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

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

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

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.

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

"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.

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.

Accurately depicting carbon emission efficiency and correctly evaluating the carbon trading policy has important theoretical and practical significance for achieving the "dual carbon" target. Based on an improved stochastic frontier model and a difference-in-difference model, this paper collected panel data on energy consumption and economic input-output for 30 provinces (cities) from 2003 to 2019, and calculated the impact and mechanism of the carbon emission trading policy on carbon emissions and carbon emission efficiency in pilot provinces. Empirical research found that the carbon emission trading policy can reduce carbon emissions by 4.4% in pilot areas, and increase carbon emission efficiency by 10.4%, which helps to promote the goal of achieving "carbon neutrality." Mechanism analysis shows that the carbon trading policy reduces carbon emissions and improves carbon emission efficiency by reducing energy consumption, adjusting energy structure, and limiting production capacity of key industries such as cement; however, the policy does not significantly promote the development of low-carbon technology. Heterogeneity analysis results show that in the implementation of the carbon emission trading policy, the trading price has an adjusting effect, that is, the higher the price, the more significant the carbon reduction effect. These findings provide important insights for promoting the national carbon emission trading policy and have high reference value for achieving the "dual carbon" target.

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.

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

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

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

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

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

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

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

The process of promoting common prosperity in China coincides precisely with the period of rapid development of digital economy, which serves as a critical engine for economic growth in the new era and a significant force to promote common prosperity in high quality development. Based on the essence of digital economy, this paper constructs a digital economy development index. Employing micro-level Chinese household data and quantile regression analysis, this paper thoroughly investigates the impact and mechanisms of the digital economy on common prosperity. This study finds that the digital economy significantly increases resident income, and there is significant heterogeneity among different income groups. Compared to high-income groups, the digital economy has stronger effect on low-income groups. Mechanism analysis indicates that the inclusiveness of the digital economy reduces the barriers to financial services, creating equal development opportunities for low-income groups, enhancing entrepreneurial activity, promoting job growth, and providing more employment opportunities. Moreover, the digital economy facilitates the shift of the rural labor force towards non-agricultural sectors, which is pivotal in increasing rural household income and reducing income inequality. This paper provides empirical evidence on how the digital economy empowers common prosperity, and also provides policy recommendations for the government to steadfastly advance common prosperity.

Identifying and addressing various financial risks on the way toward low-carbon transition is crucial for China to achieve its dual carbon goal. By performing textual analysis of newspaper articles, this paper constructs a novel index of climate policy uncertainty for China to examine the impact of the climate policy shock on the corporate bond cost as well as the mechanism behind the relationship. From a climate policy-cash flow sensitive perspective, this paper develops a theoretical model of financing decision-making under climate policy uncertainty and empirically verifies the hypothesis with a dataset of Chinese-listed companies from 2009--2020. The results show that bond spreads of climate policy-sensitive firms are significantly higher than that of climate policy-insensitive firms, which indicates that climate policy uncertainty significantly deteriorates corporate bond costs in China. Moreover, this effect increases with the maturity of the corporate bond and the level of climate policy uncertainty and is more profound in firms with a negative sensitivity to changes in climate policies. The results also prove that internal environmental governance and external regulatory enforcement intensity are two key channels by which the climate policy shock can impact the cost of corporate bonds in China. This paper contributes to the research of climate finance by providing a theoretical framework and empirical evidence on the relationship between climate policy shock and corporate bond cost and thus is crucial for policymakers to understand micro-level financing and investment risk in China under the dual carbon goal.

In the digital economy era, the content generated by consumers based on internet social media platforms, such as searches and reviews, has expanded the data sources for tourism demand prediction. Existing research focuses on mining and analyzing consumers' overall emotional tendencies towards tourist destinations, but there is little consideration of the impact of their differentiated evaluations on fine-grained levels such as dining, accommodation, transportation, and services on tourism demand prediction. Taking Jiuzhaigou scenic area's visitor flow prediction as an example, this paper uses machine learning-based fine-grained sentiment analysis methods to analyze multi-source data from Ctrip, Qunar, Dianping, and Meituan, constructing a fine-grained sentiment index covering eight dimensions to predict the scenic area's visitor flow. This is compared with models containing search engine indices and overall sentiment indices. The results show that, in three different types of prediction models based on time series, machine learning, and deep learning, the models containing the fine-grained sentiment index can significantly improve the accuracy of tourism demand prediction. In the out-of-sample forecasting, the model incorporating a fine-grained sentiment index demonstrates an average prediction accuracy improvement of 17.78% and 6.53% compared to models that include search engine indices and a general sentiment index. This study provides innovative research methods for multi-dimensional data-driven tourism demand prediction in the era of big data.

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

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.

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

Based on the large-scale residential electricity demand response (EDR) experiment and household survey data, this study uses machine learning and SHAP (Shapley additive explanatory) value algorithm to identify and analyze the influencing factors of residents' participation in demand response from the whole and individual levels. Our study found that whether residents participate in EDR activities is the result of the joint action of external incentives, family structure, electricity use habits, electricity use knowledge, and the value and polarity of the effect is varied in heterogeneity. Among them, external incentives such as telemarketing have the greatest impact on customers' participation in demand response, and the effect is more obvious among older and more educated groups; customers with a baseline electricity consumption of about 1 kW$\cdot$h during the response period have a higher tendency to participate; households with a stronger awareness of energy conservation and higher conditions for saving electricity have a higher probability of participation. At the same time, according to the interaction and decomposition properties of SHAP value, classified marketing for users in subsequent EDR activities can save 93.9% of marketing costs and increase the number of participants by 46.4%. This study has carried out a more detailed analysis and research on the heterogeneity of different groups, providing an important support for more accurate and intelligent EDR to China's new power system.

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.

To vigorously promote the initial processing of fresh agricultural products at their origin, and to address the question of how the government chooses an appropriate strategy to intervene in the fresh supply chain effectively, we construct four different game models: No government intervention, value-added tax reduction for producers, production cost subsidies, a combination of value-added tax reduction and production cost subsidies. These models analyze the optimal government intervention strategy from the perspective of improving overall social welfare and dissect how different strategies impact the equilibrium decisions of the fresh supply chain. The results show that when the government implements a value-added tax policy, the collected value-added tax first increases and then decreases as the tax rate rises. The negative impact of the tax rate increasing on each member's profit strengthens step by step from the production side to the consumption side in the supply chain. Considering government intervention costs, the optimal government intervention strategy, as the unit subsidy rate increases, is the combination strategy, the production cost subsidy strategy and the value-added tax reduction strategy orderly. All these three strategies can effectively stabilize prices, expand supply, and enhance freshness. The incentive effect of the combination strategy is relatively significant.

Stock comovement is the main manifestation of risk contagion in the financial market, and it is of great significance to study the influencing factors of stock price linkage to prevent and resolve systemic financial risks. Few literatures pay attention to the relationship between the similar information contained in analyst reports and stock price linkage, but securities analysts play an important role as information intermediaries in the stock market, and the similar information produced by them will affect the stock price linkage by causing investors to trade in the same direction. Based on the analyst reports of A-share listed companies from January 2015 to May 2023, this paper introduces the TF-IDF text analysis method to measure the textual similarity of joint analyst reports and individual analyst reports, and discusses the impact of similar information in analyst reports on stock price linkage. The study finds that: 1) Similar information in analyst reports can significantly promote stock comovement. 2) The trading behavior of individual and institutional investors plays an intermediary role in the impact of text similarity on stock comovement. 3) Co-analysts produce more similar information, exacerbating the stock comovement vision, and further research shows that the conclusion is more significant if stocks belong to the same industry. The research results are helpful to deeply understand the influence mechanism of information produced by analysts on stock price linkage, and provide a theoretical basis for investors to invest rationally and regulators to strengthen market management.

Disruptive events, such as natural disasters and manmade attacks, could lead to multi-site structural damages and functional failures with various severities for road transportation systems. These events result in degraded system level of service and even disconnectivity between some origins and destinations. To enhance the resilience of road transportation system, various protection/restoration countermeasures can be taken for system components before/after the occurrence of disruptive events. These countermeasures help the system to resist to, to response to, and to recovery from disruptive events with better accumulated system performance over the time span. This paper briefly reviews the definition of transportation system resilience and the state of art for transportation system resilience assessment methods. Then, research efforts regarding the strategies' optimization for transportation system components' protection and restoration to enhance system resilience is highlighted. Last but not least, the outlook for future research directions in this field is discussed for the reference of audience.

Aiming at the problem of multi-vehicle dynamic routing optimization under low carbon conditions, the research process was divided into two stages: Pre-optimization and dynamic adjustment. The optimization model was constructed with the lowest total cost as the objective function, and the improved adaptive genetic algorithm (IAGA) was used to solve the model. In the pre-optimization stage, IAGA algorithm is used to generate the initial distribution scheme under the constraints of meeting the store demand, returns, vehicle fuel volume, working hours and road conditions. In the dynamic adjustment stage, the changes in store demand, road conditions, temporary returns, as well as the current distribution vehicle location, cargo load and fuel volume are integrated. Through the experimental analysis, the feasibility of the model and algorithm is verified, and the total cost is effectively reduced, which provides a good reference for the formulation of enterprise distribution strategy.

This paper builds a parallel CNN-LSTM deep learning model, excavates the interactive text between investors and the management of listed companies on the interaction platform of Shanghai and Shenzhen Stock Exchanges, quantifies the information interaction process between investors and the management from the text content and semantic characteristics, and analyzes its impact on the company's stock price foam by identifying the content of investors' inquiries and the quality of company responses. The study found that investors' questioning and attention to different contents had significantly different effects on the stock price foam. Among them, questioning and attention to the company's stock and financial information could inhibit the frequency and intensity of the stock price foam, while questioning and attention to the company's research, development, production and sales information increased the frequency and intensity of the stock price foam; The higher the clarity of the management's response to investors' questions, the more significantly it can reduce the frequency of stock price foam, but it has no significant impact on the strength of stock price foam. Further, the worse the external information environment is and the lower the shareholding ratio of institutional investors is, the more obvious the impact of information interaction on the foam of the company's share price is. The transparency of company information plays a part of the intermediary effect in this impact. The research findings contribute to a micro perspective understanding of the impact mechanism of information exchange behavior on the operation of the stock market in China's investor interaction platforms.

This study constructs a Chinese provincial-world multi-regional nested input-output table to estimate China's embodied carbon emissions in exports to the European Union (EU) at the provincial scale, and furthers investigates the driving factors of embodied carbon emissions changes using structural decomposition analysis. The results show that China embodied carbon emissions in exports to the EU in 2017 were 249 Mt$\mathrm{CO}_2$, equivalent to 2.45$\%$ of China's carbon emissions. The provincial distribution of embodied carbon emissions varies significantly, with seven provinces——Jiangsu, Guangdong, Shandong, Hebei, Zhejiang, Liaoning, and Inner Mongolia which all exported more than 10 Mt$\mathrm{CO}_2$——together accounting for 56$\%$ of China's total embodied carbon emissions in exports to the EU. The four industries directly related to the EU's carbon border adjustment mechanism——basic metals, metal products, non-metallic mineral products, and chemical industry——exported 21.44 Mt$\mathrm{CO}_2$ to the EU, accounting for 9$\%$ of China's total. From 2012 to 2017, China's embodied carbon emissions in exports to the EU decreased by 16.62 Mt$\mathrm{CO}_2$, with 12 provinces increasing their embodied carbon emissions and 18 provinces realizing a decrease. The production structure effect or the direct carbon intensity effect is the primary driving factor of embodied carbon emissions changes in most provinces. The results of this study could provide some references for China to manage embodied carbon emissions and address the EU's carbon border adjustment in the future.

As a representative of the transportation systems in China, high-speed railway (HSR) network plays an extremely important role at many levels of economy and strategy. This paper focuses on the topological structure and resilience of China's HSR transportation network from a hyper-network perspective. Initially, we construct a hyper-network representation of China's HSR system. We visualize this network, revealing instances where specific HSR lines detach from the main network. Subsequently, we choose metrics tailored to the characteristics of HSR transportation for topological analysis. Finally, we employ natural connectivity as an indicator to quantify network resilience. Dynamic assessments using both random and intentional strategies are conducted to evaluate the network's ability to withstand disruptions. Our results highlight the pivotal role of a few key nodes within the HSR hyper-network, such as Zhengzhou East Station and Nanjing South Station, which hold significant value. Experimental findings underscore that prioritizing attacks on these nodes has the most significant impact on the network's natural connectivity. This paper integrates hypergraph theory with the HSR network, taking into account geographical relevance and operational considerations. This integration enhances the precision of our study on the topological characteristics and resilience of the HSR transportation network. The outcomes of our research offer valuable scientific insights for the planning, construction, and operation of HSR networks. They hold substantial practical significance in advancing China's railway transportation sector.

This paper introduces the concept of "green growth" for the first time, emphasizing high value-added economic activities coupled with low carbon emissions. It also elucidates the asymmetric effects of industrial upgrading and transfer that contribute to the realization of green growth. Furthermore, the paper employs the hierarchical structural decomposition analysis (HSDA) method to examine the impact of inter-regional industrial transfers on China's domestic economic and ecological balance. It identifies key industrial pathways that lead to the net transfer of value-added carbon emissions between regions. The primary findings are as follows: 1) The industrial structure of China's regions is currently undergoing upgrades and adjustments, resulting in a noticeable "decoupling" between economic value-added and carbon emissions from 2012 to 2017; 2) Nevertheless, the process of industrial transfer that accompanies asymmetric industrial upgrading exacerbates the green growth imbalance among regions. This is particularly evident in the net transfer of value-added carbon emissions from developed coastal provinces and cities to Hebei, Shanxi, and Inner Mongolia; 3) Two dominant factors contribute to this imbalance: The industrial transfer effect, which played a crucial role between 2007 and 2015, and the differences in carbon emission coefficients that prevailed from 2015 to 2017; 4) The key industrial pathways driving these transfers involve the final demand for construction services, medium-high/high-technology manufacturing, and other services in developed coastal provinces and cities. This results in the net transfer of production and value-added carbon emissions from medium- and low-technology manufacturing and other manufacturing industries in Hebei, Inner Mongolia, and Shanxi. These findings hold significant implications for proposing a differentiated carbon emission rights allocation mechanism, particularly at the industrial chain level.

With the development of deep learning, deep autoencoder has been widely applied in anomaly detection with efficient data encoding and reconstruction mechanisms. However, some existing deep autoencoder-based anomaly detection algorithms still face many problems, such as complex and diverse data distributions, high false alarm rate, and high missing alarm rate, etc. To overcome the above-mentioned problems, we propose a deep autoencoder-based adaptive anomaly detection algorithm. The algorithm utilizes an adaptive landmark filtering mechanism via density peak, which can select some normal samples with high density as candidate landmarks, aiming to discover the diversity of normal samples in the latent feature space. Subsequently, the landmark filtering mechanism is employed to filter and optimize the candidate landmarks to enhance the representativeness and sparsity of the landmarks. Furthermore, we design a novel loss function to optimize the model parameters iteratively with the aim of enhancing the correlation between normal samples and landmarks. Finally, the proposed anomaly detection algorithm is applied to a battery fault diagnosis, and the experiment results demonstrate that this work outperforms the existing anomaly detection algorithms in terms of accuracy, false alarm rate, and missing alarm rate. It can identify faulty batteries effectively, and provide the technical support and precise services for the battery fault identification and state management.

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.

In order to alleviate traffic pressure, multiple cities in China have introduced various policies restricting the fuel vehicles, which to some extent has affected the competition between the new energy vehicles and the fuel vehicles. Based on the above reality, this paper constructs an automobile market competition decision-making model composed of a fuel vehicle automaker and a new energy vehicle automaker in consideration of consumers' anxiety about the new energy vehicles' range or energy replenishment. This paper analyzes the optimal decision-making of the two types of automakers under the government's three policy scenarios (license plate policy, license plate lottery, and the combination of the above two policies), and compares the equilibrium results of the various policies scenarios to explore the threshold conditions for the dominant policy choice of these two automakers in terms of demand and profit. The results show that these two automakers can always realize the optimal profits under the license plate policy, while they can realize the optimal profits under the license plate lottery and the combination of these two policies only when the degree of anxiety of consumers about the fuel vehicles caused by the license plate lottery is lower than that of the new energy vehicles. In addition, the license plate lottery and the license plate policy will not change the prices of these two types of vehicles. Finally, from the perspective of demand and profit, the combination of the license plate policy and the license plate lottery cannot be an optimal choice for the fuel vehicle automaker, while the license plate lottery cannot be an optimal choice for the new energy vehicle automaker. When consumers are highly anxious about the fuel vehicles and the new energy vehicles, the license plate policy can be an optimal choice for these two types of vehicle automakers. However, as the government imposes stricter restrictions on the lottery of the fuel vehicles, the likelihood that the license plate policy becomes a predominant choice for these two automakers will gradually decrease.

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.

With the growing maturity of the domestic margin market and the continuous advancement of financial technology, the development of intelligent margin trading strategies has emerged as a critical topic and challenge in the field of quantitative finance. This article employs long short-term memory (LSTM) networks to construct expert strategies based on predictive information and proposes an online margin portfolio trading strategy that integrates expert opinions. Firstly, multiple technical indicators are utilized as input variables to anticipate the trend of stock prices using the LSTM neural network model. Next, an expert specializing in investing in a single stock is considered, and buying or selling strategies for each expert are formulated based on the LSTM model's prediction results. Then, a weight optimization model based on expert performance is proposed to determine the weight of each expert by solving the model. Finally, to demonstrate the efficacy of the proposed strategy, historical trading data from stock markets is utilized for empirical analysis. The results demonstrate that the developed strategy is capable of achieving better performance than some benchmark strategies, even when taking transaction costs into consideration.

The prevalence and prevention of epidemic are influenced by complex socio-economic conditions. By integrating social network models and epidemic propagation models, we propose a contacting network based S-E-$I_ {N} $-$I_ {F} $ (CNSEII) model to investigate dynamic transmission of epidemic. Unlike existing static social networks, which fail to capture the impact of control policies on network structures, this study addresses this limitation by constructing a three-level network comprising individuals, households, and the society, employing a scale-free structure as the foundation. The proposed CNSEII model, built upon this network structure, effectively captures the dynamic impact of control policies on epidemic transmission, helping to develop scientific and effective measures for epidemic prevention and control. Furthermore, we propose and prove the necessary condition for epidemic elimination and the effectiveness theorem of prevention and control measures, providing theoretical support for the scientific prevention and control of major infectious diseases. Through simulation, this study evaluates the impact of different parameter combinations on epidemic control outcomes. The experiments reveal that the virulence of the virus and the intensity of virus screening significantly influence the time required for epidemic elimination. Moreover, in terms of reducing the number of infected individuals, limiting interconnections between different households proves more effective than controlling the contact probability of individual nodes with the primary social network.

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.

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.