In the context of the "double carbon" target, promoting the green and low-carbon transformation of economic and social development is a major systemic project. Developing renewable energy and improving energy efficiency will help to build a more efficient green energy system. Analyzing the effect of energy transformation has important reference value for formulating a reasonable carbon emission policy and achieving medium and long-term emission reduction targets. This study takes this as a starting point. Firstly, static panel and dynamic panel system generalized method of moments (GMM) are used to estimate the impact of energy transformation, renewable energy efficiency and non-renewable energy efficiency on major macroeconomic variables. Secondly, the intermediate production sector is subdivided into renewable energy production sector and non-renewable energy production sector. The dynamic stochastic general equilibrium (DSGE) model is constructed to analyze the short-term impact of energy transformation impact, renewable energy efficiency impact and non-renewable energy efficiency impact on major macroeconomic variables. The analysis shows that: 1) energy transformation promotes the transfer of resources between sectors, the output of renewable energy production sector will increase, while the output of non-renewable energy production sector and carbon emissions will decrease; 2) The improvement of two kinds of energy efficiency will produce economic expansion effect, but it will also produce energy rebound effect and increase carbon emissions; 3) At the end of the simulation period, the implementation of the carbon emission intensity policy will promote the growth effect of three shocks on output, but will also hinder the emission reduction effect and aggravate the rebound effect in the process of energy transformation. The implementation of the carbon tax policy will inhibit the rebound effect of two types of energy efficiency shocks on carbon emissions. In the process of energy transformation, we should rely on a reasonable carbon emission policy and formulate medium and long-term emission reduction targets. This study has important reference value for China to analyze the effect of energy transformation.

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.

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.

It is of great theoretical and practical significance to accurately model and forecast the volatility of financial assets in the complex and changeable financial market environment. Therefore, based on a variety of volatility decomposition methods, and embedded with the Markov regime-switching approach, this study reconstructs multiple new heterogeneous autoregressive realized volatility models, and further takes Shanghai Stock Exchange 50ETF as the research object to compare the prediction accuracy of each model. The main empirical results are as follows. First, the model confidence set (MCS) test shows that the newly constructed model (MS-PHAR) combined with Markov regime-switching and quantile array volatility has the best predictive performance and various robustness checks confirm the above conclusion. Second, during the periods of high and low volatility, before and after the COVID-19 epidemic, and considering the leverage effect, the newly constructed MS-PHAR model still has a good performance.

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).

"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 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.

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.

Under the real demand that Chinese manufacturing industry is advancing to the high end, servitization empowered by digital technology has become an important strategy for manufacturing enterprises to achieve high-quality development, which can stimulate the innovation vitality and create new development opportunities, enabling them to provide customers with intelligent solutions that integrate products, software and services. This study firstly collects foreign and domestic literature that is closely related to digital technology and servitization, selects to form a sample database, and constructs a theoretical analysis framework. Secondly, on the basis of introducing the servitization and digital technology, the evolution trend of the digital servitization is revealed, and the realization approach of the empowerment is analyzed. Thirdly, based on the division of the value chain of manufacturing enterprises, this study expounds the application scenario of servitization empowered by digital technology from the perspective of front-end product innovation, middle-end hybrid product generation and back-end service innovation. Finally, the "double-edged sword" effect of the empowerment with potential value and risks is analyzed, the countermeasures and suggestions are put forward, and the future research directions of this topic are provided.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Various companies that once focused on online sales channels have considered expanding offline channels. In particular, their possible channel development strategies include no offline, direct, or indirect offline channels. By constructing a Stackelberg model, we study the offline channel development strategy of a supplier who only sells online through an e-commerce platform. As a result, we obtain the supplier's optimal offline channel development strategy and corresponding pricing decisions by comprehensively considering different online sales modes, consumer preferences about different channels, and sales costs in different channels. The findings show that the supplier should always extend the offline channel. In addition, the supplier's existing online sales mode vigorously influences his offline development strategy. Specifically, the supplier's optimal strategy is always developing the offline direct channel if the existing online sales model is direct sales. Alternatively, if the existing online model is indirect sales, his offline development strategy depends on the sales costs of different channels. When the sales cost of the online channel is higher, or the online and offline channel sales costs are both lower, the supplier's optimal strategy is offline direct sales. However, when the sales cost of the online channel is low, and the sales cost of the offline channel is high, the supplier's optimal strategy is offline indirect sales.

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.

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.

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 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.

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.

Additive network DEA model is one kind of highly nonlinear programming problem, which is difficult to be solved directly. The existing heuristic algorithm cannot be applied to obtain the exact solution of the model, and will consumes a lot of computing resources and times in the big data environment. In order to solve these two issues, the presented study mainly does two aspects of work. Firstly, aiming at the issue of solving precision of the model, a solving method of quadratic fractional programming is proposed in this study, which decomposes the model into a finite quadratic programming, and it is proved theoretically that the presented method can be applied to obtain the exact solution of the model. Then, in view of the issue of the slow solving speed in the big data environment, this work optimized the model's constraints by reducing the number of constraints and the consumption of computing resources while the feasible region remained unchanged, so as to improve the solving speed of the model. The numerical case results show that the proposed method can effectively improve both of the calculation accuracy and speed of the additive two-stages DEA model.

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.

The change of trade cost in services affects the cross-border flow of service elements, and under the division of global value chains, it can influence the production cost and final product price of each country through the input-output linkages and then affect the welfare of consumers. Based on WIOD database, this paper firstly calculates the level of China's trade cost in services from 2000 to 2014. On this basis, using global input-output price model and consumer welfare model, this paper examines the impact of the change of China's trade cost in services on consumer welfare in different countries from the perspective of equivalent variance and compensating variance. The results show that China's trade cost in services showed a downward trend from 2000 to 2014, with the most obvious decline in air transportation and financial services. The price effect of the change of China's trade cost in services on manufacturing industry is higher than that of service and agriculture, and the effect on knowledge-intensive manufacturing industry is the highest, followed by capital-intensive and labor-intensive manufacturing industries. The analysis of consumer welfare shows that the change of China's trade cost in services has improved consumer welfare of all countries, and it has brought more obvious welfare improvement to the United States, Japan, South Korea and Germany besides itself and the rest of the world. In terms of detailed service sector, distribution, postal transportation and finance and commerce services have the largest welfare effect among all services industry. Furthermore, international comparison shows that the welfare improvement to the rest of the world brought by the change of China's trade cost in services was about 3.3 times as much as that by the Unites States from 2000 to 2014, and China shows greater positive externality and provides more universal benefit to the world than the United States, Japan and the European Union. This paper can provide policy reference for further promoting the service trade openness and strengthening mutual cooperation among countries with the consensus of the community with a shared future for mankind.

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.

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.

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.

In online retail markets, uncertainty about the quality of emerging products and firms' dynamic pricing lead to the possibility that consumers may engage in strategic waiting and delay the purchase of products. When considering the endogenous time preferences, consumers choose their own purchasing strategy (myopic or strategic), while spending an additional sunk cost of time and effort (called strategic cost) if choosing to be strategic. Based on consumer utility theory, we develop a two-period game-theoretic model, where the firm and consumers simultaneously learn and update perceived quality from online reviews in the second period. We study consumers' purchasing strategies when they have endogenous time preferences, and the impact of such consumers on the firm's pricing strategy. The results suggest that the perceived quality and strategic cost affect consumers' purchasing strategies and lead the firm to implement different dynamic pricing strategy depending on consumers' purchasing strategies. An increase in strategic cost may either increase the firm's profit, consumer surplus and social welfare, or increase the firm's profit at the expense of consumer surplus and social welfare. In addition, an increase in the accuracy of review information does not always benefit the firm. When both the strategic cost and perceived quality are at a high level, more accurate review information may even hurt both the firm's profit and consumer surplus. Also, consumers do not always benefit from strategic waiting behavior compared with the case where all consumers stay myopic.

Based on the customs trade database from 2000--2015, this paper identifies Chinese firms' tendency to export competitive strategies and examines the impact of internal and external product networks on firms' changing competitive strategies and their mechanisms. It is found that firms generally adopt cost-competitive strategies to participate in exports, but firms can reduce their cost-competitive tendencies by converging on internal and external networks. And when both convergence levels reach a high level, firms can shift from cost-based competence to quality-based competence. The mechanism analysis shows that higher product quality differentiation and greater market demand will weaken firms' cost competitive tendency; proximity to external product networks weakens cost competitive tendency mainly through the channel of strengthening product quality differentiation, and proximity to internal product networks improves quality competitive tendency mainly through the channel of strengthening export market demand. The heterogeneity analysis shows that this network effect is more effective for new entrants and domestic firms but is weakened by policy support, as the impact of information flow will be limited by the fluency of knowledge extraction, transfer and diffusion. The findings of the study not only reveal the feasibility of enterprises using information flow of product networks to enhance export competitiveness, but also suggest more diversified thinking on the optimization of industrial policy implementation.

Propose a complete framework for decomposing value added, the multiplier decomposition and structural decomposition methods are extended to the multi-country and multi-sector input-output model to quantitatively analyze the dual circulation evolution trend of China and the endogenous power of the domestic circulation from 2000 to 2020. Research Findings: The overall dependence of China's economic development on the domestic circulation is in a "U" shape, and the inflection point appeared in 2006. On the left side of the inflection point, the main reason for the decline of the domestic circulation is China's decreasing value-added rate; on the right side of the inflection point, the improvement of the final demand rate, the toal demand coefficient and the value-added rate become the main driving force for the domestic circulation to rise in turn. Compared with investment, consumption has always been the main factor affecting the domestic circulation change in final domestic demand. The countries relying on the external circulation have shifted from developed countries to developing countries and developed countries. The dependence of the development of manufacturing industry on the domestic circulation has also increased since 2008. During this period, the increase of the toal demand coefficient of the service industry for the manufacturing industry, the upgrading of the industrial structure, and the improvement of the manufacturing value-added rate have successively occupied the main position in the cyclical rise of the manufacturing industry. The change in the domestic circulation of digital industry is dominated by its own final demand rate or the total demand coefficient of non-digital industry, and the dominant industries in the domestic circulation of non-digital industry are mainly themselves.

The high-quality development of enterprises is the micro foundation for the high-quality development of China's economy, and high-quality development of enterprises requires high-quality asset structure allocation. Based on the data of A-share listed companies from 2010 to 2020, this paper studies the impact of corporate asset structure mismatch on the high-quality development of enterprises, and tests the mediation effect of technological innovation and financing constraints between them. Research has found that asset structure mismatch significantly hinders the high-quality development of enterprises. In terms of mechanism, technological innovation has played a significant part of the mediation effect, and asset structure mismatch has hindered the high-quality development of enterprises by weakening their technological innovation capabilities; Financing constraints have a masking effect, and asset structure mismatch further hinders the high-quality development of enterprises by exacerbating financing constraints. The above conclusions are still valid after the robustness test. Further research shows that, except for accounts receivable, asset mismatches of various types are generally not conducive to high-quality development of enterprises, and the impact of asset structure mismatches on different dimensions of high-quality development of enterprises varies. Moreover, the negative impact of asset structure mismatch on the high-quality development of enterprises is significant in state-owned and non-state-owned enterprises, large-scale and small-scale enterprises, enterprises in the eastern and central western regions, and enterprises with low R&D intensity, but not significant in enterprises with high R&D intensity. The research conclusion can provide reference for effectively alleviating asset structure mismatch, optimizing strategic deployment, enhancing technological innovation level, improving financing constraints, and promoting high-quality development of enterprises.

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 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.