Table of Content

25 August 2026, Volume 34 Issue 8 Previous Issue   

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The Impact of Monetary Policy on the Fairness of Wealth Distribution from the Perspective of Risk Aversion Gap Chaoying Lin, Yanqing Chen, Zhigang Huang, Heng Lin 2026, 34 (8):  1-11.  doi: 10.16381/j.cnki.issn1003-207x.2024.2039 Abstract ( 27 )   HTML ( 0 )   PDF (1991KB) ( 14 )   Save In the distribution of social wealth， inadequate funds serve as the external catalyst for the wealth disparity， while psychological factors， notably the disparity in risk aversion， constitute the core drivers of the persistent poverty cycle. However， the existing scholarly discourse predominantly emphasizes external factors， with scant attention paid to analyzing the internal dynamics influencing the wealth distribution process within monetary policy frameworks through external triggers， or contemplating the profound implications of micro-level risk aversion mitigation from a macro perspective. The DSGE model is employed to investigate the ramifications of narrowing the risk aversion gap among residents on the wealth distribution mechanism of monetary policy. The findings reveal that a loose monetary policy has a more pronounced impact on suppressing risk aversion of non-Ricardo households compared to Ricardo households， thereby narrowing the absolute risk aversion gap and augmenting the wealth-distributive efficacy of a loose monetary stance. Under the combined influence of the external impetus of loose monetary policy and the endogenous convergence of risk aversion gap， the wealth disparity is exhibiting a trend towards contraction. The integration of tax and monetary policies amplifies the regulatory impact of a standalone monetary policy on wealth gap. As the disparity in residents’ risk aversion narrows， the synergistic effect of combined tax and monetary policy regulation progressively intensifies. In the process of promoting common prosperity， the efficacy of the monetary and tax policy mix evolves， necessitating corresponding adjustments to the optimal policy combination. It helps to clarify the impact mechanism of narrowing the risk aversion gap of residents on the wealth distribution of monetary policy in theory and to provide a reference basis for the formulation of the policy combination of “psychological poverty alleviation” and “policy poverty alleviation” in practice. Figures and Tables | References | Related Articles | Metrics

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Research on the Risk Contagion Effect of Global Foreign Exchange Rate Markets under Extreme Event Shocks Haixiang Yao, Feiting He, Xiaoguang Yang 2026, 34 (8):  12-27.  doi: 10.16381/j.cnki.issn1003-207x.2024.2113 Abstract ( 159 )   HTML ( 2 )   PDF (3402KB) ( 60 )   Save Against the backdrop of global instability and intensifying economic deglobalization， the increasing frequency of extreme risk events—including financial crises， public health emergencies， and geopolitical conflicts—has heightened abnormal fluctuations and cross-border risk transmission in global foreign exchange markets. Understanding the heterogeneous contagion effects of different types of major shocks is thus critical for safeguarding exchange rate stability.Risk contagion in the global forex market is systematically examined through a dual-network framework， focusing on two dimensions： risk linkage （comovement） and risk spillover. Methodologically， two advanced approaches are employed. First， the R-Vine Copula model is used to construct risk linkage networks， capturing complex nonlinear and tail-dependent structures among currency pairs. Second， an Elastic Net-VAR model is applied to build directed and weighted risk spillover networks. This high-dimensional framework incorporates volatility estimates from a Skew-t-GARCH model and utilizes elastic net shrinkage techniques to effectively measure the direction and intensity of volatility spillovers. Topological analysis is further conducted by grouping currencies according to geographic region and capital openness.The empirical analysis draws on a comprehensive dataset of daily nominal broad effective exchange rate indices for 27 major economies from July 2005 to July 2023， comprising 14 developed and 13 emerging market currencies to ensure representativeness. The network structures are compared across four periods： a baseline tranquil period and three extreme event episodes—the Subprime Crisis， the Major Public Health Security Event （COVID-19）， and the Russia-Ukraine Conflict.Key findings reveal that the US dollar and Saudi riyal consistently serve as pivotal nodes in both linkage and spillover networks， acting as significant net transmitters of risk across all periods. Moreover， the evolution of risk networks is strongly event-driven： extreme events disrupt regionally clustered patterns observed during tranquil times and facilitate cross-regional contagion. A notable structural shift occurred after the Russia-Ukraine conflict， as the global exchange rate landscape exhibited signs of bloc formation. This is marked by weakened Europe-Asia cross-regional linkages and spillovers， alongside intensified risk spillovers from the US dollar to other regions. Cross-regional contagion became more concentrated among cooperating， economically similar， or geographically proximate regions. Topological analysis further shows that cross-regional risk spillovers are most severe during the Major Public Health Security Event， followed by the Subprime Crisis， and least pronounced during the Russia-Ukraine Conflict.It contributes to the existing literature by integrating the two complementary perspectives of linkage and spillover into a unified analytical framework， offering a more holistic view of risk contagion in this study. The application of R-Vine Copula and Elastic Net-VAR methods provides robust tools for capturing the high-dimensional and heterogeneous nature of global forex market interconnections. By systematically comparing the network evolution across multiple diverse extreme events， it provides nuanced insights into how different shocks reshape the global risk landscape， which is vital for formulating targeted risk monitoring and prevention policies in an era characterized by deglobalization pressures and recurrent extreme events. Figures and Tables | References | Related Articles | Metrics

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The Impact of ESG Rating Divergence on Corporate Tax avoidance Yue Cao, Yike Tang, Wenjun Hu, Furong Yu 2026, 34 (8):  28-37.  doi: 10.16381/j.cnki.issn1003-207x.2025.0164 Abstract ( 15 )   HTML ( 0 )   PDF (929KB) ( 5 )   Save ESG has emerged as a pivotal investment strategy in global capital markets and a mainstream evaluation criterion for corporate sustainable development capabilities. However， significant divergence persists in ESG ratings assigned to Chinese listed companies by domestic and international rating agencies， which may distort capital market valuations and exert tangible operational impacts on corporate decision-making. Based on the data of Chinese A-share listed companies from 2018-2023， the impact of ESG rating divergence among eight representative domestic and international rating agencies on corporate tax avoidance decisions is empirically examined， and further the underlying mechanisms is analyzed through financing constraints effects， information noise effects， and performance pressure channels. It is found that first， ESG rating divergence significantly increases the degree of corporate tax avoidance. Second， mechanism analysis shows that ESG rating divergence affects corporate tax avoidance through increasing external financing constraints， intensifying information asymmetry and raising short-term performance pressures. Third， heterogeneity analysis shows that the positive relationship between ESG rating divergence and corporate tax avoidance is weakened when firms have strong internal and external financing ability， high quality of internal control and more attention from external analysts， while the positive relationship is reinforced when capital market performance expectations pressure and investor sentiment are higher. Furthermore， extensive analysis finds that corporate tax avoidance caused by ESG rating divergence can improve short-term corporate performance， but is detrimental to long-term corporate performance. The enhancement of corporate ESG report reliability and the implementation of mandatory ESG disclosure frameworks both help to mitigate e the adverse operational impacts stemming from ESG rating divergence. Finally， targeted recommendations tailored to three key stakeholder groups are presented： regulators， rating agencies， and corporations. The unintended consequences of ESG rating divergence on corporate financial decision-making is systematically revealed， and useful references are provided for regulators to formulate ESG standards， rating agencies to improve ESG rating system and enterprises to implement the ESG development concept. Figures and Tables | References | Related Articles | Metrics

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Micro-Level Capacity-Driven Container Freight Rate Forecasting: An Empirical Study Using AIS Data and Decomposition-Based Prediction Models Cong Sui, Shang Wang, Jingmin Liang, Haibo Kuang 2026, 34 (8):  38-48.  doi: 10.16381/j.cnki.issn1003-207x.2024.1984 Abstract ( 161 )   HTML ( 0 )   PDF (1209KB) ( 33 )   Save Container shipping serves as a core pillar of global trade， bearing 52% of maritime trade value by worth. However， its freight rates exhibit extreme volatility - the China Containerized Freight Index （CCFI） surged by 215% year-on-year in August 2021， presenting severe challenges for shipping operations and derivative market risk management. Traditional research predominantly relies on macro-level indicators to analyze rate fluctuations but struggles to capture dynamic micro-level market heterogeneity. Although seasonal patterns have been identified by some scholars， their micro-level determinants lack empirical validation. Resolving how to analyze the dual supply-demand driving mechanisms through high-frequency microdata and enhance freight rate prediction accuracy has become an urgent scientific challenge. An operating capacity index is first constructed using massive AIS data， quantifying actual deployed capacity across shipping routes to precisely map supply-side capacity adjustments and demand-side cargo flow cyclicality at the micro level. Secondly， Seasonal-Trend decomposition using Loess （STL） is employed to decompose operating capacity and freight rates into seasonal， trend， and residual components. Leveraging the economic interpretability of these components， micro-level evidence of freight rate formation mechanisms from supply-demand perspectives is provided. Furthermore， a “decomposition-forecasting” approach is proposed. Through empirical comparisons of six capacity models （incorporating operating capacity and its decomposed components） against the ARMA（1，1） benchmark model， it is found that models considering only trend components or simultaneously incorporating all components achieve the highest prediction accuracy， with MAPE improving by 7 percentage points over the benchmark. Finally， robustness checks using Empirical Mode Decomposition （EMD） confirm the method's effectiveness， though its weaker seasonal component extraction capability results in 5 percentage point lower accuracy compared to STL. This micro-level evidence confirms that seasonal-incorporated prediction models are more suitable for container freight rate forecasting. The operating capacity index developed in this study addresses the limitations of traditional macro-indicators in capturing micro-market heterogeneity. By revealing dual supply-demand driving mechanisms through decomposition frameworks， it provides micro-empirical support for dynamic pricing models and offers decision-making foundations for shipping companies' capacity allocation and derivative pricing （e.g.， freight futures）. Figures and Tables | References | Related Articles | Metrics

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The Time-Space Design Theory of Leadership Development: Perspectives, Theoretical Framework, and Research Directions Guoquan Chen, Fan Wu 2026, 34 (8):  49-63.  doi: 10.16381/j.cnki.issn1003-207x.2024.2197 Abstract ( 7 )   HTML ( 1 )   PDF (965KB) ( 18 )   Save In the context of rapid global changes and intensified organizational competition， the demand for effective leadership development has become a critical challenge for nations， organizations， and individuals. Traditional leadership theories， predominantly rooted in Western paradigms， emphasize natural emergence through traits， behaviors， or situational factors， yet lack systematic frameworks for actively designing and optimizing leadership growth. To address this gap， the Time-Space Design Theory of Leadership Development， a novel theoretical framework is proposed that integrates Chinese philosophical and practical wisdom with modern management science. The core premise of this theory is that leadership development is not entirely a process of natural emergence， but can be designed and optimized to a certain extent through specific methods within the time-space context. A framework is proposed for this theory， defining and explaining four key elements： the drivers， pathways， resources， and outcomes of leadership development， while exploring the interrelationships among these elements. This theory is grounded in the Three Laws of Time-Space Theory. These Laws state that the effectiveness （efficacy） of human thought and action depends on the time-space views （TV， SV） one holds and the time-space resources （TR， SR） one possesses， the degree of alignment between these views/resources and specific contexts， as well as the interaction between views and resources. This relationship can be expressed as the function E=f（（TV， SV）， （TR， SR））. According to these Laws， the drivers of leadership development and developmental pathways， derived from a design-optimization perspective， belong to the realm of time-space views. Meanwhile， the resources for leadership development constitute time-space resources. Together， these factors collectively determine the outcomes of leadership development. The literature synthesis， theoretical modeling， and case analysis are employed to develop and elaborate the proposed model， culminating in the formulation of twelve key propositions. The aim is to provide a new theoretical perspective for the field of leadership development research and practical guidance for nations， organizations， and individuals in cultivating capable leaders efficiently. Suggestions are concluded with for future research directions based on the Time-Space Design Theory of Leadership Development， including theoretical refinement， empirical validation， and practical guidelines. Figures and Tables | References | Related Articles | Metrics

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Research on Personal Default Prediction Methods Based on the AutoGluon Framework Gang Li, Boxiong Cao, Simeng Qin, Jingyi Cheng, Fang Zhao, Yajing Zhang 2026, 34 (8):  64-75.  doi: 10.16381/j.cnki.issn1003-207x.2024.0800 Abstract ( 9 )   HTML ( 0 )   PDF (1019KB) ( 9 )   Save Accurately assessing borrower default status is a fundamental aspect of personal credit risk evaluation and a critical factor in lending decisions made by financial institutions. However， conventional statistical and machine learning techniques often have limited generalization capability and unstable individual model performance. Furthermore， ensemble learning methods often require labor-intensive hyperparameter configuration， considerable manual intervention and a tendency to overfit. To address these challenges， an automated， highly accurate framework for predicting personal credit default is proposed. The core methodology leverages the Auto Gluon （AGT） framework， an automated machine learning system specifically recognized for its efficacy with tabular data. The model uses a multi-layer stacking ensemble architecture， where each layer incorporates predictions from the previous layer and the original feature set. Predictions from various base models are amalgamated via a weighted aggregation mechanism， thereby automating the processes of model selection and hyperparameter optimization. Furthermore， to mitigate the prevalent issue of class imbalance in financial datasets， the framework incorporates a class-balanced modification of the cross-entropy loss function. This recalibrates the loss function in proportion to the inverse class frequency， enhancing the model’s sensitivity to minority-class instances. To evaluate the proposed AGT-multi-layer-stacking-MCE model， both proprietary and public datasets are utilized. This included real-world lending data from Lending Club and Paipaidai， as well as publicly available credit data from the UCI repository： specifically， the German， Japanese， and Australian datasets. Performance is assessed using key metrics including Type II error， the area under the ROC curve （AUC） and accuracy. Comparative benchmarks include traditional machine learning models， Super Learner ensemble techniques and previously published results on the same datasets. The empirical results show that the proposed model produced Type II errors of 0.0676 and 0.0226 on the two main datasets， representing reductions of 9.2% and 28.7% respectively compared to the most effective baseline model. The model outperformes conventional data-balancing techniques across all evaluated datasets， highlighting its robustness and superior discriminative capacity. The substantial potential of automated machine learning frameworks supplemented by tailored loss functions in advancing credit default prediction is illustrated. The AGT-multi-layer-stacking-MCE model elevates predictive accuracy， particularly in minimizing Type II error， which is critically important in financial risk assessment， and diminishes reliance on expert-driven manual tuning. Consequently， the proposed approach provides a scalable， efficient and highly adaptive solution for credit scoring， facilitating more reliable and automated decision-making in financial practices. It contributes to the broader adoption of advanced machine learning techniques in operational environments， offering meaningful insights into how to handle class imbalance in financial risk modelling. Figures and Tables | References | Related Articles | Metrics

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Dynamic Dual-drive Evaluation Method Based on Q2-DI and Coupling Coordination Faming Zhang, Siqi He, Siyu Liao, Yunfeng Gao 2026, 34 (8):  76-91.  doi: 10.16381/j.cnki.issn1003-207x.2024.2035 Abstract ( 10 )   HTML ( 0 )   PDF (1752KB) ( 10 )   Save With the growing complexity of evaluation issues， fully excavating dynamic temporal information characteristics and guiding the evaluated objects toward benign development through effective dynamic incentive mechanisms have become critical steps and essential components for enhancing evaluation scientificity and guidance. Furthermore， in practical evaluation problems， evaluation indicator systems tend to be multi-dimensional， while indicators are often interdependent and may exhibit interactions， making it particularly crucial to consider inter-indicator relationships during evaluation.To address current limitations in dynamic incentive evaluation research—particularly insufficient attention to coupling coordination among indicators and lack of differentiation and appropriateness in incentive methods—reinforcement theory and equity theory are introduced to propose a novel dynamic dual-drive evaluation method integrating “quantity-quality dual incentives” （Q2-DI） with coupling coordination. First， development prediction points of evaluated objects at various time nodes are determined based on temporal data， gain information， and time factors， which are then connected to form an incentive reference line for implementing "quantity" incentives reflecting current states. Second， by comprehensively considering the evaluated objects’ opportunity pressure and development velocity， "quality" incentives targeting trends are applied from both absolute and relative dimensions. Subsequently， the Q2-DI dynamic evaluation model is constructed by integrating "quantity" and "quality" incentives to derive dual-incentive composite values. Building on this， a coupling coordination model is introduced to obtain coupling coordination values. The SCD-Ward method clusters data based on dual-incentive composite values and coupling coordination values respectively， with group weights and contribution degrees aggregating information to produce comprehensive evaluation results driven by both Q2-DI and coupling coordination.Applied to business environment evaluation， case studies demonstrate the rationality and effectiveness of the proposed method. Comparative analyses with existing incentive methods further highlight its superiority： it thoroughly exploits dynamic temporal data to achieve differentiated and appropriately calibrated incentives while incorporating indicator coupling coordination into dynamic incentive mechanisms， thereby enhancing comprehensiveness and accuracy. This enriches dynamic incentive evaluation methodologies and provides support for evaluation challenges in various practical scenarios. Figures and Tables | References | Related Articles | Metrics

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Research on the Effects of Prior Beliefs and Task Knowledge on Human-AI Collaborative Decision-Making Zihao Wang, Xuanhua Xu, Zhongrun Wang, Yangyang Qian 2026, 34 (8):  92-103.  doi: 10.16381/j.cnki.issn1003-207x.2024.1385 Abstract ( 10 )   HTML ( 0 )   PDF (2422KB) ( 5 )   Save The growing integration of artificial intelligence （AI） into human workflows has given rise to a new paradigm of human–AI collaborative decision-making， in which human judgment and AI recommendations are combined to achieve superior performance. However， effective human–AI collaboration remains challenging because people often use AI advice inappropriately. Hence， understanding how to build effective human-AI teams has become a central question for both researchers and practitioners. Existing research has primarily approached this issue through the lens of trust calibration， seeking to improve collaboration by encouraging people to rely on AI systems to an appropriate degree. These approaches typically focus on subjective psychological factors， such as trust in AI， self-confidence， and perceived usefulness. While insightful， such affective factors are difficult to manage through organizational interventions， and therefore their practical value for AI deployment may be limited. Drawing on the knowledge-belief framework in cognitive science， we argue that two task-related factors—prior beliefs and task knowledge—play a fundamental role in shaping how individuals process and integrate external information， including AI advice. Unlike affective factors， these cognitive factors can be influenced through training and task design， making them particularly relevant for organizational practice. As such， the present study examines how prior beliefs and task knowledge influence human–AI collaboration. To investigate these relationships， we conducted a between-subjects experiment on academic performance prediction， in which prior bias was manipulated （biased vs. unbiased）， and task knowledge was measured as a continuous variable. The results yield three main findings. First， biased prior beliefs impair human-AI collaboration. This effect arises because individuals with biased priors are more likely to make false-positive errors， causing them to incorrectly reject valid AI recommendations. Second， greater task knowledge mitigates the negative effect of prior bias. As individuals have more task knowledge， they become better able to recognize and avoid false-positive errors induced by distorted prior beliefs. Third， the relationship between task knowledge and the effectiveness of collaboration follows an inverted U-shaped pattern. Collaboration improves as task knowledge increases， but only up to a certain point. Beyond that point， additional task knowledge reduces overall team performance. This decline is driven by an increase in false-negative errors： individuals with very high levels of task knowledge become more likely to accept incorrect AI recommendations. We argue that this effect reflects overconfidence， whereby highly knowledgeable decision-makers become less vigilant in monitoring AI outputs. Based on these findings， we provide practical guidance for the design， formation， and management of human-AI teams in organizational settings. Figures and Tables | References | Related Articles | Metrics

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Optimizing Tourism Development Scale around Nuclear Power Plants: A Trade-off between Safety Margin and Comprehensive Benefits Yamin Ji, Feng Gao, Mingliang Qi, Hong Chi 2026, 34 (8):  104-114.  doi: 10.16381/j.cnki.issn1003-207x.2025.1417 Abstract ( 12 )   HTML ( 0 )   PDF (2366KB) ( 5 )   Save Under the dual-carbon and high-quality development agenda， areas surrounding nuclear power plants have emerged as tourism destinations integrating nuclear science popularization with regional economic activities. However， expanding tourism scale increases population density and mobility， thereby intensifying the complexity and operational burden of off-site nuclear emergency management. This creates an inherent trade-off between tourism development and emergency safety， making it essential to determine an appropriate tourism scale under institutional safety constraints. From a government decision-making perspective， this study formulates the problem as a joint optimization of tourist scale， spatial distribution， and emergency infrastructure investment under constraints of ecological capacity， tourism resources， and emergency preparedness systems. A safety margin indicator， defined as the ratio of emergency response capacity to exposed population， is introduced and decomposed into evacuation and decontamination components. A mixed-integer multi-objective optimization model is developed to simultaneously maximize comprehensive benefits and safety margins. Comprehensive benefits integrate economic， employment， and science popularization effects， while safety margins are derived from the comparison between system capacities and emergency demand. The model incorporates constraints such as ecological carrying capacity， accommodation limits， and emergency response time. Due to the NP-hard nature and conflicting objectives， an NSGA-II-based approach is employed， with pre-simulated evacuation and decontamination capacities embedded to reduce the search space. A case study based on a coastal nuclear power plant in China is conducted under three accident scenarios. Results show that： （1） a stable trade-off exists between benefits and safety； （2） coordinated multi-objective optimization significantly improves safety with limited benefit loss； （3） tourist structure and spatial allocation critically affect outcomes； and （4） optimizing composition and distribution is more effective than simply reducing tourist numbers. This study provides an integrated decision-support framework linking tourism development with nuclear emergency management， offering practical insights for sustainable development in high-risk energy regions. Figures and Tables | References | Related Articles | Metrics

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Capacity Replenishment Channel Selection and Pricing Decisions Considering the Operational Modes of the Platform Wei Xiao, Kai Li, Rui Xu 2026, 34 (8):  115-126.  doi: 10.16381/j.cnki.issn1003-207x.2023.1260 Abstract ( 81 )   HTML ( 1 )   PDF (1668KB) ( 30 )   Save Capacity sharing in the manufacturing industry provides a novel way to overcome capacity bottlenecks. To address uncertainties in market demand， capacity cooperation agreements are usually reached between manufacturers and backup capacity suppliers. The manufacturer’s dual-channel capacity supplement strategy and the platform’s capacity pricing strategy are investigated in this paper under both the bilateral pricing model and the fixed commission rate model by establishing a platform-leader Stackelberg game model. Using the standard backward induction， it is shown that， compared to the fixed commission rate model， the manufacturer faces a higher threshold for reserving capacity from backup suppliers and relies heavily on platform capacity supply in the bilateral pricing model. In addition， the commission rate， capacity sharing price， and supply price are higher， as well as the platform’s profit. Furthermore， the impacts of the capacity reservation contract on the manufacturer's decisions and each supply chain player’s profit are discussed through numerical analysis. Consequently， some valuable insights into the design of capacity-sharing platforms and the selection of optimal business models are provided. Figures and Tables | References | Related Articles | Metrics

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Analysis of O2O Food Delivery Platform Commission Model Selection and Pricing Strategy Considering Bounded Rationality Xinyue Tan, Jiafu Tang, Tingting Li 2026, 34 (8):  127-138.  doi: 10.16381/j.cnki.issn1003-207x.2024.2121 Abstract ( 15 )   HTML ( 0 )   PDF (2029KB) ( 29 )   Save The fixed and structured commission models are the main O2O food delivery platform payment methods. Specifically， the Fixed Commission Model （FC）， dominant early in O2O food delivery， is simple and easy to operate. This model is still widely adopted in rural areas. Additionally， the fixed commission model can be further categorized into the Single Proportional Fixed Commission Model （SP） and the Multi-Proportional Fixed Commission Model （MP）. Under the SP model， the platform applies an identical commission rate across all food items. Conversely， under the MP model， the platform employs distinct commission rates for orders of varying distances， differentiating between those placed for nearby and far-off locations. The Structured Commission Model （SC）， a newly introduced commission approach by O2O food delivery platforms in recent years， takes into full consideration various key factors， including meal prices， delivery distances， and delivery time slots. Currently， this model is predominantly utilized in urban areas. The SC model primarily comprises two components： proportional commission and service commission. For the proportional commission， the platform charges a fee based on a certain percentage of the meal price. As for the service commission， it is imposed based on the sales territory and order time distribution chosen by the merchant. For O2O food delivery platforms， selecting an appropriate commission model in different business contexts is a pivotal concern that affects the platform's commercial sustainability， merchant cooperation loyalty， and the harmonious development of user consumption experiences.Based on bargaining power， O2O food merchants fall into two types： seller accept （SA） and seller design （SD）. SA merchants operate in a perfectly competitive market environment， where product prices are dictated by the market. This category specifically encompasses merchants who reference market average prices for their own pricing， franchise merchants required to adhere strictly to unified commodity circulation prices， and merchants maintaining consistent pricing both online and offline. In contrast， SD merchants， situated in a monopolistic competitive market environment， possess the capacity to set prices. Examples include merchants catering to high-end consumer groups and those employing differentiated pricing strategies across online and offline platforms.An interesting finding is that consumers' willingness to buy depends not just on meal prices but also on the meal-to-delivery fee ratio in the total price. This implies that when meal prices are equal， consumers prefer options with a lower delivery fee ratio. According to the theory of psychological accounting， this phenomenon arises because consumers categorize meal fees and delivery fees into distinct psychological accounts， leading to differing perceptions of value for the same monetary amount. Such behavior exemplifies bounded rationality in the context of O2O food delivery services. Now， how does this behavior impact the platform's selection of a commission model？ The existence of bounded rationality among consumers is postulated， characterized by sensitivity to the ratio of delivery fees to meal fees， and the extent of this impact is defined and quantified using the concept of consumer sensitivity. Against this backdrop， the most advantageous commission models are identified for the platform across various scenarios.The pricing negotiation sequence between the platform and merchants is as follows　The platform selects a commission model and sets rates. Subsequently， merchants determine the proportion of delivery fees within the total price， taking into account the platform's chosen commission model and rate. The platform's selection of the commission model and the set rate directly influence merchant profits， while merchants' decisions on delivery fee proportions， in turn， impact platform profits through demand. Consequently， a game relationship emerges， with platforms taking the lead and merchants following suit. Commission scenarios are classified by platform models and merchant types， building Stackelberg models for each. From these models， the optimal decisions and profits for both platforms and merchants are derived. Drawing on the optimal decisions and profits of the platform， a comparative analysis of the revenue performance of two commission models is conducted， as well as the influence of factors such as consumer sensitivity， order distance distribution， and order time distribution on these models.It is found by the study that　1） Commission mode selection on the platform is not impacted by merchant type， but the pricing impact of different factors varies. 2） Only with a structured commission model is merchant encouragement for global sales needed. 3） Under the structured commission model， the distribution ratio of order time periods should be reasonably set by the platform to avoid being greatly affected by the merchant's choice of sales scope. Figures and Tables | References | Related Articles | Metrics

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Re-export Trade, Setting up Factories Overseas or Bearing High Tariffs: Reconstruction Strategy for International Supply Chain under Tariff Increases Nengmin Zeng, Lean Yu 2026, 34 (8):  139-149.  doi: 10.16381/j.cnki.issn1003-207x.2023.0764 Abstract ( 9 )   HTML ( 0 )   PDF (1210KB) ( 6 )   Save With the downturn of world economy and the rise of anti-globalization， increasing number of countries begin to take the way of tariff intervention to build trade barriers. The supply chain decision-making models are constructed based on re-export trade， setting up factories overseas and bearing high tariffs under tariff intervention. Under the re-export strategy， the manufacturer of the country of origin exports products to a third country， and the middleman of the third country labels the products and exports them to the intervention country to avoid the additional tariffs； under the strategy of setting up factories overseas， the manufacturer move their factories to the intervention country or a third country to avoid tariff intervention； under the strategy of bearing high tariff， the manufacturer maintains the production in the country of origin and bears the high tariff imposed. It is found that if the manufacturer adopts the strategy of bearing high tariffs， it will bear most of the losses caused by the imposition of tariffs， and only a small amount of losses can be passed on to the retailer and consumers in the tariff intervention country. Under the strategy of bearing high tariffs， when the market size of products is small， the tariff revenue of the intervention country is lower than the sum of the utility losses of the retailer and consumers； that is， tariff intervention makes the intervention country suffer losses. When the market size of products is large， tariff intervention will benefit the intervening country. Whether the manufacturer chooses the strategy of re-export trade， setting up factories overseas or bearing high tariffs， compared with the benchmark model， tariff intervention always leads to a net loss of social welfare. In addition， if the fixed cost of setting up factory overseas is high and the additional tax rate is not too high， the manufacturer does not need to reconstruct its own supply chain； that is， the manufacturer chooses to bear the high tariff； Otherwise， the manufacturer should restructure its supply chain： when the fixed cost is low， it should adopt the strategy of setting up factories overseas； when the fixed cost is high and the tax rate is too high， it should select the strategy of re-export trade. However， for the downstream retailer， the strategy of setting up factories overseas is most beneficial. Figures and Tables | References | Related Articles | Metrics

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Bundling Strategy in a Capital-constrained Supply Chain Fuhai Nie, Diansheng Li 2026, 34 (8):  150-159.  doi: 10.16381/j.cnki.issn1003-207x.2023.2069 Abstract ( 87 )   HTML ( 0 )   PDF (1079KB) ( 4 )   Save When facing a shortage of funds， firms cannot operate normally and have to formulate their management strategies again， thus reducing the efficiency of supply chains. Thus， most companies raise capital through external financing schemes， which incurs high debt or default risk. To enhance repayment capacity， some capital-constrained firms often employ bundling selling， bundling complementary products into packages and selling these to customers. Despite reducing uncertainty in demand and process， this bundling selling method might reduce the product prices and sales revenues as well， thus altering the firms’ strategic choices.To study the impacts of selling strategy within a supply chain under external financing modes， it focuses on a supply chain with complementary products and a decision model is built for a manufacturer and a supplier in presence of bank financing. Specifically， the manufacturer first determines the sales model， and subsequently the supply chain members respectively set component or product prices. Then， the manufacturer produces and sells the products， thereby generating sales. In this model， the optimal prices of complementary products under the independent and bundling selling situations are explored， and further the sales strategies are examined through comparative analysis.Through the model analysis， the results are summarized as follows. First， with an increase in market size， the link between the bundling sales price and the sensitivity to discounted price changes from positive to negative. Second， affected by market demand， the manufacturer is likely to choose bundling selling when the product complementarity is low， high， or moderate. Third， a stronger bargaining power does not necessarily incentivize the supplier to increase the component price when the supply chain participants negotiate the price. It focuses on the impact of financing programs on the selling strategy of a supply chain with complementary products， which enriches the studies on marketing and operations management， and also provides managerial insights for companies to know when and how to utilize bundling sales based on product and financial markets， thus improving the effectiveness of their decisions. In the future， it can be extended by considering the impact of other financing modes and loss attitudes. Figures and Tables | References | Related Articles | Metrics

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Mobile Precooling Resource Layout and Operation Path Planning of “the First Kilometer” in Villages and Towns in China Xiaochun Feng, Jilu Guo, Xiangpei Hu, Nana Yao, Tianjun Liu, Xuexi Huo, Junhu Ruan 2026, 34 (8):  160-170.  doi: 10.16381/j.cnki.issn1003-207x.2023.1548 Abstract ( 11 )   HTML ( 0 )   PDF (2148KB) ( 6 )   Save Pre-cooling of agricultural production base is the biggest shortcoming in the whole cold chain logistics system of agricultural products in China. In the current context， establishing a comprehensive pre-cooling system and popularizing the application of rapid pre-cooling equipment are crucial tasks for the development of the full cold chain of agricultural products in our nation. The mobile precooling resource has the advantages of low input cost and high flexibility. It is helpful to reduce cost and improve efficiency to carry out scientific and reasonable resource allocation and operation path planning for mobile precooling resources. With a specific focus on the “first kilometer” mobile pre-cooling resource layout and operation path planning in villages and towns across China， a mixed integer mathematical programming model is formulated. This model simultaneously determines the site selection of mobile pre-cooling resource stopovers， the quantity distribution of mobile pre-cooling vehicles， and the multi-round-trip pre-cooling path planning for these vehicles. In light of the NP-hard， nonlinear， and multi-stage decision-making challenges inherent in the model， a rapid solution framework is further proposed. This framework combines the step search method， K-means cluster analysis technique， simulated annealing algorithm， and variable neighborhood search algorithm.The main research results are as follows　（1） With the increase of the example size， the proposed algorithm in this paper is superior to the traditional simulated annealing algorithm and local neighborhood search algorithm in terms of service cost and total cost， although it takes longer in calculation time， the gap is gradually narrowing. （2） Considering the round trip of vehicles can effectively reduce the total cost. When considering the round trip， the vehicle purchase cost and service cost are relatively high， but the total cost is also high； when not considering the round trip， the vehicle purchase cost is relatively high， but the service cost is relatively low， and the total cost is also low. （3） The sensitivity analysis shows that with the increase of the proportion of service cost in the total cost， the number of vehicle round trips and the number of vehicles increase； with the increase of the single maximum path value of the mobile pre-cooling vehicle， the number of vehicle round trips first decreases and then increases slightly， and the number of vehicles first decreases and then increases slightly； with the increase of the end time range and span of the time window， the number of vehicle round trips first increases slightly and then increases significantly， and the number of vehicles continues to increase； increasing the step length can save calculation time， but the total cost will be relatively worse. Figures and Tables | References | Related Articles | Metrics

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Integrated Scheduling Problems of Distributed Flexible Job Shops and Distribution Considering Automated Guided Vehicle Transportation Zhengpei Zhang, Hongyu Dong, Yaping Fu, Min Huang 2026, 34 (8):  171-181.  doi: 10.16381/j.cnki.issn1003-207x.2024.0888 Abstract ( 7 )   HTML ( 0 )   PDF (1333KB) ( 10 )   Save In recent years， distributed production scheduling has received significant attention from both researchers and practitioners. However， existing studies often neglect two crucial aspects： （1） job transfer processes among machines within distributed systems and （2） the distribution of finished jobs to customers. To address these gaps， an integrated scheduling framework is proposed that combines distributed flexible job shops with logistics distribution， explicitly incorporating automated guided vehicle （AGV） transportation operations. First， a mixed-integer programming （MIP） model is formulated to minimize two objectives： the makespan and total tardiness. Second， a learning-driven multi-objective artificial bee colony （ABC） algorithm is developed to efficiently solve the proposed model， leveraging problem-specific heuristics to enhance the search performance. Finally， the effectiveness of the proposed approach is validated through extensive experiments on benchmark instances and compared against two state-of-the-art metaheuristics. The results demonstrate that the proposed model and algorithm achieve superior performance in both solution quality and computational efficiency. Figures and Tables | References | Related Articles | Metrics

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Modeling and Layout Optimization Solution of a Smart Manufacturing Cell under Customized Production Huiyu Zhang, Yong Liao, Qingxin Chen, Ning Mao, Chengfeng Peng, Xiang Li 2026, 34 (8):  182-194.  doi: 10.16381/j.cnki.issn1003-207x.2024.0252 Abstract ( 14 )   HTML ( 1 )   PDF (2295KB) ( 13 )   Save Intelligence and servitization are important trends in the development of industrial modernization. Smart factory is the carrier to realize smart manufacturing， factory planning is the first step to build a smart factory， and the layout of production facilities is an important part of smart factory planning. The layout of production facilities has an important impact on the productivity， material handling efficiency， and production cycle of manufacturing systems. For the smart manufacturing cell system with resource coordination constraints， a quadratic assignment problem of facility layout to maximize the average throughput of the system is studied， and a queuing network-based meta-heuristic optimization algorithm is proposed. Due to the stochastic property of the system under the customized production mode， an open queuing network model with blocking and generally distributed service time is established， and an approximate solution method is proposed to evaluate the system throughput. Then， a non-linear stochastic programming model for the facility layout problem is developed， and an improved variable neighborhood search algorithm based on queuing network is proposed to solve the solutions. In the variable neighborhood search algorithm， the initial solution generation method based on the processing route is improved， and the scale of the solution space of the neighborhood search is reduced by the isomorphism determination of directed graphs， to improve the efficiency of the algorithm. The comparison of experiments proves that our proposed algorithm has obvious advantages in both the quality of the optimal solution and the efficiency of the solution. Experiments show that the improved R-VNS* and D-VNS* algorithms have similar solution results due to the improved initial solution generation method， which avoids getting trapped in local optimum， and both of them outperform the improved R-VNS and D-VNS algorithms before the improvement. Due to the improvement of the initial solution generation method and the strategy of eliminating the homomorphic coding to reduce the solution space， both the improved R-VNS* and D-VNS* algorithms have improved the algorithm time complexity compared with the pre-improved algorithms， and the proposed R-VNS* algorithm based on the reduced-neighborhood search strategy operates more efficiently. Figures and Tables | References | Related Articles | Metrics

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Comparison of Probabilistic Sales and Quick Response under Demand Uncertainty Mingyang Zhang, Lan Deng, Juliang Zhang, Jiantao Guo 2026, 34 (8):  195-209.  doi: 10.16381/j.cnki.issn1003-207x.2024.1677 Abstract ( 33 )   HTML ( 0 )   PDF (1047KB) ( 9 )   Save With the rapid development of the digital economy and consumers' growing pursuit of personalization， consumer demand has become more uncertain. To help retailers make inventory management and pricing strategies better， three ways to address this challenge are analyzed， that is， probabilistic selling， quick response， and a hybrid mode combining the two. The probabilistic selling mode refers to the retailer combining normally sold products into probabilistic products and offering consumers the opportunity to obtain one of them at a lower price. The quick response mode involves timely replenishment of out-of-stock products after the retailer observes the actual market demand. The two-stage sales model is constructed and the impact of different selling modes on retailers' decisions （e.g.， order quantity and pricing） and profits is explored in traditional and strategic consumer markets， respectively. Firstly， the performance of the three modes in different market environments is compared. Secondly， the effects of strategic consumers on the retailer's optimal strategies are analyzed. The research shows that the probabilistic selling and hybrid mode generate more profits for retailers than the quick response mode in most cases. The hybrid mode outperforms when the quick response cost is low and the ordering cost is high； otherwise， the probabilistic selling mode is more advantageous. Moreover， the relative advantage of the probabilistic selling mode increases as market demand uncertainty rises under the high quick response cost. Figures and Tables | References | Related Articles | Metrics

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Research on Pricing and Recycling Strategies of Closed-loop Supply Chain Considering Inter-chain Competition and Intra-chain Cooperation Lixiao Wei, Chunxiang Huang, Dengfeng Li 2026, 34 (8):  210-221.  doi: 10.16381/j.cnki.issn1003-207x.2023.0809 Abstract ( 20 )   HTML ( 0 )   PDF (1626KB) ( 5 )   Save As an indispensable part of the circular economy， the importance of closed-loop supply chains is self-evident. When closed-loop supply chain members pursue their own economic benefit maximization， it is easy to produce a double marginalization effect， which leads to supply chain profit loss. The deposit return system can bring both motivation and pressure to members， urging them to actively participate in recycling and remanufacturing activities. Under the government’s implementation of the deposit return system for the retail platform， how to establish a unified game model and obtain the optimal strategy for the closed-loop supply chain members and realize the coordination of the closed-loop supply chain has become an important issue.For the competitive closed-loop supply chains of deposit return retail platform， a noncooperative-cooperative biform game approach is adopted to study and analyze the problem of pricing and profit allocation among the competitive closed-loop supply chains， ensuring that the closed-loop supply chains can be coordinated under profit maximization decisions. Firstly， the competitive and cooperative relationship between the two closed-loop supply chains is analyzed for two inter-chain competitive closed-loop supply chains consisting of manufacturer and retail platform， and manufacturer and recycler respectively. Then， a noncooperative-cooperative biform game model is constructed for pricing and profit allocation in the inter-chain competitive closed-loop supply chains that deposit return retail platform， and a model solution method integrating Shapley value， Nash equilibrium and Stackelberg game is proposed to obtain both benefit maximization strategy and cooperative profit allocation. Finally， a static comparative numerical analysis is carried out， with influencing factors including deposit， substitution factor， difficulty of recovery and cost of recovery.The results of the study show that in the inter-chain competitive closed-loop supply chain， the government’s implementation of the deposit return system on the retail platform recycling channel can effectively improve the enthusiasm of the retail platform to recover used products， but it needs to set a reasonable deposit， otherwise it will damage the supply chain profits； the； Substitution coefficient has positive effects on pricing and profit allocation in inter-chain competitive closed-loop supply chains， while the difficulty and cost of recovery can seriously affect the incentive of recovery for members of inter-chain competitive closed-loop supply chains； The manufacturer’s remanufacturing cost advantage is conducive to closed-loop supply chain members actively participating in recycling activities， increasing profits， promoting green production development and environmental protection. The research results provide theoretical and methodological support and practical reference for competition and cooperation in closed-loop inter-chain competitive supply chains. Figures and Tables | References | Related Articles | Metrics

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Optimal Channel Integration Strategy for Online Retailer under Omni-channel Retailing Chunhong Lu, He Huang, Liang Jin 2026, 34 (8):  222-233.  doi: 10.16381/j.cnki.issn1003-207x.2023.0914 Abstract ( 10 )   HTML ( 0 )   PDF (1506KB) ( 4 )   Save In recent years， the rapid development of e-commerce and increasing market competition have prompted online retailers to explore new business modes， among which omni-channel retailing has gained significant attention. Omni-channel retailing enables online retailers to interact with consumers across multiple channels， offering a variety of purchasing options. However， the integration of channels by online retailers influences consumers' channel selection and purchasing behavior， as well as the operational and decision-making processes of the e-commerce supply chain. This integration may result in imbalanced decision-making incentives within the supply chain and lead to performance losses for online retailers.In this study， a system composed of one manufacturer， one online retailer and one traditional retailer is examined， focusing on the effects of channel integration on consumer purchasing behavior and the selection of BOPS （Buy-Online-and-Pickup-in-Store） channel. A consumer utility function is developed to derive product demand and subsequently construct game models for both pre- and post-channel integration， utilizing backward induction to determine the equilibrium of the model. Based on the equilibrium outcomes， the online retailer's channel integration strategy and its effects on product pricing， firm profits， and consumer welfare are analyzed.Several findings are obtained. First， in the competition between online and offline channels， offline channels reduce consumer uncertainty regarding products， leading to higher retail prices for products sold through these channels. This conclusion remains unaffected by channel integration. Second， from the perspectives of market share and profit maximization， the online retailer is incentivized to adopt a channel integration strategy. Furthermore， when the online retailer integrates offline channels， the， manufacturer adjusts the wholesale price of the product based on the proportion of consumers choosing BOPS channels. This adjustment， in turn， impacts the retail prices of products in both online and offline channels， as well as the profits of each firm. However， this effect is not necessarily advantageous for the manufacturer or the overall supply chain system. Third， from the consumer behavior perspective， channel integration may motivate some low-valuation consumers to make purchases， while some consumers who initially prefer offline channels may exhibit channel migration behavior， shifting from online to offline channels for their purchases. Given these changes in consumer purchasing behavior and product retail prices， channel integration may not enhance consumer welfare.It highlights the significance of channel integration strategies for online retailers in this study. It not only enhances the research on channel management and consumer behavior theory within the e-commerce context but also provides a theoretical foundation for online retailers to innovate their business modes and operational practices. Future research could further investigate scenarios involving multiple competing manufacturers collaborating with online retailers to examine their channel integration strategies， which would contribute to addressing various practical issues. Figures and Tables | References | Related Articles | Metrics

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Advance Selling and Cooperation Strategy of E-tailing Supply Chain for Fresh Produce Considering Supplier's Freshness-keeping Effort Xumei Zhang, Jixin Fan, Bin Dan, Zhenjiang Chen, Ting Lei 2026, 34 (8):  234-245.  doi: 10.16381/j.cnki.issn1003-207x.2023.1240 Abstract ( 9 )   HTML ( 0 )   PDF (1661KB) ( 7 )   Save In recent years， with the rapid development of e-commerce technology and the popularity of online shopping among consumers， the fresh produce e-tailing market has developed rapidly， and more and more fresh suppliers cooperate with online sellers to sell fresh agricultural products online. Suppliers mainly cooperate with online sellers in agency mode or wholesale mode. The agency mode refers to the supplier selling products directly to consumers through the online channel and paying a certain commission to the online seller； and the wholesale model refers to the wholesale of products by suppliers to online sellers and then resold by online sellers to consumers. Under different cooperation modes， suppliers and online seller have different pricing power and different profit methods. At the same time， the development and innovation of e-commerce technology have provided convenient conditions for the implementation of the fresh produce advance selling strategy. In order to cope with the challenge of the short sales cycle of fresh produce， some suppliers or online seller begin to consider selling fresh produce before being launched to extend the sales cycle and expand market demand. However， under different cooperation modes， the advance selling strategy has different impacts on the supplier， the online seller and the overall performance of the supply chain， at the same time， the implementation of the advance selling strategy will affect the freshness-keeping effort and price decision， making the choice of cooperation mode of fresh produce e-tailing supply chain more complicated. Based on the above background， the selection of cooperation mode and advance selling decision of fresh produce e-tailing supply chain， as well as how to motivate suppliers and online seller to optimize strategy selection to achieve supply chain performance improvement are studied.The research content of this paper includes the following three parts. First， considering the interaction between cooperation mode and advance selling strategy， a dynamic game model between fresh produce supplier and online seller is constructed. Second， the advance selling strategy preference of fresh e-tailing supply chain under different cooperation modes is analyzed， and the optimal cooperation mode and advance selling strategy decision game equilibrium between the supplier and online seller are solued. Third， based on the equilibrium， incentive contracts are designed based on transfer payment to encourage the supplier to change the choice of cooperation mode or encourage the online seller to adopt advance selling strategies to achieve Pareto improvement of the overall performance of the supply chain.The results show that， first， the cooperation mode and advance selling strategy of fresh e-tailing supply chain depend on the proportion of consumers in the advance selling period， the degree of consumer loss aversion and the commission rate. Second， under the agency mode and wholesale mode， the implementation of advance selling strategy will promote the supplier to improve the level of freshness-keeping efforts， and increase the spot selling price. Third， the implementation of the advance selling strategy will affect the selection of cooperation mode by the supplier. Under certain conditions， suppliers may choose the agency mode even with higher commission rate. Fourth， in the wholesale mode， when the proportion of consumers in the advance selling period is high， or the proportion of consumers in the advance selling is low with high level of consumer loss aversion， the implementation of advance selling strategy is beneficial to the profit of the overall supply chain， but the supplier has no motivation to adopt the advance selling strategy. At this time， the contract designed based on transfer payment can encourage the supplier to adopt advance selling strategy or encourage the supplier to choose agency mode to achieve Pareto improvement of the overall supply chain. Figures and Tables | References | Related Articles | Metrics

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Sales Model Choice and Pricing Strategy in the Presence of Discount Factor and Supply Constraint Zhenkai Lou, Zhiying Wang, Xuming Lou, Xiaozhen Dai 2026, 34 (8):  246-256.  doi: 10.16381/j.cnki.issn1003-207x.2023.1479 Abstract ( 7 )   HTML ( 0 )   PDF (783KB) ( 3 )   Save Based on the time property of revenue， sales model choice and pricing strategy are considered under price regulation. First， three models corresponding to advance-only， spot-only， and advance+spot sales are constructed under no supply constraint， and all possible optimal strategies are obtained. It is shown that advance sale is always beneficial， and spot-only is an inferior strategy. Second， the impact incurred by supply constraints is analyzed. Some significant conclusions are drawn for situations such as： （a） when the previous mode is “advance+spot” sale， it may convert to advance-only or spot-only under the considered supply constraints； and （b） when the previous mode is advance-only， it remains as it is no matter whether supply constraint exists or not. Finally， some numerical examples are designed to analyze the sensitivity of selling strategy with respect to parameters， and examine the impact of supply constraint on sales mode. The presented results provide a well theoretical basis for certain cases in which spot-only sale is the optimal strategy under certain supply constraints. Figures and Tables | References | Related Articles | Metrics

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A Study on the Competitive Strategy of Freight Insurance for E-retailers with Different Retail Quality Peng Zhang, Huijuan Wang, Jun Ma, Xiaowu Zhu 2026, 34 (8):  257-268.  doi: 10.16381/j.cnki.issn1003-207x.2022.2112 Abstract ( 44 )   HTML ( 0 )   PDF (1208KB) ( 6 )   Save Return freight insurance has become a common service offered by major e-commerce platforms. Retailers differ substantially in how they provide this service—some offer it for free， while others only allow consumers to purchase it themselves. These differences have significant implications for conversion rates， return behavior， and price competition. At the same time， retailers on the same platform exhibit substantial heterogeneity in retail service quality， such as the richness of product information， responsiveness of customer service， and the availability of sizing or fitting tools. Retail service quality affects consumers’ satisfaction probability and return likelihood， and also shapes insurance companies’ risk-based pricing of return freight insurance. However， existing research has primarily focused on vertical product quality， and has not systematically examined return freight insurance competition under retail service quality differences combined with horizontal product differentiation. A Hotelling-type duopoly model with heterogeneous retail service quality is developed. Retail service quality is incorporated into consumers’ satisfaction probabilities and how differences in service quality， insurance premiums， and refund compensation jointly influence retailers’ pricing， demand， and profitability under both monopoly and competitive settings is analyzed. The results show that， in a monopoly， retailers with higher service quality have a stronger incentive to offer return freight insurance， and the choice between offering it for free or providing only a purchase option depends primarily on the retailer’s own insurance cost rather than the consumer-paid premium. In a competitive market， return freight insurance affects not only total demand but also the location of the marginal indifferent consumer， thereby reshaping the demand boundary between the two retailers. Importantly， in contrast to findings in the vertical‐quality literature， it is shown that when retail service quality differences and horizontal differentiation interact， the two competing retailers can never simultaneously benefit from offering return freight insurance. Any insurance-provision strategy generates a clear demand-shifting effect in favor of one retailer at the expense of the other. It concludes with several managerial implications， highlighting that retailers should carefully consider their own service quality and insurance cost structure when designing return freight insurance policies， and strike a cost-effective balance among service-quality investment， return compensation， and insurance-premium subsidies in this paper. Figures and Tables | References | Related Articles | Metrics

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Green Transformation of Small and Medium-Sized Manufacturing Enterprises under the Dynamic Government Reward and Punishment Mechanism Shuyu Shao, Qing Wang, Yan Liu 2026, 34 (8):  269-279.  doi: 10.16381/j.cnki.issn1003-207x.2024.0001 Abstract ( 14 )   HTML ( 0 )   PDF (1397KB) ( 4 )   Save Under the global climate change and China’s “dual-carbon” policy background， it delves into the critical issue of promoting the green transformation of small and medium-sized manufacturing enterprises （SMEs）， and constructs a two-player evolutionary game model involving local governments and SMEs to explore the complex interplay of factors influencing green transformation in this study. The model incorporates key parameters such as cost differences， reward intensity， and punishment intensity， as well as the probability of local government supervision and the probability of SMEs' active participation in green transformation. It is revealed that under static reward-punishment mechanisms， no evolutionarily stable strategy （ESS） exists， highlighting the superiority of dynamic mechanisms. It also demonstrates that the cost difference is negatively correlated with the probability of SMEs actively participating in green transformation and positively correlated with the probability of local government supervision. Importantly， it is found that increasing the intensity of punishments for inactive participation in green transformation has a more pronounced effect on achieving a coordinated and stable state of green transformation than increasing rewards for active participation. These findings underscore the importance of dynamic reward-punishment mechanisms in incentivizing and regulating the behavior of SMEs and local governments， thereby fostering sustainable green transformation. Through numerical simulations， the theoretical model is validated and a visual representation of the evolutionary paths under different reward-punishment mechanisms is provided. The results show that dynamic reward-punishment mechanisms， particularly those that adjust rewards and punishments based on the strategies chosen by SMEs， are more effective in achieving a stable state of green transformation. It not only contributes to the existing literature on green transformation and evolutionary game theory but also offers practical insights for policymakers in designing effective incentive mechanisms to promote sustainable development among SMEs in this research. Figures and Tables | References | Related Articles | Metrics

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The RDEU Game Model Analysis of Legitimacy Spillover of the Industrial College Huifeng Jiang, Yiping Liu 2026, 34 (8):  280-291.  doi: 10.16381/j.cnki.issn1003-207x.2024.1554 Abstract ( 10 )   HTML ( 0 )   PDF (1277KB) ( 8 )   Save The organizational legitimacy spillover of industrial colleges refers to the gradual accumulation of legitimacy of their organizational structure as the scale expands. Incorporating risk preference to explore organizational legitimacy spillover， introducing rank-dependent expected utility functions to measure corporate risk preference， and performing nonlinear transformations on the probabilities of utility outcomes after ranking through decision weights， thereby constructing an RDEU game model. The existence conditions of the game equilibrium for leading and following enterprises under different risk preferences and the influence laws of the risk characteristics of both parties on the equilibrium are analyzed in this model. It is found that when the benefits are greater than the costs， regardless of the risk preference， there exist pure strategy equilibria of “sharing” and “imitating” which are conducive to promoting the legitimacy spillover of industrial colleges； when the benefits are less than the costs， the risk attributes have a significant impact on the game equilibrium. Specifically， when the leading enterprise is rational， the following enterprise determines the unique equilibrium solution based on the risk attributes of the leading enterprise. Similarly， the leading enterprise will also change its own decisions based on the risk attributes and decisions of the following enterprise. If the following enterprise is risk-averse， the leading enterprise tends to choose the “sharing” strategy； if the following enterprise is risk-seeking， the leading enterprise tends to choose the “precautionary” strategy. When both are irrational， being risk-averse is conducive to advancing the legitimacy spillover of industrial colleges， while being risk-seeking hinders it. When one is risk-averse and the other is not， the equilibrium of the latter is greater than that when both are rational. Based on this， relevant countermeasures and suggestions are proposed from the perspective of risk preference to promote the legitimacy spillover of industrial colleges， in the hope of providing a reference for advancing the high-quality development of industrial colleges. Figures and Tables | References | Related Articles | Metrics

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Double Differential Game Model of the Carbon Neutrality in Supply Chain considering Government Participation Yuelong Zheng, Lingyue Zhang, Chunguang Bai 2026, 34 (8):  292-304.  doi: 10.16381/j.cnki.issn1003-207x.2023.0372 Abstract ( 11 )   HTML ( 0 )   PDF (1703KB) ( 12 )   Save Nowadays， low-carbon development has become a global consensus. America， Japan， European Union have all committed to achieve carbon neutrality by 2050， and China also pledged at the 75th Session of The United Nations General Assembly to strive to peak carbon dioxide emissions before 2030 and achieve carbon neutrality before 2060. This paper focuses on the carbon neutrality in supply chain which is triggered by the following three factors： Firstly， government subsidies for firms to reduce carbon emissions can provide incentives for firms to reduce the negative externalities on the environment. Secondly， the environmental protection awareness for consumers is gradually increasing， and they tend to buy environmentally products even if they are more expensive than ordinary products. Thirdly， supply chain sides also actively cooperate to enhance consumers’ low-carbon preferences. Scholars have conducted researches on carbon emission reduction and carbon trading， the research gaps on the carbon neutrality in supply chain is obvious.This paper explores a two-stage supply chain consisting of a manufacturer and a retailer， where the manufacturer endeavors to achieve net-zero carbon emission production by carbon emission reduction and carbon absorption， the retailer impacts consumers’ preference for carbon-neutral products by publicity and promotion. Government participate in the carbon neutrality by subsidies or interventions. Accordingly， we build a double differential game model， and comparatively analyze the decentralized decision-making without government subsidies （Scenario N）， decentralized decision-making with government subsidies （Scenario S）， centralized decision-making with government subsidies （Scenario C）， and the two-way cost-sharing contract under the government intervention （Scenario I）. The results show that Scenario S is the optimal choice for the carbon neutrality in supply chain. The natural attenuation rate has a negative impact on consumers’ preference for carbon neutrality products， product carbon neutrality level and supply chain profits， while parametters such as the influence coefficient of effort level， consumers' preference for carbon-neutral products， and the impact coefficient of product carbon neutrality level on market demand have a positive influence. with the introduction of government's role， the effort levels of carbon emission reduction and marketing changes from being positively affected by its own marginal profit to being positively also affected by the other party's marginal profit. Government needs to provide some subsidies to optimize the profits of both parties in supply chain in scenarios I and S. Whether government provide direct subsidies or intervenes in the supply chain， which can increase consumer preference for carbon neutrality products and their carbon neutrality level.Some management implications are revealed as follows. Firstly， Scenario S is the preferred choice for supply chain carbon neutrality， and the relevant influencing factors should be clarified in practice to help Scenario S to carry out smoothly. Secondly， government should actively participate in the carbon neutrality in supply chain to provide the supply chain parties with a good market expectation. Finally， supply chain carbon neutrality is affected by different factors， in practice， we should clarify the influence mechanism of the relevant factors to provide a support for the realization of the carbon neutrality in supply chain， to realize the “win-win” situation in economic and environmental benefits. Figures and Tables | References | Related Articles | Metrics

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Research on the Impact of Supplier Demand Information Acquisition on Encroachment Strategies Tianci Pang, Tong Shao, Yu Cao 2026, 34 (8):  305-316.  doi: 10.16381/j.cnki.issn1003-207x.2025.0220 Abstract ( 7 )   HTML ( 0 )   PDF (1113KB) ( 10 )   Save The impact of a supplier's demand information acquisition strategy on its encroachment strategy in a supply chain composed of one supplier and one retailer is investigated， under the retailer’s demand information advantage. The results indicate that information acquisition does not always promote the supplier’s encroachment strategy but may instead weaken its encroachment motivation. The supplier’s encroachment behavior is influenced by the dual effects of information accuracy and cost. Specifically， when the supplier possesses a high probability of high-demand market distribution， its motivation for information acquisition strengthens. In such cases， the supplier’s encroachment motivation increases with higher information accuracy. However， when the sales cost in the encroachment channel is elevated， this motivation is suppressed by cost factors. Conversely， when the supplier holds a low probability of high-demand market distribution， it tends to adopt a centralized sales mode to transfer risk to the retailer， thereby maintaining sustained profit growth. Furthermore， when evaluating market distribution scenarios， the supplier exhibits a stronger preference for leveraging historical information over proactive acquisition， particularly when cost considerations are incorporated into the analysis. Figures and Tables | References | Related Articles | Metrics

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Behaviour-based Pricing of Green Products Considering Cross-border Market Differences Kanying Liu, Wei Li, Ningyao Sun 2026, 34 (8):  317-328.  doi: 10.16381/j.cnki.issn1003-207x.2024.0005 Abstract ( 6 )   HTML ( 0 )   PDF (1637KB) ( 3 )   Save With behaviour-based pricing （BBP）， enterprises can fully concern the various behavioral characteristics and the payment willingness of new and repeat customers. Prior studies have investigated BBP in the domestic market. Considering the case of BBP in cross-border sales of green products， this paper examined BBP in cross-border market between green and conventional products. The feasibility and applicability of using BBP are discussed， focusing on the impact of trade costs on BBP， market share， profits and social welfare. It is shown that whether enterprises can fully or partially use BBP in cross-border market will be affected by the difference in trade costs between green and conventional products. With the increase in the enterprise's own trade costs， BBP will focus on protecting loyal customers， and with the increase in the competitor's trade costs， BBP will focus on attracting potential customers. The use of BBP helps to not only strengthen the market share advantages of green products in cross-border market but also improve the consumer surplus and total social welfare of domestic and overseas consumers who buy green products. When the trade cost difference between green and conventional manufacturers is small， the market equilibrium is that both types of enterprises adopt BBP. Figures and Tables | References | Related Articles | Metrics

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Study on the Influence of Carbon Option Purchase Quota Based on Demand Uncertainty on Supply Chain Decision-making and Coordination Mechanism Wenfang Shang, Xintao Gu, Tao Li 2026, 34 (8):  329-344.  doi: 10.16381/j.cnki.issn1003-207x.2024.1404 Abstract ( 10 )   HTML ( 0 )   PDF (3595KB) ( 5 )   Save The uncertainty of product market demand leads to the uncertainty of emission dependent manufacturers' demand for quotas. Carbon option gives manufacturers the right to lock the price of quotas before the implementation of the contract， which can not only avoid the risk caused by the uncertain price of carbon trading market， but also mitigate the impact of the uncertain quantity of quota demand. Aiming at the two-stage supply chain formed by emission-dependent manufacturers and their retailers， the impact of carbon option procurement on supply chain decision-making and coordination mechanism is discussed. The findings are as follows： （1） Under decentralized decision-making， carbon option procurement expands manufacturers' quota procurement volume， making their production more flexible， thus better meeting retailers' flexible order demand and driving retailers' profits to increase； In addition， when the option price and exercise price meet the threshold conditions， the manufacturer can hedge the risk caused by the uncertainty of carbon quota purchase at a lower cost and improve its own profits. （2） Under centralized decision-making， the risk weakening effect of carbon option procurement extends to the entire supply chain system， and the total profit of the supply chain can also be improved within a reasonable parameter range. （3） When retailers and manufacturers share the cost of carbon option purchase and exercise， if the cost sharing coefficient and fixed cost meet the incentive compatibility conditions， the optimal profit of the whole supply chain and the profit improvement goal of the two members can be achieved at the same time， and the profits of both manufacturers and retailers exceed the decentralized decision-making without coordination mechanism， which verifies the effectiveness of the carbon option cost sharing mechanism. Figures and Tables | References | Related Articles | Metrics

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Research on the Development Path of BEVs in China Based on Multi-objective Optimization Ru Yu, Xiaoli Wang, Xiaojun Xu, Lu Wang 2026, 34 (8):  345-355.  doi: 10.16381/j.cnki.issn1003-207x.2024.0744 Abstract ( 104 )   HTML ( 0 )   PDF (991KB) ( 26 )   Save The development of the battery electric vehicle （BEV） industry is of great significance for energy conservation， carbon reduction， and industrial restructuring in China. This study investigates the development path of China’s BEV industry based on a multi-objective optimization algorithm. First， the generalized Bass model is employed to forecast the sales trend. Meanwhile， the two-factor learning curve model is used to analyze the relationship among technology， production scale， and cost， thereby predicting the trend of cost changes. Second， based on the above forecasts， a multi-objective optimization model is constructed to explore the development path. Finally， simulation analysis is conducted. The results show that sales and cost reach a balanced state in 2028， and the period from 2026 to 2030 represents a key stage for the coordinated advancement of industrial sales expansion and cost reduction. In terms of sales， China’s BEV sales are expected to maintain relatively rapid growth before 2025， after which the growth rate will gradually slow down and decline further after 2029. By 2035， cumulative sales are projected to reach the preset target， with an average annual increase of 14.0978 million vehicles from 2024 to 2031. In terms of production cost， affected by product development costs， the unit production cost of power batteries remains high and fluctuates significantly during 2016-2025. From 2025 to 2031， the cost shows a steady downward trend and is expected to approach the target range by 2031. Figures and Tables | References | Related Articles | Metrics

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New Expanded 3D Opportunity Mining Algorithm: Opportunity Mining Based on the New Energy Vehicle Market Meng Zhao, Hailong Wang, Wenshuai Wu, Hanlin Wang, Yajun Wang 2026, 34 (8):  356-368.  doi: 10.16381/j.cnki.issn1003-207x.2024.0790 Abstract ( 12 )   HTML ( 0 )   PDF (2025KB) ( 23 )   Save In the increasingly competitive field of new energy vehicle manufacturing， market opportunities must be accurately grasped for new energy vehicle manufacturers to gain competitive advantages. The existing market opportunity mining algorithms mainly analyze potential consumer demand satisfaction and demand importance based on online comment information to explore potential demand as market opportunities. However， critical limitations are exhibited in these methods： interactive information is neglected conveyed by consumers when measuring demand satisfaction， and multiple influencing factors are not considered and their interrelationships when assessing demand importance， resulting in distorted reflection of genuine consumer needs through online reviews. Furthermore， temporal dynamics are overlooked in identifying potential demands which may hinder the ability to dynamically capture evolving market opportunities. Based on the above shortcomings， a new extended 3D opportunity mining algorithm is proposed and applies it to identify opportunities in the new energy vehicle market： firstly， the satisfaction measure is proposed based on the identification of false reviews and the consideration of interactive information； secondly， the importance measure is proposed by combining the Choquet integral with the effective frequency of demand， the influence of demand on satisfaction， and the Baidu index， etc； and lastly， based on the satisfaction and importance， satisfaction measure is proposed based on the interaction information of consumers. Finally， on the basis of satisfaction and importance， a measure of demand potential by considering the time factor is proposed， which extends the traditional opportunity algorithm to the three-dimensional space of "potential-demand satisfaction-demand importance". By applying the method framework to 7，606 text reviews of the Tang new energy vehicle series from 2015 to 2022， the ranking of consumer attention to demands and improvement suggestions are obtained. And the changes in demand importance and demand satisfaction within different time periods are analyzed. The results of comparative analysis demonstrate the effectiveness and necessity of the new method. In future research， a set of comprehensive indicators and systems are provided for measuring importance， and a method is provided for measuring demand potential considering the time factor. In practice， market opportunities for new energy vehicles can be accurately and effectively identified， and enterprises are helped to upgrade their vehicle models in a targeted manner. Figures and Tables | References | Related Articles | Metrics