Search Results (675)

To address the “greenwashing” trust crisis induced by information asymmetry in sustainable supply chains, this study develops a comprehensive game-theoretic model integrating Stackelberg and evolutionary game theories (EGT). We quantitatively investigate the dynamic interactions among multi-dimensional consumer preferences, blockchain implementation costs, and boundedly rational government interventions. Our analysis yields three core contributions. First, we analytically reveal the “double-edged sword effect” of blockchain adoption. While structural transparency unlocks a trust dividend, exorbitant technological costs trigger a “budget crowding-out effect.” Quantitative results demonstrate that breaching the absolute Feasibility Threshold completely cannibalizes the environmental budget, driving substantive green investments strictly to zero. Second, EGT analysis proves that isolated punitive carbon taxes trap supply chains in a suboptimal “shallow greening” equilibrium. A composite tax-subsidy policy is structurally required to expand the feasible cost space and hedge against technological risks. Finally, we formulate a dynamic policy exit mechanism. As blockchain infrastructure matures and the endogenous green premium effectively offsets implementation costs, regulators must systematically phase out subsidies and converge toward a single-taxation regime to prevent corporate policy arbitrage and alleviate long-term public financial burdens. Full article

This paper studies a dynamic price adjustment system in platform markets, where sellers continuously revise prices, and examines its implications for market stability. We develop a platform-led discrete-time Stackelberg game model to describe the evolution of sellers’ prices and price adjustment speeds under bounded rationality. Unlike previous studies that treat adjustment speed as exogenous, we model it as an endogenous state variable shaped by profit incentives, behavioral inertia, and price fluctuations. We derive the interior symmetric equilibrium and show that profit-driven acceleration increases sellers’ adjustment speed. When this speed exceeds the stability threshold, the system may leave the stable region, causing bifurcations and complex dynamics. We then introduce a platform-imposed upper bound on adjustment speeds and demonstrate that appropriate regulation can restore stability while balancing market responsiveness and efficiency. Numerical simulations illustrate that moderate acceleration improves profitability, whereas excessive acceleration can lead to low-profit regimes. Entropy-based metrics are used to quantify system complexity, and an entropy-triggered feedback-control mechanism is proposed to mitigate excessive volatility while maintaining flexibility. Overall, the study highlights the importance of governing adjustment dynamics rather than solely focusing on price levels. Full article

With the ongoing low-carbon transition of energy systems and the increasing penetration of distributed energy resources, the coordinated operation of heterogeneous prosumers has become essential for improving the economic and environmental performance of integrated energy systems. However, existing studies have not sufficiently addressed the joint coordination of electricity sharing, carbon emission trading, green certificate trading, and demand-side flexibility. To address this gap, this paper proposes a hybrid game-based optimal operation model for a multi-energy prosumer alliance coordinated by an Electricity Balance Service Provider (EBSP). The model is developed under coupled carbon emission trading (CET) and green certificate trading (GCT) markets. A piecewise linear dynamic pricing mechanism and a mutual recognition rule are introduced to describe the interaction between CET and GCT. Meanwhile, a price-based demand response model considering reducible and shiftable loads is incorporated to exploit load-side flexibility. On this basis, a Stackelberg-cooperative hybrid game is formulated to coordinate electricity pricing, integrated dispatch, electricity sharing, and benefit allocation between the EBSP and the prosumer alliance. The proposed model is solved using particle swarm optimization and the alternating direction method of multipliers. Case studies show that, compared with the corresponding benchmark scenarios, the proposed method reduces the alliance operating cost by 7.19%, the carbon trading cost by 41.35%, and total carbon emissions by 3.66%. It also decreases the peak-to-valley load difference ratio by 3.78 percentage points. These results demonstrate the effectiveness of the proposed method in improving economic performance, promoting low-carbon operation, and enhancing the peak-shaving and valley-filling capability of the prosumer alliance. Full article

This study examines how bank financing and direct financing provided by a third-party logistics (3PL) firm affect the operational and financing decisions of a capital-constrained retailer. It focuses on a dual-channel financing setting in which both funding sources are available and investigates whether the retailer uses them simultaneously, how creditor priority affects equilibrium outcomes, and how procurement cost and logistics pricing shape financing choices. A Stackelberg game model is developed for a supply chain comprising a retailer, a 3PL firm, and a bank. Two benchmark settings, namely bank financing only and direct 3PL financing only, are first analyzed. The study then examines the dual-channel financing equilibrium when the 3PL firm acts as the senior creditor and further extends the model to consider bank seniority and endogenous logistics pricing. When the 3PL firm is the senior creditor, the retailer does not use both funding sources simultaneously in equilibrium; instead, it chooses either bank financing only or direct 3PL financing only. The 3PL firm prefers bank financing when logistics pricing is low and procurement cost is high, whereas it prefers direct financing when logistics pricing is high or when both logistics pricing and procurement cost are low. When logistics pricing is endogenous, the optimal lending rate set by the 3PL firm is zero. This study extends the literature on 3PL financing by explicitly incorporating a dual-channel financing structure that includes both bank credit and direct 3PL lending. It highlights the strategic role of creditor priority and shows how procurement cost and logistics pricing jointly shape the financing equilibrium, thereby providing managerial insights into financing design and operational decision-making in capital-constrained supply chains. Full article

Green investment is increasingly important in sustainable supply chain management, but it remains unclear whether the associated costs should be borne by manufacturers or retailers in competitive markets. To address this issue, this study develops a two-tier green supply chain model with one manufacturer and two competing retailers, where demand depends on retail prices and product greenness. A Stackelberg game framework is used to compare two green cost-bearing structures: manufacturer-borne green cost (MBG) and retailer-borne green cost (RBG). The results show that neither mode is universally superior. When green investment costs are low, both modes lead to the maximum feasible green level. When costs are higher, their relative performance depends on product substitutability and green cost sensitivity. Stronger substitutability increases the strategic value of greenness and may favor RBG, whereas higher green cost sensitivity tends to favor MBG because manufacturers can recover green investment through wholesale pricing. This study contributes by clarifying how green cost allocation affects pricing, demand, and profit distribution under retail competition, and it provides guidance for designing green investment arrangements in practice. Full article

Power electronic systems are often engineered through a sequential–iterative workflow in which hardware parameters are initially sized from steady-state, ripple, thermal, and electromagnetic-compatibility constraints, and controllers are subsequently tuned to satisfy dynamic and closed-loop performance requirements. While converters are inherently designed for closed-loop operation, increasing power density, uncertainty, and distributed interaction make the underlying design process resemble a strategic interplay among multiple decision-makers, including hardware designers, control algorithms, loads, disturbances, and manufacturing constraints. This paper develops a unifying game-theoretic perspective on the optimal design and control of power electronic systems. Classical concepts—such as robust control, worst-case design, droop-based load sharing, and tolerance allocation—are reinterpreted as equilibrium solutions of zero-sum, Stackelberg, non-cooperative, or cooperative games. Beyond a conceptual taxonomy, two illustrative simulation case studies are provided: (i) a Stackelberg hardware–controller co-design of a buck converter, demonstrating simultaneous passive-component reduction and improved transient performance relative to a conservative sequential design; and (ii) a droop-controlled parallel-converter example contrasting Nash and cooperative equilibria, explicitly quantifying trade-offs between bus-voltage regulation, current-sharing fairness, and conduction losses. By framing power electronic design and control as interacting strategic processes rather than isolated optimization stages, the paper aims to show that game theory can serve as a structured and practically interpretable framework for distributed and uncertainty-aware power electronic systems. Full article

This paper presents an integrated techno–economic framework for coordinated grid-to-vehicle and vehicle-to-grid (G2V–V2G) operation in unbalanced distribution networks. A hardware-compatible bidirectional charger with nested AC/DC and DC/DC control loops, together with a rule-based energy management system (EMS), enables seamless mode transitions while enforcing state-of-charge (SoC) and network constraints. A probabilistic Monte Carlo study on the IEEE 13-bus feeder shows that uncoordinated G2V charging induces adverse grid impacts such as voltage stress, line-ampacity violations, and transformer overloading, whereas EMS-driven V2G support improves voltage by 2–4%, reduces line loading by 15–25%, and lowers transformer stress by up to 10%. To align these technical benefits with economic incentives, a bi-level Stackelberg model is formulated where the utility updates locational energy prices based on combined voltage, line ampacity, transformer loading stress indices and EVs choose profit-maximizing nodes, modes and power levels. The interaction converges to a Stackelberg equilibrium with a clear win–win situation; the feeder’s average locational energy price falls entirely within the win–win region, yielding positive per-session profits for both the EV (≈$0.80) and the utility (≈$0.48) while reducing feeder stress. These results demonstrate that stress-aware locational pricing, combined with detailed converter-level control provides a technically robust and economically sustainable pathway for large-scale EV integration. Full article

Collaborative operation of heterogeneous UAV swarms in dense urban environments remains challenging because right-of-way allocation is often rigid, frequent replanning consumes considerable onboard computation, and paths obtained by purely mathematical optimization may not be easy to execute under real dynamic constraints. This paper presents a physics-informed, event-triggered path planning and control framework, termed Physics-Informed SSA-MPC. Its global search layer is built on the Sparrow Search Algorithm (SSA), whose search mechanism originates from sparrow foraging and anti-predatory behaviors. On this basis, the method combines an event-triggered Stackelberg game for airspace coordination, a physically constrained SSA for global path generation, and an event-triggered MPC for local replanning. Battery State of Health (SoH) is incorporated into the adaptive search process, while Lévy-flight updates are limited by the maximum available acceleration to avoid infeasible path mutations. Local replanning is activated only when predicted safety ellipsoids overlap or tracking errors exceed prescribed thresholds, which helps reduce redundant computation. Simulations in a digital twin of Lujiazui, Shanghai, show that the proposed method shortens path length by 3.3% to 14.9%, reduces obstacle-avoidance latency to 45 ms, and achieves a 100% engineering feasibility rate. Full article

As the integrated energy market evolves toward a multi-stakeholder coexistence model, balancing economic efficiency, user well-being, and system-level sustainability among interacting stakeholders has become a key challenge, particularly in the rapidly developing regional integrated energy markets in China. Thus, to satisfy user comfort and energy substitution requirements while achieving cost-effective electricity and heating supply, this study proposes a Stackelberg game-based market trading framework involving an integrated energy producer (IEP), an integrated energy operator (IEO), and a load aggregator (LA). First, the integrated energy market framework and transaction modes are established, and the profit models of IEP and IEO are formulated. Considering users’ energy substitution behavior, user comfort is quantified to explicitly reflect user welfare in market decision making, and a consumer surplus model is developed for LA participating in market transactions. Second, a Stackelberg game framework is constructed to coordinate the strategies of all participants by incorporating source–load energy flows, and the equilibrium solution is proven to be unique and solvable using quadratic programming. Finally, a case study based on historical data from Hebei Province, China, is conducted to validate the proposed strategy. The results demonstrate that the proposed method effectively coordinates the interests of all stakeholders, enhances demand response capability without reducing user comfort, and improves economic benefits for both supply and demand sides in regional integrated energy markets. Full article

This paper proposes a low-carbon optimization dispatch method for hydrogen-based multi-agent regional integrated energy system (RIES) that incorporate CET-GCT. The approach aims to coordinate the interests of all parties within the regional integrated energy system while reducing overall system carbon emissions. First, based on Stackelberg game theory, the interactions between the energy operator and agents on the supply side and demand side are fully characterized, establishing a “one main, two subordinate” multi-agent game model. Second, the model incorporates refined power-to-gas (P2G) technology to enhance system flexibility. Subsequently, a combined carbon trading-green certificate trading mechanism is introduced to effectively constrain the carbon emission behaviors of all stakeholders. Finally, an improved Ivy algorithm is integrated with the CPLEX solver to solve the proposed model. Simulation results demonstrate that while each entity maximizes its own benefits by adjusting its strategy, the system’s overall carbon emissions decrease by 5.12% and total revenue increased by 11.49%, yielding significant low-carbon economic benefits. This validates the effectiveness of the proposed model and methodology. Full article

With the rapid integration of distributed energy resources, achieving a balance between economic efficiency and environmental sustainability in multi-microgrid (MMG) systems is critical. However, existing studies typically treat microgrid operators as fully compliant entities. They often neglect the “trust-risk” dimension along with potential default behaviors in decentralized markets. This paper proposes a novel co-optimized scheduling model for urban MMG systems, centered on a unified “Social–Economic–Physical” coupling framework. To ensure transaction integrity, a robust reputation evaluation framework is developed using Root Mean Square Error (RMSE), mean absolute error (MAE), plus Dynamic Time Warping (DTW). This framework effectively identifies fraudulent data or contractual breaches. Furthermore, to enhance fairness while promoting decarbonization, the model integrates a dynamic network pricing strategy based on the Shapley value. It works alongside a reputation-weighted reward–penalty step-type carbon trading scheme. The proposed model is formulated as a mixed-integer linear programming (MILP) problem and solved using MATLAB R2025b with CPLEX 12.10. Simulation results demonstrate that the integrated approach significantly optimizes system performance. Total carbon emissions are reduced by 49.6 tons. Meanwhile, revenues for the MMG Alliance, individual microgrids, and shared energy storage operators increase by 4.08% to 33.00%. The proposed framework provides a practical governance solution for Smart City multi-microgrid systems, effectively addressing the “trust-risk” challenge in decentralized urban energy markets. The findings validate that the proposed mechanism effectively fosters a trustworthy trading environment, achieving a “win-win” outcome for economic profitability and urban energy resilience. 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

The resource utilization of construction and demolition waste (CDW) is crucial for advancing the green transformation of the construction industry, but it faces challenges such as insufficient upstream R&D motivation and low downstream market acceptance. To investigate the impact of corporate social responsibility (CSR) and sales effort on the recycled cement supply chain, in this study, a Stackelberg game model of a two-tier supply chain comprising a single recycled cement manufacturer and retailers is constructed. Under government subsidy conditions, four CSR sharing modes are systematically compared: no CSR (NS), manufacturer-borne (MS), retailer-borne (RS), and shared by both (TS). The results indicate the following: (1) CSR implementation reduces wholesale and retail prices while increasing sales effort, the incorporation rate of recycled aggregates, and market demand, with retailers bearing CSR yielding the most significant pull effect; (2) heightened sensitivity to sales effort incentivizes retailers to increase sales investment and encourages manufacturers to increase the incorporation rate of recycled aggregates, thereby increasing overall supply chain profits and utility; and (3) when the CSR coefficient does not exceed the critical value of 0.97, both manufacturer profits and retailer profits increase as the CSR level increases under the TS model; under the RS model, total supply chain profits and total utility reach their maximum. Corporate social responsibility (CSR) undertaken or jointly undertaken by retailers can better align economic and social objectives. This study provides theoretical foundations and practical insights for policy formulation and corporate decision-making in construction waste resource management. Full article

To address the challenges of low-carbon economic optimization and collaborative management for multiple Virtual Power Plants (VPPs), this paper proposes a low-carbon economic optimization and collaborative management method based on a Stackelberg game framework. Firstly, a Stackelberg game model is constructed with the Distribution System Operator (DSO) as the leader and multiple VPPs as followers. The leader (DSO) guides the followers’ behavior through dynamic pricing strategies to maximize its own utility. Meanwhile, the followers (VPPs) develop energy management strategies to minimize their individual costs, taking into account factors such as energy transaction costs, fuel costs, carbon trading costs, operation and maintenance (O&M) costs, compensation costs, and renewable energy generation revenues. Furthermore, the strategy spaces of all participants are defined, and an optimization model is established subjected to constraints including energy balance, energy storage operation, power conversion, and flexible load response. The CPLEX solver and Nonlinear-based Chaotic Harris Hawks Optimization (NCHHO) algorithm are employed to solve the proposed game model. Simulation results demonstrate that the proposed method effectively facilitates collaboration between the DSO and multiple VPPs. While ensuring the safe operation of the system, it balances the profit between the DSO and VPPs, and incentivizes renewable energy consumption and indirect carbon reduction, thereby validating the effectiveness and superiority of the method and providing reliable technical support for the low-carbon collaborative operation of multiple VPPs. 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