Search Results (76)

Global supply chains face unprecedented complexity as organizations must simultaneously achieve sustainability objectives and operational resilience amid evolving risk landscapes. Despite extensive research, the absence of systematic knowledge synthesis has limited understanding of how these dual imperatives intersect. This study conducts the first comprehensive scientometric analysis of global supply chain risk management research, examining 1228 peer-reviewed articles from major databases published from 2016 to June 2025. The study employed co-occurrence analysis, temporal burst detection, and network visualization to map the intellectual structure and evolutionary dynamics of this field. Our study reveals four distinct research clusters: risk factor identification (traditional and unconventional threats), environmental and social sustainability integration, technology-driven challenges, and innovative risk management methodologies. Temporal analysis demonstrates significant research acceleration post-2020, driven by pandemic disruptions, with emerging focus on cyberattacks, geopolitical conflicts, and ESG compliance challenges. The findings reveal critical gaps at the sustainability-resilience intersection, particularly paradoxical tensions where short-term resilience measures may compromise long-term sustainability goals. We propose four priority research directions: digital transformation frameworks balancing sustainability-resilience trade-offs, ESG-integrated early warning systems, adaptive governance mechanisms for unconventional risks, and policy frameworks addressing regulatory complexity. This systematic knowledge mapping provides theoretical foundations for future research and practical guidance for supply chain managers navigating dual sustainability-resilience objectives in an uncertain global environment. Full article

Ensuring product quality and safety is fundamental to sustainable production and consumption. With the rapid advancement of digital technologies such as blockchain and big data, quality and safety traceability systems have become essential tools to enhance transparency, accountability, and governance efficiency across supply chains. The sustainable functioning of these systems, however, depends on the coordinated actions of multiple stakeholders—including governments, enterprises, consumers, and industry associations—making the study of technological and institutional interactions particularly significant. This paper extends evolutionary game theory to the context of technology-enabled sustainable governance by constructing a tripartite game model involving government regulators, traceability enterprises, and consumers from both technological and institutional perspectives. Unlike existing studies, which focused solely on government regulation, this research explicitly incorporates the role of industry associations in shaping stakeholder behavior and integrates consumer rights protection mechanisms as well as the adoption of emerging technologies such as blockchain into the model. Analytical derivations and MATLAB-based simulations reveal that strengthening reward–penalty mechanisms and improving digital maturity significantly enhance enterprises’ incentives for truthful information disclosure; consumers’ verification and reporting behaviors generate bottom-up pressure that encourages stricter governmental supervision; and active participation of industry associations helps share regulatory costs and stabilize cooperative equilibria. These findings suggest that combining technological innovation with institutional collaboration not only improves transparency and strengthens consumer trust but also reshapes the incentive structures underlying traceability governance. The study provides new insights into how multi-stakeholder coordination and technological adoption jointly foster transparent, credible, and resilient traceability systems, offering practical implications for advancing digital transformation and co-governance in sustainable supply chains. Full article

Amidst the challenges posed by global climate change and China’s dual-carbon objectives, the advancement of low-carbon innovative development within urban renewal projects faces obstacles arising from the divergent interests of multiple stakeholders. This research identifies the government, social capital entities, and design research institutes as principal stakeholders and develops a tripartite evolutionary game model incorporating sixteen critical variables. The findings indicate that governmental incentive policies facilitate the system’s progression toward a stable equilibrium. Notably, when the intensity of incentives surpasses a specific threshold, a positive feedback mechanism emerges between social capital engagement and design quality. Consequently, the study proposes a collaborative framework characterized by “dynamic incentives, risk sharing, and mutual recognition of standards,” which underscores the co-evolutionary dynamics among system design, technological innovation, and market participation. This framework offers a novel approach to addressing prevalent challenges in urban renewal, including inadequate incentives, elevated risks, and low efficiency in outcome conversion. Full article

Rural resilience building has gained increasing scholarly attention, yet existing literature overlooks the temporal dynamics of resilience evolution and lacks an integrative framework to explain cross-level mechanisms. This paper uses a longitudinal case study to explore how rural resilience transitions from a low-equilibrium to a high-equilibrium state and how neo-endogenous practices emerge in a weak institutional context. The study reveals three key findings. First, the village’s resilience evolved through three phases—institutional intervention, community capital activation, and resilience self-reinforcement—driven by co-evolutionary interactions between an enabling government and the rural community. This process is marked by chain effects of multidimensional community capital (e.g., cultural capital enhancing social capital) and overflow effects from resilience amplification (e.g., multi-scalar network). Second, exogenous resources and endogenous community capital are critical in the neo-endogenous model, but their synergy relies on vertical institutional interventions that foster horizontal networks and enhance communities’ resource absorption capacity. Third, the government enables resilience building by creating a support ecosystem that transitions from institutionally bundled resources to a higher-order composite space, facilitated by urban–rural interactions and community restructuring. The study makes three theoretical contributions: (1) it proposes an analytical framework integrating an enabling government, community capital, and ecosystem upgrading, thus advancing beyond the current community capital-centric paradigm; (2) it introduces a three-phase process model that unpacks spatiotemporal interactions across urban-rural interfaces, multi-scalar networks, and state-community relations, addressing the limitations of static factor-based analyses; (3) it reconceptualizes the role of government as an “enabling government” that mediates local and extra-local resource interfaces, challenging the neo-endogenous theories’ neglect of institutional agency. These insights contribute to rural resilience scholarship through a complex adaptive systems lens and offer policy implications for synergistic urban-rural revitalization. Full article

The spread of epidemic-associated panic information through online social platforms, as well as the allocation and utilization of therapeutic defensive resources in reality, directly influences the transmission of infectious diseases. Moreover, how to reasonably allocate resources to effectively suppress epidemic spread remains a problem that requires further investigation. To address this, we construct a coupled three-layer network framework to explore the complex co-evolutionary mechanisms among false panic information, therapeutic defensive resource transmission, and disease propagation. In the model, individuals can obtain therapeutic defensive resources either through centralized distribution by government agencies or through interpersonal assistance, while the presence of false panic information reduces the willingness of neighbors to share resources. Using the microscopic Markov chain approach, we formulate the dynamical equations of the system and analyze the epidemic threshold. Furthermore, systematic simulation analyses are carried out to evaluate how panic information, resource-sharing willingness, centralized distribution strategies, and resource effectiveness affect epidemic prevalence and threshold levels. For example, under a representative parameter setting, the infection prevalence decreases from 0.18 under the random allocation strategy to 0.03 when resources are allocated exclusively to infected individuals. Moreover, increasing the total supply of resources under high treatment efficiency raises the epidemic threshold by approximately 2.5 times, effectively delaying the outbreak. These quantitative results highlight the significant role of allocation strategies, resource supply, and treatment efficiency in suppressing epidemic transmission. Full article

Driven by the rapid evolution of AI technology, compatible management mechanisms have become a systematic project involving the participation of multiple stakeholders. However, constrained by the rigidity and lag of traditional laws, the “one-size-fits-all” regulatory model will exacerbate the vulnerability of the complex system of AI governance, hinder the sustainable evolution of the AI ecosystem that relies on the dynamic balance between innovation and responsibility, and ultimately fall into the dilemma of “chaos when laissez-faire, stagnation when over-regulated”. To address this challenge, this study takes the multi-stakeholder collaborative mechanism co-established by governments, enterprises, and third-party technical audit institutions as its research object and centers on the issue of “strategic fluctuations” caused by key factor disturbances. From the perspective of the full life cycle of technological development, the study integrates the historical compliance performance of stakeholders and develops a nonlinear dynamic reward and punishment mechanism based on Credit Entropy. Through evolutionary game simulation, it further examines this mechanism as a realization path to promote the transformation from passive campaign-style AI supervision to agile governance of AI, which is characterized by rapid response and minimal intervention, thereby laying a foundation for the sustainable development of AI technology that aligns with long-term social well-being, resource efficiency, and inclusive growth. Finally, the study puts forward specific governance suggestions, such as setting access thresholds for third-party institutions and strengthening their independence and professionalism, to ensure that the iterative development of AI makes positive contributions to the sustainability of socio-technical systems. Full article

To mitigate explosion hazards arising from hydrogen leakage and subsequent mixing with air, the injection of inert gases can substantially diminish explosion risk. However, prevailing research has predominantly characterized inert gas dilution effects on explosion behavior under quiescent conditions, largely neglecting the turbulence-mediated explosion enhancement inherent to dynamic mixing scenarios. A comprehensive investigation was conducted on the combustion behavior of 30%, 50%, and 70% H₂-air mixtures subjected to jet-induced (CO₂, N₂, He) turbulent flow, incorporating quantitative characterization of both the evolving turbulent flow field and flame front dynamics. Research has demonstrated that both an increased H₂ concentration and a higher jet medium molecular weight increase the turbulence intensity: the former reduces the mixture molecular weight to accelerate diffusion, whereas the latter results in more pronounced disturbances from heavier molecules. In addition, when CO₂ serves as the jet medium, a critical flame radius threshold emerges where the flame propagation velocity decreases below this threshold because CO₂ dilution effects suppress combustion, whereas exceeding it leads to enhanced propagation as initial disturbances become the dominant factor. Furthermore, at reduced H₂ concentrations (30–50%), flow disturbances induce flame front wrinkling while preserving the spherical geometry; conversely, at 70% H₂, substantial flame deformation occurs because of the inverse correlation between the laminar burning velocity and flame instability governing this transition. Through systematic quantitative analysis, this study elucidates the evolutionary patterns of both turbulent fields and flame fronts, offering groundbreaking perspectives on H₂ combustion and explosion propagation in turbulent environments. Full article

Given the great proportion of CO₂ emissions from electricity generation in total energy-related CO₂ emissions, this article constructs a tripartite evolutionary game model consisting of vertical governments and power generation groups (PGGs), where the vertical governments include the central government (CG) and local governments (LGs), considering the externalities of different power generation modes on energy security and the environment. This article analyzes the stable strategies of the three players through replicator dynamics equations, draws the evolutionary phase diagrams, and analyzes the asymptotic stability of equilibrium points by using Jacobian matrices. To validate and broaden the results, this article also provides a numerical simulation. This article concludes that (1) a reduction in the supervision, enforcement, or low-carbonization costs of the CG, LGs, or PGGs motivates it or them to choose “supervision”, “enforcement”, or “low-carbonization” strategies; (2) an increase in penalty incomes or expenses encourages the CG or LGs to choose the “supervision” or “enforcement” strategies; (3) a rise in extra tax expenses motivates PGGs to choose the “low-carbonization” strategy; (4) a change in the externalities of energy security or the environment has no impact on the CG’s strategy. The above conclusions offer the CG and LGs with references for making effective low-carbon policies and provide PGGs with references for choosing an appropriate power generation mode. Full article

The integration of renewable energy into the grid has led to problems such as low utilization rate of energy storage resources (“underutilization after construction”) and insufficient system stability. This paper studied the co-evolution mechanism of power market entities empowered by shared energy storage. Based on the interaction among power generation enterprises, power grid operators, and government regulatory agencies, this paper constructed a three-party evolutionary game model. The model introduced a dynamic reward and punishment mechanism as well as a random interference mechanism, which makes it more in line with the actual situation. The stability conditions of the game players were analyzed by using stochastic differential equations, and the influences of key parameters and incentive mechanisms on the stability of the game players were investigated through numerical simulation. The main research results showed the following: (1) The benefits of shared energy storage and opportunistic gains had a significant impact on the strategic choices of power generation companies and grid operators. (2) The regulatory efficiency had significantly promoted the long-term stable maintenance of the system. (3) Dynamic incentives were superior to static incentives in promoting cooperation, while the deterrent effect of static penalties is stronger than that of dynamic penalties. (4) The increase in the intensity of random disturbances led to strategy oscillation. This study suggested that the government implement gradient-based dynamic incentives, maintain strict static penalties to curb opportunism, and enhance regulatory robustness against uncertainty. This research provided theoretical and practical inspirations for optimizing energy storage incentive policies and promoting multi-subject coordination in the power market. Full article

Malaria persists as a paradigmatic model of co-evolutionary complexity, emerging from the dynamic interplay among a human host, Anopheles vectors, and Plasmodium falciparum parasites. In human populations, centuries of selective pressures have sculpted an intricate and heterogeneous immunogenetic landscape. Classical adaptations, such as hemoglobinopathies, are complemented by a diverse array of genetic polymorphisms that modulate innate and adaptive immune responses. These genetic traits, along with the acquisition of functional immunity following repeated exposures, mitigate disease severity but are continually challenged by the parasite’s highly evolved mechanisms of antigenic variation and immunomodulation. Such host adaptations underscore an evolutionary arms race that perpetually shapes the clinical and epidemiological outcomes. Intermediaries in malaria transmission have evolved robust responses to both natural and anthropogenic pressures. Their vector competence is governed by complex polygenic traits that affect physiological barriers and immune responses during parasite development. Recent studies reveal that these mosquitoes exhibit rapid behavioral and biochemical adaptations, including shifts in host-seeking behavior and the evolution of insecticide resistance. Mechanisms such as enhanced metabolic detoxification and target site insensitivity have emerged in response to the widespread use of insecticides, thereby eroding the efficacy of conventional interventions like insecticide-treated bed nets and indoor residual spraying. These adaptations not only sustain transmission dynamics in intervention saturated landscapes but also challenge current vector control paradigms, necessitating the development of innovative, integrated management strategies. At the molecular level, P. falciparum exemplifies evolutionary ingenuity through extensive genomic streamlining and metabolic reconfiguration. Its compact genome, a result of strategic gene loss and pruning, is optimized for an obligate parasitic lifestyle. The repurposing of the apicoplast for critical anabolic functions including fatty acid, isoprenoid, and haem biosynthesis highlights the parasite’s ability to exploit host derived nutrients efficiently. Moreover, the rapid accumulation of mutations, coupled with an elaborate repertoire for antigenic switching and epigenetic regulation, not only facilitates immune escape but also accelerates the emergence of antimalarial drug resistance. Advanced high throughput sequencing and functional genomics have begun to elucidate the metabolic epigenetic nexus that governs virulence gene expression and antigenic diversity in P. falciparum. By integrating insights from molecular biology, genomics, and evolutionary ecology, this study delineates the multifaceted co-adaptive dynamics that render malaria a recalcitrant global health threat. Our findings provide critical insights into the molecular arms race at the heart of host–pathogen vector interactions and underscore promising avenues for the development of next generation therapeutic and vector management strategies aimed at sustainable malaria elimination. Full article

A complex network topology marked by co-competitive relationships between manufacturing enterprises can meaningfully influence low-carbon technology selection, thereby affecting the low-carbon technology diffusion process. This study develops a small-world network game model based on an optimal update strategy involving the government and manufacturers with co-competitive relationships, and then uses it to assess the evolutionary dynamics of low-carbon technology selection and diffusion among manufacturers. The results indicate that the government should identify the critical threshold for subsidies based on the carbon tax to optimize the regulatory and incentivizing effects of government subsidies. The topological structure of manufacturers’ small-world networks is the key to low-carbon technology selection and diffusion. In favorable conditions, when a small-world network approaches a regular network in terms of structure, the extent of low-carbon technology diffusion is maximized; in unfavorable conditions, diffusion is minimal. Thus, the government can tighten or relax market access restrictions on the manufacturing industry and encourage the development of manufacturing clusters to change the structure of market competition. Compared with the random selection, the optimal update strategy can increase the probability density of low-carbon technology diffusion among manufacturers and rapidly achieve a balanced, stable state. Full article

Integration of IoT and predictive modeling is critical for optimizing microclimate management in urban-agglomeration vertical farming. In this study, we present a hybrid digital twin approach that combines a physical microclimate model with a distributed IoT monitoring system to simulate and control the phytotron environment. A set of heat- and mass-balance equations governing the dynamics of temperature, humidity, and transpiration was implemented and parameterized using a genetic algorithm (GA)—an evolutionary optimization method—with real-time data collected over three intervals (72 h, 90 h, and 110 h) from LoRaWAN sensors (temperature, humidity, CO₂) and Wi-Fi-connected power meters managed by Home Assistant. The optimized model achieved mean temperature deviations ≤ 0.1 °C, relative humidity errors ≤ 2%, and overall energy consumption accuracy of 99.5% compared to measured values. The digital twin reliably tracked daily climate fluctuations and system energy use, confirming the accuracy of the hybrid approach. These results demonstrate that the proposed framework effectively integrates theoretical models with IoT-derived data to deliver precise environmental control and energy-use optimization in vertical farming, while also laying the groundwork for scalable digital twins in controlled-environment agriculture. Full article

Rapid regional development and intensified human activities increasingly disturb ecosystems, posing substantial threats to the integrity of large-scale ecological zones. As a World Natural Heritage site and a crucial national ecological zone, the Zhangjiajie Scenic Area plays a pivotal role in China’s environmental conservation efforts. To comprehensively assess tourism ecological security in the Scenic Area and strengthen the scientific basis for resource management and policymaking, this study developed a multi-dimensional ecological security evaluation system covering 2010–2024, incorporating dynamic changes in perturbation, reaction, and governance. Using entropy weight–TOPSIS and coupling coordination models, combined with obstacle degree analysis, we examined the temporal trajectory of ecological security and analyzed its underlying driving mechanisms. The study also examined factors influencing the sustainable development of the ecosystem. The results indicate the following: (1) Tourism ecological security in the Scenic Area followed a V-shaped trajectory of “rapid degradation—steady recovery—impact and rebound.” It declined sharply to an unsafe level between 2010 and 2014, steadily recovered from 2015 to 2019, briefly dropped in 2020, and then rebounded, reaching a peak evaluation value of 0.519 in 2024. (2) The co-evolution of perturbation, reaction, and governance subsystems has matured: their coupling coordination degree has increased annually and has remained at the level of “intermediate coordination” since 2020. The reaction subsystem plays a central role, serving as a bridge between perturbation and governance. (3) The driving factors exhibit a phased evolutionary pattern of “elements—facilities—structure—function.” Cultivated land area, total road mileage, and artificial afforestation area constitute the main long-term constraints. This research provides important insights for strengthening ecological security and sustainability in the Scenic Area while advancing regional ecosystem development. It also offers valuable guidance for ecological security management and policymaking in similar nature reserves. Full article

The interconnections among water, energy, and food (WEF) systems are growing increasingly complex, making it essential to understand their evolutionary mechanisms and coordination barriers to enhance regional resilience and sustainability. In this study, we investigated the WEF system in Northeast China by constructing a comprehensive indicator system encompassing resource endowment and utilization efficiency. The coupling coordination degree (CCD) of the WEF system was quantitatively assessed from 2001 to 2022. An obstacle degree model was employed to identify key constraints, while grey relational analysis was used to evaluate the driving influence of individual indicators. Furthermore, a co-evolution model based on logistic growth and competition–cooperation dynamics was developed to simulate system interactions. The results reveal the following: (1) the regional WEF-CCD increased from 0.627 in 2001 to 0.769 in 2022, reaching the intermediate coordination level, with the CCDs of the food, water, and energy subsystems rising from 0.39 to 0.62, 0.38 to 0.60, and 0.40 to 0.55, respectively, highlighting that the food subsystem had the most stable and significant improvement; (2) Jilin Province attained the highest WEF-CCD, 0.850, in 2022, while that for Heilongjiang remained the lowest, at 0.715, indicating substantial interprovincial disparities; (3) key indicators, such as food self-sufficiency rate, electricity generation, and ecological water use, functioned as both core constraints and major drivers of system performance; (4) co-evolution modeling revealed that the food subsystem exhibited the fastest growth, followed by water and energy (${\alpha }_3$ >${ \alpha }_1$ > ${ \alpha }_2$ > 0), with mutual promotion between water and energy subsystems and inhibitory effects from the food subsystem, ultimately converging toward a stable equilibrium state; and (5) interprovincial co-evolution modeling indicated that Jilin leads in WEF system development, followed by Liaoning and Heilongjiang, with predominantly cooperative interactions among provinces driving convergence toward a stable and coordinated equilibrium despite structural asymmetries. This study proposes a transferable, multi-method analytical framework for evaluating WEF coordination, offering practical insights into bottlenecks, key drivers, and co-evolutionary dynamics for sustainable resource governance. Full article

This study investigates how new R&D institutions mediate policy–market disjunctures to foster integrated innovation and entrepreneurship ecosystems. Employing a longitudinal case analysis (2013–2023) of the Jiangsu Industrial Technology Research Institute (JITRI), we delineate a three-phase evolutionary process: (1) an initial government-dominated phase, stimulating foundational capability development through contract R&D; (2) a subsequent marketization phase, enabling systemic resource integration via co-creation centers and global networks; and (3) a culminating synergy phase, where policy–market alignment facilitates ecosystem optimization through crowdsourced R&D and cross-domain collaboration. Three core mechanisms underpin this adaptation: policy–market coupling (providing external momentum), endogenous capability development (absorption to innovation), and dynamic resource orchestration (acquisition to optimization). JITRI’s hybrid governance model demonstrates that stage-contingent interventions—specifically, policy anchoring in early stages followed by market-responsive resource allocation—effectively transmute inherent tensions into productive synergies. These findings yield implementable frameworks for structuring innovative ecosystems and underscore the necessity for comparative studies to establish broader theoretical generalizability. Full article