Search Results (455)

During the critical period of agricultural green transformation, clarifying the evolutionary logic of farmers’ green production behavior under a multi-stakeholder framework provides significant insights for implementing “Dual Carbon” goals, establishing long-term mechanisms for high-quality agricultural development, and resolving deep-seated contradictions in agricultural non-point source pollution. Based on the social co-governance and public participation framework, this paper constructs a tripartite evolutionary game model involving government departments, farmer groups, and the general public, grounded in cost–benefit analysis, social governance friction, and evolutionary game theory. Through simulation, the study explores the equilibrium states and the specific impacts of varying parameter values on stable points. The findings reveal that: (1) The “interest price scissors” (benefit disparity) between green and conventional production is the key determinant of farmers’ strategic equilibrium. Once this structural contradiction is resolved, green production becomes the optimal strategy. (2) Farmers are highly sensitive to marginal cost–benefit fluctuations, leading to a sequential behavioral cascade: farmers retreat first, followed by the government, and finally the public. (3) Public participation cost is the pivotal variable for activating the co-governance mechanism, and the application of digital governance tools determines the time required to reach equilibrium. (4) A “Success Paradox” exists in government regulation; incentive mechanisms must be adjusted promptly after initial success. (5) Integrated policy combinations outperform single instruments; breaking the “locked-in” state requires a policy shock of sufficient intensity. This research offers a theoretical basis and policy enlightenment for optimizing the social co-governance landscape and promoting sustainable agricultural modernization. Full article

The increasing pressure on natural resources and the environment has intensified the need for sustainable cascade utilization in closed-loop supply chains (CLSCs). This study develops a game-theoretic framework to examine cascade utilization under both constant and heterogeneous quality restoration costs across three collection structures: centralized, manufacturer-led, and third-party collection. The results show that the relative performance of different structures depends on key economic conditions, including material recycling revenue and the comparative advantage of remanufacturing. No single structure dominates across all dimensions: a manufacturer-led collection tends to promote new product sales, while a third-party collection enhances remanufacturing and recovery levels, particularly under cost heterogeneity. Environmental performance, evaluated through collection quantity, cascade utilization efficiency, and an environmental impact indicator, also varies across structures, with cost heterogeneity shifting advantages toward the third-party collection. Policy analysis further indicates that both collection and remanufacturing subsidies increase recovery volumes but operate through distinct mechanisms. The collection subsidy expands return flows but may reduce cascade utilization efficiency by directing more low-quality products to recycling, whereas remanufacturing subsidy promotes higher-value reuse pathways and improves environmental performance. These findings highlight the importance of aligning collection structures and policy instruments under different cost conditions to enhance resource efficiency and support the circular economy and sustainable consumption and production objectives. Full article

This study aimed to identify structural differences in shot characteristics between world-class badminton players and regional collegiate players using a hierarchical comparative framework. Match data were collected from top-ranking players in the Badminton World Federation (BWF) World Series and from regional university league players. All shots were recorded using a custom VBA-based notational analysis system, including player identity, court position, shot type, and rally outcome. Statistical analyses were conducted using chi-square tests with residual analysis and logistic regression modeling incorporating competitive level and tactical patterns. The results revealed that BWF players exhibited significantly lower error rates and higher proportions of building shots, indicating superior rally stability and tactical consistency. In contrast, collegiate players demonstrated higher variability in performance, including both higher ace rates and error rates. These findings suggest that world-class performance is characterized by the ability to sustain rallies while minimizing errors, rather than relying solely on offensive success. Although effect sizes were relatively small and the predictive performance of the regression model was modest (AUC = 0.53), the analysis successfully captured structural differences in tactical patterns between competitive levels. This supports the value of the model as a tool for understanding game dynamics rather than prediction. From a theoretical perspective, the findings align with the view of sport performance as a dynamic, self-organizing system, where outcomes emerge from interactions between players. Practically, the results suggest that improving defensive stability, reducing errors, and maintaining rally continuity are critical for achieving higher competitive performance. This study demonstrates the usefulness of a hierarchical comparative approach for bridging the gap between domestic and international performance standards and provides a foundation for future data-driven research in badminton. Full article

The study explores the relatively underexamined role of artificial intelligence policies in sustainable agricultural development by investigating how governments, enterprises, and farmers interact under different policy incentives. A combination of tripartite evolutionary and Stackelberg game models is employed to examine how artificial intelligence can support more effective policy design, improve the speed of response, and foster greater collaboration among stakeholders. The analysis primarily draws on simulated data, reflecting the impact of policy incentives across various contexts. Findings suggest that artificial intelligence policies can meaningfully enhance cooperation, thereby promoting sustainable agricultural development. Higher levels of government incentives appear to encourage participation from both enterprises and farmers, while artificial intelligence contributes to faster and more precise policy adjustments. Theoretically, the study offers a framework for understanding artificial intelligence policy in agriculture and elucidates the mechanisms governing stakeholder interactions. From a practical perspective, the results provide cautious guidance for the design of artificial intelligence policies aimed at fostering sustainability. Full article

Against the backdrop of a technological revolution centered on green and low-carbon development, the deep integration of digitalization and greening has become a core engine for high-quality progress. Moving beyond linear perspectives of environmental governance, this study constructs tripartite evolutionary game models to dissect the strategic interactions among government, enterprises, and consumers. Focusing on the institutional context of e-commerce, we examine how platform-enabled transparency mechanisms (e.g., blockchain traceability and carbon labeling) shape these interactions through key parameters: greenwashing detection (θ), premium loss coefficient (η), and information screening cost (C_D). The analysis reveals that the long-term trajectory is fundamentally determined by the intrinsic economic viability of corporate transformation. Government intervention acts as an equilibrium selector, influencing the speed of convergence, while product value (consumer utility and premium) and platform transparency determine the sustainability of the equilibrium. Critically, the tripartite model shows that the optimal outcome—full enterprise transformation and consumer adoption—can be achieved without sustained government intervention when product fundamentals are sufficiently attractive. This demonstrates the potential for market self-regulation to sustain digital–green synergy. The study makes three contributions: it captures the full tripartite feedback loop, reveals the saturation effect of policy intensity, and embeds platform transparency mechanisms into an evolutionary framework. The findings reframe the government’s role as a temporary enabler and position e-commerce platforms as key governance intermediaries, offering a theoretical basis for adaptive governance strategies in digital commerce. Full article

Building world-class ports requires not only scale expansion but also sustainable structural capability. However, the concept of port “hardcore strength” remains insufficiently clarified and operationalized in existing sustainability and port evaluation research. In this study, port hardcore strength is understood as an integrated capability framework covering infrastructure efficiency and logistics capability, connectivity and regional integration, maritime services and industrial clustering, strategic leadership and innovation capability, and sustainable governance and green port development. This study proposes a sustainability-oriented evaluation framework for assessing the “hardcore strength” of world-class ports through a multi-dimensional indicator system. Methodologically, the study integrates the EWM and CRITIC, and introduces Bland–Altman analysis to examine whether the EWM and CRITIC weight vectors exhibit an obvious systematic bias prior to game-theoretic integration. Using 18 representative global ports from 2019 to 2023 as a case study, the results show that the overall ranking structure remains broadly stable, with Singapore Port and Shanghai Port consistently ranking first and second, respectively, while some middle-ranked ports exhibit moderate positional changes. The findings suggest that differences in world-class port development are rooted not only in operational scale, but also in the coordination of multiple capability dimensions. The study enriches the understanding of world-class port evaluation from a sustainability-oriented perspective. Full article

This study investigates trade-in channel selection in a closed-loop supply chain under a hybrid carbon policy framework that integrates cap-and-trade and carbon taxation. Game-theoretic models are developed for three manufacturer-led channels: manufacturer trade-in (M-CX), retailer trade-in (R-CX), and third-party trade-in (T-CX). The analysis examines pricing strategies, profitability, and carbon emission reductions across these channels. The key findings are as follows: (1) Carbon tax consistently compresses manufacturer profits, whereas cap-and-trade mechanisms exhibit a non-linear U-shaped effect. Manufacturer profits remain highest under the M-CX channel, irrespective of policy intensity. (2) Retail prices are most sensitive to carbon policies under the T-CX channel, where trade-in rebates increase with carbon intensity. The R-CX channel sustains higher retail prices and rebates than M-CX, while T-CX surpasses both under conditions of high carbon intensity. (3) Carbon emission reductions decline sharply under M-CX and R-CX as policy stringency increases. In contrast, the T-CX channel establishes a buffering mechanism through rising rebates, exhibiting the slowest rate of decline. At low carbon intensity, T-CX yields the lowest reduction levels; however, under high intensity, it overtakes the other channels to achieve the highest reduction. This study offers insights for manufacturers’ channel selection and government policy coordination under hybrid carbon regulation regimes. Full article

Against the dual backdrop of worsening air pollution and industrial intelligent transformation, industrial robot technology has become an important means to promote air pollution synergistic governance. This study innovatively incorporates public environmental satisfaction and industrial robot application as dynamic mechanism variables, constructing an evolutionary game model involving the government, industrial enterprises, and the public. Through theoretical analysis and numerical simulation, the study reveals the influence mechanism of key cost–benefit parameters on stakeholders’ strategic interaction and the system’s evolution path. The conclusions are as follows: (1) The government’s environmental supervision directly affects enterprises’ green transformation willingness, and enterprises’ behavior reversely impacts public satisfaction and supervision effectiveness, forming a “supervision–response–feedback” closed-loop. (2) The cost and benefit parameters related to industrial robots are crucial for the evolution of the game system, and there is significant heterogeneity in their impact on the strategic choices of the three parties. The robot adaptation transformation of enterprise industrial depends on the comprehensive consideration of the transformation cost and the green benefits. Public supervision is regulated by both the supervision cost and the incentive benefit. The government regulation takes into account both the regulatory cost and the loss of social reputation. Various parameters dynamically regulate the system’s equilibrium by altering the party’s cost–benefit structure. (3) The application of industrial robots and the feedback of public environmental satisfaction form a coupling effect, jointly determining the long-term evolution direction of the game system. When the cost benefit and supervision incentives are well-matched, enterprises will actively promote the green transformation of industrial robots in order to achieve intelligent pollution control. The effectiveness of public supervision has also been fully realized. The dynamic adaptation of the two components can lead the system towards an efficient and stable equilibrium in air pollution governance. Full article

Background: Circular economy (CE) practices, such as glass bottle refilling, are critical to the beverage industry’s sustainability. However, coordinating manufacturer marketing efforts with collector reverse logistics investment remains a strategic challenge. Methods: This study develops a Stackelberg game-theoretic model featuring a manufacturer and a collector. The model incorporates communication effort as a demand driver and analyzes the role of bottle quality (damage rates) and the reusable bottle unit cost on the optimal decisions of the players and the collection rate. Results: Equilibrium analysis shows that the quality of the reusable bottle and the rate of bottle damage are crucial in reducing the operational costs of the refilling program. Additionally, these factors significantly influence the decisions made by manufacturers and collectors regarding their investments in communication and collection systems. Conclusions: The study demonstrates that successful refilling requires strategic coordination between manufacturers and collectors, particularly in terms of communication and investment in reverse logistics. Managerial insights indicate that investing in the quality of bottles is the key factor for achieving joint profitability. Full article

The rise in aggregation platforms has reshaped the competitive ride-hailing market. Display slots (i.e., platform-determined ranking priority) have become a key tool for influencing order allocation. Their interaction with drivers’ multi-homing behavior poses new challenges for platform sustainability. This paper constructs a two-stage Stackelberg game model with one aggregator and two underlying ride-hailing platforms. Display slots enhance supply-side lock-in, while a waiting time function links passenger utility to demand allocation. Building on theoretical analysis of two-sided market competition and multi-homing effects, we propose two hypotheses: (H1) under specific conditions, competition for display slots may lead to a Prisoner’s Dilemma equilibrium, and (H2) the proportion of multi-homing drivers positively moderates this dilemma, thereby expanding its occurrence range. Numerical simulation results under baseline parameter settings reveal that display slots generate a supply-side amplification effect by locking in multi-homing drivers. In symmetric markets, a prisoner’s dilemma range exists where mutual purchase erodes collective profits; this range expands with the share of multi-homing drivers. Higher driver profit sensitivity raises the threshold required for display slots to be profitable. In asymmetric markets, dominant platforms (strong brands, low costs) gain more from display slots, potentially leading to unilateral purchasing. Social welfare effects of display slot competition depend on a critical threshold of waiting-time sensitivity: social welfare improves above the threshold and declines below it. This study clarifies the boundaries of display slots as supply-side non-price competitive tools, offering quantitative insights for aggregator platform design and regulatory policy. The findings carry managerial implications for platform strategy and policy aimed at sustainable development. Full article

This study employs a reflexive autoethnography, guided by flow and place attachment theory, to examine how gaming experiences influence attachments to virtual environments and inspire real-world travel intentions. Data comprise reflexive journal notes written over a 10-month period after playing multiple video games and analysed using reflexive thematic analysis following a hybrid deductive–inductive approach. The analysis identified eight themes across three dimensions: temporal immersion, escapism, narrative immersion, and self-expression under flow; emotional, cognitive, and behavioural attachment under place attachment; and place-induced travel intention as the behavioural outcome. The findings establish flow as a critical antecedent to the development of place attachment within virtual environments. Consistent with emerging scholarship, the study confirms that attachment formation does not require physically tangible places; rather, it can emerge through digitally mediated presence and interaction, indicating that virtual environments are capable of eliciting place attachment. More significantly, it demonstrates that these virtual attachments can fluidly extend toward real places depicted in games, revealing a cross-environmental continuity in attachment processes. The integrated framework thus contributes a novel theoretical proposal linking flow, virtual and real place attachment, and tourism behaviour, an area that remains conceptually fragmented and empirically underdeveloped. Full article

The growing flexibility of load dispatching in modern smart grids has exposed critical limitations in conventional capacity pricing mechanisms, which calculate charges based solely on monthly maximum demand without distinguishing when peak demand occurs. This approach fails to reflect the temporal value of capacity and provides insufficient incentives for demand-side optimization. To address these challenges, this paper proposes a time-of-use (TOU) capacity pricing method that integrates user load characteristics to enable more equitable cost allocation and optimized electricity consumption patterns. The methodology employs K-means clustering analysis of user load profiles to partition pricing periods, accurately capturing differential capacity value across temporal intervals. We validate the clustering approach through the elbow method and silhouette analysis, confirming k = 3 as optimal and demonstrating K-means superiority over hierarchical and density-based alternatives. This data-driven approach ensures that period delineation reflects actual consumption patterns of commercial and industrial users. A capacity cost allocation model is established using the Shapley value method, incorporating maximum demand in each designated period while maintaining revenue neutrality for the grid operator. The 80% load simultaneity factor is empirically validated using 12 months of Shanghai industrial data (May 2023–April 2024). A Stackelberg game-based pricing model for TOU capacity tariffs is developed, incentivizing users to deploy energy storage systems and optimize charging strategies. We prove game convergence theoretically and demonstrate equilibrium achievement within 3–5 iterations across diverse initialization scenarios. Energy storage capacity is optimized by sector (3.5–6.5% of peak demand) rather than uniformly, and realistic battery self-discharge rates (0.006%/hour) are incorporated. Case study analysis using real operational data from 11 commercial and industrial sub-sectors in Shanghai demonstrates effectiveness. Extended to 12 months with seasonal analysis, results show the proposed strategy reduces the peak-to-valley difference ratio by 2.4% [95% CI: 1.9%, 2.9%], p < 0.001; increases the system load factor by 1.3% [95% CI: 0.9%, 1.7%], p < 0.001; and achieves reductions in users’ total capacity costs of 3.6% [95% CI: −4.2%, −3.0%], p < 0.001. Comparative analysis shows the proposed method significantly outperforms simple TOU (improvement +1.2 pp) and peak-responsibility pricing (improvement +0.6 pp). Monte Carlo robustness analysis (1000 scenarios) confirms performance stability under demand uncertainty. This research provides theoretical foundations and practical methodologies for capacity cost allocation, offering valuable insights for policymakers and utilities seeking to enhance demand-side response mechanisms and improve power resource allocation efficiency. Full article

The emergence of smart healthcare platforms has significantly enhanced the accessibility of medical services, yet it has also introduced critical challenges such as information overload and patient decision-making dilemmas. This study investigates the interaction and synergistic optimization of a dual-drive mechanism—comprising ‘patient proactive search’ and ‘artificial intelligence (AI)-driven recommendations’—within healthcare platform recommendation systems. By developing a game-theoretic model that incorporates heterogeneous users (including random single-search users and rational multi-stage decision-makers) and competitive medical institutions, we systematically analyze how different recommendation strategies influence market equilibrium, patient utility, and platform profit. The findings reveal that in the absence of AI-driven recommendations, a higher proportion of random users intensifies price competition among providers. In contrast, the integration of AI-driven recommendations with proactive search behavior effectively mitigates price wars and enhances matching efficiency. Furthermore, our analysis identifies an optimal recommendation strategy weight that enables the platform to simultaneously improve both equilibrium price and user demand. This research offers a theoretical foundation for the design of efficient and sustainable recommendation systems in smart healthcare platforms and provides practical managerial insights for improving medical service efficiency and optimizing resource allocation. Full article

Newsgames integrate journalism and digital game design to communicate news through interactive storytelling. This study examines how narrative journalism can function as a design framework for newsgames by exploring how its storytelling techniques—such as characterisation, scene construction, and narrative structure—can inform the design of interactive journalistic experiences while maintaining factual integrity. Using a narrative literature review, the research synthesises scholarship from journalism studies, narrative theory, and game studies to analyse how narrative structures and gameplay systems shape the communication of news in digital games. The paper proposes a conceptual model that integrates narrative journalism and newsgames with Symbolic Interaction Theory (SIT) and the Values at Play (VAP) heuristic, providing a theoretical framework for interactive journalistic storytelling. Within this framework, gameplay operates as a narrative structure through which players engage with journalistic content by interacting with simulated environments, characters, and decision-making processes. The analysis indicates that the communicative capacity of newsgames depends on how journalistic information is embedded within gameplay mechanics and narrative systems, where interactivity, player agency, and ethical design shape how audiences interpret complex social and political issues. The study concludes that newsgames function as interactive narrative systems of journalism, in which gameplay serves as a storytelling mechanism that enables audiences to engage with news through participation and interpretation. By positioning narrative journalism as a design framework for interactive news experiences, this research contributes a theoretical foundation for analysing and developing narrative-driven newsgames. Full article

Financial time series forecasting remains a challenging task due to the high non-stationarity and concept drift inherent to market data. Existing deep learning models, such as LSTMs and transformers, typically employ static weights after training, limiting their ability to adapt to rapid market regime shifts (e.g., from trends to reversals). To bridge this gap between static parameters and dynamic environments, we propose a novel framework named Game-Theoretic HyperNetwork (GTH-Net), which introduces a context-aware meta-learning mechanism to achieve adaptive forecasting. Specifically, we first introduce an Evolutionary Game-Theoretic Correction Module (E-GTCM) to explicitly extract latent buying and selling pressure based on market microstructure priors through an iterative gated evolution process. Subsequently, we propose a HyperNetwork-based fusion mechanism that treats the extracted game state as a meta-context to dynamically generate the weights of the forecasting head. This allows the model to automatically switch its prediction rules in response to shifting market regimes. Extensive experiments on real-world stock datasets demonstrate that GTH-Net significantly outperforms baselines in terms of machine learning predictive accuracy and simulated financial profitability. Furthermore, ablation studies and parameter analysis confirm that the dynamic weight generation mechanism effectively captures market reversals caused by overcrowded trades. Full article