Table of Content

25 June 2026, Volume 34 Issue 6 Previous Issue   

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Bank Lending Preference, Network Structure Evolution and Financial Risk Contagion Tao Xu, Tingqiang Chen 2026, 34 (6):  1-12.  doi: 10.16381/j.cnki.issn1003-207x.2024.0135 Abstract ( 4 )   HTML ( 0 )   PDF (2953KB) ( 4 )   Save The interbank market plays a crucial role in the modern financial system. It serves as a platform for the exchange of bank liquidity， facilitating interbank lending relationships that enable the seamless flow of funds between banks. However， these lending linkages also expose banks to potential contagion risks. In the event of bank failures， interbank lending relationships can act as conduits for risk transmission， potentially triggering a cascade effect that threatens the stability of the entire banking system. Risk contagion has been examined within bank networks of various structures， as well as the impact of bank behaviors on this contagion. Nevertheless， further research is needed to understand how bank lending preferences influence network structure and evolution， how these preferences affect risk contagion， and how the evolution of interbank lending networks impacts financial risk contagion.To investigate the relationship between bank lending preferences， network evolution， and risk contagion， a dynamic exogenous interbank lending network model is introduced based on banks' lending behaviors and balance sheets. Four foundational hypotheses reflecting the formation and evolution of the actual interbank lending market are proposed. Using these hypotheses， an initial interbank lending market is constructed， the information pertaining is updated to bank assets and liabilities， and the interbank lending network according to established construction rules is developed and revised. Subsequently， the impact of bank lending preferences on the structure and evolution of the bank network is analyzed， as well as on financial contagion within the banking system.Values to the model parameters are assigned based on data from the China Financial Statistical Yearbook， the lending rates in the Chinese interbank market， and relevant literature. Simulation analysis is employed to investigate the relationships between bank lending preferences， bank network structure evolution， and financial risk contagion. The simulation results indicate that： （1） As lending behavior preferences increase， the distribution range of bank node degrees （both in and out） gradually expands， suggesting heightened lending activity in the interbank market. Consequently， the clustering coefficient and efficiency of the network improve， while the average shortest path length decreases. （2） The cumulative distribution of the degree of bank nodes follows a single-stage power law distribution initially but gradually evolves into a two-stage power law distribution over time. （3） The structure of the interbank lending network post-evolution differs from its initial state， with increased divergence in assets and liabilities among banks. For the interval 0<T<20， as lending linkages within the network increase， both aggregation and accessibility are enhanced. When T≥20， the topology of the interbank lending network remains dynamic yet stable. （4） Financial contagion within the interbank market is influenced by bank lending preferences. When a certain threshold is exceeded， an increase in interbank lending linkages can enhance risk-sharing capabilities among banks； however， it also creates additional pathways for risk contagion， potentially amplifying the system's vulnerability and leading to systemic collapse. （5） During the evolution of the interbank lending network， an increase in the interbank lending preference coefficient raises the volatility of the ratio of failed banks， enhances the risk-sharing capacity of the network， and concurrently increases systemic vulnerability.The research presented in this paper contributes to a deeper understanding of the relationship between bank lending preferences， dynamic bank network evolution， and financial risk contagion， thereby enriching the theoretical framework for bank network evolution and financial risk management. Moreover， the findings offer practical guidance for financial regulatory authorities. First， due to the randomness of external shocks in the banking system and the nonlinear characteristics of financial risk contagion， regulators must enhance risk monitoring within the banking sector， improve their ability to diagnose and identify financial risks， and intervene promptly when bank failures occur to prevent the spread of financial risks. Second， regulators should conduct regular stress tests on the banking system to monitor changes in asset quality in real time and adjust relevant regulatory indicators accordingly to bolster the banking system's resilience against risks. Third， in the event of a banking crisis， the central bank should maintain high liquidity levels in the interbank market through policy tools such as open market operations. This approach helps prevent asset prices from declining due to liquidity exhaustion and mitigates the risk of contagion by safeguarding liquidity channels within the banking system. 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Research on Accounts Receivable Auction Financing under the Empowerment of Federated Learning and Blockchain Qiyou Liu, Jia Chen, Chengke Zhang, Huainian Zhu 2026, 34 (6):  13-21.  doi: 10.16381/j.cnki.issn1003-207x.2024.2280 Abstract ( 3 )   HTML ( 0 )   PDF (1623KB) ( 12 )   Save The insufficient penetration of full chain information and the difficulty in balancing data sharing and security make it difficult to effectively promote the application of supply chain financing in the agricultural field. The combination of blockchain and federated learning can fully leverage the technological advantages of both， effectively compensating for the shortcomings of blockchain in data privacy and security， and federated learning in data storage， synchronization， and tamper prevention. However， it still faces problems such as low computational efficiency and insufficient incentive mechanisms. Receivable financing business is taken accounts as the research object and an agricultural supply chain financing system empowered by hierarchical federated learning and blockchain is constructed； On this basis， based on deep learning auction algorithms， the financial resource allocation strategy is constructed as an auction model with the goal of maximizing seller returns， in order to achieve individual rationality， incentive compatibility， and maximization of cooperative auction revenue for financial institutions. Research has shown that： （1） through auction strategy optimization， auction mechanisms based on deep learning can maximize seller returns； （2） The auction revenue of cooperatives is directly proportional to their data coverage and the data coverage requirements of financial institutions； （3） The higher the approximate optimization rate of neural networks， the greater the difficulty of system optimization. Therefore， cooperatives need to invest more computing resources and training time， which in turn affects their profits. New ideas for promoting the application of digital intelligence technology in the field of agricultural supply chain financing are provided， and certain practical significance is given in promoting data information security and sharing， fund integration and optimization， and assisting rural industrial revitalization. Figures and Tables | References | Related Articles | Metrics

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Optimal Investment Timing of Venture Capital Projects Based on Prospect Theory and Bayesian Posterior Beliefs Model Liang Wang, Junjie He, Guoqing Huang, Jinhui Zhang, Xiaohan Wang 2026, 34 (6):  22-35.  doi: 10.16381/j.cnki.issn1003-207x.2024.1485 Abstract ( 15 )   HTML ( 0 )   PDF (2569KB) ( 2 )   Save In practice， investors must continuously update their beliefs about project quality through costly information acquisition， and they actively incorporate behavioral preferences toward risk and uncertainty into their decision-making. A unified analytical framework is developed that integrates Bayesian learning and prospect theory to study optimal venture capital investment timing in a dynamic setting. Research background In the post-pandemic era， venture capital markets have been facing increasing uncertainty due to macroeconomic fluctuations， monetary tightening， and geopolitical risks. Venture capital projects are typically characterized by long investment horizons and severe information incompleteness， making optimal investment timing a crucial determinant of investment performance. The optimal investment timing decision of venture capital projects is studied under incomplete information. The investor does not directly observe the true success probability of the project but forms a Bayesian posterior belief {\pi }_t through continuous information collection. The investor must decide whether to invest immediately or delay investment while updating beliefs， taking into account information costs and uncertainty.Research methods and models， the irreversible investment decision is modeled as an optimal stopping problem under uncertainty. The project’s investment prospect evolves stochastically， and the Bayesian posterior belief follows a learning process with mode-specific information quality and acquisition costs. Prospect theory is incorporated to describe investors’ reference-dependent preferences and asymmetric attitudes toward gains and losses. The decision problem is formulated using a Hamilton–Jacobi–Bellman （HJB） equation.The results indicate that optimal investment timing follows a belief threshold， at which the investor is indifferent between immediate and delayed investment. Under the delayed investment strategy， both the expected value and the choice of learning mode are jointly affected by information volatility and the discount rate. Higher information volatility， which reduces information quality， raises the investment threshold and leads to delayed investment. Conversely， a lower discount rate induces investors to control information acquisition costs by choosing low-cost learning modes and postponing investment. Numerical simulations are conducted using parameter values commonly adopted in the venture capital literature. The simulation results are consistent with the theoretical analysis and illustrate the effects of information volatility and discount rates on investment timing.Bayesian learning and prospect theory are integrated into a unified dynamic investment timing framework， offering a coherent explanation of how belief updating， behavioral preferences， and learning costs jointly determine venture capital investment timing in highly uncertain environments. Figures and Tables | References | Related Articles | Metrics

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Personalized Pricing Strategy for Online Merchant Driven by Data Transactions Jianhong Chang, Peng Song, Lirong Wu 2026, 34 (6):  36-49.  doi: 10.16381/j.cnki.issn1003-207x.2024.0979 Abstract ( 127 )   HTML ( 0 )   PDF (1843KB) ( 38 )   Save With the development of the information economy， data has emerged as a pivotal production factor within the digital economy， and it has led to the emergence of data transactions involving consumer information in the market. Although data brings tremendous benefits to both enterprises and consumers， the practice of enterprises utilizing consumer data to implement discriminatory pricing has raised significant societal concerns. It is imperative to prioritize research focusing on achieving a balance between the effective utilization of data and preventing the adverse effects of discriminatory pricing on consumer welfare for the sustainable development of the current platform economy. To address this issue， a platform economy is constructed with the participation of three players， namely， platform-merchant-consumers. A game-theoretic framework encompassing three scenarios is proposed： no data transactions， data transactions， and optimized data transactions between the merchant and the platform. The findings indicate that data transactions segment the consumer data acquired by the merchant into various types with distinct values. Consequently， the platform should adopt differentiated data pricing strategies tailored to these distinct data types. Furthermore， a comparative analysis of consumer surplus across the three scenarios reveals that personalized pricing by the merchant， particularly under the optimized data transactions scenario， enhances the welfare of consumers with low willingness to pay. This situation creates a mutually beneficial outcome for the platform-merchant-consumers triad. However， the impact of personalized pricing on consumers with high willingness to pay largely hinges on the extent to which the platform can convert accumulated data into consumer utility. In instances of high conversion rates， personalized pricing contributes positively to the welfare of such consumers. Ultimately， it is demonstrated that whether personalized pricing in data transactions can enhance total consumer surplus， producer surplus， and social welfare depends critically on the accuracy of the platform’s data and the conversion of data into utility. A novel approach is introduced to mitigating the issue of consumer welfare detriment resulting from discriminatory pricing. Additionally， it also holds value and significance in efficiently vitalizing data resources， converting them into valuable data assets， and enhancing the efficacy of data resource distribution. Figures and Tables | References | Related Articles | Metrics

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Mining Critical Nodes and Critical Paths in Multi-source Risk Transmission Networks in High-end Equipment Manufacturing Chen Wei, Pingfeng Liu, Jian An 2026, 34 (6):  50-65.  doi: 10.16381/j.cnki.issn1003-207x.2024.0738 Abstract ( 7 )   HTML ( 0 )   PDF (4100KB) ( 1 )   Save High-end equipment is characterized by high technological complexity and significant reliance on critical external components， making enterprises in this sector particularly vulnerable to multi-source risks such as technological blockades， trade sanctions， and geopolitical conflicts in unstable and uncertain environments. These risks often overlap， propagate， and amplify， leading to cascading disruptions， operational instability， and substantial losses. Constructing multi-source risk transmission networks and identifying critical nodes and paths within these networks is essential for effectively preventing and mitigating risks in high-end equipment manufacturing.In this study， a systematic methodology is developed to construct multi-source risk transmission networks and identify critical nodes and paths. Potential risk factors are identified using phrase extraction techniques combined with the BERTopic model. Semantic association rules between risk factors are defined， and their associations are determined using a sliding window similarity method， which facilitated the construction of a multi-source risk factor association network. The intrinsic risk strength of each risk factor， as well as the transmission strength between factors， is quantified to transform the association network into a multi-source risk transmission network. Weighted closeness centrality is employed to measure the transmission strength of risk factors in the network， and critical nodes are identified by integrating their intrinsic risk strength with transmission potential. Finally， the Choquet fuzzy integral is applied to non-linearly aggregate multi-source risks， and an ant colony algorithm is exploited to determine critical transmission paths within the network.Risk texts are collected through a mixed-method approach： online texts are crawled from prospectuses and annual reports of listed high-end equipment manufacturers， while offline texts are gathered through on-site interviews with managers and employees at a CRRC subsidiary to supplement operational risk-related narratives.Key findings include　1） Identification of 91 risk factors and their associations， forming a multi-source risk factor association network； 2） Derivation of 28 two-source concurrent risk scenarios from eight primary risk sources， leading to the construction of 28 two-source risk transmission networks； 3） Frequent identification of critical risk factors such as insufficient production capacity， rising production costs， and declining market demand in two-source risk transmission networks， alongside other critical factors like damaged production equipment， reduced industrial investment， and reliance on imported raw materials； 4） Observation that two-source risks tend to converge and amplify at critical factors such as insufficient production capacity， rising production costs， and declining market demand； 5） The most detrimental critical paths emerged from the concurrent occurrence of public health emergencies and macroeconomic downturns， causing peak-level risks and severe operational damage to enterprises. Figures and Tables | References | Related Articles | Metrics

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Transportation Network Optimization of Hazardous Chemicals Considering Risk Equity under Depot Disruptions Liping Liu, Rui Wang, Shilei Sun, Xiaofeng Gan, Tijun Fan 2026, 34 (6):  66-76.  doi: 10.16381/j.cnki.issn1003-207x.2024.0126 Abstract ( 104 )   HTML ( 0 )   PDF (1193KB) ( 34 )   Save The optimization of hazardous materials （hazmat） transportation networks under depot disruption scenarios are investigated， motivated by the increasing occurrence of “low-probability， high-consequence” events such as natural disasters and facility failures. Traditional models often minimize total risk and cost but overlook two critical factors： potential depot disruptions and risk equity—the fair distribution of risk among regions and populations. These omissions limit model applicability in complex real-world environments where governmental， public， and industrial demands must be balanced.To address this gap， a multi-objective optimization model is proposed that simultaneously minimizes （1） total system risk， （2） logistics cost （including leasing， storage， and transport）， and （3） risk compensation costs that penalize deviations from average risk exposure. A novel risk assessment function integrates depot disruption probabilities into both route and node-level risk. Risk equity is captured through compensation coefficients that quantify disparities and encourage balanced risk allocation.To solve this complex problem， a hybrid metaheuristic algorithm combining Ant Colony Optimization （ACO） and Genetic Algorithm （GA） is deueloped， which enhances solution quality and convergence speed under various disruption scenarios.A real-world case study of Shanghai’s road hazmat network—with 5 candidate depots and 20 customers—is used to validate the model. Parameter values are calibrated using government and industry data. Results show that considering depot disruptions significantly alters warehouse selection and routing strategies， leading to reduced system risk and improved network resilience. The hybrid algorithm outperforms standalone ACO and GA in all key objectives.Sensitivity analysis further reveals that　（1） higher compensation improves equity but may increase total risk； （2） expanding depot capacity reduces the number of active depots， raising both risk and inequality； and （3） ignoring depot disruptions may significantly increase systemic risk. It enriches the theoretical framework of risk assessment and equity under uncertainty and offers a practical decision-support tool for designing safer， fairer， and more resilient hazmat logistics systems in this study. Figures and Tables | References | Related Articles | Metrics

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Research on the Model of Government and Enterprise Three-level Joint Reserve of Emergency Supplies under Hybrid Contract Keting Guo, Lingjun Gong 2026, 34 (6):  77-90.  doi: 10.16381/j.cnki.issn1003-207x.2023.2130 Abstract ( 3 )   HTML ( 0 )   PDF (1563KB) ( 1 )   Save In recent years， various emergencies and natural disasters have occurred with alarming frequency， endangering the lives and property of the general public. In China， the government assumes the role of primary responder to disasters， bearing the significant responsibility of expeditiously dispatching supplies to affected regions. This requires the government to reserve sufficient emergency supplies before a disaster occurs in order to be prepared for potential crises. However， the government’s stockpiling capacity is limited， making it difficult to independently meet the surge in demand for emergency supplies triggered by a disaster. Additionally， in the event that no emergency occurs during the stockpiling period， the government may be compelled to dispose of its stockpile at salvage value， which would undoubtedly increase the government’s financial burden.To mitigate the risks and costs associated with sole government stockpiling， existing studies have proposed a joint stockpiling model between the government and manufacturers. This model leverages the strengths of social enterprises in material management while reducing the risks associated with government stockpiling. Concurrently， in order to reduce the risks and costs associated with stockpiling， manufacturers can also adopt a combination of physical stockpiling and capacity stockpiling， whereby some emergency supplies are stockpiled as reserved capacity and produced only when needed. This approach reduces the manufacturers’ stockpiling risk and cost without significantly delaying the delivery of emergency supplies post-disaster. However， the COVID-19 pandemic highlighted challenges such as the sharp increase in the price of meltblown nonwoven fabric， a key raw material for masks. During the crisis， the price surged from 18，000 yuan to 29，000 yuan per ton， an increase of 61.11%， which undeniably raised the conversion costs for mask manufacturers. To address this issue， an innovative three-tier joint reserve model is proposed that includes suppliers’ raw material reserves. This model is designed to mitigate the problem of raw material shortages and price increases post-disaster.A two-tier joint stockpiling model between the government and manufacturers is constructed based on a quantity flexibility contract as a baseline. Then， the supplier’s raw material stockpiling is integrated into the model， which is the main research model of this paper. The optimal stockpiling quantities and the respective profit and cost for each decision-maker are derived using the inverse solving method. The results of the study indicate that （1） the probability of a disaster is the most critical factor in determining whether the government initiates emergency stockpiling； （2） for materials with prices likely to fluctuate significantly post-disaster， the government should increase stockpiling before the disaster； （3） from an economic standpoint， there is an optimization interval that can improve the profit of manufacturers and suppliers while simultaneously reducing the cost of the government in the three-tier emergency stockpile supply chain； （4） the three-tier stockpiling strategy may not always be the optimal choice， so the government should flexibly adjust the strategy according to the option price between manufacturers and suppliers. Should the option price be higher， the government should adopt the new three-tier stockpiling strategy； otherwise， it should maintain the original two-tier stockpiling strategy. Theoretical guidance for the establishment of a three-tier joint stockpiling cooperative relationship between the government and enterprises is provided in this study. Figures and Tables | References | Related Articles | Metrics

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Research on Collaborative Emergency Reserve between Government and Enterprises Considering Different Risk Preferences under Government Subsidies Li Zhu, Xiao Yang, Jie Cao 2026, 34 (6):  91-102.  doi: 10.16381/j.cnki.issn1003-207x.2024.0560 Abstract ( 6 )   HTML ( 0 )   PDF (2218KB) ( 1 )   Save In today’s society， where natural disasters and emergencies occur frequently， the effective reserves of emergency supplies is crucial for enhancing our country’s capacity to prevent and withstand major risks， as well as for ensuring social security and stability. However， the government often faces dual challenges of funding pressure and storage capacity limitations in physical reserve strategies. To address this dilemma， the government actively advocates for the social transformation of emergency supply reserves， aiming to create a new ecosystem for collaborative emergency reserves between the government and enterprises.A review of the relevant literature shows that early studies on the collaborative reserve of emergency supplies by the government and enterprises primarily considered the potential advantages of cooperation from a risk-neutral perspective. In recent years， the focus has shifted to more complex and realistic scenarios that incorporate risk aversion. While some studies have attempted to explore subsidy strategies to alleviate cooperation barriers caused by risk aversion， most have concentrated on the risk attitudes of a single party （such as enterprises） and have rarely expanded the perspective to the complex emergency environment where both the government and enterprises exhibit risk-averse characteristics. Furthermore， there is a lack of research that deeply explores how to optimize subsidy strategies to more effectively promote cooperation between risk-averse government and enterprise parties in emergency supply reserves.To further enhance efficient collaborative cooperation in emergency supply reserves between the government and enterprises， a residual supply subsidy strategy within a cooperative framework based on flexible quantity contracts is introduced. Two models of collaborative reserves are constructed： one as a benchmark model for risk-neutral government and enterprises， and the other as a risk-averse model based on Conditional Value-at-Risk （CVAR） decision criteria. Using the Stackelberg game model and backward induction method， the optimal decision-making strategies of the government and enterprises under different risk preference scenarios are analyzed. Finally， a case simulation is conducted on the collaborative reserve issue of disaster relief tents， investigating how key factors such as risk aversion coefficients and the government’s regular reserves influence the formulation of subsidy strategies.The research findings indicate that compared to risk-neutral scenarios， risk-averse enterprises adopt more conservative reserve strategies， reducing reserve quantities to mitigate potential economic losses. Meanwhile， risk-averse governments are more sensitive and vigilant to reductions in the reserve quantities held by enterprises， which may lead them to increase the optimal reserve subsidy to address enterprise concerns and maintain the stability and adequacy of emergency reserves. Moreover， as both parties’ risk aversion intensifies， as well as the government’s own reserve scale increases and spot market procurement prices rise， the optimal reserve subsidy that the government is willing to provide will also increase. By considering the risk-averse behavior of both the government and enterprises， it aligns more closely with real-world conditions and offers theoretical support for developing more accurate emergency supply reserve strategies. Furthermore， by optimizing subsidy strategies， it provides new ideas for establishing a stable collaborative reserve relationship between the government and enterprises. Figures and Tables | References | Related Articles | Metrics

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The Joint Decisions of Automakers' Production and Charging Infrastructure Investment Under the Double Credit Policy Mengyao Hu, Dengfeng Li 2026, 34 (6):  103-116.  doi: 10.16381/j.cnki.issn1003-207x.2024.1056 Abstract ( 6 )   HTML ( 0 )   PDF (3213KB) ( 3 )   Save The production competition and charging infrastructure investment collaboration strategies between traditional automobile manufacturers and new energy vehicle （NEV） manufacturers under a dual oligopoly market structure are investigated. By constructing and solving both non-cooperative cooperative biform game model， how manufacturers determine the optimal production and investment strategies is revealed， as well as how profits are reasonably distributed. Theoretical and numerical analyses lead to the following conclusions： 1） The substitution rate between NEVs and fuel vehicles （FVs） significantly impacts manufacturers' production and charging infrastructure investment decisions； 2） Adjustments to charging service fees should be implemented in stages. When the NEV substitution rate is low， reducing service fees can effectively expand the NEV market and drive traditional manufacturers to transition. However， in the high substitution rate phase， excessive intervention should be avoided； 3） The effectiveness of the dual credit policy varies with the substitution rate. NEV credit values and trading prices have a positive incentive effect in low substitution rate markets， but their effectiveness reverses in high substitution rate markets. Additionally， the sensitivity of NEV credit price changes is higher than that of credit value changes； 4） Charging infrastructure subsidies have a significant effect during the early stages of NEV adoption， but their marginal benefits gradually decline as the substitution rate increases. The analysis of the impact of the dual credit policy on manufacturers' production decisions is expanded， the strategic role of charging infrastructure investment in a competitive cooperation environment is deepened， and theoretical foundations and decision-making references for policymakers and industry practitioners are provided. Figures and Tables | References | Related Articles | Metrics

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The Vehicle Manufacturer's Original Design Manufacture (ODM) Strategy and Coordination under Dual-credit Policy Jianbin Li, Hongjie Yu, Zhiying Tao, Qifei Wang 2026, 34 (6):  117-132.  doi: 10.16381/j.cnki.issn1003-207x.2023.1503 Abstract ( 7 )   HTML ( 0 )   PDF (1332KB) ( 2 )   Save The implementation of the dual-credit policy makes traditional fuel vehicle enterprises lacking new energy vehicle business face high compensation cost of credits. Some fuel vehicle enterprises seek strategic partners， expecting to obtain positive new energy credits to compensate for negative fuel credits by the original design manufacture （ODM） strategy， and explore the new energy vehicle market to make up for the shortcomings of the new energy vehicle business. However， the ODM strategy of fuel vehicle enterprises may lead to the encroachment of the new energy vehicle market on the fuel vehicle market and intensify the competition between new energy vehicle enterprises and fuel vehicle enterprises. Based on the above analysis，the dual-credit policy is considered， the Cournot game model between fuel vehicle enterprises and new energy vehicle enterprises is established， the optimal output decisions under the ODM strategy are explored， and the impact of ODM strategy on market competition and enterprise competition is analyzed. Furthermore， the coordination of Revenue Sharing Contract and Two-part Tariff Contract on ODM competition is studied. It is found that although ODM strategy intensifies the market competition and enterprise competition， the profits of fuel vehicle enterprises and new energy vehicle enterprises are increased compared with the direct purchase of credits. The ODM strategy increases the market of new energy vehicles， which indicates that ODM strategy is conducive to the development and promotion of new energy vehicles. In addition， revenue-sharing contracts cannot coordinate supply chain conflicts caused by double marginalization and competition， but two part-tariff contract can coordinate the system. Figures and Tables | References | Related Articles | Metrics

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The Construction and Optimization of a Drone Delivery System for Dual use of Usual and Emergency Situations Yulong Li, Han Su, Guobin Wu 2026, 34 (6):  133-145.  doi: 10.16381/j.cnki.issn1003-207x.2024.1981 Abstract ( 4 )   HTML ( 0 )   PDF (2283KB) ( 1 )   Save To address the challenges of inconvenient transportation and weak disaster prevention capabilities in closed areas with complex terrain， an optimization model of drone distribution point location and flight route planning is proposed for “dual use of usual and emergency situations”. The model is based on the emergency and emergency reconstruction of food security infrastructure in the closed area with complex terrain. Firstly， the investment and operation mode of drone distribution system， the selection of drone distribution mode and the selection of distribution point construction mode are discussed. Secondly， based on the selection of the above mode， the full life cycle cost of food infrastructure construction and operation is considered. The construction cost considered in this system includes the cost of transforming the distribution point to adapt to the operation of drone system， the cost of expansion due to insufficient capacity of the distribution point after the new demand is generated， and the cost of drone system due to the addition of new drone and other accessories. The operation cost considered includes the loss cost of drone system， the labor cost due to the addition of porters， the power cost and water cost due to the daily operation of the distribution point and drone transportation； In case of emergency， the main objective of the optimization model constructed in this paper is to minimize the distribution time， meeting the efficiency demand of emergency material distribution， without considering other costs. The main optimization objective is to minimize the sum of construction cost and operation cost of the drone “dual use of usual and emergency situations” distribution system， and reduce the distribution time in case of emergency. To achieve the optimization goal， an improved chaotic adaptive genetic algorithm （ICAGA） is proposed in this paper. Firstly， the joint optimization framework， solution model and optimization algorithm of the “dual use of usual and emergency situations” drone food distribution system are combined by the “dual use of usual and emergency situations” integrated fitness solution method. Then the PWLCM chaotic mapping and the introduction of multi module improved adaptive genetic algorithm are used to improve the optimization speed of genetic algorithm and the ability to jump out of the local optimal solution. Finally， the effectiveness and reliability of the proposed drone emergency and emergency distribution model are verified by a real case in BZ Town， which achieves the goal of “dual use of usual and emergency situations” and maximizes economic benefits at the same time. At the same time， by comparing with the traditional genetic algorithm， chaotic genetic algorithm and adaptive genetic algorithm， it is found that the ICAGA algorithm proposed in this paper performs better in solving speed and the final optimal solution. In addition， an additional disaster simulation test is conducted to verify the ability of drone distribution system based on the model to ensure material supply in the event of disasters. Through the real case verification， disaster simulation test and sensitivity analysis of BZ Town， it is found that the optimization model proposed in this paper can maximize the economic benefits of the “dual use of usual and emergency situations” drone distribution system， and has the robustness to respond to disasters and demand changes， which provides strong support for the construction of a more comprehensive drone distribution system. Figures and Tables | References | Related Articles | Metrics

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Evaluation of Operational Efficiency of “SRDI” Enterprises Based on Weighted Dynamic Network SBM with Consistent Objective Meijuan Li, Hongcheng Xu, Wei Yang, Jincheng Lu 2026, 34 (6):  146-156.  doi: 10.16381/j.cnki.issn1003-207x.2024.0988 Abstract ( 6 )   HTML ( 0 )   PDF (1341KB) ( 1 )   Save Specialized， refined， distinctive， and innovative enterprises （SRDI） are nurtured by the government to promote the high-quality development of industrial and supply chains， as well as to address critical “bottleneck” technologies. The report of the 20th National Congress of the Communist Party of China explicitly called for strong support for the development of “SRDI” enterprises. However， in reality， “SRDI” enterprises are often faced with severe capital shortages. In this context， scientifically evaluating the operational efficiency of such enterprises and exploring pathways for improvement are crucial for promoting their high-quality development. A weighted dynamic network SBM with consistent objective is constructed from both R&D and operational perspectives. Inspired by the network SBM model with consistent objective and exponential decay method， the model avoids the subjective bias of the traditional dynamic network SBM model in determining the weights of sub-stages and sub-period， and eliminates the potential contradiction between the improvement direction of the intermediate products and that of the objective function by adjusting the treatment of the intermediate products. Furthermore， by leveraging the base-point transformation method to handle negative-value datasets， it effectively addresses the limitations of traditional models in processing negative data. Finally， the model is used to measure the operational efficiency of 28 “SRDI” listed enterprises in China during the period from 2018 to 2022， and from the analysis of the results， it is found that during the whole evaluation period， the average value of the operational efficiency of the “SRDI” enterprises shows an “M” trend； the average value of R&D efficiency of “SRDI” enterprises shows a “V” trend； the average value of operational efficiency of “SRDI” enterprises shows an “M” trend， reflecting the large fluctuation range of the overall operational status of “SRDI” enterprises in China. Secondly， by changing the attenuation coefficient in the model， the degree of advantages and disadvantages of the development of each “SRDI” enterprise in different periods is found. Third， the comparative analysis of this paper’s model with the traditional dynamic SBM model reveals that this paper’s model has greater advantages in terms of effectiveness and differentiation， and is more suitable for the efficiency evaluation of China’s “SRDI” enterprises. The findings provide a scientific basis for policymakers to design targeted support measures and for firms to optimize operational efficiency. Figures and Tables | References | Related Articles | Metrics

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Volatility-Embedded Quantitative Risk Assessment of Liner Shipping Freight Rate Portfolios Fangping Yu, Lei Zhang, Bin Meng 2026, 34 (6):  157-170.  doi: 10.16381/j.cnki.issn1003-207x.2024.0319 Abstract ( 4 )   HTML ( 0 )   PDF (2935KB) ( 0 )   Save Driven by a series of anti-globalization trade measures taken by the United States， as well as the sustained impacts of events such as the COVID-19 pandemic， the Russia-Ukraine war， and the Red Sea crisis， the resilience of the global maritime supply chain has increasingly faced severe challenges. The container liner freight rates， with an annual global freight volume exceeding one trillion U.S. dollars， have fluctuated significantly， bringing unprecedented risks to stakeholders in the global liner market.It aims to construct a risk measurement framework and model for liner shipping freight rate portfolios that match volatility characteristics. First， considering multiple typical characteristics of liner freight rate volatility， different pairing models are used to measure the freight rate risk of individual liner routes. Aiming at the volatility clustering， periodicity， and fat-tailedness of freight rates， three complementary CVaR （Conditional Value at Risk） models—AEG， PDF， and CAV—are employed to measure the risk exposure of container liner freight rates on single routes. Second， a CVaR risk measurement model for multi-route portfolios is constructed using implied tail correlation coefficients and covariance matrices. Although the popular Copula function can effectively quantify the non-linear correlation of “cut-off point” risk measurement tools such as variance and VaR， it ignores the impact of tail "average" risks （e.g.， CVaR and ES）， failing to accurately measure the non-linear relationships between multi-route freight rates caused by such tail “average” risks. Meanwhile， significant correlations exist between freight rates of different routes， especially in extreme situations， where fluctuations in one route often cause significant spillover effects. Third， based on the multi-route model， a multi-characteristic portfolio CVaR risk measurement model is developed using the minimum variance principle. Existing studies often focus on comparative analyses of single or multiple risk measurement models for shipping market risks， making it difficult to balance the accuracy and comprehensiveness of risk measurement. Weights are allocated to different multi-route CVaR models based on the minimum variance approach to construct a multi-characteristic composite CVaR risk measurement model.Taking the container liner spot freight market as the research target， four round-trip routes operated by a Chinese container liner company are selected： Far East-N.Europe， N.Europe-Far East， Far East-USWC， and USWC-Far East. The sample covers container freight rate data from May 2， 2018， to December 30， 2022， spanning 1，172 trading days.The research results show： First， the multi-route CVaR model demonstrates superior effectiveness in measuring the portfolio risk of liner freight rates， as it can effectively quantify risk exposure and better reveal the evolutionary patterns of freight rate risks， helping market stakeholders make more targeted management decisions. Second， the risk measurement of liner route freight rate portfolios based on multiple characteristics yields better results， with multi-characteristic CVaR models all reducing risks in the liner freight market， and the portfolio risk effect being optimal. Third， the portfolio optimization model， by assigning weights to CVaR results of different characteristics， can better mitigate risks in the liner market during periods of high volatility and provide more effective risk measurement results. Whether facing periods of severe volatility or extreme conditions in the liner freight market， the accuracy of risk exposure measurement exceeds 90%， and the probability of prediction failure under a 99% confidence level is less than one-third of that of single models. A theoretical reference for market stakeholders is provided to dynamically evaluate and monitor the portfolio risks of liner route freight rates. Figures and Tables | References | Related Articles | Metrics

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An Online Quality Design Method Using Active Learning-Based Stochastic Kriging Model Chen Du, Chenglong Lin, Yuwei Shi, Yizhong Ma 2026, 34 (6):  171-186.  doi: 10.16381/j.cnki.issn1003-207x.2024.1370 Abstract ( 6 )   HTML ( 0 )   PDF (2661KB) ( 1 )   Save To address the low robustness of quality characteristics caused by unknown random noise， an online quality design method based on the active learning stochastic Kriging model is proposed. The online quality design method uses the active learning characteristic to select new sample points through the expected improvement criterion formulated under noisy response， then updates the model to improve prediction accuracy. The stochastic Kriging model is first constructed based on the initial design in the implementation process， then updated using active learning method， and finally， quality design is achieved through the expected quality loss function model. Numerical examples and simulation results demonstrated that the proposed method can obtain more accurate response surface model compared with methods using the ordinary Kriging model and offline stochastic Kriging. The proposed method can filter the noise and get more robust parameter design solutions with the same computation resources by utilizing the expected quality loss function model. Lastly， a quality cost evaluation method is proposed from an economic perspective， which evaluates the parameters of the design solutions obtained based on historical production data， providing managers with valuable information for decision-making. Figures and Tables | References | Related Articles | Metrics

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Optimization of Multi Skill Project Group Scheduling Based on Global Idle Resource Reallocation Songqing Guo, Zhe Xu, Yixuan Su 2026, 34 (6):  187-201.  doi: 10.16381/j.cnki.issn1003-207x.2024.1406 Abstract ( 5 )   HTML ( 0 )   PDF (1705KB) ( 1 )   Save Large engineering projects are often managed by project groups. There is a logical relationship between subprojects， and the project information is fully shared. At the same time， it is necessary to consider the overall optimization objectives of the project group and the optimization objectives of each subproject. Therefore， the project group scheduling problem is a special multi project scheduling optimization problem with practical application scenarios. When considering multi skilled human resources， the difference in the skill level of global resources and the change in the use of local resources will lead to the change of activity duration. Large projects tend to be completed and put into use as soon as possible， that is， project group managers usually need to consider how to reduce the project group duration； When the sub project is responsible for its own profits and losses within the budget， it often reduces the cost of the sub project as much as possible to protect its own interests， that is， the sub project manager usually needs to consider how to reduce the cost of the sub project. Therefore， the global and local objectives are to minimize the total duration of the project group and the cost of each sub project.To solve this problem， a global and local two-stage hierarchical scheduling optimization model is established. Under the global and local resource constraints， the project group manager establishes a global scheduling optimization model to minimize the total duration of the project group. Under the global resource constraints and sub project duration constraints given by the project group manager， the sub project managers establish a local scheduling optimization model with the goal of minimizing the sub project cost. To solve this problem， a two-stage solution mechanism based on idle global resource reallocation is proposed， and an adaptive improved multi population genetic algorithm is designed to solve the global scheduling problem.The experimental research was carried out based on Ran Gen randomly generated project group example set. According to the problem size， the example is divided into three problem subsets， and each problem subset contains two groups of project group examples generated by 27 different parameter combinations. The experimental results show that under the same problem scale， the stronger the global resource conflict intensity and the greater the network density of the project group， the longer the total duration of the project group， and the greater the local resource demand intensity， the greater the average cost of the sub project. The experiment further proves that the idle global resource reallocation mechanism designed in this paper can reasonably allocate the global multi skilled human resources and effectively reduce the cost of sub projects on the premise of ensuring the total duration of the project group. In addition， the idle resource reallocation strategy can provide a reference for project group managers when allocating project group resources.The research gap of project group scheduling problem is made up for considering multi skilled human resources， and the door for further research on project group scheduling problem considering human resources balance and multi skilled human resources project group scheduling problem under uncertainty is opened. Due to the multi skill heterogeneity of human resources and the different needs of activities， the working hours of employees tend to be unbalanced， which is not conducive to the use and management of human resources. Therefore， in the follow-up study， the work time balance will be considered for further research. At the same time， more efficient meta heuristic algorithm or artificial intelligence algorithm is explored to solve the problem， so as to further improve the optimization effect of project group scheduling problem. Figures and Tables | References | Related Articles | Metrics

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Research on Integrated Production and Maritime Transportation Scheduling Problem with Batch Delivery Limits Yingying Chen, He Luo, Xiangcai Xiao, Zhiming Cai 2026, 34 (6):  202-214.  doi: 10.16381/j.cnki.issn1003-207x.2024.1529 Abstract ( 7 )   HTML ( 0 )   PDF (2138KB) ( 1 )   Save With the advancement of export trade， the integrated production and maritime transportation scheduling for overseas orders confronts substantial challenges. Compared to domestic logistics， maritime transportation is more complex， involving container leasing and vessel selection. In this study， two critical characteristics of overseas orders are considered： the widespread application of batch delivery strategies and loose due dates. Subsequently， the integrated production and maritime transportation scheduling problem with batch delivery limits （IPMS-BDL） is proposed， which is a variant of the classic integrated production and distribution scheduling problem and is NP-hard.In the IPMS-BDL， production scheduling is formulated as a parallel machine scheduling problem， while maritime transportation optimizes both container leasing and vessel selection. A mixed-integer programming model （ISM） is constructed with dual objectives aimed at minimizing total cost and the total deviation in delivery times. To efficiently solve the ISM， a memetic algorithm （NGMA） is developed， which combines global NSGA-II search with local greedy search. NGMA incorporates a rule-guided initial population generation operator and an adaptive selection operator.Through comparative experiments conducted on both small and large-scale problem instances， the NGMA has demonstrated a significant enhancement in the quality and diversity of the Pareto optimal solution set. Additionally， validity comparison experiments reveal that the ISM achieves reductions of 20.87% in average total cost and 50.57% in average delivery time deviation. Notably， the ISM illustrates strong applicability under conditions of larger fluctuations in maritime transportation costs and fewer delivery batches.Ultimately， a case analysis is performed utilizing business data from representative domestic manufacturers， underscoring the considerable advantages of integrated scheduling in terms of cost control and timely delivery. This further elucidates the practical feasibility of integrated scheduling within manufacturing business processes. These research findings not only offer novel perspectives and methodologies for effectively managing overseas orders but also provide essential theoretical support for decision-making in this domain. Figures and Tables | References | Related Articles | Metrics

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A Multi-attribute Large Group Decision-making Method Considering Modular Distrust Relationships under Group Intelligence Faming Zhang, Luping Lin, Zhaoqing Ye 2026, 34 (6):  215-227.  doi: 10.16381/j.cnki.issn1003-207x.2023.1302 Abstract ( 8 )   HTML ( 0 )   PDF (1082KB) ( 2 )   Save Due to the complex social connections between individuals， considering different decision-making behaviors has important theoretical value and practical significance in solving decision-making problems in large groups. However， most studies are based on different decision-making behaviors to enhance group consensus， and there is relatively little research on considering decision-making behaviors to enhance group intelligence at different levels of consensus， ultimately achieving the goal of improving the quality of group decision-making. Meanwhile， most studies focus on trust relationships， with less emphasis on modular thinking that treats clustering as a small group and considers situations where decision-makers have distrust relationships. To solve these problems， a new multi-attribute large group decision making method with interval intuitionistic is proposed fuzzy sets considering modular distrust relations. Firstly， based on the social network structure among decision experts， the Louvain method is used to divide the group decision-makers into subgroups and calculate the weights of the decision-makers and subgroups； Secondly， based on the density of social relationships and considering both trust and distrust relationships， the preference of decision experts is adjusted， and the level of group consensus is measured based on the similarity of individual and group preferences in position and direction； Then， fuzzy entropy and cross entropy are used to determine the attribute weight of the scheme， and the scoring function and accurate function of the scheme are calculated to determine the optimal scheme； Finally， taking the selection of emergency plans in major natural disaster events as the background， the effectiveness of the method are verified.This method considers the modular distrust relationship of decision-makers and selects the preference adjustment model of decision experts based on social relationship density to stimulate decision-makers to jointly explore better solutions. The example results demonstrate that the proposed consensus model can not only explore better decision-making solutions， but also effectively improve decision-making speed and meet the urgent needs of emergency decision-making. Figures and Tables | References | Related Articles | Metrics

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Pricing Strategy of a Ride-hailing Platform with Concern on Driver Welfare Yiyi Jiang, Chen Hu, Yongbo Xiao, Shuna Wang 2026, 34 (6):  228-238.  doi: 10.16381/j.cnki.issn1003-207x.2024.1927 Abstract ( 5 )   HTML ( 0 )   PDF (1346KB) ( 1 )   Save The proliferation of ride-hailing platforms has transformed urban mobility by offering convenient travel solutions and generating widespread employment opportunities. As the industry evolves， the focus is gradually shifting toward sustainable development， with driver welfare emerging as a critical concern. An increasing number of platforms recognize that prioritizing driver welfare is not only a matter of corporate social responsibility but also essential for their long-term sustainable development. While prior research has examined the social responsibilities of ride-hailing platforms， there is a lack of studies investigating the platform’s optimal operational decisions when driver welfare is explicitly considered， as well as the impact of such considerations on market equilibrium. This gap is addressed by studying the optimal decisions of a socially responsible ride-hailing platform that operates with a mixed objective： maximizing profit while enhancing driver welfare. The platform determines both the wage paid to drivers and the fare charged to passengers， and it facilitates the matching of available drivers with passenger ride requests. Three widely used pricing strategies are examined： （i） two-sided pricing， where the platform determines the passenger fare and driver wage separately； （ii） fixed commission， where the platform pays drivers a fixed percentage of the fare； and （iii） minimum wage， where drivers receive guaranteed minimum earnings. By studying the platform’s optimal pricing and wage decisions under each strategy， how different levels of concern for driver welfare influence the platform’s decisions and market outcomes is analyzed， including platform profit， driver welfare， and passenger welfare. Using the two-sided pricing strategy as a benchmark， a comparative analysis of the fixed commission and minimum wage strategies is conducted through numerical experiments. The analysis yields several important results. Under the two-sided pricing strategy， the platform can achieve a perfect matching between supply and demand. When driver welfare becomes a higher priority， the platform tends to respond by increasing per-trip wages， thereby directly improving driver earnings. Under either the fixed commission or minimum wage strategies， there may be an oversupply equilibrium in the market. When the emphasis on driver welfare increases， the platform might reduce service prices to boost driver utilization rates， thereby indirectly enhancing drivers’ expected earnings. Importantly， across all pricing strategies， a higher emphasis on driver welfare not only leads to improvements in driver welfare， but also increases passenger welfare. Moreover， platforms can achieve substantial improvements in driver welfare with only marginal reductions in profit. Valuable insights into how ride-hailing platforms can balance profitability and social responsibility are provided. The findings can also inform policymakers in designing regulations that promote both the financial sustainability of ride-hailing platforms and drivers’ well-being. Figures and Tables | References | Related Articles | Metrics

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Optimum of Pricing and Reimbursement Policy for Mixed Online and Offline Medical Service Considering Heterogeneous Patients Xiaoyan Zhu, Jing Cui, Ting Zhang, Yunzhi Cao 2026, 34 (6):  239-249.  doi: 10.16381/j.cnki.issn1003-207x.2024.0595 Abstract ( 6 )   HTML ( 0 )   PDF (1369KB) ( 1 )   Save As a novel medical service， telemedicine contributes to alleviating the difficulty of seeing a doctor for the patients and the unbalanced distribution of medical resources in the traditional offline medical system in China. For a medical service system composed of the government， a hospital， and patients， the geographically heterogeneous patients are considered that face to visit the clinic periodically with chronic disease. A three-stage sequential game model is constructed and the equilibrium solution of the government’s medical reimbursement ratio and hospital’s medical service price for the mixed online and offline medical service is deduced. Benchmarking the traditional single offline medical service， through theoretical analysis and numerical experiments， the impact of clinical feasibility， fixed online setting fees， etc.， on patients’ choice of medical treatment， hospital profits， social welfare， etc.， after the adoption of telemedicine is compared and analyzed. It shows that the government’s medical reimbursement ratio is positively correlated with the hospital’s medical service price， and the adoption of telemedicine does not change the relationship between offline medical price and reimbursement ratio. The mixed online and offline medical service is a potential and efficient approach to improve social welfare and realize that all patients visit the clinic. Improving the method and technology of telemedicine by artificial intelligence， reducing the membership fee of telemedicine， decreasing the setting fees of patients’ hardware and software equipment， and developing the traffic system to control the traffic cost are all conducive to the implementation of telemedicine. Figures and Tables | References | Related Articles | Metrics

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Pricing Strategies of Supply Chain with Asymmetric Demand Information Considering Equity Holding under Different Power Structures Liangjie Xia, Mengxian Gu, Youdong Li, Jun Wang 2026, 34 (6):  250-260.  doi: 10.16381/j.cnki.issn1003-207x.2023.0709 Abstract ( 2 )   HTML ( 0 )   PDF (1057KB) ( 0 )   Save In the context of supply chains， there is an emerging trend where retailers acquire equity in manufacturers， and gain a position of dominance. This equity holding， coupled with the inherent power structure in supply chains， significantly impacts the flow of demand information signals between upstream and downstream enterprises. A two-tier supply chain comprising a manufacturer and a retailer is considered， where the retailer holds equity in the manufacturer and possesses private information about market demand scale. The aim is to analyze pricing decisions made by both upstream and downstream enterprises， taking into account scenarios where either the retailer or the manufacturer dominates the supply chain. Furthermore， it aims to explore the impact of equity holding on signal transmission and pricing decisions. The findings reveal that， regardless of who leads the supply chain， the retailer's marginal revenue exhibits a negative correlation with both the equity holding ratio and the probability of a high-type market size. Conversely， the wholesale price demonstrates a positive correlation with these factors. In scenarios where the market size is low-type， supply chain members tend to achieve higher profits when acting as leaders， regardless of their equity holding ratio. However， when the market size is high-type， being a leader may not always be advantageous. Notably， when the manufacturer leads the supply chain， the profits of supply chain members remain unaffected by the equity holding ratio. Under retailer leadership， the retailer's signaling behavior is influenced by market size and equity holding ratio. Specifically， when the market size is low-type and the equity holding ratio is low， the retailer must signal the market size information costly， while it can signal the market size information cost-free if the equity holding ratio is high enough. Furthermore， at lower equity holding ratios， both the retailer's marginal revenue distortion and signaling costs increase as the equity holding ratio rises. Finally， an elevated equity holding ratio can indeed boost the retailer's profitability， though it does not guarantee a corresponding increase in the manufacturer's earnings. Nevertheless， under specific circumstances， both entities can achieve a harmonious outcome that leads to mutual advantages. Figures and Tables | References | Related Articles | Metrics

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The Exclusivity of the Super Seller in Two-Sided Platform Competition Haijun Chen, Qi Xu 2026, 34 (6):  261-274.  doi: 10.16381/j.cnki.issn1003-207x.2023.2029 Abstract ( 4 )   HTML ( 0 )   PDF (1666KB) ( 1 )   Save In two-sided markets， the heterogeneity on the supply side and differentiation strategies have given rise to a super seller with significant bargaining power， becoming a crucial player in platform competition. Platforms cannot interfere with the independent operation of merchants， but merchants can proactively enter into exclusive agreements with platforms. A horizontal differentiation model of two-sided platform competition is constructed， analyzing the impact of the super seller's adoption of exclusive and non-exclusive strategies on platform competition， market participation of ordinary sellers， and welfare of two-sided users. It is found that the participation of the super seller changes market equilibrium， enhances the platform's pricing power over consumers， and affects the market position of ordinary sellers. Additionally， exclusive agreements enhance platform competitiveness by expanding user base and increasing pricing power. When the super seller chooses exclusive or non-exclusive agreements， they should consider both network influence and the intensity of inter-platform competition. Furthermore， it is revealed that the presence of the super seller often reduces the total welfare of ordinary sellers， while consumers tend to prefer the super seller choosing non-exclusive agreements. The main contribution of this paper lies in elucidating the impact mechanism of the super seller and their exclusive agreements， thereby providing theoretical guidance for the super seller to formulate exclusive strategies and offering policy suggestions to antitrust policymakers. Figures and Tables | References | Related Articles | Metrics

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A Study on Imitation and Innovation Strategies of Platform Retailers Considering Antitrust Regulations Pei Li, Hang Wei 2026, 34 (6):  275-290.  doi: 10.16381/j.cnki.issn1003-207x.2023.1478 Abstract ( 61 )   HTML ( 0 )   PDF (1649KB) ( 7 )   Save With the rapid development of the digital economy， the imitative innovation behavior of platform retailers has become one of the focuses of attention for governments and academia. For example， Amazon's "dual identity" gives it a data and traffic advantage that other merchants do not have. Amazon uses these advantages to monitor and analyze data from third-party merchants on its platform， quickly identifying current best-selling products and products with market potential， and independently developing its own brands or collaborating to develop exclusive brand products. Therefore， for platform retailers with abundant data resources， it is necessary to consider whether to utilize their data advantages for imitative innovation behavior. At the same time， considering the continuous increase in attention to platform monopolistic behavior worldwide， if governments take anti-monopoly regulatory measures， whether platform retailers can still implement these imitative innovation behaviors， and whether these possible anti-monopoly regulations can play a role in protecting innovation， increasing consumer surplus， and social welfare.Based on this， a supply chain model composed of one platform retailer and two manufacturers was constructed， using the classic Hotelling utility function model as the basic function. The model considers three strategic choices for the platform retailer： not engaging in imitative innovation， engaging in independent imitative innovation， and engaging in collaborative imitative innovation. By comparing the profits of the platform retailer， the level of product innovation， consumer surplus， total social welfare， and market concentration under these three strategies， the optimal imitative innovation strategy selection conditions for the platform retailer are provided. The analysis examines the impact of different imitative innovations on product innovation and social welfare， and， in conjunction with existing anti-monopoly rules， designs two regulatory strategies. Additionally， the analysis considers scenarios where imitated manufacturers may switch platforms and where platform retailers provide open data information to manufacturers. It assesses whether these scenarios would change the platform retailer's choice of imitative innovation strategy and whether they can effectively improve the level of product innovation and social welfare. It aims to provide monitoring， identification， and governance references for government antitrust departments.Firstly， government regulations to some extent alter the strategy choices of platform retailers. In the absence of regulations， not engaging in imitative innovation is not the optimal choice for platform retailers； when the government prohibits imitative innovation， independent research and development becomes the optimal choice for platform retailers； and when innovation subsidy regulations are implemented， both independent and collaborative imitative innovation may be the optimal choices for platform retailers. Secondly， in the absence of regulations， if the innovation imitation coefficient is small， the highest level of product innovation occurs under independent imitative innovation， while the highest level of product innovation occurs under no imitative innovation if the innovation imitation coefficient is large. Innovation subsidy regulations can enhance the level of product innovation. Thirdly， only when the commission coefficient of switching platforms is relatively small can the level of product innovation be increased， and only then will imitated manufacturers choose to switch platforms. Fourthly， opening data information by platform retailers can enhance the level of product innovation， and the cost of data information will affect the strategy choices of both platform retailers and imitated manufacturers. Figures and Tables | References | Related Articles | Metrics

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Research on Trade-in Pricing Strategies for Consumer Goods Considering Customer Privacy Concerns Xiaoqing Zhang, Xigang Yuan, Jiajia Chen, Qiang Wei, Baofeng Huo 2026, 34 (6):  291-302.  doi: 10.16381/j.cnki.issn1003-207x.2025.0588 Abstract ( 8 )   HTML ( 0 )   PDF (1271KB) ( 9 )   Save As an effective way of consumer goods recycling， the protection of customers' privacy information when they participate in trade-in activities is an urgent problem to be solved. From the perspective of game theory， considering customers' privacy concerns， a two-period game model is constructed to explore the optimal trade-in pricing strategy for enterprises. The results show that （1） In both cases without privacy protection and with privacy protection， the retail prices of the two generations of new products are the same； When privacy protection is in static or dynamic pricing strategies are adopted， consumers participating in the trade-in program can enjoy more trade-in discounts. （2） No matter which pricing strategy is adopted， enterprises are more willing to offer privacy-protected trade-in strategies when the cost of providing privacy protection is low. （3） When the innovation level of the second-generation new products is significantly enhanced， the dynamic pricing strategy is more beneficial to enterprises； Conversely， the static pricing strategy is better. The research of this article provides a reference for the formulation of pricing strategies for trade-in of consumer goods considering customers' privacy concerns. Figures and Tables | References | Related Articles | Metrics

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Research on Ordering Behavior Decision Considering Supply-demand under Combined Behavioral Operation Shijie Zhao, Xuejiao Rong, Leifu Gao 2026, 34 (6):  303-318.  doi: 10.16381/j.cnki.issn1003-207x.2023.0890 Abstract ( 7 )   HTML ( 0 )   PDF (2501KB) ( 1 )   Save Given the increasing complexity of supply chain management and the increasing uncertainty of managers' decision-making environment， behavioral experiments are applied to investigate managers' ordering decisions in three environments： demand uncertainty， supply uncertainty， and supply-demand uncertainty， constructing behavioral models in the three environments based on their behavioral decisions. The theoretical model and its optimal solution under specific uncertainty， and complete behavioral experimental data collection through experimental design and decision system development are provided. The ordering decision behavior in specific experimental situations is explored by using statistical analysis methods， corresponding behavioral models with ordering behavior as an element is constructed and the validity of the models under specific situations is tested， and an anchor chasing adjustment behavior model with a learning effect is proposed by coupling the “anchoring before adjustment” and “demand chasing” heuristic strategy with the ordering behavior as the element. In addition， the ordering decision-making behavior model in the environment of uncertain supply and uncertain supply and demand is constructed， and the validity test of the model in specific contexts is carried out. Research shows that there are deviations from theoretical optimal ordering decisions， chasing behavior and anchoring behavior in single-sourcing ordering decisions under supply uncertainty， demand uncertainty， and supply-demand uncertainty environments. The ordering behavior of ordering decision makers under supply-demand uncertainty environments is influenced by the combined effect of demand uncertainty and supply uncertainty at the same time. A new expression of an appropriate anchoring coefficient is proposed in the evaluation of anchoring behavior to overcome the phase-out effect of positive and negative anchoring values of Bostain's anchoring coefficient. The predicted performance of the constructed behavioral model is better than Bostain's demand chase heuristic model and Benzion's central chemotaxis bias model in many indicators， and it can better fit the real ordering decision data. Figures and Tables | References | Related Articles | Metrics

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Purchase Order Financing and Supply Chain Operations Strategies under Blockchain Smart Contract-driven Dynamitic Interest Rate Pricing Chengfu Wang, Xiangfeng Chen, Wei Jin, Wen Ding 2026, 34 (6):  319-330.  doi: 10.16381/j.cnki.issn1003-207x.2023.1934 Abstract ( 6 )   HTML ( 0 )   PDF (1597KB) ( 1 )   Save With the initial practical application of the blockchain smart contract-driven dynamic interest rate pricing method （DIP）， it is worth exploring compared to the stable interest rate pricing method （SIP）， what the value of DIP is， and how to introduce DIP into a supply chain financing scheme. Two types of bank interest rate pricing methods， SIP and DIP， are considered when a manufacturer applies for financing from a bank through the purchase order financing mode. Under SIP， before granting financing funds to the manufacturer， the bank decides a stable interest rate that remains unchanged throughout the loan cycle. Whereas， under DIP， after granting financing funds， the bank determines a detection time point to check the manufacturer’s production process， and the interest setting of the bank is contingent on whether the manufacturer has finished production and delivery at the detection time point. It aims to investigate how DIP changes participants’ decisions， and compared to SIP， whether DIP can bring extra profit for each participant， and under what conditions DIP should be applied. Stackelberg game and newsvendor models are used to analyze the bank’s interest rate pricing decisions， manufacturer’s production decisions， and retailer’s order decisions under SIP and DIP， and based on this， each participant’s profits under SIP and DIP are compared and DIP’s applicable conditions are identified. It is suggested that： （1） SIP can lead to the manufacturer’s and retailer’s deviations from their respective optimal decision profits under certain conditions， namely， under SIP， the manufacturer and retailer obtain lower profits than their optimal decision profits. （2） DIP can always completely repair the manufacturer’s deviation from its optimal decision profit caused by SIP， thereby improving the manufacturer’s profit to the level of its optimal decision profit， but DIP cannot guarantee the repair of the retailer’s deviation from its optimal decision profit caused by SIP. （3） There exist upper and lower bounds of the probability of the manufacturer completing delivery at the order deadline， as well as upper and lower bounds of the cost saving the manufacturer obtains from the delayed delivery； Within these upper and lower bound ranges， DIP can synchronously repair the manufacturer’s and retailer’s deviations from their optimal decision profits caused by SIP （namely， DIP can improve the manufacturer’s and retailer’s profits to their respective optimal decision profits）， and DIP should be introduced. （4） Moreover， considering the retailer’s additional operating cost caused by the manufacturer’s delayed delivery， it is found that： If this retailer’s additional operating cost caused by the manufacturer’s delayed delivery is bigger than the cost saving the manufacturer obtains from delayed delivery， DIP can synchronously repair the manufacturer’s and retailer’s deviations from their respective optimal decision profits caused by SIP； Otherwise， DIP still cannot guarantee the repair of the retailer’s deviation from its optimal decision profit caused by SIP. The findings can help banks and enterprises make decisions on whether to introduce DIP and meanwhile provide guidance for a supply chain to achieve Pareto improvement through DIP. Figures and Tables | References | Related Articles | Metrics

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Reselling, Agency Selling, or Hybrid Mode? Sales Mode Selection of Competing E-commerce Platforms Yu Zhao, Benrong Zheng, Liang Jin 2026, 34 (6):  331-343.  doi: 10.16381/j.cnki.issn1003-207x.2024.0530 Abstract ( 4 )   HTML ( 0 )   PDF (1365KB) ( 0 )   Save The rapid development of e-commerce has accelerated the evolution of platform sales modes. Agency selling， a direct sales model for suppliers， can improve channel efficiency through a revenue sharing mechanism. Unlike the traditional reselling mode， where the platforms control the sales price， the agency model allows suppliers to directly set prices while paying a commission to the platform. This mode has been widely adopted by giant platforms such as JD.com and Amazon. However， many manufacturers continue to use the reselling mode. Consequently， it is crucial for platforms to identify which mode is more optimal. Moreover， many platforms often choose different sales modes for the same brand， driven by the intensity of platform competition and platform power difference.Motivated by practical examples that competing platforms adopting different sales modes， a stylized supply chain consisting of one supplier and two competing platforms is analyzed， differentiated by market power. Four distinct channel modes are developed： （a） Mode RR， where both platforms adopt the reselling mode； （b） Mode AA， where both platforms use the agency selling mode； （c）Mode AR， where platform PA adopt the agency selling mode while platform PB uses the reselling mode； and （d）Mode RA， where platform PA adopt the reselling mode while platform PB uses the agency selling mode. The sequence of the game divides into two stages： channel mode selection and pricing stages. In the channel mode selection stage， platforms choose their preferred sales mode. In the pricing stage， the supplier and platforms determine wholesale or retail prices based on the chosen four channel modes. Using backward induction， the equilibrium outcomes are derived and the profits across the four models are compared.The following results are derived. First， the channel mode choice of platforms is influenced by the competition intensity and the platform's market power. When competition is low， RR mode is preferred， while the AA mode becomes optimal under high competition. When competition is moderate and there is a significant power imbalance between platforms， the RA mode becomes the equilibrium strategy. Second， the impact of different channel modes on both the supplier and the platform varies. Under weak competition， the AA mode is more advantageous for the supplier， whereas in highly competitive environments， the RR mode benefits the supplier more. When competition is moderate， the AR mode is optimal for the supplier. Furthermore， under moderate competition， if platform PA holds weaker market power， both the platforms and the supplier can achieve a win-win-win situation under either the RR or AA modes. Third， these findings remain robust even when considering variations in supplier channel costs or differences in platform commissions. Specifically， an increase in supplier channel costs reduces the likelihood of the AA mode being dominant， while a rise in platform PB's commission makes the AA mode more attractive.The research findings offer significant managerial implications for platforms and suppliers. First， platforms should consider three key factors when making channel decisions： the level of competition， the disparity in platform power， and commission. In less competitive markets， platforms benefit more from adopting the reselling mode. However， in a competitive environment， the optimal channel mode for platforms does not necessarily align with maximizing supplier profitability. Therefore， suppliers should adapt their pricing strategies in response to the platforms’ chosen channel mode strategies. Figures and Tables | References | Related Articles | Metrics

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Coordination of Blockchain Adoption and Sales Mode for Green Products Xiaoyu Shen, Li Dai 2026, 34 (6):  344-355.  doi: 10.16381/j.cnki.issn1003-207x.2024.0496 Abstract ( 4 )   HTML ( 0 )   PDF (1546KB) ( 1 )   Save In order to facilitate the promotion of green products production and consumption， it aims to explore how green product firms should balance between the cost of blockchain adoption and uncertain short-term benefit， given that blockchain technology can be adopted to improve consumers’ belief on green products sold through e-commerce platforms. To do so， the coordination of blockchain adoption and sales mode （either marketplace mode or reselling mode） between the green product manufacturer and the e-commerce platform is studied. By considering whether or not blockchain technology is adopted， and whether the green products are sold through marketplace mode or reselling mode， four models are built and compared. The results are as follows. In the marketplace mode， as compared to the case where blockchain technology is not adopted， blockchain adoption leads to a higher retailing price， but the effort exerted by the manufacturer to improve the greenness of product and the demand of green products may decrease. Meanwhile， the e-commerce platform will ask for a lower service fee. In the reselling mode， adopting blockchain technology does not necessarily lead to increase of retailing price， and wholesale price， greenness effort and demand may also decrease. In the presence of blockchain cost per unit product， blockchain adoption is less likely to occur in reselling mode than marketplace mode， and the manufacturer is less likely to prefer blockchain technology than the e-commerce platform. Both parties in either sales mode will agree on blockchain adoption only if the unit cost of using blockchain is relatively low and the consumers’ sensitivity of green products is relatively high. In the marketplace mode the impact of blockchain adoption on consumer surplus and social welfare depends on consumers’ sensitivity of green products， while unit cost of using blockchain also plays a role in the reselling mode. Blockchain adoption will not affect the e-commerce platform’s preference on sales mode， but will make the manufacturer more likely to prefer marketplace mode， which indicates that adopting blockchain leads to a higher chance for both parties to achieve coordination of sales mode. Besides， no matter blockchain adoption occurs or not， marketplace mode generates more consumer surplus and social welfare than reselling mode. It sheds light on the literature from three aspects. （i） Considering that the e-commerce platform endogenously decides market service fee， this paper complements the study on sales mode by finding that， in the presence of blockchain adoption cost， marketplace mode still dominates reselling mode with respect to consumer surplus and social welfare. （ii） The model of green products production and consumption is constructed with factors like greenness belief， consumer sensitivity on greenness， blockchain adoption cost， which enriches the literature on green products. （iii） This paper helps to clarify how green technologies like blockchain affect firms’ production & operation decisions. The main managerial insight of this paper lies in that， marketplace mode is more likely to promote the adoption of blockchain on green products than reselling mode， and the e-commerce platform should play a major role in expediting the circulation of green products supported by green technologies. Figures and Tables | References | Related Articles | Metrics

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An Analysis of Inter-provincial Carbon Emission Efficiency and Its Influencing Factors in China: A Multi-period Cross-efficiency Approach Zeshui Xu, Mei Chang, Xunjie Gou 2026, 34 (6):  356-368.  doi: 10.16381/j.cnki.issn1003-207x.2024.0626 Abstract ( 3 )   HTML ( 0 )   PDF (1265KB) ( 0 )   Save As the world’s largest carbon emitter， China is facing particularly severe pressure on carbon reduction. Improving the carbon emissions efficiency is an inevitable choice to obtain a win-win situation between promoting economic growth and reducing carbon emissions. Consequently， numerous scholars conduct extensive research on carbon emission efficiency and its influencing factors. Due to the unique advantages of data envelopment analysis （DEA） in measuring the efficiency of decision-making units （DMUs） with multiple inputs and outputs， a lot of DEA extension approaches are proposed to measure carbon emission efficiency. However， most of these methods either focus solely on comparability between DMU efficiencies or on comparability of efficiencies over time， with fewer addressing both aspects. This results in biased measurements of carbon emission efficiency across multiple time periods. To measure multi-period carbon emission efficiency scientifically and effectively， a multi-period cross-efficiency model with undesirable outputs is constructed to measure the carbon emission efficiency of 30 provinces in China from 2006 to 2021. Furthermore， recognizing the significant spatial spillover effect of inter-provincial carbon emission efficiency in China， a panel spatial lag model is employed to examine the impact of various factors， such as industrial structure change and technological innovation， on carbon emission efficiency. The results show that there are large inter-provincial differences in carbon emission efficiency of China’s provinces， which display a significant right skewed distribution from an overall perspective， that is， the carbon emission efficiency of most provinces is relatively low. From the regional perspective， the carbon emission efficiency in the eastern region of China is the highest， followed by the central and western regions， and the gap in carbon emission efficiency among the three regions is increasing year by year； In terms of dynamic evolution trend， the carbon emission efficiency of the whole country， the eastern and central regions showed a fluctuating upward trend during the sample period， while the western region showed a downward trend due to the impact of the rough mode in the western development. The spatial autocorrelation test shows that there is a significant positive spatial spillover effect on carbon emission efficiency. Factors such as industrial structure upgrading， technological innovation， population density and external development show a significant positive impact on carbon emission efficiency， whereas the energy consumption structure and production factor structure exhibit a significant negative impact on carbon emission efficiency. Therefore， China should make concerted efforts in industrial structure adjustment， energy structure optimization， technological innovation breakthroughs and regional cooperation to effectively promote a win-win situation for energy conservation and carbon reduction， as well as economic growth. Figures and Tables | References | Related Articles | Metrics