Exploring the underlying causes and contexts of corporate ESG greenwashing is essential due to its adverse consequences, such as the harm of consumer benefits and social trust crisis. This paper takes Chinese A-share listed companies from 2011 to 2021 as samples to test the relationship between legitimacy pressures from different institutional sources and corporate ESG greenwashing, as well as the moderating effects of financing constraints and industry competitiveness. The results show that regulatory legitimacy pressure significantly negatively relates to corporate ESG greenwashing; The pressure of standardization and imitation legitimacy significantly positively relates to corporate ESG greenwashing. Mechanism analysis shows that financing constraints and industry competitiveness strengthen the negative effect of regulatory legitimacy pressure on corporate ESG greenwashing, whilst financing constraints strengthen the positive effect of imitation legitimacy pressure on corporate ESG greenwashing; the institutional legitimacy pressure affects corporate ESG greenwashing through internal control. Heterogeneity analysis further shows that the relationship between institutional legitimacy pressure and corporate ESG greenwashing is more pronounced, in state-owned enterprises and heavily polluting industry enterprises. Based on the perspective of organizational decoupling, this paper contributes to clarify the deeper motives and constraints of corporate ESG greenwashing, which is significant for promoting ESG practices, green transformation, and sustainable development in China.

In recent years, China's food delivery industry has undergone substantial growth, driven by the rapid expansion of the platform economy and the influence of the COVID-19 pandemic. Food delivery services have not only lessened customers' sensitivity to delays associated with in-person dining but have also generated increased market demand for merchants. It is noteworthy that the majority of merchants employ a centralized operational mode, which combines food delivery and dine-in services within a single establishment. However, certain merchants opt for a decentralized approach, wherein they establish dedicated food delivery outlets exclusively handling food delivery orders while maintaining an offline restaurant. To examine the impact of food delivery channels on merchant decision-making, this study establishes a dual-channel service system operating within a congestion-prone environment. It characterizes the equilibrium strategy of customers under the two operational policies and investigates how the quality of food delivery services affects merchant profits. Furthermore, the research reveals the optimal operational approach based on varying levels of delivery quality. The key findings of the study are as follows. 1) In the case of decentralized operations, the service capacity allocated to the food delivery channel by the merchant exhibits a non-monotonic relationship with its quality. This implies that higher food delivery quality may gradually prompt the merchant to shift its focus toward the offline channel. 2) Despite the fact that higher food delivery quality has the potential to attract more customers, the study surprisingly finds that improving food delivery quality may actually reduce merchant profits in both centralized and decentralized scenarios. 3) While decentralized operations may lead to decreased order processing efficiency, adopting this approach can effectively mitigate the cannibalization effect of the food delivery channel and result in higher profits, particularly when food delivery quality is high. Consequently, centralized mode is recommended only when the food delivery quality falls within an intermediate range. Additionally, we further validated the robustness of this conclusion from various perspectives, including marginal costs and delivery fees.

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

Under the background of the “dual credit” policy of promoting the healthy development of new energy vehicles and energy saving and emission reduction of fuel vehicles, a competitive game model including the government, fuel vehicle manufacturers, new energy vehicle manufacturers and consumers is established. The emission reduction R&D investment of fuel vehicle manufacturers is included in the model, and the impacts of the dual credit policy and the government's gradually declining subsidies are theoretically studied. The findings are as follows: 1) Under certain conditions, the gradual decline of government subsidies will be more conducive to the increase of R&D investment of fuel vehicle manufacturers; 2) The impacts of the credit transaction price in the dual credit policy on the emission reduction R&D investment of fuel vehicles and the automobile market demand are non-monotonic. Therefore, under the background of the decline of government subsidies, a low credit transaction price should be set to stimulate the R&D investment of fuel vehicles and stimulate the market demand for new energy vehicles; 3) The impact of the decline of government subsidies on carbon emissions is non-monotonous, and the decline of subsidies will reduce carbon emissions in the automobile market.

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

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

This paper empirically examines the relationship between customer enterprises' ESG rating and supplier enterprises' green innovation, using all A-share listed companies from 2009 to 2022 as the research samples. The empirical results show that customer enterprises' ESG rating can positively influence supplier enterprises' green innovation; customer enterprises reduce the amount of funds absorbed from supplier enterprises, encourage supplier enterprises to increase innovation investment, and improve the managers' green cognition of supplier enterprises are the three mechanisms of promoting supplier enterprises' green innovation through customer enterprises' ESG rating; supplier enterprises' market power will negatively moderate the positive relationship between customer enterprises' ESG rating and supplier enterprises' green innovation. Heterogeneity analysis shows that the positive influence of customer enterprises' ESG rating on supplier enterprises' green innovation is more significant both when customer enterprises are under greater legitimacy pressure or have more substantive ESG practices, and when supplier enterprises lack credibility or face stricter environmental regulations. Further research shows that customer enterprises' ESG rating is more effective in promoting supplier enterprises' green innovation when the uncertainty of ESG rating results is low, and that supplier enterprises' green innovation contributes not only to supplier enterprises' own ESG rating but also to supplier enterprises' total factor productivity. This paper highlights the spillover effects of ESG rating pressure from the perspective of supplier enterprises, and provides empirical evidence and management enlightenments for effectively promoting enterprise green innovation.

Carbon neutrality is of great significance to the sustainable development of human society, and carbon neutrality technology and ecological carbon sequestration are two important factors affecting carbon neutrality capacity. In this paper, we develop an economic growth model that takes into account both factors, while also considering the deadline for carbon neutrality. By the theory of optimal control, we obtain closed-form formulas for optimal consumption, investment, capital stock, and carbon neutrality capacity. Based on theoretical and numerical analysis, several policy recommendations are proposed. Specifically, countries need to set carbon-neutral targets that match their own endowments and target capital stocks. Countries or regions within the same country should choose different technical levels of carbon-neutral investment according to their different stages. Unlike usual expectations, the path of carbon neutralization capacity may decrease with the elasticity of output to investment. As the deadline approaches, investment strategies may be abnormal.

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

Improving firm ESG performance is an important measure to achieve sustainable economic development. This study takes the implementation of the “Broadband China” strategy released in 2013 as a quasi-natural experiment and uses differences-in-differences method to evaluate the impact of digital infrastructure on firm ESG performance. The study finds that digital infrastructure has a significant promoting effect on firm ESG performance. The mechanism analysis shows that the impact of digital infrastructure on firm ESG performance is mainly achieved through pathways such as increasing government environmental concerns, incentivizing firms to fulfill social responsibilities, and improving firm information transparency. Heterogeneity analysis reveals that the promoting effect of digital infrastructure on firm ESG performance is more significant in large-scale firms, firms with high customer concentration, high-polluting industries, and firms with strong green innovation capabilities. This study evaluates the practical role of digital infrastructure from a sustainable development perspective, providing new empirical evidence for understanding the influencing factors of firm ESG performance and offering policy recommendations for strengthening digital infrastructure construction and promoting economic green transformation.

Promoting carbon emission reduction of urban residents is of great significance for mitigating climate problems. Based on the panel data of 288 cities above prefecture level in China from 2009 to 2019, this paper calculated the living carbon emissions of urban residents, and combined population and economic characteristics to cluster cities into four types for analysis, and analyzed the influencing factors of living carbon emissions of urban residents. And BP neural network and scenario analysis were used to predict the carbon reduction potential of various urban residents. The results show that: 1) The total carbon emission of urban residents in China is increasing year by year, and the proportion of carbon emission from electricity is the highest, and the growth rate of carbon emission from heating is the highest. 2) Urbanization level, per capita disposable income, energy structure and total population size all have positive effects on the carbon emissions of urban residents, while energy intensity and consumption tendency of urban residents have negative effects, and the influencing factors of carbon emissions of various cities have certain differences. 3) All kinds of cities have great carbon reduction potential in residents' life, and there are great differences. The carbon reduction potential of the second type of cities is significantly higher than that of other cities. The first type of cities has the lowest carbon reduction potential overall. The change degree of carbon reduction potential of the third and fourth types of cities is similar, showing a trend of first increasing and then decreasing. All localities may formulate and implement carbon reduction measures for residents according to local conditions.

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

This paper addresses the location and capacity planning of battery swapping stations of electric vehicles, combining the charging and swapping operations in the stations. The charging and swapping operations within the swapping station are a crucial link connecting the swapping demand with the decisions on station location and capacity planning. However, previous research has overlooked providing a detailed characterization of this process. This study models the internal operations of the swapping station as a multi-period optimization problem and provides insights into the structural properties of the optimal solution to this problem. Building upon this foundation, considering the uncertainty in swapping demands, we integrate the internal operational aspects with the station location and capacity planning to construct a distributionally robust optimization model and a robust satisficing model. To deal with the hard multistage problem in the model, we utilize the linear decision rule to approximately solve the two models and extend the lifting technique by incorporating auxiliary variables into multistage scenario-wise robust optimization models. The theoretical analysis establishes the relationship between the models before and after lifting. Finally, numerical experiments are conducted to validate the effectiveness of the proposed model and lifting techniques.

In the digital economy era, data has emerged as a new factor of production. However, pervasive data quality issues pose significant challenges to releasing the value of data elements and may potentially become "grey rhinos" for digital economy development. Currently, the field of data science is advancing rapidly, highlighting the pressing need for further consolidation and summarization of research related to data quality. This is essential to effectively support the practice of data quality management and the establishment of reliable data circulation. This paper takes a systematic approach to explore the trajectory of data quality research. By employing a synthesis of diverse methodologies, we conduct a comprehensive review of relevant literature from domestic and international sources during the past 30 years. Our review reveals a logical progression in data quality research, characterized by the interconnected stages of "connotation-theory-method-application". Building upon this, we develop a framework for data quality research. Subsequently, we provide a retrospective summary encompassing the data quality connotation and dimensions, theoretical foundation development, assessment and optimization methods, and influencing factors and value effects. Finally, we explore trends in the development of data quality research and offers insights into future directions.

The international economic circulation affects China's economic growth through the production process of the product and the market demand of the product. In terms of production process, the mutual substitution of imported intermediate products and domestic intermediate products affects economic growth; in terms of market demand, foreign demand for China intermediate and final products affects China economic growth. Based on the structural decomposition analysis method, this paper decomposes the change of the Leontief inverse matrix into technology level change and import substitution, and decomposes the final demand change into domestic final demand change and export change, so as to measure the impact of international economic circulation on China economic growth from two aspects. The results show that: 1) Import substitution is an important channel for the international cycle to affect China economic growth, and it shows periodic characteristics. From 2000 to 2005, imported intermediate products replaced domestic intermediate products, which had a negative impact on economic growth; From 2005 to 2014, domestic intermediate goods substituted imported intermediate goods, and China gradually took control of more intermediate goods production processes. From 2015 to 2021, the share of imported intermediate goods again increased. 2) Compared to domestic final demand, the contribution of exports to China's economic growth has been continuously decreasing, and China's dependence on the final demand of international circulation has been gradually declining.

Enhancing human capital and attaining high-quality economic progress are fundamental imperatives for the comprehensive construction of a modern socialist nation. Against this backdrop, our study employs data from Chinese A-share listed companies spanning 2011 to 2019 to explore the influence of local economic growth targets on corporate human capital structure and its underlying mechanisms. Our findings reveal that heightened local economic growth targets tend to hinder the optimization and advancement of corporate human capital structures, consequently diminishing enterprises' total factor productivity. Further analysis indicates that these effects are more pronounced in regions exhibiting greater governmental intervention capacity and willingness, companies facing severe financing constraints, and industries characterized by non-high-tech and labor-intensive sectors. Mechanistically, elevated economic growth targets prompt local government officials to skew fiscal expenditure structures, curtail public service outlays, steer enterprises towards increased fixed asset investments at the expense of innovation expenditures, thereby impeding the optimization and enhancement of human capital structures. This research not only contributes to the body of literature on economic growth targets and corporate human capital but also furnishes empirical insights to facilitate the optimization of official assessment systems and the realization of high-quality economic development objectives.

Under the background of the accelerated reconstruction of the global industrial chain and supply chain, the United States tries to implement the friend-shoring and near-shoring strategy to reduce the dependence of its industrial chain and supply chain on China. Economies such as Southeast Asia and Mexico have become the main destination of China's industrial transfer, which is bound to have a negative impact on the impact scope, profitability and risk resistance of China's industrial sector in the global value chain. This paper uses the trade data of intermediate goods from the multi-regional input-output (MRIO) database to construct the global production network model, and extract the real network (null model) and artificial network (counterfactual model) that reflect the backbone of the global value chain from different perspectives, respectively. On this basis, it analyzes the potential impact of the United States' trade policy towards China on the restructuring of the global production network and the relocation risk of China's industrial chain. The results show that the friend-shoring strategy of the United States relying on Altasia and the near-shoring strategy relying on the United States-Mexico-Canada Agreement, and Canada will lead to the partial decoupling of the industrial chain and supply chain in the global scope, and moreover, the friend-shoring strategy has intensified the trend of economic anti-globalization and the risk of relocation of China's industrial chain. Finally, this paper puts forward policy suggestions to improve the resilience and security level of China's industrial chain and supply chain under the background of the United States' de-risking China-reliant supply chains.

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

Different from the traditional financial sector, science and technology finance can effectively support scientific and technological activities, which is of great significance to enhance our country's independent innovation capacity and achieve high-quality economic development. This paper uses the entropy method to comprehensively evaluate the development level of science and technology finance from four dimensions: resources, funds, financing and output. Based on the panel data of 31 provinces from 2007 to 2021, a spatial econometric model is constructed to empirically test the impact of science and technology finance on technological innovation. It is found that sci-tech finance not only has a significant promoting effect on local technological innovation, but also has an obvious spatial spillover effect. If the spatial spillover effect is not considered, the impact of sci-tech finance on technological innovation will be underestimated. Further research shows that in the eastern region, the direct effect and spatial spillover effect of sci-tech finance on technological innovation are more significant, and sci-tech finance improves the level of regional technological innovation by easing the financing constraints of enterprises and optimizing the industrial structure. The research of this paper provides data support for evaluating the impact of science and technology finance, and also provides policy reference for exploring the path of technological innovation promotion.

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

The environment of international politics and economics is becoming increasingly complex and ever-changing, posing great challenges to the resilience and security of industry chains and supply chains. As an important part in supply chain management, procuring decisions are now influenced by various uncertain factors (such as supply disruption, transportation disruption, price volatility), thus directly affecting the cost of enterprises and the resilience of supply chains. This paper provides a review of the resilient supplier selection and order allocation problem, providing a basic description and a general framework for the problem. Especially, this paper focuses on different aspects (such as the four different types of risk and the corresponding modeling, the risk response strategies, the three mainstream mathematical modeling methods, commonly considered factors, and the solving algorithms etc) to review the problem. Finally, this paper states insights into future research trends.

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

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

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

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

Increasing labor income share is the core essence of optimizing the income distribution pattern and achieving common prosperity. This paper examines the impact of local fiscal pressure on the labor income share of enterprises using difference-in-difference method with a quasi-natural experiment of the nation-wide abolition of agriculture tax in 2005. We find that local fiscal pressure significantly reduces the share of labor income of enterprises. Moreover, we find that the negative relationship is more pronounced in small and medium-sized enterprises, regions with lower financial development, regions with lower fiscal self-sufficiency, and firms in labor-intensive industries. Further mechanism analysis shows that fiscal pressure leads local governments to raise tax and non-tax enforcement, increase debt raising, and thus intensify firms' financing constraints. Financing constraints firms cut human capital investment to smooth out fixed asset investment, which in turn leads to a decrease in firms' labor income share. This study not only enriches the research on local fiscal pressure and labor income share of enterprises, but also provides empirical evidence and policy implications on how to improve the primary distribution structure and achieve common prosperity.

New urbanization construction is an essential driver for expanding domestic demand and a critical measure to facilitate internal circulation. This paper evaluates the impact of new urbanization pilot projects on the consumption levels of the rural migrant using data from the China Migrants Dynamic Survey. The study reveals that, compared to non-pilot areas, the consumption levels of rural migrants in pilot areas have significantly improved, and a series of robustness checks support this conclusion. Mechanism analysis indicates that the pilot projects have increased income, enhanced access to public services, and strengthened a sense of identity, all of which contribute to the increased consumption levels of rural migrants. The pilot projects have a more substantial effect in regions with low dialect diversity and more effectively raise the consumption levels of employed and intra-provincial migrants. Additionally, the pilot projects have boosted both daily and housing consumption of migrants, with a more pronounced effect in counties and county-level cities. The findings provide theoretical explanation and empirical evidence for establishing a long-term mechanism to expand domestic demand.

Nowadays, although global economic rebalancing has made great progress, seeking inclusive growth is still an important issue to be solved. We propose a global value chain-based accounting framework to quantify the global trend of inclusive growth, and further use structural decomposition analysis (SDA) to explore the driving factors accounting for the change of inclusive growth. Finally, considering the deviation between domestic value-added and national income, we also conduct a comparative study on the inclusiveness of global economic growth under the territorial and ownership calibers. We find that the global income gap (especially south-north gap) under the territorial and ownership calibers shows a downward trend, indicating that the inclusive level of global economic growth is improving. However, it should be alert that the global economic inclusive growth measured by national income shows sign of deterioration at the end of the analysis period. Further analysis shows that the input structure, final demand and population jointly contribute to the reduction of the global income gap. Among them, the derivation of value-added ratio between North and South has widened the global income gap, which is offset by the increasingly strengthened intermediate and final trade linkages between North and South. In addition, the expansion of final demand scale is also an important factor to narrow the global income gap. Our paper sheds light on the global economic inclusive growth and its driving factors, and also on how to boost inclusive growth.

How digital platforms enable enterprises to achieve disruptive innovation is a key concern for managers in the context of the platform economy. Building on the literature on platform ecosystem and dynamic capability, we explore the effect of digital platform capabilities on disruptive innovation. Leveraging data from 209 Chinese high-tech enterprises that have either developed their digital platforms or integrated with existing ones, we find that digital platform capabilities have a significantly positive impact on disruptive innovation. We further show that structural flexibility and organizational unlearning partially mediate the relationship between digital platform capabilities and disruptive innovation. Specifically, digital platforms empower shaping structural flexibility, dismantling rigid organizational routines, and identifying emerging niche markets targeted for disruptive innovation on the one hand. On the other hand, they facilitate organizational unlearning, breaking away from existing knowledge path dependencies, and acquiring complementary knowledge required for disruptive innovation. Additionally, structural flexibility has a significant positive impact on organizational unlearning, and both factors serve as chain mediators between digital platform capabilities and disruptive innovation. This study deepens our understanding of the formation mechanisms behind disruptive innovation in high-tech enterprises within the platform economy framework. It addresses the practical question of which capabilities high-tech enterprises need to cultivate for disruptive innovation from a micro perspective. These insights provide valuable theoretical guidance for enterprises seeking to leverage digital platforms for achieving disruptive innovation in the context of ongoing digital transformation.

In this article, we use text analysis to extract implicit information such as specialization of economic governance from government work reports, providing a new explanation for the sources of deviation in local economic growth goals. The results are that the specialization of economic governance can bring economic growth exceeding expectations, which is reflected in the fact that the actual economic growth rate exceeds the expected goals announced in the government work report. This is related to the effective allocation of resource elements, and is also motivated by factors such as “promotion championships”. With the transformation of local government performance evaluation system, the impact of economic governance specialization on the deviation of economic growth goals has decreased. However, in cities with different regions or administrative levels, professional officials are effective in promoting economic growth. Furthermore, if there are too many prospects for the future, weak execution ability, and lower innovation as well as higher compliance with previous policies in the local government’s economic governance, that may reduce the impact of specialization in economic governance on the deviation of economic growth targets, which is not conducive to achieving economic growth exceeding expectations. This study has reference significance for better leveraging the role of the government in resource allocation as well as in economic growth.

The digital transformation of enterprises has changed from internal business, organization and business model to digital synergy of production factors and organizational relationship in the upper and lower reaches of supply chain. Based on the data-to-information-to-knowledge-to-wisdom model (DIKW) of data value chain, this paper constructs a multi-stage model of enterprise digital transformation, and uses the data of Chinese A-share listed companies from 2007 to 2021, this paper examines the impact of digital transformation on supply chain resilience. The study found that digital transformation can significantly improve supply chain resilience, with different effects at different stages. In mechanism testing, digital transformation promotes supply chain resilience by promoting information and knowledge spillover in supply chain. Further heterogeneity analysis found that when the enterprise is located in the eastern region, the economic policy uncertainty is high and the industry competition degree is strong, as well as the large-scale, high-tech industry and non-manufacturing industry, for enterprises with low supply chain integration, the impact of digital transformation on supply chain resilience is more significant. Moreover, the digital transformation has the diffusion effect on the upstream and downstream enterprises of the supply chain, which can improve the digital transformation degree and value creation level of the upstream and downstream enterprises. The research results reveal the impact and mechanism of enterprise digital transformation on the resilience of supply chain, provide a new idea for building a resilient supply chain system, and promoting coordinated development of supply chain.

Federated learning is a distributed machine learning technique which enables clients with limited resources to collaboratively train models without sharing private data, effectively protecting the data privacy of the clients. Classic federated learning systems lack a strict mechanism for selecting clients, but generally use an average strategy to aggregate the local model parameters, which may lead to the inclusion poor-quality clients in the training process of the federated learning, consequently affecting the overall performance of the final models. To address the above issues, a federated learning client selection method based on dynamic programming is proposed (FedWeight). The proposed method uses the Shapley Value method to measure the contribution of each client in different communication rounds, addressing the inherent difficulty of evaluating clients' data quality directly. Using the Shapley Value as an important measurement to dynamically select high-quality clients by the server, and then the server improves the overall performance of model by aggregating these high quality clients. To construct different federated learning scenarios, MNIST, CIFAR-10, Fashion-MNIST, EMNIST and KMNIST datasets are used in our experiments. The experimental results demonstrate that the proposed method can effectively identify high-quality clients, and the performance of the obtained final federated model is almost unaffected by poor-quality clients. Furthermore, our method exhibits significant advantages in terms of convergence speed and model stability.

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

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

Existing literature mainly focuses on the impact of investor attention on stock return predictability and asset dynamics. However, less research interest is paid on the impact of investor attention allocation on asset prices. In this paper, we employ the COVID-19 event as the attention-distraction shock and empirically investigate the impacts of investor attention allocation on the return comovement. This attention-distraction shocks are characteristics by have been salient nationwide, repeated, and suitable for the stock market dominated by individual investors. The empirical results mainly reveal that: 1) return comovement is significantly increased when investors are distracted; 2) analyst following, market openness, information transparency could significantly alleviate the magnitude of return comovement. We mainly contribute to the literature in two aspects: First, with the novel attention-distraction event, we confirm that substitution effect of investor attention allocation; second, we also contribute to literature on the determinants of return comovement by documenting a new determinant of return comovement, i.e., investor attention allocation. All these findings should be of great interest to investment professionals interested in the investor attention allocation on financial markets. Much work needs to be done on the impacts of investor attention allocation on micro-behavior, e.g., order imbalance, probability of informed trading, anomalies, as well as cross-assets and cross-markets movements. We leave these for future research.

This paper examines the impact of emerging technology-oriented venture capital on corporate innovation by analyzing pre- and post-listing samples of A-share companies. The study finds that technology-oriented venture capital significantly enhances both the quantity and quality of innovation in the invested companies through post-investment technological empowerment. This effect is sustained over time and exhibits an innovation imprint. Mechanism tests show that technology-oriented venture capital institutions promote corporate innovation through human capital support mechanisms and innovation network support mechanisms. Further research reveals that the innovation-enhancing effect of technology-oriented venture capital is influenced by the heterogeneity of the venture capital institution’s characteristics and investment situation. Specifically, the impact on corporate innovation is more pronounced when the venture capital institution has a lower reputation, intervenes earlier, invests in more rounds, maintains a higher level of focus, and is geographically closer to the invested company. This paper reveals that technology-oriented venture capital is more effective than traditional venture capital in enhancing corporate innovation, providing breakthroughs and decision-making references for guiding which type of venture capital can better support the advancement of national innovation strategies.

Wind power has high intermittent, which brings challenges to the accurate prediction of wind power and better management and decision-making of power system scheduling and wind field operation and maintenance. For this reason, a short-term wind power prediction model based on multi-feature fusion and power series decomposition is proposed. Considering the limitation of single prediction model, the original power sequence is decomposed, predicted and reconstructed by variational mode decomposition (VMD), and the VMD algorithm is optimized by fuzzy self-tuning particle swarm optimization algorithm (FST-PSO), which improves its adaptability and the accuracy of prediction results. The model then considers the feature fusion from two aspects: Multi-point numerical weather prediction (NWP) data features and multi-layer semantic information features. First of all, a feature selection network (FSN) is designed to adaptively screen the multi-point NWP data features to make full use of the multi-point information. Furthermore, a multi-layer semantic fusion attention mechanism (MSA) is designed between the network layers to fuse different levels of semantic information, which realizes the full representation of the semantic information in the recurrent highway-network layer and improves the prediction performance of the model. Finally, the point prediction results are extended to probability density prediction, and the prediction interval and probability density curve including future power series are obtained, which provides a more flexible decision interval for wind field and power grid decision-makers. Through the numerical calculation and analysis of the actual wind field data, the results show that the proposed method is more effective in prediction accuracy, reliability and decision-making support.

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

During the process of using drones for power inspection, a component to be inspected usually corresponds to multiple task points that are different in location but all meet the photography requirements. These task points form a collective task. To ensure the quality of inspection, it is required for the drone to take multiple shots of the components to be inspected, that is, to visit multiple task points in the collective task. In light of the above characteristics, the problem of task allocation for multiple drones power inspection for collective tasks was modelled as minmax multi-depot family traveling salesman problem (Minmax-MDFTSP). A framework that combines reinforcement learning and genetic algorithm was designed to solve the problem. This framework contained a mechanism for checking and correcting chromosomes, a combination exchange mutation operator, a local optimization operator based on a greedy strategy and a parameter tuning method for genetic algorithm based on reinforcement learning. The results of the performance experiment showed that the proposed method in this paper exhibited significant improvements in both solution quality and solving efficiency. Besides, the ablation experiment confirmed the effectiveness of each part in the framework. Finally, in combination with real-world scenarios, the advantages of the proposed method over existing inspection methods were verified through on-site flight validation.

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