China’s capital market has made multiple efforts to introduce financial derivatives. In 1992， the Shanghai and Shenzhen Stock Exchanges launched the “Dafeyue Warrant” and the “Bao’an Warrant，” respectively， marking the initial introduction of option-based financial instruments in China. However， due to excessive speculation， the China Securities Regulatory Commission （CSRC） suspended warrant trading in June 1996. It was not until 2005， following the progress of the split-share structure reform， that warrants were reintroduced as part of the reform process. Academic research and regulatory attention have primarily focused on the causes and economic implications of speculative bubbles in China’s warrants market. It aims to contribute to the existing literature by addressing a critical yet underexplored question： whether the warrants market， within the framework of the split-share structure reform， successfully protected the interests of holders of tradable shares？

The impact of the reform on 28 publicly listed companies is investigated that utilized warrants as a form of consideration. By leveraging both public market data and proprietary brokerage information， the fluctuations in public shareholders’ wealth before and after the reform are assessed and performance disparities among different categories of tradable shareholders are explored to evaluate the role of reform-related warrants. Specifically， public market data are employed to compute the adjusted price changes of individual stocks pre- and post-reform， thereby gauging shifts in the wealth of tradable shareholders. An event study methodology is then utilized to determine whether the issuance of warrants resulted in excess returns for tradable shareholders， focusing on abnormal returns during pivotal events such as the announcement of the reform， its implementation， and the listing of warrants. Subsequently， proprietary brokerage data is analyzed to directly assess the gross profits of tradable shareholders during the reform period. This empirical analysis centers on shareholders who were allocated warrants as consideration in the split-share structure reform， specifically investors who received warrants in the primary market for free. By examining account-level trading data for both stocks and warrants， the total profits accrued by these tradable shareholders， encompassing gains from both warrants and the underlying stocks are calculated.

The analysis of public market data reveals that， following the split-share structure reform， the adjusted prices of individual stocks for the 28 companies examined increased by an average of over 10% compared to the period preceding the reform announcement. Account-level data further indicates that approximately 90% of tradable shareholders who held stocks before the reform announcement and received warrants as consideration realized positive returns. It is suggested that the primary market for warrants effectively provided compensation to tradable shareholders. However， previous study in the literature also suggests that， due to shortcomings in secondary market trading mechanisms and inadequate management of investor suitability， speculative bubbles emerged following the listing of the warrants. These bubbles had a detrimental effect on investor wealth and market stability. Complementing the existing literature， a direct analysis of the returns is offered for tradable shareholders who received warrants in the primary market for free， providing robust empirical evidence regarding the compensatory role of warrants in the split-share structure reform. The focus of this paper is shifted from the market bubbles to the role of warrants within the split-share structure reform， highlighting the positive impact that warrants had on the reform process and affirming the potential positive role of financial derivatives in the domain of corporate governance.

With the continuous maturation of capital markets， financialization has become a significant characteristic of modern corporate operations. The widespread use of the internet and social media has increasingly highlighted the role of small and medium-sized shareholders' online expressions in corporate financialization. As the volume of online posts by these shareholders increases， investor sentiment is transmitted to company management through online expressions， thereby exerting pressure on the management from small and medium-sized shareholders. Although the increase in financial asset investments is accompanied by higher risks， it can effectively enhance the short-term excess returns of enterprises. The pressure from small and medium-sized shareholders makes the management more inclined to allocate financial assets， thereby exacerbating corporate financialization.

To verify the above hypothesis， non-financial firms listed on the A-share market from 2008 to 2022 are used as samples and empirically examines the impact of small and medium-sized shareholders' online expressions on corporate financialization using posting data from stock forums. Most Chinese companies completed the split-share structure reform at the beginning of 2007， and since 2008， the volume of posts on stock forums has stabilized， gradually highlighting its impact on the stock market. Therefore， the sample of stocks in this paper includes all A-share listed companies， covering the period from 2008 to 2022. Additionally， the online expressions of small and medium-sized shareholders are measured using data on posts， comments， and readings from the Eastmoney stock forum available on the China Research Data Service Platform （CNRDS）. Financial data， executive characteristics， board data， and shareholder holding data are all sourced from the CNRDS database. News data and analyst report data come from the China Stock Market & Accounting Research （CSMAR） database， while market data for the stock market is obtained from the Wind database.

It is found that online expressions convey pressure to corporate management by small and medium-sized shareholders， incentivizing managers to reallocate resources toward financial assets that are more likely to generate short-term excess returns， thereby intensifying the degree of corporate financialization. Furthermore， this positive relationship between online expressions and corporate financialization is more pronounced under greater performance pressure， competitive pressure， and weaker external regulation. The impact of small and medium-sized shareholders' online expressions on corporate financialization is discussed from the perspective of social media's role in corporate governance， which has significant positive implications for the regulation of social media public opinion and the healthy development of corporate financialization.

The power-rationing event implemented in many provinces in 2021 is the first major event that China has encountered in the process of economic green and low-carbon transformation under the background of the “double carbon” strategy， and it can be regarded as a natural experiment to affect the Chinese stock market. Will the power-rationing event have a significant impact on the Chinese stock market？ How will investors choose under the impact of power-rationing？ Will this event change people’s confidence in China’s economic development？ To answer these important questions， 48 trading days from August 1st， 2021， to October 15th， 2021， are selected as the sample period to study the impact of the power-rationing event on China's A-share market by using the event analysis model， time-varying Difference-in-Differences and Difference-in-Differences-in-Differences models. The specific empirical work and the empirical conclusions in this article are as follows： First， based on the event analysis model， the impact of power rationing on different industries in power-restricted provinces is examined. The results show that after companies in high-energy-intensity industries are affected by power rationing， the stock price change， abnormal return， and institutional net buying orders decrease significantly； at the same time， companies in non-high-energy-intensity industries in power-restricted provinces are obviously affected by power rationing. However， the specific direction of the impact is not consistent with companies in high-energy-intensity industries. Second， based on the time-varying Difference-in-Differences model， whether the power rationing event had a significant impact on the A-share listed companies in power-restricted provinces is tested. The results show that the power-rationing event has a significant positive impact on the stock price change and abnormal return of companies located in power-restricted provinces， which are mainly driven by the rise in stock in non-high-energy-intensity industries in power-restricted provinces. Further testing of the dynamic effects shows that the power rationing had no long-term impact on the stock market. Third， based on the Difference-in-Differences-in-Differences model， the heterogeneity of investor buying behavior in the impact of power rationing on the stock market is examined. The results show that the positive short-term impact of the events on the stock price change and abnormal return in the restricted provinces is mainly driven by the investment behaviors of institutional investors and big investors. The events make these investors increase the net buy of stocks in non-high-energy-intensity industries in the power-restricted provinces. To sum up， the research in this article shows that the securities market has maintained smooth operation under the impact of power rationing， reflecting investors' stable expectations for the long-term optimism of the securities market and their firm confidence in the green transformation under the dual-carbon strategy. To a certain extent， it proves that China's economy has strong resilience in dealing with shocks.

Food plays a critical role in human survival and development， and has always been the global focus. Recently， natural and social events such as rising energy prices， frequent extreme weather， the severe COVID-19， and the Russian-Ukrainian conflict have jointly pushed up the food prices. Keeping food price staying in a reasonable range and preventing extreme fluctuations are crucial to ensure national food security. It is of strategic significance to pay attention to the stable operation of the grain futures market， extreme risk management and early warning.By utilizing the prices of grain futures in the US （CBOT rice， CBOT wheat， CBOT corn） and the Chinese markets （ZCE rice， ZCE wheat， DCE corn）， the data are described and visualized， and the clustering characteristics of extreme risks are uncovered.Then event flow models are employed to model the occurring process of extreme fluctuations by means of the ACD-POT model and Hawkes-POT model and derive the formula of extreme risk measures （VaR）.Finally， three accuracy test methods are used to evaluate the fitting and prediction effects of the benchmark models （POT， EGARCH-POT）， the event flow models （ACD-POT model， Hawkes-POT model）， and the ensemble model.The results show that event flow models are able to capture the fat-tailed and clustering characteristics of extreme price fluctuations and fits the price data very well. In addition， further comparison finds that the in-sample and out-of-sample accuracy of Hawkes-POT models is better than other models， and the power-law Hawkes-POT model performs the best. The results not only deepen our understanding of the occurring pattern of extreme price fluctuations in grain futures， but also provide a new avenue for risk management and market supervision in food markets.

In most existing models of capital structure， the dynamics of cash flow/asset value are known to bondinvestors. However， there are three good reasons for us to think about departures from this assumption. First， in practice， it is typically difficult for bond investors to observe a firm's assets directly due to delayed accounting reports. Second， the Ellsberg paradox and related experimental evidence demonstrate that people deal with risk and ambiguity in different ways. Risk refers to the case where the probability distribution over the state of the world is known， while ambiguity refers to the situation where the distribution itself may be unknown to the economic agents. Finally， as Hansen and Sargent （2001） pointed out， economic agents believe that the observed economic data come from a set of unspecified models. Concerns about model misspecification make a decision maker desire robust decision rules.How model uncertainty distorts a firm's leverage decision is investigated. The robust control method is adopted and an alternative explanation is provided for the debt conservation puzzle. The standard capital structure model implies the optimal leverage ratio should be 70%， while the average leverage ratio in the data is only 25%. To resolve this puzzle， it is assumed the bond investors are ambiguous averse about the firm's cash flow dynamics. Since the existence of model uncertainty reducesthe debt value and increases the cost of debt financing， the firm would issue less debt and choose a lower leverage. Quantitatively， the optimal leverage ratio in our model is 25.1%， which is consistent with the data.

E-commerce platform plays a key role in alleviating the financing difficulties of small and medium-sized enterprises in the digital economy era. Meanwhile， in a complex and ever-changing development environment， resilience factors have a significant impact on the stability of capital flow and the selection of financing models for platform supply chains. It systematically explores financing supply and selection strategies in the platform supply chain， conflicts between strategies， and proposes corresponding conflict resolution mechanisms in this study. A decision-making system consisting of an e-commerce platform， a manufacturer， and a retailer is constructed that faces the risk of demand interruption and has a certain resilience to resist risks. The e-commerce platform provides digital credit financing and guarantee credit financing for the financially constrained retailer. Meanwhile， the manufacturer provides trade credit financing to the retailer. First， it explores the optimal financing supply strategy of the e-commerce platform and the optimal financing selection strategy of the retailer. Second， the strategic conflict between financing supply and financing choice is analyzed. Finally， the interest rate dynamic adjustment mechanism is designed to resolve the conflict between financing supply and demand.The results show that when the initial capital of the retailer is low and the production cost of the products is high， the e-commerce platform can obtain the highest profit in trade credit financing. When the initial capital of the retailer is low and the production cost of the product is low， the platform prefers to provide digital credit financing and guarantee credit financing when the resilience is high and low， respectively. When the initial capital of the retailer is high， the best choice for the e-commerce platform is trade credit financing. Retailers with low， medium and high initial capital prefer to select trade credit， guarantee credit and digital credit financing models respectively. The dynamic interest rate adjustment mechanism designed in this research can effectively alleviate the financing choice conflicts between e-commerce platform and retailer and achieve win-win situation for both sides. Methodological tools and strategic recommendations for selecting financing models and resolving financing conflicts in the platform supply chain with resilience considerations are provided in this study.

Reputation management is crucial to e-commerce platforms’ healthy and sustainable development. A positive reputation is key to these platforms’ competitive advantage and dynamic competitiveness； however， platform sellers are their main source of reputation crisis. E-commerce platforms focus on regulating sellers through a credit rating system to ensure reputation management. The centralization of the traditional credit rating system provides objective conditions for sellers to alter their ratings and distort the supply of market information. Our research innovatively describes the game relationship between consumers and sellers to outline the role of consortium blockchain in reputation management and its accompanying mechanism. Findings reveal obvious principal–agent problems in the traditional centralized credit rating system， leading to a “lose–lose” situation among platforms， sellers， and consumers. The use of consortium blockchain technology can impede sellers’ opportunistic behavior to realize a “win-win” situation for all parties. Additionally， we analyze the impacts of using consortium blockchain technology along with major parameters （e.g.， profit margin and reputation sensitivity） on sellers’ welfare. Our conclusions are verified via numerical simulation. Results offer a novel managerial view of how e-commerce platforms can leverage consortium blockchain in reputation management as well as a solution for the development of high-end e-commerce.

Vaccination is one of the most effective measures to prevent and control epidemic events， and governments often use vaccination subsidies to increase residents’ willingness to be vaccinated. Given the university and the importance of the vaccination subsidy budget constraint， a decision approach for differentiated allocation of vaccination subsidies based on the subsidy effectiveness is designed to maximize its utility. The approach consists of two parts， first， by integrating the interpersonal contact characteristics of different age populations and the immunity enhancement of vaccination， an improved SIR-based epidemic dynamic model is constructed to simulate the local epidemic trend， if there is an imported new round of outbreak， under a certain subsidy allocation scheme. Then， taking into account the principles of allocation efficiency and allocation equity， a decision model for vaccination subsidy allocation is constructed based on the characteristics of different subpopulations to optimize the subsidy effect. Finally， a case study of the COVID-19 vaccination subsidy in Hubei province is conducted to verify the feasibility and effectiveness of the developed decision approach. The present research finds that （1） The vaccination subsidy can affect the development of epidemics by influencing the vaccination willingness of the population， and the optimal value of the subsidy budget exists in terms of delaying the peak time； （2） The frequency of interpersonal interactions is an important factor influencing the vaccination subsidy allocation scheme， and more subsidies should be allocated to populations with higher interpersonal interaction frequencies； （3） Consideration of the allocation fairness will lead to the reduction in the effectiveness of the epidemic prevention and should be taken into consideration in practice.

In the context of globalization， as “non-core business outsourcing” and “lean production” have become industry trends， while the supply chain has obtained lower operating costs， it is also more likely to be affected by sudden events and encounter supply disruption. Once the supply disruption occurs， enterprises in the supply chain will suffer huge losses， and if the appropriate procurement strategy is adopted in advance， it will effectively reduce the losses caused by the disruption. At the same time， consumers have different preferences for components from different sources and their assembled products， which will further affect the selection of procurement strategies for enterprises. Based on this， it focuses on a secondary supply chain system consisting of a high-quality supplier， a general supplier， and a manufacturer. Considering that both suppliers have disruption risks and consumers have different preferences for core components from different sources and their assembled products， game models for single source procurement and multiple source procurement are constructed. Based on solving the optimal equilibrium solution， comparative analysis and parameter sensitivity analysis are conducted.The results of the study show that compared to single source procurement， dual source procurement reduces the procurement costs of two core components and the sales prices of their products， but the procurement volume of the high-quality core component is reduced when the probability of disruption for the general supplier is higher， and the procurement volume of the general core component is reduced when the value perception factor is higher， or the value perception factor and the probability of disruption for the general supplier are both lower； Dual source procurement is always the best choice for the manufacturer， but the high-quality supplier tends to accept single source procurement when its own disruption probability is lower， and the general supplier tends to accept single source procurement when the value perception factor is higher， or the value perception factor and its own disruption probability are both lower； In the dual source procurement strategy， the manufacturer will always set a higher sales price for the high-quality core component product， but when the value perception factor is lower and the probability of disruption for the high-quality supplier is higher， the wholesale price of the high-quality core component is lower than that of the general core component， and the procurement volume of the high-quality core component is also higher； The increase in both the value perception factor and the probability of disruption will lead to an increase in the wholesale prices of both types of core components and the sales prices of their products； if both suppliers are able to supply normally， as the probability of disruption for one supplier increases， the manufacturer will increase the procurement volume of its core component and reduce the procurement volume of the core component from the other supplier； When the disruption probability of one supplier is less than a certain threshold， the manufacturer will reduce the procurement volume of the core component from the other supplier as the value perception factor increases.

Online consumer reviews in the online channel have an essential impact on consumers’ purchase decisions and have attracted the attention of manufacturers. The impact of the manufacturer’s online review strategy on each retailer’s pricing decisions and the manufacturer’s quality decision in the presence of horizontal competition between the online and offline retailers is analyzed. A multi-stage Stackelberg game is formulated in which the manufacturer acts as the leader and the retailers are the followers. Additionally， horizontal competition is measured as the price difference between the online and the offline retailers. The results show that the online review strategy will reduce （or increase） the retail price in the offline （or online） channel during the continuous sales phase， widening the gap between the retail prices in the online channel and the offline channel. Therefore， the intensity of horizontal competition decreases. Moreover， cost efficiency， the manufacturer’s bargaining power， and consumers’ perceived level of quality are the three influencing factors of the manufacturer’s optimal quality. Under the constraint of ensuring that the equilibrium result exists and is positive， the results of the numerical analysis suggest that the manufacturer’s decision of online review strategy depends on the direction and extent of inter-channel consumer shifts. A gap in the existing literature is filled by considering that the role of online consumer reviews differs between the online and the offline channels in a horizontal competitive environment.

In recent years， there are more natural disasters occurring in China， which lead to more demand of relief supplies. Facing different scenarios of disasters， the decision-maker has to select suitable modelling tools to figure out the optimal solution based on the practical situation. However， recent modelling studies in emergency logistics tend to ignore the feeling of victims and are far from practice. To solve these problems， relevant literature and practical operations in emergency logistics are studied. The deprivation cost is suggeted as the measurement， new objective functions and constraints are considered based on scenarios of disasters， and it is pointed out that different types of decision-makers such as governments and non-governmental organizations should be distinguished. Based on them， the emergency logistics decision-making models are constructed in different scenarios of disasters and their application is shown through simple numerical experiment. It is shown that emergency logistics modelling needs to identify and distinguish different scenarios of disasters and types of decision-makers， and make trade-offs between different objective functions.

Volatility matrix is the basis and core of quantitative analysis of many high-dimensional financial activities. Multivariate volatility models mainly include multivariate GARCH model， high-frequency volatility matrix model and network volatility model. There are three main difficulties in their modeling process， including the problem of dimensional disaster， meeting the requirements of the mathematical and empirical characteristics of volatility matrix， and the model has a certain economic interpretation. In order to alleviate these three kinds of problems， the integration of two kinds of modeling ideas is realized by utilizing the advantages of high frequency volatility model， which contains rich volatility information and flexible modeling， and the advantages of network volatility model， which has economic significance. Based on the traditional high-frequency CAW model， the conduction structure is presupposed between fluctuations by introducing positive and negative correlation networks with the characteristics of risk accumulation and risk dispersion， and further a two-layer network volatility matrix model （DNVM model） is established based on the heterogeneous market hypothesis. Under this framework， the network effects and spillover effects of asset volatility conduction along different network paths are derived. The model in this paper realizes the structural dimension reduction of parameters and the improvement of the economic significance of the model.The empirical research based on the 50 constituent stocks of Shanghai Stock Exchange in China shows that the correlation between assets is heterogeneous in time dimension， and there is significant positive and negative asymmetry in cross section. The volatility transmission between assets has a significant network effect， and the network effect is stronger in the short term than in the medium and long term. The short-term volatility spillover effect between assets is stronger than that in the medium and long term， and the positive volatility spillover effect is stronger than the negative volatility spillover effect on the whole， and the short-term positive volatility spillover plays a dominant role. Both the statistical prediction effect and the minimum variance portfolio strategy show that the DNVM model is better than the non-network high frequency model and the low frequency multivariate GARCH model such as BEKK model. DNVM model provides a new guidance and research method for the study of high-dimensional volatility matrix prediction of financial market， and also provides a new perspective for the expansion of traditional network econometric model based on vector data to matrix data.

In response to the national policy guideline of developing and improving the market allocation of data elements， an innovative market allocation model of data elements is proposed from the perspective of data trading platforms. First， the unique content of the data trading system is analyzed in terms of data products and their characteristics， data trading system and its components， as well as risks in data trading. Then， existing studies related to the data trading system are reviewed to sort out their contributions and shortcomings. On this basis， four sorts of scientific issues required to be solved urgently in future research on the allocation model of data trading system based on data trading platforms are distilled： （1） construction of the supply chain of data products and identification of price influencing factors； （2） the price formation mechanism for the design of various types of data products； （3） design of the revenue sharing mechanism and incentive mechanism for data trading system； and （4） establishment of the quality and security monitoring system of data products for the whole chain. Finally， possible research solutions for the above scientific problems are proposed， providing some reference and direction for future research.

The super-efficiency DEA model is widely used because it can measure the efficiency of effective decision-making units （DMUs）. However， there are still several key problems that have not been well addressed. （1） The uniform evaluation criteria. The super-efficiency DEA model assumes the DMU being evaluated should be excluded from the production possibility set， which indicates that the evaluation standard （Production Frontier） is different for each DMU， so whether the measurement results under different standards are comparable requires new theoretical support. （2） The lack of theoretical basis in economics. Due to the DMU being evaluated is excluded from the production possibility set， the super-efficiency production frontier constructed by other DMUs is an incomplete “production function”， and such a frontier lacks economic implications. If these two problems can't be solved well， then the rationality of the super-efficiency DEA model will never be guaranteed. （3） DEA production frontiers can be achieved in theory， but the difficulty of achieving these production activities is very different. From the existing literature of DEA methods， there are still few methods to research the difficulty of implementing production frontiers. In order to solve the above problems， the basic ideas of the generalized DEA method is refered to. Firstly， a strong ordinary axiom is proposed to give the construction method of robust production frontiers. Next， a modified DEA model （RSD） based on the robust production frontiers is proposed， and it is proven that the efficiency value given by the model （RSD） is equal to the super-efficiency of DMUs， and a new explanation is given for the economic meaning of DEA super-efficiency by the production theory. Furthermore， the concept of robust projection based on the model （RSD） is proposed， and a calculation formula for the difficulty of implementing DEA projection is provided. Finally， the proposed method and the original method are applied to compare and analyze the technological innovation efficiency of China's biomedical listed enterprises.According to empirical analysis， it is found that the proposed method explains the economic meaning of the radial super-efficiency DEA model， clarifies its axiom system， and makes the efficiency measurement results obtained based on the modified model more reliable and the projection improvement goal more feasible. In addition， the proposed method in this paper not only further improves the super-efficiency DEA effectiveness measurement method， but also provides theoretical support for the application of this method in economic management problems. All the data are derived from CSMAR， CNRDS， WIND database and the annual report of listed companies from CNINFO.

Supply chain finance can help small and medium-sized enterprises（SMEs） to improve the shortage of funds. Dynamic discounting（DD） as an innovative model of supply chain finance， can promote the coverage of supply chain funds to more SMEs. DD program refers to a dynamic settlement that utilizes the visibility of the trade process provided by the platform and enables suppliers and buyers to reach an agreement on a fixed daily discount on the nominal invoice value， finally realizing a “customized” application of early payment discounts. Comparing dynamic discounting programs based on business chain， hub network and digital ecology， the influence mechanism of this new supply chain finance model is revealed to improve funds shortage of more than one small and medium-sized enterprises. The main decision parameters in this paper include dynamic discount rate dd， early payment time ep（t）， and Working capital liq. There are differences in decision parameters in different stages. The decision parameters in the business chain mode are dd and ep（t）， and the decision method is bargaining game； the decision parameter in the hub network mode is dd and liq， and the decision method is expected profit distribution； the decision parameter in the digital ecological mode is dd， ep（t）and liq， the decision method is based on the buyer's benchmark profit， supplier's best profit. It is found that in the first two stages， discount rate are relatively close， while those in digital ecological mode have an inflection point when the buyer’s working capital cost exceeds the bank financing cost. In addition， the amount of working capital will affect participants’ profits， and the impact in hub network mode is higher than in digital ecological mode. Moreover， there are differences in the optimal early payment time among the three stages， in business chain mode the optimal early payment period will not change with the discount rate and working capital， but in digital ecological mode this value is related to both the two parameters， in hub network mode there is no optimal early payment period. Finally， with the dynamic discounting program developing to a higher stage， participants can enjoy a more profitable multi-lateral relationships， and efficiency of the supply chain finance has also been significantly improved.

A Stackelberg dynamic game model is constructed to obtain the equilibrium decisions of wholesale price， retail price and product green level when the manufacturer are overconfident and capital-constrained. Under three financing models， the optimal decisions are achieved by backward induction. Using impact analysis， the impact of overconfidence on the decisions and profits is explored， and the financing strategies under different scenarios are obtained through comparative analysis. Numerical simulations are used to verify the theoretical results.Firstly， the game model under three financing models （bank loan， retailer advance and equity financing） is constructed to analyse the supply chain operation decision of each financing model considering overconfidence and to analyse the impact of overconfidence on the operation decision； Secondly， the financing strategy is obtained by comparing the manufacturer's profit under the three financing models and the impacts of overconfidence on the financing strategy are investigated. Research Results The choice of manufacturer financing strategy is associated with the level of overconfidence and the proportion of equity dividends， which differs from the finding that trade credit is often the best choice in previous studies of rational financing for decision makers； Overconfidence of decision makers is also found to affect operational decisions in green supply chains differently under different financing models， with overconfidence of the dominant supply chain player damaging the interests of other participants. The operational decisions and financing strategies considering the makers’ overconfidence are significantly different from the rational state， moreover， the operational decisions and benefits of followers are influenced by the overconfidence of the dominant person. Therefore， decision makers in the dominant position should take appropriate measures to eliminate the negative impact of overconfidence on supply chain partners， otherwise the imbalance of supply chain benefits caused by the power structure may lead to the breakdown of the partnership within the supply chain. Although supply chain operations and financing decisions when firms are short of capital have attracted much attention， the existing financing research related to behavior preferences of decision-makers still is relatively weak. There exists some literature considering the effects of risk-averse and fairness concerns， but less studies on financing and operational decisions with overconfidence. In fact， many firms are overly optimistic about the market status and their own operations state， and make irrational operational and financing decisions， which resulting in a failure to optimize the efficiency of supply chain operations， or even to go bankrupt. The gap between the operations as well as financing decisions and overconfidence preference is filled by investigating the optimal financing stratgy and pricing decisions of overconfident anufacturers with capital-constrained in a green supply chain. In a two-echelon green supply chain comprised of a dominant and capital-constrained manufacturer and the follower retailers， which manufacturers are overconfident in the market demand for their green products， while retailers have sufficient funds. Manufacturers have three financing ways： bank credit， trade credit and equity financing. The pricing and financing strategies of the supply chain are examined under the three financing models and the impact of overconfidence on operational decisions and the choice of financing strategies is explored under manufacturers’ overconfidence.

China has become an aging society and the pension issue has become a focus of national concern. With the increase of the number of elderly people， traditional family support mode has encountered many problems， such as family size shrinking and birth rate declining. Institutional pension is the key to solve the above problems. However， the nursing resources in nursing institutions are in short supply， especially in public institutions. Therefore， it is necessary to improve the utilization rate of nursing resources by the effective matching of elderly people and caregivers. To address this issue， a two-sided matching decision-making method is proposed based on preference-approval structure and prospect theory considering different opinions of different decision makers on attribute weights and the psychological behavior of bounded rationality. Firstly， a fusion model for aggregating preference-approval structures information from two-sided decision makers for attributes is proposed. Then， prospect theory is used to construct a comprehensive perceived utility model of the two-sided agents based on the new method of constructing individual reference points and social reference points of both agents. And a two-sided matching method with the objective of utility maximization is constructed. Finally， a case of matching elder people with caregivers in an elderly care institution is used to illustrate the effectiveness and feasibility of the proposed method. It can be seen through case analysis that our proposed method can effectively solve the problem of the effective matching of elderly people and caregivers.

Since its implementation in 2009， China's hierarchical medical system has faced the dilemma of structural resource imbalance： Grade 3A hospitals （China’s highest-ranked medical facilities）， accounting for only 7.6% of the total number of hospitals， bear nearly 50% of the patient visits， operating under prolonged high load； while the utilization rate of primary medical institutions （such as community hospitals） remains below 60%. Due to information asymmetry and a lack of trust in community hospitals， patients with minor illnesses flock to Grade 3A hospitals， exacerbating the issues of “difficult and expensive access to healthcare”. Internet of Things （IoT） technology offers a new pathway to optimize medical resource allocation and enhance the efficiency of hierarchical diagnosis and treatment. However， its investment decisions require systematic analysis of the interactive effects of patient behavior choices， hospital competitive strategies， and policy constraints.The core problem addressed in this paper is， within a two-level referral system consisting of Grade 3A hospitals and community hospitals， how can IoT investment by community hospitals alter patient choice of healthcare providers， alleviate congestion in Grade 3A hospitals， and simultaneously optimize the pricing strategy of Grade 3A hospitals to achieve the goals of hierarchical medical care.Achieves Hierarchical Care　IoT supports information sharing and a two-way referral system， optimizing resource matching.The IoT investment level of community hospitals is a key lever for the success of hierarchical medical care. When investment is high， it creates a virtuous cycle of "patient diversion → Grade 3A hospital price reduction → decrease in both cost and waiting". Conversely， the hierarchical system fails. The government needs to constrain Grade 3A hospital pricing via guided prices and incentivize IoT construction in community hospitals. Future research could explore cross-hospital cost-sharing mechanisms and empirical validation.

The integration of medical care and elderly care is an important elderly care model to alleviate the shortage of medical and elderly care resources in China's communities. Therefore， activating the limited medical and elderly care resources is the key to improving the quality of elderly care services. It focuses on the problem of optimizing the scheduling of service personnel in the dynamic medical and elderly care demand scenarios of community elders for this paper. By constructing a multi-agent perturbation measurement model based on prospect theory， with the maximization of the comprehensive prospect value of community institutions， service personnel， and elders as the perturbation optimization objective， a multi-objective interference management optimization model is established. An improved Grey Wolf Optimization algorithm （IGWO） embedded with genetic operators is innovatively proposed. Finally， the effectiveness of the model and algorithm in this paper is verified through a standard case set. The results show that the non-inferior solution set obtained by the improved Grey Wolf Optimization algorithm embedded with genetic operators is significantly superior to the Grey Wolf Optimization algorithm （GWO） and the multi-objective genetic algorithm （NSGA-II） in terms of distribution， dominance， and convergence indicators. A decision-making framework is provided that balances efficiency and fairness for the scheduling of medical and elderly care resources in dynamic environments. The proposed behavioral modeling method expands the application boundaries of traditional operations research in the field of public services， and the algorithm innovation provides a new tool for solving complex scheduling problems.

In recent years， Electric Vehicles （EVs） have been widely spread and used in logistic distribution. Due to the fact that EV route planning is directed associated with time-varying properties of energy consumption and traffic conditions， the Time Dependent Electric Vehicle Routing Problem with Nonlinear Energy Consumption （TDEVRPNEC） deserves deeper study. The TDEVRPNEC discussed in this paper involves set constraints， including （1） distribution center locations， （2） a homogeneous EV fleet， （3） customers' number， locations， needs， time windows， and （4） time-varying speeds on several time sections. The optimal plan is expected to realize the goal of total cost minimization under the premise of satisfying customer' expectations， i.e.， needs and time windows， by means of reasonable departure time choice， optimal charging strategy and vehicle routing optimization. Specifically， the total cost includes energy consumption cost， charging time cost， usage-based time cost and fixed dispatch cost. In the design， a specific energy consumption rate function is firstly applied to calculate total rate of energy consumption during trips. And then， a partial charging strategy is proposed with special consideration of the influence of time-varying speed on charging capacity. Based on the fact of time-varying traffic conditions on different road sections， a road segment-based vehicle travel time calculation method is proposed. Finally， a TDEVRPNEC optimization model is constructed. In order to solve the TDEVRPNEC model， an Improved Whale Optimization Algorithm （IWOA） is designed. The basic idea of the algorithm can be described as follows. （1） The crossover techniques in genetic algorithm is introduced to derive a refined position updating formula in the whale optimization algorithm， with application to the solution of discrete problems. （2） The greedy algorithm is used to construct initial solution for shortening the computing time of optimization. （3） The scheduling of vehicle departure times is proposed to avoid traffic congestion. （4） A very important place for the design of operators， i.e. reversal operator， energy exchange operator and charging station insertion operator， is provided to expand the solution space， avoid local optima and improve global search capability. Experimental simulation results show the following. From a perspective of model， the TDEVRPNEC model is verified to achieve multi-objective balancing among logistics cost， energy consumption， and travel time. From a perspective of strategy， the partial charging strategy is tested effective to shorten charging time， save logistics cost and reduce the energy consumption； the scheduling of vehicle departure times can contribute to traffic congestion avoidance and high logistics efficiency. From a perspective of algorithm， the proposed IWOA is confirmed to achieve fast convergence， better generalizability and optimality-seeking ability.

Sudden natural disasters pose a great threat to social stability and economic development， and effective emergency response is an effective means to reduce the threat. As the core link of emergency response， emergency resource allocation is a complex system engineering with multi-subject participation and cooperation under the environment of time crunch， resource shortage and budget pressure， which inevitably leads to conflicts between subjects. Therefore， resolving the subject conflict in the emergency resource allocation system is the prerequisite to ensure the smooth development of resource allocation activities. Most of the existing studies focus on the resolution of the subject conflict of emergency response， and the methods of the resolution of the subject conflict of emergency resource allocation need to be further studied. Therefore， in order to solve the problem of subject conflict resolution in emergency resource allocation， based on the three-layer， two-stage multi-period emergency resource allocation model， the representation of resource allocation subjects and their conflicts is given， and the subject conflict measurement method is defined. Secondly， considering that the purpose of subject conflict resolution is to improve the efficiency of resource allocation， the mapping relationship between the resource allocation objectives， such as minimizing rescue cost， maximizing life-saving utility and optimal fairness， and the interest demands of multi-subjects is constructed， and the corresponding theorems are given and the positive correlation between the optimization of the above objectives and the subject conflict resolution is proved. On this basis， a multi-period and multi-objective dynamic programming model for subject conflict resolution is established. Thirdly， based on the idea of approximate dynamic programming， a rollout algorithm based on greedy stochastic adaptive search algorithm is designed to solve the model， which can optimize the objectives of emergency resource allocation and resolve subject conflicts. Finally， based on the background of a huge earthquake in a certain place， an example of post-earthquake emergency resource allocation is constructed through systematic investigation， analysis and summary of the data obtained from statistical bulletin， relevant documents of the Ministry of Emergency Management of the People’s Republic of China， disaster reports， satellite maps and references， and the scientificity and effectiveness of the proposed method are verified by example analysis. The results of research show that the objective differences between subjects are the internal causes of subject conflicts， and the subject objective changes dynamically with the evolution of disaster scenarios in each period of resource allocation. The multi-period and multi-objective dynamic programming model built on the basis of the mapping relationship between the objective preference of resource allocation and the interest demands of multi-subjects can balance the objectives and demands of subjects in each period and multi-stages of resource allocation， achieve dynamic multi-subject consensus and optimal decision-making， effectively resolve the subject conflicts in the emergency resource allocation system， and in the process of resource allocation， the cost can be reasonably controlled， the life-saving utility and fairness can be taken into account， and the deprivation suffered by the victims can be significantly alleviated， which provides some reference for the further improvement of the theory and method of subject conflict resolution of emergency resource allocation.

The rise of social e-commerce has benefited from innovative marketing strategies. Among them， group-buying model is a precise marketing strategy that targets consumers with different characteristics. It has combined social attributes and differentiated pricing properties， making it the most effective marketing model today. An optimal decision-making model for merchants and platforms is provided under information asymmetry by comparing uniform pricing models with differential pricing models that provide group buying channels. The model has taken into account consumer heterogeneity and valuable consumer feature information to offer optimal group buying conditions， pricing， and scale. It has also categorized consumers into group-buying preference type and waiting aversion type， analyzing the impact of potential consumer scale， consumer composition， and differences in group formation costs on business decisions. After obtaining precise decision-making on optimal pricing combinations and group-buying scales for each mode， it is found that （1） when the proportion of waiting aversion consumers is slightly higher， providing group-buying options not only does not reduce brand value but also increases both the profits of businesses and platforms. （2） The more pronounced the heterogeneity of consumers， the more profit can be gained from opening group-buying channels. （3） Information on consumer proportions collected by platforms has a more significant impact on large-scale businesses. As the proportion of impatient consumers increases， businesses should widen pricing differentials and set larger group-buying scales.（4） Within a certain range， platforms can obtain additional profits through selling valuable information. However， when businesses have low market forecasting capabilities， choosing to share information with them for free can actually achieve a win-win situation for both the platform and the businesses.

It originates from the complexity of dual-channel supply chains for this study， encompassing both online direct sales by manufacturers and offline distribution through retailers. The focal point of investigation is the optimal pricing and service decision-making in the context of consumer “free-riding” behaviors， where consumers leverage offline channels for product assessment but turn to online channels for purchase， due to perceived differences in product quality across these channels.The core of the problem is encapsulated in a formulated model that considers variables such as the initial quality perception coefficient， the degree of competition between online and offline channels， and the extent of free-riding behavior. These variables are instrumental in determining the optimal strategies for pricing and service provision in a dual-channel supply chain setting. The model is structured to quantify the impact of these variables on decision-making processes and overall profitability within the supply chain.Methodologically， this research adopts a mixed approach that combines theoretical modeling with empirical data analysis. The theoretical framework is built upon the principles of supply chain management and consumer behavior theories， particularly focusing on how quality perception differences between online and offline channels influence consumer purchasing decisions.The primary findings reveal several key insights　Firstly， a high initial quality perception and a preference for offline services， coupled with channel competition， lead to product price premiums in both channels and enhance the service levels of offline retailers， thereby increasing the profitability of the entire supply chain system and its participants. Secondly， when the manufacturer's offline market share is relatively small and consumer free-riding behavior is prevalent， offline prices tend to be lower than online prices， which enhances the manufacturer's profitability and benefits the operation of the dual-channel supply chain. Conversely， when offline prices exceed online prices， it results in reduced profits for both the manufacturer and the supply chain system. Thirdly， as the degree of consumer free-riding increases， the price of products in the online direct sales channel tends to rise， while the price in the distribution channel decreases， consistently impacting the retailer's profit negatively.

The business model usually refers to the transaction relationship and interest connection between enterprises， which is the mode by which a company relies for survival and brings benefits. In reality， the application of different business models is extremely important for long-distance transportation of fresh agri-products. This is because the business model adopted by the supply or demand sides will involve a series of questions such as how to pay for the goods， who will pay for the freight and bear the risk of product loss during transportation， which will affect the decisions and profits of all members of the supply chain.A fresh agri-product supply chain composed of a supplier， a third-party logistics service provider（TPL）and a retailer is considered. The impact of two different business models， i.e.， FOB and CIF， on the supply chain’s decision-making and operations is explored by considering the products’ value loss during the long-distance transportation. Two non-cooperative game models are built under two business models in which the supplier and the TPL are the leaders， and the retailer is the follower. Under the FOB model， the retailer bears the freight and pays to the supplier according to the offshore products’ freshness， while under the CIF model， the supplier bears the freight and the retailer pays to the supplier according to the landed products’ freshness. It proves that the equilibrium solutions of the two business models exist and are unique. Comparing the interior-point equilibrium results under different business models， it is found compared with the CIF model， the FOB model is more conducive to motivate the TPL to improve the logistics service level， and motivate the supplier to reduce the wholesale price， whereas the retail price remains unchanged. The change of the business model won’t change the ratio of retailer’s profit to supplier’s profit， however， the ratio of the TPL’s profit to supplier’s profit under the CIF model is greater than that under the FOB model， which means the TPL will benefit from the CIF model from the perspective of fair allocation. Furthermore， an interesting counter intuitive result is found that all supply chain members prefer the FOB model， which means the FOB model is a Pareto improvement of the CIF model. This is due to the unilateral payment behavior of the freight under logistics outsourcing， which makes the CIF mode lack of incentive for the TPL to improve logistics service level. Then， a logistics service cost sharing contract between the supplier and the TPL is introduced， which can effectively motivate the TPL to improve logistics service level. Further， through the analysis of examples， it is found there exists a specific range of parameters， which can make the CIF model to be a Pareto improvement of FOB model.The research enriches the research results of fresh agri-product supply chain operation management， and also provides theoretical references for exploring the selection of fresh agricultural product supply chain business models in long-distance transportation processes.

Under the strict regulation of modern anti-monopoly policy， some large retailers commonly reach virtual bargaining through “unspoken agreement” and horizontal bargaining mechanisms to achieve benefits for both parties. However， such an agreement is reached through ideology and has no concrete proof of monopoly. Furthermore， virtual bargaining is not only influenced by its own factors， but also by the strategies of upstream members in the supply chain，such as manufacturer's pricing strategies. The concealment of the retailers' virtual bargaining will make the government encounter a dilemma and hinder the high-quality development of manufacturing enterprise. Therefore， an oligopoly competitive supply chain with one powerful manufacturer and two weak retailers is considered as the research object. A model of retailer virtual bargaining mechanism is built and the effects of retailer virtual bargaining on the supply chain are analyzed as well as the effects of manufacturer pricing strategies and bargaining mechanisms on retailer virtual bargaining. The effects of retailer virtual bargaining on the profits of supply chain members and the entire supply chain under the manufacturer's discriminatory wholesale pricing strategy are analyzed. The findings suggest that retailers do not always have an incentive to achieve the virtual bargaining equilibrium， and it is closely related to the channel status. In particular， the virtual bargaining equilibrium can be achieved only when the channel status is comparable. When retailers engage in virtual bargaining， virtual bargaining benefits the retailers but hurts the manufacturer and the entire supply chain. The effects of manufacturer pricing strategies on retailer virtual bargaining are then investigated. It is uncovered that the virtual bargaining equilibrium has a smaller related with the wholesale pricing strategy. That is， when the channel status is comparable， retailers always have incentives to engage in VB regardless of the manufacturer's uniform wholesale pricing and discriminatory wholesale pricing strategies. The profits of two wholesale pricing strategies are further compared and it is found that the manufacturer prefers discriminatory wholesale pricing strategies， while the supply chains prefer uniform wholesale pricing strategies. This observation can be used to explain the reason why the government bans the manufacturer provides discriminatory wholesale pricing strategy. In the extension section， the effects of bargaining mechanisms on retailer virtual bargaining are investigated. It is found that when retailers bargaining with manufacturer， vertical Nash bargaining can undermine the disadvantageous degree of the virtual bargaining to the supply chain system under certain conditions， while vertical egalitarian bargaining do not work well， and may worsens the supply chain's double marginalization impact. The above findings can broaden the concept of tacit collusion and enrich the theoretical framework for supply chain management， as well as have practical significance in guiding the high-quality development of manufacturing enterprises and the design of the government's per-regulatory mechanism for anti-monopoly.

Emerging technology systems are having a broad and deep impact on the entire business community， and have also become a potential driving force for technology applications and technological innovation related to the retail industry. The retail industry often introduces emerging technologies such as blockchain and artificial intelligence to promote innovation and change in the industry， and technology providers also focus on providing differentiated technology solutions to meet the specific needs and technology application scenarios of retailers. However， not all enterprises are suitable for directly introducing new technologies， and adoption timing is crucial to the survival and development of enterprises. There are two important problems to solve： （1） Given the incentive to introduce emerging technologies， what strategies should competitive retailers choose to maximize their respective profits？ Are emerging technologies introduced at the same time as competitors or introduced sequentially？ （2） When retailers introduce emerging technologies sequentially， due to the existence of differentiated technology solutions from technology providers， what technology supply strategies do retailers that introduce emerging technologies first and those that introduce them later expect technology providers to provide for them respectively？ A dynamic multi-stage supply chain model including technology provider and two competitive retailers is constructed， exploring the strategic choice of technology introduction for retailers and the impact of technology provider's coping strategies on retailers' decision-making results. It is found that a long period of monopoly period can stimulate retailers to introduce new technology， and lower the technical service fee will encourage the latter to enter the market ahead of time， it has an incentive to shift strategic choices and introduce emerging technologies together with competitors. The technical service fee acts as a "management lever" that can strengthen or weaken the retailer's alliance. Under certain conditions， the technology provider can satisfy the strategic needs of both the former and the latter. Moreover， the span of the monopoly period acts as a “Time lever” that enables technology providers to achieve their profit maximization while encouraging retailers to choose different strategies.

With intense market competition and rapid product updates， price reduction promotions have emerged as a crucial strategy for merchants to gain market share and optimize inventory management. However， such promotions also foster consumer behavior characterized by strategic waiting. Increasingly， consumers are deferring purchases upon learning about price reductions， anticipating further price drops. This strategic behavior introduces demand uncertainty， resulting in challenges such as stock shortages and inventory overhang， significantly impacting business decisions and the profitability of supply chain participants. Consequently， the implementation of flexible pricing and ordering strategies becomes imperative for ensuring business profitability， while effectively addressing strategic consumer behavior remains a noteworthy challenge. In response， many retailers have adopted rapid response mechanisms to promptly adjust supply in accordance with market demand fluctuations， thereby enhancing order fulfillment capabilities. Research has demonstrated the substantial impact of rapid response strategies on pricing， inventory management， and overall profitability. Additionally， return guarantees can bolster consumer confidence， stimulate early purchases， and mitigate strategic waiting behavior. Returns can be classified as either defective or non-defective， with the latter constituting the majority. While offering return services encourages customer spending， it also results in an increased volume of returns. However， the existing related literature is mostly based on the assumption that decision makers are completely rational. Overconfidence is a common cognitive bias. Under stochastic market demand， the retailer may estimate demand excessively precisely， leading the decision to deviate from the rational state and affecting profits. Therefore， four scenarios considering the retailer' overconfidence and return policies under the rapid response mode are examined. It investigates the optimal pricing and ordering decisions of the retailer when confronted with strategic consumers， analyzes the influence of the retailer' overconfidence and return policies on its decisions and profit， and explores the interplay between overconfidence and return policies.It focuses on the interaction between a responsive online retailer and strategic consumers. Under stochastic market demand， the retailer orders the product before the sales period and conducts two sales stages： the first at regular prices and the second with price reductions. Strategic consumers aim to maximize intertemporal utility by strategically timing their purchases. At the start of the selling period， market demand information is updated， and the responsive retailer replenishes inventory if the initial stock cannot meet the demand for the first stage. Two scenarios are considered for the retailer： non-overconfidence and overconfidence， with overconfidence manifesting as excessively accurate estimation of market demand. Both retailer types have expectations aligned with actual market demand， but the overconfident retailer believes the stochastic demand has a lower variance. Considering the possibility of customer dissatisfaction with online purchases， two return policies are examined： allowing returns and not allowing returns. Models are established for four scenarios： RN （allowing returns with a rational retailer）， RO （allowing returns with an overconfident retailer）， NN （not allowing returns with a rational retailer）， and NO （not allowing returns with an overconfident retailer）.The models are solved using backward induction to determine the retailer's optimal pricing and ordering decisions. Subsequently， an analysis of the impact of parameters such as the level of overconfidence and customer satisfaction on the optimal outcomes in each scenario is conducted. The results across different scenarios are compared to explore the effects of overconfidence and the allowance of returns， as well as their interactions. Furthermore， numerical simulations are performed to further analyze the findings. The primary findings of this study are as follows： whether returns are allowed or not， the average of market demand affects the impact of overconfidence on the retailer’s pricing and ordering decisions， and overconfidence doesn't necessarily cut into retailers' profits. Whether the retailer is overconfident or national， the retail price is higher and the order quantity is lower when return is allowed than those when return is not allowed， and the retailer can earn more profit in the case returns are allowed than in the case returns are not allowed only the satisfaction rate of consumers meets certain conditions. Also， allowing consumers to return goods amplifies the influence of overconfidence on retailers to deviate from optimal pricing and order quantity decisions. These can provide meaningful references for retailers and consumers.

Power battery is the most core and highest value of the key components of new energy vehicles， and the choice of power battery procurement channels is an important strategy for new energy vehicle firms. Based on game theory and taking the electric vehicle industry as the research object， the influence of product substitutability on production strategy and power battery purchasing strategy of electric vehicle enterprises is analyzed under duopoly where one company has the power battery production capacity and the other does not. The results show that when the company with power battery production capacity adopts the vertical integration of vehicle-electric production， the new electric vehicle company without power production capacity tends to purchase power batteries from external battery suppliers. However， when the company with the production capacity of power batteries adopts the strategy of vehicle-electric separation， the new energy vehicle company without power production capacity tends to purchase power batteries from the company with battery production capacity. At the same time， the research shows that， no matter what battery purchasing strategy is adopted by electric vehicle company without power production capacity， for the electric vehicle company with battery production capacity， the vehicle-electric separation production strategy is always superior to the vehicle-electric integration production strategy.

How to transmit the redundant photovoltaic power to the power grid is currently an important obstruction to the development of China's photovoltaic industry， and the coordination and cooperation among generation， transmission， distribution， and sales of electricity is an effective way to solve this problem. The cooperation and operational decision- making on pricing， the energy storage capacity， carbon emission reduction in a photovoltaic supply chain， which is composed of two heterogeneous power plants （i.e. a photovoltaic power plant and a thermal power plant）， single grid company and single power sale company under the renewable energy portfolio standard are studied. First， the game models are respectively constructed for three scenarios： non-cooperative mode， the model of cooperation between the grid company and two heterogeneous power plants， and the model of cooperation between the grid company and the power sale company. Second， the optimal energy storage capacity level， carbon emission reduction level， electricity price and total electricity demand are acquired and analyzed in each scenario. The results show that compared with non-cooperative model， the sale price and the photovoltaic power curtailment radio reduce， the energy storage capacity level， the carbon emission reduction level and the total electricity demand increase， and the overall profit of the supply chain is improved， and its members achieve three-way Pareto improvement in two cooperative models. With the increase of the unit excess consumption voucher price， the photovoltaic on-grid electricity price or the photovoltaic on-grid electricity quantity increases， while the thermal on-grid electricity price or the thermal on-grid electricity quantity decreases. As the unit excess consumption voucher price rises， the proportion of photovoltaic on-grid electricity quantity to total on-grid electricity quantity increases. However， increase of the weight of renewable energy consumption responsibility is not always able to increase the proportion.

Data Envelopment Analysis （DEA） is a non-parametric technique for evaluating the relative efficiency of a group of homogeneous Decision-making units （DMUs）. Unlike other efficiency evaluation methods， DEA does not require pre-specification of production functions， making it widely applicable for performance analysis across various organizations. However， traditional DEA assigns different weights to each DMU， leading to a lack of consistent basis for comparison between different DMUs. Additionally， traditional DEA assumes input-output data are accurate， yet data uncertainty is an inevitable issue in the real world.To address the shortcomings of traditional DEA， a robust DEA model with common weights is developed using the robust optimization method. Specifically， a robust counterpart of the CCR-DEA model is first proposed. Then， the ideal efficiency values of each DMU are obtained from the robust CCR-DEA model. After that， a multi-objective programming model is developed to obtain a set of common weights. Finally， the efficiency scores of each DMU under the common weights are calculated.Based on the data of crowd innovation spaces in 30 provinces and cities in China， the feasibility and effectiveness of our method are verified. Empirical analysis reveals that under different levels of data perturbation， Beijing， Jiangxi， Hubei， Gansu， and Guizhou are always in the top 5 in terms of efficiency ranking， while Hainan， Guangxi and Shanxi are always in the last three. Furthermore， the concept of the price of robustness is used to evaluate the ability of regions to cope with data uncertainty. A Monte Carlo simulation is designed to analyze the consistency of efficiency rankings under different conservatism levels. Lastly， the constructed method with existing models is compared， and the results indicate that our method has a smaller computational burden and is more suitable for real-world performance evaluation problems.

AI-based personalized recommendation services are now widely used by companies in various marketing areas. Companies analyze consumers’ purchasing behavior using AI and understand their preferences through user profiling. It allows them to recommend better-match products to consumers in future sales periods. AI can improve matching accuracy between recommended products and consumers， stimulate consumption thus expanding the market and promoting sales. However， it also may lead strategic consumers to expect better product recommendations in the future， wait and delay their purchases， thus resulting profit loss. As a result， companies need to be more cautious using AI-based recommendations. How companies should strategically use AI-based personalized recommendation services is explored through commitment strategies. A two-period dynamic game model is used to explore two commitment strategies：（1）Price commitment strategy， where the company announces the product prices of two periods in advance， similar to real-world price protection strategies；（2）Enhanced-match commitment strategy， where companies promote their AI recommendations to ensure that consumers have accurate expectations about the enhanced-match precision of future recommended products. Commitment strategies influence consumers' value assessments of products across two sales periods， thereby regulating purchasing behavior and creating strategic interactions for AI-based recommendations. The results indicate that AI-based personalized recommendation service is not necessarily beneficial. Only when the originally existence of purchase delay is not severe or the sales-promotion effect is strong enough， can the adoption of AI benefit the firms. Interestingly， although Enhanced-match strategy may seem to encourage delayed purchases， it actually reduces this effect and requires only a modest improvement in matching accuracy. Additionally， the relationship between the AI use extent and the costs of enhanced-match accuracy is not necessarily positive. Companies may need to heavily promote AI-based recommendations to enhance product matching accuracy， even with high costs， depending on the trade-off between sales promotion and delayed purchase effects. When the cost of enhanced-match accuracy is relatively low， the Enhanced-match strategy plays better than traditional price commitment strategies. The consumer surplus and total social welfare are also compared under different strategies.

The rapid development of artificial intelligence and information technology has propelled collaborative group decision-making to become a key approach for addressing complex systemic decision-making problems in emergency management， engineering demonstration， and other fields. Its characteristics of scalability， social interactivity， and dynamism fundamentally necessitate adaptive capabilities in decision-making models. However， the traditional paradigm of non-consensus sequential identification struggles to meet the demands of efficient group preference aggregation and real-time response in dynamic environments， urgently necessitating the exploration of adaptive intelligent group decision-making methods. Considering that the trust structure and preference information of decision-makers can reveal the latent inter-individual relationships and consensus foundation， and their coupled effects during dynamic interactions are recognized as directly influencing the group decision-making process. Therefore， a reinforcement learning-based intelligent group consensus decision-making model with the “structure-information” coupled network is proposed in this study. First， trust relationships and preference information similarity among decision-makers are quantified to construct the structure network and the information network， and the coupling interaction mechanisms of these two networks are then investigated. Furthermore， an integrated reward function with group consensus level， collaborative incentives， and collaborative costs is designed. To address the discrete characteristics of decision-making behaviors （action space） and the continuous characteristics of decision environments （state space） in group decision-making with the coupled network， the Deep Q-Network （DQN） algorithm is introduced to adaptively optimize consensus strategies. Finally， simulation experiments demonstrate that the proposed model effectively enhances consensus-reaching efficiency while reducing collaborative costs， and its applicability in differentiated behavioral mechanisms is verified through parameter sensitivity analysis.