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.

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

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

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

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.

Solving the problem of outstanding accounts of private enterprises is of great significance for activating market entities, increasing labor income share, and promoting high-quality economic development. This paper selects data from A-share private listed companies from 2011 to 2021 and uses difference-in-differences method to estimate the impact, channels, and heterogeneity of local government debt liquidation special supervision on the labor income share of private enterprises. The results have found that special supervision of local government debt liquidation can significantly increase the share of labor income in private enterprises, and this conclusion still holds after a series of robustness tests. Alleviating financial pressure, improving labor employment levels, and optimizing human capital structure are the channels through which local government special supervision on debt liquidation increases the share of labor income in private enterprises. Research combining production, operation, financing, and governance shows that the higher the intensity of labor, the greater the pressure of operation, the smaller the business scale, the higher the financing constraints, and the higher the concentration of equity, the more significant the positive impact of local government debt liquidation special supervision on increasing the labor income share of private enterprises. Further analysis reveals that the special supervision of local government debt liquidation has significantly promoted the fairness of internal income distribution and labor productivity of private enterprises, and increased the high-quality development level. The research findings enrich the economic effectiveness of government debt liquidation special supervision work and have important policy implications for how to improve the labor income share of private enterprises at present.

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.

Traditional linear predictive regression models perform poorly in out-of-sample stock return predictions. One competing hypothesis for this result is that structural changes in the financial markets introduce model instability. This paper constructs a two-state multi-asset time-varying regime switching (TVTP-RS) model to investigate industry stock return predictability. In this model, the time-varying industry expected returns are driven by economic variables, but the relation between them may change due to shifts in market states, where market states are unobservable and follow a Markov chain with time-varying transition probabilities. In-sample estimation results reveal that jointly utilizing information from industry returns and economic variables can effectively identify latent market states, and the relation between economic variables and expected returns indeed depends on market states — The coefficients of some economic variables reverse in different market states. Out-of-sample industry return predictions and industry allocation strategies based on this model consistently outperform benchmark models and linear predictive regression models. This study provides new evidence of the time-varying relation between economic variables and industry expected returns and demonstrates that considering the impact of market state transitions can effectively reduce the model instabilities. The proposed TVTP-RS model also offers a reliable solution for industry rotation strategies in practice.

As an emerging economic community with distinctive competitive advantages, the platform ecosystem has garnered significant attention in academic research. However, due to differences in research perspectives and contexts, a clear and unified theoretical framework has yet to be established. Based on bibliometric analysis and a systematic literature review, this study examines the existing literature on platform ecosystems, clarifies the concept and characteristics of platform ecosystems, summarizes the theoretical framework, and explores potential future research directions. The study first identifies that platform ecosystems encompass core elements such as modular architecture, value propositions, and ecosystem governance, alongside characteristics such as modular complementarity, non-hierarchical control, multi-party interactions, and network effects. It then constructs a theoretical framework for platform ecosystems, specifically elaborating on the foundational roles of modular architecture and value propositions, the governance structure formed by open access, power distribution, and benefit-sharing mechanisms, and value co-creation driven by mechanisms, processes, and value capture. Finally, based on the theoretical framework, the study proposes future research directions, aiming to provide valuable insights for both theoretical research on platform ecosystems and practical management applications.

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.

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

The 20th National Congress of the Communist Party of China emphasized promoting common prosperity through high-quality development, but the the rural-urban income disparity is still relatively large. The adoption and diffusion of artificial intelligence, a major general-purpose technology represented by robots, has a profound impact on the labor market and income distribution. Based on the characteristic facts of China’s urban-rural dual economic structure, using the IFR and provincial panel data from 2005 to 2020, as well as the data of CLDS in 2014 and 2016, this paper empirically analyzes the impact of robot application on urban-rural income gap and its internal mechanism from the macro and micro levels. The results show the application of industrial robots and the income gap between urban and rural areas presents an inverted U-shaped trend, which increases first and then decreases. From a micro perspective, the growth rates of total income and wage income for rural residents are higher than those of urban residents, but this impact is mainly concentrated in the eastern regions. This papar not only provides policy reference for the government to promote the development of artificial intelligence technology, industrial structure upgrading and high-quality economic development, but also provides policy enlightenment for narrowing the urban-rural income gap, rural revitalization and common prosperity.

Technological innovation is an important breakthrough for realizing a strong manufacturing country and building a modern industrial system. China issued a strategic document on comprehensively promoting the implementation of intelligent manufacturing in May 2015. Intelligent manufacturing policy is a key institutional arrangement that promotes the transformation and upgrading of manufacturing enterprises and enhances the global competitiveness of manufacturing supply chains. It is important for enhancing the resilience and security level of the supply chain to explore how the intelligent manufacturing policy promotes the digital innovation of manufacturing enterprises by increasing their profits from intelligent production, easing their financing constraints, enhancing the efficiency of supply chain collaboration and increasing the government’s subsidies so as to realize the digital transformation of the supply chain of manufacturing enterprises. This paper constructs an evolutionary game model of government-bank-manufacturing enterprises-distribution enterprises, and based on the data of Chinese A-share listed companies from 2007 to 2022, it empirically tests the mechanism and effect of the intelligent manufacturing policy on technological innovation of manufacturing enterprises from the perspective of the supply chain of manufacturing enterprises by using a DID model. It has been found that the intelligent manufacturing policy can effectively incentivize firms to choose technological innovation strategies while further strengthening active cooperation with distribution firms. Mechanism analysis finds that the intelligent manufacturing policy promotes technological innovation in manufacturing firms mainly by increasing government subsidies, lowering interest rates of innovation and increasing corporate profits. Heterogeneity analysis shows that the intelligent manufacturing policy effectively promotes technological innovation in state-owned enterprises, non-eastern enterprises and large-scale enterprises, but is detrimental to the development of technological innovation in private enterprises, eastern enterprises and small enterprises. This paper contributes to a comprehensive understanding of the micro-mechanism and differentiated effects of the intelligent manufacturing policy, provides a reliable basis for optimizing the intelligent manufacturing policy system and boosting the development of technological innovation, and is also an important reference and guidance for the current in-depth promotion of the digital transformation of the manufacturing enterprises.

Data elements are a new driving force for innovation in high-tech industries in the digital age, and market integration is an external environmental support for promoting innovation in high-tech industries. It is necessary to study the impact of data elements and market integration on innovation in high-tech industries within the same framework. On the basis of theoretical analysis, this paper conducts empirical research based on China’s provincial panel data, and comprehensively uses panel regression model, mesomeric effect model and panel threshold model to study the impact of data element development and market integration on high-tech industrial innovation. The results show that: 1) The development of data elements has a significant positive impact on innovation in high-tech industries, and market integration has strengthened the innovation driving effect of data elements. 2) Market integration has a significant mesomeric effect, and data elements can drive high-tech industrial innovation by promoting market integration. 3) The threshold effect indicates that as the threshold value of market integration increases, the impact of data elements on high-tech industry innovation shows a trend of first increasing and then decreasing. When the market integration is at a moderate level, it is more conducive to unleashing the innovation driving effect of data elements. 4) Multidimensional regression analysis shows that the positive impact of data element development on high-tech industry innovation in the central region is significantly greater than that in the eastern and western regions. Compared with medium-sized enterprises, data element development has a stronger driving effect on the innovation of large high-tech enterprises. Therefore, high-tech enterprises should continuously enhance their ability to mine, apply, and transform data elements. Relevant government departments should pay attention to the development and utilization, efficient circulation, and ownership protection of data elements, actively cultivate a unified and standardized data element market, promote regional collaborative development of data elements, and strengthen the innovation driving effect of data elements.

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.

As a vital component of urban transportation systems, the bike-sharing system operates on a time-based billing mode and offers “point-to-point, door-to-door” rental services, enabling users to conveniently pick up and drop off bicycles at their desired locations. At present, bike-sharing platforms encounter operational deficiencies, including inaccurate demand prediction, suboptimal bicycle allocation, and delayed collection of faulty bicycles, resulting in a significant mismatch between supply and demand. To address these challenges, this study investigates a spatio-temporal demand prediction method incorporating multi-task learning and a dynamic shared-bikes repositioning and collection approach. Firstly, a multi-gate mixture-of-experts with a bidirectional long short-term memory network is employed to jointly predict the pick-up and drop-off demands by considering the correlation between the pick-up and drop-off demands corresponding to stations. To alleviate the dependency on long time sequences, an attention mechanism is introduced to enhance the attention given to the crucial information. Furthermore, a collaborative optimization model is proposed to address the dynamic repositioning and faulty bicycle collection in the bike-sharing system, which accounts for charging decisions and mileage constraints associated with vehicles. To meet the time-sensitive requirement of large-scale dynamic repositioning management, a simulated annealing-based adaptive large neighborhood search is customized to solve the model. Finally, a comprehensive case study utilizing bike-sharing data from the New York City Citi Bike is conducted to validate the effectiveness of the proposed approach across various performance metrics: Predictive accuracy, computational efficiency, and operating costs.

The Yangtze River Economic Belt is a major national strategic development area. Green and innovation development is one of its important missions. This article combines panel data of Chinese cities from 2008to 2022 and uses a double difference model to examine the impact of the development strategy of the Yangtze River Economic Belt on the performance of green technology innovation. The results indicate that the development strategy of the Yangtze River Economic Belt can effectively improve the green technology innovation performance of the areas along the route. Mechanism analysis finds that environmental regulations, industrial agglomeration, foreign direct investment, and government subsidies are effective paths for the development strategy of the Yangtze River Economic Belt to promote the performance of green technology innovation. Heterogeneity analysis finds that the development strategy of the Yangtze River Economic Belt has a more significant promoting effect on the performance of green technology innovation in cities along the Yangtze River, coastal cities, large cities, and downstream cities. This article integrates the development strategy of the Yangtze River Economic Belt and the performance of green technology innovation into a unified analytical framework, analyzes the impact and mechanism of national strategies on the performance of green technology innovation, and provides important policy implications for how to adjust strategic regulation methods in the next stage and promote high-quality development of the Yangtze River Economic Belt.

In the context of population aging, stabilizing the industry is crucial for achieving high-quality economic development. However, it remains to be tested whether aging will inhibit industrialization. From a supply and demand perspective, this paper uses newly collected panel data from fifty economies spanning the years 1990 to 2018. By constructing a two-way fixed effects panel model, it aims to identify the impact of aging on industrialization and its underlying mechanisms. The research finds that there exists a U-shaped relationship between population aging and industrialization, and this conclusion remains valid after a series of robustness tests. Despite the negative supply-side mechanism of an aging labor force structure and the two negative demand-side mechanisms of domestic demand shifting towards services and international demand relocating abroad, there are also three positive supply-side mechanisms: Improvements in labor quality, technological upgrades, and capital-biased technological progress. The negative supply-demand mechanisms play a major role when the level of population aging is relatively low, while the positive supply mechanisms take the lead at higher levels of population aging. In view of this, this study enriches the empirical evidence and causal pathways of the impact of population aging on industrialization. The research conclusion provides decision-making references by advocating for sustained supply-side reforms to promote industrial development through “talent dividends” and “innovation dividends”, while also emphasizing the expansion of demand to maintain the international competitiveness of manufacturing industry.

In this paper, we have set up a synergy degree model of pollution and carbon emission reductions to measure the synergy degrees of pollution and carbon emission reductions for China’s 30 provinces during2014–2021, and geographically and temporally weighted LASSO regression model to identify their key driving factors. The results obtained show that the synergy degrees of pollution and carbon emission reductions in China’s 30provinces show an upward trend with a range between 0.11 and 0.71, which also shows significant spatiotemporal characteristics with the spatial trend of “northeast-southwest”, the spatial pattern of “hot in the south and cold in the north”, and the temporal evolution of “increasing hot spots and decreasing cold spots”. Temperature, humidity, water resource utilization, energy intensity, energy structure, common wealth, environmental protection investment, and artificial intelligence technology are identified as the key drivers of the synergy of pollution and carbon emission reductions in China. Our findings not only help deeply understand pollution and carbon emission reductions, but also help improve the provincial targeted policies for pollution and carbon emission reductions in China.

In the era of the platform economy, the competitive landscape among enterprises is undergoing a shift from traditional product or customer-driven competition to one characterized by platform ecological competition. However, existing literature has yet to provide a comprehensive understanding of this evolutionary mechanism. This study employs an evolutionary game approach to construct a model of ecological cooperation comprising e-commerce platforms, logistics firms, and businesses. Through an analysis of evolutionary paths, we simultaneously consider three key mechanisms: Resource sharing, mutual benefit, and collaborative innovation. Our investigation aims to elucidate the influence of these mechanisms on the establishment and maintenance of ecological cooperation. The finding shows that resource sharing, mutual benefit, and collaborative innovation among multiple agents are essential prerequisites for fostering an ecological cooperation network in the age of platform economy. Failure to satisfy any of these conditions can lead to the collapse of such cooperation network. Furthermore, we identify several determinants, i.e. the sensitivity coefficient of services, the degree of mutual trust, and the discount associated with collaborative innovation, which positively impact the formation of ecological cooperation. Conversely, another factors such as the costs associated with ecological cooperation, the risks associated with collaborative innovation, and speculative returns exert inhibitory effects on ecological cooperation. Additionally, the efficacy of resource sharing levels on ecological cooperation is contingent upon the absorption capacity and willingness of stakeholders to engage in resource sharing. Similarly, the impact of collaborative innovation research and development investment on ecological cooperation hinges on the level of innovation risk. This study not only presents a theoretical framework for understanding the strategic decision-making process among multiple agents engaged in ecological cooperation within the context of the platform economy but also offers practical insights for enterprises seeking to establish or integrate into ecological cooperation alliances.

This paper clarifies three economic characteristics of AI technology: Substitution, collaboration, and creativity. Categorizes AI into “labor-saving” technologies (represented by embodied AI) that replace production labor and “augmenting” technologies (represented by generative AI) that enhance R&D labor. A multi-sector dynamic general equilibrium model is constructed based on the dual nature of AI and its three economic features, revealing the intrinsic logic of how AI reshapes high-quality economic development. Simulations and empirical tests are conducted. Model analysis and numerical simulations show that “labor-saving” technologies drive economic scale expansion by replacing non-skilled labor and collaborating with skilled labor to enhance marginal returns on technology. Meanwhile, “augmenting” technologies achieve dual breakthroughs in scale and quality by empowering R&D actors in knowledge production, simultaneously improving marginal returns and total factor productivity (TFP). Empirical results indicate: 1% increase in “labor-saving” technologies leads to a0.035% expansion in economic scale, with substitution contributing $\sim $22.9% and collaboration $\sim $34.2% of the positive effects. 1% increase in “augmenting” technologies drives a0.117% economic scale expansion, with creativity contributing $\sim $11.1% of the effects. 1% increase in “augmenting” technologies raises TFP by 0.0026%, with creativity contributing $\sim$27% of the improvement. Finally, policy recommendations are proposed for advancing AI and fostering high-quality economic development.

As implicit government guarantees disappear and the rigidity of bond market payments is broken, the efficiency of the corporate bond market is gradually improving, highlighting the significance of studying bond market pricing patterns. Therefore, this paper proposes a parametric pricing method based on machine learning and bond characteristics. By leveraging machine learning technology to utilize high-dimensional bond characteristics, we estimate the stochastic discount factor for corporate bond pricing, fully extracting both linear and non-linear pricing information. This method can obtain analytical solutions and has economic interpretability. Theoretically, it is demonstrated that this method is equivalent to the parametric portfolio approach, enriching the economic connotation and estimation method of the stochastic discount factor for corporate bonds. Research on Chinese corporate bond market shows that: 1) The parametric pricing model extracts more corporate bond pricing information than the classical factor model by capturing complex pricing relationships and weak factors from high-dimensional bond characteristics. 2) Return-related and liquidity-related bond characteristics are most important for corporate bond pricing, and the importance and predictive direction of these characteristics exhibit strong time-varying properties. The fundamental characteristics of the issuer cannot provide additional pricing information beyond bond characteristics. 3) The parametric pricing model has stronger pricing capabilities for bonds with high duration, high volatility, low credit ratings, low liquidity, and those issued by non-state-owned enterprises and non-listed companies; its pricing ability weakens in an expanding macroeconomic state and strengthens otherwise. This paper expands the research framework of corporate bond pricing theory, helps to understand corporate bond pricing patterns, improves market pricing efficiency, and prevents bond risks.

Based on the production perspective of final products, this paper constructs an input-output analytical model to measure the role of the “dual circulation”. The aim is to reveal the linkages between the three types of economic circulation — namely, the internal circulation, the external circulation, and the internal and external intertwined circulation — and the creation of value added. Accordingly, this paper explores the characteristics of China’s “dual circulation”, the contribution of circulations to China’s value-added creation, the main factors affecting the functioning of circulations. It also analyses the contribution of China’s “dual circulation” to value-added creation in major economies such as the US, Japan, the EU and ASEAN. The results show that China’s internal circulation is the main driving force for development, and the final goods production is the main factor affecting the creation of value added in the internal circulation. The contribution of the other two types of circulation should not be neglected, and the cross-border trade linkage factor has a greater impact on their contribution to value creation. In addition, while China reaps its own benefits from “dual circulation”, it also contributes significantly to the value creation process of other countries. Following these conclusions, this paper proposes policy recommendations on how China can build a new development pattern of “dual circulation” that is more efficient and of higher quality.

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.

Based on the data of A-share listed companies from 2010 to 2022, the article examines the impact of digital infrastructure construction on supply chain resilience using an asymptotic double-difference model by considering the Broadband China pilot policy as an exogenous shock to digital infrastructure construction. The study finds that digital infrastructure development significantly enhances supply chain resilience, with reduced credit mismatch, increased risk-taking level and improved inventory turnover efficiency being the channels through which digital infrastructure development enhances supply chain resilience. Heterogeneity analysis suggests that the promotion effect of digital infrastructure construction on supply chain resilience is more significant in growing and maturing firms and competitive industries, and when the city is far away from neighboring prefectures and provincial capitals, the promotion effect of digital infrastructure construction on its supply chain resilience is more significant. Further research finds that digital infrastructure construction only has a positive spillover effect on supply chain resilience in cities with neighboring cities as pilot cities, and the spillover effect decreases with increasing geographic distance.

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.

Compared with traditional off-line shopping, online shopping has the dilemma of information asymmetry. As an important means to solve the problem of information asymmetry in online shopping, online comments can significantly affect customer purchasing decisions and thus firms’ decisions. With respect to a supply chain competition system consisting of two manufacturers and two retailers, considering the influence of online comments on customer choice behaviors, this paper builds a game model to explore how retailers adjust product pricing and how manufacturers adjust wholesale price in response to their rivals’ decisions. First, a customer utility function considering the impact of online comments is developed; next, we construct competitive pricing models of retailers and manufacturers based on Nash game; then, we derive the models to determine the equilibrium pricing decisions for retailers and manufacturers; finally, the effects of online comments on retailers’ pricing decisions, manufacturers’ wholesale price decisions, and profits of all players are analyzed. The results show that both better online word-of-mouth and customers’ greater focus on online comments do not always induce retailers and manufacturers to increase product prices. However, when online comments provide more information about product fit, price competition between the firms weakens, that is, both retailers and manufacturers raise their respective prices. From the perspective of profit, opening up online comments in a competitive supply chain could reduce profits for both retailers and manufacturers.

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.

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.

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.

As a critical strategic resource for enterprises, data assets play a pivotal role in risk management and have become a significant factor influencing corporate risk-taking. This study examines the impact of data assets on corporate risk-taking, its mechanisms, and potential heterogeneity using data from Chinese A-share listed companies from 2011 to 2022. The findings reveal that data assets significantly reduce corporate risk-taking, and this conclusion remains robust after addressing endogeneity issues and conducting rigorous tests. Moderating effects indicate that fintech development and corporate financialization enhance the inhibitory effect of data assets on corporate risk-taking. Mediation mechanisms demonstrate that data assets mitigate corporate risk-taking by reducing agency costs, decreasing strategic deviation, and improving corporate ESG performance. Further analysis shows that the risk-reduction effect of data assets is more pronounced in state-owned enterprises, firms with high R&D subsidies, low equity incentives, a higher proportion of executives with IT backgrounds, and those operating in environments with high institutional innovation. These findings provide empirical evidence and policy implications for understanding how data asset allocation influences corporate risk-taking mechanisms, optimizing data asset management, and supporting high-quality development.

Actively participating in the international macro cycle and enhancing the influence of foreign trade is pivotal for China to shape its new development paradigm and seize the initiative in growth. Using the natural matrix structure of monthly bilateral goods trade data from 23 major economies, this paper incorporates a cutting-edge matrix autoregression model to capture the intricate contemporaneous and intertemporal dependencies present within the trade matrix. Based on this, we extend the spillover index measurement method and combine it with the spillover network analysis method to construct international import and export trade spillover networks. Further, from a China-centric perspective, we quantitatively investigate the changes in China’s import-export trade influence under the international cycle. Results show that from a global standpoint, bilateral trade networks undergo significant structural shifts, with overall spillover intensity first increasing and then gradually weakening, embodying a transition from “globalization” to “de-globalization” traits in the international macro cycle. From China’s perspective, import spillover remains stable, while export spillover has gradually weakened since the global financial crisis and remained low during the US-China trade war and the COVID-19 pandemic. Analysis of influencing factors suggests that international total trade spillovers are significantly affected by the US Federal Reserve’s interest rate, and the geopolitical risk index of the US Granger-causes China’s export spillover index. Evidently, the dual circulation strategy, emphasizing domestic macro circulation while promoting mutual advancement with international circulation, is valuable for guarding against potential “de-globalization” risks in the international cycle and ensuring the stability of China’s economic trade. This research offers insights for understanding the international macro cycle in the new development paradigm, adjustments to the dual circulation strategy, and related policy formulation.

Corporate fundamentals consist of multidimensional information that affects operation and development of firms, such as financial data and non-financial data. Different from the existing research on market anomalies of single indicators at the firm level, this paper attempts to utilize machine learning methods to integrate the information of 50 dimensional fundamental variables and to innovatively predict the intrinsic valuation of firm. We construct a valuation mismatch indicator by comparing the difference between predicted corporate value and the real market value. This article tries 4 linear machine learning models (RIDGE, LASSO, ELASTICNET, PCR)and five nonlinear models (DT, RF, GBDT, XGBOOST, FNN) one by one, and further integrates the algorithmic models to aggregate the predictive ability of multiple machine algorithms as the final valuation mismatch indicator. The results show that market long-short portfolios constructed based on the valuation mismatch measure can earn up to 32% raw annualized returns and 22% Fama-French 5-factor adjusted annualized returns. This valuation mismatch anomaly is more significant in stocks with limited investor attention and higher limits to arbitrage, which act as potential explanatory mechanisms for the valuation mismatch anomaly. Further analysis reveals that the valuation mismatch anomaly is affected by certain macroeconomic state changes, and that firm-level valuation mismatch indicators can predict the occurrence of future firm-level real risk events as well as market-level changes in systemic financial risks. This paper proposes a new enterprise intrinsic valuation mismatch indicator and reveals the accompanying market anomalies, which is instructive for preventing and resolving financial risks and enhancing the information efficiency of China’s capital market.

Driven by digital technology, more and more platform enterprises promote products through live-streaming sales, group chat forwarding, video promotion and other ways. The social e-commerce based on the social interaction is on the rise. In order to study the impact of social behaviors on the platform supply chain, our paper designs a two-level manufacturing platform supply chain composed of a manufacturing platform, a manufacturer of the check-in platform and a traditional retailer. Considering the diversity of sales channels, the manufacturing platform allows buy-online-and-pickup-in-store (BOPS). The price competition and BOPS cooperation coexist in the manufacturing platform supply chain. We construct a noncooperative-cooperative biform game model and solve it to get the optimal strategies and profits of manufacturing platform supply chain members. We analyze the effects of inconvenience cost, consumer preference for the manufacturer of the check-in platform and social behaviors on equilibrium results. Some findings are as follows: In the social e-commerce era, the manufacturing platform and traditional retailer can achieve BOPS cooperation by profit-sharing and create the maximum benefit for the grand coalition, which realizes the win-win situation between the manufacturing platform and traditional retailer. Based on the co-existence of pricing competition and BOPS cooperation, improving the influence of social behaviors is not always beneficial to the manufacturing platform and the entered manufacturer, but can effectively increase the consumer surplus.

The new infrastructure based on information technology is not only a carrier for new technologies, elements, and business forms, but also a crucial lever to achieve high-quality development in China. This paper takes new infrastructure as the research object and analyzes the influence mechanism of the government’s new infrastructure investment in promoting China’s economic development by constructing a dynamic stochastic general equilibrium (DSGE) model including the new infrastructure sector. Furthermore, this paper makes targeted policy recommendations for China to implement the strategy of expanding domestic demand and promoting high-quality economic development from the perspective of infrastructure investment strategy. Research shows: 1) New infrastructure investment shows a differentiated growth path for the economy compared to economic and social infrastructure investment; 2) New infrastructure investment is the largest economic enabler, followed by social infrastructure investment, while economic infrastructure provides a relatively small boost; 3) Excessive increases in new infrastructure investment may weaken their investment multiplier effects, while appropriate increases in social infrastructure investment can help to strengthen the role of new infrastructure investment as a driver of the economy. The conclusion of the study shows that in the process of vigorously promoting the construction of new infrastructure, a sound new factor market system should be established, the transformation and upgrading of economic infrastructure should be intensified, and the construction of convergent infrastructure in social and livelihood areas such as medical care and education should be expedited.

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.

Under extreme disaster situations, the traditional bureaucratic emergency management model has many problems, such as serious misallocation of resources, information asymmetry, lack of dynamic adjustment ability and so on. The intelligent emergency mutual aid information platform relies on the interactive socialized emergency rescue model to provide new ideas for solving these problems. Based on the view of physical-social-information triple spatial resources, this paper has constructed a differential game model for the dynamic allocation of emergency resources between help-seekers and rescuers, and analyzed the influence of important parameters, such as information sharing, the random interference with insufficient or distorted information, emergency rescue time and efficiency factor of resource allocation, on the equilibrium decision-making and utility steady state of resource allocation between help-seekers and rescuers. This paper also explores the role of heterogeneity of help-seekers’ informational ability on the behavioral decision-making and system utility of participants. The study shows that: 1) Under the condition of random interference factors such as insufficient or distorted information on the platform, the amount of emergency mutual aid resource allocation of help-seekers and rescuers will decrease, and the utility will also be restrained and reduced. 2) As participants share more information on the platform, more resources will be devoted to help-seekers and rescuers, which can effectively enhance the level of resilience and security in disaster relief. 3) Only when the degree of information sharing is large, the amount and utility of resource allocation between the two groups increase with the increase of resource allocation efficiency factor, and it is independent of the heterogeneity of the assistance-seekers. 4) It is not always wise to recognize the heterogeneity of the informational ability of help-seekers. When the informational ability of the help-seekers is at a low level and below a certain threshold, identifying the heterogeneity can improve the allocation of emergency resources and system utility of the participants by accurately locating requirements and individualized response; as the informational ability of the help-seekers gradually increases, the homogenization of the help-seekers is more helpful to achieve accurate resource matching and uniform resource allocation. This study provides theoretical support for improving the resilience of disasters through research on the maximum allocation and dynamic optimization of emergency mutual aid resources in the emergency mutual aid information platform.

This paper verifies the optimization effect of customer digital transformation on supplier resource allocation and its transmission mechanism from the perspective of supply chain spillover. The research results show that customer digital transformation has a backward spillover effect, which significantly improves the resource allocation efficiency of supplier. When the geographical distance of the supply chain is long, and the supplier’s customer concentration is low and the scale is large, the spillover effect is more significant. The mechanism test shows that the spillover effect of customer digital transformation mainly improves supplier’s resource allocation efficiency by optimizing the matching of supply and demand, reducing capital costs, and facilitating digital innovation collaboration. Further research shows that the spillover effect is mainly reflected in the reduction of supplier labor mismatch, and the negative demand shock caused by the COVID-19 pandemic weakens the spillover effect. Based on the perspective of supply chain spillover, this paper expands the application scope of the theory of vertical relationship of industrial organization in the field of digital supply chain management and resource allocation, and enriches the research on the interaction mechanism between upstream and downstream firms in the supply chain. It provides practical inspiration on how to use digitalization to empower supply chain management, promote the construction of vertical coordination mechanism of supply chain and improve supply chain resilience.