Table of Content

25 April 2026, Volume 34 Issue 4 Previous Issue   

Select

Optimal Consumption and Portfolio Choices with Time-inconsistent Preferences and Consistent Performance Yuanping Wang, Jinqiang Yang, Xiangyu Meng 2026, 34 (4):  1-12.  doi: 10.16381/j.cnki.issn1003-207x.2022.2149 Abstract ( 118 )   HTML ( 16 )   PDF (1615KB) ( 86 )   Save It is based on the self-control problem in real economy and the consistent performance requirement in investment practice. The existing literature has not researched on consumption and portfolio choices in a framework integrating time-inconsistent preferences and consistent performance. Considering time-varying impatience risk and consistent-performance constraint simultaneously， their joint impacts on optimal consumption and portfolio allocation are studied. According to Grenadier and Wang （2007）， Harris and Laibson （2013）， time-inconsistent preference is characterized by a quasi-hyperbolic discount function in continuous time. According to Chen and Tian （2012）， consistent-performance constraint is defined by that the current wealth must stays at least on the level given by a weighted average of wealth in the past. The results show that the tolerance to the withdrawal in wealth and the optimal proportional allocation in risky asset are determined by the degree of consistent performance constraint and the change of time preference， which can provide theoretical support for the relationship between the time preference and risk taking and give better prediction for empirical facts such as investment behaviors in risky assets， financial wealth effect on consumption. Figures and Tables | References | Related Articles | Metrics

Select

The Impact of Green Credit Policies on Corporate Social Responsibility Fulfillment: Empirical Evidence from the Perspective of Resource Reallocation Yuejun Zhang, Wei Qiang 2026, 34 (4):  13-21.  doi: 10.16381/j.cnki.issn1003-207x.2024.1610 Abstract ( 119 )   HTML ( 7 )   PDF (925KB) ( 51 )   Save A difference-in-differences model is developed to examine the impact of China’s green credit policies on corporate social responsibility （CSR） performance. The results indicate that China’s green credit policies have significantly differentiated impacts on companies with different risk levels. After the implementation of the 2012 Green Credit Guidelines， the overall CSR performance of high-risk companies experienced a temporary decline， while clean companies showed a positive but insignificant change. Mechanism analysis reveals that these results stem from resource reallocation behavior constrained by green credit policies. High-risk companies prioritize resource allocation in environmental governance and social compliance-related areas during the initial transformation phase， thus creating a crowding-out effect on CSR performance in the short term， and this effect varies significantly under different compliance cost conditions. Furthermore， green credit policies， by tightening credit and increasing financing costs， exacerbate corporate funding constraints， providing a transmission path for resource reallocation behavior. The phased characteristics of the role of green credit policies are revealed from the perspective of corporate transformation， providing new empirical evidence for objectively assessing its micro-governance effects. Figures and Tables | References | Related Articles | Metrics

Select

Option Pricing with Component Realized EGARCH Model Xinyu Wu, Xuebao Yin, Haibin Xie, Chaoqun Ma 2026, 34 (4):  22-33.  doi: 10.16381/j.cnki.issn1003-207x.2022.1305 Abstract ( 68 )   HTML ( 4 )   PDF (1190KB) ( 52 )   Save Options are a type of financial derivative， which are mainly to be used as a tool for building investment strategies and managing financial risk. Options play an important role in developing the financial system， and generating economic growth. One of the important issues for trading options is to address the question on how options can be valued correctly. It aims to develop a reasonable model for pricing options.Classical option pricing model， such as the Black-Scholes （B-S） model， relies on the assumption that the underlying asset returns are normally distributed with constant volatility. However， the assumptions are inconsistent with empirical findings， resulting in option pricing biases and ``volatility smirk". It is well recognized that asset return distribution exhibits characteristics such as negative skewness and excess kurtosis. Moreover， asset returns exhibit volatility clustering， asymmetric volatility and long memory volatility behaviors.To overcome the drawbacks of the conventional option pricing approach， the GARCH option pricing models have been developed. However， the GARCH option pricing model fails to account for the intraday information as well as the complex volatility dynamics （such as asymmetric volatility and long memory volatility） for pricing options. In light of this， the Component Realized EGARCH （CR-EGARCH） model is proposed， which extends the Realized EGARCH （R-EGARCH） model through the incorporation of component volatility structure， to price options. The proposed CR-EGARCH model could more adequately capture the volatility dynamics， such as the long-memory volatility and the long-term and short-term leverage effects. Meanwhile， the model exploits the intraday information from realized measure， which is expected to improve return fitting and volatility estimates. The risk-neutral return dynamic is derived relying on the Radon-Nikodym derivative with dual shocks （return and volatility shocks）. Using Monte Carlo simulation method， the prices for European options are computed. A sequential maximum likelihood estimation method is developed to estimate the parameters of the pricing model using data on the underlying asset and option prices.An empirical analysis based on Shanghai Stock Exchange （SSE） 50ETF options shows that in terms of implied volatility root mean squared error （IVRMSE） the CR-EGARCH model offers a 13.52% improvement over the standard R-EGARCH model. The R-EGARCH model offers 10.95%， 29.48% and 25.02% improvements over the Component EGARCH （C-EGARCH） model， the EGARCH model and the B-S model， respectively. The results provide strong support for incorporating the component volatility structure as well as the intraday extreme-value information from realized measure （range） to improve option pricing performance， with the proposed CR-EGARCH model offers the best option pricing performance. Finally， it is confirmed that the superior pricing performance of the CR-EGARCH model is robust to different evaluation criteria， different sample period， out-of-sample analysis， different realized measure and different option type. Figures and Tables | References | Related Articles | Metrics

Select

Modelling and Application of Economic Time Series Forecasting Considering Spatial Proximity Effect Song Ding, Xingao Shen, Yaoguo Dang, Xupeng Guo 2026, 34 (4):  34-46.  doi: 10.16381/j.cnki.issn1003-207x.2024.0854 Abstract ( 77 )   HTML ( 4 )   PDF (2066KB) ( 59 )   Save In real-world economic systems， the development trends of major systems are often influenced by spillover effects from neighbouring regions. However， existing grey multivariate models mainly focus on the temporal utility of correlated variables while overlooking the dynamic spatial relationships between neighbouring entities. To address this gap， a novel integrated spatial proximity term is introduced based on geographic and economic distances to measure spatial proximity effects. On this basis， three types of grey multivariate models that consider spatial proximity effects have been developed， namely STGM（1， N， M）， STGMC（1， N， M）， and STDGM（1， N， M） models. Moreover， the challenge of selecting model hyperparameters is tackled by designing an innovative intelligent algorithm selection framework. This framework introduces three criteria for identifying optimal algorithms， including accuracy verification， statistical testing， and parameter sensitivity analysis， thus enhancing the reliability， stability， and interpretability of the model solutions. The effectiveness and applicability of the spatial proximity term are validated through various methods， such as the HLN test， Monte-Carlo simulations， and probability density analysis， from the perspectives of accuracy and stability. Finally， a case study on the economic prediction of Shanghai demonstrates that the integration of the novel spatial proximity term significantly improves the ability of all three grey multivariate models to capture the spatial proximity effects of neighbouring regions， offering enhanced prediction accuracy， reliability， and stability. Figures and Tables | References | Related Articles | Metrics

Select

Can Interaction and Dissemination of Mixed-Frequency Information in Companies' Multi-Layer Relationship Networks Improve Asset Pricing? Asset Pricing Study Based on Graph Neural Networks Zezhou Wang, Qifa Xu, Cuixia Jiang 2026, 34 (4):  47-62.  doi: 10.16381/j.cnki.issn1003-207x.2024.0896 Abstract ( 65 )   HTML ( 1 )   PDF (4247KB) ( 66 )   Save The interconnections among listed companies are intricate and constitute multi-layer relationship networks. The contemporary interaction and intertemporal dissemination of mixed-frequency information in multi-layer relationship networks will impact asset prices and returns. To this end， supply chain， equity， and industry networks among listed companies are established. Then low-frequency company characteristic information， low-frequency macroeconomic information， as well as high-frequency market， trade， and sentiment information are integrated as network node features. As a result， a novel MF-IAMGCN model is constructed to investigate how mixed-frequency information interaction and dissemination in multi-layer networks predict asset prices. In the spatial dimension， the mixed data sampling （MIDAS） method is incorporated into the attention multi-layer graph convolutional network （AMGCN） framework. The MF-IAMGCN model can capture the contemporary interaction of mixed-frequency information in multi-layer networks， including high-order dependencies and nonlinear relationships between nodes. In the temporal dimension， the MF-IAMGCN model leverages the gate mechanism to capture the intertemporal information dissemination between nodes in consecutive time steps. All listed companies in the Chinese A-share market from January 2003 to December 2022 are choosed and the proposed MF-IAMGCN model’s pricing power from individual stock pricing， test asset pricing， and portfolio performance is examined. The empirical results show that： （1） The pricing performance of the MF-IAMGCN model outperforms four competitive models on both individual stock and test asset levels. （2） The portfolios constructed by the MF-IAMGCN model achieve optimal risk-adjusted performance in terms of high returns and low volatility. （3） The mixed-frequency data processing module， network information contemporary interaction module， and network information intertemporal dissemination module within the MF-IAMGCN model jointly learn dynamic dissemination patterns of pricing information. The mixed-frequency data processing module contributes greatly to improving pricing performance by exploiting high-frequency pricing information. （4） Equity networks contain information about capital activities （such as strategic investments， mergers and acquisitions， insider trading， and “backdoor listings”）， significantly reducing pricing errors of ST stocks and playing a vital role in asset pricing. Figures and Tables | References | Related Articles | Metrics

Select

Can State-owned Enterprises as “Chain Leader” Lead the Improvement of Innovation Capabilities of Private Enterprises on the Chain? From the Perspective of Spillover Effects in the Industrial Chain Guangqian Ren, Man Jing, Hao Jiao, Kunkun Xue 2026, 34 (4):  63-76.  doi: 10.16381/j.cnki.issn1003-207x.2025.0278 Abstract ( 124 )   HTML ( 15 )   PDF (764KB) ( 76 )   Save Against the background of global economic downturn， intensified trade friction， and frequent geopolitical conflicts， the risks of “stuck chain” and “broken chain” in China’s industrial chain continue to rise， and enhancing the resilience and innovation effectiveness of the industrial chain has become an important proposition for achieving high-quality economic development. The “chain leader” enterprises are the key force to strengthen， supplement， and extend the industrial chain， and the state-owned enterprises （SOEs） have the core advantages to become the “chain leader” enterprises， so they should give full play to the radiation and driving role of the “chain leader” SOEs and lead the private enterprises in the chain. To give full play to the role of “chain leader” SOEs and lead the collaborative development of private enterprises in the chain is an important way to enhance the resilience of the industry chain. Based on the above research background， the data of A-share listed private enterprises in Shanghai and Shenzhen and their top five suppliers and customers from 2009 to 2023 are taken as samples， and the influence of “chain leader” SOEs on the innovation ability of private enterprises in the chain and its mechanism is examined from the perspective of the association of industrial chain enterprises.The conclusions are as follows. Firstly， “chain leader” SOEs can play the “wild goose effect” and lead the improvement of the innovation capacity of private enterprises in the chain. Secondly， the mechanism test finds that “chain leader” SOEs enhance the innovation capacity of private enterprises in the chain through three paths： technological innovation spillover， alleviating financing constraints， and optimizing resource allocation. Thirdly， the heterogeneity test finds that there is heterogeneity in the innovation-leading effect of the “chain leader” SOEs on the private enterprises in the chain， and the benefits are more significant for the private enterprises in the chain that are not in the western region， with a high degree of market competition， and in the geographic distance between the “chain leader” SOEs and the private enterprises in the chain that are closer to the “chain leader” SOEs. In addition， when the “chain leader” SOEs belong to high-tech industries and have a higher degree of digital transformation， their innovation leadership effect on the private enterprises in the chain is more significant. Fourthly， the economic consequences test finds that the “chain leader” SOEs further enhance their enterprise value by improving the innovation capabilities of private enterprises on the chain. Figures and Tables | References | Related Articles | Metrics

Select

A Prospect Theory and Attribute Association Based Probabilistic Linguistic Fuzzy SIR Method for Multi-attribute Group Decision-making and Its Application Jie Yang, Zhikun Cai, Zhiwen Zheng, Libang Lai, Zeshui Xu 2026, 34 (4):  77-88.  doi: 10.16381/j.cnki.issn1003-207x.2023.2030 Abstract ( 75 )   HTML ( 5 )   PDF (1102KB) ( 54 )   Save In real life， it’s inevitable that the multi-attribute group decision-making has a series of problems such as fuzzy information， individual preferences and attribute association， which makes it difficult for decision-makers to accurately determine which alternative is the best alternative. How to abstract these practical decision-making problems into theoretical problems， and select the best alternative is the focus of this paper. To solve such a kind of problems， a multi-attribute group decision-making SIR （superiority and inferiority ranking） method based on Choquet integral and prospect theory is proposed. Firstly， a probabilistic linguistic Choquet integral operator is used to aggregate the decision-making information， and construct the group decision-making matrix. After that， considering that the SIR method ignores the risk preferences of experts， the proposed approach introduces prospect theory into the SIR method to construct the comprehensive prospect superiority and inferiority index. Then， to address the problem of attribute association， Choquet integral and attribute comprehensive weights are used to construct a new comprehensive prospect superiority and inferiority flow that can characterize the difference of alternatives. And then， alternatives are sorted according to the sorting rules of the SIR method. Finally， the feasibility and effectiveness of the proposed approach is indicated by an example of overseas location problem for cross-border e-commerce. The conclusion of this article is as follows： （1） Decision makers' risk preferences and linguistic preferences significantly affect the ranking of the alternatives， for example， risk aversion decision makers will tend to choose conservative strategies. （2） Compared with the classic SIR method and prospect theory， the proposed method can more intuitively analyze the differences between alternatives through pairwise comparison. In a word， the proposed approach can comprehensively consider the subjective and objective uncertainties of the decision-making environment， and is a further extension of the classic SIR method. Figures and Tables | References | Related Articles | Metrics

Select

Group DEMATEL Factor Analysis Method with Hierarchy-situation Type for Complex Systems Yonghe Sun, Zihang Huang, Bin Miao, Fudong Chi 2026, 34 (4):  89-99.  doi: 10.16381/j.cnki.issn1003-207x.2023.1784 Abstract ( 57 )   HTML ( 2 )   PDF (2062KB) ( 40 )   Save The group decision-making trial and evaluation laboratory （DEMATEL） is a commonly used method of factor analysis in complex systems， which can reveal causal relationship among the factors. In recent years， the group DEMATEL has been widely used in various fields. As a DEMATEL expansion method of how to solve complex hierarchical structure problems， hierarchical group DEMATEL has attracted the attention of some experts and scholars. However， existing hierarchical group DEMATEL methods still have the following deficiencies such the inherent “method-structure” mismatching， logical confusion and fragmentation of the correlation among the different levels of factors， as well as the expert judgments being difficult. To overcome the above-mentioned drawbacks， a group DEMATEL factor analysis method with hierarchy-situation type for complex systems is suggested in this paper. In the method， a new analytical structure is first proposed by devising the state situations of the system factors and adding multiple alternative situations. Then， the factor knowledge reasoning mechanism from a holistic perspective is given based on polychromatic set theory. Next， the corresponding method implementation steps are given in detail. Finally， a case study on the identification of risk factors affecting oil and gas leakage from offshore platforms is shown to illustrate the proposed method and verify the feasibility and validity of the proposed method. The result shows that the method is scientific and reasonable and can be well applied to solve the issue of analysis of complex system factors. It should be noted that this suggested method not only clarifies the action mechanism of group DEMATEL complex factors reflecting well hierarchical structure characteristics from both vertical and horizontal dimensions， but also can realize the varying weight analysis of the system factors. Figures and Tables | References | Related Articles | Metrics

Select

Theoretical Analysis and Empirical Test of the Impact of Inter-provincial Trade on Residents' Consumption Jianyue Ji, Yutong Sui, Lina Yu 2026, 34 (4):  100-110.  doi: 10.16381/j.cnki.issn1003-207x.2024.0614 Abstract ( 40 )   HTML ( 0 )   PDF (618KB) ( 24 )   Save Inter-provincial trade is an important focus to release the consumption potential of residents in the domestic cycle. The indefinite utility function is used to analyze the impact of inter-provincial trade on residents’ consumption in the pursuit of maximizing lifetime utility， and a theoretical model of inter-provincial trade affecting residents’ consumption is constructed. On this basis， using panel data encompassing 29 provincial-level administrative regions spanning the years 2000 to 2022， the effect and mechanism of inter-provincial trade on residents’ consumption is empirically tested. It is revealed that： First， inter-provincial trade can significantly promote residents’ consumption， and this conclusion is still valid after a series of endogenous and robustness tests. Second， the results of mechanism analysis show that inter-provincial trade can produce employment quantity expansion and product market upgrading effects， that is， expanding employment scale on the demand side to increase income， promoting product innovation and upgrading on the supply side to improve residents’ consumption. Third， the heterogeneity analysis results of different regions and different types of residents’ consumption show that the promotion effect of inter-provincial trade on residents’ consumption in the central and western regions is greater than that in the eastern regions； inter-provincial trade can promote the survival， enjoyment and development consumption expenditure in the central and western regions， and promote the survival consumption expenditure in the eastern region. The results of the heterogeneity analysis of the degree of marketization show that the lower the degree of marketization， the greater the promotion effect of inter-provincial trade on residents’ consumption. This research provides reference and policy implications for expanding domestic demand and promoting residents’ consumption under the background of the new development pattern of dual circulation. Firstly， it is essential to accelerate the establishment of a unified national market to minimize inter-provincial trade barriers， thereby facilitating domestic trade. This initiative will significantly bolster consumer spending. Secondly， it is crucial to align supply-side strategies with evolving consumer demands. Figures and Tables | References | Related Articles | Metrics

Select

Rescheduling Optimization of Virtual Cell Considering State Transition Information When New Orders Arrive Liang Mei, Qiqing Shen, Shilun Ge, Lin Wang 2026, 34 (4):  111-127.  doi: 10.16381/j.cnki.issn1003-207x.2023.0778 Abstract ( 33 )   HTML ( 0 )   PDF (4365KB) ( 20 )   Save The rescheduling optimization problem in virtual cell （VC） manufacturing systems triggered by the dynamic arrival of new orders， a prevalent disturbance in make-to-order production environments is investigated. The arrival of new orders disrupts initial schedules and induces state transitions in parts or machines， challenging traditional heuristic rules that focus solely on static process or capacity constraints and fail to account for dynamic state transitions during rescheduling. To address this gap， a joint decision-making model is proposed that integrates process route reconstruction with state transition-guided rescheduling. The model optimizes three objectives： minimizing the maximum completion time， total queuing time， and total transportation time， with weighting parameters to balance their relative importance. A novel feature of the model is its introduction of dynamic constraints for schedulable states upon new order arrivals， enabling real-time adaptability. Additionally， the model employs an assignment problem framework to precisely compute queuing times， eliminating the need for assumptions about order arrival distributions， which enhances its practicality in complex manufacturing systems. To solve this high-dimensional and multi-constrained problem， a Differential Evolution Hybrid Algorithm Using State Transition Information （DEHAUSTI） is developed. This hybrid algorithm combines the global search capability of differential evolution （DE） with the local state analysis of finite state machines （FSM）. DEHAUSTI incorporates a dual-layer encoding mechanism for part sequencing and machine allocation， state transition rules to evaluate rescheduling feasibility， and adaptive selection strategies， including greedy and simulated annealing approaches， to mitigate the risk of local optima. The experimental validation involves four large-scale random instances， comparing DEHAUSTI with four benchmark algorithms： genetic algorithm （GA）， standard DE， fruit fly optimization （FFOA）， and forest optimization （FOA）. The tests are conducted at ten rescheduling time points to evaluate performance under varying conditions. Results demonstrate that DEHAUSTI significantly outperforms the benchmark algorithms. Specifically， it reduces the maximum completion time by 4.73% in initial scheduling and 4.43% in rescheduling， cuts queuing time proportions by 44.96% and 85.50%， and lowers transportation time proportions by 3.96% and 1.39%， while simultaneously increasing the proportion of effective production time. Further analysis reveals that initiating rescheduling earlier yields additional performance benefits， highlighting the algorithm's adaptability to dynamic environments. Case studies， including detailed Gantt chart analyses， illustrate DEHAUSTI's effectiveness in balancing machine workloads， reducing bottlenecks， and enhancing system flexibility. The research contributes both theoretically and practically by establishing a state transition-aware rescheduling framework and providing actionable insights for VC systems operating under dynamic disruptions. The methodology's robustness is confirmed through parameter sensitivity analyses and comparative benchmarks， underscoring its superiority in handling high-dimensional， constrained optimization scenarios. Future research could extend this framework to address other disturbances， such as machine failures or workforce changes， further broadening its applicability in industrial settings. Figures and Tables | References | Related Articles | Metrics

Select

Research on Optimization of Rural Passenger-Deliveries Integrated Logistics Collaboration and Cost Allocation Problems in China Weizhen Rao, Xiaohe Miao 2026, 34 (4):  128-143.  doi: 10.16381/j.cnki.issn1003-207x.2023.1900 Abstract ( 51 )   HTML ( 1 )   PDF (4138KB) ( 42 )   Save The demand for logistics in China's rural areas continues to expand rapidly. More than 100 million parcels enter and exit the countryside every day. However， rural logistics in China is plagued by problems such as unreasonable transport routes， single capacity and high costs. At the same time， traditional passenger transport in rural areas faces the problems of low occupancy rates and serious unoccupied loads， and spare capacity is not being used efficiently. How to smooth the first-mile of residents' travel and rural logistics has become a key point to stimulate the vitality of rural development. Based on this， a new model of collaborative transport is proposed. A mixed fleet consisting of multiple enterprise vehicles and rural buses picks up parcels and carries passengers to the township service center， and then the enterprises collaborate to transport the parcels from the township to the county-level co-delivery center. In this paper， a joint transportation model for passengers and parcels is constructed based on hybrid fleet collaboration and a collaborative pickup model for enterprises， which are used to plan transportation routes and calculate costs. The two-stage costs are fairly shared with the help of Shapley value method. The experimental results show that the two-stage collaborative transportation model saves nearly 5913.1 yuan in cost and 451.03 kilometers of transportation distance per day compared to the traditional transportation model. The average cost savings for companies reached more than 30.03%， and the average revenue-to-cost ratio for rural transit operators reached 17.17%. And， passenger travel time is extended by no more than 5 minutes. This suggests that the government should actively encourage collaboration between logistics companies， passenger transportation companies and other entities. When courier vehicles are limited or the destination village is very remote and the demand is low， it is recommended to adopt the RCBF model， which is able to minimise the frequency of use and distance of logistic vehicles. In addition， the RCBF model enables all parties involved in the collaboration to obtain considerable income， achieving the multi-objective unity of cost savings， efficiency and increased income. Overall， the implementation of RCBF in rural areas can achieve a win-win situation for rural residents， express delivery companies and public transport operators. At the same time， the key operational mechanisms of the RCBF model is examined， including route optimisation， cost quantification and sharing， to provide a more general transport model design idea that can be applied to countries and regions with large rural areas. Figures and Tables | References | Related Articles | Metrics

Select

An Adaptive Large Neighborhood Search Algorithm for Electric Vehicle Pickup-and-Delivery Routing Problem with Pickup-Points Selection Tingying Wu, Bingbing Liu, Yugang Yu 2026, 34 (4):  144-155.  doi: 10.16381/j.cnki.issn1003-207x.2024.0142 Abstract ( 48 )   HTML ( 2 )   PDF (1044KB) ( 39 )   Save In the consumption mode of combining online e-commerce and offline stores， the goods purchased by consumers can be provided by multiple offline stores at the same time， which leads to the unfixed pickup point of goods. Therefore， it is necessary to make decisions on the selection of pickup points and the delivery routing when distributing goods. The electric vehicle pickup and delivery problem with pickup-point selection in the case of unfixed pickup point is studied. After characterizing the constraints such as time window， charging time and battery capacity of the pickup point and the customer delivery point， a mixed integer programming formulation is formulated to minimize the total distribution cost. According to the characteristics of the model， an improved adaptive large neighborhood search algorithm is developed. In order to expand the search space of feasible solution， a variety of destroy operators and repair operators are designed for charging station nodes and pickup-and-delivery nodes， and a new solution acceptance criterion based on simulated annealing is used to improve the search efficiency. In this paper， the greedy random adaptive repair operator and the minimum spanning tree destruction operator are first proposed. The effectiveness and superiority of the proposed algorithm are verified by a comprehensive numerical experiment analysis， The numerical results show that： （1） The influence of the selection of the pickup points on the total cost and the improvement effect of the proposed new operators on the algorithm are emphatically analyzed. （2） Considering the selection of pickup points can greatly reduce the number of vehicles and delivery costs. When logistics enterprise operators plan the route of pickup and delivery vehicles， the selection of pickup and delivery points should be made at the same time as the planning of the pickup and delivery path， rather than first deciding the pickup point and then optimizing the pickup and delivery routes. The selection of pickup points and the pickup and delivery routing are isolated into two independent issues for decision-making.The research results of this paper not only enrich the study of the route optimization of electric vehicles for pickup and delivery， but also provide a decision-making reference for logistics enterprises to design distribution and delivery schemes. Figures and Tables | References | Related Articles | Metrics

Select

Optimization Model and Strategies for Route and Fleet Allocation Considering Port Congestion and Carbon Emission Reduction Shaolong Hu, Xudong Wang, Chuanfeng Han, Lingpeng Meng 2026, 34 (4):  156-167.  doi: 10.16381/j.cnki.issn1003-207x.2024.0283 Abstract ( 43 )   HTML ( 2 )   PDF (1759KB) ( 25 )   Save In recent years， a discernible incongruence has emerged between the slowly improving service capacity of coastal ports and the escalating pace of maritime requisites. This asymmetry in growth has engendered a recurrent proliferation of port congestion， precipitously impacted the scheduling of maritime fleets and thereby culminated in the clustering of vessels within ports. The confluence of these ramifications has reverberated with profound repercussions upon both regional economies and environmental domains. In response to the entwined issues of port congestion and emissions reduction， a variation inequality model is proffered that optimizes ship routes and allocation strategies. Encompassing considerations of ship propulsion types， varying congestion degrees across distinct ports and alternative harbor options， this model crystallizes around the manifold facets of temporal costs， transport expenditures， and carbon emissions. An improved diagonalization algorithm is devised to solve this problem. Rooted in the context of China's coastal routes， the empirical investigation proffers some insights.As the standard diagonalization algorithm uses network allocation and scalar solutions to determine the iteration direction and step size， it saves 14.61% of the solution time on average to find the same optimal solutions for small-scale problems， compared to the projection algorithm. Furthermore， the improved diagonalization algorithm saves 28.36% of the time compared to the projection algorithm. More importantly， the improved diagonalization algorithm can find the optimal solution within 12，000 seconds for large-scale problems. Lastly， as port congestion intensifies， （1） Shipping enterprises grapple with escalating costs associated with waiting times and voyage schedule losses. （2） A predilection emerges within shipping entities to divert vessels towards alternative harbors. （3） Maritime enterprises gravitate towards more eco-friendly fuels when confronted with the convergence of lowered clean energy costs or elevated carbon taxation rates. Figures and Tables | References | Related Articles | Metrics

Select

Study on the Order and Rack Sequencing Problem with Rack Buffer Zones Qianqian Han, Kang Wang, Zhenping Li 2026, 34 (4):  168-177.  doi: 10.16381/j.cnki.issn1003-207x.2024.0255 Abstract ( 33 )   HTML ( 1 )   PDF (1124KB) ( 27 )   Save With the rapid development of e-commerce enterprises and the wide application of robotic mobile fulfillment systems， the importance of order picking for warehousing operational efficiency is increasingly evident， and configuring rack buffer zones can enhance order picking efficiency. Therefore， it focuses on the research of the order and rack sequencing problem with rack buffer zones. In the scenario of limited capacity in the rack buffer zone， it aims to jointly optimize the order picking sequence， rack retrieval sequence， and buffering strategy. Taking into consideration factors such as the shared storage mode for products， the quantity of each product stored on individual racks， the ordered quantities in customer orders， and the limited capacity of the buffer zone， an integer programming model is established for the order and rack sequencing problem with rack buffer zones. The objective is to minimize the number of rack retrievals while considering these factors. Considering the characteristics of the problem， an interactive heuristic algorithm is designed to synchronously address the three sub-problems. Through case study experiments， the correctness and effectiveness of the proposed model and algorithm have been verified. The experimental results indicate that the average relative deviation between the feasible solutions obtained by the interactive heuristic algorithm and the exact solutions is approximately 5.4%， demonstrating that the proposed algorithm can achieve high-quality feasible solutions in a relatively short time. Furthermore， comparisons with the baseline algorithm and the heuristic algorithm without the rack buffer zone further validate the efficiency of the proposed algorithm. Specifically， the average improvement in the objective function achieved by the proposed algorithm is about 9.6% compared to the benchmark algorithm， and the implementation of the rack buffer zone significantly reduces the number of times racks are transported during the order fulfillment picking process by about 14.1%. The proposed model and algorithm in this paper provide decision-making foundations for enterprises to strategically set up rack buffer zones， reduce the number of rack retrievals， and enhance order-picking efficiency. Figures and Tables | References | Related Articles | Metrics

Select

Joint Pricing and Fulfillment Optimization for Omni-channel Retailers Considering Fulfillment Location Selection and Resources Sharing among Channels Bilin Zou, Xiaohui Lyu, Xiaohong Zhang 2026, 34 (4):  178-191.  doi: 10.16381/j.cnki.issn1003-207x.2024.1365 Abstract ( 30 )   HTML ( 0 )   PDF (3336KB) ( 16 )   Save The omni-channel retail model reconstructs the way in which the traditional retail industry creates value online or offline. With the implementation of this model， a large number of retailers blindly imitate the business model and make separate decisions on online and offline channel pricing and fulfillment optimization， which makes it difficult for online and offline channels to exert complementary advantages. Therefore， in view of the actual situation that omni-channel retailers can choose fulfillment centers and physical stores to circulate and process goods in multi-period and multi-region， and in order to improve the utilization efficiency of online and offline channel resources， the joint optimization problem of pricing and fulfillment decision is studied considering fulfillment location selection and resource sharing among channels. Among them， the fulfillment decision includes fulfillment location selection decision， inventory decision and transportation decision. Focusing on research problem， a mixed integer nonlinear programming model for joint optimization of pricing and fulfillment decision of omni-channel retailers is established with the goal of maximizing the total profit of omni-channel retailers. Based on this model， an optimization model considering resources sharing among online and offline channels is established to further explore how to rationally allocate the resources of each channel. Then， a co-evolutionary algorithm is proposed to solve the model. Finally， the effectiveness of the co-evolutionary algorithm is verified by numerical experiments. The results show that： there is a correlation between pricing decision and fulfillment decision， and considering the fulfillment location selection in fulfillment decision can significantly improve the total profit of the omni-channel retailers； fully sharing and integrating resources among online and offline channels enables omni-channel retailers to make decisions that reduce fulfillment costs， thereby increasing the total profit of the omni-channel retail network， and the larger the network size， the more obvious the increase in the total profit of omni-channel retailers. Figures and Tables | References | Related Articles | Metrics

Select

Quantity-limited Optimization Design with the WOM Effect in the Context of Scarcity Marketing Bo Tan, Zhiguo Zhu, Jang Pan, Ming Gao 2026, 34 (4):  192-204.  doi: 10.16381/j.cnki.issn1003-207x.2022.2610 Abstract ( 31 )   HTML ( 0 )   PDF (3089KB) ( 19 )   Save With the improvement of production capacity of enterprises and the change of market structure， more and more high-tech enterprises use limited sales to provide consumers with new products. Limited sales mode is the main way for enterprises to use the scarcity effect for marketing. The limited sales approach is affected by a variety of factors， including price， fans， brand awareness， final volume， and marketing costs. However， there is a lack of research on this aspect. Therefore， a profit maximization model considering the network word-of-mouth effect is constructed， variational method is used to solve the limited optimal path， the influence of factors such as the size of fans， the ending number and price on the quantity path and the level of marketing effort is analyzed， and the design methods of the quantity path in two cases are given. And how to make decisions on the level of marketing effort under the environment of network word-of-mouth. The model is hypothesized and described， then a differential equation is established to describe the dynamics of limited sales in the network environment， and a profit maximization model under limited sales is also established， which is solved and analyzed in two stages under the basic model of construction： Firstly， the optimal quantity path is obtained by the variational method， and then the parameter variables that affect the profit are analyzed and decided under this path. A profit maximization model considering the network word-of-mouth effect is further constructed， the influence of network word-of-mouth effect on profits， and how enterprises make decisions on marketing effort level are analyzed. Finally， a case is used to analyze and verify the practicability of the model. The conclusions point out that： 1） Under the condition that the quantity at time T is determined： first， the enterprise can set the final quantity N in advance， design the optimal quantity path according to the size of fans， and finally make a decision on the price to obtain the maximum profit； Second， by setting the price in advance， and then setting the final quantity N according to the price and the size of fans， the optimal quantity path is designed to obtain the maximum profit. By deducing the calculation， a final value N is determined which makes the enterprise profit maximum. 2） In the case of quantity uncertainty at time T： enterprises can determine the optimal quantity path through the combined effect of fan size and price. And it is given that under the situation of limited production capacity or limited edition， the optimal quantity supply decision of the enterprise is to implement the equivalent quantity in the cycle T. In both cases， the size of fans plays a key role in the design of the optimal quantity path， and efforts to expand the size of fans play an important role in the design of the acquisition of greater profits. 3） Enterprises should pay attention to the interactive impact of positive and negative word-of-mouth effect on profits. Negative word of mouth influence plays a key role in the decision-making of marketing effort level and the design of quantity path of enterprises， especially in enterprises with high brand awareness. 4） When the size of fans is fixed， in order to obtain the maximum profit， the higher the pricing， the larger the total number of sales. The results of paper will provide theoretical support and decision-making suggestions for enterprises to implement limited sales practice in scarcity marketing. Figures and Tables | References | Related Articles | Metrics

Select

Impact Factors of Consumer Return Behavior in Jade Products Live-streaming E-commerce Demei Kong, Yongbo Xiao, Zhao Yang, Jihong Zhang 2026, 34 (4):  205-217.  doi: 10.16381/j.cnki.issn1003-207x.2024.0523 Abstract ( 84 )   HTML ( 7 )   PDF (1404KB) ( 63 )   Save Live broadcasting platforms， encompassing both general platforms （e.g.， TikTok and Kuaishou） and specialized platforms （e.g.， Jade Empire）， have emerged as innovative sales channels for jade products such as Hotan Jade and Jadeite. These platforms have revolutionized the marketing of high-value， non-standardized products like jade， offering consumers immersive and interactive shopping experiences. However， the rapid growth of the jade live-streaming has introduced several operational challenges， most notably the exceptionally high return rate. According to data provided by Jade Empire Co.， Ltd.， the return rate for jade products in live-streaming can reach up to 80%， significantly exceeding other product categories. This issue poses critical challenges to both merchants and platforms in managing costs and maintaining customer satisfaction. The determinants of consumer return behavior in the jade live-streaming context is investigated， focusing on two key dimensions of return behavior： the likelihood of returns and the return time interval. Drawing on data from two distinct platforms， Jade Empire （a specialized jade platform） and TikTok （a general live-streaming platform）， the influence of platform characteristics， product features， and merchant operation strategies on return behavior is examined. Employing empirical methods， including logit regression and linear regression models， the following key insights are derived： （1） Jade products sold on TikTok exhibit significantly higher return rates compared to those on Jade Empire. This difference can be attributed to the broader， less specialized audience of TikTok， which includes a higher proportion of impulsive buyers with limited knowledge of jade products. （2） The relationship between product prices and return rates follows an inverted U-shaped curve. Low-priced and high-priced jade products are associated with lower return rates， while mid-priced products exhibit the highest return rates. Additionally， bracelet products， characterized by high matching uncertainty （e.g.， size， fit）， show a notably higher return rate compared to other jade product types. （3） Operational factors such as price discounts and live-streaming schedules significantly influence return rates. Offering price discounts increases return rates by encouraging impulsive purchases. Furthermore， daytime live-streaming events are associated with higher return rates compared to nighttime events， as daytime viewers often face external distractions that impair their ability to make informed decisions. （4） The type of live-streaming platform moderates the relationship between product features， operational strategies， and return behavior. General platforms like TikTok amplify the effects of product and operational characteristics on return rates， while specialized platforms like Jade Empire mitigate these effects by attracting a more knowledgeable and engaged audience. （5） By analyzing return time intervals， two distinct return behavior patterns are identified： impulsive returns （occurring before product delivery） and experiential returns （occurring after product receipt and usage）. Mid-priced products and bracelet products are more likely to prompt impulsive returns due to heightened expectations or mismatched features. Conversely， price discounts and nighttime live-streaming extend return time intervals by encouraging consumers to experience the product before deciding to return it. It contributes to the literature on live-streaming commerce and product return behavior by integrating insights from platform-specific dynamics and consumer decision-making processes in this paper. The findings offer valuable insights into live-stream scheduling， product assortment management， and platform selection， all of which can help reduce return rates and enhance consumer experience. Figures and Tables | References | Related Articles | Metrics

Select

Price Discrimination and Personal Information Protection: Based on a Two-Stage Dynamic Duopoly Model Zhiyu Wang, Chuan Ding, Liyuan Wang 2026, 34 (4):  218-230.  doi: 10.16381/j.cnki.issn1003-207x.2024.0756 Abstract ( 71 )   HTML ( 3 )   PDF (1458KB) ( 31 )   Save In the era of digital economy， with the advancement of artificial intelligence and digital mining technologies， firms are increasingly able to extract valuable information from consumer data. By creating precise consumer profiles， firms can engage in third-degree or even first-degree price discrimination. This gives rise to issues such as “big data discriminatory pricing” and personal information protection， posing significant challenges to government policymaking. A two-period dynamic duopoly model is constructed to study firms' pricing strategies and consumers' purchasing decisions. Based on the model， the fundamental characteristics and mechanisms of personalized pricing by firms are analyzed based on consumers' purchasing histories. Furthermore， a comparative analysis of different personal information protection policies is conducted from the perspective of welfare metrics and implications for management are provided.The main conclusions of this paper are as follows　First， under both mandatory and no personal information protection policies， the phenomenon of “big data-driven price discrimination” emerges. Under voluntary personal information protection policy， firms raise uniform prices in the second period to erect a “price barrier” for consumers， colluding tacitly to avoid repeated competition for new and existing consumers， while simultaneously engaging in first-degree price discrimination toward their loyal customers. Although this eliminates the appearance of “big data discriminatory pricing”， it weakens market competition and deprives all consumers of their surplus in the second period through personalized pricing. Second， under voluntary personal information protection policy， first-degree price discrimination does not result in deadweight loss to social welfare but does lead to an inequality in producer and consumer welfare. Optimal social welfare and producer surplus can be achieved， but the consumer surplus reaches its lowest level. In contrast， under no personal information protection policy， market competition is intensified by addressing initial market share asymmetries， resulting in suboptimal social welfare and consumer surplus but the lowest producer surplus. Inefficient allocation of information resources leads to unnecessary social welfare losses， suggesting that mandatory personal information protection policy only achieve minimal social welfare. Third， given that optimal producer and consumer surplus cannot be achieved simultaneously， the government should adopt a problem-oriented approach when selecting policies and carefully balance efficiency and equity considerations. Specifically， to address efficiency issues in the digital economy， the government could adopt voluntary personal information protection policy， transferring consumer surplus to firms to curb “big data discriminatory pricing” and thereby enhance overall social welfare. To address equity issues， the government could implement no personal information protection policy to foster market competition， improve consumer welfare， and achieve suboptimal social welfare. Figures and Tables | References | Related Articles | Metrics

Select

Follow-up Strategies on Technology Investment for the Software Platform Minqiang Li, Nan Yuan, Haiyang Feng 2026, 34 (4):  231-242.  doi: 10.16381/j.cnki.issn1003-207x.2023.0244 Abstract ( 42 )   HTML ( 1 )   PDF (1434KB) ( 31 )   Save As emerging technologies such as artificial intelligence， blockchain and augmented reality grow， an increasing number of software platforms are investing in new technologies to foster continuous innovation. In practice， some lead software platforms are forward-looking in the development of emerging technologies. Due to the difficulty and uncertainty of developing emerging technologies， investments by various software platforms in the same technology often occur sequentially in the market. When one software platform has already invested in a new technology， the subsequent investment strategies pursued by other platforms may vary.Therefore， the software platform’s follow-up strategies on technology investment are examined by considering the functional level of the technologies invested by competing platforms and cross-side network effects. A game-theoretical model of platform investment strategy in a duopoly setting is developed， to study how the platform makes a follow-up investment choice and the optimal pricing decision when one of the competing platforms has invested in a new technology. Specifically， the optimal follow-up technology investment strategy of the software platform is first analyzed， followed by an exploration of the impact of cross-side network effects on the platforms’ follow-up investment decisions. Finally， the optimal decision regarding the technological functional level is examined when the platform engages in follow-up technology investment.The main findings are as follows， the platform should follow up the technology investment when the marginal integration cost of developers is low， the marginal development cost of the platform is high， and the functional level of the technology invested by the platform is low. As the user-to-developer network effect increases， the platform's willingness to follow up the technology investment increases. The impact of the developer-to-user network effect on the platform's willingness to follow up the technology investment is affected by the functional level of its investment technology. It provides insights into the investment strategy decisions of software platforms regarding emerging technologies， while also addressing the gap in existing literature concerning the research on follow-up investment strategies of competitive software platforms. Figures and Tables | References | Related Articles | Metrics

Select

Closed-loop Supply Chain Decision of Hybrid Recycling Channels Considering Fairness Concerns Xiaogang Cao, Bowei Cao, Hui Wen 2026, 34 (4):  243-255.  doi: 10.16381/j.cnki.issn1003-207x.2023.0619 Abstract ( 49 )   HTML ( 3 )   PDF (2501KB) ( 39 )   Save With the continuous improvement of social material living standards， the amount of waste is also increasing. Although China 's resource recycling capacity has been significantly enhanced， the overall efficiency of resource utilization is still not high， and the problem of low standardization level of renewable resource recycling has not been completely solved. How to improve the recycling system of waste materials in China and improve the level of resource recycling is still an urgent problem to be solved. Some enterprises improve the recycling of resources by multi-channel recycling. For example， in addition to carrying out recycling business on the official website and self-operated offline service stores， Huawei also cooperates with Jingdong， Aihuishou and other enterprises to carry out electronic equipment recycling and trade-in； in addition to recycling directly from consumers through after-sales channels， Midea also opens old machine recycling services to its distributors. However， when enterprises cooperate with enterprises in different channels， the behavioral preferences of different enterprises， such as fairness concerns， will affect their willingness to cooperate and bargaining power. In view of this， the following research questions are proposed： （1） What is the optimal decision of supply chain members under fairness concerns？ （2） How does the fairness concerns behavior of supply chain members affect the optimal decision and profit？ （3） Which recycling mode is optimal for the supply chain when the supply chain members have fairness concerns？To solve these problems， the closed-loop supply chain （CLSC） structure of hybrid recycling channels composed of different recycling participants is investigated. Considering the fairness concerns behavior of the retailer and the third-party recycler， four kinds of hybrid recycling channel models are established to compare the equilibrium decisions and profit of supply chain members： the hybrid recycling channel model of the manufacturer and retailer （MR）； the hybrid recycling channel model of the manufacturer and third-party collector （MT）； the hybrid recycling channel model of the retailer and third-party collector （RT）； and the hybrid recycling channel model of the manufacturer， retailer and third-party collector （MRT）. The result shows that： （1） the recycling competition among members in the CLSC will reduce the total recycling quantity of waste products； （2） when the fairness concerns of the retailer and the third-party collector exceed a certain range， the optimal recycling mode of both members witnesses a change； （3） the influence of fairness concerns coefficient on utility is diverse under different models， and the retailer and third-party collector have the maximum utility in RT mode. Finally， the effectiveness of the conclusion is demonstrated through numerical analysis. Figures and Tables | References | Related Articles | Metrics

Select

Co-construction Strategy of New Energy Vehicle Industry Xiaohong Chen, Ningyi Yang, Yanju Zhou 2026, 34 (4):  256-265.  doi: 10.16381/j.cnki.issn1003-207x.2023.0430 Abstract ( 99 )   HTML ( 7 )   PDF (1390KB) ( 104 )   Save Against the backdrop of global energy transformation and industrial policy adjustments， the new energy vehicle （NEV） industry is confronted with multiple challenges， such as subsidy retreats and core technological bottlenecks. Accordingly， enhancing supply chain resilience has emerged as a critical issue. This study addresses the supply chain collaboration problem within the NEV industry under the condition of diseconomies of scale. By comprehensively incorporating manufacturers’ fairness preferences and consumers’ heterogeneous preferences， it analyzes the decision-making models of NEV supply chain members and the coordination mechanism of cost-sharing contracts. The findings indicate that diseconomies of scale in the production context can reduce both the greenness level of products and the overall supply chain profit. However， a well-designed cost-sharing contract can effectively facilitate coordinated improvement of the supply chain. Notably， the retail price of products is not only correlated with manufacturers’ fairness preferences but also with the magnitude of the product carbon coefficient，which exerts heterogeneous impacts on enterprise decision-making. When manufacturers exhibit fairness preferences， designing a cost-sharing contract can achieve a Pareto improvement in profits， ultimately leading to the dual enhancement of economic and social benefits. Figures and Tables | References | Related Articles | Metrics

Select

Distributor Information Sharing with Manufacturer Channel Diversification and Cross-channel Conflict Ying Han, Guijun Zhuang 2026, 34 (4):  266-275.  doi: 10.16381/j.cnki.issn1003-207x.2023.0520 Abstract ( 47 )   HTML ( 2 )   PDF (838KB) ( 28 )   Save Manufacturers have been investing in establishing multiple channels for selling products to customers. Drawing on prior studies concerning multichannel marketing， information sharing， and complex adaptive system theory emphasizing the interplay between a system and its environment， the impact of distributor information sharing on manufacturer channel diversification and cross-channel conflict in multichannel settings， and how the impact is moderated by sales growth rate and environmental uncertainty are proposed and tested. Survey data are gathered from a sample of 484 manufacturers in China to test the hypotheses through regression analysis. It is found that， first， distributor information sharing positively affects manufacturer channel diversification， and can mitigate cross-channel conflict. Second， sales growth rate strengthens the positive effect of distributor information sharing on manufacturer channel diversification， and the negative effect of information sharing on cross-channel conflict. Third， environmental uncertainty weakens the positive effect of distributor information sharing on manufacturer channel diversification， and the negative effect of information sharing on cross-channel conflict. The major contribution of this paper is investigating the effect of distributor information sharing in multichannel settings. The findings enrich the literature on multichannel marketing and information sharing by confirming the important role of distributor information sharing to promote manufacturer channel diversification and mitigate cross-channel conflict. Furthermore， in accordance with complex adaptive system theory， the results support two boundary conditions at environmental factors （i.e.， sales growth rate and environmental uncertainty）. In practice， the findings have several implications for marketing channel managers in properly conducting multichannel governance and management. Manufacturers are suggested to be aware of cross-channel conflict in multichannel marketing systems and properly leverage information shared by distributors according to environmental conditions to restrain cross-channel conflict. Figures and Tables | References | Related Articles | Metrics

Select

Research on OEM/ODM Supply Chain Decision Making Considering Supply Risk Haonan Xu, Jiaguo Liu 2026, 34 (4):  276-286.  doi: 10.16381/j.cnki.issn1003-207x.2023.0351 Abstract ( 44 )   HTML ( 2 )   PDF (1476KB) ( 36 )   Save In the supply chain system comprising an Original Equipment Manufacturer （OEM）， an Original Design Manufacturer （ODM）， and a Contract Manufacturer （CM）， a multi-party game model based on competitive-cooperative relationships is developed to address upstream supply risks and determine equilibrium decisions. Furthermore， the impacts of economies of scale and brand advantage on supply chain management strategies are explored. The research findings indicate that even in the presence of supply risks， the CM can squeeze the profits of ODM， while the OEM can achieve profit gains through upstream competition. When the supply from the CM is disrupted， the ODM with weaker OEM brand advantage may choose to participate in end-market competition to further enhance profits. However， when the brand advantage of the OEM is significant， the ODM may focus on upstream supply to mitigate competition in the end market. Nevertheless， economies of scale have heterogeneous impacts on the profits of ODM and OEM， and manufacturers should be cautious of the potential negative effects that economies of scale may bring. It is noteworthy that the decision-making of ODM and OEM remains in a “lose-lose” situation， where the OEM as “follower” can only rely on measures such as brand advantage to ensure own profit realization， while the ODM’ decision can achieve a “win-win” outcome for both itself and social welfare. Not only a comprehensive understanding of the impact of supply risks on diversified procurement is provided but also the influence of supply disruptions on firm decision-making is explored. It offers strategic guidance for firms on decision-making in risk-operating environments and provides theoretical analysis for policy management departments in addressing supply risks and enhancing social welfare. Figures and Tables | References | Related Articles | Metrics

Select

Research on Supply Chain Decision-making Considering Retailer Information Sharing Strategy under Carbon Trading Mechanism Peng Tong, Xiang Ding, Wenbin Wang, Zhanpeng Mao, Ying Xiao 2026, 34 (4):  287-297.  doi: 10.16381/j.cnki.issn1003-207x.2023.1484 Abstract ( 50 )   HTML ( 2 )   PDF (1295KB) ( 46 )   Save In recent years， the emission of greenhouse gases has led to the intensification of global warming， which has brought about serious environmental problems. In order to achieve the goal of emission reduction， countries have adopted a series of policies， such as carbon tax， carbon cap-and-trade and so on， among which the carbon cap-and-trade mechanism is widely used because of its obvious effect of emission reduction. Under the carbon cap-and-trade scheme， enterprises can invest in carbon reduction technologies to reduce the carbon emissions in the production process. However， carbon emission reduction will increase the operating costs of enterprises and directly affect the profits of enterprises. Therefore， under the carbon cap-and-trade mechanism， how to choose the operational strategy of emission reduction and production of supply chain member enterprises has high research value. In addition， downstream enterprises in the supply chain are closer to the market or the final consumer than upstream enterprises， and thus have more demand information. However， the party with the information advantage， in order to maximize its own interests， often will not share the forecast information， and eventually form the situation of information asymmetry.Therefore， the common historical method is adopted as the quota allocation method under the carbon cap-and-trade mechanism. Considering the uncertainty of market capacity in the supply chain composed of manufacturers， retailers and consumers with low carbon preference， two models are constructed respectively according to whether retailers share forecast information with manufacturers. The results show that the information sharing behavior can promote manufacturers to reduce emissions， and the higher the accuracy of prediction information， the more obvious the emission reduction effect； When the emission reduction cost coefficient is small， retailers take the initiative to share information with manufacturers. When the emission reduction cost coefficient is moderate， an information sharing subsidy mechanism can be introduced to encourage retailers to share information. When the emission reduction cost coefficient is large， retailers always do not share information. When the emission reduction cost factor is below （above） the threshold， information sharing behavior will increase （decrease） the expectation of consumer surplus. In the extension part of the model， the combination of datum line method and historical datum line method is considered， and it is found that the conclusions obtained by the historical method are still applicable.Some management insights are offered based on our findings. i） When the emission reduction cost factor is moderate， information sharing will reduce the retailer's own profits. In this case， an information sharing subsidy mechanism should be formed in the supply chain， and the manufacturer should subsidize the retailer's information sharing behavior to achieve mutual benefit and win-win results. ii） Supply chain should be driven by big data technology， improve the accuracy of forecast information， promote information sharing， and help achieve the goal of “double carbon”. Figures and Tables | References | Related Articles | Metrics

Select

Carbon Price Interval Forecasting Study Based on the Hybrid Quantile and Time-Varying Weights Jujie Wang, Xin Zhang 2026, 34 (4):  298-308.  doi: 10.16381/j.cnki.issn1003-207x.2024.1451 Abstract ( 45 )   HTML ( 1 )   PDF (1999KB) ( 31 )   Save The accurate prediction of carbon prices provides valuable information to practitioners， supports market development， and help the dual-carbon target to be realized on schedule. Therefore， how to construct an efficient， accurate and stable carbon price prediction model has become a major research issue. However， carbon price interval prediction poses significant challenges due to the need to balance interval width and coverage. An innovative time-varying weighted ensemble interval prediction model， based on mixed quantile regression， is proposed to quantify the uncertainty of carbon prices. Two hybrid quantile models are constructed using advanced time-series deep learning algorithms for predicting carbon price intervals. To improve prediction precision， a multi-objective optimization algorithm is developed， dynamically assigning time-varying weights to optimize the upper and lower bounds of combined interval predictions， striking a balance between interval width and coverage. Historical carbon trading data from two representative Chinese pilots， sourced from the Wind database， are used for empirical validation. The results demonstrate that the proposed model performs robustly across various pilot applications， significantly enhancing the reliability and precision of prediction intervals. A valuable tool is provided for practitioners， enabling carbon price predictions with high coverage while effectively controlling interval width. Figures and Tables | References | Related Articles | Metrics

Select

High-Resolution Modeling of a New Power System with Source-Load Interactions under the Carbon Peaking and Carbon Neutrality Goals Bo Wang, Jingyun Li, Zhaohua Wang, Bin Lu, Bin Zhang 2026, 34 (4):  309-318.  doi: 10.16381/j.cnki.issn1003-207x.2023.1676 Abstract ( 46 )   HTML ( 3 )   PDF (2533KB) ( 25 )   Save Under carbon peaking and neutrality goals， coal power is being phased out. The rise of fluctuating renewable energy presents challenges in maintaining power system balance. And meeting peak load demands requires substantial investments in complementary facilities such as energy storage. However， long-term planning involving investments in backup capacity may result in excess power generation capacity. Research indicates that governments often increase power investments to address peak loads， despite their limited occurrence （5% or less annually）. Future research should focus on dynamically reducing peak loads through interactions between generation and load demand， while adapting to variable renewable energy output.Traditional power system capacity models neglect the evolving demand-side characteristics in new systems， inadequately capturing demand-side response interactions. Long-term models typically focus on reducing electricity consumption， missing the true source-demand dynamics. Short-term demand-side management often fails to impact capacity planning， potentially leading to the unnecessary planning of redundant backup capacity.The long-term capacity planning is combined with hourly operational simulations， creating a high-resolution model for the new power system with global optimization. It integrates demand-side management from electric vehicles， industrial， commercial， and residential sectors， thoroughly depicting the “generation-demand” interactive feedback mechanism. It relies on data from the China Statistical Yearbook， China Energy Statistical Yearbook， and Global Energy Monitor. Relevant parameters are extracted from reports by the State Grid Energy Research Institute and other expert studies.The findings indicate that， influenced by “generation-demand” interactions， China’s emission reduction trajectory has shifted from an initial rapid decline followed by a slower pace to a slower pace followed by accelerated reduction， aligning with a phased and steadily advancing transformation model. By 2060， China’s wind and solar capacity is expected to reach 5.75 to 6.48 billion kW， while coal power will maintain 300-500 million kW. Demand-side management is projected to replace 23% of energy storage and 38% of coal power capacity， reducing power system investment by 5.8 trillion CNY. Additionally， demand-side management will handle 9.5% of peak load pressures during peak hours. A planning methodology is proposed considering “generation-demand” interactions and demand-side management to guide the power system’s new development path， serving as a scientific reference for energy system transformation planning. Figures and Tables | References | Related Articles | Metrics

Select

Research on Differential Strategies Considering the Endurance and Advertising Advertising of New Energy Vehicles under the Dual Credit Policy Shuaicheng Lin, Guitao Zhang 2026, 34 (4):  319-329.  doi: 10.16381/j.cnki.issn1003-207x.2024.0653 Abstract ( 47 )   HTML ( 0 )   PDF (1158KB) ( 26 )   Save In the context of the decline of government subsidy， dual credit policy has become the main policy for promoting new energy vehicles （NEV）. BYD， NIO and Ideal are rapidly rising as auto companies， and large companies such as Huawei and Xiaomi are also shifting their focus to the research and development and production of NEV. So， what impact will the further popularization of dual credit policy have on the decision-making and revenue of NEV company？ It is necessary to conduct research from the perspective of the goodwill of NEV company from a dynamic perspective. In addition， NEV is new mode with uncertainties， and downstream dealer is likely to have fair concerns. What impact will their fair concerns have on the decision-making of NEV company？ To solve the above problems， under the background of dual credit policy， NEV company makes efforts to improve its endurance and advertising to enhance goodwill. Differential game and continuous dynamic programming theory are used to study the pricing strategy， endurance strategy and advertising strategy of the NEV supply chain under general and ideal models. The dynamic changes of automobile company’s goodwill and its performance are further given， and a two-way cost-sharing contract is used to coordinate the automobile supply chain. Among them， \stackrel{·}{G}\left(t\right)=\mu A\left(t\right)+\lambda B\left(t\right)-\delta G\left(t\right) shows the trajectory of goodwill change， \sigma p_c represents the credit calculation coefficient and the credit price， which is the profit obtained by the manufacturer from selling each unit of NEV； in addition， U_R={\pi }_R-\theta \left({\pi }_M-{\pi }_R\right) is used to express the fair concern behavior of the dealer. Through model solving and numerical analysis， it is found that： The popularization of dual credit policy has increased the utility of consumers purchasing NEV and contributed to the promotion of NEV. However， the government must pay attention to balance when formulating policy parameters to improve the effectiveness of the policy. NEV company should actively invest in battery life costs and advertising costs， and improve the efficiency of related investments. NEV with high battery life and efficient advertising will stimulate market demand and make NEV companies profitable， creating a virtuous cycle. Although the dealer’s fair concern behavior will increase its own profits， it will reduce the manufacturer’s profit and the total revenue of the NEV company. Within a certain feasible domain， a two-way cost-sharing contract can achieve Pareto improvement and make the total revenue of the automobile company reach a fair and neutral state， but the fairness concern behavior of dealer will always affect the normal profit distribution within the NEV company， which is not conducive to the long-term development of the company. Not only enriches dual credit policy and related theories in the field of NEV are enriched， but also theoretical guidance is provided for the government to formulate relevant policies and for automobile companies to formulate strategies in this context. Figures and Tables | References | Related Articles | Metrics

Select

Carbon Regulation and Firm’s Investment under the Perspective of Regional Carbon Migration Han Zhang, Hua Zhao, Zhiguo Li, Li Jiang 2026, 34 (4):  330-342.  doi: 10.16381/j.cnki.issn1003-207x.2024.1358 Abstract ( 34 )   HTML ( 0 )   PDF (1516KB) ( 38 )   Save The “East Data West Computing” project， which guides digital firms to deploy production capacity in the western region where factor costs are lower， which is accompanied by a shift in carbon emission areas and may exacerbate the growth of total carbon emissions. To address this issue， the government has set emission-reduction technology thresholds for new projects in the West. In order to investigate the impact of the government's access threshold regulation on controlling the growth of carbon emissions， a game model involving both a government and a firm is constructed. In this model， the government sets emission-reduction technology thresholds for new capacities in the West， while the firm chooses either to expand existing capacity in the East using high-emission technology or to move to the West， investing in improved emission-reduction technology to meet the threshold. The impact of the government’s emission-reduction technology thresholds on the firm’s capacity investment decision and corresponding production decision is analyzed， and how factors such as cost differences between the East and West regions and carbon price fluctuations in the East affect the government’s equilibrium threshold decision is discussed. It is found that when the government sets a lower threshold， the firm opts to build new capacity in the West and invest in emission-reduction technology just enough to meet the threshold. As the government’s threshold is decreased， the firm increases the capacity scale in the West and produce more in this region， resulting in a greater total increase in carbon emissions compared to expanding existing capacity in the East. The government’s threshold does not always increase with the widening cost differences between the regions； it is lowered when the differences are small. Moreover， the government’s threshold decreases as carbon price fluctuations in the East intensify； stabilizing these fluctuations is more beneficial for enhancing social welfare. The government’s optimal threshold increases with the strengthening of its environmental regulations. At a moderate level of regulatory strength， the government’s optimal technological threshold maximizes the benefit of the firm building new capacity in the West， while significantly reducing total carbon emissions. Figures and Tables | References | Related Articles | Metrics

Select

The Optimal Number of the Charging Station and Government Subsidies Considering the Electric Vehicle's Adoption Target Linghong Zhang, Yuwei Xia, Lilong Zhu 2026, 34 (4):  343-357.  doi: 10.16381/j.cnki.issn1003-207x.2024.0151 Abstract ( 56 )   HTML ( 0 )   PDF (1818KB) ( 35 )   Save Promoting the use of electric vehicles is the main way to achieve the “dual carbon” goal of the automotive industry， but inconvenient charging is the main factor hindering consumers from purchasing electric vehicles. Considering the impact of the number of Charging station on the demand for electric vehicles， and taking the target market share of electric vehicles as a constraint， the charging station builders are divided into two categories - the government and the third party. At the same time， two types of government subsidies - electric vehicle purchase subsidies and charging station construction subsidies are added. Using the Stackelberg game model， the optimal number of charging stations constructed by different charging station construction entities and the price of electric vehicles for vehicle enterprises are given. Then compare the impact of different government subsidies on the number of charging station construction and electric vehicle sales. Finally， based on practical numerical simulations， relevant suggestions for electric vehicle sales， charging station construction， and government subsidies were provided. The research shows that the number of Charging station built by the government and the third party is closely related to the sales target of electric vehicles and consumers' sensitivity to the number of Charging station. In the early stages of electric vehicle sales， the government should simultaneously build charging stations and implement subsidies； With the increase in sales of electric vehicles， the government can stop providing consumer subsidies but continue to participate in the construction and subsidy of charging stations， further withdraw from charging station subsidies， and ultimately completely withdraw from charging station construction. Figures and Tables | References | Related Articles | Metrics

Select

Evaluation of Energy Conservation and Emission Reduction Impacts of Fuel Vehicle Ban Policies: Insights from Lifecycle and Regional Analysis Feng Dong, Jiaojiao Sun, Shouyang Wang 2026, 34 (4):  358-368.  doi: 10.16381/j.cnki.issn1003-207x.2024.2252 Abstract ( 56 )   HTML ( 1 )   PDF (7683KB) ( 27 )   Save As countries around the world accelerate toward carbon neutrality， many have announced official timelines for phasing out internal combustion engine vehicles. As the world’s largest automobile market， China faces mounting pressure to decarbonize its transport sector and mitigate air pollution. However， significant regional disparities in power supply structures have introduced considerable uncertainty regarding the emission reduction benefits and implementation feasibility of the fuel-vehicle ban policies across different regions. Therefore， scientific identification of the phase-out paths of fuel vehicles and their synergistic environmental benefits in different regions of China is essential for achieving low-carbon transport transitions and regionally targeted emission reductions. It focuses on six major power grid regions in China and systematically assesses the optimal timing and environmental impacts of implementing fuel vehicle bans. Based on life-cycle operating cost （LCOD） analysis， and informed by China’s automotive development trajectory and international electric vehicle （EV） adoption experience， potential policy implementation time points across regions is determined. Accordingly， it establishes four policy scenarios of the baseline （BAU）， the ban on fuel vehicle （BFV）， the promotion of clean energy power （PCEP） and the hybrid scenario （BFV+PCEP）. A Long-range Energy Alternatives Planning （LEAP） model is constructed to simulate the regional energy consumption and environmental impacts under different scenarios from 2021 to 2050， incorporating fuel life-cycle emissions across multiple passenger vehicle technologies. The results indicate that implementing fuel vehicle bans in conjunction with power structure optimization can significantly reduce energy demand and achieve synergistic reductions in CO2， CO， and NOx emissions across all regions， with earlier implementation yielding more substantial benefits. However， in regions with high reliance on coal-fired power， such as East， North， and Northwest China， the policy may increase SO2 and PM emissions in the medium to long term， particularly by 2040 and 2050， resulting in net negative environmental outcomes. Thus， accelerating coal power decarbonization is crucial to avoid the “carbon reduction but pollution increase” dilemma.Model input data are primarily drawn from historical records on regional vehicle ownership， the 14th Five-Year Plans， EV penetration forecasts， China’s national greenhouse gas inventory， and pollutant emission factor databases， ensuring both temporal relevance and regional representativeness. Regional timelines are identified for fuel vehicle bans， the energy and environmental co-benefits are quantified under different policy combinations， and an integrated “ban-plus-clean-electricity” transition pathway is proposed. The findings provide theoretical and practical insights to guide China’s regionally differentiated， phased transition toward low-carbon transport and energy systems. Figures and Tables | References | Related Articles | Metrics