To address the problem of difficulty and high cost in obtaining labeled samples in credit scoring， a new credit scoring model is proposed based on semi-supervised support vector machines. By introducing new parameters to the unlabeled samples， the model need not satisfy the random missing assumption and has good applicability. Meanwhile， adding a semi-supervised part to the loss function encourages the similarity between the coefficients of labeled and unlabeled samples， which can effectively fuse the unlabeled sample information and improve the estimation effect. In addition， Group LASSO is used for variable selection， which can make full use of the group structure information and screen important variables. The feasibility of the proposed method and its excellent results in variable selection， coefficient estimation and classification prediction are demonstrated by numerical simulations and an example data of credit card risk default prediction.

The digital economy is a key driving force for stable economic growth and a new engine for high-quality economic development. With the maturity of new-generation information technologies such as big data and artificial intelligence， a large number of commercial banks have embraced digital technology and initiated digital transformation， making digital transformation a new round of arms race among banks. However， it is worth noting that the essence of digital transformation is an innovation， which does bring new opportunities for the development of the banking industry， but any kind of innovation means the re-setting of the rules of the game， thus triggering a new change in the market structure. In other words， the dividends brought by digital transformation should be seized， but also whether digital transformation will bring new uncertainties and challenges to the banking system and financial supervision should be considered. This shows that the impact of digital transformation on commercial banks and financial supervision has become an important topic that scholars pay close attention to. Then， the following questions should be considered： Has the digital transformation of banks exacerbated or reduced systemic risks in the banking industry？ If the influence relationship between them exists， what is the internal transmission mechanism？ There are important theoretical and practical significance for the promotion of digital transformation of commercial banks， and useful reference for the improvement of the financial regulatory policy framework in this paper.Factors such as digital transformation， network relevance and external shock are introduced into the classic bank moral hazard model to analyze the inherent impact of digital transformation on banking systemic risk. On this basis， the 2012-2020 quarterly panel data of China Commercial Bank Digital Transformation Index established by manual collection and text mining methods are used to conduct empirical testing. The results show that： （i） Digital transformation can effectively reduce network relevance and banking systemic risk. （ii） Digital transformation will curb banking systemic risk by significantly reducing the degree of network relevance of commercial banks. That is， network relevance is an important channel through which digital transformation affects banking systemic risk. （iii） The digital transformation has a more significant restraining effect on the systemic risk level of small banks. （iv） Digital transformation can weaken the negative impact of negative external shocks such as weakening the new crown pneumonia epidemic and international financial market volatility on banking systemic risk. Digital transformation can reduce the adverse impact of negative external shocks such as the fluctuations in international financial markets on banking systemic risk.The findings have important policy implications. The research results will help to promote the risk management of commercial banks and the integrated development of digital technology and commercial banks， such as encouraging commercial banks to carry out digital transformation and improving their digital risk management capabilities. Different types of commercial banks can carry out differentiated layout for digitization and try to find the most suitable digital strategy for themselves. Regulators should promulgate management measures for the digital transformation of the financial industry， formulate relevant standards in the fields of artificial intelligence， big data and other digital technologies， so as to regulate the application of digital technologies in the financial system.

Collaborative filtering （CF for short） technology， as an important component of recommender system， plays a key role in personalized advertising. On the other hand， although various CF models are aimed at capturing personalized preferences of individuals， their training process are without regarding differences among the individual， where given a training algorithm， the model is always trained with the same training data， no matter what the target object is. This approach， however， is too general to the individuals， for given a specific target user， in order to personalize the recommendations， it might be more reasonable to first personalize the training process. In this work， MORE， a collaborative filtering model with MinOrity REport is presented. MORE trains the CF model in an user specific mode. Given a target user， MORE essentially works in three steps： First， it constitutes a personalized expert set for the target user with the Elastic Net model； Second， it constructs a sub matrix of the origin “user-item” rating matrix， where all values are only from the target user and the expert users. Third， it performs matrix imputation on the newly constructed matrix， and obtain estimations for the target user’s uncollected rating values. MORE is evaluated with two different CF models on two real movie rating data sets， all results show that the proposed model is promising.

The cooperation level of the core enterprises of the prefabricated construction industry chain affects the integrity of the industry chain and the improvement of production performance. The government usually adopts administrative intervention means such as financial subsidies and land support to promote the integration and development of the industry chain. An evolutionary game model of cooperative behavior among three groups of prefabricated construction core enterprises （design units， component manufacturers and construction companies） is constructed under government intervention， and the influence of initial strategy and key parameters on the evolution path of tripartite strategies is analyzed by using the system dynamics method. The research results show that under the positive government intervention， the design unit， the tripartite strategies of the design unit， component manufacturer and construction company influence each other. When the initial probability of the two parties choosing cooperation strategy is higher， the system is more likely to reach the stable state of tripartite cooperation. The factors such as original transaction cost and its reduction coefficient， government subsidies and cooperation benefits of the three parties have a positive effect on the evolution of the system towards cooperation. Setting a reasonable distribution coefficient of cooperation benefits can effectively drive tripartite cooperation. The cooperation cost of the three parties has a negative effect on the evolution of the system towards cooperation， and the lower the cost of each entity choosing to cooperate， the faster the system converges to the stable state of cooperation. The research results have a certain reference significance for optimizing the cooperation mechanism of the main body of the assembly building industry chain and guiding the cooperative development of the core enterprises in the industry chain.

The classic evolutionary cobweb model with heterogeneous expectations has been extensively developed since it was proposed by Brock and Hommes， and has been deeply applied in the aspects of stocks， futures and asset pricing.However， unlike Western countries， state-owned enterprises play an important role in the national economy of China and have made important contributions to the healthy and stable development of Chinese economy. Therefore， state-owned enterprises whose goal is to maximize national welfare are included in the model of Brock and Hommes. Not only that，the variability of the number of private firms is considered since we observe that firms in the real economy are not static and private firms aiming to maximize their own profits are always entering and exiting the market. A cobweb model of heterogeneous expectations with state-owned enterprises and a variable number of private enterprises which is more in line with China’s reality are considered.It is found that with the change of private enterprises' intensity of choice to switch predictors and production technology， the entire market will have complex dynamic characteristics， such as bifurcations and chaos. With the increase of private enterprises' intensity of choice to switch predictors， the complexity of the market equilibrium continues to increase， while the degree of market competition first increases and then decreases， while consumer welfare first increases and then falls into a cyclical cycle or even chaos. What’s more， the degree of market stability and competition of manufacturers varies with different production technologies. Under the condition of medium technology level， the market competition is more intense and market stability is difficult to achieve. Finally， it is found that state-owned enterprises play an irreplaceable role in stabilizing the market， and this stabilizing role continues to become prominent with the increase in the level of production technology. The results of this paper are of practical significance for analyzing the stability of the market and the necessity of the existence of state-owned enterprises.

Speeding up the construction of the electricity market is of great significance to the realization of the “dual carbon” goal. The Unified Settlement Point （USP） mechanism on the user side is considered an effective measure to smoothly promote electricity marketisation and is being piloted in a number of provinces in China（e.g. Guangdong， Zhejiang， etc.）. Financial Transmission Rights （FTRs） are important electricity price risk aversion tools in the electricity market. Therefore， a comprehensive theoretical analysis of the impact of the new electricity price mechanism is required. However， up to now， there has been a lack of in-depth research on issues relating to the practical operation of the new electricity price mechanism， such as its impact on generators' bids and on the hedging effect of FTRs. In view of this， the impact of the introduction of FTRs on generators' bidding behavior under the user-side USP price mechanism is analyzed， and a model for generators’ bids is constructed， taking into account FTRs， forward contracts and the spot market. The model also takes into account power system security constraints and demand response factors to make the model more realistic. The basic conclusion of this paper is that the USP price mechanism affects the bids of power generators through two ways： profit and risk， and the introduction of FTRs can help power generators avoid the risk of price difference. The simulation results show that under the scenario that the bid factor of the other generator set in this paper is 1.2， the bid factor of the own generator in the forward market is 2 and the bid factor in the spot market is 1.55， the optimal FTRs market strategy is to purchase， with a purchase quantity of 448.70MW. Under the scenario of uncertainty about other generator’s bid factor， 500 simulations show that the optimal bid factor in the spot market ranges from 1 to 1.75， the optimal bid factor in the contract market ranges from 1.45 to 2. The purchase of FTRs increases generators' profits by approximately 1%， indicating that FTRs effectively assist generators in reducing losses under the user-side USP price mechanism. Based on the results， policy recommendations are put forward such as the introduction of transmission rights， the disclosure of historical bid information， the strengthening of grid construction of congestion lines， and the increase of the proportion of power generation companies participating in the market. The bidding model developed in this paper can provide a theoretical basis for electricity market analysis and forecasting， as well as for the choice of bidding strategies by power generators.

In order to solve the problem of distributed multi-energy grid connection， distribution and scheduling coordination， considering that the interaction of distributed energy is dynamic and real-time， and the supply and demand sides have data privacy and security requirements， a distributed energy heterogeneous is constructed. Blockchain network model. In this model， a consortium chain mechanism of cross-chain information collaboration and multi-organization complementary transactions is designed to reduce the traditional blockchain consensus confirmation and transaction delay. Combined with the need to improve energy utilization efficiency， control indicators such as low-carbon factor， decay service quality and comprehensive energy utilization rate are introduced to build a reputation value bidding strategy based on electricity sellers， and the automatic transaction and evaluation functions are embedded in the smart contract of the model. Provide support for the settlement of this bidding strategy. Through the multi-scenario setting， the energy blockchain bidding model with the participation of virtual power plants is compared and analyzed. The calculation example shows that the model： on the basis of ensuring the interests of energy transaction participants and data security， the market transaction mechanism is more flexible and promotes Local consumption of clean energy contributes to low-carbon sustainable development. At the same time， the bidding strategy can provide a theoretical reference for exploring the optimal control strategy for participants in the future energy utilization system.

A theoretical analysis and empirical examination of the convergence of pollution emissions in China is conducted under the modified spatial green Solow model framework. The following key conclusions are primarily drawn： Firstly， theoretical analysis suggests that when the growth rate of emission reduction technologies exceeds the overall output growth rate， the economy will surpass the turning point of the environmental Kuznets curve， providing theoretical support for the existence of pollution emission convergence. Subsequently， the results of the spatial panel model estimation indicate that urban pollution emissions in China currently exhibit convergent characteristics， with “club convergence” being the first to materialize. Finally， the results of the grouped estimation reveal that there are three categories of urban clusters in China， namely， low-pollution， moderate-pollution， and high-pollution. Among them， the low-pollution city cluster has entered a virtuous development model for pollution control. The moderate-pollution club has experienced pollution transfer phenomena， reflecting a certain degree of “pollution haven” effect. In contrast， the high-pollution club exhibits a typical pollution-positive feedback loop mechanism， indicating the continued risk of worsening pollution.

Technological innovation is crucial for the renewable power generation enterprises as it can help the enterprises to gain additional profits and reduce their costs. However， the cost of technological innovation is huge. Therefore， the Chinese government tend to provide subsidy to facilitate the innovation process related to renewable technology. As a result， the Chinese government and renewable power generation enterprises are two players with regards to technological innovation and incentive policy. The establishment of trading market of carbon emission changes the game as renewable enterprises can obtain additional profits by involving in Chinese Certified Emission Reduction （CCER） market. Therefore， it is necessary to investigate the new game between renewable power generation enterprises and the Chinese government under the background of carbon trading mechanism. The evolutionary game related to the innovation behavior of renewable energy enterprises and incentive issues of the government is studied. By considering the success probability of research and development （R&D）， R&D cycle time and other factors， the discounted costs and benefits related to the innovation behavior are obtained. Then， the payoff matrix based on these parameters are established. The evolutionary stable strategy of both the static incentive and punishment scenario and the dynamic incentive and punishment scenario are obtained. Moreover， the impact of the continuous R&D cost， success probability， additional purchase cost and other parameters is analyzed in the dynamic incentive and punishment scenario. The results indicate that： There exists a unique evolutionary stable strategy for both parties in the dynamic incentive and punishment scenario. The development of emission trading market is beneficial for the technological innovation as it utilized the power of invisible hand provided by emission trading market. The marketization of power prices can also improve the technological levels in renewable energy industry. The government can also stimulate the process of technological innovation by reducing its expenditure on providing supporting schemes. The results are useful for both the government and the renewable power generation enterprises.

Recently， carbon emissions have become one of the primary reasons for global warming， resulting in many natural problems， such as melting of glaciers and rising sea levels. To control such problems， governments have enacted many policies to reduce carbon emissions， among which， the carbon cap and trade scheme allows firms to buy and sell excess emission credits， and has become one of the most favorable modes to reduce emissions with its unique flexibility. With the advance of emission trade market， there are many firms that provide emission credits for production， namely emission allowance suppliers. Although the carbon cap and trade scheme has brought considerable benefits to firms， the fluctuation of carbon price and demand uncertainty have become the main risks of this mode. With the development of carbon financial market， a series of carbon financial products based on carbon options have become the main tools to hedge such risks of carbon price fluctuations and demand uncertainty. On the other hand， facing severe carbon emission problems and strict policies， firms have started to implement remanufacturing strategy to reduce carbon emissions and production cost.The pricing strategy and coordination in a supply chain with carbon emission option contract are investigated under a remanufacturing environment. Based on the newsvendor model， a supply chain model consisting of an emission allowance supplier （Stackelberg leader） and an emission-dependent remanufacturer （follower） is developed， and then decentralized and centralized decision-making models are established. Using backward induction， the optimal solutions of different models are derived， and the role of emission option contract in supply chain coordination is further discussed. Finally， numerical examples are conducted to illustrate sensitivity analyses of several key parameters. The results show that， the optimal total amount of carbon allowance purchased by the remanufacturer is not affected by the wholesale price of the wholesale contract. Compared with the emission option contract， the amount of carbon allowances purchased through the wholesale contract is more sensitive to the emission option price and exercise price. Under a certain condition， a single emission option contract can coordinate the supply chain. However， when the supply chain is coordinated， the emission option contract cannot achieve the optimal pricing strategies of decentralized scenario. An increase in remanufacturing rate and carbon emission ceiling can increase both the total expected profit of the supply chain and the channel efficiency. If the demand of carbon emission allowance is large， the remanufacturer prefers emission option contract than the wholesale price contract.These findings enrich the theory of supply chain with remanufacturing under emission option contract and can help stakeholders make decisions to align the financial and environmental objectives.

On February 9， 2015， the Shanghai Stock Exchange （SSE） launched its first exchange-traded option， the SSE 50 ETF option. The SSE 50 ETF option is an European-style option written on the 50 ETF. The SSE 50 ETF option provides an effective hedging instrument for the investors in China's stock market. One of the important issues for the development of derivatives markets is to address the question on how derivatives can be valued correctly. It aims to develop an appropriate model for pricing the SSE 50 ETF option in this paper.Classical option pricing theory （such as the Black-Scholes model） is based 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. It is well recognized that asset returns exhibit characteristics such as skewness and heavy tails， which cannot be captured by using a normal distribution. Moreover， asset returns exhibit the volatility clustering property： the volatility changes over time and its degree shows a tendency to persist. To overcome the drawbacks of the conventional option pricing approach， the GARCH option pricing models have been developed. In particular， the GJR-GARCH-FHS option pricing model has been proved to be useful in fitting the option prices.However， the GJR-GARCH-FHS option pricing model does not exploit the high-frequency information for pricing options. The usefulness of high-frequency information to price options has been well established in the literature. In light of this， this paper proposes the REGARCH-FHS model which combines the realized EGARCH （REGARCH） model with the filtered historical simulation （FHS） method for pricing option. The model extends the conventional GJR-GARCH-FHS option pricing model by incorporating the rich high-frequency intraday information from the realized measure to price options. The model is easy to implement， allows for flexible change of measure and is able to capture volatility asymmetry （leverage effect） as well as non-Gaussian innovation distribution.Empirical analysis based on SSE 50 ETF options shows that our proposed REGARCH-FHS model outperforms the Black-Scholes and GJR-GARCH-FHS models in both in-sample and out-of-sample option pricing. Specifically， the root-mean-square error （RMSE） of the REGARCH-FHS model is 77.70% and 15.64% lower than the RMSE of the Black-Scholes and GJR-GARCH-FHS models in in-sample option pricing， while it is 64.16% and 5.40% lower than the RMSE of the Black-Scholes and GJR-GARCH-FHS models in out-of-sample option pricing. Moreover， the REGARCH-FHS model improves the GJR-GARCH-FHS model most significantly for the pricing of the short-term （days to maturity less than 60 days） in-sample and for the pricing of the short-term （days to maturity： 30-60 days） out-of-sample. Our results are robust to alternative criteria for pricing performance evaluation. In summary， our study highlights the value of incorporating the realized measure （price range） and the flexible FHS method for option pricing.

Supplier relationship is an important precursor relationship of enterprises' production and operation activities， which not only affects the distribution of enterprises' working capital， but also inevitably reshapes corporate financialization. However， it is unclear how a firm’s supplier relationship affects its financialization. Based on industrial competition theory and relational contract theory， a hypothesis of “risk effect” of high supplier concentration is proposed. According to the supply chain management theory， it is assumed a “synergic effect” of high supplier concentration. In order to verify these two hypotheses， the relation between supplier concentration and corporate financialization is examined.A-share listed firms from 2010-2018 as research sample are selected， and financial industry firms are excluded， samples of missing supplier information are eliminated， and firms without financial investment are removed. Adopting the purchase ratio from the largest supplier and top five suppliers as the measuring of supplier concentration， the ratio of financial assets is used to total assets as a proxy variable of financialization. How supplier concentration impacts on financialization is investigated using OLS regression model， aiming to reveal the mechanism and the resulting economic consequences between supplier concentration and financialization.The results show that there is an inverted U-shaped relationship between supplier concentration and financialization. Mechanism analysis finds that it is “synergistic effect” of supplier concentration on financialization on the left of the inflection point， namely the higher supplier concentration， the lower the operating cost rate， the faster the inventory turnover， the less cash to pay on procurement. The synergistic effect produced by the supplier relationship leads to more free funds for the firm， and further the firm invests financial assets. On the right side of the inflection point， there is a “risk effect” of supplier concentration on financialization. That is， the higher supplier concentration is， the more resources are occupied， the more margins are eroded， the more serious risk are taken. The risk effect leads to no longer be able to bear the financialization of risk， thus reduce its financial assets. The research shows that the degree of supplier concentration in Chinese enterprises is characterized by the coexistence of risk effect and synergistic effect. Enterprises treat centralized supplier relationship cautiously and establish healthy cooperative relationship with upstream suppliers.

Because of the risks existing in supply chain finance， taking accounts receivable factoring business as the research object， the factors affecting the decision-making of the participants in supply chain finance are analyzed， an evolutionary game model between small and medium-sized enterprises and financial institutions is constructed， and the mechanism of blockchain to solve the financial risks of the supply chain is analyzed by comparing the changes of evolutionary stability strategies before and after the introduction of blockchain technology. And taking the actual case as the background for example analysis， the main conclusions are verified. It is found that firstly， credit risk plays a decisive role in whether financial institutions accept financing business decisions. Blockchain technology can reduce the operational risk of financial institutions and improve the business income of financial institutions； Secondly， the strict regulatory environment formed by blockchain technology makes the default behavior of small and medium-sized enterprises and core enterprises in a high-risk state at all times. No matter the profit distribution proportion that small and medium-sized enterprises can obtain through collusion， they will not choose to default， which effectively solves the paradox that small and medium-sized enterprises cannot obtain loans from financial institutions despite the increased probability of compliance. Then， the evolutionary game between financial institutions and small and medium-sized enterprises is balanced in that financial institutions accept business applications， small and medium-sized enterprises abide by the contract， and the convergence effect is better. Therefore， blockchain technology not only reduces the financing risk of financial institutions， but also helps to solve the financing problems of small and medium-sized enterprises.

To promote the market replacement of green innovative technology and green products， relieve the pressure of energy and environment. A two-stage evolutionary game model of green technology innovation diffusion is constructed based on complex market network， exploring the adjustment process of enterprises and consumers’ strategies under the coordination and complementarity of market mechanism and policy mechanism， and analyzing the optimal and stable conditions conducive to the diffusion of green technology innovation. Through simulation experiments， it could be found that enterprises and consumers are more sensitive to market orientation， consumer demand for green products is the main decision-making basis of enterprises and improving technology maturity is conducive to promoting the diffusion of green products among consumer groups. Meanwhile， the dominant enterprises with relative competitive advantages pay more attention to the market demand for green products， while the competitive marginal enterprises are more easily influenced by the proportion of green technology enterprises in the market. However， the double externalities caused by the diffusion of green technology innovation easily led to market failure， which requires the government to overcome the negative externalities caused by market failure through economic policy regulation. Hence， the government should take improving the consumption demand of green products as the priority to encourage more enterprises to choose green technology production. And promoting the market share by driving enterprises to improve the technology of green products. Meanwhile， it should also pay attention to the diminishing marginal utility of policy incentives.

With the development of artificial intelligence technology， humans are increasingly dependent on machines intelligence to make the correct decisions. The evolution of machine intelligence is changing from detecting and recognizing human beings to perceiving and understanding human beings. The evolution process of decision-making model from the perspective of human-machine collaboration is reviewed， the definition of human factors capability is proposed and the characteristics such as measurability， individual differences， dynamics， and strong correlation are pointed out. Thus a calculation model of human factors capability is established and each index is measured. A human factors capability assessment method that incorporates a capability scale is proposed. The task scenarios of human-machine collaborative decision-making with human factor capabilities are focused on， and the interactive activities of human， machine， and environment are studied with full consideration of human characteristics and factors. Finally， the feasibility and effectiveness of the human factor capability assessment method is analyzed in specific application scenarios. This paper also look forward to the research directions of human factors capability research， in order to providing theoretical and practical references for solving the problem of human-machine collaborative decision-making in the era of artificial intelligence.

It focuses on two common supply chain cost sharing contracts in real life in this paper： one is that the manufacturer bears part of the after-sales service cost for the retailer， the other is that the manufacturer bears parts of the after-sales service cost for the retailer， and the retailer bears parts of the quality cost for the manufacturer. The impact of using different cost sharing contracts on product pricing， quality level， after-sales service level and the profit of supply chain members is analyzed.It is found that （1） When the after-sales cost sharing ratio and the quality cost sharing ratio are lower than a certain level， the product quality level and after-sales service level under the two cost-sharing contracts can be improved， but the quality cost and after-sales cost are shared by each other The product quality level and after-sales service level under the contract will be higher. （2） The manufacturer’s profit under the contract for the mutual sharing of quality cost and after-sales cost is always higher than the profit under a single after-sales cost-sharing contract， but the manufacturer can only share a small proportion of after-sales service costs and consumers have a greater impact on after-sales service costs. When insensitive and the quality cost coefficient is large， compared with not using a cost sharing contract， the use of a single after-sales cost sharing contract can increase profits. This also explains why in emerging industries， manufacturers tend to use quality and after-sales cost sharing contracts， because consumers in emerging industries are often more sensitive to after-sales service costs， and use quality cost and after-sales cost sharing contracts and use single Compared with the after-sales cost sharing contract， the after-sales service level of the product can be improved to a higher level， thereby increasing the profit of the manufacturer. （3） Retailers may not necessarily benefit from a single after-sales cost-sharing contract. Only when the quality cost coefficient is small， compared with not using a cost-sharing contract， the retailer’s profit under a single after-sales cost-sharing contract will be higher. Compared with a single after-sales service cost-sharing contract， when both the quality cost coefficient and the after-sales cost coefficient are large， the retailer's profit under the contract for mutual sharing of quality cost and after-sales cost will be lower.

To promote sustainable supply chain development， the government usually adopts different forms of subsidies based on the cost of green R&D， and manufacturers also develop different channel structures to sell their products based on the different forms of government subsidy. A supply chain game model with a government as the Stackelberg leader， a manufacturer and a retailer is developed. Based on two forms of R&D cost subsidy and unit production subsidy provided by the government， the optimal government’s subsidy rate， the manufacturer's wholesale price， green level， and the retailer's order quantity are explored as well as the profit or utility function of each channel member under different channel structures. The results show that when the manufacturer can better control their green R&D costs， the unit production subsidy provided by the government will be better for producing environmentally friendly products， and the profit of the manufacturer. Moreover， unit production subsidy can also make the retailer more profitable if the manufacturer reduces the cost of developing green technologies. Our research reveals the close correlation between the government subsidy strategy and manufacturers' green technology input and channel structure. When the government provides with the manufacturer R&D cost form， the manufacturer’s dual-channel structure reduces government expenditures and is a better option for the manufacturer， the retailer and the government if the manufacturer reduces its green R&D cost. However， when the government provides the subsidy to the manufacturer in the form of unit production subsidies， the manufacturer needs to choose a proper channel structure based on the consumer environmental awareness， the price elasticity， the cost rate of green technology development and the marginal rate of return of environmental improvement. In addition， from the government's point of view， when the development cost of green technology or the marginal benefit of environmental improvement is low， the adoption of unit production subsidy will be more conducive to the government’s policy on promoting environmentally friendly products， and more conducive to the interests of both the manufacturer and the retailer. Otherwise， the government is suggested to adopt a strategy of R&D cost subsidy.

The vigorous development of e-commerce has made the capacity of online shopping carts continue to increase. In addition to the functions of adding products and checking out， consumers also use shopping carts to collect products， compare information， and collect orders for promotions and other functions. For this purpose， a large number of products are added to the online shopping cart， but they are not “emptied”， and the behavior of leaving the products in the shopping cart is called “online shopping cart abandonment behavior”. Previous literatures have studied a large number of antecedent variables that affect online shopping cart abandonment behavior， including platform perception factors， consumer subjective factors， shopping behavior factors， and product factors in the pre-decision stage of online shopping. However， there is no research on the impact of online shopping cart abandonment behaviors from the perspective of the decision-making stage after online shopping， that is， the sorting rules of products in digital shopping carts， and there is a lack of discussions on intermediary mechanisms and boundary mechanisms.Based on the post-decision stage of shopping， the effect of chronological order （ascending & descending） on online shopping cart abandonment behavior，and alternative the explanations for psychological ownership and oblivion are proposed. Through one interview and two studies， the results show that the sorting method in ascending time order alleviates the problem of consumers' choice overload and effectively reduces the abandonment of online shopping carts.

The high-speed rail station， as a connection point for traffic inside and outside the city， has greatly promoted the economic development of the surrounding areas. It is a comprehensive transportation hub integrating transportation， transfer and service. In the event of an emergency， it is difficult to evacuate the huge passenger flow in a short period of time， and it is easy to cause casualties. Therefore， it is necessary to study how to organize efficient and orderly emergency evacuation activities in a limited time， optimize the evacuation path of people in high-speed railway stations， and reduce casualties and property losses caused by emergencies.The first part of this paper reviews the relevant literature on the evacuation process under emergencies， and finds that there are still some shortcomings in the existing research. First， in terms of model construction， the existing research usually adopts the general evacuation model of large public places， which does not reflect the characteristics of the evacuation problem of high-speed rail. Secondly， in the method of solving the objective problem， some existing algorithms are difficult to obtain effective and stable results when solving nonlinear programming models with multiple optimization objectives and multiple constraints.The second part introduces the characteristics of the evacuation problem of high-speed railway stations and the characteristic factors that need to be considered when modeling. According to the structural characteristics of the high-speed railway station， a multi-objective optimization model for the evacuation path of people in the high-speed railway station is established. The optimization goal of the model is to reduce the total evacuation time of all evacuees’ as much as possible， balance the load of the entire evacuation network， and calculate a feasible evacuation path that can evacuate people on time under the condition of ensuring safety.In the third part， an improved quantum ant colony algorithm is designed based on the methods of increasing the quantum revolving gate adaptive improvement mechanism， increasing the individual mutation strategy， and improving the pheromone update method. And through the numerical example comparison experiment， it is verified that the improved quantum ant colony algorithm can effectively overcome the shortcomings of the traditional ant colony algorithm that the convergence speed is slow and it is easy to fall into the local optimum.In the fourth part， based on the actual survey data of a high-speed railway station， an experimental case of the evacuation problem of high-speed railway station is constructed with different scales of evacuating personnel， and the optimization results of the improved quantum ants are compared and tested， which shows the effectiveness and efficiency of the model.In summary， the emergency evacuation of high-speed railway stations is studied under the emergency situation， the relationship between evacuation efficiency and personnel density and congestion is analyzed， and finally a multi-objective evacuation path optimization model is established. In order to enhance the efficiency of model solving， an improved quantum ant colony algorithm is designed based on a mixed strategy， which overcomes the defect that the ant colony algorithm is prone to fall into prematurity. The optimization results of the algorithm can provide a more scientific and effective decision-making basis for the path selection of emergency evacuation of high-speed railway stations.

With the rapid growth of international trade， technology licensing cooperation between enterprises is becoming more and more popular. However， in the process of transnational technology licensing， unreasonable licensing contracts make it easy for the companies to occur patent infringement disputes. In this context， the following questions are studied： 1） in transnational technology licensing， how should the licensor design and choose the form of the technology license contract？ 2） How should production diseconomies of licensee and information asymmetry affect the design of technology license contract for licensor？ 3） How should the design of technology license contract and the domestic enterprise's production diseconomies affect social welfare？ Taking the foreign enterprise as licensor and the domestic enterprise as licensee， production scale diseconomy of domestic enterprise and information asymmetry about market demand are taken into account， and then games with incomplete information are constructed to investigate the optimal design of transnational technology licensing contract problem. On this basis， the conditions for choosing the form of transnational technology licensing contract are analyzed， and the influence of production scale diseconomy of domestic enterprise on social welfare is investigated.By solving and analyzing the constructed model， the optimal technology licensing contract and output decision under pooling equilibrium and separating equilibrium are found. Firstly， compared with the only choice of two-tariff contract to technology licensing under the pooling equilibrium， the foreign enterprise can strategically design fixed-fee contract or two-tariff contract under the separating equilibrium； Secondly， the production diseconomies of the domestic enterprise does not affect the choice of license form of the foreign enterprise， but it will prompt the domestic enterprise to reduce the output of the product and increase the price of the product. Moreover， the impact of production diseconomies on the performance of technical supply chain members depends on market conditions， but it always leads to the loss of consumer surplus and social welfare. Finally， the domestic enterprise's production outsourcing and the domestic enterprise's production economies of scale are considered to expand the analysis of this paper. The results show that， the domestic enterprise's production outsourcing and the domestic enterprise's production economies of scale will not affect the patent licensing form choice of the patent provider. Nevertheless， the impact of production outsourcing on the equilibrium of technology supply chain is uncertain， and scale economies will bring positive externalities to technology supply chain， that is the profit levels of the domestic enterprise and the foreign enterprise will increase.This paper also has some limitations. In order to focus on the design of transnational technology licensing contracts， the description of information asymmetry in domestic market demand is simplified. Therefore， when studying the problems of technology licensing in the supply chain， more influencing factors can be further considered， and the continuous random function can be used to describe information asymmetry in product market demand or disruption in demand/supply.

With the development of the port industry and the continuous ssprogress of the transformation of traditional industries， the “Port before Factory” was successfully born.The meaning of “Port before Factory” is “the port in front and the steel enterprise in the rear”， which is an innovative form of enhancing the cooperation between the port and the iron and steel enterprise. After the raw materials needed by steel enterprises arrive at the port， its are temporarily stored in the port yard. Then， according to the production needs of steel enterprises， after the reclaiming operation of the reclaimer， the raw materials are delivered to the production line of steel enterprises by the port in a fixed， fixed and quantitative manner. Under this mode， the port reclaiming operation is more frequently driven by the “multi-variety， small-batch” raw material distribution orders of steel enterprises， which puts forward higher requirements for its operation quality and distribution time. And there is uncertainty in the operation of the reclaimer. Therefore， how to deal with the failure of the reclaimer has become the key to affecting the cooperative production of ports and steel enterprises.In order to improve the agility of the reclaiming operation in the yard， the flexible production theory of the manufacturing industry has been introduced into the management of the reclaiming operation in the port， and the refined reclaiming process has been proposed， which includes three aspects： the layout of the yard， the process of the reclaiming operation， and the emergency scheduling scheme of the reclaimer. Among them， in the emergency scheduling scheme， through multiple scheduling， the delayed tasks caused by the failure of the reclaimer are evenly distributed to other equipment， which reduces the impact of emergencies on the overall operation progress.On the basis of the refined reclaiming process， with the goal of minimizing the job completion time of the reclaimer， an emergency scheduling model of the reclaimer is constructed. In addition， since the emergency scheduling of the reclaimer is a nonlinear NP-hard problem， the BFA swarm intelligence algorithm is used to solve it. And in order to improve the performance of the algorithm， the adaptive step size， the optimal flip angle and the expansion of the solution space are improved to make it more suitable for the solution of the model.Finally， based on the real operational data produced by a port operation and a steel enterprise， simulation experiments are used to verify the effectiveness of the algorithms and models. The improved BFA algorithm， the classical BFA algorithm and the classical PSO algorithm are simultaneously used to solve the example. The results show that the improved BFA algorithm has a faster solution speed and a stronger ability to jump out of the local optimum. The working time in the case of a failure of a reclaimer and the working time under the normal working condition of the reclaimer are solved many times， and the optimal result is taken from them. The results show that the optimal job completion time of the former is 90.7866 minutes， and the optimal job completion time of the latter is 107.3372 minutes. Under the condition that the reclaimer fails for 50 minutes， the job completion time of emergency scheduling is increased by 17 minutes， and due to frequent equipment adjustment， the equipment adjustment time is also increased（The working time of the reclaimer includes the adjustment time and the reclaiming time）. Therefore， the increase in operating time is only 1/4 of the failure time （the number of reclaimers in the experiment is 4）. It can be seen from this that emergency scheduling can distribute the impact of downtime to all operating equipment by dynamically allocating operating paths and workloads. It makes full use of the productivity of each equipment， minimizes production delays， and improves the flexibility of port reclaiming operations.A scientific theory and method for the seamless connection of “port-steel” production is provided； at the same time， the refined production process lays the foundation for the construction of smart ports.

Demand response technology based on real-time pricing improves the development and utilization of muti-energy sources based on the balance of supply and demand， thereby improving the overall efficiency of the energy market. To analyze the social welfare of real-time pricing， the relationship between real-time pricing and social welfare based on the social welfare maximization model are studied. Firstly， the analytic solution of real-time pricing under the social welfare maximization is obtained through quadratic programming. On this basis， the quantitative relationship between price and social welfare is derived. Subsequently， based on the derivation results， the impact of the user's elastic parameters and preset parameters on real-time pricing and social welfare is analyzed and explained. Furthermore， the social welfare benefits of real-time pricing compared to fixed electricity prices are explained. Finally， combined with the analytical results and Shanghai pricing standards， an empirical study is implemented. The empirical research results show that real-time prices not only increase consumer surplus， but also improve social welfare compared.

In recent years， as market competition intensifies and consumer needs become more diverse， more and more retailers are choosing to upgrade their products and services to further consolidate and expand their markets， such as enriching product offerings， improving user experience， and meeting personalized needs. One of the key strategies is to introduce store brands. Store brand reduces retailers' dependence on manufacturers， enhances their bargaining power and improves profitability. Although store brand will eat into the market share and profit margin of manufacturers' brands and cause conflicts with manufacturers， but the retailers are still important channel partners for manufacturers， so how to deal with the complex competitive partnership between products， improve channel coordination and maximize profits has become an important issue for manufacturers and retailers to focus on together. Strategic inventory can mitigate double marginalization and improve the overall supply chain profitability， which providing a new perspective for developing supply chain coordination mechanism. Based on the consideration of the retailer's ability to own store brand and hold strategic inventory， we construct a two-stage game model to obtain the optimal decisions of the retailer and the manufacturer under two contract formats： a commitment contract and dynamic contract. In a commitment contract， the manufacturer commits wholesale prices of both periods.In contrast， under the dynamic contract， the manufacturer will announce the wholesale price of each period only at the beginning of that period. The optimal contract design for the supply chain is explored under store brand introduction. The main result shows that： store brand inhibits the strategic inventory of the retailer； under the dynamic contract， as the level of store brand quality rises， the retailer's profit in equilibrium may fall. the manufacturer always prefer the dynamic pricing contract； The retailer prefer the commitment contract if and only if the quality of store brand is relatively low and inventory holding cost is moderates； the supply chain’s preference for contracts is influenced by store brand quality and holding cost； when retailer can choose the optimal pricing contract， it is not necessarily harmful to the manufacturer for the retailer to increase the quality of store brand. Numerical study indicates that under the dynamic contact inventory level and the manufacturer’s profit are decreasing in store brand quality.

Frequent public health emergencies have brought severe challenges to the life and property of the country and the people， and strengthening the collaborative prevention and control of public health emergencies has become an important issue in social governance. Aiming at the high uncertainty in the process of strategy interaction and behavior evolution between local governments and the public in public health emergencies， a stochastic evolutionary game model for collaborative prevention and control of public health emergencies is constructed to analyze the evolutionary stability strategy and evolution process of local governments and the public. It is found that the local government evolves to the stable strategy faster than the public； The stochastic interference factors will slow down the speed at which local governments and thepublic evolve into stable strategies； When the probability of epidemic spreading gradually increases， the impact on the change of local government strategy is greater， followed by the public. With the increasing of punishment coefficient to local government and the public， local government and the public are more inclined to choose （active prevention and control， voluntary isolation） strategy， and the local government has a stronger response to punishment. The stochastic evolution decision-making model for collaborative prevention and control of public health emergencies constructed in the article provides theoretical reference and practical basis for scientific prevention of public health emergencies.

The research on movie recommendation algorithms developed rapidly because of the generation of new technologies such as Web 2.0， Big data， and cloud computing. Most recommendation systems operate based on preference similarity or social distance. However， current movie recommendations still face sparse issues on item ratings and social relationships. According to sociological theory， preference similarity is one of the important factors in the formation of social relationships， and social relationships also contribute to the emergence of similar preferences. Thus， considering multiple association relationships between users and the interaction between these relationships is the key to accurately capturing user needs and improving recommendation accuracy. A double-layer network-based recommendation model is proposed that simultaneously considers users’ social relationships and preference similarity. The social network is obtained based on the "follow" relationship and the preference similarity network is built based on similar reviews. Based on the single-layer processing of the double-layer network， the Louvain method is used to reduce the dimension of a large group of users. Then， the influence of intra-class users is calculated based on multi-level relationships and the personalized recommendation is provided for target users. Three main contributions are made by this paper. Firstly， the sparsity problem of users’ evaluation of the target movie is improved by extending the target movie to a collection of similar movies. Secondly， the sparsity of users’ social relationships is alleviated by preference similarity. Thirdly， considering the double-layer relationships， the needs of target users and recommendation lists can be accurately predicted. This paper takes Douban data （including 384548 users， 269469 movies， and the Top 250 movie list in 2022） as an example to verify the effectiveness of the recommendation model. Through experimental analysis， the sparsity problem of user relationships is effectively alleviated， the communities that users are interested in are efficiently identified， and more novel items are recommended to target users. Additionally， this method is also suitable for predicting missing information and identifying similar preference communities， providing data support for a more comprehensive understanding of user preferences.

Based on consumer decision model theory， goal theory， the influence of social comparison tendency on consumer purchase decision is discussed from the perspective of emotional-rational decision in marketing field. The study was carried out by experimental method， and a total of 4 experiments were designed for verification. The research shows that consumers' social comparison tendency has a significant impact on consumption decisions. Consumers with high social comparison tendency are more inclined to buy hedonic products， while those with low one are more inclined to buy practical products. This study has enriched the research of the psychological variable of social comparative tendency in the field of marketing， and provided suggestions for merchants to promote the sale of different types of products， and also helped to avoid the risk caused by consumers' impulsive consumption.

In order to fuse carbon constraints into service supply chain decision， a Supply-Delivery problem is broken down into two sub-processes： retailers choose distributors according to their needs； the distributors receive the retailer's order to complete the delivery. In view of the above， four kinds of strategy model-periodicity， cumulativity， globality， and volatility-for distributors with different intensities of carbon constraints are constructed， so that carbon emissions from transport can comply with carbon-bound and the distributors pay the lowest cost. The results show that the periodic restraint strategy is the ideal state with the highest intensity； the cumulative restraint strategy and full-cycle one have some limited flexibility； the full cycle carbon restraint strategy will result in the decrease of the order quantity； the volatility carbon restraint strategy is more suitable for the distributors to respond to carbon restrict and so it is relatively better strategy. In addition， the relationship between the total amount of emissions and the number of deliveries under the four kinds of carbon constraints is analyzed through numerical experiments， and a detailed strategy plan is given.

Previous studies have failed to reach a consensus regarding the impact of performance pressure on employee innovative behavior. While some argue that performance pressure can facilitate employee innovative behavior， others contend that it may decrease such behaviors. The current study employed experience sampling methodology to investigate the impact of daily performance pressure on innovative behavior. Drawing on the cognitive appraisal theory of stress， we proposed that for employees with high levels of general challenge appraisal of performance pressure， daily performance pressure might facilitate next-day innovative behavior through problem-solving pondering at night. However， for employees with high levels of general hindrance appraisal of performance pressure， daily performance pressure might decrease next-day innovative behavior via affective rumination at night.To test our hypotheses， we conducted two experience sampling studies. In Study 1， participants were asked to report their general challenge and hindrance appraisal of performance pressure and demographic information in the baseline survey. One week later， participants were invited to complete two daily surveys every workday over a two-week period. In the afternoon survey， they assessed daily performance pressure and daily innovative behavior. In the evening survey， they reported problem-solving pondering and affective rumination at night. We obtained 834 day-level observations from 133 participants. In Study 2， data collection also occurred in two phases： a baseline survey and daily surveys over a three-week period. We obtained 802 day-level observations from 71 supervisor-subordinate dyads in this study.The results of two-level pathanalyses and Monte Carlo simulation showed that： general challenge appraisal of performance pressure moderated the indirect effect of daily performance pressure on next-day innovative behavior through problem-solving pondering at night. Additionally， general hindrance appraisal of performance pressure moderated the indirect effect of daily performance pressure on next-day innovative behavior via affective rumination at night. The present study reconciled the discrepant findings in previous research by demonstrating both positive and negative mediating mechanisms in the relationship between daily performance pressure and innovative behavior， offering valuable implications for innovation management.

The rapid development of a new generation of information technology， such as big data， Internet of Things， and artificial intelligence， has provided opportunities for the transformation and development of the manufacturing industry. To enhance the competitiveness of China’s manufacturing industry， it should focus on quality improvement， starting from the digital， network and intelligent transformation of quality management， to drive the upgrading of manufacturing industry. At present， there is no theoretical guidance for the upgrading and transformation of quality management in China’s manufacturing industry. Therefore， it aims to propose the concept of intelligent quality management based on the current research progress and the actual development of China’s manufacturing industry is this paper. The theory model of intelligent quality management from the technical， activity， and value dimensions is proposed， and nine key technologies （i.e.， Internet of Things， big data platform， cloud computing and edge computing， machine learning， machine vision， digital twins， wireless communication， visualization， and blockchain） and their application scenarios are introduced. Finally， the future researches of intelligent quality management are pointed out from the perspectives of theoretical research， technical research， and application research. This study can provide strong guidance for the transformation and upgrading of quality management in China’s manufacturing industry by establishing the theoretical framework of intelligent quality management， exploring the development path of quality， and promoting the development of China’s manufacturing industry with high quality.

Continuous mass production not only requires enterprises to identify the state of the production process more accurately and efficiently， but also needs to identify potential quality problems in the production process， so as to avoid quality risks in advance. The existing control chart abnormal pattern recognition method can monitor whether the production process is abnormal in the short term， but it is difficult to identify and analyze the potential quality problems in the long-term production process. In order to overcome this limitation， this study proposes a method， using relative entropy to identify the quality problems through distribution pattern. Firstly， five training sets and test sets of distribution anomaly patterns are generated by simulation. Then， the probability distribution of the mass characteristic values of the samples under the test set or the probability density after fitting by kernel density estimation is used as the input of the model. Finally， the relative entropy is used to quantify the similarity and divergences between the distribution of the actual production sample and each distribution of the estimated abnormal pattern， the production state classification index is output， and further pattern recognition is completed by constructing two quality pattern judgement criteria. It is shown that the proposed method in this paper can accurately identify the abnormal quality patterns in both the discrete and continuous states of the quality characteristic parameters. Through comparative analysis with the correlation method， it is found that our method has a higher classification accuracy， and thus it can recognition the quality abnormal pattern of the process more effectively.

With the continuous advancement of standardization reform and the implementation of the new standardization law， Chinese group standards have shown a vigorous development trend in recent years. But the low quality of standards has become a key problem that plagues the development of group standards. In this paper， from the perspective of group standard development subject， four different complexity situations can be distinguished based on the number of components and dependency of the developed standard， which are simple， slightly complex， more complex and complex situations. Based on the NK model， the process of standard development in modeled as a search， evolution and collaboration process of multiple actors in a technical field to find feasible solutions. The impact of the number of actors involved in standard development， the complexity of the standard itself and the coordination level of the group standard organization on the quality of the standard is studied under these four scenarios by means of simulation.It is assumed that there are M subjects involved in standard development and coordination activities in this process， and the standard development process can be modeled as a collaborative adaptive search process conducted by M subjects around a common technical problem， with the search aiming to seek the optimal technical solution in the technical landscape， thus forming a group standard. They can either improve performance by developing innovations in their own technology， i.e.， improve the performance of their own technical solutions， or switch to technical solutions with better results proposed by other participating subjects in the group organization. For the former strategy， due to limited rationality and technological path dependence， the evolution of firms' own technological landscape position is mainly achieved by means of local search. For the latter strategy， it is considered that at each stage each subject tries to imitate with probability d=(\mathrm{1}-\text{?}{A}_{\mathrm{0}}/A_{\mathrm{m}\mathrm{a}\mathrm{x}})， where A₀ is the magnitude of attractiveness of the selected subject's landscape location， numerically equal to the solution quality P₀， and A_max is the attractiveness of the landscape location of the best performing solution. In fact， group standard organizations can play an important organizational coordination role in this process， and different coordination strategies may produce different standard development processes and results. The coordination coefficient c is used to characterize the coordination ability of group standards organizations， with c taking values between 0 and 1. The model causes c·M participating subjects to turn directly to a candidate technical solution when that solution meets certain performance criteria. The experimental design first analyzes the influence of the number of subjects in different complexity contexts on the quality of the standard， and discusses the optimal number of participating subjects for a given context and the influence of contextual factors on the quality of the standard. Secondly， the changes in the quality of standards brought about by the coordination ability of group standards organizations are examined to explore the best way of coordination.The results of the study show that increasing the number of subjects involved in standard development can improve the phenomenon of technical shortsightedness， and the number of subjects can be adapted to the complexity of the technology to achieve efficient improvement of the standard quality， and the more complex the technology is， the more the number of participating subjects is required to achieve optimal standard quality， and a reasonable number of subjects needs to match the complexity of the technical standard itself. The enhanced complexity of the technical environment will lead to sub-optimal standards， in which the increase in the number of technical elements has a low impact on the quality of standards， while the increase in the degree of dependence of elements will significantly reduce the quality of standards. The coordination ability of group standards organizations has two distinct impacts on the quality of standards. In less complex technical contexts， the intervention of group standards organizations is detrimental to the quality of standards. However， the coordination level of the group standard organization should be kept within a certain range， too high coordination level is not conducive to the formation of optimal standards， and the corresponding coordination intensity should be selected according to the technical characteristics. Based on the results of simulation analysis， three strategies are proposed to improve the quality of group standards， such as improving the participation of market players， increasing the development of standards in key technical fields and optimizing the coordination mechanism of group standards. The research expands the theoretical perspective and research method of group standard research， and provides theoretical support and practical guidance for effectively improving the quality of group standards.

Blockchain-based traceability technology has contributed to the widespread adoption of formal supply chain quality information systems. Through the quality blockchain， suppliers are able to decide how much quality information to share and with whom， thereby alleviating their concerns about information leakage and loss of competitive advantage， which has significant implications for their pricing decisions and performance. However， the majority of existing studies have focused solely on the binary quality information sharing decision， sharing or non-sharing， instead of flexible decision-making. Furthermore， it needs to discuss the impact of the most important factors in the market， namely quality and price competition， on the supply chain information-related decisions.It is discussed how suppliers make quality information sharing decisions in the competitive environment， how quality information sharing level affect the decision-making and performance of the supply chain players， how quality and price competition intensity affect quality information sharing decisions and supply chain profit， and whether suppliers should share quality information horizontally with their competitors.A multi-stage game model of quality and price competition is established for two suppliers participating and their common retailer in the same quality blockchain under a competitive environment， and the optimal vertical quality information sharing decision， supply chain pricing decision and benefits of all parties are analyzed under three horizontal quality information sharing strategies of non-sharing， two-way sharing and unilateral sharing through blockchain authorization. Then the influence of quality and price competition intensity and equilibrium deviation behaviors is discussed. Finally， a robust numerical example is used to verify the effectiveness of the model and management implications are presented.It is found that （i） Regardless of the horizontal quality information sharing strategy adopted by the competing suppliers， vertical quality information sharing from either supplier can always yield nonnegative direct or spillover effects to the information recipients. This reflects the value of quality information， emphasizing the need for supply chain enterprises to prioritize and take the lead in quality blockchain information sharing and authorization management. （ii） The intensification of quality competition encourages suppliers to improve their quality information sharing level， which benefits each supply chain player， while the intensification of price competition inhibits suppliers' input in quality information， which harms any supplier's profit and the spillover effect obtained by the retailer， but helps the retailer obtain a higher certain profit. Therefore， for suppliers， keeping sufficient quality advantage is an effective way to avoid being hurt by price competition； For the retailer， it is more beneficial when price competition is intense while quality is relatively stable， such as for daily consumer goods， and more beneficial when price competition is weak while quality fluctuation is large， such as for high-end fresh food market. （iii） Suppliers' vertical quality information sharing decisions are influenced by their own strategies， and in turn by their competitors' horizontal quality information sharing strategies. When quality competition is weak， the optimal strategy of any supplier is horizontal sharing. When the quality competition is strong， the optimal strategy is horizontal non-sharing. （iv） When price competition is weak while quality competition is relatively strong， suppliers have the incentive to share quality information with each other to mitigate quality competition and reduce quality information inputs， but this may hurt the profits of the retailer and the whole supply chain， and the sharing level of supply chain quality information. From the standpoint of the supply chain， it is necessary to prevent the suppliers from conspiring to monopolize the supply.This study provides theoretical basis and practical guidance for supply chain enterprises to decide quality information input and product price when facing competitive products， and to use quality blockchain for information authorization and management. It also has reference value for the retail to improve marketing management of competitive products. In the future， the research can be extended to more supply chain structures， decision influencing factors and empirical research.