With the development of e-commerce, consumers purchasing sports goods online face not only quality information asymmetry but also common fit uncertainty due to differences in size, sports habits, and usage scenarios. To alleviate these issues, this paper constructs a three-tier game model using backward induction. Key findings: (1) Without certification, low-end manufacturers can offset brand disadvantages through stronger fit disclosure; high-end manufacturers do not always lead in quality or disclosure. (2) Platform certification creates market stratification, high-end manufacturers' advantage grows with certification rigor, but only when they participate. (3) The platform's optimal certification standard rises with consumer trust and commission rate, and falls with certification cost; platforms prefer standards that induce only high-end participation. (4) Certification's effect on social welfare is non-monotonic, with a unique socially optimal level. (5) Excessive certification can crowd out manufacturer disclosure when the two are substitutes. The study reveals the synergistic and substitutive mechanisms of certification and disclosure, providing theoretical and managerial implications for platform certification design and manufacturer information strategies. (c) 2026 by the authors; licensee Growing Science, Canada
To examine the impact of different tariff structures on cross-border supply chains and support multinational corporations in addressing trade frictions and policy formulation, this paper constructs a three-tier cross-border supply chain model comprising domestic suppliers, domestic manufacturers, and multinational manufacturers. It analyzes equilibrium decisions under tariff-free, unilateral tariff, and bilateral tariff scenarios. Using reverse induction, the optimal pricing and profits of supply chain members are determined, with afocus on analyzing the effects of key factors such as the tariff application stage, raw material costs, and market competition intensity. Findings reveal that tariffs influence upstream and downstream pricing through cost pass-through: when applied to final goods, suppliers gain bargaining power and raise wholesale prices; when applied to intermediate goods, suppliers are forced to lower prices to maintain sales volume. Manufacturers' selling prices and market demand are jointly regulated by cost and competition effects, exhibiting non-monotonic changes under bilateral tariffs that reflect the dynamic trade-off between cost-driven increases and demand contraction. Profit analysis indicates that when tariffs are applied to final goods, multinational manufacturers can pass on the tax burden by raising prices when raw material costs are low, potentially achieving higher profits than in a tariff-free scenario. When tariffs target intermediate goods, multinational manufacturers bear the brunt of the burden, exhibiting a "reverse pass-through" effect. Bilateral tariffs consistently represent a suboptimal choice for multinational manufacturers, demonstrating that retaliatory tariffs fail to improve their profitability. Under asymmetric tariff scenarios, the interaction between tariff intensity, costs, and competitive strength further influences corporate strategy choices. (c) 2026 by the authors; licensee Growing Science, Canada
Accurate short-term forecasting of metro passenger flow is crucial for metro operation management and service planning. However, conventional prediction models are typically developed using data from a single city, which may limit their ability to capture diverse passenger flow patterns and reduce forecasting performance when historical data are insufficient. To address this issue, this study proposes METcross, a cross-city knowledge integration framework for short-term metro passenger flow forecasting. The proposed framework incorporates both static and dynamic covariates, including economic indicators, weather conditions, and passenger flow characteristics, to comprehensively represent station-level features. METcross consists of two stages. In the first stage, passenger flow patterns and feature representations are learned from a source city. In the second stage, feature embeddings from the source and target cities are integrated to enhance the representation of metro passenger flow characteristics and generate forecasting results for the target city. By incorporating knowledge from multiple cities, the framework can exploit shared mobility patterns while preserving city-specific characteristics. To evaluate the effectiveness of the proposed framework, experiments are conducted using metro passenger flow datasets from Wuxi and Chongqing. The results demonstrate that METcross consistently outperforms conventional single-city forecasting models. Compared with the No Fusion (NF) baseline, the proposed framework reduces the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) by 22.35% and 26.18%, respectively. The findings indicate that cross-city knowledge integration can effectively improve short-term metro passenger flow forecasting accuracy and provide a practical solution for metro systems with limited historical observations.
The operation of the automotive supply chain in China is primarily influenced by government subsidy policies and the dual credit policy. Based on a two-echelon automotive supply chain consisting of one manufacturer and one retailer, this paper incorporates the technological decisions regarding battery performance improvement of new energy vehicles, including battery electric vehicles and plug-in hybrid electric vehicles, as well as the energy-saving efficiency improvement of traditional fuel vehicles. Four policy scenarios are considered, namely a baseline scenario with no policy intervention, scenario of government subsidy-only policy, scenario jointing subsidy-dual credit policy, and scenario of dual credit-only policy. A supply chain pricing and technological performance decision model is developed for each policy scenario. Using a Stackelberg game approach, we derive the optimal decisions and profits of supply chain members under the four policy scenarios. The results show that: (1) an increase in the credit trading price generates a stable positive price incentive effect on battery electric vehicles, whereas its impact on the prices of plugin hybrid electric vehicles and traditional fuel vehicles is jointly moderated by government subsidies and consumers' green preferences. (2) R&D subsidies and credit trading price effectively promote improvements in the battery performance of new energy vehicles, whereas purchase subsidies may weaken incentives for the battery performance enhancement of new energy vehicles. Meanwhile, the energy-saving efficiency of traditional fuel vehicles exhibits differentiated responses depending on policy instruments and the level of green utility. (3) R&D subsidies and purchase subsidies can simultaneously improve the profits of all members in the automotive supply chain. Moreover, under the scenario joining government subsidy-dual credit policy, the credit trading price can further enhance the profitability of supply chain members. This paper clarifies the differential impacts of various policy instruments on automotive supply chain decisions and benefit incentive mechanisms. (c) 2026 by the authors; licensee Growing Science, Canada
Water-infrastructure public-private partnerships combine long-duration public-service obligations with incomplete knowledge about governance costs, opportunistic gains, enforcement consequences, and the public-loss implications of hydrological-risk exposure. This study develops a two-population evolutionary game in which government decision makers choose routine governance or enhanced monitoring and private participants choose cooperative performance or opportunistic behavior. Interval grey numbers represent defensible epistemic bounds, while conventional worst-case inequalities establish robust stability over the resulting sets. The reformulated payoff mechanism embeds water risk through public loss, separates latent trust from observable choices, and evaluates all four boundary rest points. Under the maintained assumption that c>0, RC, RO, and MO have nonempty strict stability regions, whereas MC is unstable; the model also yields an interior center. A separable first integral proves that regular interior level sets are closed orbits; the center is not a convergent state. The computational analysis covers 16 effective-box vertices, 50,000 independent-box observations, five linked designs based on a shared 50,000-row whitening sample, and 336 sensitivity cells. In the independent coverage sample, 27,689 rows exhibit the interior center, whereas the midpoint classification disagrees with 22,311 rows (coverage share 0.44622). Positive affine linkage reduces disagreement and improves several worst-case margins, but every baseline robust margin remains negative, so no strict robust regime holds over the full admissible set. The contribution is a water-specific, analytically proved, and reproducibly computed framework that distinguishes grey epistemic representation from robust-set mathematics and supports conditional governance implications tied to named parameters and interval bounds.
