Search Results (812)

Global supply chains are increasingly exposed to systemic disruptions driven by environmental pressures, geopolitical instability, and social unrest. Although Green Supply Chain Management (GSCM) is a strategic approach balancing sustainability and competitiveness, current research remains fragmented and regionally focused. Prior research has identified critical chokepoints and conceptualized disruption propagation through simulation and event system theory, yet few studies have operationalized large-scale empirical datasets to quantify cross-domain resilience. Addressing this gap, we collected and analyzed over 1.8 million news articles from more than 705 global portals spanning October 2023 to September 2025. Using GPT-based autonomous classification, approximately 67,434 disruption events directly related to GSCM were extracted and categorized by event type, geography, and significance. A system-of-systems framework was employed, linking seven domains: environment and climate, energy and resources, manufacturing and production, logistics and transportation, trade and commerce, agri-food systems, and labor and social systems. The results demonstrate that disruptions are unevenly distributed. The United States (8945 events), China (7822), and India (5311) emerged as global hubs, while Saudi Arabia acted as a single-domain chokepoint in energy. Energy and resources accounted for 22 percent of all events, followed by logistics (19 percent) and manufacturing (17 percent). Temporal analysis revealed major spikes in February 2024 (56,595 weighted intensity units) and June 2024 (10,861 units). Correlation analysis confirmed strong interdependencies across domains with average values greater than 0.7. This study contributes a globally scalable, data-driven framework to quantify disruption intensity, frequency, and interdependence in GSCM. It advances resilience research and offers actionable insights for policymakers and industry leaders. Full article

At present, the construction of China’s shared manufacturing platform is developing rapidly. However, it is still in the stage of practical exploration, facing numerous challenges, such as difficulties in resource integration, immature business models, and a weak digital foundation. This paper takes Changzhou Zhiyun Tiangong’s “Super Virtual Factory” as an example, utilizing the grounded theory to conduct a case study on this shared manufacturing platform. Using a ‘condition-action-result’ framework, this paper explores the value co-creation (VCC) mechanism in a shared manufacturing ecosystem. We analyze how digital intelligence convergence (DIC) and supply chain collaboration (SCC) facilitate the digital intelligence transformation of consumption, production capacity, and products. The study finds that consumer insight, technological drive, government support, enterprise challenges, and the Changzhou home appliance industry cluster are the internal driving forces for the shared manufacturing ecosystem to carry out industrial ecological VCC; DIC and SCC are the two key elements for digital intelligence technology empowerment. Digital intelligence technology is empowered from three aspects—technology, resources, and structure—enabling organizational members with capability and authority while achieving “decentralization” of industrial chains. Finally, digital intelligence empowerment enables the shared manufacturing ecosystem to achieve VCC of the industrial ecosystem, thereby establishing a VCC model for the digital intelligence empowerment shared manufacturing ecosystem. The results of the study not only help enrich the theory of VCC in shared manufacturing platforms but also provide practical insights for the digital intelligence transformation of traditional manufacturing enterprises. Full article

Integrated permitting systems are being progressively implemented by governments worldwide owing to their beneficial effects on site-level environmental management. However, different countries have implemented different systems that reflect their own national conditions, resulting in varying efficacy. Based on experience from Korea, here, we propose an e-permit system that implements (i) package management of industries related to or within the value chain of permitted industries, (ii) site-specific best available techniques (BATs), (iii) automatic calculations of BAT-associated emission levels (BAT-AELs), and (iv) code-based management of environmental factors using a ReGreen-BAT (RG-BAT) diagram. The e-permit system improves permitting process efficiency and reduces the review period from 35 days to 2–23 days depending on the industry sector. Additionally, by leveraging big data collected through the Integrated Environmental Permitting System for post-permit management, administrative efficiency could be further improved. Integrating industries related to or within the value chain of the target industries in the Integrated Environmental Management System (IEMS) can reduce pollution, improve resource circulation, and promote energy efficiency and cost savings. When selecting BATs, the balloon effect and internal site-specific factors should be evaluated to realize more tailored and acceptable BAT selection. Automation of BAT-AEL calculations using Python considerably reduces the processing time to 2–3 weeks. The RG-BAT diagram helps visualize BAT effectiveness and allows businesses to easily identify suitable BATs. The insights gained herein indicate that countries implementing IEMS can benefit from sharing implementation experiences and should collaborate on the development of advanced systems. Full article

The forest-based bioeconomy is increasingly recognized as a key pillar of European bioeconomy strategies, with potential to drive sustainable innovation, rural development, and climate action. However, regional disparities persist, particularly in Southern Europe. This study assesses the development of a forest-based entrepreneurial ecosystem located in the Spanish region of Castilla-La Mancha, using an adapted multidimensional framework that considers institutional, supply, and demand-side drivers. Fifteen interviews were conducted with key players in the forestry sector. Results indicate an incipient and fragmented ecosystem: while initiatives such as UFIL Cuenca foster entrepreneurship and innovation, the region lacks three main and different aspects: (i) a coherent strategic vision, (ii) cluster development, and (iii) presents coordination failures. Coordination structures as sectoral roundtables are viewed as critical for the forestry value chain but currently underutilized. The study emphasizes the importance of aligning forest-based resources with supportive entrepreneurial environments—where networks, infrastructure, and institutional mechanisms interact—to enable systemic innovation and sustainable regional development. The findings highlight the need for integrated regional strategies, strengthened governance mechanisms, stable financial resources for regional structures and expanded entrepreneurship support to advance the forest-based entrepreneurial ecosystems in Spain. Full article

This paper proposes an enhanced hierarchical fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) integrated with the Analytic Network Process (ANP) for solving multi-criteria decision-making (MCDM) problems under uncertainty. Conventional fuzzy TOPSIS models often face significant challenges, such as restrictions to specific fuzzy number formats, difficulties in normalization when zero or very small values appear, and limited capacity to capture hierarchical interdependencies among criteria. To address these limitations, we develop a generalized fuzzy geometric mean approach for deriving weights from pairwise comparisons that can accommodate multiple fuzzy number types. Moreover, a novel normalization function is introduced, which ensures mathematically valid outcomes within the [0, 1] interval while avoiding division-by-zero and inconsistency issues. The proposed method is validated through both a numerical building selection problem and a practical supplier selection case study. Comparative analyses against established fuzzy MCDM models demonstrate the improved robustness, flexibility, and accuracy of the approach. Additionally, a sensitivity analysis confirms the stability of results with respect to variations in criteria weights, fuzzy number formats, and normalization techniques. These findings highlight the potential of the proposed fuzzy hierarchical TOPSIS-ANP framework as a reliable and practical decision support tool for complex real-world applications, including supply chain management and resource allocation under uncertainty. Full article

This paper reveals profound obstacles to sustainable supply chain integration in Industry X.0 in the United Arab Emirates (UAE) by utilizing a hybrid Fuzzy Delphi-TOPSIS approach and enriching the viewpoints of 102 experts in oil/gas (45%), logistics (30%), government (15%), and academia (10%). The top obstacles are a lack of favorable leadership (Fuzzy Delphi Threshold (FDT), FDT = 0.82) and insufficiency of sustainability professionals (FDT = 0.82), with strategy prioritization training (Rank 1, Closeness Coefficient Index (cci) cci = 0.1255) and employee engagement (Rank 2, cci = 0.1499) being among the most important solutions as opposed to technological solutions. Most importantly, AI-related technologies had a low ranking of seventh place because of their lack of implementation, which proves that human capital enhancement is always prioritized before technological adaptation. The oil/gas industry values AI with respect to regulatory compliance commitments to emissions monitoring, whereas SMEs accentuate the problem of training because of the limited resources available to them, which also indicates the societal relevance of the concept of AI to social entrepreneurship and the blockchain-based transparency and access to green technologies. This study contributes (1) a decision-oriented framework bridging the traditional 2050 vision of the UAE and the realities it faces day to day, (2) empirical insights into the need for cultural principals within governance so as to prevent the so-called paperwork syndrome, and (3) a theoretical advancement that sees AI as an enhancer of human-centric methodologies. The conclusions provide policymakers with knowledge of the importance of the ability to contextualize investments in organizational culture prior to technology implementation in order to provide effective sustainability transitions. Full article

In the context of a complex and volatile domestic and global environment, Chinese enterprises face frequent risks of supply chain disruption that seriously hinder their operations. The rise of the digital economy offers new opportunities to strengthen supply chain resilience. Building on supply chain collaboration and value co-creation theories, this study conceptualizes supply chain collaboration through three dimensions, namely information collaboration, governance collaboration, and innovation collaboration, and explores their role in enhancing resilience. Using panel data of Chinese A-share listed firms from 2011 to 2023, this study investigates the impact of focal firm digitalization on supply chain resilience and its underlying mechanisms. The results indicate that focal firm digitalization generates significant backward spillover effects, enhancing the resilience of its upstream suppliers. Although its positive influence on supply chain stability (measured by supply chain demand and supply fluctuations) is not statistically significant, it substantially enhances recovery (measured by supply chain efficiency) and adaptability (measured by supplier innovation). Mechanism analysis further reveals that digitalization strengthens supply chain collaboration through information, governance, and innovation channels, thereby reinforcing resilience. Moreover, the positive effects are heterogeneous, varying with industry competition intensity, the closeness of upstream–downstream relationships, and suppliers’ regional resource endowments. These findings highlight the need to design digitalization strategies centered on focal firm leadership and upstream–downstream collaboration, thereby advancing both resilience improvement and collaborative mechanism development through differentiated and targeted approaches. Full article

The growing environmental concerns related to climate change and resource depletion urgently call for a global and profound innovation of industrial production models and energy systems to foster the adoption of environmentally efficient technologies, circular economy principles, and long-term sustainable value chains [...] Full article

The emergence of Generative Artificial Intelligence (GenAI) is reshaping logistics and supply chain operations, offering new opportunities to improve efficiency, accuracy, and responsiveness. In the automotive manufacturing sector, where high-volume throughput and precision are critical, the integration of AI technologies into warehouse management represents a strategic advancement. This study presents a case analysis of the implementation of AI-driven reception processes at an Automotive facility in Blaj, Romania. The research focuses on the transition from manual operations to automated recognition using industrial-grade imaging systems integrated with enterprise resource planning platforms. The integrated approach used combines Value Stream Mapping, quantitative performance analysis, and statistical validation using the Wilcoxon Signed-Rank Test. The results reveal a substantial reduction in reception time up to 79% and significant cost savings across various operational scales with improved data accuracy and minimized logistics failures. To support broader industry adoption, the study proposes a Cleaner Logistics and Supply Chain Model, incorporating principles of sustainability, ethical compliance, and continuous improvement. This model serves as a strategic framework for organizations seeking to align AI adoption with long-term operational resilience and environmental responsibility. The findings validate the operational and financial advantages of AI-enabled warehousing management in achieving sustainable digital transformation in logistics. Full article

The surge in electric vehicles (EVs) and renewable energy has made lithium-ion batteries (LIBs) critical to the global energy transition. This review examines how LIBs contribute to a circular economy, focusing on China and the United States as key actors shaping the battery value chain. We analyze technological advancements, market growth, supply chain dynamics, ESG risks, and strategies for recycling, reuse, and next-generation chemistries. China’s approach centers on vertical integration and scale, while the U.S. emphasizes innovation, policy incentives, and diversification. Despite progress, gaps remain in closed-loop systems, ethical sourcing, and supply chain resilience. Realizing sustainable battery growth will require coordinated efforts in technology, governance, and international collaboration to align resource efficiency with long-term environmental and economic goals. Full article

The deep integration of artificial intelligence (AI) is a core driver for digitalization and intelligence in agricultural and food engineering, boosting production efficiency, resource optimization, and product quality. This review systematically analyzes AI’s application scenarios, technical pathways, and challenges across the agricultural value chain. It aims to develop a structured taxonomy of AI-driven technical application mechanisms in agriculture, highlighting their roles in optimizing core agricultural processes. A systematic literature review was conducted using reputable databases, including Google Scholar, IEEE Xplore, ScienceDirect, Web of Science, SpringerLink, and Scopus, focusing on peer-reviewed articles from the last decade. Findings show that AI-enhanced techniques improve product quality and safety inspection efficiency. However, challenges like multi-source data synchronization barriers, high intelligent equipment costs, and model adaptability limitations in complex agricultural environments remain. This review contributes to the field by providing a unified framework for understanding AI applications in agri-food engineering, identifying key research gaps, and highlighting pathways for sustainable technology adoption that can benefit diverse agricultural stakeholders. Full article

Livestock and poultry genetic resources form the cornerstone of elite population breeding, new breed development, and global food security. The yak (Bos mutus), endemic to the Qinghai–Tibet Plateau, is indispensable for maintaining regional biodiversity and ecological stability. The Datong yak—China’s first nationally recognized cultivated yak breed and the world’s inaugural domesticated yak variety—plays a pivotal role in enhancing yak production performance, mitigating grassland–livestock conflicts, and restoring degraded grasslands. This study aimed to provide a scientific basis for the conservation and sustainable utilization of yak genetic resources by comprehensively evaluating the genetic resource value of Datong yaks. We employed the market price method, opportunity cost method, and shadow engineering method to assess four value dimensions—aligned with the Food and Agriculture Organization (FAO) livestock genetic resource value framework and adapted to China’s yak production context: direct use value (DUV), indirect use value (IUV), potential use value (PUV), and conservation value (CV). Data were collected through expert consultations, semi-structured interviews, and questionnaire surveys in Datong County (Qinghai Province, the core production area of Datong yaks) between August and September 2024, with the widely distributed Qinghai Plateau yak serving as the control breed. Based on a recent market survey, the total genetic resource value of Datong yaks in China was estimated at CNY 2.505 billion in 2024, highlighting the increasing economic and strategic significance of yak genetic resources. Among the four value dimensions, PUV accounted for the largest share (65.67%), driven by superior production performance, market price premiums, and reduced feeding costs. DUV contributed 20.72%, reflecting the value of biological assets and beef products; IUV represented 7.10%, primarily associated with grassland conservation benefits; and CV constituted 6.51%, encompassing costs for genetic resource preservation and cultural heritage contributions. These results underscore the substantial potential of Datong yak genetic resources, particularly given their unique adaptation to high-altitude environments and their critical role in supporting local livelihoods and ecological stability. Future research should focus on expanding breeding programs and genetic conservation, optimizing industrial and value chains, and strengthening genetic improvement initiatives to promote ecological security and sustainable development of the yak industry on the Qinghai–Tibet Plateau. Full article

While weighted multi-model approaches are widely used to improve predictive capability, hydrological models (HMs) and their weighted combinations that perform well under past conditions may not guarantee robustness under future climate scenarios. Furthermore, the extent to which weighting schemes influence the propagation of runoff projection uncertainty remains insufficiently explored. Therefore, this study evaluates the capacity of strategies that weight monthly scale HMs to narrow runoff projection uncertainty. Since standard approaches rely only on historical simulation skill and offer static weighting, this study introduces a refined framework, the Uncertainty Optimizing Multi-Model Ensemble (UO-MME), which dynamically considers the trade-offs between calibration performance and projection uncertainty. In performing the uncertainty decomposition, a total of 140 ensemble runoff projections, generated through a modelling chain comprising five GCMs, two emission scenarios, two downscaling methods, and seven HMs, were analyzed for Beydag and Tahtali watersheds in Türkiye. Results indicate that standard techniques, such as Bayesian model averaging, ordered weighted averaging, and Granger–Ramanathan averaging, led to either marginal reductions or noticeable increases in projection uncertainty, depending on the case and projection period. Conversely, the UO-MME achieved average reductions in projection uncertainty of around 30% across the two watersheds by balancing the influences of climate signals produced by GCMs that are reflected in the projections through HMs while maintaining high simulation accuracy, as indicated by Nash–Sutcliffe efficiency values exceeding 0.75. Although not designed to eliminate inherently irreducible uncertainty, the UO-MME framework helps temper the inflation of noisy GCM signals in runoff responses, providing more balanced hydrological projections for water resources planning. Full article

To overcome the production bottleneck induced by the high viscosity of extra-heavy oil and resolve the issues of limited efficiency in traditional thermal oil recovery methods (including cyclic steam stimulation (CSS), steam flooding, and steam-assisted gravity drainage (SAGD)) as well as the fragmentation of existing viscosity reducer evaluation systems, this study establishes a multi-dimensional evaluation system for the effectiveness of viscosity reducers, with stage-averaged remaining oil saturation as the core benchmarks. A “1D static → 2D dynamic → 3D synergistic” progressive sequential experimental design was adopted. In the 1D static experiments, multi-gradient concentration tests were conducted to analyze the variation law of the viscosity reduction rate of viscosity reducers, thereby screening out the optimal adapted concentration for subsequent experiments. For the 2D dynamic experiments, sand-packed tubes were used as the experimental carrier to compare the oil recovery efficiencies of ultimate steam flooding, viscosity reducer flooding with different concentrations, and the composite process of “steam flooding → viscosity reducer flooding → secondary steam flooding”, which clarified the functional value of viscosity reducers in dynamic displacement. In the 3D synergistic experiments, slab cores were employed to simulate the SAGD development process after multiple rounds of cyclic steam stimulation, aiming to explore the regulatory effect of viscosity reducers on residual oil distribution and oil recovery factor. This novel evaluation system clearly elaborates the synergistic mechanism of viscosity reducers, i.e., “chemical empowerment (emulsification and viscosity reduction, wettability alteration) + thermal amplification (steam carrying and displacement, steam chamber expansion)”. It fills the gap in the existing evaluation chain, which previously lacked a connection from static performance to dynamic displacement and further to multi-process synergistic adaptation. Moreover, it provides quantifiable and implementable evaluation criteria for steam–chemical composite flooding of extra-heavy oil, effectively releasing the efficiency-enhancing potential of viscosity reducers. This study holds critical supporting significance for promoting the efficient and economical development of extra-heavy oil resources. Full article

Polyhydroxyalkanoate (PHA) bioplastics are produced from wastewater as a carbon recovery strategy. However, the tuneable characteristics of PHAs and wastewater biorefinery potential have not been comprehensively reviewed. The aim of this study is to review the main challenges and strategies for carbon recovery from wastewater feedstocks via PHA production, assessing potential target biopolymer applications. Diverse PHA-accumulating prokaryotes metabolize organic pollutants present in wastewater through different metabolic pathways, determining the biopolymer characteristics. The synthesis of PHAs using mixed microbial cultures with wastewater feedstocks derived from municipal, agro-industrial, food processing, lignocellulosic biomass processing and biofuel production activities are described. Acidogenic fermentation of wastewater feedstocks and mixed microbial culture enrichment are key steps in order to enhance PHA productivity and determine biopolymer properties towards customized bioplastics for specific applications. Biorefinery of PHA copolymers and extracellular polysaccharides (EPSs), including alginate-like polysaccharides, are alternatives to enhance the value-chain of carbon recovery from wastewater. PHAs and EPSs exhibit a wide repertoire of applications with distinct safety control requirements; hence, coupling biopolymer production demonstrations with target applications is crucial to move towards full-scale applications. This study discusses the relationship between the metabolic basis of PHA synthesis and composition, wastewater type, and target applications, describing the potential to maximize carbon resource valorisation. Full article