Search Results (11,207)

The rapid transition toward electrified mobility and climate neutrality has prioritized the structural and environmental optimization of battery electric vehicle (BEV) subsystems. While vehicle lightweighting enhances operational efficiency, the production phase of structural enclosures and battery cells frequently introduces severe environmental and economic impacts and supply chain vulnerabilities. This study presents a comprehensive cradle-to-gate environmental life cycle assessment (LCA), life cycle costing (LCC), and semi-quantitative social assessment of alternative battery housing materials and battery cell architectures. To achieve a functionally accurate comparison, alternative materials, including a novel recyclable thermoplastic acrylic sheet molding compound (SMC), commercial thermoset SMCs, aluminum (AlMg3), and stainless steel, are evaluated using an analytical stiffness- and strength-equivalent methodology across three real-world geometric demonstrators. Simultaneously, lithium iron phosphate (LFP) liquid electrolyte prismatic cells and solid-state polymer pouch cells are assessed. Material-level results indicate that, while aluminum minimizes the structural mass, primary aluminum manufacturing exhibits the highest global warming potential and processing costs. Conversely, Polytec SMC and Elium SMC achieve the lowest environmental impacts alongside competitive total production costs. At the cell level, prismatic LFP architectures display superior environmental performance compared to solid-state pouch cells, which suffer from energy-intensive processing and lower volumetric capacity normalization. Demonstrator-level aggregation reveals that the electrochemical cells heavily dominate the environmental and economic footprint of the complete assembly, with the housing accounting for less than 5% of the total global warming potential (GWP) and 1% of the total costs. The social assessment reveals moderate and comparable performance across all systems, with slight advantages for thermoplastic composite-based configurations in terms of circularity potential and innovation perception. Overall, the study highlights the critical importance of the cell architecture and manufacturing processes in determining battery system sustainability, while demonstrating the relevance of lightweight composite housings in reducing the structural mass with a minimal environmental penalty. Full article

The study addresses the design and development of a unified corporate information system for multimodal transport and logistics companies engaged in maritime and railway transportation. Analysis of the existing literature revealed the absence of a coherent methodological framework for the design of corporate information systems tailored to the specific operational characteristics of multimodal transport and logistics enterprises. To bridge this gap, a design methodology for corporate information system databases is proposed, intended for subsequent deployment in companies operating multimodal supply chains. The development of the unified corporate information system was guided by the principle of “total costs,” which requires that the decision-maker—when selecting transport modes, methods of transportation, carriers, routing, and auxiliary intermediaries (insurer, stevedore, bank, and customs broker)—address the problem as an integrated whole rather than optimizing individual components in isolation. The study encompasses information modeling of the business processes of multimodal transport and logistics companies, construction of an optimal model of the transport process for maritime and railway transportation using integrated computer automated manufacturing definition (IDEF) and structured analysis and design technique (SADT) modeling, and the design of a multilevel unified database structure for the coordination of different transport modes. A decision-making and support system has been developed for managing the operational activities of a multimodal transport and logistics company engaged in maritime and railway transportation. The proposed unified corporate information system enables the replacement of task resolution by local optimization criteria applied separately to each transport mode—such as freight cost and delivery time—with a single global optimization criterion for the multimodal supply chain. Full article

Using 1135 customer–supplier–year observations involving Chinese A-share listed firms and their major suppliers from 2011 to 2023, together with prefecture-level government work reports and patent-assignment records, we examine whether environmental policy pressure faced by downstream customers shapes upstream suppliers’ external green technology acquisition. External green technology acquisition is measured as the log-transformed annual number of green patents assigned to each supplier. We find that suppliers acquire more external green patents when their customers face stronger local environmental policy pressure, with a one-standard-deviation increase in policy intensity associated with approximately 5.7% higher external green patent acquisition. The result is robust to placebo tests, alternative measures, and PPML estimation, while instrumental-variable estimates point in the same direction. Further analyses document stronger customer green-transition urgency and supplier-perceived supply-chain uncertainty under greater downstream policy intensity, and they show that the relationship is stronger among suppliers with weaker bargaining power or lower R&D intensity. Environment-related policy intensity is more strongly associated with end-of-pipe technology acquisition, whereas energy-transition and market-incentive policy intensity are more strongly associated with source-control technology acquisition. Overall, the findings indicate that environmental policy intensity in downstream customers’ cities is associated with upstream suppliers’ external green technology acquisition within observed customer–supplier relationships. Full article

Stable and efficient supply chain relationships are essential for small and medium-sized enterprises (SMEs) to strengthen competitive advantage and achieve sustainable growth. However, how resource-constrained SMEs overcome internal resource constraints and enhance competitiveness through supply chain integration remains underexplored. Taking China’s Supply Chain Innovation and Application Pilot Program (SCIAPP) as a quasi-natural experiment, this study uses panel data of SMEs from 2014 to 2024 and employs a SCIAPP significantly improves SMEs’ competitive advantage (${\beta }_1$ = 0.016, p < 0.01). Mechanism analyses reveal that SCIAPP enhances competitive advantage by mitigating the bullwhip effect and increasing innovation investment. The positive effect is stronger for firms with higher supply chain dependence, for non-capital-intensive firms, and for smaller firms. Further analysis shows that improvements in competitive advantage contribute to higher firm value. This study extends research on SMEs’ competitive advantage by demonstrating how government-led supply chain governance facilitates capability development among resource-constrained firms. The findings also provide implications for policymakers seeking to improve supply chain governance and promote the high-quality development of SMEs. Full article

This study provides a comprehensive quantitative assessment of the carbon footprint of a four-star hotel in Istanbul, Türkiye, spanning the period from 2022 to 2024, to identify critical operational emission drivers. Methodologically grounded in ISO 14064-1 and the Greenhouse Gas Protocol, the research integrates direct (Scope 1), indirect energy (Scope 2), and selected value-chain (Scope 3) emissions. The operational data underwent rigorous uncertainty and sensitivity analyses, alongside multi-tier benchmarking against global, climatic, and regional datasets. Findings reveal that total emissions increased from 1444.93 tCO₂e in 2022 to 1592.61 tCO₂e in 2024, predominantly driven by grid-connected electricity, which accounted for approximately 47.1% of the total footprint in 2024. Key operational hotspots include the continuous baseload energy demands of mechanical rooms and lifts, fugitive R-404A refrigerant leakage, and Scope 3 impacts from red meat procurement and organic waste. Furthermore, sensitivity analyses confirmed the footprint’s high elasticity to the national grid’s carbon intensity. Despite the facility outperforming benchmarked peers in overall eco-efficiency, the study concludes with actionable, stratified recommendations for facility engineers, hotel operators, and tourism policymakers to overcome technical, supply-chain, and grid-level barriers to decarbonization. Full article

With the continued advancement of low-carbon campus transformation and the increasing penetration of renewable energy, campus integrated energy systems have become key infrastructure for green campus development. However, the highly random nature of student behavior causes dynamic fluctuations in electricity, heating, and cooling loads, creating major challenges for real-time supply-demand balance and economic system scheduling. To address this problem, this paper takes student behavior uncertainty as the core disturbance factor and proposes a flexible architecture-driven autonomous adaptation and multi-energy complementary optimization strategy. A closed-loop operation paradigm of signal–response–complementarity–regulation is established, in which dynamic electricity price signals, comfort-oriented guidance, and campus functional energy-zone division are combined to form a multi-level autonomous response chain. To improve solution efficiency, the electromagnetic wave propagation algorithm is further enhanced, and a Multi-Objective Electromagnetic Wave Propagation Algorithm (MEMWPA) is developed. Wave-impedance matching and energy-flux-density feedback mechanisms are introduced to strengthen convergence performance in complex multi-objective optimization problems. Comparative case studies show that the proposed strategy can effectively smooth the net load curve, reduce the campus peak load by 26.73%, and increase the load factor by 14.533 percentage points, thereby improving both operational flexibility and energy efficiency. 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

Fruit and vegetable supply chains generate heterogeneous data across production, storage, logistics, and sales, creating challenges for trusted data sharing, privacy protection, and real-time traceability across distributed supply-chain information systems. Conventional single-chain blockchains suffer from limited scalability, data redundancy, and low retrieval efficiency, making them inadequate for high-frequency full-process information management. This study proposes a multi-chain blockchain framework for trusted full-process information management of fruit and vegetable supply chains. The framework integrates traceability, enterprise, notary, and regulatory chains to support hierarchical data management and privacy isolation. A reputation-based notary node election mechanism and a threshold-signature scheme based on Shamir secret sharing are designed to enhance cross-chain security and distributed regulatory consensus. To improve retrieval efficiency, a Cuckoo-Augmented Merkle Tree (CMerkle) and a skip-list-based block index are developed. Simulation results show that all malicious nodes were restricted by the 19th round, signature aggregation required 70.16 ms in a 500-node setting, and CMerkle achieved retrieval speedups of 14.7 and 153 times at data scales of 500 and 10,000 records, respectively. The framework supports trusted data governance, real-time traceability, privacy-preserving sharing, and regulatory decision support in blockchain-enabled supply-chain information systems. Full article

Amid increasing upstream raw-material disruptions and battery supply uncertainty, NEV manufacturers may obtain emergency supply from vertically integrated competitors after a disruption, but must incur technology conversion costs. We develop a differentiated Cournot duopoly model with an outsourcing manufacturer and an integrated manufacturer to compare equilibrium, profit, and welfare outcomes with and without emergency procurement cooperation. The results show that cooperation has a clear feasibility boundary jointly determined by market potential, relative costs, and conversion costs, giving rise to competition-only, cooperation-only, and coopetition regimes. The cooperation option reshapes pre-disruption quantity decisions: the outsourcing manufacturer increases regular procurement, while the integrated manufacturer reduces its own-brand output by a larger amount. Within the cooperation region, higher conversion costs continuously reduce the outsourcing manufacturer’s profit, whereas the integrated manufacturer’s profit can be U-shaped. Greater product substitutability makes cooperation more fragile and widens the divergence between private profit incentives and supply-chain resilience. Consumer surplus and social welfare may also move in different directions, depending on the outsourcing manufacturer’s benchmark market share and conversion cost. A Nash-bargaining extension confirms the robustness of the activation condition and pre-disruption quantity-adjustment mechanism, while reducing emergency markups and improving consumer surplus. Full article

Geopolitical conflicts, resource-protection policies, and unexpected disruptions have heightened supply-security concerns across the global nickel industry chain. This study constructs a multilayer trade network based on complex network theory to characterize structural evolution across the upstream, midstream, and downstream segments and applies a cascading-failure model to simulate the propagation of supply risks. There are four main findings: (1) The global nickel trade network exhibits pronounced layer heterogeneity, with the midstream layer acting as the principal amplifier of cascading failure risks. (2) Nodes with high centrality and broad cross-layer participation largely coincide with the countries that generate the largest systemic risks. (3) A small group of countries controls most trade flows and dominates risk transmission. (4) The center of systemic risk is shifting from traditional industrial and trading economies toward resource suppliers and countries that integrate resource extraction with processing. These findings support a risk-governance strategy based on diversified supply sources, dynamic monitoring of critical nodes, improved resilience in midstream smelting and refining, strategic resource stockpiling, and the circular utilization of nickel resources. Full article

With the rapid development of E-commerce platforms (EPs) and the accumulation of consumer data, demand information sharing has become increasingly important in green supply chains. However, the interaction between selling mode selection and information-sharing strategies remains insufficiently explored. This study develops a game-theoretic model for a green supply chain consisting of a manufacturer and an EP to examine selling modes and information-sharing decisions under reselling and agency modes. The results indicate that information sharing is highly dependent on information accuracy. Specifically, when information accuracy exceeds a threshold, the manufacturer no longer purchases demand information because the marginal benefit cannot compensate for the acquisition cost. Under the reselling mode, the EP shares information only when information accuracy falls within an intermediate range, whereas under the agency mode, it always chooses to share information due to additional information-sharing revenue. Moreover, the manufacturer prefers the agency mode when the service cost performance factor is below the critical threshold and switches to the reselling mode otherwise. Information sharing consistently improves environmental performance, while the preferred selling mode depends on the service cost performance factor. The extension further shows that introducing an information collection cost narrows the information-sharing region and weakens the EP’s incentive to share information. Full article

Artificial intelligence (AI) is transforming dairy quality assessment by enabling rapid, non-destructive, and data-driven monitoring across the dairy supply chain. Conventional analytical methods, although accurate, are often labor-intensive, time-consuming, and unsuitable for real-time applications. The integration of advanced sensing technologies with AI has therefore emerged as a promising approach for improving dairy quality control and food safety. This review examines recent advances in AI-enabled dairy quality assessment, covering spectroscopy-based sensing technologies, biomimetic sensing systems, machine vision, and multimodal data fusion. Particular attention is given to their applications in major dairy products, including raw milk, milk powder, cheese, and yogurt. Comparative analysis shows that AI-assisted sensing significantly enhances the accuracy, efficiency, and automation of compositional analysis, adulteration detection, microbial screening, freshness evaluation, and defect inspection, while multimodal approaches offer new opportunities for comprehensive quality assessment. The review further highlights the emerging role of explainable artificial intelligence (XAI) in improving the transparency and trustworthiness of dairy detection systems. Recent advances in feature attribution, decision interpretation, and uncertainty quantification are discussed, together with challenges related to data scarcity, model generalization, cross-instrument transferability, and explainability evaluation. Future developments in multimodal foundation models, standardized datasets, and explainable intelligent systems are expected to accelerate the deployment of trustworthy and scalable dairy quality assurance frameworks. Full article

Food spoilage along the supply chain represents a major global challenge, contributing to economic losses, environmental impacts, and food safety concerns. Graphene-based materials have emerged as promising platforms for real-time food freshness monitoring owing to their high surface area, electrical conductivity, chemical sensitivity, and compatibility with flexible sensing architectures. This review critically examines graphene-based sensing strategies for food freshness and spoilage monitoring, including chemiresistive, dielectric, field-effect, optical/fluorescence, photoelectrochemical, mass-sensitive, and colorimetric approaches. Representative sensing platforms are compared in terms of analytical performance, including detection range, limit of detection, selectivity, response and recovery times, calibration, reproducibility, and stability. Particular attention is given to machine-learning-assisted sensing, multifunctional platforms for temperature, humidity, and gas monitoring, and the challenges associated with real-world implementation, including environmental interference, sensor fouling, signal drift, long-term stability, and cross-matrix validation. Finally, commercialization readiness, integration into intelligent packaging, and safety and regulatory considerations related to graphene-based food-contact applications are discussed. Overall, the review identifies the main technological gaps and future research priorities toward robust, scalable, and practical graphene-based systems for real-time food freshness monitoring. Full article

Sustainable supply chains carry a cost premium that can make their demand base fragile when economic shocks tighten household budgets, because the more expensive environmental, social, and governance (ESG)-aligned product is often among the first that consumers forgo. We propose extending resilience thinking to the downstream, demand-side node of the supply chain: we frame premium-tolerant ESG consumption as a conceptual indicator of demand-side resilience—an absorptive buffer that may help keep sustainable supply chains viable through disturbance—and draw on signaling theory to model how it is formed. Using a nationally structured, quota-controlled survey of 3000 Korean consumers collected by the Korea Consumer Agency, we estimate a moderated-mediation structural equation model in which ESG signal trust is associated with resilient sustainable consumption through perceived ESG legitimacy, conditioned by income and perceived greenwashing. Because the data are cross-sectional, we report associations rather than causal effects. Trust was positively associated with perceived legitimacy, which in turn was associated with resilient sustainable consumption, so that legitimacy is the proximal correlate linking trust to resilience. Income moderated this conversion only modestly—the sustainability–resilience trade-off is statistically present but small in magnitude—while perceived greenwashing did not attenuate it, and its unexpected positive coefficient proved unstable across specifications and is not interpreted. A multi-group comparison indicated that the pattern of associations differs with the perceived credibility of the ESG signal environment: when third-party signals were perceived as credible, the trust–legitimacy–consumption route was more pronounced, whereas when they were not, trust was associated with consumption more directly. Consumers also expected ESG far more of large firms than of micro-enterprises. No supply-chain-level outcome was measured; the cross-level link is advanced as a proposition for future work. The study reframes supply chain resilience as partly demand-side, potentially sustained by credible, independently verified ESG signals rather than by firms’ own disclosure. Full article