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.

The application of industrial robots has significantly advanced the process of intelligentization within the manufacturing sector, fundamentally transforming firm labor structures and providing critical opportunities for high-quality economic development. Utilizing data on robot applications in Chinese manufacturing firms from 2012 to 2022, we analyze the impact of industrial robot usage on the human capital structure of firms. Our findings indicate: 1) The application of industrial robots exerts substitution effects and creation effects, optimizing the human capital structure of firms. 2) Channel analysis indicates that industrial robot application optimizes the human capital structure through three pathways: enhancing safety production, improving innovation levels, and upgrading labor quality. 3) The heterogeneity test indicates that the use of industrial robots significantly enhances the upgrading of human capital structures in labor-intensive firms, firms experiencing rapid technological advancements, and regions with a more developed factor market. Against the backdrop of the national policy of "delayed retirement" aimed at mitigating the impacts of an aging population and rising labor costs, this study offers policy insights and empirical evidence to support firm quality enhancement and high-quality economic development.

Artificial intelligence innovation is an engine for the formation of new quality productivity, which not only generates substantial social wealth but also exerts profound influence on employment opportunities and household consumption patterns. The key to promoting high-quality economic development lies in addressing the realistic dilemma of insufficient household consumption through enhancing demand-side capacity and optimizing the product supply system with the aid of artificial intelligence innovation. Based on the artificial intelligence innovation task model, this paper establishes a theoretical framework for the impact of artificial intelligence innovation on household consumption, providing a comprehensive understanding of the channels through which artificial intelligence innovation influences household consumption. Additionally, numerical simulations are conducted to validate the proposed theoretical model. Furthermore, empirical tests are conducted on the theoretical model using input-output data from various provinces between 2012 and 2020, as well as patent data from the State Intellectual Property Office. The findings indicate that: 1) Artificial intelligence innovation stimulates household consumption, with the promotion effect observed across various categories of consumption. Among them, the impact on enjoyment consumption is particularly significant, serving as a driving force for enhancing the quality and expansion of household consumption. Heterogeneity analysis reveals that artificial intelligence innovation has a stronger promotion effect on household consumption in the eastern region, urban areas, and industries characterized by high-skilled factor intensity. 2) The analysis of supply paths reveals that artificial intelligence innovation enhances household consumption through enhanced productivity and the realization of product innovation. Moreover, product innovation serves as the primary driver for promoting this effect. 3) The demand path analysis reveals that the "job creation effect" induced by artificial intelligence innovation exerts a lesser impact on the skill premium compared to the inhibitory "catfish effect", thereby enhancing household consumption capacity and fostering consumption growth.

Empowering green innovation with digital elements is a key path for enterprises to cultivate competitive advantages and achieve green upgrading. The accompanying question is whether digital transformation can effectively empower comprehensive iterative optimization of green innovation? Previous studies have only discussed the positive role of digital transformation in green innovation, but have overlooked the status and structural changes of innovation activities in different fields within green innovation during digital transformation, failing to clarify the current stage matching problem and internal mechanism between digitization and greenization. Based on this, this article classifies the green patent information retrieval of A-share listed companies from 2007 to 2022 according to the "International Patent Classification Green List", and summarizes the green innovation of management and design, production energy conservation, and end of pipe treatment according to application fields. Based on the resource allocation theory and technology consistency theory analysis, the asymmetric effect of digital transformation on green innovation is empirically examined. Research has found that digital transformation has significantly promoted green innovation in business management and design, but its empowering effect on green innovation in production energy conservation and end of pipe treatment is insufficient; compared to the application of digital business scenarios, the layout and development of digital underlying technologies have a more significant asymmetric effect on green innovation; from the perspective of R&D resource allocation, digital transformation promotes green innovation in business management and design, which is not only the result of adding new R&D resources, but also comes at the cost of squeezing out R&D resources for green innovation in other fields. Meanwhile, the tripartite governance factors of green innovation (market, government, and society) play an important role in the coordinated evolution of digitization and greening. The research conclusion of this article reflects the current situation of cultivating green competitive advantages through digital elements, that is, digital transformation mainly achieves the improvement of resource utilization efficiency in business processes through green upgrading in the field of information technology, and has a significant "local empowerment" effect. This discovery not only provides practical reference for the government to formulate more targeted policies and measures for the coordination of industrialization and informatization, but also provides practical basis for the optimization of enterprise innovation resource allocation and the formulation of innovation strategies.

Green innovation is a key factor for firms to promote the sustainable development. Its relationship with firm performance has received extensive attention. What is largely missing from the existing studies, however, is the in-depth analysis and comparison on the impacts of diverse green innovation on different firm performance. Therefore, based on the data of Chinese listed firms from 2008 to 2022, this paper analyzes and compares the impacts of green management innovation and green technology innovation on firm short-term and long-term performance. The results show that, within the sample interval, green innovation can improve firm performance. However, green management innovation only has a significant positive effect on short-term performance, while green technology innovation only has a significant positive effect on long-term performance. This paper further finds that stakeholder engagement significantly strengthens both of these two boosting effects. Regional marketization only significantly strengthens the boosting effect of green technology innovation on long-term performance, and industrial competition has no significant moderating effect in the relationship between green innovation and firm performance.

Digital twin map physical entities through simulation modeling, significantly enhancing system reliability and reducing maintenance costs by utilizing data fusion, behavior simulation, optimization decision-making, and visualization through virtual-physical interaction. In intelligent operation and maintenance practices, common challenges include poor data quality, scarcity of abnormal samples, and unclear degradation processes. Digital twin technology offers a novel paradigm to address these issues. This paper systematically reviews simulation and modeling techniques within digital twin applications, summarizing recent research advancements in key areas such as anomaly detection, remaining useful life prediction, fault diagnosis, and operation and maintenance decision-making. Focusing on the demands for intelligent equipment operation and maintenance, we summarize the research findings and technical pathways related to digital twin-driven intelligent maintenance. Based on prior theoretical research and practical applications, we propose a four-level hierarchy for digital twin-driven intelligent operation and maintenance. Furthermore, we illustrate the application of digital twin-driven intelligent maintenance in real-world scenarios with a case study on naval equipment. Finally, considering current research and engineering practices, this paper proposes future research directions to provide insights and guidance for digital twin-driven intelligent maintenance across the equipment life cycle.

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.

Corporate transnational operation is an important link that cannot be ignored in building a new development pattern of dual circulation, but whether it will exacerbate internal shareholder opportunistic behavior has not been answered by existing research. The article takes Chinese A-share listed companies from 2007 to 2021 as samples and empirically explores the impact of corporate transnational operation on the stock selling behavior and stock selling motivation of internal shareholders based on the "Stock Selling Triangle Model" proposed in this article. Research has found that transnational operations will significantly exacerbate the stock selling behavior by internal shareholders of enterprises. By analyzing the motivation, we find that internal shareholders' stock selling behavior in multinational enterprises has obvious opportunistic characteristics and arbitrage tendency, which means that multinational corporations have a high risk of illegal stock selling behavior. This conclusion still holds after a series of robustness tests, such as multiple time point difference-in-difference model, Bartik instrumental variable method. The mechanism analysis found that transnational operation aggravated the market risk, information asymmetry and foam phenomenon faced by enterprises, which constituted the pressure, opportunity and excuse for stock selling behavior of internal shareholders. The heterogeneity tests find that corporate multinational operation mainly aggravated the stock selling behavior of directors, but does not exacerbate the stock selling behavior of supervisors and executives. The economic consequences tests find that the arbitrage stock selling of international enterprises will aggravate the risk of stock price collapse. However, continuous and stable export scale, strong policy supervision and good institutional investor governance environment, internal control environment and audit governance environment can effectively curb the internal shareholder arbitrage and stock selling under the background of enterprise internationalization strategy. This paper enriches the literatures in the field of corporate internationalization strategy and internal shareholder arbitrage stock selling. The research conclusions have practical significance for guiding stakeholders in the capital market to pay attention to the potential illegal stock selling risks of internationally operated enterprises.

Servitization has emerged as a vital strategic option for manufacturing enterprises in mitigating market challenges. Extensive research has been conducted in various domains of business operations to investigate the key aspects of implementing this strategy. However, there is currently a dearth of observations from the supply chain perspective. Hence, this study aims to explore the influence of trade credit from suppliers as a focal point in the execution of servitization strategy. By employing signaling theory and empirical data from publicly listed manufacturing companies, it examines the potential impact of signals emanating from enterprise servitization on trade credit. The empirical analysis reveals a U-shaped relationship between servitization and trade credit. Moreover, the firm's financial flexibility negatively moderates this U-shaped relationship, whereas the relevance of services does not demonstrate a significant influence. Furthermore, subgroup analysis indicates that state-owned and light industry enterprises exhibit a relatively attenuated U-shaped relationship between servitization and trade credit compared to non-state-owned enterprises and equipment manufacturing firms. This article provides empirical evidence validating the effect of servitization on trade credit in the manufacturing industry, thereby offering crucial theoretical and managerial insights to scholars and practitioners in the field of operations management.

The rise of social media has altered the information landscape and participant behavior in capital markets. Against this backdrop, this study utilizes Sina Weibo data and explores the impact of listed companies' social media account usage on market reactions to analyst reports. Using regression analysis and instrumental variable methods, we find that if a listed company updates its Sina Weibo within a week before the release of an analyst report, market reactions to that report will significantly decrease. This suggests that a company's Weibo can partially substitute analyst reports in conveying information to the market. Furthermore, the more frequent the Weibo posts, the lengthier the posts, and the higher the volume of comments and reposts, the lower the proportion of institutional shareholdings in the listed company and the fewer analysts following it. The more pronounced the diminishing effect of Weibo usage on market reactions induced by analyst reports becomes. This study enriches our understanding of the interplay between information intermediaries in capital markets and the role social media plays in information dissemination.

This paper innovatively investigates the relationship between abnormal tone of China's listed companies, decided by managers' strategic choices, and bond credit spreads, from the perspective of earnings communication conferences, which are highly interactive in real time and difficult to prepare fully beforehand. The results show that the managers' abnormal tone at earnings communication conferences is significantly and negatively related to bond credit spreads. The mechanism analysis finds that abnormal tone is significantly and positively related to firms' future performance, and negatively associated with firms' bankruptcy risk, indicating that the abnormal tone, with incremental information, can be used as a reliable signal. This shows that the abnormal tone is consistent with the incremental information view, with incremental information other than financial quantitative information, which makes the pricing of the credit spread of the bonds more accurate. In addition, the negative relationship between abnormal tone and bond credit spreads is more pronounced in firms with less information transparency, lower institutional investor ownership, private firms, and bonds with higher bond coupon. Finally, the abnormal tone is significantly and positively associated with credit ratings and issue sizes of bond, significantly and negatively associated with issue spreads. The above results suggest that the abnormal tone of Chinese listed company's earnings presentations influences the cost of direct bond financing for companies, making higher-quality companies have lower financing costs and resulting in a multidimensional positive cross-market spillover effect on the bond market. These findings have important implications for improving bond market pricing efficiency, resolving bond market risk, and promoting high-quality information disclosure in the capital market.

In the carbon neutrality background, forestry carbon sink projects have high investment cost and long return cycle, and the carbon emitting enterprise faces difficulties such as capital constraints. Based on realistic forestry carbon sink financing mechanisms, we develop three financing modes, i.e., bank carbon sink expected return pledge, industrial investment fund and BOT. In this paper, we compare and analyze the equilibrium results of the carbon emitting enterprise under different financing modes from the perspective of profit and carbon sink output. The study shows that when carbon emission is high, the enterprise prefers the industrial investment fund financing mode based on profit maximization, regardless of the change of own capital; When own capital is high but carbon emission is low, the enterprise prefers bank pledge financing mode; When both own capital and carbon emission are low, BOT financing mode is the best financing mode for the enterprise. Based on the perspective of sustainable development, the government should promote enterprises to adopt the industrial investment fund financing mode to achieve a win-win situation for both social economy and environment.

According to the three different forms of extended producer responsibility (EPR) system in practice, aiming at the manufacturing-remanufacturing competition system composed of an original equipment manufacturer (OEM) and an independent remanufacturer (IR), three kinds of mixed regulations with EPR characteristics combined with the carbon tax are designed by constructing a dynamic game model. This paper discusses the effect of introducing the connotation of the EPR system on improving the efficiency of carbon tax policy. The results show that the existence of the carbon reduction technology spillover effect is significant for the performance of mixed regulations. Compared with the carbon tax policy, implementing the three mixed regulations can increase the consumer surplus and environmental performance and have more robust incentive effects on emission reduction and remanufacturing. However, the scope of the application is quite different. The mixed regulation based on levy and subsidy and reward and penalty can better balance the incentive effect of emission reduction, corporate profits, and environmental performance and bring higher social welfare to some extent. The conclusion of this study has a specific reference value for the combination design of EPR and carbon tax.

The increase of data scale and the acceleration of data update frequency bring challenges for efficiency evaluation. Free Disposal Hull (FDH) is a classical efficiency evaluation method under non-convex technology. Compared with data envelope analysis under convex technology, the efficiency solving process of FDH is more complicated, and it is difficult to ensure the timeliness of the evaluation results under the situation of rapid data updating. To address the above problems, firstly, the fast enumeration algorithm (FEA) based on the dominance and reference relationship between decision-making units is proposed on the basis of the existing enumeration algorithms, which is used to calculate the FDH efficiency of large-scale samples. Furthermore, based on the transitivity of the dominance and the reference relationship, the dynamic fast enumeration algorithm (DFEA) is proposed to update the efficiency results. Finally, the effectiveness of the algorithm is verified through numerical simulations and the application of the evaluation of doctors in the Haodaifu platform. The experimental results show that, compared with the enumeration algorithm, the time for FEA to complete the evaluation of the FDH efficiency of large-scale samples is significantly reduced, and the DFEA is capable of updating the FDH efficiency of large-scale samples in real time.

The accelerated degradation testing (ADT) under constant stress is an effective means for reliability assessment. It extrapolates product reliability under normal stress conditions by analyzing degradation data at elevated stress levels. Common approaches for handling degradation data include the one-step and two-step methods. The one-step method entails model parameter estimation and subsequent inference by maximizing the log-likelihood function based on degradation data. On the other hand, the two-step method first estimates pseudo-lifetimes for each sample, which is then converted into accelerated life testing (ALT) analysis. With the advancement in computational capabilities, the one-step method has become computationally more tractable, and previous research has compared both one-step and two-step methods in non-accelerated contexts. However, literature comparing these two methods in the context of ADT remains scarce. This study aims to systematically compare these two methods for constant stress ADT systems, providing more accurate and efficient guidance for selecting reliability assessment methods suitable for ADT. In this paper, we introduce criteria for distinguishing between overestimation and underestimation of true lifetimes using pseudo-lifetimes and compare the performance of the one-step and two-step methods in estimating the mean time to failure (MTTF) through numerical simulations under linear degradation conditions. Furthermore, we apply these methods to a set of classic datasets, and the results align with the simulation findings. In summary, the simulation results indicate that, for various sample sizes and numbers of observations, the one-step method demonstrates higher accuracy in product MTTF assessment compared to the two-step method based on pseudo-lifetimes with different distributions. This advantage is particularly pronounced in small sample scenarios.

Making full use of the current uncertain information in high-frequency trading data can help improve the modeling and prediction performance of asset volatility in the complex and volatile financial market environment. This article incorporates it into the realized multiplicative error model to develop the realized real-time MEM model for joint modeling of volatility and realized volatility. Unlike existing models, the new model treats the random error term obtained from current realized measure scaled by its volatility as the real-time intraday factor of high-frequency information, thereby characterizing the conditional volatility of asset returns as a mixed function driven by both historical realized measures and real-time intraday factor. Under the framework of the new model, we discuss some important properties such as the conditional distribution theorem and related properties, the weak and strict stationary conditions, the quasi-maximum likelihood estimation method, and the out-of-sample multi-step-ahead volatility prediction theorem. In addition, the proposed model is further extended to incorporate the leverage effect and volatility feedback effect of high-frequency current information. Taking four international stock datasets as the research object, the empirical results show that: 1) The current uncertain information of high-frequency data makes the conditional distribution of the realized measure have time-varying kurtosis characteristic, which enhances the ability to model volatility of financial returns. 2) Compared to benchmark models, the realized real-time MEM models provide higher out-of-sample forecasts in terms of volatility, conditional distribution of realized measure, and volatility at risk (VolaR).

In recent years, the global capital market is in a sharp shock situation. Gold's safe-haven and value-protection functions are more prominent, and its price prediction is highly regarded by investors. We combine MEMD's multi-frequency scale synchronous decomposition function and WGAN-GP's efficient extraction capability for complex patterns, and propose a multi-frequency scale integrated prediction model MEMD-WGAN-GP based on an influencing factors system using LASSO. Firstly, we select 30 indicators from macro policy, gold, stock and crude oil market, and construct an influencing factors system with LASSO. Secondly, we decompose explanatory variables and gold price synchronously using MEMD, to obtain IMFs under different frequency scales, and build WGAN-GP prediction model for each IMF. Then, we optimize the combination of IMFs and integrate the predicted values of optimized IMFs to obtain the overall predicted gold price. Finally, the predictive performance of MEMD-WGAN-GP is evaluated under different market conditions, and the results show that our model has the best trend prediction ability, the smallest regression prediction error and the lowest prediction lag.

The fast-growing social media not only provides merchants with a platform for product marketing but also serves as a channel for product sales. The introduction of social e-commerce channels alongside traditional e-commerce channels broadens the consumer market but intensifies channel competition. Therefore, manufacturers should fully consider user characteristics to make effective channel strategy selections. Based on three different supply chain structures: not introducing social channels, introducing self-operated social channels, and introducing third-party social channels, we build a model to study the impact of social channels on manufacturers. We show that when manufacturers introduce self-operated social channels, as the price competition between channels intensifies, if the potential demand for social channels is lower, it will reduce the wholesale price; conversely, it will increase the price. Moreover, when the potential demand for social channels is larger or when it is smaller, the degree of price competition is weaker, the proportion of fans is lower, and the difference between fans and general users is higher, social channels are always introduced. Furthermore, under the condition that manufacturers introduce social channels, as the difference between fans and general users widens, if the potential demand for social channels is lower, the possibility of serving as social retailers increases; conversely, the possibility of cooperating with third-party social retailers increases.

Three types of parking facilities, i.e., on-street, off-street, and shared parking facilities, often coexist in an urban region, and their service characteristics are significantly different. The traffic system within the region typically exhibits uncertainty both in its evolutionary processes and in the observation of its state indicators. This study aims to minimize the total travel cost of participants in the regional traffic system, including area transit costs, parking search costs, management costs, and walking costs. To achieve this, a dual-driven model based on rolling optimization and data fusion estimation is proposed to design dynamic supply strategies for multi-type parking services in the region. The effectiveness of the strategy is verified through Monte Carlo numerical simulations. As a result, it is found that: First, the proposed dynamic optimization problem can be equivalently transformed into a quadratic programming problem with inequality constraints, and if a solution exists, it is guaranteed to be the unique global optimum; second, when considering uncertainties in both process and observation, a significant discrepancy may arise between the system observation outcomes and the system's target trajectory; finally, the introduction of Kalman filter can effectively reduce the gap between the posterior state estimation and the target trajectory, thereby enhancing traffic efficiency in the region and reducing the total travel cost.

In the port hinterland drayage operation, the uncertainty of turnaround time in ports brings significant challenges. This paper proposes an optimization method for external truck scheduling based on the prediction of turnaround time in ports. By analyzing the historical gate data of ports, extracting relevant features, and applying random forecast methods, a prediction model for the turnaround time of trucks is trained. In the optimization model for external truck scheduling, the turnaround time is fed back into the optimization model through repeated calls to the random forecast prediction model, thus formulating a more reliable container truck scheduling scheme. With the gate data of a container terminal in South China, the prediction model is trained, and the results show that it has high accuracy in fitting the prediction of port container truck turnaround time, and the goodness of fit is more than 0.9. In the numerical experiment of the optimization model, the median error is all distributed in 1%, indicating that the combination of machine learning-based prediction of turnaround time in ports and scheduling optimization can reduce the disturbance of uncertainty in turnaround time on the scheduling plan, and improve the reliability and effectiveness of the scheduling provided by drayage companies.

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.

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.

The digital transformation of government is a major initiative to improve the government's governance capacity in the new era, which provides a new idea to promote "tax governance by numbers". In order to explore the micro tax governance effect of government digital transformation, this paper takes the establishment of Big Data Bureau as a quasi-natural experiment, takes listed companies from 2008 to 2020 as the research samples, and uses the DID model to test the micro tax avoidance governance effect of government digital transformation based on the A-S model and information asymmetry theory. The results showed that government digital transformation can effectively inhibit corporate tax avoidance, and its inhibition is mainly generated through joint tax audits, optimization of business environment and government-enterprise cooperation and sharing. Further research shows that government service-oriented Big Data Bureaus and regions with stronger tax collection and management are more conducive to the micro tax governance effect of government digital transformation, and that the tax avoidance governance effect of digital government is more pronounced for highly digitized enterprises. This paper can extend and deepen the governance effect of government digital transformation and provide theoretical basis for the government to further promote digital transformation and create a fair tax environment.

Digital transformation is an important micro foundation of China's industrial upgrading, and the characteristics of CEO play a key role in the digital transformation of enterprises. Based on relevant data from listed companies spanning the years 2011 to 2021, this study constructs adaptive LASSO and QUBO models using machine learning to select trait variables. The goal is to grasp the main contradictions and systematically study the influence mechanism of some important CEO characteristics on digital transformation. According to the results of the machine learning model, this paper argues that the characteristics of old, female, financial experience, management experience are the main traits promoting digital transformation, and production experience is the main inhibiting trait. Mechanism study shows that age of CEO will affect his experience richness in a reversed U-shaped relationship and affect his risk tendency in a U-shaped relationship, this makes a reversed U-shaped relationship between CEO age and digital transformation. Moreover, financial experience and management experience promote digital transformation by alleviating information asymmetry and improving corporate governance, production experience increases the short-termism characteristic of the CEO, which inhibits digital transformation, gender will promote digital transformation by less risk tendency. In this paper, two machine learning models are used for variable selection, it provides a reference for solving the problem of subjective variable selection, this paper enriches the research on CEO traits and digital transformation and provides some enlightenment for the design and adjustment of company management.

Strengthening compliance management is a key measurement for Chinese central state-owned enterprises to comprehensively implement the strategic deployment of governing the country in accordance with the law. After the quasi-natural experiment of the implementation of the "Guidelines for Compliance Management of central state-owned enterprises (Trial)'', based on the empirical analysis of the panel data of China's Shanghai and Shenzhen A-share listed central enterprises and local state-owned enterprises from 2016 to 2021, studied the time difference and individual difference before and after the implementation of the guidelines by using the duel-differential model, the aim of this article is to examines the impact of the implementation of the guidelines on the ESG performance of listed state-owned enterprises. The study found that the implementation of compliance guidelines significantly improved the ESG performance of listed companies of central enterprises, and the research conclusion remained valid after the robustness test such as placebo test was adopted. Moreover, the compliance guidelines of central enterprises mainly enhance the ESG rating of enterprises through external supervision, which is complementary to the company's internal governance capabilities, especially those with stronger internal governance capacity or weaker external supervision effects. This article provides micro-evidence for Chinese central state-owned enterprises to improve corporate governance and achieve high-quality development by strengthening compliance management construction. The study provides positive inspiration for listed companies to strengthen compliance management in order to optimize ESG performance.

A brand owner, which sources from an upstream supplier and sells to the downstream consumers, normally has private information about the supplier's corporate social responsibility (CSR) and has to decide whether or not to disclose such information to consumers. Consider the upstream supplier can be more responsible (h-type) or less responsible (l-type), facing different responsible violation risks, which impacts consumers' purchasing utility. The brand owner chooses whether or not to disclose its private information on the supplier type to the consumers. Under the disclosure scenario, the brand owner informs the consumers about the supplier's type. Under the non-disclosure scenarios, the brand owner signals the supplier's type to consumers through CSR efforts and the price. By comparing equilibrium outcomes under two information scenarios, it is shown that the brand owner sourcing from an h-type supplier prefers to disclose the information, but it may not necessarily invest higher CSR efforts depending on the brand owner's CSR cost coefficient and consumers' belief regarding the brand owner sourcing from the h-type supplier. Whereas, the brand owner sourcing from an l-type supplier will choose not to disclose information. This is because the brand owner can achieve higher profits from pooling and reducing the cost of investing in CSR efforts. As consumers' belief on the probability of sourcing from the h-type supplier increases, the brand owner's motivation to disclose the h-type supplier will be reduced, and it is more incentive to not disclose l-type supplier. In expectation, the brand owner has an ex-ante incentive to audit the supplier's CSR information in advance and disclose the information to consumers. Finally, this paper extends to examine the robustness of the results by considering the scenarios of myopic consumers and heterogeneous consumer valuation.

In the context of the normalization of bidirectional fluctuations in the RMB exchange rate, the bank-firm relationship, as a crucial linkage mechanism between firms and financial institutions, plays an essential role in enhancing corporate exchange rate risk management capabilities. Based on relationship banking theory and the optimal currency area theory, this study empirically investigates the impact and mechanisms of the bank-firm relationship on corporate exchange rate exposure, using a sample of A-share listed companies from 2016 to 2022. Empirical evidence shows that the bank-firm relationship significantly reduces corporate exchange rate exposure, with this result remaining robust across various endogeneity and sensitivity tests. The mechanism suggests that the bank-firm relationship mitigates exchange rate exposure by facilitating the use of foreign exchange derivatives and foreign currency debt. Further analysis indicates that increased RMB internationalization weakens firms' dependence on the bank-firm relationship for exchange rate risk management. Heterogeneity analysis reveals that ownership structure and regional marketization levels significantly influence the bank-firm relationship's effectiveness in reducing exchange rate exposure. This study advances the literature on the economic consequences of the bank-firm relationship and corporate exchange rate risk, providing both theoretical and practical insights for enhancing corporate risk management and promoting financial services in the real economy.

Recently, defaults in the global sovereign bond market have occurred successively. As the largest creditor country, China is likely to be significantly impacted by overseas debt market. However, due to the time-varying characteristics of debt risk spillover effect, the traditional linear analysis framework may not be applicable, and may even lead to significant deviations in the conclusion. In view of this, this paper adopts the newly developed nonlinear smooth-transition vector autoregression model (STVAR), to conduct research on sovereign debt risk contagion relationship in 16 countries (regions) including China's mainland and the United States. Specifically, based on commodity research bureau index, global economic policy uncertainty, CBOE implied volatility index, American excess bond premium, nominal dollar index and federal funds rate, this paper constructs asymmetric risk contagion matrix in different periods, analyzes the role of countries (regions) in debt risk contagion and the path of contagion, examines the influencing factors and mechanisms of sovereign debt risk transnational contagion. It is found that China's mainland has been the net risk importer in the sovereign debt risk contagion network for a long time, and the rising of inflation, the increase of economic policy uncertainty, the global market sentiment turns pessimistic, the boost of financial risks, the strengthening of the US dollar and the monetary policy turns hawkish will amplify the imported impact of global sovereign debt risk on China's mainland. On this basis, we put forward relevant policy suggestions to prevent imported debt risks from the perspectives of preventing debt risks at key nodes, monitoring the macroeconomic environment and controlling the transmission channels of sovereign debt risks.

To deal with the complex and ever-changing business environment, startups employ diversification techniques. As a result of these diversification techniques, entrepreneurs must put effort over many tracks. Diversification creates new tasks for corporate development, and these duties may be cooperative or competitive in nature. The impact of inter-task linkages on dynamic contract design and investor expected returns is investigated in this study. Furthermore, due to the disparities in agency structures amongst organizations, this study is separated into two cases: Multi-agent and single-agent. This study gives optimal dynamic contracts and investor expected profit equations under both agency structures using the martingale approach of continuous-time principal-agent theory, and explores the impact of varied task relationships on them. Under the multi-agent model, the entrepreneur's response function to incentives is independent of task relationships, whereas task relationships only affect the entrepreneur's response function under the single-agent model. There is a fixed ratio between the entrepreneur's multiple efforts in the single-agent model, and the ratio is governed by task linkages and cost considerations. Using the background of venture capital, this study builds a particular example of entrepreneurs who consistently exert utmost effort and discovers that conflict connections diminish expected returns while collaborative partnerships increase expected returns. When there is poor task collaboration or disagreement, investors prefer the multi-agent model. But as task collaboration improves, the single-agent approach is preferable.

This paper uses textual analysis techniques to identify anti-takeover provisions existing in the company's articles of association, and focuses on the mechanism of anti-takeover provisions on company performance from the perspective of supply chain stability. It is found that anti-takeover provisions can significantly enhance firm performance, and this positive effect is particularly significant in firms with large customers or dependent suppliers. This suggests that anti-takeover provisions contribute to maintaining supply chain stability and thus enhance firm performance. The results of the mechanism of action test show that from the perspective of customers, anti-takeover provisions can help firms obtain more orders from large customers, which in turn generates scale effects and reduces selling expenses. From the supplier's perspective, anti-takeover provisions can help the company obtain raw materials from suppliers in a timely manner, which can reduce operating costs and increase the turnover rate of raw materials. This paper expands the literature on the economic consequences of anti-takeover provisions, which is important to understand the role of anti-takeover provisions in promoting supply chain stability.

Supply chain disruptions caused by unexpected events lead to economic loss risks for supply chain node companies. Based on the demand-side risk of the supply chain, this paper portrays three scenarios: No business interruption insurance, separate retailer insurance and manufacturer-retailer joint insurance, and considers the endogenous problem of insurance rates to incorporate insurance companies into the supply chain system. The study investigates the risk transfer effect of business interruption insurance on insured subjects, the mechanism of business interruption insurance influencing the decision making of supply chain participants and the value realization process of business interruption insurance under different insurance decision scenarios. The study finds that: The joint insurance strategy of manufacturers and retailers has the best risk transfer effect in the supply chain and the strongest value-added effect of business interruption insurance; the insurance company has the boundary of insurance rate decision, and the insurance company can only maximize its own profit by setting the appropriate insurance rate level to achieve a win-win situation for manufacturers, retailers and insurance companies; the impact of retailers' overconfident behavior on supply chain participants' profit enhancement and value addition of business interruption insurance is closely related to the insurance strategy.

With the development of informatization and networking and the acceleration of urbanization, the breadth and depth of the impact of disasters have been deepened. While as an effective measure and essential guarantee in response for disaster, the inter-government coordination still has a lot of problems to address, such as unclear division of rights and responsibilities, blocked resource sharing, and unreasonable interest coordination. Based on these problems, this study proposes an emergency response strategy model considering information sharing and resource sharing according to evolutionary game theory, and analyzes the external factors (e.g., disaster characteristics, regional location) and internal factors (e.g., cooperation efficiency, rescue efficiency, interest coordination, etc.) that affect inter-government coordination from a micro perspective, as well as guiding effect of the construction of the vertical government information sharing platform on horizontal inter-government emergency coordination, so as to analyses and compare the strategy selection and evolutionary path of governments. The following results and policy suggestions can be obtained through the numerical analysis of case of the Guangdong-Hong Kong-Macao Greater Bay Area: 1) The information sharing platform promotes the efficiency of horizontal inter-governmental communication and coordination to some extent. 2) External factors have a significant impact on horizontal inter-governmental coordination. Besides, horizonal governments determine the coordination amount of relief supplies with overall consideration. 3) The form of interest distribution affects the horizontal inter-governmental coordination. Both sides need to comprehensively consider the disaster situation, relief benefits, costs and other factors to allocate supplies and benefits so that increase the possibility of collaboration. 4) The traceability of information sharing platforms can provide more reasonable incentives, subsidies and punishments for vertical governments, which promotes the horizontal inter-government division of power and responsibility and prevent from causing the failure of emergency management. It is suggested that the horizontal government should design an emergency plan and cooperation evaluation mechanism in the disaster preparation stage, formulate an appropriate benefit distribution mechanism based on internal and external factors, and conduct regular drills to improve the efficiency of cooperation. The vertical government should actively promote the construction of information sharing platform, use blockchain and other information technology to improve the sharing process and supervision system, and set up reasonable punishment and incentive mechanism, so as to promote the efficient response of disaster emergency under inter-government coordination.

Energy storage for new energy vehicles (NEVs) is of great significance for power grid load shifting, ensuring power supply security, and promoting clean electricity consumption. The competitive charging demand function during the peak power period and the off-peak power period is first proposed, and then an energy storage game model including grid companies, charging companies and NEV users is established. Furthermore, the influence of key factors such as peak and valley electricity prices, charging service fees, energy storage participation, and peak power grid operating costs on energy storage decisions is further investigated. Finally, the incentive efficiency of three kinds of NEV energy storage policies including valley power subsidy policy under power price regulation, peak power taxation policy under power price regulation and marketization of power price is also comprehensively examined. The results demonstrate that: 1) The NEV energy storage will not change the charging service fee and the total power consumption of the system during the peak and valley periods, but it can effectively increase the charging amount and the benefits of all parties during the valley power period at night; 2) NEV users will obtain most of the NEV energy storage benefits, and the peak-to-valley price difference and peak power grid operating costs are the key factors driving all parties to participate in NEV energy storage; 3) the valley power subsidy policy is better than the peak electricity tax policy, and when the peak electricity price is high, continuing the electricity price regulatory policy will have a better incentive effect than the marketization of electricity prices. This study will be beneficial to improve China's NEV energy storage operation and incentive policies.

With the development of power exchange technology, China's new energy vehicle industry proposes battery as a service under the separation of vehicle and battery, which allows consumers to rent batteries on demand. Against this background, this paper constructs a Stackelberg model composed of the new energy vehicle manufacturer and the battery supplier. Two battery service models are considered: Not providing battery rental service (N) and providing battery rental service (B). The results show that, when the body price is relatively high (low), the new energy vehicle manufacturer should choose (not) to provide the battery rental service, but it is harmful (beneficial) to the battery supplier. Therefore, when the body price is moderate, the battery rental service can achieve a win-win outcome for the new energy vehicle manufacturer and the battery supplier. Meanwhile, we reveal the impact of the battery rental service on the decision of the new energy vehicle supply chain and analyze the impact of the battery life level and the body expectancy life on the win-win outcome's realization conditions. We also provide useful managerial implications for both the new energy vehicle supply chain and the overall industry.

The lack of charging infrastructure has severely limited the large-scale promotion of electric vehicles in China, and how to design an effective incentive policy to promote investment in charging infrastructure is an urgent problem to be solved. By constructing a tripartite game model, this paper investigates the effects of two types of EV charging infrastructure subsidy policies in the presence of consumer range anxiety and discusses the choice of the optimal government policy for promoting electric vehicles. The results of the study show that: 1) Compared with the charging infrastructure subsidy policy, the charging service fee subsidy policy not only has a good effect on the promotion of EVs but also improves social welfare. Therefore, the optimal charging infrastructure subsidy policy for the government is the charging service fee subsidy.2) As consumers' range anxiety increases, the optimal charging service fee subsidy rate tends to decrease and then increase. As the externality coefficient of charging infrastructure increases, the optimal charging service fee subsidy rate should continue to increase. 3) The effect of the combination of charging infrastructure subsidy and charging service fee subsidy on the promotion of EVs is not better than that of the single charging service fee subsidy policy. 4) Under the circumstances of changes in charging infrastructure construction and operation, the existence of government budget constraints, the existence of competition from fuel vehicles, and changes in the type of consumer travel demand, the conclusions of the base model are still robust, and the charging service fee subsidy policy is still the optimal choice for the government. The results of this paper have important implications for the promotion of electric vehicles and the achievement of the "double carbon" goal of the transport sector.

Effective incentives are crucial to driving the reclaimed water market. Aiming at the reclaimed water use system composed of local governments, manufacturers, and industrial enterprises, a three-stage dynamic game model was constructed considering water rights trading and tiered subsidy to analyze the impact of the government incentive model (S mode) and dual government and market incentive model (SP mode) on the system optimal decision. The results showed that: 1) Tiered subsidies can increase the total demand for reclaimed water, manufacturer profits, and government benefits, and the water rights trading price can positively moderate this incentive effect of the subsidy policy. There is an optimal boundary between the two measures; whether the government implements subsidy policies depends on the water rights trading price limit. 2) Whether the enterprise receives high-level subsidies determines the impact of tiered subsidies on reclaimed water use decisions of individual industrial enterprises. And there is a threshold effect of the residual transfer ratio of water rights on promoting individual enterprises' reclaimed water use by the water rights trading price. 3) The government's preference for economic benefits has a better pull on demand for reclaimed water than its preference for resource and environmental benefits.

The annual schedules generation for military construction planning is to develop the annual construction schedule for the next year based on the actual situation and feedback in the current year. Based on deep reinforcement learning and multi-objective optimization theory, this paper proposes a dynamic multi-objective optimization algorithm improved by the Deep SubQ-Network. It is used to assist in the generation of annual schedules for military construction planning. First, from the process of implementing military construction planning and generating annual schedules, we analyze the dynamic characteristics of the problem; then, the mathematical model of iterative multi-objective optimization of annual construction schedules for projects is constructed, and the objective function is described in recursive form. Based on a typical deep reinforcement learning algorithm framework, we innovatively propose a SubQ network, which makes it possible to solve multi-objective optimization problems using deep reinforcement learning. We design an iterative optimization algorithm, that can gradually generate annual construction plans for each year; the illustrative part is based on simulated data and set up comparison experiments with other optimization methods to verify the feasibility and advantages of the model and algorithm in this paper.

Taking naval battles as an example, due to the swift movement and constraints posed by distance and obstructions, radar and satellite systems face challenges in swiftly and accurately capturing the motion patterns of maritime targets, thereby impacting combat precision and response speed. To address this, a collaborative mission planning approach involving unmanned aerial vehicles (UAVs) and unmanned vessels is proposed. Initially, integrating motion data acquired by UAVs, a method for tracking and predicting maritime target trajectories is devised to accurately ascertain the real-time positions and forecast the subsequent motion states of dynamic targets. Subsequently, upon acquiring target information, a task planning model for multi-objective weapon-target allocation in naval unmanned vessel operations is established. Finally, considering the influence of mutation operators on weapon-target allocation, a deep Q-learning network-based artificial bee colony algorithm is proposed for resolution. Simulation results demonstrate that the proposed method accurately locates dynamic maritime targets, enhances tracking precision, and exhibits significant advantages in solving multi-objective optimization problems and scalability.

This paper proposes a novel reliability solving algorithm for non-network reliability block diagram (RBD) with complex logical relationships, such as series, parallel, voting, and plus structure, as well as shared components. Motivated by the water flow in a pipe system, we call it water flow algorithm. Initially, some virtual nodes are introduced into the classic RBDs, and a new encoding scheme is designed to store RBD structure. Subsequently, three types of algorithms are designed, namely the node inflow and outflow probability computing, the search for branching nodes, and the reliability solving of systems with shared components. These algorithms constitute the water flow algorithm and enable the accurate solving of complex system reliability within approximate polynomial time. Some numerical examples and a real-world fleet task reliability evaluation case study indicate that in certain application scenarios, this method demonstrates advantages over traditional techniques such as binary decision diagrams (BDD) and Monte Carlo simulation in terms of efficiency, accuracy, and applicability.