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

Using detailed data of interest-bearing debts from more than 3{,}000 local financing platforms and the 2021 annual reports from 60 major banks in China, this paper evaluates the systemic risk of the banking system potentially caused by local government debts in terms of total debt volume, economic industry and economic region, respectively. The results of stress testing show that: 1) Financing platform loans in the leasing and business service industry have the largest risk exposure among all industries and are closely connected to the real estate industry. An increase in default rate in the leasing and business service industry alone can trigger systemic risk in the banking system. 2) In terms of the regional comparison, the implicit debt of local governments exhibits higher default rates in the west and higher risk exposures in the east. The systemic risk of the banking system presents a noticeable "high in the east and low in the west" pattern under the same default rate. Additionally, the safe interval of default rates for implicit debts is narrower in the east compared to the mid-west. 3) At present, either defaults in local implicit debt (financing platform loan) or liquidity crisis triggered by explicit debt (government bond) is unlikely to cause the systemic risk in the banking system. However, combining with the risk contagion from banking networks, they can jointly cause significant losses to the banking system.

With the development of digitalization, networking and intelligence in social and economic activities, the connections between firms are becoming close, and the impact of related risks on the financial distress of firms is increasing. Existing research usually uses social network analysis methods to quantify topological structures and related risk impacts, but those methods are not applicable to heterogeneous networks containing different types of entities and relations. In particular, the related risk paths are long and the quantification of higher-order related risks propagated through indirect paths faces challenges. To end this, we design a framework for predicting financial distress of firms by incorporating higher-order related risk features. In the framework, we propose an unsupervised heterogeneous graph representation learning model to construct higher-order related risk features and develop an explainable method to mine higher-order related risk paths. Experimental evaluations demonstrate the superior predictive power of the unsupervised heterogeneous graph representation learning model over benchmark methods for financial distress prediction. In addition, the experimental results show that there are two types of higher-order related risk paths that help predict the financial distress of firms.

Under the orientation of high-quality development, the incentive problem for green transformation of Chinese enterprises urgently needs to be solved. From the perspective of regional trade agreements (RTAs), we construct a theoretical model to analyze the impact mechanism of "Open Environmental Regulations" on the green technology progress of enterprises in developing countries, and conduct an empirical study taking Chinese enterprises as an example. First, we develop a model of enterprise green innovation decision-making in an open economy and find that RTAs' environmental provisions influence the direction of enterprise technological progress through two channels: The technological incentive effect and product structure effect. Then, using the text of RTAs, country-specific bilateral trade data, and import and export data from Chinese customs, we construct the "environmental provisions embeddedness" of enterprises and employ a panel fixed-effects model to empirically test the effect of "Open Environmental Regulations" on green technological progress in Chinese enterprises. Our study finds that embedded environmental provisions significantly promote substantive green innovation in enterprises but have no significant impact on strategic green innovation. Mechanism analysis shows that RTAs' environmental provisions drive enterprises to increase R&D investment and green transformation of product structures, thereby promoting green technological progress. The green innovation effect of environmental provisions is more evident in non-heavy polluting industries, enterprises with low financing constraints, enterprises with high social attention, and enterprises with a high degree of overseas market diversification. Further research finds that some market-based environmental regulation tools amplify the green innovation effect of environmental provisions, indicating the necessity of coordinating internal and external environmental policies in the process of institutional openness. These conclusions provide new theoretical perspectives and empirical evidence for China to promote high-quality economic development and high-level environmental protection through high-level openness. Our study finds that embedded environmental provisions significantly promote substantive green innovation in enterprises but have no significant impact on strategic green innovation. Mechanism analysis shows that RTAs' environmental provisions drive enterprises to increase R&D investment and green transformation of product structures, thereby promoting green technological progress. The green innovation effect of environmental provisions is more evident in non-heavy polluting industries, enterprises with low financing constraints, enterprises with high social attention, and enterprises with a high degree of overseas market diversification. Further research finds that some market-based environmental regulation tools amplify the green innovation effect of environmental provisions, indicating the necessity of coordinating internal and external environmental policies in the process of institutional openness. These conclusions provide new theoretical perspectives and empirical evidence for China to promote high-quality economic development and high-level environmental protection through high-level openness.

Climate policy is the key to addressing climate change and realizing carbon neutrality. The assessment of climate policy is filled with uncertainties affecting economy, climate and energy through parameter uncertainty and model uncertainty. We develop a robust assessment framework based on the expected output from IAM for climate policies performance by incorporating the integrate assessment models (IAMs) and distributionlly robust optimization to address the impacts of model uncertainty and joint parameters uncertainties simultaneously on the assessment of climate policy. Besides, Wasserstein ambiguity set is utilized to demonstrate information of uncertain parameters. The results show that: 1) The stricter mitigation efforts, the less impacts of uncertainty on climate performance but the larger economic costs. 2) Uncertainty requires stronger mitigation efforts for the realization of carbon neutrality. 3) The worst-case distribution from the expected output of IAM has less effects on the expected net present value and expected global warming but brings great effects on the risk of future abrupt climate damage. 4) The temperature increase performance of climate policies is relatively robust across IAMs. Our work is the first study that develops a framework for assessing climate policies on the realization of carbon neutrality under uncertainty by integrating distributionally robust optimization and general climate-economic model, which provides a conservative and robust assessment for climate policy.

Climate change and its associated risks have become a significant concern for sustainable development in society. Specifically, the stranding of fossil fuel assets has emerged as a potential factor affecting long-term investments by companies and the stability of financial markets. To understand the impact of asset stranding risk on investor decision-making, this study examines data from Chinese energy-intensive companies listed on the A-share market from 1998 to 2021. The findings of the study are as follows: 1) The risk of stranding fossil fuel assets in Chinese enterprises exhibits an upward trend, particularly after the introduction of the "dual carbon" goal. 2) There is a positive correlation between the stranding risk of fossil fuel assets and stock returns, indicating a premium associated with fossil fuel assets in the Chinese capital market. 3) The risk of asset stranding can be transmitted through the capital market, influencing investors' asset allocation and risk decisions. However, there are variations in decision-making among rational investors. 4) Investors mitigate the risk of stranding assets through various internal and external channels, including green finance, ESG performance, and business transformation. ESG and green finance play a crucial role in guiding investors to adjust their investment portfolios and mitigate the impact of asset stranding risk. This study offers insights for energy-intensive enterprises in China on how to proactively pursue low-carbon transformation and address the risks associated with asset stranding. Additionally, it provides new evidence for investors to reevaluate their involvement with high-carbon enterprises and make informed decisions regarding corporate climate governance.

Preventing and resolving financial risks and promoting enterprises to "transform from virtual to real" is an inevitable requirement for China's manufacturing industry to realize high-quality development and implement economic structural transformation. Based on the research perspective of intelligent manufacturing, this paper takes the pilot demonstration project of intelligent manufacturing implemented by the Ministry of Industry and Information Technology as a quasi-natural experiment, and adopts a multi-period double-difference research method to systematically investigate whether intelligent manufacturing can effectively promote enterprises to "transfer from virtual to real". The results of the study show that the implementation of intelligent manufacturing significantly reduces the financialization level of enterprises by suppressing the profit-seeking and reservoir motives of enterprises, and the above results are more obvious in enterprises that are not state-owned, have higher minimum wage standards in their respective regions, adopt diversified development modes, and are farther away from the banks and enterprises. The analysis of economic consequences reveals that the implementation of intelligent manufacturing can not only promote the enterprise "de-virtualization", but also promote the enterprise "to the real", and alleviate the crowding out effect of financialization on the enterprise's real investment. This paper further expands the research literature on intelligent manufacturing and enterprise financialization by examining the impact of the implementation of intelligent manufacturing on enterprise financialization, which is of reference value for promoting the transformation and upgrading of manufacturing enterprises and realizing high-quality development.

The industry 4.0 technology makes the cooperation of manufacturing enterprises and logistics enterprises more convenient, accelerating the integration of the two industries. Logistics enterprises or manufacturing logistics departments join in the procurement and production of manufacturers, so as to achieve the deep strategic cooperation and "you have me, I have you". However, it is not clear how Industry 4.0 technology affect the integration of the two industries. This study uses qualitative comparative analysis (QCA) and multiple regression, and proposes the influence path of Industry 4.0 technologies including big data, Internet of Things, artificial intelligence, blockchain, augmented reality and 5G on the integration of the two industries from both qualitative and quantitative perspectives, based on the empirical research on Typical Cases of Deep Integration and Innovation Development of Logistics and Manufacturing Industry (2021), and finds that: From the overall path, enterprises can integrate from the innovation, cost and efficiency, such as using the dual factors of process and assets to drive innovation, using the three factors of mode, process and assets to drive cost optimization, and using technology to achieve efficiency optimization. From the perspective of technology, the single application of blockchain, augmented reality technologies with low popularity can improve enterprise efficiency, while more mature technologies require overlapping applications, such as the Internet of Things, big data, artificial intelligence, 5G. Smaller enterprises apply industry 4.0 technology more obvious effects for the integration, but the Internet of Things and big data show opposite characteristics for different enterprises, when the enterprises use them, larger manufacturing enterprises have more significant effects, while smaller logistics enterprises are more obvious.

The upcoming "mandatory + voluntary" carbon market mechanism, which allows carbon cap-and-trade mechanisms to coexist with nationally certified emission reduction trading, is a new mechanism that has not been thoroughly studied, which will promote the formation of a new "carbon complementary supply chain" mode of operation. That is, supply chain members promote internal cooperation and complementary advantages of resources through internal trading of carbon emission rights to achieve high-quality, low-cost energy saving and emission reduction. Therefore, under the new mechanism of "mandatory + voluntary", the study of carbon complementary energy supply chain cooperation mode selection and benefit distribution is conducive to further enhance the operational efficiency and synergistic effect of the supply chain, which is of great theoretical significance for promoting the low-carbon transformation of China's energy industry. This paper designs five cooperation models for the carbon complementary energy supply chain composed of emission reduction enterprises, emission limited enterprises and the consumer side, and comprehensively uses non-cooperative and cooperative game theory methods to explore the choice of cooperation models and the distribution of benefits under the "mandatory + voluntary" mechanism and renewable energy subsidy policy. The results of the study show that the cooperation model of the alliance between emission limited enterprises and emission reduction enterprises makes the supply side more competitive relative to the consumption side, with the lowest total quantity of energy products and the highest wholesale price, while the model of the alliance between emission reduction enterprises and the consumption side is just the opposite. Under the premise of the same subsidy level, the cooperation model of the grand alliance eliminates the influence of the double marginal effect, with the highest total profit, which is the optimal cooperation model. The weighted revenue allocation scheme proposed based on the cooperation game model can ensure the stability of the grand alliance under the different subsidy levels, and it has a certain degree of superiority.

More and more manufacturers are not only selling products through traditional retail channels, but also establishing channels directly targeting consumers to sell products. Faced with the threat of direct channels, retailers can respond to channel competition by switching to external suppliers. Considering the dual channel management problem of manufacturers when retailers hold external choices, a secondary supply chain consisting of one manufacturer and two retailers holding different external choices is constructed. The wholesale price decisions of manufacturers and the switching supplier decisions of retailers are explored, and the impact of outside options by retailers on the manufacturer's optimal channel strategy is analyzed. Research has shown that manufacturers' wholesale price decisions are constrained by retailers' external choices, but adopting customized wholesale price strategies can weaken the constraints of retailers' external choices; Abandoning some or retaining all retailers may become the optimal channel strategy for manufacturers, and abandoning some retailers when the efficiency of direct sales channels is low can improve the profitability of manufacturers; When retailers have limited external selection advantages, closing inefficient direct sales channels may become the optimal channel strategy for manufacturers.

By considering the inability to independently manufacture and remanufacture core components, suppliers are introduced to provide core components to downstream firms. This paper develops a supply chain model consisting of one supplier, one manufacturer and one remanufacturer. In the supply chain, both the manufacturer and the remanufacturer purchase key components from the supplier to produce final products. We examine the outsourcing and authorization remanufacturing modes. Subsequently, we analyze and compare the equilibrium solutions and corresponding consumer surplus, environmental impact and social welfare under the two remanufacturing modes considering the supplier-led and the manufacturer-led scenarios. The results show that, under the supplier-led scenario, the supplier keeps the procurement prices of core components under the two remanufacturing modes unchanged. Under the manufacturer-led scenario, the supplier changes the procurement price for the remanufacturer; in any scenario, compared to the authorization remanufacturing mode, the outsourcing remanufacturing mode leads to higher collection rate, supply chain profit, consumer surplus, and social welfare, but lower environmental impact. Therefore, the outsourcing remanufacturing mode should be adopted between the manufacturer and the remanufacturer; however, under the outsourcing remanufacturing mode, when the collection scale coefficient is relatively large, the manufacturer should be encouraged to be the leader in the supply chain; on the contrary, the supplier should be the leader in the supply chain.

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

Consider a green supply chain composed of a manufacturer and a retailer. We construct two decision models for the retailer: not adopting AI technology and adopting AI technology. Compared to not adopting AI technology, retailers can more accurately predict potential demand when adopting AI technology. The retailer's AI technology adoption strategies and their impact on the green supply chain, environmental benefits, and social welfare are studied. Our paper shows that the retailer's estimations of potential demand and the green input cost coefficient are important factors affecting their AI technology adoption strategies. Specifically, when the retailer underestimates potential market demand, it prefers to adopt AI technology as the estimation bias increases, and as the green input cost coefficient decreases, the retailer prefers to adopt AI technology. On the contrary, when the retailer overestimates potential market demand, it should not adopt AI technology when the green input cost coefficient is low, and when the green input cost coefficient is higher than the given threshold, the retailer should adopt AI technology. Meanwhile, we find that the adoption of AI technology may harm the profits and overall social welfare of the supply chain. Further research shows that both the unit cost of retailer's application of AI technology and prediction accuracy of AI technology can affect the retailer's AI technology adoption strategies.

In the context of technology patent licensing, the game models for the supply chain of new energy vehicles have been established based on four management strategies: no introduction, individual introduction by suppliers/manufacturers, and mutual introduction of professional managers. The aim is to comprehensively explore the impact of introducing professional managers on innovation decision-making within companies. Additionally, we have investigated the question of whether supply chain members should introduce professional managers from various perspectives. The study reveals a negative correlation between the innovation level of companies and incentive levels. Enterprises that introduce professional managers experience an increase in profits as their self-set incentive levels increase, but a decrease as the incentive levels set by the other party increase. From the standpoint of enhancing innovation levels and expanding market demand, the decision to have both supply chain members introduce professional managers is considered the most prudent choice. However, a comprehensive consideration of various interests indicates that both parties may opt for two extreme scenarios: Either not introducing professional managers at all or introducing them mutually. From the standpoint of enhancing innovation levels and expanding market demand, the decision to have both supply chain members introduce professional managers is considered the most prudent choice. But, a comprehensive consideration of various interests indicates that both parties may opt for two extreme scenarios: Either not introducing professional managers at all or introducing them mutually. However, the profit optimization of supply chain members can be realized by designing benefit-cost-sharing contract under the mutual introduction of professional managers. This indicates that when all supply chain members introduce professional managers, setting up a proper coordination mechanism is helpful to promote enterprise innovation and maintain the stable operation of supply chain.

In the process of guaranteed delivery of display advertising, the publisher needs to design allocation strategies in advance based on contracts signed with advertisers, delivering impressions to various campaigns according to specified proportions. However, the existence of multi-level demand contracts in reality and the significant uncertainty in impression supply present substantial challenges to this process. To address this issue, this paper proposes a distributionally robust chance-constrained programming model, where the determination of impression demands is regarded as a decision-making process. Adopting a conservative approximation approach, the model is transformed into an easy-to-solve mixed-integer second-order cone programming (MISOCP) model. Besides, considering the problem in which the risk level is a decision variable, a feasible MISOCP model is also designed. Extensive out-of-sample testing, compared with the benchmark model, validates the robust performance of the proposed model and solution method.

To mitigate the high incidence of order cancellations by passengers in ride-sourcing systems and enhance platform operational efficiency, this study adopts a data-driven approach within the theoretical framework of "predict then optimize". By integrating predictive methodologies from data science with operations research optimization techniques, this research analyzes the complex dispatching challenges in ride-sourcing systems, specifically considering passenger order cancellations. The study reveals that: i) The predictive-then-optimization framework effectively simplifies the platform's dispatching optimization problem with consideration of passenger's order cancellations into a linear programming model, which significantly improving the solvability of the model and reducing the difficulty of theoretical analysis; ii) Employing real data, machine learning models can effectively predict whether passengers will cancel orders, thus avoiding the limitations of assumptions inherent in mathematical modeling of passenger decision-making processes; iii) Compared to dispatching models that do not consider passenger's order cancellation behavior, the model proposed in this paper can effectively improve the revenue of the ride-sharing platform. Numerical experiments indicate that as the supply-demand ratio (drivers/passengers) increases, the solutions of the dispatching strategies that consider passenger's order cancellation behavior and those that do not gradually converge; compared to orders with short or long travel distances, orders with medium travel distances contribute more significantly to the platform's revenue; compared to cost-priority and profit-priority strategies, the dispatching strategy that accounts for passenger's order cancellation behavior achieves higher revenue and can effectively reduce the total waiting time of passengers, among other benefits. This paper provides a modeling approach and solution method for the optimization of ride-sharing platform's dispatching considering passenger's order cancellation behavior, offering theoretical reference for the improvement of dispatching strategies.

The complex computational rules associated with multivariate interval-valued time series (ITS) pose challenges for conventional point-valued methods. This paper treats multivariate interval-valued variable as elements in a high-dimensional random set space and employs the principles of decomposition-ensemble forecasting to construct a MITSSD-FC-IVAR model. Firstly, this paper defines a unit directional discrete weighted inner product and provides formulas for the operations of multivariate ITS distance and variance. Second, this paper defines and calculates the spectral characteristics of multivariate ITS, introducing the multivariate ITS spectral decomposition (MITSSD) method to decompose the original multivariate ITS into multiple sub-sequences with specific frequency components. Finally, to accommodate the frequency domain properties of ITS, this paper builds a Fourier coefficient-interval vector autoregressive (FC-IVAR) model to forecast the sub-sequences of multivariate ITS and ensemble them into the original multivariate ITS predictions. Empirical findings using multivariate energy assets and weather conditions data show that the model proposed in this paper outperforms various benchmark models.

In robust parameter design, the quality of data and model parameter uncertainty often affect the modeling and the acquisition of optimal parameters. In addition, batch effects need to be considered in the actual product manufacturing process. To address the above problems, a robust method has been proposed that can handle batch effects in products or processes and is insensitive to outliers. This method is a multi-response modeling and optimization approach based on Bayesian quantile mixture models. The quantile mixture model is first incorporated into a Bayesian framework. Bayes' theorem is then used to derive the posterior distributions of the model parameters and the Markov chain Monte Carlo algorithm is used to calculate the estimates of the parameters. Next, the multivariate expected loss function is constructed. Finally, the expected quality loss function is minimized to find the optimal parameter settings and the Euclidean distance of the predicted responses between batches is calculated. The simulation example and 3D printing example show that the proposed method is resistant to outliers and reduces the impact of model parameter uncertainty on modeling and optimization.

Sequential accelerated life tests (SALT) can update prior information of model coefficients via collecting lifetime data under different stress levels, and then get better experimental strategy. SALT has been applied in industries successfully, but this method has its drawback. If the model is unknown, then the analysis results may deviate from the true values. To address above-mentioned problem, we propose a SALT for planning and analyzing accelerated lifetime experiment considering model uncertainty. First, we get lifetime data from high stress level, and update the prior information of model coefficients; next, the SALT plan is optimized based on the prior information of candidate models under the criterion that incorporates both asymptotic prediction variance and squared bias; finally, a Bayesian model averaging (BMA) framework is used to derive the posterior model and the posterior distribution for the low lifetime quantile. The proposed method can get the optimal solution of SALT through update prior information sequentially. Furthermore, the method is robust. It can avoid the uncertainty of results caused by a single specified distribution model.

Understanding the architecture and genesis mechanisms of echo chambers is important for dismantling the information cocoon, removing filter bubbles, and mitigating the phenomena of information constriction and group polarization in online social media. For this purpose, this study has constructed a novel model for opinion interaction networks, incorporating two indicators for the distribution of user and neighbor stances, and has developed an effect function that can accurately describe the state of echo chambers in the network in terms of distinct stances. To further explore the neighbor effect on the stance of users in the echo chamber, a network causal inference model has been established, and a Dose-Response function is used to quantify the neighbor effects on the stance of users. In the empirical analysis, this study utilizes two datasets about Russia-Ukraine discussions. The experimental results demonstrate that the constructed echo chamber effect function is effective in quantifying the state of the echo chamber in the opinion interaction network. Furthermore, the neighbor effect in the echo chamber not only strengthens the stance of users with similar opinions but also induces users with conflicting opinions to align with the group stance, thereby intensifying the echo chamber architecture.

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.