Search Results (1,042)

Optimizing layout schemes for multi-branch river hubs is complex due to the need to balance conflicting goals—safety, ecology, and economy—under significant uncertainty. To address these challenges, this study proposes a comprehensive evaluation method integrating a dynamic weighting mechanism and a two-dimensional cloud model. First, we constructed an evaluation index system covering engineering safety and benefits. A multi-agent game theory approach was employed for combination weighting to reconcile the diverse interests of government, environmental, and community agents. Furthermore, a dynamic mechanism was introduced to adjust indicator importance across three key stages: dam site selection, hub layout, and detail optimization. Subsequently, the schemes’ uncertainty and risk status were quantified using a two-dimensional cloud model within a “probability-loss” framework. The methodology was validated using the Ganjiang River Hub Project. The results demonstrate that the method effectively captures the evolutionary path of decision-making priorities, transitioning from “safety-first” in early stages to “benefit-maximization” later. This study provides robust, stage-aware, and visual decision support for complex hydraulic engineering layouts, ensuring a scientific trade-off between risk control and comprehensive benefits. Full article

In the modern era, the green manufacturing innovation ecosystem is vital for promoting sustainable development. It significantly contributes to achieving carbon peak and carbon neutrality goals. Current research regarding the coevolution of the green manufacturing innovation ecosystem predominantly utilizes deterministic models. These models do not account for the inherent stochasticity present in interactions within the innovation ecosystem, and they also neglect quantitative analyses pertaining to ecosystem resilience and stability mechanisms. This study explores the core mechanisms that drive the stability of the green manufacturing innovation ecosystem. It is based on theories of ecology and innovation. This study employs the Lotka–Volterra model to characterize the stochastic evolutionary process of symbiotic interactions among innovation groups. Compared to deterministic models, the stochastic approach has significant advantages. It captures the inherent uncertainties of human behavior and subjective decision-making. Additionally, it accounts for dynamic environmental changes. This approach provides more realistic insights into the evolution of green manufacturing innovation ecosystems amid complex conditions. The findings yield three key conclusions. First, the mutualistic symbiosis model is more stable than other models. This includes independent, competitive, parasitic, and commensal symbiosis models. This stability underscores the mutualistic model’s critical role in sustaining the ecosystem’s development. Second, the return time for a mutualistic symbiosis ecosystem is notably shorter than for a stochastic interaction ecosystem. This indicates that mutualistic symbiosis is more effective in fostering growth within the green manufacturing innovation ecosystem. Third, participant relationships in this ecosystem are complex. They encompass competitive, parasitic, and commensal dynamics, among others. Furthermore, the ecosystem’s resilience improves as the rate of mutually beneficial interactions increases. These findings provide direct policy and management guidance for optimizing the symbiotic mechanisms of green manufacturing innovation ecosystems, enhancing ecosystem resilience, and advancing carbon peaking and carbon neutrality goals. Full article

Digital transformation (DT) is reshaping manufacturing, with core enterprises (CEs) leveraging their resources to build industrial Internet platforms (IIPs) that support ordinary enterprises (OEs) in adopting DT. Differences in enterprise roles lead to varying impacts of government subsidies, necessitating careful policy design. Crucially, IIP adoption involves higher-order, multi-player interactions beyond conventional pairwise relationships—a dimension often overlooked in existing quantitative studies. This research employs hypergraph theory to model these complex interactions on IIPs and applies evolutionary game theory to analyze how enterprise decisions and government subsidies shape DT dynamics in manufacturing supply chains. The findings reveal that: (1) The network effect is the primary driver for DT via IIPs, but its promotional impact exhibits diminishing marginal returns. (2) Governments should prioritize subsidizing CEs for platform establishment, as subsidies directed at OEs for DT adoption are less effective. (3) Before withdrawing subsidies, governments must ensure a sufficiently high IIP adoption rate to sustain DT autonomously. This study introduces a novel methodology for examining DT and offers theoretical insights to guide enterprise strategy and policy implementation. Full article

Green building incentives constitute a policy instrument for mitigating economic, technical, and behavioral barriers to the adoption of green buildings, yet existing studies remain fragmented across incentive types, stakeholders, and building life cycle stage. A coherent synthesis that explains how incentive strategies evolve and interact across these dimensions is still missing. This study addresses that gap through a systematic literature review guided by the PRISMA 2020 protocol. A total of 69 peer-reviewed journal articles published between 2016 and 2025 were identified from Scopus and analyzed using thematic synthesis. The review maps temporal trends, incentive typologies, stakeholder roles, and implementation challenges across different regional and market contexts. The findings indicate that incentive effectiveness depends on alignment between life cycle stage, market maturity, and stakeholder capacity, rather than on any single policy instrument. Financial incentives remain critical in early market phases, while non-financial and regulatory instruments gain prominence as markets mature. The synthesis also demonstrates how evolutionary game theory has been increasingly applied to analyse dynamic incentive and penalty strategies under bounded rationality, offering a structured lens for adaptive policy design. By integrating life cycle perspectives, stakeholder interactions, and game theoretical insights, this study advances current understanding of these incentive designs. The results provide a foundation for more adaptive and context-sensitive incentive frameworks and identify clear directions for future empirical and comparative policy research. Full article

Soil erosion control is an important aspect of promoting ecological civilization and a key support mechanism for achieving the ‘dual-carbon’ goals. The successful implementation of PPP (Public–Private Partnership) projects for soil erosion control requires widespread participation from farmers. Therefore, it is necessary to study the evolutionary mechanisms of farmer participation behavior and the process of their state transformation, as well as exploring how to enhance farmers’ participation willingness. First, a dynamic group model of farmers’ participation behavior was constructed by dividing them into five states: unknown, observing, participating, rejecting, and immune. Then, the strategic interactions between the government, social capital, and farmers under the PPP model were considered, and this was coupled with the dynamic group model. Finally, Chongqing City was taken as a typical case for numerical simulation to analyze the evolutionary patterns of farmers participation behavior. The results indicate that: (1) synergistic effective government regulation and active enterprise governance can elevate the farmer participation rate to approximately 71% and facilitate the convergence of the system toward a stable high-participation equilibrium; (2) government subsidies need to be controlled within a reasonable range to ensure policy effectiveness; (3) improving government publicity and enhancing the social atmosphere can increase farmers’ participation rate to approximately 71% and 78%, respectively, significantly boosting their willingness to participate; (4) improving the social security system and reducing perceived risks can help increase farmers’ participation rate. The research conclusions can provide a valuable reference for local governments in China in formulating soil erosion control policies. Full article

At the critical juncture where Chinese cities are transitioning toward intensive urban renewal and sustainable development, the revitalization and adaptive reuse of industrial heritage face a collective action dilemma stemming from the misaligned interests among three key stakeholders: the Local Government (LG), the Industrial Heritage Developer (IHD), and the Neighboring Complementary Merchants (NCMs). To address this challenge, this study constructs a tripartite evolutionary game model and innovatively proposes an analytical framework of a Multi-Dimensional Policy Lever System, which integrates spatial synergy (k, w, v), economic incentives (p₁, p₂, q), and behavioral regulation (m, n). Numerical simulations reveal that the successful regeneration of industrial heritage does not rely on any single policy but fundamentally depends on the systematic coordination and dynamic adaptation of these three-dimensional levers. The nonlinear coupling of spatial elements forms the foundation for value leapfrogging. The economic driving force requires a critical shift from government subsidies (p) towards a market-based value capture and recycling mechanism (q). Behavioral interventions provide the necessary cognitive and normative safeguards for cooperation. The research elucidates a three-phase evolutionary pattern of the system, transitioning from a stalemate to synergy, and emphasizes the need for an adaptive and sequential combination of policies. The theoretical contribution of this study lies in providing an integrative quantitative analytical framework. Its practical significance is to offer a scientific basis for decision-makers to construct a dynamic policy toolbox and promote the sustainable collaborative governance of industrial heritage. Full article

Under the constraints of China’s “Dual Carbon” targets, promoting green consumption has emerged as a critical market-based strategy to drive industrial decarbonization and achieve sustainable development. However, the existing literature primarily focuses on supply-side government regulation, leaving the mechanism of how demand-side constraints influence the strategic interaction between the government and enterprises under-explored. To bridge this gap, this paper constructs an evolutionary game model incorporating performance-based government incentives, consumer low-carbon preferences, and corporate abatement costs. Unlike theoretical models with hypothetical parameters, this study calibrates the simulation parameters using empirical data from the steel industry in Zhejiang Province, a pilot zone for China’s ecological civilization construction. The simulation results indicate that: First, under the current empirical parameters, the system fails to spontaneously converge to the ideal equilibrium state, highlighting a “governance deadlock”; second, consumer preference intensity serves as a vital external force that can effectively break this deadlock and reduce the government’s regulatory burden; and finally, sensitivity analysis reveals the critical thresholds for the synergistic effect between regulatory policies and market demand. Based on these findings, policy recommendations are proposed to foster a collaborative governance mechanism integrating government guidance, market-driven approaches, and demand-side driving forces. Full article

We detail a procedure to transform the current empirical stage in the study of complex systems into a predictive phenomenological one. Our approach starts with the statistical-mechanical Landau-Ginzburg equation for dissipative processes, such as kinetics of phase change. Then, it imposes discrete time evolution to explicit back feeding, and adopts a power-law driving force to incorporate the onset of chaos, or, alternatively, criticality, the guiding principles of complexity. One obtains, in closed analytical form, a nonlinear renormalization-group (RG) fixed-point map descriptive of any of the three known (one-dimensional) transitions to or out of chaos. Furthermore, its Lyapunov function is shown to be the thermodynamic potential in q-statistics, because the regular or multifractal attractors at the transitions to chaos impose a severe impediment to access the system’s built-in configurations, leaving only a subset of vanishing measure available. To test the pertinence of our approach, we refer to the following complex systems issues: (i) Basic questions, such as demonstration of paradigms equivalence, illustration of self-organization, thermodynamic viewpoint of diversity, biological or other. (ii) Derivation of empirical laws, e.g., ranked data distributions (Zipf law), biological regularities (Kleiber law), river and cosmological structures (Hack law). (iii) Complex systems methods, for example, evolutionary game theory, self-similar networks, central-limit theorem questions. (iv) Condensed-matter physics complex problems (and their analogs in other disciplines), like, critical fluctuations (catastrophes), glass formation (traffic jams), localization transition (foraging, collective motion). Full article

The empowerment provided by third-party intelligent information platforms has emerged as a crucial driving force for facilitating the digital transformation of supply chain enterprises. Whether and how intelligent information platforms are engaged is a key to the digital transformation of the supply chain. This essay endeavors to establish theoretical underpinnings for supply chain digital transformation and assist enterprises in resolving transformation-related issues. Based on the tripartite evolutionary game, this essay analyzes the digital transformation of supply chain from the behavioral strategies of the third-party intelligent information platform, manufacturers and retailers, respectively. Combined with numerical simulation analysis, we explored the digital transformation patterns and characteristics of supply chains under different scenarios. The results suggest that joining an intelligent information platform will always be a balanced strategy for supply chain digital transformation, and that an increase in the level of platform enablement will accelerate the process, while potential threats to cybersecurity will slow it down. However, manufacturers may also build their own platforms when their initial digitization level is high or the cost of building their own platforms is not prohibitive, resulting in higher word-of-mouth benefits and digital longevity. This paper provides new perspectives for analyzing the impact of intelligent information platforms on supply chain digital transformation decisions, and ultimately proposes practical operation and management recommendations for related practices. Full article

Background: From the perspective of product liability, this study explores how agricultural product e-commerce enterprises can enhance the quality of the agricultural product supply chain through quality incentive strategies. Methods: Based on a tripartite evolutionary game model, the strategic interactions among farmers, agricultural product e-commerce enterprises, and the government are analyzed. Results: The research finds that whether the system converges to the ideal equilibrium of “high-quality production—ex-ante quality cost-sharing—collaborative governance” depends on the combined effects of revenue distribution, liability costs, and external incentives or penalties. Among these, government-led collaborative governance plays a key guiding role in incentivizing enterprises and influencing farmers’ behaviors. The incentive measures implemented by e-commerce enterprises and government penalties can effectively curb farmers’ low-quality production behaviors. Conclusions: The study further reveals how factors such as ex-ante cost-sharing, liability allocation, and farmers’ conformity psychology affect the stability of agricultural product supply chain quality, thereby providing theoretical support for constructing a “policy-platform-farmer” collaborative governance framework. Full article

The development of the new energy vehicle (NEV) industry has become a key driver of the global low-carbon transition. Understanding the policy effect on NEV diffusion is essential to promote sustainable growth. In this study, we propose a new approach that combines a two-layer small-world network involving consumers and enterprises and evolutionary game theory to study the diffusion effect of industrial and trade policies on enterprises’ low-carbon production strategies and consumer preferences. Different from existing diffusion models, we integrate reinforcement learning (RL) into the decision-making process of enterprises and use SHapley Additive exPlanations (SHAP) to decode the micro-level decision logic of enterprises. In terms of the decision-making mechanism, the simulation results show that the Q-learning algorithm better fits the real market diffusion trend of NEVs compared with traditional algorithms; in terms of policy effects, industrial policies and trade policies exhibit a synergistic effect. SHAP analysis reveals that enterprises are more concerned about NEV market maturity than the impact of policy parameters on decision-making; Sobol sensitivity analysis indicates that consumer subsidies have a greater impact on the market diffusion of NEVs than trade policies. Full article

To address collaborative decision-making challenges in the pharmaceutical logistics supply chain amid public health emergencies, this study integrates disappointment aversion, delay effects, and pharmaceutical value attenuation, constructing a three-echelon system. It adopts a “differential game-system dynamics (SD)” two-layer dynamic research method for in-depth synergy. The differential game model focuses on multi-agent dynamic strategic interactions, deriving time-series equilibrium solutions for the optimal effort levels, transportation efficiency, and profits under four decision modes (decentralized, government subsidy, cost-sharing, centralized) to clarify the dynamic impact laws of the core parameters. Compensating for its limitations in complex environmental coupling and practical variable integration, the SD model incorporates the patient consumption rate, inventory fluctuations, weather disturbances and other real factors to build a dynamic feedback system. It not only verifies the practical adaptability of the differential game equilibrium solutions but also reveals the evolutionary laws of supply chain performance and the amplified inter-mode performance differences under multi-factor superposition. This study finds that centralized decision-making performs the best in terms of transportation efficiency peaking, profit stability, and attenuation control. Pharmaceutical stability and enterprise effort levels positively drive benefits, while disappointment aversion and excessive delays exert inhibitory effects. Government subsidies need to be paired with collaborative mechanisms to avoid policy dependence. Management implications suggest that enterprises should prioritize the collaborative centralized-decision-making mode, establish risk-sharing and benefit-sharing mechanisms, precisely regulate key variables, and implement gradient subsidies with exit mechanisms to enhance the supply chain’s dynamic adaptability and achieve the triple optimization of “efficiency–profit–stability”. Full article

The promotion of shore power is a key pathway for reducing port-related emissions and achieving sustainable maritime development. This study analyzes the strategic interactions among governments, ports, and shipping companies by constructing a tripartite evolutionary game model. Specifically, it addresses three core questions: (1) how stakeholders’ initial intentions and strategic choices influence the system’s evolutionary path and eventual equilibrium; (2) how critical parameters—including subsidies for shore power infrastructure, wind turbine installation, and ship retrofitting, as well as electricity price support, carbon pricing, and policy implementation costs—shape the dynamics of the system and the equilibrium strategies of the three parties; and (3) how heterogeneity in national energy mixes, particularly the roles of wind turbine, affects decision-making behaviors across different countries. Simulation experiments are conducted to explore the effects of varying policy interventions and energy conditions on the stability of cooperative strategies. The results provide insights into the design of differentiated policy instruments that promote shore power adoption while accounting for the structural characteristics of national energy systems. This research enriches the theoretical application of evolutionary game theory to maritime sustainability and offers practical guidance for governments and stakeholders in advancing decarbonization in the port and shipping sectors. Full article

Distributed production systems have to increasingly balance economic goals such as energy efficiency and productivity with critical technical requirements such as flexibility, real-time capability, and reliability. This paper presents a novel approach for distributed optimization by means of Evolutionary State-based Potential Games with dynamic grid structures. More in detail, we leverage the combination of Potential Games which provide rigorous convergence guarantees with population-based optimization to improve the efficiency of the learning process. Specifically, we address challenges of previous approaches including inefficient best response strategies, insufficient coverage of the state–action space and the lack of knowledge transfer among agents. The developed strategies are evaluated on a industrial system of laboratory scale. The results highlight advances in evolutionary state-based knowledge transfer and an improved coverage resulting in efficient control policies. By leveraging dynamic grid structures, Evolutionary State-based Potential Games enable the maximization of weighted production targets while simultaneously eliminating process losses resulting in improvements in the considered metrics compared to state-of-the-art methods. Full article

Consumer attitude uncertainty can hinder disruptive innovation (DI) diffusion in the new energy vehicle (NEV) market and weaken enterprises’ incentives to adopt new technologies. This study develops a dual-layer coupled network model linking consumer attitude dissemination and enterprise R&D strategy evolution under bounded observability. Our simulations show three main findings. First, stronger discouragement of counter-attitudinal dissemination markedly suppresses diffusion and lowers steady-state adoption. Second, diffusion strengthens when consumers weight public information more and firm messaging less, particularly under stronger policy support. Third, network structure shapes diffusion: stronger inter-enterprise connectivity increases adoption, and consumer topology and interaction breadth exert different effects under different network types. These results clarify how information environments, policy support, and cross-layer behavioral modulation jointly shape diffusion regimes. Full article