Search Results (857)

Driven by the rapid proliferation of generative artificial intelligence, the computing power industry is undergoing a paradigm shift from traditional linear supply chains toward complex, interdependent innovation ecosystems. This study investigates the evolutionary dynamics of the computing power ecosystem, specifically examining the strategic interplay between antitrust regulation and vertical integration. We construct a tripartite evolutionary game framework involving the government regulators, leading computing power incumbents, and downstream AI innovators. By deriving evolutionarily stable strategies, we analyze the underlying mechanisms of system transitions and employ numerical simulations to explore key parametric sensitivities. The theoretical analysis suggests that the evolution of the AI computing power innovation ecosystem manifests distinct stage-based progressions and threshold-driven bifurcation characteristics—potentially transitioning from an initial efficiency-based state of “natural monopoly and passive dependence” during the industry’s emergence, through transitionary states such as the “comfort zone trap” or “regulatory stalemate” during the expansion phase, and ultimately converging toward a mature configuration of “co-opetition and endogenous growth.” The model suggests that downstream AI firms may benefit from advancing vertical integration, achieving hardware–software co-optimization through self-developed domain-specific architectures, The analysis further implies that the leading computing power firm could strengthen its ecological niche by opening its underlying interfaces and software stacks to maintain its ecological niche as the industry cornerstone in integrated form. For the government, it is necessary to establish precise dynamic intervention and orderly exit mechanisms. Full article

This study investigates the interaction between a manufacturer’s blockchain adoption strategy and a retailer’s demand information sharing strategy in a green supply chain. For four strategy combinations, we establish a multi-stage game-theoretical model of a green supply chain consisting of a single manufacturer and a single retailer. We first derive the optimal pricing, greenness, service level, and profits, followed by sensitivity and comparative analyses. Next, by examining how consumer price sensitivity and the unit adoption cost of blockchain technology interact, we identify equilibrium strategy combinations. Finally, we validate the relevant findings through numerical analysis. The results demonstrate that adopting blockchain can mitigate the double marginalization effect when consumer price sensitivity is moderate, and can enhance product greenness and service level when the adoption cost remains low. Interestingly, the manufacturer is inclined to adopt blockchain irrespective of the degree of consumer skepticism. Meanwhile, the implementation of blockchain may motivate the retailer to share information when price sensitivity falls within a moderate range. These findings present actionable guidance for green supply chains regarding blockchain and information-sharing strategies. Full article

Background. Ethanol distribution in the energy supply chain can be maximized by solving a Location Routing Problem (LRP). Manifold LRP (MLRP) expands on the classic domain assumptions of LRP to manifold surfaces, and it can be applied to profit maximization in ethanol supply chains. Methods. In this work, a hybrid MLRP (H-MLRP) is introduced as a new mixed integer nonlinear programming NP-hard problem assuming discrete facility allocation that requires a mix of truck and train transportation for ethanol distribution from the facility to its customers. Ethanol supply chain profit can be maximized by solving a stochastic nonlinear integer programming problem (SNLP) using ethanol raw materials, production quantity, logistics, railcar shipments, and transit times as the decision variables. H-MLRP and SNLP are combined as a two-stage optimization methodology to design a biofuel energy distribution system for making optimal decisions to maximize ethanol profit. Results. A case study demonstrated the effectiveness of the proposed method on the relocation of an ethanol producer that is currently located in North Dakota (ND) to Oklahoma (OK). In this case study, customer demand destinations and suppliers of raw materials are located in different regions of the United States. Conclusions. The results indicate a good use of the new model for decision-making. Full article

Cruise ship manufacturing is a high-tech, complex industry where development depends on coordination across stages and organizations. We advance the coordination literature by treating the supply chain, industry chain, and value chain as a complex system, and by linking cross-chain coordination to high-quality development in a way that is comparable to theoretical debates on capability building and productivity-oriented development. Empirically, we collect city-level panel data for ten Chinese cruise port cities from 2008 to 2023 and combine a coupling–coordination framework with a panel data qualitative comparative analysis (PD-QCA) to capture both coordination dynamics and configurational causality. Our results show substantial heterogeneity in coordination trajectories, which can be grouped into decline–recovery, high-level stability, and persistent decline/high-variability patterns. We also show that high coupling does not guarantee high-quality outcomes, which are jointly shaped by industrial foundations, high-end value creation, and innovation capacity. Moreover, we identify two main pathways: an anchoring pathway that depends on output capacity and resource inputs, and an optimizing pathway that mainly relies on investment intensity, demand-side output, and value efficiency, with cross-chain coordination acting as an enabling condition that helps improve cross-chain matching. Full article

Scientific assessment of energy conservation, emissions reduction, public health externalities, and economic costs is crucial for the sustainable development of new energy vehicles (NEVs). Despite minimal emissions during the operational phase of NEVs, the production process of energy, such as electricity and hydrogen, contributes to pollution across the full supply chain, shifting environmental and health burdens to upstream sectors and raising concerns about the overall societal benefits. To address this, we apply a full-chain life cycle assessment (FC-LCA) framework that integrates emissions from vehicle production, energy supply, and end-of-life stages, while simultaneously quantifying health-related mortality attributable to key pollutants. By incorporating upstream energy production structure and downstream industry emissions, this approach captures the complete energy supply chain and enables a systematic comparison between NEVs and conventional vehicles. We further employed and compared ARIMA, LSTM, and Bi-LSTM models to forecast future vehicle demand and defined different forecasting scenarios for China’s passenger vehicle sector. Results provide policy-relevant insights for decision-makers to make informed policy choices concerning the widespread implementation of NEVs in a sustainable manner. Full article

Real-time coordination across OEM–Tier-1 manufacturing networks remains challenging due to delayed shop-floor data, stochastic machine availability, and the need for schedule stability. This paper presents a protocol-agnostic, two-stage mixed-integer linear programming (MILP) framework for real-time family-level scheduling. The method integrates MTConnect-like data streams without requiring adherence to any single communication standard. In Stage 1, a baseline plan is generated using expected capacity; in Stage 2, a rolling-horizon recourse model adapts the plan to observed (possibly lagged) capacity while incorporating a stability penalty to control resequencing. A synthetic OEM–Tier-1 testbed with three machines (two Tier-1, one OEM) is used to benchmark performance under real-time (L = 0) and delayed (L = 5) data scenarios. Across these scenarios, the real-time rolling scheduler improves strict on-time fulfillment by approximately 70% and eliminates terminal backlog relative to static planning, while MILP solve times remain under 0.1 s per cycle. Sensitivity experiments that vary disruption intensity, replanning interval (Δ), and stability weight (λ) show consistent qualitative trends and illustrate how the framework can be tuned to balance service performance against schedule stability without sacrificing computational tractability. Full article

Carbon emission regulations and customers’ green preferences require ports and shipping companies to develop green services, but green investments entail significant costs. Vertical alliance cooperation between ports and shipping companies through sharing costs can address this issue. Most studies use non-cooperative game to analyze the competitive relationship between ports and shipping companies. Although such research can capture price competition, they struggle to address the distribution of cooperative benefits within an alliance. They also fail to simultaneously reflect the coexistence of competition and cooperation. So, we constructed a non-cooperative–cooperative biform game to analyze green investment under vertical alliance. In the non-cooperative stage, the model captures vertical price competition between ports and shipping companies, as well as horizontal competition among supply chains. In the cooperative stage, the Shapley value is used to allocate the coalition profits from green investment cooperation. The results indicate that alliance cooperation can promote the green development of shipping. Moderate green competition can promote the green development of shipping. Route substitution competition will increase service prices and green investment level and reduce the cost-sharing ratio for shipping companies. Port congestion prompts ports to increase green investment level. These findings offer references for the green collaborative development of ports and shipping companies across different countries, thereby enriching the research framework for global sustainable development in shipping. Full article

Pesticide residues in food and agricultural products continue to constitute a significant concern for food safety, particularly when rapid decision-making is required across production and supply chains. Although chromatographic methods such as GC-MS and LC-MS/MS remain essential for confirmatory analysis, their dependence on central laboratories limits their applicability for field screening. Consequently, portable biosensor-based detection platforms have attracted increasing attention as rapid screening tools. This review synthesizes 26 peer-reviewed studies published between 2010 and 2025 on portable biosensor-based screening tools for pesticide detection in food and agricultural matrices, including electrochemical sensors, immunoassays, aptamer-based systems, paper-based lateral flow devices, and smartphone-assisted platforms. Given the heterogeneity of analytes, sensing mechanisms, and study designs, a narrative synthesis approach was applied. Overall, the evidence suggests a shift from laboratory-centered detection toward field-deployable technologies that may support preliminary screening within food safety monitoring frameworks. Paper-based lateral flow assays are widely reported as deployable formats, while electrochemical and affinity-based platforms are often positioned as intermediate solutions for mobile or semi-controlled testing environments. However, most platforms remain at the proof-of-concept or early validation stage, and challenges related to matrix interference, long-term stability, reproducibility, standardization, and large-scale implementation persist. This review highlights the potential role of portable biosensor technologies as complementary tools within tiered food safety monitoring systems and outlines key priorities for further development before wider regulatory integration can be considered. Full article

This study develops a Life-Cycle Sustainability Framework for circular business models in the context of post-war economic reconstruction and sustainable value chain transformation. Ukraine is used as the main case study due to its post-war reconstruction context and the need for resource-efficient economic recovery strategies. Under conditions of disrupted supply systems, resource constraints, and structural economic change, circular economy principles are conceptualized as strategic mechanisms for enhancing resilience, resource efficiency, and long-term competitiveness rather than solely as environmental policy instruments. Building on a structured hierarchy of circular business models aligned with product life-cycle stages, the framework emphasizes value retention through functional and usage extension beyond material recovery. The framework includes a hierarchical classification of 12 circular business models and a sustainability evaluation approach based on four criteria (K1–K4), which allow for the comparative assessment of circular business models and their combinations across life-cycle stages. Using secondary statistical data and policy review as analytical inputs, the study identifies sectors with high potential for circular transformation and sustainable investment, including agriculture, energy, industry, construction, and logistics. The results indicate that circular business models applied at early life-cycle stages, such as reuse, repair, and remanufacturing, provide the highest potential for reducing resource intensity and improving long-term economic sustainability, while recycling and energy recovery play a supporting role. These findings highlight how life-cycle-oriented circular strategies can support sustainable reconstruction pathways, strengthen international cooperation, and inform policy and managerial decision-making in transitional economic contexts. Full article

Based on the panel data of Chinese A-share listed companies from 2012 to 2023, this paper takes the pilot policy of Pilot Zones for Innovative Application of Artificial Intelligence as an exogenous shock, and adopts a multi-period difference-in-differences (DID) model to systematically examine the causal effect of this policy on the quality and efficiency of enterprise innovation and its mechanism of action. It is found that the Pilot Zones for Innovative Application of Artificial Intelligence significantly improve enterprises’ innovation quality and efficiency. Mechanism tests show that the pilot policy enhances enterprise innovation quality and efficiency by driving digital transformation, eliminating information barriers, and upgrading supply chain collaboration. Heterogeneity analysis confirms that the policy dividends are more fully released in non-state-owned enterprises, high-tech enterprises, labor-intensive and technology-intensive enterprises, as well as enterprises located in cities with a higher degree of marketization. In addition, the life-cycle heterogeneity analysis shows that the pilot policy exerts the strongest and most comprehensive innovation-promoting effect on maturity-stage firms, mainly improves innovation efficiency for decline-stage firms, and does not produce significant effects for growth-stage firms. The findings offer practical insights for policymakers and local governments in refining AI-related innovation policies and pilot-zone implementation, and for enterprise managers in strategically adopting AI to strengthen innovation capability and long-term sustainable development. Full article

This study investigates the comminution behavior and beneficiation potential of lithium-bearing ores, zinnwaldite from Cínovec (Czech-Germany border) and lepidolite from Villasrubias (Spain) by integrating mineralogical analysis and mechanical characterization. The research is driven by Europe’s need for secure lithium supply chains. In particular, it focuses on the challenges associated with low-grade, fine-grained lithium micas found in hard-rock ores, which offer significant potential to supply in Europe but also pose substantial processing challenges. QMA (Quantitative Microstructural Analysis) revealed distinct differences in the textural and structural characteristics of the studied ores. Zinnwaldite-bearing rocks are coarser-grained with high interlocking and roughness, while lepidolite-bearing samples showed finer grains, lower roughness, and more disseminated mica distribution, indicated by their low clustering degree. In terms of mechanical characterization, zinnwaldite-rich ores have the lowest compressive strength, while lepidolite-rich samples showed the highest values, attributed to their finer grain size and more cohesive structure. This suggests that lepidolite may require higher energy input and finer crushing stages to achieve the target liberation size. These features influenced the breakage behavior observed during mechanical testing and comminution and are essential for enabling selective comminution, separating mica from gangue material. This study contributes to analyzing the potential of European hard-rock lithium resources from the perspective of upstream comminution, which is an essential step influencing downstream energy consumption, reagent use, and overall recovery efficiency. The results of this research emphasize that selective comminution should not rely solely on mineral hardness contrasts but must incorporate microstructural parameters such as clustering, grain size distribution, and orientation. Full article

As an effective approach to boosting consumption and facilitating the recycling of consumer goods, trade-in programs have been widely adopted by branders and e-commerce platforms. A platform supply chain system comprising e-commerce platforms and branders is investigated in this paper for this purpose. We construct a two-stage dynamic game model encompassing eight scenarios, discussing the provision of trade-in programs and product recycling issues under the resale and agency selling modes. Below are the key findings: (1) Trade-In Programs: In the resale mode, both branders and platforms prefer to adopt self-recycling when market potential is large, while opting for recycling undertaken by the other party when market potential is small. In the agency selling mode, branders prefer to adopt self-recycling (B-II) when fixed costs are high and the salvage value of used products is high, while platforms choose platform-led recycling (P-II) when fixed costs are low and the salvage value of used products is high. (2) Product Recycling: In the resale mode, branders should opt for self-recycling when facing high fixed costs, small market potential, and high salvage values, while outsourcing is more appropriate when salvage values are low. When the market potential is low, the platform ought to prefer self-recycling if the salvage value is either sufficiently high or sufficiently low; otherwise, outsourcing is preferable. In the agency selling mode, when the salvage value of used products is relatively high, platforms tend to have a free-riding mentality. When platforms provide trade-in programs, they will prioritize self-recycling if the salvage value is higher. In contrast, branders consistently achieve maximum profits when platforms adopt self-recycling. (3) Selection of Selling Mode: Branders always prefer the agency selling mode, while platforms’ mode selection depends on the trade-off between salvage value and commission rate. This study provides strategic insights for platform-based supply chain decisions. Full article

The growing digitalization of supply chains and increasing sustainability requirements create the need for structured tools that assess organizational readiness for Cognitive Twin (CT) adoption. However, existing digital twin and sustainability maturity models rarely integrate technological architecture, governance, and circularity within a unified framework. To address this gap, the study proposes the Supply Chain Twin Sustainability–Cognitive Maturity Model (SCT-SCMM), a novel framework that explicitly integrates governance structures, sustainability objectives, and a hierarchical system architecture into the assessment of Cognitive Twin readiness. Unlike existing models, the proposed framework captures the interdependencies between technological capabilities, decision intelligence, and governance mechanisms across multiple system layers, providing a systemic perspective on sustainable digital transformation. The framework structures organizational readiness through five interdependent layers: Physical, Control, Communication, Decision-making, and Governance, and defines staged maturity levels reflecting progression toward sustainable cognitive autonomy. This layered architecture enables the simultaneous evaluation of operational automation, digital intelligence, and institutional governance as co-evolving dimensions of Cognitive Twin adoption. The model was developed through a structured literature review and operationalized using a hybrid multi-criteria and fuzzy-based evaluation approach, enabling the evaluation of complex socio-technical systems under uncertainty. The framework was applied in an automated product-to-human warehouse case study to evaluate technological, sustainability, and governance readiness. The results demonstrate the model’s ability to identify maturity gaps, reveal inter-layer dependencies, and prioritize transformation pathways toward more resilient and circular logistics systems. By integrating governance, sustainability, and system architecture into a single maturity model, SCT-SCMM extends existing digital twin maturity approaches and provides a transparent decision-support tool for guiding staged Cognitive Twin adoption in next-generation sustainable supply chains. Full article

Chinese multinational enterprises, as the most active emerging-market investors, face mounting challenges in sustaining outward foreign direct investment (OFDI) under increasingly volatile global environments, yet how ESG performance shapes firms’ capacity to withstand and recover from external shocks remains poorly understood. This study investigates whether and how ESG performance enhances the OFDI resilience of Chinese multinational enterprises across the resistance phase and the recovery phase. We hypothesize that ESG performance enhances OFDI resilience through phase-specific mechanisms: in the resistance phase, ESG functions as a static resource buffer grounded in the resource-based view, while in the recovery phase, it operates as a dynamic reconfiguration mechanism consistent with the dynamic capabilities view. Using a panel dataset of 19,691 firm-year observations from Chinese A-share listed firms spanning 2008 to 2024, we employ a fixed-effects panel model to test these hypotheses. The results show that ESG performance significantly enhances OFDI resilience in both phases, and this conclusion holds after robustness and endogeneity tests. Mechanism analysis reveals that green innovation mediates the effect in both the resistance and recovery phases, while supply chain resilience and investment efficiency serve as additional mediating channels exclusively in the resistance phase. By introducing a phase-dependent perspective and highlighting ESG’s distinct roles across shock stages, this study provides practical guidance for emerging-market multinational enterprises on how to leverage ESG performance to build sustainable OFDI resilience in volatile global environments. Full article

Whether artificial intelligence (AI) can effectively enhance the operational resilience (OR) of enterprises is of great significance for the manufacturing industry to resist risks and achieve sustainable development. Employing a staggered difference-in-differences (DID) model, this paper utilizes data from Chinese A-share listed manufacturing companies from 2012 to 2023 and takes the National Artificial Intelligence Innovative Application Pioneer Zone (AIIAPZ) policy as a quasi-natural experiment to examine the impact of AI applications on the OR of enterprises. The results indicate that AI significantly enhances corporate OR. Mechanism tests reveal that AI promotes OR by reducing management agency conflicts and optimizing supply chain allocation performance. Heterogeneity analysis shows that the enabling effect of AI is more pronounced for enterprises located in the coastal eastern region, those in the growth stage, and those that are technology-intensive and capital-intensive. Further analysis indicates that the improvement in OR effectively reduces corporate operational risk and enhances their capacity for sustainable development. This study provides crucial insights for enterprises to explore synergistic pathways integrating intelligentization and promoting OR under the AIIAPZ framework. Full article