The impact of leveraged trading on stock market liquidity is investigated in this study， addressing a critical issue in financial markets where liquidity shocks have become increasingly frequent. The research is anchored by the implementation of margin trading in China since 2010， which was aimed at enhancing liquidity by amplifying securities supply and demand. However， the effects of leveraged trading on liquidity remain contentious， with varying opinions on whether market conditions are improved or deteriorated by it.The core research question is addressed by focusing on the asymmetrical effects of leveraged trading on liquidity， particularly distinguished between short-term and long-term impacts， as well as differential effects during market upturns and downturns. A panel regression model is employed to analyze individual stock data， with the liquidity index being constructed using the Amihud illiquidity measure.The empirical analysis is based on a data set comprising stocks listed on the Shanghai and Shenzhen exchanges from January 2014 to November 2021， with stocks eligible for margin trading being the focus. It is revealed by the findings that liquidity is enhanced by leveraged trading in the short term but is led to deterioration in the long term， demonstrating a "short-term vs. long-term asymmetry." Additionally， a more pronounced positive effect on liquidity is exerted by leveraged trading during significant market upturns， whereas liquidity issues are exacerbated by it during downturns， confirming the "upturn vs. downturn asymmetry." It is suggested by the extended research that the negative impacts of leverage become more significant in high-leverage market environments， indicating that excessive leverage can lead to liquidity crises.An understanding of the complex dynamics between leveraged trading and liquidity is contributed to by this research， providing insights for policymakers regarding the regulation of margin trading practices. By highlighting the dual nature of leverage as both a facilitator and a potential source of liquidity risk， the need for careful monitoring and management of leverage in financial markets is underscored by the study to mitigate systemic risks.

Due to the effective implementation of targeted poverty alleviation （TPA）， absolute poverty has been historically eliminated in China by 2020. Compared with traditional poverty alleviation， the market forces represented by listed companies are more encouraged to actively and extensively participate in “targeted poverty alleviation”. It is necessary to investigate whether the TPA behaviors of listed companies are effective and whether regional differences have an impact on the effectiveness.Based on the data of 334 Chinese listed companies committed to TPA from 2016 to 2019， the TPA behaviors by listed companies are divided into two categories： “blood transfusion” and “blood production”. Further based on the disposable income of rural residents from 455 poor counties in 27 provinces alleviated by the above-mentioned listed companies， the effect of the above two types of TPA behavior on increasing farmers’ income and the moderating effect of the livelihood characteristics （risk and capital） in poor areas on this effect are investigated empirically in this paper. Our results show： Firstly， “blood transfusion” TPA can effectively increase the disposable income of rural residents and narrow the income gap between urban and rural areas. Secondly， at current stage， considering that the “blood transfusion” remains in the ‘incubation and cultivation’ phase during the sample period， its efficacy in enhancing disposable income and narrowing the urban-rural income gap is not yet statistically significant. On the whole， the TPA behaviors of listed companies increase the income of poor farmers. Thirdly， livelihood risk in poor areas plays a negative moderating role in the income effect of TPA， while livelihood capital plays a positive moderating role. The evidence and conclusion summarize the Chinese unique experience of TPA from the perspective of the company， providing positive support for the rationality of “precise” and “targeted” policies， as well as guidance for improving the efficiency of the companies’TPA and enhancing the quality of “blood production” TPA.

Under pressure from successive interest rate hikes by the US Federal Reserve， Silicon Valley Bank collapsed due to maturity mismatch problems in liquidity management， affecting the global financial system. The tightening of macro liquidity has added to the pressure of micro liquidity risk outbreaks. In this paper， the competitive equilibrium conditions are mapped for bank liquidity holding decisions into a regression model， corresponding to the connotation of the interbank network structure to a spatial econometric model that describes the implications of interactions between entities in a competitive equilibrium. Using the listed banks from 2010 to 2021 as samples， the transmission and accumulation mechanism of liquidity risk changes resulting from the aggregation of micro-level decision-making behaviors combining spatial econometric model and minimum density algorithm is elucidated. Additionally， it employs structural estimation to identify the contagion effect within the bank network.The research results demonstrate three main conclusions. Firstly， The source of liquidity funds obtained by banks is externally dependent due to the existence of bank network. Secondly， the contribution of banks to systemic risk is influenced by the magnitude of exogenous shocks and the mechanism of network propagation， with the risk of contagion from network propagation being the main source. This network propagation risk is the result of the gradually decaying impact of random error shocks in the form of a covariance structure on the bank in question. Thirdly， Against the backdrop of China’s deleveraging policy， as bank network density declines， the contagion effect of liquidity risk among banks exhibits a downward trend and the topology of bank networks shows a trend toward decentralization，while individual bank liquidity risks increase. To effectively prevent and mitigate systemic risks， it is imperative to address the liquidity risk management challenges arising from macro-level liquidity adjustment policies for micro entities. It is crucial to establish an integrated early warning mechanism by building a framework system of macro liquidity volatility affecting financial institutions' decision making. This necessitates enhancing banks' capacity for emergency liquidity risk management and bolstering monitoring， early warning systems， and control mechanisms pertaining to liquidity risk in banking institutions.

Measuring the digitalization level of enterprises systematically and comprehensively is crucial for an in-depth exploration of the effectiveness of digital transformation. It has received wide attention from academic community by statistically analyzing the frequency of digitalization-related terms in annual reports of companies. However， previous studies have overlooked the importance of terminology dictionaries， and usually adopt small-scale， narrow coverage and poor scalability， which leads to improper measurement of enterprise digitalization levels. To address this issue， two term extraction models are proposed， namely BERT-GlobalPointer and BERT-GlobalPointer-Mask， for efficient identification of digital terms. Furthermore， utilizing the large-scale terminology dictionary built by the proposed BERT-GlobalPointer-Mask， a Digital Transformation Index （DTI） for Chinese listed companies is developed and their evolution patterns over the past two decades are analyzed. The results indicate that the proposed models significantly outperform the benchmark models in identifying digital terms， new terms and long terms. Based on BERT-GlobalPointer-Mask model， a digital terminology dictionary containing 273，634 terms is constructed， which covers more comprehensive and diverse digital terms than previous studies. With this terminology dictionary， a more long-term and comprehensive measurement of the digitalization of listed companies in China over the past two decades can be conducted. The fundamental data support is provided for quantitative measurement and effectiveness analysis of enterprise digital transformation， and there is significant methodological and practical values.

The mean variance （MV） model is the basis of modern asset allocation theory. MV disperses risk through minimizing the correlation of asset returns within the portfolio. Since the variance is not applicable to the non-Gaussian distribution of asset returns and the covariance can only describe the linear correlation between assets， MV is interfered by modeling random errors and the internal or external risks in the market， resulting in poor generalization performance. The existing portfolio researches mainly improve the MV modeling error by modifying the risk measurement and optimizing the risk structure， but those models still have strong data dependence which makes the risk offset unstable. Besides， those models are limited to risk dispersion which cannot resist exogenous shocks. Risk conduction exists partially deterministic causal relationship. However， financial risk forms a complex association through multiple paths of conduction， which makes the overall behavior show high order nonlinear characteristics. In this paper， an improved idea of risk auto disturbance rejection is proposed. Using the sequential risk actions， MV is improved to construct Portfolio Risk Active Disturbance Rejection Model （PRADR）. Empirical research is conducted using A-share stock market data from July 10， 2017 to December 30， 2022， and the results show that the stock market risk is formed by the high-order of independent risk causes interacted and sequentially conducted along the supply chain. The high-order risk conduction network only needs \mathrm{1}/\mathrm{7} risk causes of the first-order linear network， which achieves the same risk interpretation degree； Through two-dimensional cross repeated test， PRADR Sharpe ratio is larger and less volatile， which illustrates its portfolio performance is better than MV； Suffering from the exogenous stock， PRADR has stronger risk auto disturbance rejection ability and higher generalization performance. The key to portfolio risk autoimmunity is to extract the deterministic relationships contained in uncertain risks and resist uncertainty interference. The deterministic risk transmission laws contained in various enterprise relationships need to be explored and applied.

The COVID-19 epidemic in 2020 aggravated the fluctuation of global crude oil prices. Meanwhile， systemic risks spread in global crude oil futures market. In this context， preventing imported financial risks has become an important challenge. Therefore，identifying the impact of emergency events on global crude oil prices can be very valuable. It helps to find the origin of risk in global crude oil futures market， so as to prevent imported financial risks.There is a rich literature on risk contagion， nevertheless， extensive research focuses on stock market and pay less attention to the risk spillover of global crude oil futures market. Hence， few studies on the factors of risk contagion，and even less research risk spillover of global crude oil futures market under emergency event. Meanwhile， extensive research focuses on static models. However， this obviously cannot identify the dynamic risk contagion characteristics of global crude oil futures market. Therefore， based on the risk spillover intensity index， the risk contagion of global crude oil futures market under the COVID-19 pandemic is studied， and then the driving factors of financial risk transmission are clarified.The main findings of this paper are as follows　（1）The risk impact of single market is significantly positively correlated with its severity of pandemic. Europe and the United States are the risk spillover centers of global crude oil futures market， but China is the risk receiver.（2）The increase of overseas pandemic risk will exacerbate the vulnerability of domestic financial market， and then China will be compelled to face increasingly imported financial risks.（3） The volatility of European and America’s stock markets and the appreciation of US dollar all strengthen the risk input of crude oil futures market. From the perspective of market depth and maturity， the deep improvement of the market helps to absorb the risk of price fluctuation， while the rising maturity increases the external risk spillover. （4） The robustness test shows that the impact of COVID-19 epidemic on crude oil market’s risk still exists after various treatments. Although China’s crude oil futures market is stabilizing now，we still need to remain cautiously optimistic. Since the pandemic risk is rebounding，China still faces uncertainty shocks.

Since the 2008 financial crisis， the traditional aggregate monetary policy in western developed economies has been extremely limited. Subsequently， the European debt crisis and the impact of the COVID-19 pandemic further accelerated the process of the world entering the era of low interest rates. Against this backdrop， advanced economies， such as Japan and the Eurozone， have implemented negative interest rate policies to alleviate the severe economic situation like rising unemployment， deflation， and economic malaise. Although in the short term， the traditional negative interest rate policy can stimulate consumption to some extent. However， in the long run， it distorts market price signals and makes it difficult to effectively recover the economy. The reason is that the zero lower bound constraint of deposit interest rates limits the effectiveness of traditional negative interest rate policies on economic growth and inflation.Therefore， how to overcome the zero lower bound constraint of deposit interest rate and improve the effectiveness of the traditional negative interest rate policy on macroeconomic regulation and control has become the global focus. The negative interest rate tool of the central bank digital currency can effectively solve the constraint of the lower bound of the deposit interest rate， and inhibit the negative impact of the traditional negative interest rate policy on the deposit-to-loan spread and the transmission process of credit supply by reducing the cost of bank information and increasing the currency multiplier. In this paper， a dynamic stochastic general equilibrium model including currency multiplier characteristics and information cost characteristics is constructed， and the background of the Great Recession is fitted through risk shocks to better explore the impact of the negative interest rate tool of central bank digital currency on the macroeconomy. It is found that first， in the context of economic recession， the impact of zero interest rate central bank digital currency on the macroeconomy is not obvious. And the negative interest rate tool of central bank digital currency can make the negative impact of bank profits and credit supply less than the traditional negative interest rate policy， which is helpful to alleviate the sustained economic recession. Secondly， increasing the currency multiplier or reducing the cost of bank information is conducive to improving the effect of the negative interest rate tool of the central bank's digital currency on macroeconomics and social welfare， and the two have an interactive stimulating effect. These findings are helpful to better answer the questions about the effect and role of the negative interest rate tool of central bank digital currency on the macro-economy during the economic downturn. And it also provides a good analytical framework for the research on the monetary policy effect of central bank digital currency and the research on negative interest rate policy.

In the context of global industrial and supply chains， multiple stochastic factors such as input prices （such as steel， cement， etc.）， wage costs （including management and labor costs during the construction process）， financing costs （such as interest rates）， and demand are considered to construct a multi cycle investment and pricing joint decision-making model for real estate enterprises. Using stochastic processes to characterize the volatility of input prices， wage costs， and financing costs， incorporating them into a decision model with stochastic demand， and providing an evaluation mechanism for risk and reliability. The research conclusion indicates that， firstly， when the interactive effect of fluctuations in input prices， wage costs， and financing costs shows a downward trend， enterprises should increase their investment to obtain greater expected profits； When the three are irreducible traversal Markov chains， the downward risk of the model has good stability； Decision makers can predict the trend of cost increases in the future cycle through the wear and tear evaluation mechanism， in order to select the best supplier and avoid operational risks. Secondly， analyzing the impact of fluctuations in international factor market prices on corporate profits and the impact of policy uncertainty on corporate costs and optimal strategies， it can be concluded that the greater the volatility risk of both， the higher the cost； Decision makers can explore the long-term operational risks faced by enterprises based on the statistical regularity of international input markets and financing policy fluctuations， in order to provide solid theoretical support for their long-term decision-making. Thirdly， adopting the key hypothesis of flexible decision-making， real estate enterprises dynamically select suppliers for each period under constrained cash flow. The greater the internal fluctuation of each supplier's input quotation， the greater the expected profit the enterprise can obtain， reflecting the value of uncertainty in flexible decision-making.

Forming a multi-supply system by stabilizing government investment and attracting private investment into basic public services， is an important policy arrangement to address the unbalanced development of basic public services among regions in China. Nevertheless， little is known about the practical effect of this policy arrangement， and its inner theoretical mechanism remains unclear. In addition， the existing literature fails to clarify the manifestation of the current relationship between the government and the market in the process of basic public service development.It attempts to fill the gaps by not only examining the impacts of government and private investment on regional equalization of basic public services， but also exploring the interactive effect between government investment and private investment in this study. Also， it tries to investigate the optimal allocation and benign interactive strategies regarding the investment by the government and market， distinguishing samples based on various economic catch-up levels of different provinces， subdivided categories of basic public services， and time trends over the sample period.For methodological approaches， a series of theoretical deductions are developed and three hypotheses are proposed regarding not only the effects of government investment and private investment but also their interactive effect on the interregional equalization of basic public services. Then， focusing on the provincial scope of view to evaluate the inequality among cities within provinces， and capturing the time inertia effect of the spatial distribution of basic public service development， a set of panel data and two dynamic models are developed. Finally， using the generalized method of moments method， the core models are examined and evidence is explored to test the hypotheses. Moreover， regarding the differentiated interactive effect between government investment and private investment， additional evidence is obtained through heterogeneity analyses based on the differentiation of the samples.The findings are as follows. First， there is a nonlinear inverted U-shaped relationship between government investment and regional equalization of basic public services. Second， private investment has a positive effect on regional equalization of basic public services， but this effect is inhibited by the crowding-out effect of government investment on private investment. Third， private investment in regions with lower levels of economic catch-up is more sensitive to the crowding-out effect than in regions with higher levels of economic catch-up， thus failing to promote the regional equalization of basic public services. Fourth， private investment is more likely to be crowded out by government investment in equalization of basic public services concerning social welfare than economic benefits. Also， the crowding-out effect shows a weakening trend and instability during the sample period， indicating that the synergetic supply of government and private investment in basic public services is not fully formed in current China.

Intellectual property pledge financing is a vital means to alleviate the shortage of funds for small and medium-sized scientific and technological enterprises. However， the small scale of pledged fixed assets and high credit risk have become the key problem faced by small and medium-sized enterprises in intellectual property financing. Based on the perspective of supply chain， a secondary supply chain consisting of a single supplier and a single manufacturer is constructed， taking into account the two different power structures of supplier-led and manufacturer-led as well as the different relationships between suppliers’intellectual property valuation and their own capital， and four models of supply chain are established based on this model. The optimal decisions of suppliers and manufacturers are compared and analyzed under the four models， and numerical simulation is used to further verify the effects of supplier product price coefficients， credit rates and their impact coefficients on the product prices， output and profits of suppliers and manufacturers. It is shown that， if the production demand of supply chain enterprises can be met after carrying out IPR pledge financing， the operation mode in which suppliers dominate the whole secondary supply chain is more conducive to suppliers' intellectual property pledge financing and promotes the coordinated operation of the whole supply chain system. In order to ensure that the relevant parties in the supply chain can co-create the value of intellectual property， the supplier should increase the participation of multiple parties such as core enterprises by giving reasonable price discounts and other forms of contracts. A theoretical basis and practical guidance is provided for the operation of intellectual property pledge financing in the supply chain and the solution to alleviate the capital shortage of SMEs.

One of the key problems in multi-attribute group decision-making （MAGDM） is how to reach a consensus on multi-attribute opinions， while the interaction between attributes will affect the adjustment of decision-makers' opinions on each attribute and the consensus outcome. However， the extant research often assumes that attributes are independent of each other and ignores the impact of their interactions on consensus efficiency and decision-maker's consensusutility. It aims to explore the consensus problem of how to obtain the maximumconsensus utility with a limited budget considering interacting attributes. First， Choquet integral is introduced to solve the interacting relationship among multiple attributes， and a minimum cost consensus model with interacting attributes is built. Second， considering thedecision-maker's consensus utility and limited budget， a maximum utility consensus model with interacting attributesunder budget constraints is constructed. To ensure that the proposed model has the optimal solution， an interaction optimization model is built to determine the range of the interaction weight between attributes. Finally， the consensus model is validated by thecontract negotiation between the government and farmers. Results show that： （1） Compared with independent attributes， the negative interaction between attributes can increase the consensus cost， while the positive one reduces the consensus cost； （2） Both attribute complementary and redundant relationship will improve the group consensus utility， and the stronger the interaction relationship， the more significant the effect on consensus utility； （3） The redundant relationship between attributes has a greater impact on consensus cost and consensus utility than the complementary relationship. Thus， the interaction of attributes should be fully considered in consensus decision-making， and a reasonable balance between the cost budget and consensus utility will help to maximize consensus utility.It can also provide theoretical reference and methodology support forMAGDMwithinteracting attributes in this study.

With national efforts to achieve carbon neutrality goals， electric vehicles have gradually become the preferred choice for logistics enterprises. However， electric vehicles require planning additional charging routes during delivery， which results in high logistics costs. To reduce the logistics cost of the electric vehicle delivery system， integration of drones is considered into the existing delivery system， and the vehicle routing problem with time windows is investigated under the collaborative delivery mode of electric vehicle-drone. The primary goal is to determine the optimal routes for both electric vehicles and drones to minimize total costs while satisfying the customer's time window requirements. During the delivery process in this mode， electric vehicles may require recharging at charging stations due to their limited battery capacity， when drones are loaded with batteries and goods， launched from， and recovered to the depot. To solve this problem， a mixed-integer programming-based mathematical optimization model is constructed. Then， an extended adaptive large neighborhood search algorithm （EALNS） is proposed， which integrates a construction heuristic algorithm to quickly obtain the initial feasible solution. In the algorithm， new insertion criterion and removal criterion of charging station are incorporated to satisfy the battery capacity constraint， and a shortest path removal operator is designed to accelerate the algorithmic convergence. Finally， simulation experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm. The experimental results show that： （1） Compared with the Gurobi solver and ALNS algorithm， the EALNS algorithm obtains a better solution with a shorter running time in solving the proposed model； （2） Compared to existing delivery systems， integrating drones into the delivery systems can yield cost savings ranging from 1.07% to 19.50%， with an average saving of 5.97%； and （3） The changes in model parameters affect the cost of the solution. Specifically， the costs of the solution decrease as the load capacity and flight duration of drones increase， or as the load capacity and battery capacity of electric vehicles increase. Furthermore， as the customer's time window constraints are tightened， the cost of the solution increases. In conclusion， a new variant of the electric vehicle routing problem with time windows is presented， verifying that integrating drones into electric vehicle delivery systems can significantly reduce logistics costs， and the proposed EALNS algorithm is effective in solving this problem.

Rapid fragmentation of the ride-hailing market has led to issues like “difficulty in getting a car during peak periods and low user matching rate，” for which resource-based businesses like Amap have begun to develop an integration model in which various travel service providers come together on an integrated platform to provide services to customers. Integration models can significantly aid small ride-hailing platform market entry， competition， and profit， however there is some influence from the growth of big platform firms. How to choose the integration approach for ride-hailing platforms is a crucial matter in light of this integration background. Using the two-sided market of ride-hailing as a backdrop， a duopoly platform competition model is developed based on the Hotelling model， which includes an integration platform（I） of small platforms（A） and a large platform（B）， and the integration strategy selection of large platform in different market environments and user characteristics is investigated. In this paper， the bilateral market size decisions， platform bilateral pricing decisions， and the impact of pricing on customers shifting under the non-integration strategy （N）， follow strategy （F）， and open strategy （O） are theoretically derived. Further， numerical simulations are conducted to compare the profit levels of large platforms under different integration strategies. Cross-side network effect and customer’s travel cost are major factors influencing the adoption of large-scale platform integration solutions， according to the findings. Large-scale platforms prefer not to participate in integration when the customer-side and driver-side Cross-side network effects are both low. When the customer’s travel cost is below a certain threshold， the large platform adopts the follow strategy； otherwise， it does not participate in the integration. Interestingly， neither the driver's travel cost nor the commission ratio of the integration platform will affect the choice of large-scale platform integration strategy， which might explain the integration platform’s early stage-free development strategy in reality.

Prices and service levels are the main decision-making issues in the daily operation of shared mobility platforms. Based on the different stages of competitive development of shared mobility platforms， decision-making models are constructed for prices and service levels of two platforms under different competition modes， the equilibrium results of different models are solved， and the decisions and profits under different models of game equilibrium are compared and analyzed. This provides theoretical support for the price and service decisions of two platforms in different stages of competition development and verifies the relevant conclusions through case analysis. The research indicates that： Introducing service competition is conducive to improving the service level of shared mobility platforms； when there is a significant difference between the two platforms， under the service competition mode， the platform with high commission rates demonstrates a competitive advantage in service， and profits increase with the degree of competition enhancement； when the service cost coefficient is low， the platform with low commission rates seizing the market with price advantages is the dominant strategy； when platforms adopt a service competition mode， the degree of change in shared service system revenue caused by changes in platform service levels is higher than the degree of change in platform revenue. The results provide a theoretical basis for the price and service level decisions of shared mobility platforms under different competition modes in different periods.

It aims to examine the optimal offline channel strategy and operations model adoption in a supply chain consisting of a manufacturer and a platform. To this end， three scenarios， i.e.， no offline channel， the platform introducing offline channel and the manufacturer introducing offline channel are considered under reselling model and agency selling model， respectively. The results show that： （1） If the online product matching probabilityis highenough， both the platform and the manufacturer will establish a physical store when the operating cost of the physical store is low enough. If the online product matching probabilityisintermediate， neither of them has the motivation to establish a physical store. If the online product matching probabilityis low enough， it is profitable to establish a physical store only when the travel cost of consumers is low. When the travel cost is high， even if the operating cost of the physical store is zero， none of themwill establish an offline channel. （2）When there is no physical store or the physical store only acts as a showroom， the operations model adoption of the platform and manufacturer only depends on the commission rate. When consumers buy in the physical store， the operationsmodel adoption is not only affected by the platform’s commission rate and consumers’ travel cost， but also by who establishes the physical store. The research findings provide a scientific basis for decision-making on off-line channel management and operations model adoption of platform supply chains.

The production of hazardous chemicals poses a complex and severe safety risk， with maintenance frequently resulting in accidents. The high complexity of maintenance tasks， including various sources of hazards and overlapping risk factors， such as “human-machine-environment，” increases the likelihood of accidents. To reduce risks during maintenance of hazardous chemical facilities， it focuses on optimizing the “personnel-task” assignment scheme， specifically studying the method for minimizing comprehensive risks in a typical maintenance scenario where one person is assigned multiple tasks.A mathematical model and intelligent algorithm are designed to provide an assignment scheme for this problem. The objective function focused on the “personnel-task” assignment and directly affected human risk. Furthermore， the different operating times of different personnel on different tasks resulted in varying total maintenance periods， which impacts equipment failure and environmental accident risks during the maintenance project's existence period. The overall safety risk loss function factors in human， machine， and environmental risks and considers the comprehensive index of task complexity， as well as hard constraints such as one person multiple task assignments with non-overlapping time and qualification matching. From an algorithmic perspective， the solution difficulties of the model， such as non-linearity， non-convexity， double MAX function， 0-1 matrix matching discrimination， and NP-hard， are addressed through an improved genetic algorithm based on greedy rules and adaptive learning mechanisms. The feasibility of this algorithm is verified in two scenarios， random and practical， the calculation results show that the Greedy Algorithm has a fast computation speed， high solution accuracy， and an acceptable error rate. AGA uses a greedy solution as the initial solution， which greatly improves the initial solving performance. However， due to algorithm limitations， its local search capabilities are weak， and it cannot search for better solutions based on the initial solution. HLGA， relative to AGA， has increased local search capabilities due to the addition of learning operators. Overall， the calculation results show that HLGA is indeed more suitable for solving this type of problem than AGA. It demonstrates its advantages on convergence speed， solution efficiency， and quality. A framework for modeling one-to-many personnel assignment problems with non-overlapping is provided. It offers a new reference for solving models with MAX function and matching matrix. The proposed algorithm also provides inspiration for designing algorithms that combine heuristic algorithms and intelligent algorithms. By focusing on personnel assignment， a practical solution is offerred for safety management in the hazardous chemical industry.

In practical project management， there exist many uncertain factors， such as weather changes， shortage of resource supply， untimely capital payment， may influence the activity duration and the contractor’s cash flow arrangement during the whole project implementation process， and finally cause the loss of project profit. At the meanwhile， different clients payment modes determine the time and scales of cash inflow， the project contractor optimizes the arrangement of project activities and cash outflow according to the cash inflow by client， so as to ensure the smooth implementation and completion of the project. Therefore， it has a very important guiding significance for the optimal scheduling and management of cash flow of the actual project， and how to choose a reasonable client payment mode while dealing with the interference of uncertainty factors so that the project can obtain the maximum benefit.Considering the impact of the clients payment modes on project cash flow distribution and profit under uncertain environment， the robust Max-NPV project scheduling optimization problem is studied on different clients payment modes， where time buffer strategy is applied to strengthen the baseline schedule robustness， and at the same time the robustness threshold constraint is formulated. Firstly， the research problem is defined， and on basis of the robustness threshold， a two-stage project payment Max-NPV scheduling optimization model under different payment modes （progress-based， cost-based， time-based， milestone-based） is constructed； The first stage is a robustness maximization scheduling model， to obtain the maximal robustness value and solve the resource constraint based on the resource flow network， and the second stage is to solve the Max-NPV optimization problem under the given robustness threshold constraint. Then， according to the NP-hard property of the problem， a tabu search heuristic algorithm is designed， and considering the algorithms’ neighborhood quantity and search strategy on performance， the variable neighborhood heuristics， multi-start iteration improvement and random generation algorithms are adopted as benchmarks. The four algorithms are tested on a set of randomly generated large scall standard instances， and moreover， the sensitivity of key parameters on project net present value is analyzed.The conclusions of the research are as follows. Firstly， compared with the four algorithms， the designed tabu search algorithm performs better other three algorithms with acceptable time. Then， compared with the four clients payment modes with different algorithms， the results show that different payment modes have an important impact on the contractor’s project net present value， among which， the progress-based payment mode performs the best， and the cost-based and milestone-based rank the second， while the time-based payment mode is the worst. Moreover， the influences of the key parameters including the payment numbers， the project deadline， the payment proportion， the discount rate， the buffer cost per unit time， the robustness threshold coefficient on the project net present value are analyzed， and the following results are drawn： the contractor's net present value increases with the increase of the client payment numbers and the payment proportion， and decreases with the increase of the discount rate， the buffer cost per unit time and the robustness threshold coefficient， and first rises and then decreases with the increase of the project deadline. It is more important to note that， a reasonable robustness threshold can increase the project’s net present value， but too high or too small may induce a decrease conversely.The research in this paper can help project managers to determine the optimal payment strategy and schedule under uncertainty environment， ensuring the smooth implementation and the best benefits of the project. Therefore， it can provide effective decision supports for project scheduling of maximizing the net present value in reality.

The unexpected disaster refers to a disaster that is difficult to prevent and has sudden and highly uncertain characteristics. After the outbreak of unexpected disasters， there will be a shortage of materials. Efficient distribution of emergency materials is the key to ensuring people’s life safety， maintaining their living needs and reducing disaster losses. In the complex and dynamic environment after the disaster， the demand for emergency materials at each demand point is highly uncertain， and the emergency management department cannot predict the demand in advance.The route selection problem for emergency materials distribution is proposed in this paper. The service requests with service time requirements are considered to be sent from each demand point in turn， and the objective is to make the total travel cost add total delay cost as small as possible. An on-line route selection model with unpredictable service requests is established. A comparison strategy is designed based on the comparison between the cost from the current point of the distribution vehicle to the first-to-go demand point and the cost from the current point of the distribution vehicle to the demand point with new service request. The competitive performance of the comparison strategy is discussed in two different cases， and the competition ratio of the comparison strategy is proved.Finally， the implementation of the comparison strategy is validation by a practical case based on the real road network in Jianghan district of Wuhan city. The ratio of the total cost of the online strategy to the optimal cost of the offline problem is 1.9， which shows that the implementation of the strategy is good. A reliable research idea for route selection for emergency materials distribution with unpredictable service requests of demand points is provided. Future research can investigate how to design a better online strategy.

Road capacity plays a key role in emergency evacuation planning， and it is difficult to accurately predict the road capacity when we formulate an evacuation plan before a disaster occurs. Based on the different degrees of impact of disasters on road capacity in reality， the emergency evacuation planning model with capacity uncertainty caused by the road stability coefficient is considered， to obtain the choice of evacuation route and the corresponding flow arrangement over time. In order to avoid the disadvantages of overly strong assumptions about the exact value or accurate probability distribution of road capacity， it is assumed that only the mean and the partial distribution information of the upper and lower bounds of the support set of the road stability coefficient are known， and the uncertain distribution set characterized by known information is formulated， then the distributionally robust optimization method is used to construct a reliable evacuation path plan under the worst-case scenario of the uncertain set； based on the network breakdown minimization principle， an opportunity constraint model with the goal to minimize congestion probability is established to further ensure the reliability of the route planning scheme. Then the solution method is given， and the simulation analysis is carried out to verify the effectiveness of the method and the reliability of the evacuation path scheme.

In recent years， the frequent occurrence of emergencies has brought severe challenges to the supply and price stability in the fresh produce market， causing supply shortages and price fluctuations of fresh produce and damaging the interests of consumers. In this context， the government may rely on the retailer to release emergency-used fresh produce that has been reserved in advance （briefly， emergency products） to the market. To stabilize market supply and price better， the government will also set a price limit for emergency products. However， the retailer may be reluctant to purchase and sell emergency products because they are not profitable or may damage its own profit under a price limit， which will affect the effective release of emergency products and consumers’ living security. Therefore， it is necessary to analyze whether the retailer will actively release emergency products under the government’s price limit policy and how the government sets the price limit to promote the effective release of emergency products.The above problem is discussed in the following three parts. First， considering that the government provides the emergency products and sets a price limit， a multi-stage game model of the fresh produce supply chain is constructed when the emergency products are released or not released， and the optimal decisions and profits of supplier and retailer are analyzed. On this basis， the release strategy of emergency products in the supply chain is discussed， and the government’s price limit policy to promote the effective release of emergency products is explored. Third， the effects of emergency products’ release on the price and supply of fresh produce， consumer surplus， social welfare， and the profits of supply chain members are explored. Finally， through numerical analysis， the model is tested and management inspiration is provided， which offers guidance for the government to tackle supply risks and provides theoretical support for market members’ decision-making under the government’s price limit policy.The results indicate that the government can rely on the retailer to release the emergency products to the market by setting a relatively high price limit. The release of the emergency product can always exert the effectiveness of stabilizing supply and price for fresh produce， and the effectiveness is more significant when the price limit is lower， the yield for fresh produce is higher， and the freshness difference between emergency products and fresh produce is smaller. The release of the emergency product can improve consumer surplus and social welfare. Under a certain price limit， it will also promote the supplier’s and retailer’s profit improvement and bring about an “all-win” situation. It is also shown that there exists an optimal price-limit level to maximize social welfare， and when fresh produce has a higher yield， the optimal price-limit level is higher. Finally， even if the sale of emergency products becomes an “unprofitable business”， the retailer may introduce emergency products， prompting the supplier to reduce the wholesale price of fresh produce， thereby increasing its overall profit.

In the flourishing digital economy， the imperative to facilitate an effective collaboration between local governments and traditional businesses in the context of consumer participation forms the core of this study. It stems from the need to understand and optimize the strategic interplay that drives digital transformation in traditional enterprises， which has become a pivotal concern in the realm of digital economy studies. The problem is articulated by questioning how consumer involvement can enhance government and business collaboration for digital transformation. The focus is on identifying and implementing strategies that allow for efficient integration of consumer feedback and demands into the digital transformation initiatives of traditional businesses. Employing differential game theory， the methodology adopted in this study involves constructing dynamic models to analyze the strategic interactions among local governments， businesses， and consumers. The models incorporate various strategic frameworks including non-cooperative， Stackelberg， and cooperative games， which help in understanding the impacts of different strategic interactions on the digital transformation process. The approach to resolving these issues is through a rigorous analysis of game-theoretical models， which are used to predict and analyze the behavior of stakeholders under different strategic setups. By simulating these interactions， it aims to derive optimal strategies that promote sustainable digital transformation practices while maximizing the collective benefits of all parties involved in this study. The main findings of this research highlight the nuanced dynamics of the strategic interactions. For instance， it is shown that non-cooperative strategies， though primarily focused on individual gains， can inadvertently result in benefits to the overall ecosystem—thereby providing a new perspective on digital transformation. On the other hand， cooperative strategies are found to significantly enhance the ecosystem's integration and optimization， which are crucial for the robust digital transformation of businesses. Furthermore， it contributes to existing literature by offering a detailed theoretical framework that elucidates the role of consumer involvement in digital transformation strategies. It also provides strategic guidance for policymakers and businesses on implementing effective digital transformation strategies that incorporate consumer feedback and demands. It not only aids in understanding the strategic dynamics between governments， businesses， and consumers but also serves as a blueprint for developing policies that foster an environment conducive to digital innovation.

The digital and intelligent transformation of the agricultural product supply chain is considered crucial for improving agricultural efficiency， enhancing supply chain resilience， and promoting sustainable development. In this paper， 689 English - language literatures published by foreign scholars and 753 Chinese and English literatures published by domestic scholars， which are indexed in the Web of Science and CNKI from 1998 to 2023， are selected as samples. Bibliometric analyses on aspects such as the number of publications， keywords， and research hotspots are carried out on the data using CiteSpace and VOSviewer， and the historical evolution of the research on the digital and intelligent transformation of the agricultural product supply chain is sorted out. The research results show that： in terms of the number of publications， an upward trend year by year has been shown in the research on the digital and intelligent transformation of the agricultural product supply chain both at home and abroad； in terms of high - frequency keywords， the research focus of the digital and intelligent transformation of the agricultural product supply chain has been closely associated with technologies such as blockchain， the Internet of Things， and big data； in terms of the clustering of high - frequency keywords， eight clusters have been formed in the research on the digital and intelligent transformation of the agricultural product supply chain， and there is a common category of "blockchain" technology. Finally， the new models and new trends emerging in the digital and intelligent transformation of the agricultural product supply chain are analyzed， and prospects for future research were made.

Ever since MOGU， which is an NYSE-listed online shopping platform from China， introduced live-streaming e-commerce in 2016， live-streaming e-commerce has achieved rapid development in recent years. It has become an important sales channel for traditional sellers and brands， and has penetrated into a majority of industries. Compared with traditional e-commerce， live-streaming e-commerce is distinguished by real-time interaction between watchers and streamers， nature of social commerce， economy of fans， and traffic bi-diversion between the content and e-commerce departments of platforms. It also enables supply chain management by shortening the supply chain and scale of economics in order processing and fulfillment. Live-streaming e-commerce platforms such as Tiktok and Kuaishou， live-streamers or key opinion leaders （KOLs）， multi-channel network （MCN） institutions， brands （i.e.， manufactures， wholesalers， and retailers）， consumers， and governmental departments have formed into a complex live-streaming e-commerce ecosystem. The ecosystem contains many new business models （such as brand-based and KOL-based live-streaming， streamer as a selling agent， a purchasing agent， or self-endorsement， live-streaming auction， blind box live-streaming， and outdoors live-streaming） and new supply chain structure； and members of the ecosystem encounter many new management challenges， including the absorbing and monetization of fans， the competitive and cooperative relationship between KOLs and brand providers， and high return rate in live-streaming e-commerce. On the basis of analyzing the challenges of live-streaming e-commerce that are different from traditional e-commerce， a survey towards the existing literaturerelevant is provided to live-streaming e-commerce， and the potential research issues and research directions are pointed that deserve attention from the perspective of operation and supply chain management.

The rapid development of digital economy promotes the wide application of live streaming in network marketing in recent years. More and more brands entrust e-commerce influencers to provide product recommendation services for their online marketing. However， contracts between brands and e-commerce influencers are often different under complicatedmarket conditions. Firstly， there may be adverse selection problem， because of prior information asymmetry （e.g. consumers’ recommendation preference）. Secondly， information asymmetry after the event will lead to moral hazard problems （e.g. influencers’ effort level）. A principal-agent model consisting of a brand and an online influencer with risk aversion is built， where the brand and influencer are the principal and agent respectively. By solving the optimal incentive contract of brand and the optimal effort level of influencer under different information status， consumers’ recommendation preference and information value of influencer’s recommendation effort level are analyzed. Then the impact of consumer’s recommendation preference， influencer’s risk aversion degree and uncertain market demand on the optimal contract is explored. The results show that when the recommendation effort level is observable， the brand designs the optimal incentive contract with live streaming commission to get the whole value of the influencer. When the recommendation effort level is unobservable， the optimal incentive contract includes live streaming commission and revenue sharing fee. An increase in the risk aversion degree reduces the recommendation effort level， leading to lower profit of the brand， and thus reduces the revenue sharing fee in the optimal contract. In addition， the asymmetric information of consumers’ recommendation preference does not affect the brand’s profit when the effort level is observable， but increases the brand’s profit when the effort level is unobservable. Therefore， the brand who owns the information advantage of recommendation effort level tends to be more profitable. In all， our study provides a theoretical basis for brand’s contract design and e-commerce influencer’s recommendation effort strategy.

The time gap between advertising exposure and its effect is a common universal phenomenon， which provokes the manager's thoughtful consideration. The main reason for this phenomenon is the delayed effect. In this paper， the advertising decision and coordination problem are explored for a supply chain in the finite horizon planning when product goodwill is affected by the delayed effect. By establishing the product goodwill as a delay differential equation of brand advertising investment， an advertising decision model with finite horizon constraint is firstly constructed for a supply chain. Later， the finite horizon optimal control theory for the time-delay system is utilized to derive the optimal advertising strategies， product goodwill， and supply chain profits under the decentralized and centralized decision scenarios. Then， the equilibrium results can be compared and analyzed， including the optimal advertising effort， product goodwill， and profits. Finally， a demand-dependent compound contract is designed to coordinate the supply chain perfectly based on the centralized supply chain as a benchmark. It can be pointed out that， unlike the infinite horizon planning situation， the optimal advertising effort level of the manufacturer is time-dependent in the finite horizon planning and follows the principle of “gradual reduction-early termination.” A longer advertising period and a shorter advertising delayed time can stimulate the advertising investment of the manufacturer， improve the accumulation of product goodwill and obtain more profits. Even if the delayed effect occurs in the finite horizon planning， the centralized decision is always the best decision-making mechanism in the supply chain. The research results will provide some theoretical guidance for the marketing practice of supply chain enterprises.

The global economy remains in a precarious state， increasing risks and uncertainties in supply chain management. Many unpredictable disturbances outside the organization may induce supply chain disruption， threatening the survival and development of manufacturing firms. To adapt to the new competitive environment， novel technologies are urgently needed by manufacturing firms to optimize the process and paradigm of supply chain management. In recent years， the rise of digital technologies （e.g.， big data， cloud computing， artificial intelligence， etc.） has injected new impetus into supply chain management. As an important activity in the application of digital technologies， supply chain digitalization is gaining an increasing amount of attention in both practice and research. Many studies have shown that supply chain digitalization is conducive to the establishment of end-to-end digital connections among supply chain members （e.g.， manufacturers， suppliers， retailers， customers， etc.） and improves the efficiency of supply chain operations in demand forecasting， product design， manufacturing， logistics， and product delivery. Although supply chain digitization has attracted extensive attention from academics， few studies have focused on the impact of supply chain digitization on the survival and development of manufacturing firms from the market competition perspective. How to improve supply chain resilience and shape manufacturing firms' competitive advantage through supply chain digitization has become an important issue to be further discussed in supply chain management literature.Building on the theories of resource-based view and technology affordance， the survey data of 226 Chinese manufacturing firms are used to explore the interplay of supply chain digitalization， supply chain resilience， and competitive advantage. Moreover， considering the multi-agent feature of supply chain management， relational governance among supply chain members is also taken as a contextual factor to analyze its moderating effect on the above-mentioned relationship. Three key conclusions can be drawn from our research. First， supply chain digitization is positively related to the competitive advantage of manufacturing firms. As a management practice of digital technology application， supply chain digitization can enhance the visibility and transparency of the supply chain， improve the efficiency of business processes and value creation， and help manufacturing firms shape their competitive advantages. Second， supply chain resilience mediates the relationship between supply chain digitization and the competitive advantage of manufacturing firms. Supply chain resilience is an effective way for the supply chain to cope with disruption risks， which combines the functions of agility and robustness. Supply chain digitalization can improve the strategic coordination of the supply chain， transform the independent tasks among supply chain members into collaborative， synchronous， and integrated business processes， and respond to various potential and real disruption risks with a high degree of supply chain resilience， thus enabling manufacturing firms to occupy a dominant position in the market competition. Third， relational governance positively moderates the mediating effect of supply chain resilience between supply chain digitization and manufacturing firms' competitive advantage. Relational governance （e.g.， trust and relational norms） is a safeguard mechanism for manufacturing firms to build competitive advantages， which will promote close cooperation among supply chain members. Under the influence of relational governance， digital technology can fully empower supply chain management， enable supply chain members to effectively integrate and utilize data resources， improve the supply chain's capability to deal with disruption risks， and promote manufacturing firms to achieve higher performance than competitors.

If collaborative distribution is optimized in a low-carbon model as opposed to a low-cost model， the cost and carbon emissions will be different. Distribution companies lack motivation to reduce emissions due to higher low carbon expenditure prices and difficulties with carbon data verification. Enterprise alliances seeking to maximize economic benefits typically opt for the low-cost model， so collaborative distribution model has not yet achieved the state of minimizing carbon emissions currently. How to precisely incentivize collaborative distribution companies to adopt the low-carbon model is crucial to achieving the dual-carbon goal.A dual low-carbon incentive strategy is created based on information collected from a government-led digital platform to encourage distribution companies to adopt the low-carbon model. Firstly， adopting incentive strategies requires a scientific estimate of the benefit loss and carbon emission of the alliance members. This problem is modelled as an integer programming which minimizes vehicle routing costs or carbon emissions for sub-alliances， and then costs and carbon emissions are calculated. The cost-sharing values and carbon emissions of each participant in the two models are determined using the Shapley value method and benefit loss models and carbon emission models are created using these data. Secondly， distribution companies are only encouraged to adopt the low-carbon model when the benefit loss is fully made up for. Based on this， a dual low-carbon incentive strategy is suggested considering government subsidies and revenue from carbon quota trading， a carbon quota mechanism is designed based on emission reduction contribution， and compares and the impact of a single incentive strategy and a dual incentive strategy on the best choice made by companies and the government is analyzed. Finally， numerical experiments are used to determine whether the model and strategy are valid and workable.Results indicate that compared to the conventional model， the collaborative distribution low-carbon model based on the digital platform reduces carbon emissions by 31% to 67%. Distribution companies may be more inclined to adopt a low-carbon model if carbon quota mechanisms are based on emission reduction contributions. The dual low-carbon incentive strategy can reduce government fiscal outlay by 15% to 31%， and the carbon emissions overrun penalty mechanism can penalize high-emitting enterprises. The government can use this study as a supplement to current theory and practice to distribute carbon quotas for distribution companies， implement low-carbon incentives， impose penalties for exceeding them， and assist in meeting the double carbon target on schedule.

It is found that： （1） with introducing BT technology， both the supplier and third-party remanufacturer have improved their profits. However， whether the manufacturer benefit depends on BT technology’s impact on promoting demand and consumers’ trust in BT； （2） a higher traceability level can promote the circulation of the whole NEV closed-loop supply chain and the saving of social resources， so as to achieve the purpose of promoting circular economy； and （3） if the investment cost of BT technology is controllable and the effect of BT technology （consumption/recycling preference） is significant， the essential impact of BT technology on the closed-loop supply chain of NEVs is positive.Research Question Sources　The rapid growth in new energy vehicle （NEV） sales has been reflected in the expanding demand and scaling up of battery production， which reached 160 GWh in 2020. In reality， the average service life of the battery for electric cars is 5-8 years， and safety measures hold that the battery must be replaced before a 20-30% degradation occurs from its original capacity. The increased demand for battery power not only places increasing pressure on manufacturers to recycle retired batteries， it also poses a severe threat to the environment， owing to toxic electrolytes and chemicals. Firms are also seeking to recycle used batteries where possible. Most manufacturers choose to cooperate with third-party recyclers （3PRs） to recycle their used batteries so that they can focus on their core business. For example， NEV manufacturers such as Nissan and Volkswagen ask their consumers to return retired batteries to authorized third-party collection centers. Although cooperating with 3PRs can improve the efficiency of recycling electric vehicle batteries， there remains some technological limitations in the reverse supply chain of these batteries， such as the traceability of the energy consumption and the verification of the recyclability of the batteries. These limitations， however， may potentially be overcome by the use of Blockchain traceability （BT） technology. With China’s commitment to “carbon peak and carbon neutrality”， new problems of power battery upgrading or scrapping emerge. How to effectively track the energy consumption of power batteries and the performance of battery materials， verify the recyclability of batteries and ensure compliance？ It is not only a technical problem at present， but also an inevitable environmental problem in the future. Based on the above scenario， Stackelberg game models are employed to study the motivation and influence mechanism of key factors for NEV manufacturers or power battery suppliers to adopt BT technology to track the use of power batteries.

Under the background of China’s increased focus on the stability and security of its industrial and supply chains， as well as the growing challenges faced by Chinese enterprises in a dynamic market environment， there is a pressing need to systematically evaluate the impact of smart supply chain construction on supply chain resilience optimization and enterprise performance improvement. Employing the panel data of Shanghai and Shenzhen A-share listed companies over 2013-2020， the implementation of supply chain innovation and application policies is approached as a quasi-natural experiment in this study. To estimate the impact of constructing a smart supply chain on enterprise performance， a robust DDD model is utilized.The empirical analysis results demonstrate that the construction of a smart supply chain has significant influences on enterprise performance. Firstly， it significantly enhances enterprise performance， thereby indicating that the intelligent transformation and upgrading of the supply chain can assist enterprises in achieving sustainable performance improvement. Secondly， supply chain resilience optimization serves as a key mechanism through which a smart supply chain enhances enterprise performance. This mechanism facilitates the development of dynamic capabilities enabling professional division of labor， control of transaction costs， internal and external financing， and innovation synergy. Thirdly， the policy effect exhibits significant heterogeneity. The construction of smart supply chains has a greater impact on the performance improvement of growing period enterprises， non-state-owned enterprises， labor-intensive enterprises， and technology-intensive enterprises. Moreover， the policy effect is more favorable in situations where there is a lower degree of market segmentation for goods and labor， and when the enterprises are located in the eastern region. The findings of this study have important policy implications for expediting the intelligent transformation and upgrading of supply chains， ensuring the continuous enhancement of supply chain resilience， and accelerating the high-quality development of enterprises.

As a new product with technology that is still maturing， new energy vehicles （NEVs） often foster controversies and even negative word of mouth （NWOM） during consumer use. Thus， it is important for NEV enterprises to deploy timely webcare strategies to respond to NWOM. In this study， experiments in the Chinese market are implemented to analyze the influence of NEV NWOM on consumers’ purchase intention and the heterogeneous effects of enterprises’ webcare strategies. Firstly， experimental studies show that when the degree of NEV NWOM is high， consumers’ willingness to purchase will be more inhibited than when the degree of NWOM is low. Secondly， NEV NWOM influences consumers’ purchase intention by affecting their perceived risk. Thirdly， the interaction between NWOM and webcare strategies affects consumers’ purchase intention and perceived risk. There are differences in the effects of NEV enterprises’ webcare strategies in contexts with different degrees of NWOM. Therefore， NEV enterprises need to “adapt to the situation”：when facing a higher degree of NWOM， NEV enterprises should prioritize accommodative and no-action strategies； facing a lower degree of NWOM， enterprises should prioritize accommodative and defensive strategies. Finally， enterprises should improve products and publicize webcare strategies to reduce consumers’ perceived risk effectively to achieve sustainable consumption of NEVs.

Given the context of carbon peak and neutrality targets， the low-carbon power system can be achieved from two aspects： low-carbon policy and low-carbon technology. In this regard， these two methods are considered and the impact of carbon emission reduction technology， carbon tax mechanism， and demand response mechanism on the low-carbon dispatch of virtual power plants （VPP） is explored. On this basis， the impact of various risk attitudes of virtual power plant operators （VPPO） on scheduling decisions under the uncertainty of renewable energy is considered. Firstly， in order to maximize VPPO profits， a demand response model is built that considers various user attributes as well as a deterministic low-carbon dispatching model. Secondly， considering the uncertainty of wind and photovoltaic power， the information gap decision theory （IGDT） is used to describe the uncertain variables， and the robust model and opportunity model are constructed to explore the scheduling scheme under different risk attitudes of VPPO， so as to provide guidance for the dynamic operation of the system. Finally， the Monte Carlo simulation method is used to generate the initial wind output， photovoltaic output and user load data， and the Gurobi optimization software is used for simulation. The results show that the low carbon， economy and stability of the system can be obtained in the proposed model， and the establishment of a reasonable carbon tax price is conducive to protecting the economic benefits of the VPPO while improving the social and environmental benefits. Only by adopting a scientific attitude to determine the attitude of the VPPO towards the uncertainty of the scenery can the advantages of the robust model and the opportunity model be fully utilized.

Designing a flexible and effective storage and lending mechanism of Chinese emission allowances （CEAs） is not only an important content that needs to be improved in the carbon trading market， but also another innovative practice to improve the emission reduction efficiency of the whole society. At present， there are carbon price volatility aggregation and carbon price rising expectation in China’s carbon market. And the CEAs intertemporal consumption demand of stakeholders can not be satisfied. In view of the above problems， a CEAs storage and lending mechanism is desiqned with carbon credit center as the medium， and time preference theory is combined with evolutionary game theory to construct an evolutionary game model including government， key emission enterprises and carbon credit center. Then， using this model， the strategic interaction and stability among the tripartite participants in the CEAs storage and lending mechanism in discussed under the influence of time preference， and further the policy effects of cost sharing mechanism and income sharing mechanism are compared. The research conclusions are as follows. Firstly， the weaker the time preference of key emission enterprises are， the faster the CEAs credit market can enter the Pareto equilibrium state. Secondly， the stronger the intertemporal demand of “insufficient CEAs in the current period and surplus in the future” is， the larger the interest margin between CEAs storage and lending， the greater the value-added income of CEAs， the faster the CEAs credit market can enter the Pareto equilibrium state. Thirdly， in indirect regulation measures， the revenue sharing ratio between the government and the carbon credit center should be moderate， and the cost sharing ratio should be increased， so as to effectively promote the healthy development of the CEAS credit market. And compared with revenue sharing， cost sharing can promote the smooth operation of CEAS storage and lending mechanism more quickly.