As a result of the transition of atmospheric environmental governance from territorial administration to joint management, regional joint prevention and control has become a crucial air pollution control measure. However, joint prevention and control cannot exist wholly without territorial governance, and how to coordinate joint prevention and control with territorial governance has become the key to air pollution control. This paper explores the policy relevance and impact of territorial governance from the perspective of policy governance, using 12166 air pollution prevention and control policy texts issued by Chinese local governments from 2000 to 2018, and combining unsupervised learning and spatial econometric models. Research has found that local prevention and control policies mainly focus on supervision and regulation, including emergency management of heavily polluted weather, total pollutant emission control, project control and dust control, and mobile pollution source control, but each has its own emphasis on specific prevention and control; And the higher the correlation between regional policies, the more similar their pollution emissions, energy consumption, and industrial development are. The results indicate that pollution emissions and some influencing factors, such as the spatial spillover effect of environmental regulations, are also caused by similar policy prevention and control systems. The prospective policy relevance in territorial governance can serve as the foundation for regional joint governance, and promote regional environmental collaborative governance by further integrating regions with high policy relevance. This study provides a new explanation for the spatial dispersal and transmission of air pollution, and a feasible direction for regional joint prevention and control.

Corporate continuous green innovation activities are an important driver for promoting green and sustainable economic development. This paper uses a sample of A-share listed companies in China's capital market from 2009 to 2022 to empirically examine the influence of local government environmental protection concern on corporate green sustainable innovation levels. This study finds that local government environmental protection concern can enhance corporate green sustainable innovation levels, with stronger concern leading to higher levels of corporate green sustainable innovation. By distinguishing between types of corporate green sustainable innovation activities, the study finds that local government environmental protection concern promotes both upstream green sustainable innovation levels (source control) and downstream green sustainable innovation levels (end-of-pipe treatment), with a more pronounced impact on upstream green sustainable innovation levels. The analysis of the influencing mechanisms indicates that local government environmental protection concern improves corporate green sustainable innovation levels by increasing environmental resource compensation and strengthening managerial environmental awareness. Further heterogeneity analysis reveals that the positive impact of local government environmental protection concern on corporate green sustainable innovation levels is more pronounced in samples that CEOs with green experience, firms with stable institutional investors, heavily polluting industries, and cities with key environmental protection. The economic consequence test shows that local government environmental concern is beneficial for enhancing corporate environmental protection performance by improving corporate environmental outcomes, increasing corporate environmental advantages, and reducing corporate pollutant emissions and environmental governance costs. This paper uses the level of corporate green sustainable innovation as an entry point to explore the microeconomic consequences of local government environmental protection concern, providing theoretical references for promoting the current transition to green and sustainable economic development.

Blockchain technology changes the current financing channel of firms. It would help firms to solve the financing difficulties. This article considers a bank financing model to analyze the firm's optimal production strategy and investment of blockchain technology when the market demand is stochastic. The article also discusses the different decisions in three types of firms (the firm that initial capital to invest in the blockchain is relatively sufficient, the firm that initial capital to invest in the blockchain is insufficient, and the firm with no blockchain investment). In our model, we find that the firm's profit, production, and blockchain investment decision would be affected by initial capital, the bank interest rate, and the bank's interest rate discount coefficient of the blockchain investment. The article finds that with the difference in the level of investment efficiency and the level of profitability of the company, blockchain investment has an adverse impact. Besides, the stimulated market demand generated by blockchain investment can reduce the risk of firms' loan default. The article finds that blockchain investment can create huge value for firms and reduce actual financing costs. Moreover, the article identifies the different impacts of market risk on firm decisions. This work gives managerial insights into firms' financing and production strategy when investing in blockchain technology. The paper also finds that the discounts of interest rates and blockchain investment interest rates formulated by banks would play a guiding role in firms' production.

This paper incorporates digitalization into the theoretical framework of bank risk preference from both the perspectives of income and cost. Based on this framework, it uses panel data from the Chinese banking industry for empirical testing. The results show that digitalization significantly enhances banks' risk preference. Mechanism testing suggests that digitalization improves risk preference by increasing the risk control effect on the income side and reducing management costs on the cost side. Heterogeneity analysis finds that the effect of digitization on risk preference is more significant for banks that are smaller, have lower capital adequacy ratios and are located in areas with weak financial sector development. In terms of loan allocation, digitalization significantly promotes credit loans and corporate loans, providing indirect evidence for the increase in risk preference. Further research also finds that digitalization helps drive banks to return to their roots and better serve the real economy, while also having a more positive impact on internal bank performance, reducing bankruptcy risks and increasing profits. This paper contributes to a deeper understanding of the micro-economic consequences of bank digitalization, thereby providing empirical support for economic policies designed to improve the quality and efficiency of financial services to the real economy.

This article uses the idea of genetic algorithm to construct a new Keynesian model with social learning behavior, and explores the mechanism of the impact of central bank communication on the expected heterogeneity of economic entities. The study found that pessimistic shocks can lead to a sudden increase in the divergence of economic entity expectations. At this time, central bank communication can effectively reduce the expected divergence of economic entities through social learning channels; Pessimistic shocks can also reduce the credibility of the central bank. At this point, by improving the accuracy of information disclosure, the central bank can slowly repair its credibility and guide the expectations of economic entities; Under the pessimistic impact, although central bank communication can effectively reduce social welfare losses, it cannot fully offset the inherent welfare costs of heterogeneous expectations. Further empirical testing has found that the central bank's more relaxed and consistent words and actions, as well as the stronger learning ability of economic entities, can reduce divergence in inflation expectations. This article verifies the role of communication between the People's Bank of China in expectation management from both theoretical and empirical perspectives, providing valuable reference for the central bank's "stabilizing expectations" work.

As an important component of the financial market, the research on stock returns has always been a hot topic in the academic community. The paper proposes a new method for decomposing stock returns based on the extreme price information of high and low prices of stocks, which transforms the direction prediction of stock returns into the question of whether the stock price rise ratio is greater than 1/2. Furthermore, we predict the direction of stock returns based on B-CARS. The paper is based on the S&P 500 index and the CSI 300 index to estimate and predict the direction of stock returns and compares it with the B-CARS model based on single extreme price information and traditional models. The empirical results indicate that the B-CARS model based on two extreme value information has good predictive ability in the stock market and is superior to other models. The extreme price information of stocks can provide more effective information for predicting the direction of stock returns, thereby improving prediction accuracy. This method can be well applied to predict the direction of stock returns.

Addressing the imperative of carbon reduction goals, China's power sector is confronted with the urgent need to transition from coal-dependent energy generation to renewable alternatives such as wind and photovoltaic power. However, this transition introduces challenges associated with the intermittent nature of these renewable sources, destabilizing the power grid as their share in the energy mix expands. Simultaneously, China's power demand continues its rapid expansion. This confluence of expanding demand and shifting supply paradigms necessitates enhanced flexibility within the power grid to ensure its safety and stability. Two viable solutions to this complex scenario are the integration of "battery energy storage system (BESS) with wind and photovoltaic power" and the amalgamation of "carbon capture and storage (CCS) with coal power", both presenting flexibility without associated carbon emissions. In light of these options, this study constructs a comprehensive simulation model, based on hourly load curves, accounting for carbon emissions, escalating power requirements, and renewable energy targets to attain a balanced power production and consumption equilibrium. Upon evaluating the merits of "CCS + coal power" and "BESS + wind and PV power", it becomes evident that the latter exhibits a cost advantage at lower renewable energy source (RES) penetration rates, while "CCS + coal power" becoming increasingly economical as RES penetration deepens. It is resonable to conclude that the deployment of both "CCS + coal power" and "BESS + wind and PV power" demands focused attention. In paving the way for a sustainable energy landscape, equal emphasis should be placed on nurturing an enabling environment for "CCS + coal power", allowing it to mature and capitalize when its cost competitiveness timing came.

The cost allocation & transfer of thermal power enterprises has already attracted much attention. On the basis of clarifying the cost composition of thermal power, the concept of cost allocation & transfer is defined. From the perspective of supply chain, the cost allocation & transfer process is discussed in scenarios, and the performances of different methods and pathways in various scenarios are deduced through establishes an electricity supply chain mathematical modeling, and verified by both situational comparison and numerical simulation. The results show that: 1) If the Carbon Allowance is set, the active carbon emission reduction of thermal power enterprises in the electricity-carbon market will be more conducive to expanding their profit space. 2) The incentives for thermal power companies to channel costs to downstream nodes for large users of electricity are higher, more economical, and more environmentally friendly, while the efficiency of thermal power companies in channeling costs to small users of electricity through downstream nodes of electricity retailers is more dependent on effective partnerships. 3) In the case of imperfect cooperation mechanism, cost transfer is more conducive to ensuring the carbon emission reduction effect and the profit of thermal power enterprises than cost sharing. With the continuous improvement of markets at all levels, however, the contractual price discount cooperation mechanism between thermal power enterprises and downstream nodes will be more conducive to cost allocation & transfer.

Effectively addressing 'Not in My Backyard' (NIMBY) conflict is an urgent issue in the new era of social governance and democratic institution-building. Compensation, as an effective measure to mitigate negative externalities and regulate interest imbalance, serves as an important policy tool for NIMBY governance. This paper proposes a benefit-sharing mechanism as compensation strategy considering residents' uncertain psychology losses and the administrator's reputation fluctuation. By depicting the impact of uncertain losses on the administrator's decision-making, we introduce the reputation fluctuation function, and construct a bi-level programming model to explore the optimal benefit-sharing mechanism. The result emphasizes the adaptability of the proposed mechanism to divergent types of NIMBY facilities. Sharing total positive externalities as compensation can effectively constrain administrator's decision to balance societal and local residents' interests. Meanwhile, the analyses reveal that the public benefit-sharing mechanism will induce trade-offs between reputation improvement and incentive effects enhancement, as well as the total compensation addition. It indicates that pursuing high reputation target is unwise when facing facilities with high positive externalities and residents with extreme psychological losses. Moreover, introducing pure altruism can effectively address the inefficient incentive problem of the benefit-sharing mechanism when dealing with facilities with low positive externality effect. It alleviates and even eliminates the conflict between reputation elevation and the total compensation addition. It also relaxes the requirement for the administrator to pursue high reputation target. The analyses can provide suggestions for addressing NIMBY syndrome from compensation policy design, reputation management, and pure altruism introduction.

To address the issues of inefficiency in agricultural production and difficulty in local marketing in the context of "small and decentralized farming", considering the impact of land trusteeship services on farmers' production and marketing, a Stackelberg game model for land trusteeship services led by agribusiness firms is established. Using the scenario without trusteeship services as the benchmark, we study agribusiness firms' and farmers' service and production strategies. Then, we analyze the impacts of trusteeship services on their profits in two land trusteeship services: "full-time" and "part-time". It is found that: 1) When the cost-effectiveness is high, trusteeship services increase farmers' profits. When the cost-effectiveness is low, and the demand in the local market is high, trusteeship services cannot increase farmers' profits. When the cost-effectiveness is low, and the demand in the local market is moderate, only "full-time" services increase farmers' profits. When the cost-effectiveness is low, and the demand in the local market is down, both "full-time" and "part-time" services increase farmers' profits. 2) "Full-time" trusteeship services exhibit a more pronounced impact in assisting farmers. However, it can lead to the dilemma of "high risk, low return" for agribusiness firms and the failure to form a stable interest connection between the two subjects. 3) When there is a high cost-effectiveness of trusteeship services or a sluggish demand in the local market, "part-time" services lead to a mutually beneficial situation, unless the farmers are in a state of extreme land scarcity.

Aiming at the problem of imbalanced medical service resources in the current operation of Internet hospitals, this paper studies the service capacity allocation strategy between offline and online channels and the operations performance of the healthcare service system. By constructing the sequential game decision-making model in a queueing framework, we analyze the dynamic interaction relationships among enterprises, hospitals, and patients, and then obtain the equilibrium decisions including the payment price, service capacity allocation rate, and patient visit rates of each channel. The results show that under the Internet hospital service model, the amount of resources allocated to the platform by the hospital mainly depends on the demand and ratio of treatment time for patients in offline and online channels. For a small demand size, only when the ratio of treatment time for patients in offline and online channels is moderate, part of the service capacity should be allocated to the online channel, and the remaining capacity should be reserved for the hospital such that the two channels coexist in the market. Otherwise, whether the demand is large or small, only one channel can exist in the market. Specifically, when the ratio of treatment time for patients in offline and online channels is large, all resources should be allocated to the platform such that only online services should be carried out. When the ratio of treatment time for patients in offline and online channels is small, all resources should be left to the hospital such that only offline services should be carried out. Furthermore, one hospital-led Internet hospital is more conducive to the improvement of operating profits and social welfare, but one enterprise-led Internet hospital is more beneficial to the reduction of wait times and improvement of patient welfare.

The physical condition of patients is heterogeneous and complex, making it challenging to accurately predict surgery duration and postoperative recovery length before surgery, which brings challenges to the hospital in surgery scheduling. Medical informatization has produced a large amount of medical data, and the effective use of the diagnosis and treatment data of history patients can provide guidance for the surgery scheduling of newly arrived patients. This paper proposes a machine learning approach that constructs a patient feature-based uncertainty set, taking into account the uncertainty of surgery duration and postoperative recovery length. To minimize total costs during the scheduling period, a two-stage robust optimization model is established from the perspective of patient segmentation, and a column and constraint algorithm is designed to generate accurate solutions. Numerical experiments with real hospital data show that the proposed method can improve the quality of surgical scheduling and alleviate downstream unit shortages.

In this paper, we analyze the effects of individual protection awareness and population movement on the transmission process based on population network and construct a susceptible-infected-recovered-susceptible (SIRS) epidemic transmission model considering individual protection awareness and interregional population mobility. The degree of individual protection is introduced into the epidemic model as a random variable, and the queuing system model is utilized to describe the epidemic transmission process under population mobility. The expectations of the number of susceptible, infected, and recovered individuals are calculated using the stochastic process method to analyze the development of the epidemic. The simulation results show that the awareness of individual protection can reduce the probability of individual infection, and the increase of population input rate in a certain area will lead to an increase in the number of infected individuals, which verifies the correctness of the theorem. In addition, the model is applied to the real epidemic data, showing a good fitting effect.

In the real world, the spreading of infectious diseases is commonly co-occurring with the competitive diffusion of relevant information (or "knowledge") and misinformation (or "fallacy"). This competitive diffusion has, in turn, remarkable effects on the spreading of infectious diseases, posing great challenges to the studies of the mechanisms of epidemic transmission and the prevention and control of public health emergencies. To overcome the deficiency on the studies on the coupled dynamic models of competitive information diffusion and disease spreading, a coupled dynamic model on multiplex networks is proposed to examine how the competitive diffusion of knowledge and fallacy influences the time-scale and range-scope of disease spreading. By comprehensively utilizing the dynamic system analysis based on the microscopic-Markov-Chain-approach (MMCA) and the simulative analysis, the results indicate that the information diffusion significantly affects the spreading of infectious diseases. The group cognitive level affects the spreading of infectious diseases by influencing the competitive diffusion of knowledge and fallacy. In particular, the prevalence time of knowledge and fallacy in the information layer and the initiation time for knowledge diffusion are crucial for the spreading of infectious diseases. The findings in this study may shed some light on guiding the precise prevention and control of epidemics.

Aiming at the budget allocation problem of stochastic simulation optimization under resource constraints, this paper combines hedging idea and portfolio strategy, introduces resource allocation criteria considering deviation and variance, and proposes a two-stage Bayesian optimization method based on finite resource portfolio allocation, in order to better solve the trade-off problem between simulation budget and optimization accuracy. First of all, a heteroscedasticity stochastic Kriging model is constructed by considering the influence of internal and external noise on the output response on the initial sample set. Secondly, in the search phase, based on the portfolio strategy, the acquisition function is optimized to obtain the sequential optimization point set, and the adaptive selection probability is calculated according to its performance. Then, in the allocation stage, the resource allocation criterion considering deviation and variance is used to allocate the budget for the sampling points. In the end, the Hartmann-3 dimensional test function and the inventory problem under random demand are used to compare the proposed method with the existing portfolio optimization algorithm. The experimental results show that the proposed method has good global optimization ability and robustness.

Traditional collaborative distribution only considers logistics enterprises' willingness to participate in, and is less likely to consider whether customers are willing to accept the distribution services from other members of the alliance. For this reason, this paper analyzes the relationship between the ratio of customers' willingness to participate in collaboration and the cost savings of logistics enterprises, and designs an optimal price discount strategy to encourage customers to participate in collaborative distribution. Firstly, a vehicle routing optimization model is constructed to quantify the total distribution cost of the alliance under different customer participation ratios, and an adaptive large neighborhood search algorithm is designed to solve the model. Secondly, the functional relationship between the proportion of customer participation and price discount is described by the theory of demand elasticity, and the total distribution costs of the alliance under different discounts are shared by the Shapley value method, and then the profit of each logistics enterprise after the discount is obtained. Then, with the goal of maximizing the average profit of logistics alliance members, based on the principle of ordinary least square fitting and the Newton method, an estimation method is proposed to quickly calculate the optimal discount according to the total number of customers and distribution price of each logistics enterprise. Finally, the effectiveness of the model and strategy is verified by Solomon standard examples and numerical experiments. The results show that: in different elastic demand markets, after adopting the optimal price discount strategy proposed in this paper, the average profit of logistics enterprises can be improved to 1.5～4 times that when no discount is provided; The accuracy of the solution can be controlled at about 97%, and the average deviation between the corresponding profit and the actual optimal profit is only 3.5%.

Meal takeout routing problem is characterized by dynamic orders and uncertain preparation time, and it usually needs to be managed online in a "fast response" and "seconds-computation" manner when orders arrive. This paper studies the meal takeout routing problem with dynamic requests and stochastic meal preparation time (MTRP-DRST). We formulate a route-based Markov decision process to minimize delivery delays. We develop an effective online decision-making method for solving it. Specifically, based on offline value approximation iteration algorithm, the impact of future events is captured through the reward-to-go value associated with each action. A novel order postponement strategy and a dynamic time buffer strategy are integrated. To fast and effectively approximate the reward-to-go value, decision time, order's service status, order's time slack and order's time buffer are defined as key features of the reward-to-go value, which is approximated in per-order level. Experiments show that the proposed method can provide fast and effective online decisions, with an average decision time of 0.04 s at a decision epoch. Novel order postponement and dynamic time buffer strategies are effective. The management insights are provided to takeaway routing operations.

Extracting potential product defect information from the increasingly abundant online complaints has become a focal point and challenge in quality management for both manufacturers and regulatory agencies. To this end, this study proposes a novel product defect information detection model based on topic modeling. Firstly, the model takes advantage of the guided topic model to determine the number of defect topics through a defect dictionary, extract defect topics consistent with industry standards, and perform multi-label classification of complaints based on these topics. In addition, the model incorporates two improvement mechanisms, namely product feature modeling and adaptive term weighting, to further enhance defect recognition performance by addressing the unstructured nature of complaint information and the sparse distribution of defect words. This study collected 115,668 online complaints from a leading domestic automotive platform for experimentation. Experimental results indicate that compared to existing topic models such as latent Dirichlet allocation, the proposed model can generate defect topics consistent with actual industry defect classifications. Additionally, it can analyze the influence of product brand, energy type, and other features on the probability distribution of defect topics in complaints. The model outperforms other comparative topic models significantly in F1 score, AUC score, Hamming loss, and ranking loss. The product feature modeling, adaptive term weighting, and guided topic model structure of the model ensure its accuracy in all evaluation metrics. This research can help manufacturers and regulatory agencies monitor product quality levels in a timely manner and protect the legitimate rights and interests of consumers.

With the improvement of satellite capabilities and the normalization of emergency requirements, it becomes difficult for the traditional centralized mission planning or distributed cooperative planning for a single emergency task to meet the needs of existing satellite collaborative planning. Therefore, we study the distributed satellite online coordination problem for batch arrival emergency tasks. Firstly, based on the online collaboration mechanism of contract net, this paper aims at the problem of communication surge and cyclic solving caused by batch arrival tasks. This paper proposed an improved contract network protocol based on two-layer tabu search (ICNPTS). ICNPTS divides the original problem into two sub-problems: bid making problem and bid evaluation problem, and uses the historical bidding information to propose three improvement mechanisms. Secondly, a tabu search algorithm for conflict resolution is proposed to generate bids. The contract net bid evaluation problem is solved by multiple matching algorithm based on dominating edge set. Finally, numerical experiments verify the effectiveness of ICNPTS in reducing the communication traffic and improving the coordination effect.

Crude oil is a vital disposable energy source that is closely linked to the economic and security interests of nations. The quantitative simulation and assessment of risks in the crude oil supply chain, along with research on the formulation and improvement of regulatory measures in response to sudden disruptive events, constitute the foundation of safeguarding the security of China's crude oil supply chain and economic growth. Firstly, base on analyzing domestic and international factors influencing the security of the crude oil supply chain, a regulatory model reflecting the interaction between risk factors in the crude oil supply chain system is constructed using the system dynamics method. Through simulation, the study evaluate the impact of long-term and short-term regulatory measures on the risk of the crude oil supply chain and GDP recovery rate during the occurrence of sudden disturbances. Secondly, using the simulation model, multi-stage regulatory combinations and regulatory scales that optimize the GDP recovery rate are identified and fine-tuned. The variations in the risk of the crude oil supply chain under these combination measures are analyzed to reveal the patterns of combination and scale changes in regulatory measures. The results indicate that: 1) The implementation of both long-term and short-term control measures is more effective than the implementation of either measure alone in inhibiting the risk of the crude oil supply chain and improving the GDP recovery rate. Furthermore, the control effect on the risk increases with the increase in the scale of long-term control measures. 2) Long-term control measures on the crude oil supply chain risk suppression effect exists both directly and indirectly, and the indirect effect is mainly achieved through reducing the type and scale of short-term control measures. 3) The feasibility of emergency crude oil imports is a crucial factor influencing the risk of the domestic crude oil supply chain. The lower the feasibility of emergency crude oil imports, the higher the risk.

In the context of the new era, the fundamental way to promote high-quality economic and social development is to improve urban development efficiency, and digital economy plays an important driving role in the process. This paper constructs a theoretical analytical framework for digital economy-driven urban development efficiency improvement, and empirically tests the impact of digital economy on urban development efficiency and spatial spillover effects using a spatial and temporal double-fixed spatial Durbin model. This paper finds that: Firstly, digital economy significantly contributes to urban development efficiency in the region and surrounding areas, and the finding still holds through a series of robustness tests. Secondly, digital economy contributes to urban development efficiency by enhancing social, economic and ecological benefits, but the enhancement is limited by the reduction of land benefits, while industrial integration, technological advancement, and urban-rural integration play an important role in its mechanism. Thirdly, the effect of digital economy in driving the improvement of urban development efficiency shows a non-linear trend of "downward and then upward" and spatial spillover characteristics. Fourthly, there is city-level heterogeneity and geographic-area heterogeneity in the impact of the digital economy on urban development efficiency, which means that the role of digital economy in driving urban development efficiency is more pronounced in cities with high administrative levels and large populations, as well as in the eastern and northern regions. The above findings imply that at present, China should take urban development efficiency as an important target to consider for the high-quality economic development, and take the development of digital economy as the main driving force to improve urban development efficiency.

To systematically explore the impact of regional synergistic development on urban sprawl, this paper firstly constructs a theoretical framework of regional synergistic development on urban sprawl; Subsequently, the fixed effects model, threshold effects model, spatial measurement model and spatial threshold model were used to reveal the effects and non-linear mechanisms of regional synergistic development on urban sprawl, as well as the spatial threshold effects and spatial spillover boundaries of regional synergistic development on urban sprawl at different stages. The results show that: 1) Regional synergistic development has an inhibitory effect on urban sprawl. On this basis, the threshold effect indicates that the relationship between the two has a non-linear characteristic of "first promoting, then inhibiting, and then strengthening the inhibitory effect", and is constrained by the thresholds of population mobility, industrial development, environmental concerns and transportation construction. 2) The increase in the level of regional synergistic development of the local region will exacerbate the phenomenon of urban sprawl in the neighboring regions, which has the obvious characteristic of "beggar-thy-neighbor", but the boundary of the spatial effect of the attenuation is only 280 km. 3) As the level of regional synergistic development increases, its inhibitory effect on local urban sprawl will continue to increase, while its facilitating effect on urban sprawl in neighboring areas will continue to decrease. 4) The spatial spillover effect of regional synergistic development on urban sprawl at different stages shows a wavy spatial distance decay characteristic, and the radiation boundary shrinks as the level of regional synergistic development increases.

As a new financial service, supply chain finance plays an important role in improving financing efficiency and reducing transaction costs for enterprises. Behind the huge benefits there are often frequent incidents of pseudo supply chain finance, and "supply chain security" is gradually elevated to the level of the macro national security system. This paper uses the data of A-share non-financial listed companies in Shanghai and Shenzhen Stock markets from 2007 to 2021, and we find that supply chain finance can significantly reduce firms' risk-taking, while this negative relationship is more obvious in non-state-owned enterprises and small enterprises. Further analysis shows that supply chain finance will enhance the resilience of the industrial chain and supply chain by improving the company's operating efficiency, alleviating underinvestment, stabilizing supply chain relations to reduce the risk-taking level. In addition, enterprises with good bank-enterprise relationship, higher industry competition and higher risk preference of management can enhance the reducing effects to a greater extent. Under the background of high environmental uncertainty faced by enterprises at present, this paper provides feasible ideas for enterprises to carry out supply chain finance to reduce production and operation risks and financial risks, and then maintain the security of industrial chain and supply chain.

The strategy of green and low-carbon development brings more opportunities and challenges to the risk management of commercial banks, especially the carbon risk management. Based on the data of 240 Chinese commercial banks from 2012 to 2021, this paper constructed a measurement system for bank carbon risk, and empirically tested the effects of bank carbon risk on its loan risk, examined its mechanism from the perspective of loan scale and loan concentration in high-carbon industries, and put forward countermeasures accordingly. The empirical test results showed the following findings. First, bank carbon risk significantly increases bank loan risk. Second, the expansion of bank loan scale or loan concentration in manufacturing and construction industries will aggravate the accumulation of bank carbon risk, and then increase the bank loan risk. Third, in local commercial banks, substantial green credit practice can effectively mitigate the carbon risk of bank credit. Meanwhile, carbon emission policy has an impact on the bank carbon risk. When the low-carbon pilot city policy and the carbon trading pilot policy are superimposed, the impact of carbon risk of local commercial banks on the loan risk can be effectively mitigated. This paper provides policy reference for carbon risk management of banks under the new situation.

Using Shanghai International Energy Exchange Center crude oil futures (INE) and domestic and foreign crude oil spot, the paper proposes an integrated method with noise reduction- mixed frequency- decomposition to estimate the hedging ratios in crude oil futures and spot under GEPU. The novelty approach begins with denoising the data, and then uses mixed frequency data approach to model futures-spot structure. The original optimal hedging ratio is obtained. Basing on the idea of "Decomposition-Integration", the original ratio is further decomposed, followed by comprehensive integration of the frequency scales to obtain the final hedging ratio. The empirical results of full-sample and out-sample hedging tests indicate that the integrated method outperforms the control groups in terms of hedging effectiveness. Besides, the performance of INE with Shengli crude oil spot is the best, while that of INE with Brent crude oil spot is the worst. The robustness tests confirm the above conclusions. The study theoretically provides a new approach to estimate the optimal hedging ratio, and in practice provides a new path for crude oil investors to develop risk management strategies.

Using the survey data collected from rural households in Ningxia, Chongqing, and Sichuan provinces, this paper has identified the credit risk and measured the risk loss, under the context of land property rights controlled and the imperfect ecology of rural finance market in China. This paper uses machine learning method to identify farmers' credit risk and verifies the effectiveness of this method compared with the traditional model. Also, Credit Risk+ model is employed to evaluate farmers' credit risk. According to the survey statistics, the default rate of farmers' farmland management right mortgages is relatively high, and it was 10%. Results show that the random forest model could identify the key factors of credit risk and predict the default probability effectively. Moreover, the expected loss and risk exposure of each loan is relatively high, and the risk loss increases rapidly under the impact of extreme events. In addition, it is helpful for financial institutions to optimize the financial capital structure and improve the risk management strategy to increase the investigation of farmers' passive default motivation under the prior risk management framework. Thus, we conclude with several policy implications such as the accelerating development of fintech, improvement of rural credit investigation system, and innovation of risk pre-warning tools.

Based on the daily stock data of China's A-share main board market from 2012 to 2020, this paper establishes an unbalanced panel spatial Durbin model (SDM) with time-varying spatial weight matrices to study the spillover effect of price limit hits. The empirical results suggest that the upper price limit hit (lower price limit hit) can predict the future return of the connected stocks negatively (positively), indicating a significant negative spillover effect (positive spillover effect). This study further finds that under the influence of the price limit hits, there may be a substitution effect of liquidity between connected stocks. The upper price limit hit of a stock can increase its own capital inflow, while the capital outflow of other related stocks may increase. The situation of the lower price limit hit is the opposite. In addition, due to speculative traders, the higher the limit of arbitrage of a stock is, the stronger the spillover effect caused by its price limit hit will be. Finally, the price limits have a significant volatility spillover effect on other stocks in the short-term future.

Default prediction has become an efficient tool that allows financial institutions to differentiate their potential default borrowers, which has been applied in credit risk assessment. Due to the drawbacks of weak meta-classifier and poor predictive ability in traditional Stacking method, this study constructs a default risk warning model based on Stacking approach. Based on the motivation of multiple benchmark model comparisons, the proposed model's efficiency is confirmed from the perspective of six different performance measures including accuracy with respect to forecasting the default risk of 3425 Chinese A-share listed companies. Moreover, we use Friedman test and Bonferroni-Dunn test to verify the robustness of the proposed model based on five open credit datasets including German. There are two innovations and features in this study. First, the optimal feature set is obtained among many feature sets using Lasso-logistic model. Secondly, this study establishes a Stacking ensemble learning model that determines the optimal meta-classifier based on different base classification model combinations for warning the default risk of listed companies, which contributes to the field of credit scoring research by demonstrating that model combinations of different methods are worth considering to improve the classification performance of default prediction models. Our experimental results demonstrate that F-measure of the proposed model constructed based on the optimal meta-classifier has improved. In terms of multiple performance measures, the proposed model's predictive performance outperforms several other benchmark models including logistic regression and decision tree. These features, including interest-bearing debt/total invested capital, monetary fund ratio, and type of audit opinion, play an important role in forecasting the default risk of a company in the next 1～5 years.

In China's IPO market, characterized by parallel registration and approval systems, this study evaluates the cross-market spillover effects of registration reform. It analyzes how sponsor representatives' IPO sponsorship experience in the registration market impacts the information disclosure quality of their approved IPO clients. The research underscores that sponsor representatives with registration market experience notably enhance their clients' disclosure quality, affirming the positive economic impact of registration reform. Mechanism tests highlight the significance of experience accumulation, especially within the same industry, and the personal reputation of sponsor representatives in facilitating cross-market spillover effects. Moreover, extensibility tests reveal that the sponsor representative's reputation and the complexity of the client's business amplify the influence of registration system experience on disclosure quality. Economic consequence tests demonstrate that registration-based sponsorship experience reduces IPO price suppression and enhances long-term client performance. By focusing on sponsor representatives, crucial intermediaries in the IPO market, this study quantifies cross-market spillover effects, providing theoretical backing for China's ongoing efforts to enhance the registration system and promote high-quality capital market development.

Improving the labor market performance of migrant workers is the key to the rapid promotion of new urbanization in China, and it is also the internal demand for achieving common prosperity. Using the 2017 China Migrants Dynamic Survey (CMDS) data, this paper uses dialect as a measure of culture, systematically investigating the impact of cultural diversity of the destination on the labor market performance of migrant workers and its mechanism. This paper finds that the richer the cultural diversity of the destination, the significantly higher the hourly wages of migrant workers. This paper uses the ethnic war in Song Dynasty as instruments for cultural diversity, and the results show that for every 1 standard deviation increase in cultural diversity, the hourly wage of migrant workers increases by 13.0%. Mechanism analysis finds that the social inclusiveness of the destination and the improvement of migrant workers' non-cognitive skills are the mechanisms by which cultural diversity enhances the labor market performance of migrant workers, which enable migrant workers to enter the modern service industry with higher income, thereby improving their labor market performance. This paper expands the research literature in two areas: Immigrant employment performance and "culture and economy", and provide useful insights for understanding the role of cultural diversity in economic growth from a micro perspective. The policy implication of this paper is that promoting a diverse and inclusive cultural atmosphere in cities and enhancing migrant workers' non-cognitive skills, will contribute to the realization of urban-rural integration and common prosperity.

In order to better highlight the new information priority of the fractional order accumulation operator and improve the prediction accuracy of fluctuating data, an improved grey Verhulst model based on new information priority fractional order accumulation is proposed. Firstly, based on the Toeplitz matrix theory, some properties of the new information priority fractional order accumulation operator are studied in detail, and the conditions to satisfy the new information priority are derived. Secondly, the modeling process of the improved grey Verhulst model is presented, and genetic algorithm is adopted to search for the optimal parameters in the new information priority fractional order accumulation operator. Finally, the improved grey Verhulst model is applied to two practical cases with fluctuating data characteristics. Numerical results validate the importance of new information priority and the theoretical results of this article, and the fitting and prediction accuracy of the new model is higher than that of the traditional grey Verhulst model, the fractional order accumulation grey Verhulst model and the new information priority accumulation grey Verhulst model.

Multi-view and multi-level are the two basic principles of problem solving in granular computing. Partition order product space as a new granular computing model follows the principle of multi-view and multi-level, which can describe and solve problems from multi-view and multi-level. However, partition order product space is a lattice structure, and finding a suitable problem solving layer in partition order product space is usually an NP difficult problem, especially when there is redundancy in views and levels, which will lead to the large and complex structure of partition order product space. Therefore, by reducing the views and levels, the complexity of partition order product space can be effectively reduced. Existing metrics, such as decision support, conditional entropy, maximum inclusion and distance measurement, cannot effectively reduce the views, levels and attributes, and this paper combines the concepts of distance measurement, maximum inclusion and maximum decision in the partition order product space, and introduces a new monotonic uncertainty metric based on distance measurement of maximum inclusion entropy. On this basis, the importance of view and level importance are defined respectively, and the reduction algorithms of view and level are given respectively, which can reduce multi-views and multi-levels under each view, which effectively reduces the complexity under the partition order product space. Experimental results prove the effectiveness of the proposed reduction algorithm.

Enterprise ecosystems take into account upstream and downstream relationships as well as physical layout in the operation process of production elements to construct an association network through the coupling structure and functional mechanism. This research creates an integrated assessment approach to investigate the corporate ecosystem mechanism and analyze how to assess the adaptive reciprocity issue under various circumstances. The state of the chain cluster is assessed using the network node's structural characteristics, which are based on system clustering characteristics (such as functional or resource clustering). Taking into account the enterprise ecosystem's network structure, the chain cluster reciprocity utility is enhanced through the joint distribution of the chain cluster reciprocity utility and the chain cluster reciprocity evaluation. In order to measure the adaptive changes of network nodes in the ecosystem, the network node reciprocity judgment also makes a distinction between the types of connections—connections with newly established reciprocal nodes, connections with existing nodes, and random connections. Combining the co-circulation properties of the higher order network structural factors modifies the nodes' response to the ecosystem. The study's application scenarios cover embedded and de-embedded assessment modes in particular scenarios, as well as how to integrate assessment aspects and decide which elements to incorporate. Chains are the main emphasis of the embedded evaluation, while nodes are the main focus of the de-embedded evaluation. The enterprise ecosystem can be found in a number of states following regulation, including ideal, positive, development, structural adjustment, growth, and mismatch states. In order to assist enterprises in optimizing and regulating the ecosystem structure based on adaptive reciprocity, this study offers a research perspective of integrated evaluation for enterprise ecosystem research and analyzes the decision proposals of adaptive reciprocity in various application scenarios.

Working capital is essential for the survival and growth of companies. However, economic uncertainties, fluctuating demand, and ineffective management often result in cash flow management challenges. Factors like production costs, order expenses, and financing rates further disrupt operations, leading to system fluctuations. Collaborative control offers a solution by fostering overall cooperation and enhancing system flexibility. Enhancing collaborative fund management improves efficiency and enhances system stability. To address this challenge, this article designs a capital collaborative control strategy in a dynamic environment to achieve system cost reduction and efficiency increase. First, considering a two-echelon supply chain finance system, a basic model composed of capital transfer equations of node companies and a total system cost equation. Second, the basic model transforms into a fuzzy multi-model system by using T-S fuzzy system to reduce the interference to the system during the switching process. Third, the feasibility of the method and the effectiveness of the capital collaborative control strategy are verified by several simulation tests. Finally, some management implications and decision support are given through analysis of simulation results.

Based on a thorough literature review, a theoretical model is constructed with coopetition between owner and contractor as the independent variable, project value-added as the dependent variable, and project uncertainty as the moderating variable. A measurement scale of coopetition between owner and contractor that is highly matched to projects in the Chinese context was developed through analysis of normative documents and expert interviews. Then, the measurement scale was amended and tested by exploratory factor analysis and confirmatory factor analysis. Meanwhile, 194 valid data samples were collected to verify the measurement model of the coopetition. On this basis, the theoretical model was empirically tested by hierarchical regression analysis. The results of this research can be listed as follows. 1) The coopetition between the owner and contractor has a connotation structure of second-order dimension. Specifically, cooperation is supported by three sub-dimensions, that is, the contractor's intervening time point, the contractor's rationalization proposal, and information communication. Competition is supported by five sub-dimensions, that is, the contract pricing method, the "high quality and good price" clause, the benefit sharing of the contractor's rationalization proposal, the supervision strength, and the dispute resolution. 2) There is an inverted U relationship between coopetition and project value-added in engineering-procurement-construction (EPC) projects. 3) The external environmental uncertainty and behavioral uncertainty of project participants both have a negative moderating effect on the inverted U-shaped relationship, whereas the uncertainty of the project itself has a positive moderating effect on the inverted U-shaped relationship.

Motivated by the increasing demand for customized travel services, this study analyzes passenger travel choices and driver supply choices from the perspective of ride-hailing platform operations, considering the heterogeneous demands of regular and special passengers. We introduce an analytical model for the customary-regular dual-service mode and examine the platform's bilateral pricing strategies responding to demand. We have several interesting discoveries. First, to maximize profits, the platform should offer no service in small markets, dual-service mode in medium markets, and only customized services in large markets. Second, under the dual-service mode, the effective arrival rate of regular passengers follows an inverted-U shape with respect to market size, and the platform can significantly broaden market coverage and improve profitability by implementing price discrimination across different passenger categories. Additionally, although the dual-service mode improves overall social welfare, the consumer surplus for regular passengers is critically damaged when the market size is larger, or driver cost for providing customized services is lower. These results provide both theoretical and practical insights for the operation of customized services on ride-hailing platforms.

There is a complex dynamic feedback relationship between the brand goodwill, quality and market demand of agricultural products under the reference quality effect, which makes the performance of production, preservation, and brand marketing strategies non-linear and counter-intuitive. In this paper, a system dynamics model of a regional brand agricultural product supply chain under consumers' reference quality effects was developed, and the optimal level combinations of farmers' production effort, distributor's freshness-keeping effort, and retailer's brand marketing effort in different scenarios were obtained with the Powell hill-climbing optimization algorithm in Vensim DSS software. We designed four simulation scenarios each corresponding to an optimal level combination to simulate the dynamic evolution process of regional brand goodwill and supply chain member profits, and analyze the dynamic influence mechanism of quality, preservation and marketing strategies to optimize the operational strategies of the supply chain. The simulation results show that the level of farmers' planting effort is the key decision variable in controlling the performance evolutionary process of the system, since both of the distributor and the retailer's profits and brand goodwill of the produce are highly positively correlated with the output quality of agricultural products, but the cost of efforts to maintaining high output quality will seriously damage the profits of farmers themselves. Due to the influence of the reference quality effect, the evolution path of brand reputation from non-equilibrium dynamics to equilibrium is full of oscillations, and the trough decreases significantly with the decrease of the manufacturer's quality effort level. The context of applying system dynamics modeling and simulating to present the performance dynamics of supply chain decision-making from the perspective of behavioral operations in this paper is a beneficial supplement to current research based on analytical modeling methods and focusing on equilibrium control strategies.

In order to solve the scheduling problem of the mixed transit system with modular autonomous bus and traditional electric bus, this paper considers the functional and cost differences of the two kinds of buses, and combines cross-line scheduling and segmented charging strategy to construct a scheduling optimization model. The model minimized the operation cost, empty driving cost, purchasing cost and charging cost, and decides the fleet size of the mixed transit system and the tripd chain of each type of buses. For the proposed integer nonlinear programming model, this paper designs a two-stage solution algorithm based on the "initial solution generation and column generation framework", and selects four operating lines in a station in Beijing for case study. The results show that compared with the bus system with single line scheduling, the application of cross-line scheduling strategy can improve the bus utilization rate by 7.2%, and reduce the operation cost by 23.3% and the purchase cost by 21.93%. Compared with the traditional electric bus system with multiple models, the introduction of modular autonomous bus makes the average number of uses of each grouping unit reach 3.58 times, and the average number of uses of buses in the system increases by 0.53 times, which effectively improves the utilization rate of buses. This article provides optimization suggestions for multi-line bus scheduling under the mixed transit system.