With the explosive growth of investable assets and asset information, portfolio selection faces the dual challenges of high dimensionality in both assets and characteristics. This paper proposes a portfolio selection framework based on machine learning and asset characteristics. Leveraging the inherent advantages of machine learning, the framework utilizes asset characteristics to directly predict portfolio weights, bypassing return distribution prediction in the conventional two-step portfolio management paradigm. The framework is applied to asset allocation research in the Chinese stock market. The research results show that: 1) The proposed investment strategies capture incremental information within high-dimensional characteristics and uncover both linear and non-linear relationships between asset characteristics and portfolio weights, resulting in a significant enhancement of investment performance. 2) Trading friction-related characteristics are the most important indicators for predicting portfolio weights. 3) These strategies yield higher returns on stocks with stricter arbitrage restrictions while exhibiting lower sensitivity to changes in macroeconomic conditions. Under other economic constraints, these strategies remain robust. This paper expands the research framework of modern portfolio theory, contributing to the development of artificial intelligence and quantitative investment.

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.

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.

"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.

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).

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.

Based on the two-dimensional Hotelling model, this paper builds a game model that simultaneously considers the differentiation of platforms and merchants, to explore the adoption of different exclusive strategies by dominant platforms and the impacts of such strategy. The results show that, there are two types of exclusive strategies, namely monopoly-driven and differentiation-driven exclusivity. The monopoly-driven exclusivity can be promoted by low commission rates of strong platform, low horizontal differentiation between platforms, high vertical differentiation between platforms, and high horizontal differentiation between products; Otherwise, the differentiation-driven exclusivity will be strengthened. Second, the differentiation-driven exclusivity is also beneficial for weak platforms. To avoid the monopoly-driven exclusivity, weak platforms can increase horizontal differentiation between platforms and reduce vertical differentiation or commission rates. Finally, fierce competition among merchants can stimulate the differentiation-driven exclusivity implemented by dominant platforms, which in turn reduces competition among merchants and improves their profits. Overall, the research clarifies the drivers of exclusivity strategy of dominant platforms and distinguishes the influences of different exclusivity strategies on both platforms and merchants, providing strong policy implications for the regulation of dominant platforms and anti-monopoly in the platform industry.

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.

Against the backdrop of economic globalization, the rapid advancement of cutting-edge digital technology has catalyzed a new wave of technological revolution. The AIGC technology, represented by ChatGPT, disrupts the technical landscape of traditional artificial intelligence. And it is widely embraced for its enhanced human-like functionalities, thus emerging as a pivotal milestone in the advancement of general artificial intelligence. Through the analysis of ChatGPT's impacts on the education and employment market, this research reveals that the implementation of AIGC technology can enhance social value exchange efficiency and invigorate the education and employment market. However, it also gives rise to legal and ethical concerns such as data privacy infringement. Therefore, management and supervision recommendations are proposed to address potential risks in order to ensure seamless operation of the economy and society.

With the decreasing cost of intelligent capital compared to the labor costs of medium-skilled workers, artificial intelligence (AI) is poised to replace jobs in the middle-skilled segment, leading to labor market polarization. This article constructs a dynamic multi-sector general equilibrium model to compare three strategies to cope with this phenomenon: 1) improving the labor productivity of medium-skilled workers, 2) transitioning medium-skilled workers into low-skilled roles, and 3) upskilling medium-skilled workers into high-skilled positions. Findings reveal that all three strategies can mitigate wage polarization, but transforming medium-skilled workers into high-skilled workers can enhance the overall labor force skill level, reduce income inequality, and promote quality employment and shared prosperity. Automation of high (low) skill tasks will decrease the wages and labor income share of high (low) skilled workers, while the creation of high (low) skill tasks will increase their wages and labor income share. The government increasing the proportion of investment in new infrastructure and reducing the proportion of investment in education can increase total social output, but it will intensify wage polarization. The government increasing the proportion of investment in education and reducing the proportion of investment in new infrastructure will help reduce income inequality, but the economic growth effect will not be as good as Invest in new infrastructure.

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.

This paper uses the data of A-share listed companies in China during 2010-2019, and investigates the impacts of TMT faultlines on corporate social responsibility (CSR) by unbalanced panel fixed effect models. The results show that TMT faultlines have a significant negative impact on CSR. TMT faultlines negatively affect internal, external and positive CSR rather than negative CSR. TMT task-faultlines significantly reduce CSR, while TMT bio-faultlines have no significant effect on CSR. Corporate internal governance environment has a significant moderating effect on the relationship between TMT faultlines and CSR. CEO power intensifies the negative effect of TMT faultlines, while CSR committee and top management diversity inclusiveness weaken the negative effect of TMT faultlines. TMT faultlines can inhibit CSR by increasing agency costs and decreasing internal control quality. This paper reveals the importance of TMT construction and regulatory governance from the perspective of CSR, and provides empirical evidence for governments to formulate and improve corporate governance policies, and for firms to improve their social responsibility and enhance sustainable competitiveness.

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.

Using natural language processing (NLP) techniques to gain key information from unstructured data, such as corporate texts, news coverage and self-media language, to do financial and economic research which has attracted extensive attention from numerous scholars in recent years and a wealth of research literature has existed. This paper summaries the latest research progress on the application of NLP in financial problems to expatiate text analysis methods using NLP techniques, and focuses on literature about how to use annual reports and news text to study issues in financial areas including corporate finance, asset pricing, risk management, macro-finance and green finance. We evaluate some rough edges in the existing research literature and provide certain research directions for further research in the end.

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.

Data ownership can either be considered as an indivisible monistic right or separated into ownership and usufruct, but the economic significance of data ownership and usufruct separation remains controversial. Digital enterprises' data-based services incentivize users to provide data, but also result in loss of privacy for users through personalized pricing. This paper constructs a monopoly firm model to explore the impact of data collection behavior and welfare under the definition of lack of usufruct and possession of usufruct when there is personalized pricing and privacy loss. The study finds that without usufruct, data collection will not occur if the privacy cost is high, but excessive data collection will occur if the privacy cost is low. When the collector has the initial usufruct and the user has ownership of the data, efficient data collection can be achieved, improving user surplus and social welfare. Data ownership only affects the distribution of social welfare between the collector and user. Therefore, we should consider the right definition framework of data ownership and usufruct separation and discuss the ownership of data usufruct in different scenarios. When the privacy cost is low, data collectors should be granted usufruct, and users should be allowed to exercise ownership through the "deletion right" and other ways to improve social welfare and user surplus. When the cost of privacy is high or users underestimate the cost of privacy, the method of prohibiting data collection should be adopted to protect sensitive data, rather than confirming rights.

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.

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.

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 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.

A supply chain with the manufacturing company at its core is usually referred to as a manufacturing chain. Constructing the "Manufacturing chain + platform" dual mode by building a platform based on the manufacturing chain is an important way for China's traditional manufacturing industry to achieve digitalization and service transformation. Based on value co-creation theory, starting from the structure of dual-mode value co-creation system, this paper systematically analyzes the internal and external motivations of dual-mode value co-creation and their realization mechanism. By relationalizing the mechanism, an overall causal loop diagram of the system is formed to fully capture the feedback effects among the factors. On this basis, in order to understand the path and degree of the influence of each factor on the value co-creation system from a dynamic perspective, and then identify the key factors affecting the effect of dual-mode value co-creation, this study constructs a system dynamics model and conducts simulation experiments by using Vensim software. It is found that resource, relationship, and network structural motivations differentially contribute to value co-creation, and the technological innovation indirectly influences value co-creation through acting on the internal structural motivation. In the early stage, the flow and innovation of diverse resources and the integration and collaboration of complementary resources take the lead in stimulating the accumulation of co-creation value; in the middle stage, the building of explicit relationships and the strengthening of the interaction of implicit relationships on the platform can effectively maintain the vitality of multi-subject value co-creation; and the expansion of the scale of subjects driven by the network effect is the key aspect of the final formation of dual-mode ecology. Moreover, improving the compound ability of the focal enterprise, platform technology investment and cross-side network effect can significantly improve the value co-creation effect of dual mode and the accumulation of subject scale; reducing customer learning cost has a certain contribution to the increase of the number of subjects, but has a less significant effect on co-creation value; while the professional ability of supply-side partners has a weaker impact on the value co-creation effect and ecological construction of dual mode.

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.

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.

Based on the theoretical framework of multiple competing platforms, two forms of exclusive contracts are investigated: Traditional exclusive contracts and “pick one of two” contracts. We distinguish the differences in the incentives of the dominant platform to use these contracts, and analyze the impact of the different forms of exclusive contracts on market competition and the revenues of market participants. Our study shows that the degree of differentiation between platforms and between sellers are the core factors in determining the incentives for the dominant platform to use different forms of exclusive contracts and the impact on market participants. When the degree of differentiation between platforms and between sellers are both small, the dominant platform uses traditional exclusive contracts, while when the degree of differentiation between sellers is large, the dominant platform uses “pick one of two” contracts. Although both exclusive dealings reduce consumer surplus and social welfare, the impact on profits of other competing platforms and sellers is uncertain. Traditional exclusive contracts can reduce the profits of all competing platforms, whereas “pick one of two” contracts can reduce the profits of all sellers. This study provides a theoretical explanation for the use of different forms of exclusive dealing of platforms.

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.

Based on the research sample of Chinese A-share listed enterprises from 2010 to 2021, this paper empirically tested the synergistic effect of bank digitalization and enterprise digitalization in promoting the green transformation of enterprises. The study found that both bank digitalization and enterprise digitalization contribute to promoting enterprise green transformation, and bank-enterprise digitalization has a synergistic effect on promoting enterprise green transformation. This conclusion is still valid after considering endogeneity problems and a series of robustness tests. The results of mechanism test show that the synergistic effect of bank-enterprise digitization in promoting enterprise green transformation is due to the "information synergistic effect", "credit allocation synergistic effect" and "internal governance synergistic effect". Based on the test of the enterprise characteristics, it is found that the synergistic effect of bank-enterprise digitalization is more effective for non-state-owned enterprises, small-scale enterprises, heavy polluting enterprises and high-tech enterprises. Based on the test of the external environment of enterprises, it is found that the synergistic effect of bank-enterprise digitalization is more effective for regions with stronger environmental regulations and more intense bank competition, as well as for competitive industries. The additional test of the effects of enterprise green transformation found that green transformation promoted the carbon emission reduction without sacrificing the development of enterprises. This study provides some policy implications for giving full play to the synergistic effect of bank digitalization and enterprise digitalization, so as to empower enterprises with green transformation through digitalization.

In the era of the digital economy, the overlap between antitrust and information protection issues poses a great challenge to the formulation and implementation of public policies. This paper constructs a duopoly model where there are vertical differences in product quality and consumers' sensitivity to the quality of different products is negatively correlated. It explores the impact of different information protection policies with different strength on the pricing strategies of duopolists and consumer decisions and analyzes the specific welfare effects of policies. The conclusion indicates that in the market where consumers' sensitivity to the quality of different products is negatively correlated, a strong information protection policy leads to the impairment of social welfare, and vice versa. The reason is that information weakens the monopoly power by promoting competition. Meanwhile, considering different product characteristics, a strong protection policy and a weak one exert different effects on consumers' surplus and manufacturers' profits because information will bring about the "monopoly effect" and "competition effect", both of which play against each other to make the market present different welfare distribution patterns. Thus, the conclusion of this paper has certain policy implications for promoting and deepening the actualization of China's antitrust and information protection.

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.

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.

The generative AI technology represented by ChatGPT, considered as the second information revolution, have transformed the depth of data analysis, offering new perspectives for intelligent internal audits in enterprises. In response to the limitations in the existing audit risk warnings, such as the limited improvement in the generalization capability of traditional machine learning and the insufficient feature analysis dimensions, we propose a method based on the core technology of ChatGPT—A deep autoencoder network. This method aims to pre-determine risks in the critical accounting activity of incoming funds. First, based on influencing factors, audit features are selected and extracted from various perspectives including business matching, term structure, impairment loss, related transactions, individual statistics, and text information. Subsequently, considering the imbalance of risk samples and the temporal characteristics of financial indicators over the operating cycle, an unsupervised and deep learning-based approach is employed. This involves constructing a deep autoencoder (DAE) pre-training model with the addition of an attention mechanism and employing bidirectional long short-term memory (Bi-LSTM) as the network. Additionally, drawing from the concept of multi-task learning, an integrated framework with model transfer is utilized to quantify audit risk probabilities, ensuring the stability of warnings. Finally, real data from enterprise transactions and finances are collected by using big data technology for comprehensive comparative validation of the proposed method. Experimental results indicate that this method effectively and accurately extracts audit features under different warning time windows. In comparison to common practices like supervised learning and iterative clustering, it significantly enhances the precision and robustness of audit risk warnings. Moreover, it also identifies key factors leading to risk, enabling quickly swift localization of audit suspicions. Our study can provide intelligent decision support for enterprises to improve the quality and efficiency of internal audit.

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.

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.

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.

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.

In the current era of the digital economy, the transformation of digital technology has become a significant driving force for enterprises to achieve high-quality development. Taking advantage of the pilot program of "integration of informatization and industrialization" as a quasi-natural experiment and using Chinese listed manufacturing companies from 2007 to 2021 as the sample, this paper empirically investigates the impact of digital technology transformation on the stock price crash risk using a staggered difference-in-differences approach. The findings are as follows: 1) With the pilot program significantly promoting the digital technology transformation of enterprises, the risk of future stock price crash shows a notable decrease. This conclusion is further supported by a series of robustness tests. 2) Mechanism analysis reveals that the pilot program reduces the risk of future stock price crash through improving internal control quality, but not attracting more external monitoring. 3) The effects of the pilot program in reducing the risk of stock price crash are more pronounced when accounting conservatism is low, earnings aggressiveness is high, there is less analyst coverage, and media attention is low. These indicate the existence of complementary effects between emerging digital technologies and traditional governance mechanisms. Our research enriches the empirical evidence supporting the improvement of corporate governance in the digital economy. The conclusions provide decision-making references for enhancing corporate quality through digital governance for listed companies.

Cap and trade is considered to be the most effective market incentive mechanism for carbon emission reduction to deal with global climate change. In order to study the optimal decision-making of production, emission reduction and carbon trading as well as the diffusion of low-carbon technology under this mechanism, we build two emission reduction game models of low-carbon technology and traditional technology under cap and trade mechanism for competitive firms. The impact of various parameters on the optimal decision-making is analyzed; at the same time, taking WS small world network as the carrier, the evolution rules are designed, and the driving effect of various factors on the diffusion of low-carbon technology is explored by numerical simulation. The results show that the increase of carbon cap and competition intensity is not beneficial to the improvement of emission reduction level, carbon price and carbon trading volume in the micro level. However, output is positively correlated with carbon cap and negatively correlated with competition intensity. In the macro level, the diffusion degree of low-carbon technology under cap and trade mechanism depends on the competition intensity and cap level among firms. The reduction of cap level and the weakening of firm competition intensity can promote the diffusion of low-carbon technology. Compared with the subsidy mechanism, cap and trade mechanism can achieve “win-win” situation between economic and environmental benefits, that is, cap and trade mechanism can promote simultaneously the diffusion of low-carbon technologies and enable firms to obtain higher benefits.

The heterogeneous information network (HIN) has broad application prospects in practical problems due to its inclusion of different types of nodes and edges. The objective of representation learning models for HIN is to find an effective modeling method that represents the nodes in the heterogeneous information network as low-dimensional vectors while preserving the heterogeneous information in the network as much as possible. Existing representation learning models still have limitations in insufficient utilization of heterogeneous information. We propose a fusion encoding and adversarial attack meta-path aggregation graph neural network (FAMAGNN). The model consists of three module components, namely, node content transformation, intra-meta-path aggregation, and inter-meta-path aggregation, which aim to solve the problem of insufficient feature extraction in existing heterogeneous information network representation learning methods. At the same time, the model introduces a fused meta-path instance encoder to extract rich structural and semantic information in the heterogeneous information network. In addition, we introduce FGM adversarial training to perform adversarial attacks during model training to improve the robustness of the model. The outstanding performance in downstream tasks such as node classification and node clustering proves the effectiveness of this method.

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.