Search Results (192)

The entry of JD.com into the food delivery sector and the ensuing subsidy competition have resulted in irrational competition, merchant profit squeezes, and food safety risks in China. This study therefore investigates the collaborative governance mechanisms for food delivery platforms under involutionary competition driven by traffic contestation. A two-agent evolutionary game model between platforms and merchants is developed, and Q-learning simulations are conducted to capture dynamic learning behaviors. The analysis examines the effects of coupon face value, cost-sharing mechanisms, traffic incentives, and government incentive-penalty policies on the strategic choices of both agents. Key findings reveal that merchants are more sensitive than platforms to traffic incentives and government penalties. Traffic-dependent merchants and traffic-independent merchants exhibit significantly different responses to government interventions. The coupon face value demonstrates a threshold effect, where only a reasonable range encourages compliant behavior among both parties. Based on these results, a collaborative governance framework is proposed. For traffic-dependent merchants, the government should focus on regulating platform behaviors and supervising coupon value controls, while platforms should establish a reward-oriented, penalty-supported incentive mechanism. For traffic-independent merchants, the government should strengthen consumer-reporting penalty mechanisms and strictly control collusion risks between platforms and merchants. Platforms should increase inspection frequency and reinforce penalties to prevent, at the source, the decline in product quality and market disorder induced by involutionary competition. This study provides strategic insights for achieving collaborative governance of involutionary competition in platform economies under intense traffic contestation. Full article

The Engineering-Procurement-Construction (EPC) general contracting model has emerged as the dominant delivery method for large-scale infrastructure and industrial projects in China. However, contemporary EPC project cost control remains plagued by critical industry challenges, including fragmented cross-stage coordination, pervasive data silos, and the shallow integration of digital technologies into core management processes. This study considers three key stakeholders—government regulators, project owners, and EPC general contractors—and develops a tripartite evolutionary game model to analyze the strategic interactions underlying intelligent cost control in EPC projects. We examine the evolutionary stability of each stakeholder’s strategy selection, explore how various factors influence tripartite strategic choices, and further investigate the stability of equilibrium points in the game system. The key findings are summarized as follows: (1) Strengthening government incentives and penalties simultaneously promotes owners’ investment in intelligent cost control systems and general contractors’ active collaborative cost management. However, excessive incentive intensity undermines the government’s regulatory effectiveness. (2) Establishing a revenue-sharing mechanism for excess cost savings fully stimulates the spontaneous cooperation willingness of owners and general contractors, serving as the cornerstone for market-oriented operation of intelligent cost control. (3) Reducing owners’ intelligent construction investment costs and general contractors’ collaborative control costs effectively addresses practical implementation barriers and accelerates the digital upgrading of engineering cost management. Finally, numerical simulations are performed using MATLAB R2020b to validate theoretical findings. Full article

A common limitation of existing multi-AGV cooperative systems is their reliance on the obstacle-agnostic Manhattan distance as the basis for reward signals. This causes agents to receive misleading feedback, engage in excessive futile exploration, and ultimately achieve poor training quality. To address this, we introduce an A*-distance guidance mechanism for multi-agent reinforcement learning (MARL) path planning, built on the precise path distance computed via the A* algorithm (A*-distance). Within the QMIX framework, we incorporate an A*-distance-based guiding function into the action selection mechanism. This function evaluates candidate actions by quantifying their immediate effect on the A*-distance, providing positive incentives for actions that bring the agent closer to the goal and applying negative penalties for those that lead it farther away. This effectively biases exploration towards actions that genuinely shorten the obstacle-aware path to the goal, suppresses ineffective exploration, and accelerates policy convergence. Experiments in four warehouse environments (simple obstacles, complex obstacles, large-scale, and congested) show that, compared with standard QMIX, the proposed method achieves higher global average reward and faster convergence. The advantage grows as environment scale and obstacle density increase. In the large-scale and congested environments, standard QMIX and the other MARL baselines fail to solve the task, whereas the proposed method still succeeds. It is the only learning-based method to solve these hardest tasks while keeping path length close to that of dedicated search-based solvers. Ablation experiments further show that the A*-distance-guided action selection is the primary contributor to these gains, while the A*-distance reward plays a supporting role. Full article

Digital policy has become an important instrument for promoting sustainable agricultural modernization. However, its effectiveness depends on the strategic responses of the government, agricultural operators, and farmers. This study develops a theoretical framework to examine how digital policy affects sustainable agricultural modernization through multi-agent interaction. Specifically, it constructs a three-party evolutionary game model and a Stackelberg game model to analyze strategy evolution under different implementation costs, subsidies, and penalties, as well as the government’s first-mover role in subsidy design. The results show that digital policy does not promote sustainable agricultural modernization through a simple linear pathway. Instead, it operates by reshaping the incentive structures of agricultural operators and farmers. Lower government implementation costs increase the likelihood of active policy implementation, while subsidies for agricultural operators and farmers strengthen their willingness to adopt digital tools, engage in standardized production, and participate in digital agricultural activities. However, the marginal effect of subsidies weakens as participation and digitalization increase, indicating that unlimited subsidy expansion may reduce policy efficiency and increase fiscal pressure. This study contributes to the literature by linking digital policy design, multi-agent strategic interaction, and sustainable agricultural modernization within a unified theoretical framework. It highlights that effective digital agricultural policy requires incentive compatibility, fiscal sustainability, inclusive participation, and adaptive governance, rather than reliance solely on digital technology investment or subsidy expansion. Full article

Existing cross-chain bridges over-rely on static collateralization and post-event penalties, leaving them vulnerable to concealed on–off attacks and rational group collusion. To address these limitations, this paper proposes a Dynamic Trust Evaluation and Risk Control (DTERC) mechanism for heterogeneous cross-chain relay nodes. First, DTERC develops a multidimensional trust quantification model that combines temporal decay, robust multi-observer latency aggregation, verification accuracy, online stability, and an asymmetric one-strike penalty triggered only by cryptographic evidence. Second, DTERC constructs a threshold-aware N-player evolutionary game model to characterize the k-of-N signature structure of cross-chain relay consensus and introduces a dynamic staking function to reduce the economic incentive for collusion under bounded attack-value and parameter conditions. Third, DTERC designs a threshold-preserving FastPath mechanism to reduce redundant verification for low-risk transactions while retaining committee-level confirmation and challenge-based fallback. The empirical evaluation combines multi-agent simulation, smart-contract prototype testing, whitelist-compromise stress tests, malicious-oracle robustness analysis, network-jitter experiments, repeated trials, and parameter-sensitivity analysis. The results show that, under the tested settings, DTERC reduces the malicious transaction success rate to 0.15% under a 50% initial collusion scenario, lowers core contract Gas overhead by 35.7%, and reduces average end-to-end latency by approximately 10% in benign FastPath conditions. These findings indicate that DTERC improves the security–efficiency trade-off of heterogeneous cross-chain relay networks while making its assumptions and limitations explicit. Full article

How to promote regional agricultural sustainability through ecological compensation policy incentives and penalties has become a major concern worldwide. To evaluate the impact of ecological compensation reform on sustainable agricultural development, this study exploits China’s Ecological Compensation Incentive and Penalty Policy (ECIPP) reform as a quasi-natural experiment. Using panel data from 30 provinces (autonomous regions and municipalities) in China from 2012 to 2022, we construct a Staggered-Adoption Difference-in-Differences (SA-DID) model to identify the effects of policy implementation on the efficiency of agricultural ecological product value realization and its underlying mechanisms. The results show that: (1) the implementation of the ECIPP significantly improves the efficiency of agricultural ecological product value realization. On average, the policy increases the AEPVR efficiency score by 0.0869 units. (2) Mechanism analysis indicates that ecological compensation reform generates information transmission and structural adjustment effects. Specifically, the policy enhances government environmental attention and promotes the integration of agricultural industries, thereby improving the value conversion efficiency of agricultural ecological products. (3) Heterogeneity analysis reveals that the policy effect is more pronounced in regions with higher levels of public environmental concern and lower levels of fiscal decentralization. Furthermore, compared with the combined year-on-year and ranking-based assessment mechanism, the year-on-year assessment mechanism alone is more effective in enhancing policy performance. This study provides valuable insights for both developing and developed countries seeking to improve the effectiveness of ecological compensation policies and enhance the realization of value from agricultural ecological products. Full article

Information infrastructure is essential for digital transformation and AI-enabled services, but its operation also involves high electricity consumption and carbon emissions. This study develops a tripartite evolutionary game model involving the government, information-infrastructure operators and the public, and integrates it with system dynamics to examine how regulatory mechanisms influence operators’ net-zero behaviours. The model focuses on operational-stage information infrastructure. Initial parameters are calibrated using the 2023 China Statistical Yearbook on Resources and Environment and expert consultation, with key variables measured by operational revenue, net-zero costs, regulatory costs, incentives, penalties, public scrutiny costs and environmental losses. The results show that operators’ net-zero behaviours may fluctuate under weak or static regulation. Government incentives, penalties and public scrutiny can promote net-zero operations, while dynamic reward–penalty mechanisms are more effective in stabilising behavioural evolution. This study extends evolutionary game theory and system dynamics to the net-zero governance of information infrastructure and provides an adaptive regulatory framework for coordinating government regulation, operator behaviour and public participation. Full article

Digital technologies are increasingly promoted as enablers of decarbonization and environmental, social, and governance (ESG) compliance in shipping, yet adoption remains constrained by high upfront costs, uncertain returns, supply–demand mismatch, and the risk of symbolic ESG disclosure and greenwashing. This study develops a collaborative governance framework to explain how technology provision, enterprise adoption, and public regulation co-evolve under ESG constraints. We construct a tripartite evolutionary game involving technology providers, shipping enterprises, and the government, incorporating ESG-driven market preference, technology matching efficiency, supply- and demand-side subsidies, regulatory intensity, greenwashing detection and penalties, and system-wide ESG benefits. Replicator dynamics and equilibrium stability analysis are used to derive convergence conditions, and numerical simulations together with system dynamics are employed to examine adjustment paths and convergence speed under alternative policy scenarios. Results indicate that a high-compliance equilibrium emerges when the net benefits of supply and adoption are positive and regulatory benefits offset enforcement and subsidy costs. Matching efficiency is identified as a key friction that slows diffusion and delays convergence even under favorable ESG market signals. Subsidies reduce cost pressure on both supply and demand sides, while greenwashing penalties and effective detection strengthen compliance incentives and accelerate convergence. Overall, the findings suggest that policy packages combining targeted incentives with credible enforcement are more effective than single-instrument approaches, and that improving technology–business fit is essential for transforming ESG pressure from external compliance into sustained internal adoption. Full article

As a core component of childbearing costs, women’s opportunity costs provide a crucial perspective for explaining the current decline in fertility rates. Recognizing the reciprocal causality between women’s opportunity costs and fertility decisions, this study examines their statistical correlation using micro-level data from the China Family Panel Studies (CFPS). Building on these empirical insights, we develop a household fertility decision-making model that incorporates women’s opportunity costs, calibrating the parameters through structural estimation to quantitatively explore its impact on fertility choices. The quantitative empirical findings reveal a significantly negative correlation between women’s opportunity costs and the actual number of children in a household. The theoretical analysis demonstrates that an intensifying motherhood penalty and prolonged career interruptions due to childbirth both lead to a reduction in the equilibrium number of children. Furthermore, higher educational attainment and increasing child-rearing costs exert a pronounced inhibitory effect on fertility intentions. Policy simulations further indicate that, compared to short-term or one-off incentives, continuous fertility subsidies and the implementation of free childcare policies are more effective in offsetting opportunity costs and boosting household fertility intentions. Full article

New agricultural entities are essential to advancing high-quality agricultural development, ensuring food security, and reducing regional wealth disparities. However, due to insufficient creditworthiness, lack of collateral, natural disasters, and information asymmetry, capital shortages have become a significant barrier to their development. The development of supply chain finance offers a novel solution to mitigate financing constraints faced by new agricultural entities. This study incorporates agricultural guarantee institutions into the conventional supply chain accounts receivable financing framework and develops a three-party evolutionary game model comprising ‘new agricultural entities–banks–agricultural guarantee institutions.’ This research study examines the strategic choices of different participants, performs stability analysis and numerical simulation, and offers policy recommendations to enhance the financing accessibility of new agricultural entities. This study proves that integrating agricultural guarantee institutions into the accounts receivable financing framework can help mitigate banks’ credit risks, improve the reliability of accounts receivable, and help the three parties to achieve three-win results. Meanwhile, the findings indicate that penalty mechanisms, subsidy and reward incentives, and risk-sharing frameworks, along with the amounts of accounts receivable and the pledge rate, significantly impact the strategic evolution of financing participants toward a stable equilibrium. Full article

The advancement of rural urbanization has led to a steady increase in construction and demolition waste (CDW) in rural areas; its dispersed nature complicates management efforts, yet existing research has not sufficiently explored the synergistic effects of subsidies and transportation costs. In this paper, a Stackelberg game model is constructed among the government, farmers, and manufacturers within the framework of a reward–penalty mechanism (RPM), and rural governance efficiency is introduced to characterize regulatory enforcement losses. Furthermore, on the basis of existing research and discussions with experts in the construction industry, this study conducted numerical simulations of key parameters by integrating multiple data sources and calibrating parameters. The aim was to analyze the mechanisms through which key factors—such as differences in subsidy structures and transportation costs—influence the decision-making behavior of farmers and manufacturers, as well as the equilibrium outcomes of the supply chain. The results indicate that (1) the reward–penalty mechanism has a significant and nonlinear effect on the decision-making of the parties involved; (2) although subsidy intensity promotes technological investment, its impact on revenue and pricing varies because of transportation cost constraints; (3) the proportion of additional subsidies for farmers is key to policy coordination, and a reasonable subsidy structure can simultaneously improve both economic and environmental performance; and (4) as a key constraint, farmers’ transportation costs play a significant moderating role in the effectiveness of regulatory measures. This paper reveals the decision-making mechanisms of rural CDW resource recovery supply chains under multiple constraints from a farmer-led perspective, providing a reference for promoting rural CDW resource recovery. Full article

Emission trading scheme (ETS) promotes carbon emission reductions by leveraging market incentives for enterprises. However, the lack of enthusiasm among enterprises in China’s building carbon emission trading market signals some deficiencies in its incentive mechanism. This study establishes an evolutionary game model involving three parties, including the government and heterogeneous enterprises, to explore the behavioral evolution process of multiple game players and improve the incentive mechanism for carbon emission reduction. The results demonstrate that the development path of enterprises’ participation in the emission trading market undergoes four evolutionary stages: the initial stage, the transitional stage, the growth stage and the mature stage. Also, the results of sensitivity analysis demonstrate that (1) rewards and penalties are the tools for government regulation and market incentives; (2) enterprises are not very enthusiastic about participating in carbon trading activities and adopting carbon-reduction technologies; (3) the rise in carbon quota trading prices can prompt enterprises to actively reduce carbon emissions. Based on the results, this study proposes an executable implementation framework for the carbon-reduction incentive mechanism of public buildings under ETS, providing practical guidance for local governments to formulate and implement targeted carbon-reduction policies, aiming to boost the enthusiasm of building enterprises for low-carbon behaviors and decision-making choices. Full article

The integration of blockchain technology into the Internet of Things (IoT) offers a decentralized paradigm for data integrity. However, the emergence of 51% attacks—driven by hashrate concentration—threatens the foundational trust of these resource-constrained networks. In resource-constrained IoT-enabled blockchain environments, mining-power asymmetry and limited governance capability may amplify the impact of strategic attacks. These characteristics motivate the need to analyze long-term adversarial behavior and governance effectiveness under repeated interactions. This paper develops a threshold-based analytical framework that integrates a single-stage decision model and a multi-stage discounted decision model to analyze 51% attack decisions and governance effects in asymmetric blockchain mining environments. We characterize the interaction between competing mining pools as a multi-stage game, integrating key parameters such as the discount factor of future utility and recovery penalty cycles. Our analysis demonstrates that a multi-stage framework creates a “long-term deterrent effect” where the net present value of potential future losses outweighs the immediate gains of hashrate abuse. analytical results indicate that the strategic threshold for launching an attack is highly sensitive to the duration of punitive measures and the accuracy of IoT-based anomaly detection. The results provide useful insights into the design of governance and incentive mechanisms for blockchain systems deployed in resource-constrained and heterogeneous environments. Full article

This study formulates a corporate abatement decision problem under carbon price uncertainty as a continuous-time stochastic control model. To this end, the carbon price is modeled as a geometric Brownian motion, while abatement capacity is accumulated through costly effort and depreciates over time. Specifically, the firm chooses its abatement effort to maximize expected discounted profits while accounting for allowance purchasing costs, compliance-related penalties, abatement costs, and potential revenues from surplus allowances. The paper contributes by integrating stochastic carbon prices, endogenous abatement-capacity accumulation, allowance-shortage/allowance-surplus asymmetry, and surplus allowance monetization into a unified corporate abatement framework. Applying the dynamic programming principle, the associated Hamilton–Jacobi–Bellman equation is derived, and the bounded optimal abatement effort is characterized in feedback form. Since the resulting nonlinear HJB equation generally does not admit a closed-form solution, a finite-difference scheme with damped policy iteration is used for numerical analysis. The results show that optimal abatement effort is strongly state-dependent. Higher carbon prices strengthen abatement incentives in the allowance-shortage region, whereas effort declines sharply after reaching allowance neutrality if surplus allowances cannot be monetized. Moreover, partial monetization of surplus allowances significantly increases abatement effort in the surplus region and can shift firms’ behavior from passive compliance to active low-carbon investment. Overall, these findings suggest that surplus allowance monetization plays an important role in sustaining firms’ abatement incentives under carbon price uncertainty. Full article

Frequent occurrences of inter-regional emergencies constitute critical impediments to global security and sustainable development, necessitating enhanced intergovernmental emergency collaboration. This study employs a network evolutionary game model (NEGM) to examine how vertical interventions shape diffusion mechanisms of cooperative strategies among local governments. The results show that (1) solely intensifying penalties or rewards yields diminishing marginal returns in incentivizing local governments to adopt a proactive cooperative strategy; (2) elevating the cost-sharing index significantly accelerates the diffusion rate of cooperative strategies, effectively mobilizing broader subnational engagement in public health emergency response; and (3) the tripartite integration of penalty-based enforcement, reward incentives, and cost-sharing mechanisms demonstrates synergistic superiority over alternative policy instruments—whether implemented individually or in pairwise combinations. Full article