This article aims to explore the impact of the internet on income inequality among rural households. Through the analysis of data from China Family Panel Studies, it is found that although the internet can significantly alleviate the inequality in total income and wage income among rural households, its effect on alleviating inequality in entrepreneurial income is limited, and it may exacerbate inequality in household property income. Based on this finding, this article analyzes the mechanism of its impact from the perspective of household income sources, revealing that the internet mainly reduces the wage income gap by pulling rural labor force into the non-agricultural sector, thereby alleviating household income inequality. Meanwhile, households with original capital accumulation are more likely to benefit from the internet, which exacerbates property income inequality. In addition, this article introduces the causal forest algorithm and, from the perspective of human capital, analyzes the heterogeneous effects of the internet on individual-level inequality in wage income and property income among rural households. The results show that the alleviation of wage income inequality is mainly manifested in households with low human capital, while the exacerbation of property income inequality is mainly manifested in households with high human capital.

The vigorous development of artificial intelligence (AI) is a key initiative to drive technological innovation, achieve industrial upgrading, and enhance the resilience of the Chinese economy. As one of the important applications of AI, industrial robots have transformed the production modes of traditional manufacturing industries by leveraging digital technologies and big data algorithms. Based on data from Chinese listed manufacturing companies on the A-share market between 2012 and 2019, this study explores the impact of industrial robot applications on the resilience of Chinese manufacturing firms. The research findings demonstrate that industrial robot applications significantly enhance firm resilience, which remains robust after a series of robustness tests. Mechanism analysis reveals that industrial robot applications enhance firm resilience through two mechanisms: Improving labor productivity and promoting technological innovation. Heterogeneity analysis indicates that the positive impact of industrial robot applications on firm resilience is more pronounced in non-state-owned enterprises, firms with high technological compatibility, firms with high product technological complexity and regions with higher levels of marketization. This study adds new evidence to the study of the economic consequences of artificial intelligence and expands the literature on the influencing factors of firm resilience. This study also provides theoretical support and policy insights for enhancing firm resilience through artificial intelligence, thereby enhancing the resilience of the Chinese economy.

"The adjustment of coal-based electric energy transportation" plays a significant role in enhancing the ecological environment and reducing coal consumption in recipient areas. However, it faces challenges in practice. This study examines the "Structure adjustment of coal electric energy transport" from the "Sanxi Region" to the Beijing-Tianjin-Hebei region based on real-world experiences. By employing a multi-regional CGE model, this paper quantitatively analyzes the environmental, economic, and social impacts of this adjustment on both regions. The research reveals that the primary issue with the current transition is the imbalance of interests between the sending and receiving areas. Specifically, while the Beijing-Tianjin-Hebei region benefits from improved air quality, the "Sanxi Region" suffers from negative effects on both the atmosphere and economy. Considering China's 14th Five-Year Plan environmental protection goals, this paper suggests an optimal annual growth range for coal-based electric energy transportation from 2021 to 2025 of [14\%, 27\%]. Additionally, it proposes an optimized tax rate range for joint air pollution control and an economic compensation plan. This research offers a solution path and reference for overcoming challenges in the transformation of coal-based electric energy transportation and contributes to achieving ecological objectives in the Beijing-Tianjin-Hebei region.

Industrial policy has always played an important role in promoting industrial structure transformation and high-quality economic development. Based on the Five-Year Plan of the province level local governments and the micro-data of Chinese industrial enterprises, this paper constructs a staggered DID identification strategy to empirically analyze the impact of local key industrial policies on firms' TFP. The study found that local key industrial policies can significantly improve the TFP of enterprises through policy effects (financial subsidies, tax breaks, low-interest loans) and competitive effects. Further analysis shows that the way local key industrial policies formulated and implemented will have an important impact on the effect of industrial policies. The impact of local key industrial policies formulated combining with the regional comparative advantage, or implemented dispersedly is better. This study provides Chinese empirical evidence for the impact of industrial policies on firms' productivity, which can provide useful reference for the government to formulate and implement industrial policies and promote high-quality economic development.

Promoting enterprises to accelerate digital transformation is of great significance to enhance the core competitiveness of enterprises, empower the upgrading of traditional industries, generate new forms of business, as well as drive China's digital economy to become better and stronger. From the perspective of enterprises, this paper analyzes the antecedents of enterprises' digital transformation, constructs related indexes based on the text analysis method, proposes a two-factor theoretical model of manager characteristics and dynamic capabilities, and uses the structural equation model based on partial least squares estimation (PLS-SEM). The empirical results show that manager characteristics such as entrepreneurship, digital evangelist and coordinator, as well as corporate dynamic capabilities such as sensing, learning, integrating and coordinating, have a significantly positive role in promoting the tendency and output of digital transformation of enterprises. In addition, manager characteristics can significantly improve the level of enterprises' dynamic capabilities, and the effect of manager characteristics on enterprises' dynamic capabilities and digital transformation is moderated by managers' perception of policy uncertainty. In addition, the above effects are heterogeneous between state-owned and private enterprises, enterprises in the eastern, central and western regions, as well as enterprises in provincial and non-provincial capitals. This paper fills the research gap on the antecedents of digital transformation, and provide a feasible practical path for enterprises to cultivate managers in the digital era and improve their dynamic capabilities.

There are great differences between domestic and foreign enterprises in terms of business decisions, sensitivity to the international economic patterns, etc., which leads to their completely different characteristics in participating in the international industry relocation. Therefore, with the frequent outbreak of international emergencies that cause deep adjustment of global value chain, clarifying the heterogeneity of domestic and foreign enterprises in the international industry relocation will be an important prerequisite to promote the "dual circulation" development pattern and ensure the security of supply chain. This paper proposes a quantitative model to measure the magnitude of industry relocation that distinguishes between domestic and foreign enterprises. Based on that, the empirical study captures the scale, mode, industry heterogeneity and mutual substitution between China's domestic and foreign-funded enterprises in participating in international industry relocation during 2005--2016. The empirical evidence finds that: 1) From the magnitude perspective, both domestic and foreign enterprises in China are generally receiving production activities. However, the growth of relocation slowed down after 2014, followed with the trend of relocation outward. The magnitude of the international industrial relocation of domestic enterprises has always been about 80.0% of the total. However, in terms of the ratio of industrial relocation to output, domestic enterprises have always been much lower than foreign enterprises, and the difference reached 14.1 percentage points in 2005-2016. 2) From the mode perspective, domestic enterprises mainly participate in international industry relocation through intermediate products, while foreign enterprises participate more through final products. 3) From the industry perspective, domestic enterprises is dominated by the capital-intensive manufacturing, while foreign enterprises mainly focus on technology-intensive manufacturing and producer services. 4) Generally, China's domestic enterprises have a strong substitution effect on foreign enterprises in China. However, a "reverse substitution (foreign enterprises substitute domestic enterprises)" is continuously taken place in the technology-intensive manufacturing.

Green development involves a wide range and covers a large range, so the difference in interest demands makes the government, enterprises and financial institutions unable to reach an effective consensus in the game. This study constructs a tripartite evolutionary game model based on green preference perspective, and analyzes the relationships among green regulation, green transition, and green bond investment. The study found that the green preference of government, enterprises and financial institutions has different effects on green development. Enterprise green preference plays a decisive role in green transformation. Firstly, when the green preference of the enterprise is large, even if the government does not carry out green regulation or financial institutions do not invest in green bonds, the enterprise will still carry out green transformation production. However, when the green preference of other participants is not large enough, the phenomenon that the government makes green regulatory decisions but has no policy effect will occur. Secondly, when the size of the green preference of enterprises is in a specific range, while the green preference of financial institutions and the government is large, there are two possibilities: The simultaneous success or failure of green transition and green bond issuance. Finally, the main conclusions of this paper are verified by numerical simulation.

This study constructs the digital innovation index of A-share listed companies from 2008 to 2020, and empirically examines the impact of the "specialization effect of intellectual property (IP) judicial protection" brought by the establishment of IP courts on corporate digital innovation. We find that: 1) the establishment of IP courts has a significant positive effect on digital innovation in companies; 2) the establishment of IP courts mainly motivates digital business model innovation in companies; 3) the establishment of IP courts promotes digital innovation in companies through mechanisms such as optimizing the judicial environment, reducing spillover losses, and alleviating external financing constrains; 4) the promotion effect of the establishment of intellectual property courts on digital innovation is more pronounced in small, non-state-owned, and low-competition industry firms; 5) the establishment of IP courts significantly increases the market value of firms' digital innovation (especially digital business model innovation).

This paper constructs a panel model to conduct an empirical analysis on the impact of financial flexibility on the banking systemic risk and its mechanism with quarterly data of listed banks in China from 2010 to 2021. The research shows that: 1) Financial flexibility has a positive effect on banking systemic risk. Compared with the state-owned banking sector and the economic downtrend period, the promotion of financial flexibility to the systemic risk of banks only exists in non-state-owned banks and the economic uptrend period. 2) Excessive credit expansion and off-balance sheet business play a mediating role in the relationship between financial flexibility and banking systemic risk. Financial flexibility promotes banking systemic risk mainly through increasing excessive credit scale and improving off-balance sheet business channels. The transmission channels of "financial flexibility-excessive credit expansion/ off-balance sheet business-banking systemic risk" are all effective. 3) The new asset management regulation and monetary policy uncertainty have asymmetric moderating effect on the relationship. Implementation of new asset management regulation will weaken the promotion effect of financial flexibility on banking systemic risk, while increase of monetary policy uncertainty will aggravate the promotion effect. 4) Further research on different dimensions of financial flexibility shows that both cash flexibility and debt flexibility have a positive effect on the systemic risk of banks. The results will provide important theoretical guidance and decision-making reference for improving the efficiency of bank financial flexible supervision and preventing and controlling the systemic risk of China's banking industry.

Insufficient aggregate demand is the prominent contradiction facing the current economic operation, we must restore and expand consumption in a priority position. This paper empirically investigates the impact of the housing provident fund system on household consumption using data from three editions of the China Household Finance Survey in 2015, 2017, and 2019. The empirical results show that household contributions and withdrawals to the housing provident fund significantly increase household consumption levels and improve household consumption structure. Robustness tests show that the above conclusions remain robust after replacing the way the core variables are defined, replacing the instrumental variables, and relaxing the exclusivity constraints on the instrumental variables. The Heterogeneity analysis shows that the housing provident fund system has a greater impact on the consumption of housed, low-income, as well as young and middle-aged households. Further research finds that contributing to the housing fund reduces households' precautionary saving incentives, withdrawing from the housing fund increases households' disposable income and eases liquidity constraints, thereby boosting household consumption. This paper provides micro-level evidence that housing funds promote household consumption and improve household consumption structure, which can provide important references for relevant policy formulation.

China's high-quality development and carbon peaking and carbon neutrality goals both require an overall consideration to economic benefits and environmental cost. Transnational investment promotes the reconstruction of global industrial and supply chains, which also leads to dispute of environmental responsibilities under the accounting of economic benefits based on the ownership principle and the accounting of carbon emission based on the territorial principle. In this paper, we employed an inter-country inter-industry input-output database that distinguishes the activities of multinational enterprises (MNEs) and introduced counterfactual analysis and scenario analysis to evaluate impact of structural change in GVC on China's gross national income (GNI) and CO$_2$ emissions. There was significant industrial shift toward China from 2005 to 2016, boosting China's GNI and CO$_2$ emissions by 15.23% and 20.50% respectively compared to 2016 levels. For the future shift, the scenario analysis shows that compared with the relocation of GVC led by developed economies, the relocation led by China would yield lower negative impact on China's GNI when reducing same amount of China's CO$_2$ emissions. The negative impact on GNI and CO$_2$ emissions varies by sector initiating the relocation and by economy undertaking the relocation. Our analysis provides policy implications for China's future GVC relocation and high-quality development.

Technology innovation is a dynamic process,including two links:R&D and technology adoption.In the traditional economic model of technology innovation and wage-income inequality,the paper endogenously portrays the technology adoption,and clarifies the relationship between technology adoption and wage-income distribution.The predictions of the theoretical model are verified empirically using panel data from China.It is found that:We reconstruct the model between technology innovation and wage-income gap,prove the effect and mechanism of R&D and technology adoption on wage-income gap from the theoretical level,and highlight the importance of technology adoption.The effect of R&D and technology adoption on wage-income gap is obviously different.Technology adoption helps to reduce the wage-income gap,but R&D widens the wage-income gap.The conclusion is still valid after a series of robustness tests.The mechanism analysis shows that technology adoption plays a role in alleviating the wage-income gap mainly through marketization,intellectual property protection and technology trading market.Our results imply that government should pay sufficient attention to technology adoption,not just R&D.In other words,only when the R&D enters the production process to achieve technology adoption,can it help narrow the wage-income gap to achieve common prosperity while promoting high-quality economic development.

This paper studies the optimal decisions of the green supply chain (GSC) under demand information asymmetry, and mainly focuses the following three situations: Both the manufacturer and the retailer are risk averse, only the retailer is risk averse, and only the manufacturer is risk averse. The impacts of risk-aversion coefficient and information asymmetry on the GSC members' optimal decisions and utilities are discussed. Then, the cost-sharing contracts are introduced to optimize the GSC's performance under three situations. The results show that: The GSC's equilibrium solutions are influenced by the value of risk-aversion coefficient, and the joint impact of green degree and retail price on the market demand; under three situations, information asymmetry always reduces the manufacturer's utility, and it is not necessarily beneficial to the retailer, which is related to the demand information value evaluated by the manufacturer; whether the information is symmetric or not, the preference sequences of the manufacturer and the retailer for three situations are fixed; meanwhile, given specific conditions, the cost-sharing contracts can improve the products' green degree and help the GSCs achieve Pareto improvements under three situations.

Suppliers and customers are crucial stakeholders in a company's supply chain, and exploring the influence of supply chain structure on corporate sustainable development serves as a micro foundation for China's successful attainment of "carbon peaking and carbon neutrality" goals. This paper focuses on the sample of Chinese A-share companies from 2010 to 2022 to investigate the effects and mechanisms of supply chain concentration on corporate environmental, social, and governance (ESG) performance. Empirical results are as follows. 1) Increasing supply chain concentration suppresses corporate ESG performance, and this conclusion holds true even after considering measurement errors, omitted variables, and reverse causality. 2) Supply chain concentration hinders corporate ESG performance through heightened fluctuation in operating profit, operational slack and unabsorbed financial slack. The influence of supply chain concentration on corporate ESG performance is reinforced when the performance is above the aspiration and weakened when the performance is below the aspiration. 3) The inhibitory effect of supply chain concentration on corporate ESG performance is more pronounced in state-owned enterprises, those with larger market shares, lower innovation capabilities, and higher information transparency. This paper deepens our understanding of the relationship between stakeholders and corporate sustainable development and provides critical scientific evidence for strengthening supply chain management and enhancing ESG performance for businesses.

Aiming at the optimization of demand transportation and assignment in the express delivery network with high-speed railway, this paper proposes a two-stage demand assignment method under the passenger train delivery mode. In the first stage, we use the K-shortest algorithm to calculate the set of feasible transportation routes for freight demands. In the second stage, an optimization model for network train demand assignment plan is built by introducing the type of demand transportation path, train loading state and train cross line operation. In addition, due to the complexity of combinatorial optimization problems and the nonlinear and multivariable characteristics of the model, the traditional particle swarm optimization algorithm has low efficiency in solving transportation and assignment plan models under different freight scales. Therefore, this paper proposes an improved nested particle swarm optimization algorithm to improve the efficiency and accuracy of the solution by iteratively optimizing the transportation path and demand assignment plan. Finally, the paper takes the high-speed railway and EMU operating network composed of 10 lines, including Harbin Dalian Railway, Beijing Shenyang Railway, Changchun Baicheng Wulumuqi Railway, etc., as an example to verify the demand assignment model and the effectiveness of the algorithm. The experimental results indicate that the optimization model for the transportation and assignment plan considering the operation of cross-line trains reduces the demand backlog by 5% approximately. In addition, the nested particle swarm optimization algorithm can effectively solve the demand transportation and assignment problem for large-scale high-speed railway networks, and the efficiency of the solution is improved by about 20% compared with the classical algorithm.

In recent years, there have been plenty of natural disasters occurred in China, which greatly threaten the safety of property and lives of publics. It is proved that the cooperation between the local authority and the suppliers can help better prepare for disasters. However, though there have been plenty of such cooperation in China, their main considerations are still the cost but regardless of the human suffering due to the deprivation of relief supplies for a long time. Mitigating such human suffering is the core of emergency logistics and essential difference compared to the commercial logistics, which makes the relief operations more 'people-centered'. Hence, this paper adopts deprivation cost to measure the human suffering of victims due to the lack of relief supplies. Based on it, this paper aims at answering the following questions: 1) How to decide the reserve quantity and price of emergency supplies to achieve the win-win situation with the consideration of heterogeneous delivery conditions? 2) What are the impacts of different emergency logistics contexts (different kinds of relief supplies, demand distributions and preferences of the local authority) on the cooperation between the local authority and the suppliers? To solve these problems, this paper proposes a cooperation model based on the Stackelberg's game. A case study along with the sensitive analysis is conducted to prove the feasibility of the model. Through using deprivation cost, i.e., the measurement of human suffering, this paper can enrich the research in emergency logistics, help conduct the idea of 'people-centered' and provide scientific model and theory to the relief operations.

In order to give full play to the advantages of the enterprise reserve mode of emergency materials, a cooperation between governments and enterprises is established by using option contracts. Considering that the enterprise can choose physical reserve and production capacity reserve, the paper gives the optimal reserve form (single physical reserve, single production capacity reserve, physical reserve and production capacity reserve) and corresponding reserve quantity when the option exercise price and the probability of disaster events meet different conditions. On the basis of this, the corresponding supply chain coordination mechanism is designed, and the effects of relevant factors on the coordination mechanism, the reserve strategy and the cost-benefit of the government and the enterprise are analyzed. The results show that the designed coordination mechanism has strong robustness; When the enterprise adopts physical reserve and production capacity reserve at the same time, the higher probability of disaster events will lead to the increase of physical reserve quantity and the decrease of production capacity reserve quantity, but the increase of material spot market price will only lead to the increase of production capacity reserve quantity; No matter what kind of reserve form the enterprise takes, the cost and profit of both the government and the enterprise will increase with the increase of the probability of disaster events and the spot market price of the material, and the reduction of option price will increase the sensitivity of the cost and profit of both parties to the changes of relevant factors; With the increase of the probability of disaster events and the spot market price of the material, the superiority of the enterprise reserve mode becomes more and more significant.

The Report to the 20th National Congress of the Communist Party of China especially emphasized that the pursuit of common prosperity for all the people should be integrated into the process of modernization. By constructing the social accounting matrix of China's rural residents' consumption, this paper simulates the changes of the consumption of social goods and services and the Gini coefficient of rural residents' consumption under different distribution policies, so as to explore the distribution policy path to promote the common prosperity of rural residents. The results show that: Expanding the distribution policy of the middle and support the weak can not only promote the growth of social goods and services consumption, but also control the consumption gap of rural residents within a reasonable range, which is the relatively better choice, while the absolutely fair and absolutely efficient distribution policy is the relatively inferior choice; The policy simulation showed that consumption of education, culture and entertainment and health care increased the most, while consumption of food, tobacco and alcohol decreased, indicating that effective distribution policies are conducive to the consumption upgrading and transformation of rural residents; the primary distribution system is the leading mechanism to promote the common prosperity of rural residents, the redistribution system is an important means to promote the common prosperity of rural residents, and the third distribution system is a beneficial supplement. To this end, the paper puts forward the following policy suggestions: Improve the income distribution system, promote the realization of common prosperity; narrow the income gap between residents and promote the prosperity of farmers; we will improve the social security system and improve people's wellbeing.

Digital economy has played an important role in the process of industrial green transformation, which is of great theoretical and practical significance to explore the pollution and carbon dioxide emission reduction effects and impact mechanisms of industrial digitalization. Based on the input-output table of China's between 1991 and 2008 provided by the Eora global supply chain database and the matching industrial environment and energy consumption satellite accounts, this paper empirically investigates the possible impact and mechanism of digital investment on carbon dioxide emissions and pollution emissions from the perspective of factor productivity. Empirical results suggest that digital investment has a non-linear effect on carbon dioxide emissions and pollution emissions, which is robust after mitigating the endogenous nature of digital investment. Mechanism analysis shows that the nonlinear relationship between digital investment and pollution reduction and carbon dioxide reduction are driven by the level of digital investment and the industrial factor productivity. During the sample period investigated in this paper, empirical results verify the existence of "productivity paradox" of digital economy, which is mainly because the marginal benefit of digital investment in the digital infrastructure industry is less than the marginal cost, and the relationship between factor productivity and digital investment is "U"-shaped, while there is an inverted "U"-shaped relationship between factor productivity and emissions. The digital investment and factor productivity during the sample period are both at a low level, as a result, digital investment promotes pollution reduction and carbon dioxide reduction of industry through the effect of "negative negative gain positive". Moreover, heterogeneity analysis shows that digital investment has a stronger pollution and carbon dioxide reduction effect on medium and high energy consumption industries, manufacturing industries, resource-based industries, and when the industry is in a higher export dependence, and a series of robustness tests suggest that the conclusions of this paper are robust. The findings of this paper provide an important policy reference for formulating a digital economy development strategy to serve the achievement of high-quality development economy and strengthening green supply chain management.

The network SBM model has the advantages of considering the internal structure of the system and not requiring proportional improvement of input or output. However, traditional network SBM models do not consider the internal intermediate products, or the improvement direction of their intermediate products is unclear when calculating efficiency, resulting in the model being unable to measure sub-stages' efficiency, and the contradiction between sub-stage improvement goals and overall goals. The article proposes a two-stage network SBM model with consistent objectives, which optimizes the improvement direction of intermediate products, redefines the overall objective function and sub-stage objective functions, so that the sub-stage improvement goals are consistent with the overall improvement goals when improving the input and output level of decision making units, and the improvement process is more in line with the actual production situation. And the overall efficiency obtained by this model is equal to the product of sub-stage efficiency, avoiding the subjectivity and bias brought about by traditional weighted summation methods. Apply it to the measurement of innovation efficiency in China's high-tech industries. The empirical results indicate that the results of the SBM model with consistent objective are more reasonable compared to traditional models, and it provides a frontier projection to enable invalid decision making units to reach the DEA effective state.

In this paper, we combine data envelopment analysis (DEA) with cooperative game theory to create a fair resource allocation mechanism in alliances. We introduce a novel cross-efficiency measure that considers both internal and external effects within alliances. Weight assignments are used to reflect the importance of internal and external DMUs. We establish a cooperative game characteristic function based on this cross-efficiency, show the super-additivity of the characteristic function and prove the core non-emptiness of the cooperative game. Finally, we design a resource allocation mechanism using the Shapley value based on the characteristic function and demonstrate its practical application through a numerical example and an empirical analysis.

The fundamental question in exploring the path of economic growth is how emerging industries gain and maintain their comparative advantages.This paper investigates the impact of knowledge spillovers and policy interventions on the emergence and sustainability of comparative advantages,using big data from 1.33 million registered enterprises across provinces from 2010 to 2020.It conducts various heterogeneous analyses at the industry and provincial levels to explore this issue in depth.The study finds that knowledge spillovers contribute to the emergence of new industry comparative advantages.However,only knowledge spillovers among related industries within a province facilitate the maintenance of existing comparative advantages.National-level industrial policies do not increase the probability of the emergence of supported industry comparative advantages but enhance the share of targeted industries.Regarding the development of comparative advantages,industries with high knowledge intensity and geographic concentration receive lower benefits from knowledge spillovers.Provinces in the central and western regions and those with higher specialization rely more on inter-provincial knowledge spillovers during industry development.Further analysis reveals that a developed highway system enhances the promotion effect of inter-provincial knowledge spillovers on industry development,while provinces with a high net inflow of population act as knowledge spillover sources.Inter-provincial knowledge spillovers positively moderate the effectiveness of development policies for targeted industries.This research provides empirical evidence for enhancing industry diversity.

This paper investigates the UAV scheduling to monitor the exhaust of ships underway by considering the interruptions caused by sea wind and severe weather. In view of the complex and changing characteristics of marine meteorological conditions, a rolling planning framework is proposed. In each rolling planning horizon, we formulate the problem as a UAV-station-ship time-space network model, with the objective of maximizing the benefits of ship monitoring. Our model captures some realistic factors such as the serviceable time window of the ship, real-time and dynamic position of the ship, maximum telemetry distance of the UAVs, flight speed and battery power limitation of the UAVs, and multi-type UAV configuration. To solve this model, we design a Lagrangian relaxation algorithm incorporating several heuristic strategies, so that the upper and lower bounds of the problem can be obtained. Numerical experiments are conducted to validate the effectiveness of the proposed model and algorithm. Some managerial insights are offered by analyzing the effects of UAV configuration and wind intensity to provide decision support for scheduling multi-type UAVs to monitor the exhaust of ships underway under the sea wind and severe weather.

Using the generalized disappointment aversion model including market factors, volatility factors and downside risk related factors, this paper studies the downside risks and their risk premiums, and empirically tests the pricing ability of this model for the Chinese assets. We take the firm characteristic-sorted portfolios and the stock and bond index portfolios as test assets separately. This paper finds that the impacts of volatility factor, downward state factor and volatility downward factor can not be ignored, and the generalized disappointment aversion model show certain universality. The risk premium of voltility factor, downward state factor and downward volatility factor are all negative, indicating that investors prefer assets with positive covariance between asset returns and the related factors, so they are willing to pay premium for them. Using different disappointment thresholds and volatility measurement methods, the generalized disappointment aversion model produces robust results. The introduction of downside risk factors can effectively improve the ability of the model to explain the cross-sectional returns. In addition, disappointment threshold and the weight of market volatility corresponding to lower pricing error are also small, indicating that the disappointment thresholds of the Chinese investors are low, and they are more concerned about the decline of market returns than the rise of market volatility.

Taking the development of forestry carbon sink projects in shareholding cooperation mode as the research object, this paper focuses on the moral risks in the project development and the supervision strength of the regulatory authorities, constructs a stochastic differential game model between project development enterprises and foresters, and analyzes the conditions of spontaneous cooperation within the project and the mechanism of action of different external shocks on project development. It is found that firstly, in the case that the regulator imposes strong supervision on the project development enterprise and weak supervision on the forest farmer, and the proportion of carbon sink revenue allocation received by the forest farmer is lower than that of the project development enterprise, spontaneous cooperation within the project is formed, which alleviates the pressure of supervision. Second, tightening carbon quota increases the mean value of the price of carbon sinks, while including more industries and enterprises in carbon trading reduces the variance of the price of carbon sinks. To effectively motivate project participants in the initial phase, the implementation of a tight carbon quota or tax policy should take precedence, followed by the gradual inclusion of more industries and enterprises. Finally, the stricter the regulatory environment and the higher the proportion of carbon sink revenue allocation require the participating entities to make strategic adjustments in a shorter period of time, and the financial subsidies from the government do not have an impact on the speed of strategic adjustments, but incentivize the level of their behavioral efforts.

News recommendation is an important recommendation scenario, and its effectiveness relies on the thorough exploration of news textual information. In recent years, graph neural networks (GNNs) have gained widespread attention in the field of recommendation due to their powerful ability to mine higher-order information. However, there is limited research on the use of heterogeneous graph neural networks in the field of news recommendation, and existing heterogeneous graph recommendation models also suffer from the problem of information loss. In order to fully exploit the high-level information among news, users, textual topics, entities, and categories in the news recommendation scenario, we propose a meta-path guided neighbors interaction recommendation model (MPNRec) for news recommendation. The MPNRec model builds a heterogeneous graph with more types of nodes and edges fully mine high-level information and improve the performance of news recommendation. On two public datasets (i.e., MIND small and Adressa 1week), the MPNRec model can reach at least a 2% to 5% improvement in recommendation accuracy when compared with state-of-the-art methods.

To enhance the identification and management of credit risk for commercial bank corporate customers, we present a systematic approach for predicting default. Firstly, in constructing high-dimensional big data variable sets, we determine the optimal cutoff point for dividing the indicator interval by minimizing the Gini index. This ensures that each path of the decision tree maximizes the distinction between customer default and non-default. We treat each path as a dummy variable, where the value of the variable is 1 if the customer belongs to this path, otherwise, it is 0. Secondly, to reduce the dimensionality of dummy variables, we utilize Lasso regression to minimize prediction error and infer the optimal set of variables. Thirdly, we calculate the optimal default prediction threshold of the logistic regression model with the highest sum of customer judgment ratio, which improves the accuracy of default firm prediction. Our results show that decision tree path variables have stronger default discriminatory power than raw credit indicators and contain richer information. Additionally, the indicators of net profit cash content, per capita disposable income of urban residents, and legal dispute situation of enterprises have a significant impact on the default prediction of Chinese small enterprises. Although these three indicators represent only 3.704% of the total number of indicators, their contribution to accuracy is 41.639%. Our proposed methodology outperforms the comparison model in terms of accuracy and robustness. It can unveil the key factors and thresholds that affect the credit risk of enterprises, thus providing a basis for commercial banks’ credit approval and pre-loan review work. The methodology’s effectiveness has been proven across multiple credit datasets, and it can be extended to constructing default prediction models for individuals as well as large and medium-sized enterprises.

The uncertainty in impression supply presents a significant challenge to the optimal allocation of advertising resources. To address this uncertainty, this paper proposes a data-driven distributionally robust model for targeted display ad allocation problem. Firstly, a stochastic programming model with chance constraints is formulated, with the objective of maximizing the publisher's revenue and penalizing both the unmet demand and the excess of demand. Second, using historical impression supply data, a data-driven distributionally robust chance-constrained model is established. This model utilizes the Wasserstein ambiguity set to propose an allocation strategy that maximizes the publisher's revenue even under the worst-case distribution of impression supply. Through a conservative approximation, the model can be reformulated as an easy-to-solve mixed-integer programming problem. Finally, large-scale out-of-sample experiments are conducted to validate the feasibility, efficiently, and stability of the model and the solving approach.

In the era of digital economy,the competition pattern of China's banking industry is undergoing tremendous changes,and various digital technologies affect the business model and development level of banks.Based on the perspective of the integration of big data and traditional data,this paper uses factor analysis to measure the digital finance development level of 120 commercial banks in China from 2009 to 2019.Then,the kernel density estimation and the spatial panel Dubin model are used to investigate the temporal and spatial variation characteristics of the digital finance development for banks in China from two perspectives of time and space,and the evolution feature of regional differences and sources of bank digital finance is analyzed.Finally,the interactive relationship between the digital finance development level of banks and regional digital inclusive finance is examined.It is found that the digital finance level of Chinese commercial banks shows polarization differences over time.In the eastern region,there is a mutual promotion effect between bank digital finance and regional digital inclusive finance,while in the northeast region,there will be a suppression effect.In the central and western regions,there is no obvious interaction between them.This paper enriches the construction system of digital finance level measurement,and provides important reference experience for revealing the differences in the development of digital finance.It also explores countermeasures for synergistic improvement with regional digital inclusive finance.

Digital economy has become a new engine of high-quality development, promoting the transformation of employment structure. This paper constructs a comprehensive digital economy index from the perspective of input-output, and adopts semi-parametric generalized additive model (GAM) to study the impact and mechanism of digital economy on employment of rural women based on China Family Panel Studies (CFPS). The results show that, first, the relationship between digital economy and the employment characteristics of rural women is non-linear. With the development of digital economy, the labor participation rate and income of rural women show an upward trend, while working hours show a downward trend. Second, the higher level of digital economy has smaller positive effects on rural women's wage income, business income and the employment in manufacturing industry, while it promotes women's the employment probability of high-quality service industry more significantly. Third, digital economy influences rural women's employment by increasing their disposable time, human capital and the access to information. In addition, the results of heterogeneity show that the digital economy affects more significantly on the employment probability and individual income of highly skilled rural women; It also plays a positive effect on women with heavy parenting burden; The rural revitalization strategy can help rural women adapts the development of digital economy. Therefore, the government should not only promote digital economy, but also improve the knowledge and skills of rural female, which can magnify the cumulative effects of the digital economy on human capital, and meet the requirements of the digital economy for high-quality labor force.

The implementation of the EU Battery Act has intensified the demand for recycled batteries in the market, single-channel recycling of retired power batteries can no longer fully meet the need of production, dual-channel recycling model is becoming a trend. In this paper, a single-channel retired power battery recycling model is proposed, and two dual-channel retired power battery recycling models are extended on the basis of the proposed model. The effects of changes in key parameters on the equilibrium results are analyzed under the three models. The impacts of government subsidy on supply chain members' strategic preferences and incentives to recycle under the dual-channel recycling model are explored in the extended analysis. The study shows that: 1) Changes in market size, market discount factor, refurbishment cost and price sensitivity factor will lead to adjustments in the strategies of supply chain members. Supply chain members can always benefit from increased marketing investment to increase the demand for new energy vehicles with refurbished batteries (uv). 2) Battery manufacturers (BM) and Third party battery recycling remanufacturer (RM) will benefit from the RE model under more stringent environmental policy. Although strict environmental policies will always have a negative impact on electric vehicle manufacturer (EV), choosing the RB model can help to minimize this negative impact. 3) Government subsidy provides more significant incentives for EV and RM, while the incentives for BM are very limited. 4) Strict environmental policy always reduce the profit of the supply chain, while government subsidy is beneficial to the performance of the supply chain. Without government subsidy, RB model should be chosen to improve the profit of the supply chain under lenient environmental policy, otherwise, single-channel mode is beneficial to improve the profit of the supply chain; with government subsidy, RB model is always more beneficial to the supply chain. The above analysis leads to the following management insights: Supply chain members should increase marketing investment to increase demand for uv. Strict environmental policy always discourages BM and EV from participating in battery recycling, and there is a need to develop specific incentives for EV and BM. Government subsidy can effectively incentivize EV and RM to participate in battery recycling, incentives for BM should be more diversified. This study only considered the effect of government subsidy on the performance level of supply chain members, the impact of EPR on the strategic choices of supply chain members and battery recycling will be further explored in the future.

In recent years, with the increase of various uncertainties at home and abroad, corporate debt problems occur frequently and the risk of default cannot be ignored. Under the background of low-carbon and green development, the carbon emission trading (CET) system is an important environmental regulation set up in China for realizing carbon peaking and carbon neutrality goals. While encouraging corporates to reduce carbon emissions, does it affect their default risk? Based on a quasi-natural experiment of China's CET system, using the data of listed companies in the pilot area from 2010 to 2020, this paper studies the impact of CET on corporate default risk through the difference-in-differences model (DID) and difference-in-difference-in-differences model (DDD). This research finds that CET significantly reduces the default risk of the pilot corporates, especially high-polluting corporates. After a series of robustness tests, the conclusion is still valid. Mechanism research shows that CET reduces the default risk by increasing green innovation, strengthening environmental information disclosure and easing financing constraints. Heterogeneity analysis shows that pilot policies have a more significant mitigation effect on enterprise default risk in areas where local environmental governance is stronger and public environmental concern is higher. This paper broadens the research on the relationship between environmental risk and default risk, enriches the empirical evidence on the effect of the carbon finance market at the micro level, and also provides beneficial enlightenment for the prevention and control of financial risks in the green transformation of enterprises.

Recent years have witnessed the rapid development of new energy vehicle (NEV) industry. Process innovation and mode innovation are the two main directions for the innovative development of NEV companies at the present stage. Therefore, choosing an innovation strategy has become a key issue for NEV companies that traditionally adopt the charging mode. We construct an optimization model under process innovation and mode innovation for a NEV company that traditionally adopt the charging mode. We propose applicable conditions of two innovation strategies and the optimal pricing strategy of the NEV company under different innovation strategies. We also extend the model to include government subsidy. Our findings indicate that the optimal innovation strategy of the NEV company is determined by mode innovation cost in the case without government subsidy. However, in the case with government subsidy, the optimal innovation strategy of the NEV company is jointly determined by production cost, market structure and mode innovation cost. Furthermore, there exists an interactive effect between innovation strategy and government subsidy on the pricing of the NEV company. Meanwhile, we reveal the impact of government subsidy on the decision of the NEV company, and analyze the social welfare under different innovation strategies. We also provide useful managerial implications for both NEV companies and the overall industry.

With the expanding capital scale and membership in China's banking industry, the increasingly complex inter-institutional network has emerged as a potential risk contagion channel. Employing the GARCH-Copula-CoVaR model, this paper measures the contribution of 16 listed commercial banks to systemic financial risk. Integrating network topology theory and the sliding window method, we study the evolution of banking network structure characteristics and analyze their influence on the transmission mechanism of systemic financial risk. While the "decentralization" effect of the banking sector has grown significantly, enhancing overall network stability and risk resistance, it remains crucial to recognize the diverse roles played by network structure characteristics in systemic risk generation and radiation. Banks in different risk contagion positions are key players in the contagion process. Banks in different risk-transmitting positions face varying challenges in meeting both micro-prudential and macro-prudential requirements. Banks sensitive to extreme risks and occupying central roles in the network are inclined to export more risks to the entire system, often leading in the risk transmission process. During crises, network density undergoes drastic changes, and imbalances in relationship network stability expedite the contagion of systemic financial risk. This study proposes ideas for regulators to update the list of systemically important banks in real time and comprehensively supervise risk contagion channels, significantly enhancing the financial market's resilience to risks and mitigating systemic risks.

The evaluation of the project schedule's robustness is important and difficult in the study of robust scheduling for the project with uncertainties. Given this, a robustness evaluation indicator is designed in this paper. Firstly, the stochastic characteristics of activities within the project constraints are analyzed, and the probability of activity starting time is derived by deconstructing the relationship between activity procrastination and postponement. Then, the loss incurred by uncertainties is described as deviation cost, the expected value of which is deduced by an approximation method and used as a measure of the schedule's robustness. A settable parameter is introduced in the indicator. The precision and simplicity of the indicator can be weighed by adjusting the parameter, which improves the flexibility of the indicator in practical applications. Finally, the experiments show that the new indicator can accurately reflect the schedule's robustness and improve the performance of robust scheduling algorithms compared with the traditional ones.

The opening of public data is the government's major strategic move to release the value of data factor. However, whether these data resources are used by the public to release their value needs to be empirically tested. Therefore, based on the perspective of high-quality development of firms, this paper examines the relation between open public data and firms' total factor productivity so as to reflect the value of public data resources in driving force of promoting firms' high-quality development. Taking A-share listed firms from 2010 to 2019 as samples, using a natural experiment based on the launch of the local government data platform, and utilizing the staggered difference-in-differences model, this paper finds that as compared with their counterparts, firms located in provinces with their public data open have higher total factor productivity after the launch of public data platform, indicating that open public data resources can realize their value by promoting firms total factor productivity. The result holds after a variety of robustness tests. Mechanism tests reveal that the main regression effect is more pronounced for firms with managers' greater demand for public data resources or stronger data processing capacity, or for firms with greater demand for public data resources to supervise firms. These results indicate that the promotion of total factor productivity is due to the decrease of information processing cost, which promotes the use of public data resources by internal and external stakeholders of firms. Further analyses show that the open public data reduce the operating expenses, earnings management and financing constraints of firms. This paper enriches the relevant researches on the economic consequence of public data resources and its mechanisms, provides theoretical support for the practice of open government data, and provides reliable causal evidence for the value of data resources.

With the wide advocacy of ESG (environment, social, governance), it has been increasingly common for firms to consider ESG indicators such as social welfare and consumer welfare when they optimize their profit. Considering a two-sided sharing platform (such as Meituan) that links a group of service providers and a group of consumers, this paper studies the pricing decisions for a platform that is bilateral socially responsible. The platform seeks to optimize its profits, as well as the welfare of service providers and surplus of consumers. We model the platform's objective function as a weighted summation of these three sub-objectives, with the weights on the latter two reflecting the platform's concern level towards welfare of service providers and consumers, respectively. Starting from the choice behavior of service providers and consumers, we study the optimal pricing decisions for the platform under two scenarios, i.e., deterministic and stochastic market demands. The study shows that under deterministic market demand, the platform can always perfectly match supply and demand through two-sided pricing. Compared to a platform that only focuses on its own profit, concern on welfare of service providers and consumers will lead to an improvement of welfare on both sides, but at the cost of a decrease in platform's profit. Under stochastic market demand, the platform's concern on welfare of service providers or consumers reduces the probability of successful matching for the users on the corresponding side, but contributes to the growth in their welfare; however, the welfare of the other side may be hurt. In particular, under certain conditions, being socially responsible can induce a "win-win-win" outcome in terms of the platform's profit, welfare of service providers, and welfare of consumers.

In the trend of green economy, merchants on the platform have begun to gradually transform towards green development, where traffic marketing has had a significant impact on the merchants' transformation strategies. Therefore, this study establishes a single-product model (producing only traditional products) and a dual-product model (producing both traditional and green products), innovatively focusing on the traffic decision-making of merchants who produce both traditional and green products during the green transformation process. It analyzes the optimal traffic strategy of merchants undergoing green transformation and the impact of traffic marketing on their green transformation. The results indicate that merchants utilize traffic to provide greater support for products in weaker competitive positions, thus achieving a balance between traditional and green markets. However, excessive traffic is not necessarily beneficial, particularly in highly competitive markets, where higher traffic levels may compromise merchants' profits. With the increasing eco-consciousness among consumers, merchants consistently benefit from green transformation under traffic marketing. Furthermore, consumer preferences for eco-friendly products are a key factor influencing merchants' traffic strategies. Interestingly, as consumer preferences for green products increase, merchants tend to reduce traffic for green products, reflecting an inevitable choice by businesses to protect their traditional product lines.

Our country is in the crucial process of industrial structure reformation, and expanding the new infrastructure investment, which has a feature of digital technology application, has a practical significance in pushing the upgrading industrial structure. This article picks China's 2004-2021 provincial panel data as samples to empirically prove the spatial effect of new infrastructure investment on industrial structure upgrading. Empirical results illustrate that the new infrastructure investment will accelerate the upgrading of the province's industrial structure, and have a positive spatial spillover effect. New infrastructure investment will inhibit the coordinated transformation of industrial structure, but this negative impact is not contagious among regions; The impact of new infrastructure investment on the upgrading of industrial structure is heterogeneous among different infrastructure types and eastern, central, and western regions. The above conclusions have certain policy implications for China to grasp the direction of new infrastructure investment, improve the efficiency of resource allocation, and accelerate the pace of industrial structure optimization and upgrading.

In the local search for solving the job-shop scheduling problem (JSP), the process of generating the neighbouring solution after performing a move plays a crucial role in determining the computational efficiency of the algorithm. In this regard, a partitioning method for the topological order of a schedule solution is proposed, based on the theories related to the topological order and the maximum arc number of an operation. This method is utilized to generate the topological order of a neighbouring solution. Additionally, the proposed method accurately identifies the set of operations whose head or tail is changed in the neighbouring solution, within the resulting topological order. This enables the fast and dynamic generation of neighbouring solutions. Experimental results demonstrate that the proposed dynamic method offers significant advantages in terms of computational efficiency, leading to considerable savings in computational time compared to existing generation methods.