe, and one from Asia. A structural steel reuse classification model is introduced to support cross-case comparison of coordination demands, implementation barriers, and scalability potential within reverse supply chains. The results demonstrate that reuse pathways differ substantially in coordination requirements, implementation complexity, and scalability outcomes. They further indicate that the dominant challenges to structural steel reuse are no longer primarily technical, but instead stem from system-level coordination gaps, limited information availability, and insufficient integration across reverse supply chains. Based on these findings, targeted solutions are identified, including improved dismantling strategies, enhanced documentation systems, dedicated storage infrastructure, and stronger regulatory alignment. This study provides a structured analytical approach and practical recommendations to support decision-making, stakeholder coordination, and the scalable implementation of structural steel reuse. Full article