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.

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.

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.

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.

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 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.

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.

Energy, foreign exchange and gold, as important strategic reserves, show high sensitivity to geopolitical risks. Using time-frequency network analysis based on quantile vector autoregression, this paper reveals the risk spillovers between energy, foreign exchange, and gold markets in the Middle East, South Korea, and Western regions. The effects of geopolitical risks on risk spillovers between these markets are also examined. The results show that: 1) There are significant risk spillovers between energy, foreign exchange, and gold markets, with notable differences across various time and frequency domains. 2) Geopolitical events significantly affect the pattern of risk spillovers in Korea, Europe and the U.S. regions, and the responses of these to geopolitical events show significant differences. 3) The risk spillovers between energy, foreign exchange, and gold markets exhibit distinct time-varying characteristics, and geopolitical risk not only exacerbates the overall level of risk spillovers, but also affects risk spillovers between different regions and markets. These findings hold significant implications for policymakers and investors in understanding the risk spillovers between energy, foreign exchange, and gold markets under the impact of geopolitical events, enabling timely strategy adjustments.

Motivated by physics-informed neural networks, this paper introduces a new framework called finance-informed neural networks (FINN), which integrates financial theory with deep learning technology. The goal is to improve the transparency and accuracy of deep learning methods used in empirical financial research. FINN’s network structure is based on arbitrage pricing theory and empirical portfolio construction techniques. Market efficiency information is integrated into the loss function in the training of FINN. An empirical study of China’s A-share market demonstrates that FINN has several advantages. It outperforms conventional fully connected neural networks and traditional empirical APT factor models in terms of out-of-sample $R^2$ performance. FINN also overcomes the limitations of traditional APT factors in predicting different asset types. Additionally, FINN achieves the highest cumulative returns and Sharpe ratios in constructing mean-variance efficient portfolios, highlighting its substantial economic value. Furthermore, a comprehensive analysis of the linear and nonlinear importance of characteristics reveals that FINN’s outputs are particularly sensitive to trading characteristics, reflecting the unique attribute of China as an emerging market. Moreover, valuation, profitability, and solvency characteristics significantly influence FINN’s outputs on a linear level. The introduction of FINN offers innovative methodological guidance for conducting empirical financial research using deep learning technology.

The increasing frequency of extreme weather events caused by global warming poses a serious challenge to the high-quality development of the economy. As a key technological vehicle for intelligent manufacturing systems, the rise of industrial robots presents new opportunities for firms to address climate challenges. Drawing on the dynamic capabilities theory, this study examines the impact of climate risk on industrial robot application, based on data from listed manufacturing firms on the Shanghai and Shenzhen A-shares from 2016 to 2023. The findings reveal that: 1) Climate risk promotes industrial robot application, and this conclusion remains valid after a series of robustness tests. 2) In terms of the mechanism of influence, climate risk drives industrial robot application through the factor substitution effect and the strategic option effect. 3) Heterogeneity analysis indicates that the positive impact of climate risk on industrial robot application is more pronounced in private firms, large-scale enterprises, enterprises in coastal regions, enterprises with high carbon emission intensity, and highly competitive industries. This research provides micro-level evidence for effectively responding to climate risks and promoting the intelligent transformation of production processes, while also offering valuable insights for advancing the strategic layout of intelligent manufacturing and achieving high-quality economic development.

Based on the upper echelons theory, rising climate policy uncertainty affects management’s perception on future development, thereby influencing corporate investment decisions. According to the real option theory and growth option theory, this study investigates whether climate policy uncertainty impacts corporate investments via management perception, based on textual analysis of MDA (management’s discussion and analysis)sections in annual reports of Chinese non-financial listed companies from 2007 to 2023. We construct two dimensions of management perception indicators: risk perception, tone, and text complexity — for empirical analysis. The findings reveal that increased climate policy uncertainty significantly raises corporate financial and green investments while suppressing fixed asset investments, and these effects exhibiting long-term persistence. Mechanism analysis demonstrates that heightened climate policy uncertainty amplifies management’s perception of uncertainty and negative tone, and elevates annual report text complexity, ultimately shaping investment behavior. Furthermore, the rising climate policy uncertainty will suppress excessive investment, intensify underinvestment, and increase the default risk and stock price volatility risk of enterprises.

In the context of China’s transition to a low-carbon economy, the “dual credit” policy stands as the most significant regulatory measure for the NEV industry. Technological innovation is crucial for NEV enterprises to achieve low-carbon upgrades and transition. It remains to be explored whether the implementation of the dual credit policy can effectively incentivize technological innovation among NEV enterprises, which vary in supply chain location, region, asset size, and ownership structure. To address this, the paper combines differences-in-differences model and propensity score matching analysis to thoroughly investigate the impact of the dual credit policy on the technological innovation of heterogeneous enterprises. The results show that the policy can effectively incentivize the technological innovation of all types of NEV enterprises, with significant variations in its effects on heterogeneous enterprises. Specifically, the “dual credit” policy is more likely to stimulate innovation output in downstream enterprises, central region enterprises, small and medium-sized enterprises and state-owned enterprises. Additionally, it favors innovation input of upstream enterprises, western region enterprises, large enterprises and state-owned enterprises. However, the policy’s influence has not significantly enhanced innovation resource input for downstream enterprises. Moreover, enterprises in less economically developed regions exhibit a “high input, low output” phenomenon, characterized by increased competitive pressure and market risk.

In the past decade, the joint institutional investors in China’s capital market are developing and growing at an extraordinary speed, and the influence of joint institutional investors’ banding behavior on corporate behavior is increasingly significant. Based on the data of Chinese listed companies from 2010 to 2020, this paper innovatively constructs the network of common institutional investors and examines the impact of common institutional investors’ banding behavior on listed companies’ fulfillment of social responsibility. The study finds that the grouping of common institutional investors has a positive impact on the CSR performance of listed companies. The conclusion is still valid after the robustness test. The mechanism research finds that the joint institutional investors’ banding promotes enterprises to fulfill their social responsibilities by exerting information effect and governance effect. This paper expands the research on the effect of joint institutional investors’ banding on corporate social responsibility, and provides new evidence for informal institutions to enhance corporate social responsibility.

Under the “dual carbon” goal, designing a carbon price mechanism that can not only promote the development of the carbon market, but also drive people’s low-carbon life and improve the return on investment in green industries has become a research focus. Therefore, this paper conducts a dynamic evolution of carbon price through multi-agent gaming toward the “dual carbon” goal. Firstly, the impact pathways of multiple mechanisms-including carbon quota, covered sectors, carbon inclusion, and carbon sink-on carbon pricing are analyzed. Secondly, dynamic development pathways for these mechanisms are designed to cover the entire carbon control process. On this basis, a dynamic carbon pricing game model is constructed, incorporating multiple stakeholders such as government regulation, carbon allowance suppliers, and demanders. Finally, simulation analysis based on simulated data yields the following findings: 1) Carbon price overall exhibits a three-phase dynamic evolution characterized by “decline, then rise, peaking, and finally stabilizing”. 2) Driven by the scarcity of carbon emission rights, the government’s free carbon quota allocation coefficient shows a year-on-year decreasing trend during the 14th to 15th Five-Year Plan periods. 3) Carbon price responds asymmetrically to policy tools: relaxing free carbon quotas or narrowing sectoral coverage leads to a decrease in the equilibrium carbon price, while weakening carbon sink and carbon inclusion mechanisms drives the carbon price upward. This study provides theoretical insights for understanding the evolution of carbon pricing and constructing a phase-adaptive policy system for the carbon market.

The inefficient operation of rural express delivery services has become a bottleneck for the upward movement of agricultural products and the downward movement of industrial products. It is urgent to innovate the operation mode of rural logistics. Based on this, this article proposes a rural logistics collaborative pick-up and delivery operation model that considers the integration of public transportation passenger and freight. The corresponding cost quantification and allocation strategies have also been carefully designed. Firstly, the article constructs a mathematical model for generating $2^n-1$ ($n$ is the number of members) sub-alliances at once to correspond to the collaborative retrieval problem. This is a requirement that combines the characteristics of bus passenger and freight integration and collaborative delivery transportation. The research uses an algorithm framework based on adaptive large neighborhood search to solve and obtain cost sharing input data. Then, with the help of cooperative game Nucleolus solutions, fair allocation of collaborative costs among alliance members is achieved. Finally, the effectiveness of the proposed mode, model, and algorithm is verified through numerical experiments. The results show that compared to the independent transportation mode, cooperation among express carriers can save19.20% of transportation costs, significantly reducing distance and vehicle numbers. On this basis, considering the multi-party collaboration of bus passenger and freight integration, it can further save 23.58% of the total cost, increase the cost savings of carriers by 10.64% to 19.88%, and enable bus operators to obtain considerable benefits. Analyses of key parameters such as parcel demand, passenger travel demand and bus capacity reveal that the proposed model maintains robust cost-saving benefits under different scenarios. The operation mode and methods proposed in this article can provide reference for the government to promote rural logistics cooperation and improve the efficiency of two-way transportation of rural logistics.

In the process of China’s progress towards achieving “carbon peak and carbon neutrality”, green finance has become an important driving force for enterprises to promote green innovation, and it has become an urgent practical problem how to lead enterprises to co-create green value. Based on the open innovation theory, this study uses the difference-in-differences model to evaluate the impact of Green Credit Guidelines on corporate green value co-creation, and further excavates the mechanism and boundary conditions of the relationship. Based on the data of listed companies in China from 2009 to 2020, the empirical test results show that:
1) Green Credit Guidelines has a significant positive impact on green value co-creation. 2) The two dimensions of green collaborative innovation (breadth and depth) play a mediating role between Green Credit Guidelines and green value co-creation, respectively. 3) Environmental leadership positively moderates the relationship between Green Credit Guidelines and green value co-creation; CEO openness positively moderates the relationship between Green Credit Guidelines and green collaborative innovation. In addition, the research conclusion is still valid by conducting the endogenous and robustness tests. Overall, this study not only constructs the theoretical framework for green value co-creation between firms and external partners and explores the channels through which green credit policy enhances green value, but also expands the theoretical boundaries of research on corporate green value co-creation, which provides a theoretical reference for testing the effectiveness of green credit policy in China and promoting firms to actively engage in green value co-creation.

House prices have distinctive economic attributes. An accurate house prices forecasting is significant. To capture real-time predictive information and improve forecasting accuracy, this paper proposes a short-term house price forecasting approach with multi-source heterogeneous information and data traits, Firstly, Weibo, Baidu and stock market are collected to construct the multi-source heterogeneous dataset. Subsequently, the memory-trait and mutual-trait of house prices series are analyzed by data-trait-driven method to guide the division of sample interval and model selection. Lastly, statistical models and machine learning models are employed to verify the effectiveness of the proposed approach. The average housing prices across 70 large and medium-sized cities in China are selected as the sample data. Empirical results show that the proposed approach can achieve better prediction accuracy at the short-term forecasting, and the multi-source heterogeneous information can significantly improve the prediction performance of house prices. This provides theoretical support for scientific decision-making of management departments.

This paper investigates the optimal pricing strategies for asymmetric and competitive manufacturers (an innovative manufacturer and a regular manufacturer). Each manufacturer has two pricing strategies, uniform pricing and differentiated pricing, thus constituting four pricing combination models: Uniform pricing for both manufacturers, differentiated pricing for one manufacturer, and differentiated pricing for both manufacturers. By analyzing the prices, innovation levels and the profits under optimality, the following conclusions are obtained. First of all, when manufacturers adopt differentiated pricing strategies, they charge higher prices to old consumers, i.e., “killing the regulars”, to poach market. This strategy further incentivizes manufacturers to improve the level of innovation, resulting in the highest level of innovation. In terms of equilibrium, there are two Nash equilibriums, i.e., both manufacturers adopt uniform pricing strategy and both manufacturers adopt differentiated pricing strategy, in which the former is Pareto-optimal and risk-advantageous. This illustrates that the differentiated pricing based on consumer purchase history does not benefit either side. Therefore, the better strategy for competing manufacturers is to adopt “treating all equally” rather than “killing the regulars”. Furthermore, differentiated pricing strategy increases consumer surplus, but its impact on social welfare is related to the innovation ability.

This paper examines the relationship between litigation risk and financial distress using the data from A-share listed companies in the Shanghai and Shenzhen stock markets from 2003 to 2019. The study finds that litigation risk significantly exacerbates financial distress. The mechanical analysis shows that litigation risk increases the likelihood of financial distress by intensifying financing constraints and reducing profitability from the perspective of capital allocation path. The reputation insurance mechanism analysis reveals that corporate social responsibility has a significant negative moderating effect on the relationship between litigation risk and financial distress. Further research finds that this distress effect caused by litigation risk is more pronounced in companies that are in the growth and decline stages, have more severe information asymmetry, and have a lower proportion of female directors. Additionally, compared to securities litigation, operational litigation has a more severe impact on financial distress. This paper provides a theoretical basis for understanding the relationship between litigation risk and financial distress and offers empirical evidence for reducing corporate financial risk and promoting the healthy development of the market.

Under the preposition warehouse mode, the O2O (offline to online) orders have the characteristics of immediacy and small batch. Generally, the more frequent the delivery, the higher the distribution cost but the lower the delay cost. Consequently, there is a tradeoff between distribution cost and delay cost for the manager to make delivery decisions minimizing the total cost. The real difficulty lies in the fact that the manager cannot accurately predict the demand information of future O2O orders, and can only make real-time dynamic decisions on whether to deliver and which orders to be delivered based on the previous order information and delivery effect. In this paper, using online algorithm and competitive analysis, an online decision-making model is established for O2O order instant delivery problem with capacity limit under the preposition warehouse mode. An asymptotic lower bound of 2 is derived for this online problem and an online delivery strategy with competitive ratio of 3 is designed. The present online optimization model and strategy can not only be directly used in the practice of O2O order delivery under the preposition warehouse mode, but also have some reference value for the related problems such as make-to-order and purchase-to-order.

Supply chain network disruption risk can be propagated to upstream and downstream enterprises through the supply and demand relationship, consequently endangering the overall performance of the supply chain network. In the process of disruption risk propagation, it is often accompanied by risk information dissemination, risk information disclosure, and risk control measures adoption. The propagation scale and outbreak threshold are either directly or indirectly related to these factors. Therefore, studying the dynamic coupling propagation characteristics of risk information dissemination, risk information disclosure, risk control measures adoption and disruption risk propagation is crucial to understanding the underlying mechanisms of disruption risk propagation in supply chain network. In this paper, a three-layer complex network model is constructed to explore the risk disclosure awareness, risk control measures adoption and the direction of risk propagation on disruption risk propagation in supply chain network. Different from previous studies, this paper divides enterprises into enterprises that disclose risk information and enterprises that do not disclose risk information, and the disclosure of risk information affects the information dissemination process; moreover, this paper, unlike the assumption of risk dissemination non-directionality in previous studies, considers the influence of both forward and backward disruption risk propagation on the disruption propagation process. By building a state probability transmission tree, the state transmission equation is established using the microscopic Markov chain method, and the disruption risk outbreak threshold is derived. The results of simulation experiments show that enhanced awareness of risk disclosure can increase the disruption risk outbreak threshold and reduce the scale of disruption risk propagation. In addition, it is essential to distinguish between forward and backward disruption risk propagation because of the differences in their influence on disruption risk propagation.

In the context of manufacturer investing in quality and retailer investing in retail service, this paper investigates the encroachment and information sharing decisions in a two-level supply chain consisting of a single manufacturer and a single brick-and-mortar retailer. Four dynamic game models based on different encroachment and information sharing choices are constructed and then the Bayesian Nash equilibrium solutions are obtained. By comparing the firms’ ex ante profits, the equilibrium decisions about the encroachment and information sharing are derived, and the impact of the model parameters on the equilibrium is also examined through sensitivity analysis. The main results of this paper are as follows. First, when the quality sensitivity coefficient is high, the retailer will proactively share information. If the manufacturer chooses to encroach, the retailer’s willingness to share information decreases (increases) with the service sensitivity coefficient when the channel substitutability is high (low), and first decreases and then increases with the service sensitivity coefficient when the channel substitutability is medium. Second, the manufacturer will choose to encroach when the encroachment cost is low. The manufacturer’s willingness to encroach increases with the quality sensitivity coefficient, and increases with the service sensitivity coefficient when both the service sensitivity coefficient and the quality sensitivity coefficient are low. Third, the manufacturer can induce the retailer to share information by designing a compensation payment incentive contract under certain conditions, which can lead to the Pareto improvements in their respective profit levels.

Forward-looking information reflecting future performance and business forecasts provides significant reference for investors’ decision-making. However, due to the dispersion of equity, minority shareholders often lack effective means to participate in the governance of corporate information disclosure. The establishment of investor interactive platforms has enhanced the voice of minority shareholders, enabling them to exercise activism through proactive expression under regulatory environments. This study examines how the online voices of minority shareholders impact corporate forward-looking information disclosure and finds that online voices improve the clarity of forward-looking information, as evidenced by increased quantitative and decreased qualitative disclosures. Further analysis reveals that this effect is realized primarily through mechanisms of capital market pressure created by aggregated investor opinions and the regulatory attention imposed by regulatory authorities. Moreover, the effect is more pronounced when the topics of minority shareholders’ voices focus on business strategies, when the proportion of negative tone is high, and when the corporate response rate is high. This paper identifies the transformation of forward-looking information disclosure triggered by minority shareholders’ online voices under the context of investor interactive platforms, providing important practical insights for companies to understand and respond to the interest demands of minority shareholders, as well as for regulatory authorities to further strengthen investor protection.

The high-tech industry serves as a crucial engine for China’s transformational development, with its innovation serving as a pivotal driver for economic growth and enhanced competitiveness. Scientifically and accurately assessing the innovation efficiency of the high-tech industry and analyzing the pathways to enhance efficiency hold significant positive implications for promoting high-quality development within this sector. This study constructs a dynamic network DEA with global weight and explores the paths that drive the improvement of innovation efficiency in high-tech industries using fuzzy set qualitative comparative analysis (fsQCA). This study finds that: 1) The overall innovation efficiency of high-tech industries in China is relatively low, with imbalanced innovation development among provincial regions; 2) there are two configuration paths for improving the innovation efficiency of high-tech industries; that is, digital-driven type, industry-university-research-market-environment synergy type. Finally, according to the efficiency evaluation results and the efficiency improvement path, some policy suggestions are given: 1) Changing the orientation of innovation policy from supply-oriented innovation policy to demand-oriented innovation policy; 2) enhancing technological development, promoting subject cooperation, and optimizing the innovation environment.

In response to the rapid aging of society, implementing effective fertility support policies and delaying retirement policies has become a key focus of China’s future population policies. This paper, based on an overlapping generations general equilibrium model with endogenous fertility and endogenous retirement, assumes that the government allocates resources between fertility subsidies and pensions, and examines the impact of fertility subsidies on retirement decisions, pensions, and welfare in old age. The theoretical analysis shows that fertility subsidies not only increase the number of children born to residents but also encourage individuals to delay retirement. The impact of fertility subsidies on pensions follows an inverted U-shape, but they can increase old-age welfare, although this may reduce the welfare of retirees in the current period. Empirical results, based on OECD cross-national panel data, align with the theoretical findings. The results suggest that if the government faces resource constraints, shifting part of the resources from pensions to fertility support policies can both promote an increase in the birth rate and facilitate the implementation of delayed retirement policies. While this may affect the welfare of retirees in the current period, it will, in the long run, enhance the overall welfare level of future generations of elderly individuals. This study enriches the theoretical literature on fertility support and delayed retirement policies and provides valuable theoretical insights for the implementation of fertility support policies in China today.

As a significant force driving the leap in productivity and industrial transformation, the new generation of artificial intelligence (AI) has been deeply integrated into economic and social operations as well as industrial development. In this context, data is gradually emerging as a core element differentiating competition in the AI field, with high-quality datasets significantly enhancing the efficiency and proficiency of model training for AI service providers. This paper focuses on the collaboration between data providers and data trading platforms to improve the quality of data granules, which aims to foster the development of the AI industry. Additionally, considering the non-cooperative game in the data supply chain, this paper constructs a noncooperative-cooperative biform game model to tackle this challenge, and studies the equilibrium strategies and optimal profits of all parties involved, as well as analyzing social welfare. Finally, a numerical analysis has been performed to examine how key factors influence data supply chain strategies, offering a theoretical foundation for decision-making by data providers, data trading platforms, and government. The findings indicate that: 1) The optimal selling prices and maximum profits for data providers and data trading platforms, the quality level of data granules, and overall social welfare are negatively related to the price sensitivity coefficient. 2) As AI service providers’ preference for data granules’ quality increases, the data supply chain actively enhances the level of data granules’ quality, leading to an increase in selling prices for data providers and data trading platforms, an improvement in social welfare, while a decrease in data providers’ dependence on data trading platforms.

This paper examines the live-streaming e-commerce supply chain composed of brand owners, live-streaming platforms and consumers, and investigates the interactive impact of platforms’ quality information disclosure and channel selection. Four game models are developed to reflect different selling models and information disclosure strategies: voluntary disclosure in reselling (RV), mandatory disclosure in reselling (RM), voluntary disclosure in agency selling (AV), and mandatory disclosure in agency selling (AM). The study reveals the following insights: product quality and user loyalty affect the optimal decisions and profits of all participants, while the selling model impacts how the live-streaming platforms acquire and disclose information. Mandatory information disclosure, compared to voluntary disclosure, significantly decreases streamers’ motivation to select products and hurt brand owners profits and consumer surplus. Brand owners need to consider commission rates and product attributes to choose the right sales channel, potentially achieving a win-win outcome with the platform under certain conditions.

Urban transport carbon reduction is an important breakthrough to achieve carbon neutrality and climate change mitigation. This study takes the Guangzhou subway system as a case study to construct a comprehensive environmental-economic analysis model for public transportation using the long-range energy alternatives planning system (LEAP). The model simulates the evolution of carbon reduction potential and external economic effects in the metro system from 1997 to 2060. The results indicate that the emission reduction potential associated with the transformation of transportation modes surpasses that of energy technology transformation, and subway transportation can significantly reduce transportation carbon emissions in all scenarios. Notably, substituting private cars with subway transportation demonstrates the highest potential for carbon emission reduction, achieving a maximum reduction of 3811 kt CO₂ emissions at equivalent transport intensity — constituting 93.3% of the total carbon emissions from private transportation. Although the carbon reduction potential of subway is expected to decrease as electrification and vehicle efficiency improve, it is projected to remain significant until 2060. The carbon return for the urban subway system is anticipated as early as 2029, but the speed of infrastructure development and changes in transportation mode will impact this time point. The benefits derived from mitigating a portion of road transport emissions surpass the social costs incurred by the subway, resulting in a net annual profit of approximately 0.4 to 0.8 billion Yuan after 2030. By 2060, the cumulative benefit-cost ratio is projected to reach 2 to 7 times. Consequently, the paper puts forward three policy suggestions on promoting carbon emission reduction in China’s urban subway system.

In the context of accelerating the global value chains restructuring, China, Japan, Republic of Korea, Australia, New Zealand and ten ASEAN countries signed the Regional Comprehensive Economic Partnership(RCEP), the largest in the world. How will RCEP reconstruct the global value chain, especially what opportunities will it provide for the development of China’s value chain? Firstly, this paper adopts the global multi-regional input-output model to analyze the evolution characteristics of GVC division in RCEP members and three regions (Asia, North America and Europe) from 1995 to 2018 from two dimensions of GVC participation depth and breadth. Then the structural model is used to measure the restructuring effects of RCEP tariff reduction on the depth and breadth of GVC participation. The results show that all RCEP members will benefit from tariff reductions, and most smaller economies will benefit more than larger ones. RCEP will alleviate the current recession of globalization and the declining trend of GVC participation in ASEAN, China, Republic of Korea and other RCEP members. Meanwhile, RCEP will significantly enhance the GVC forward and backward linkages between members such as China, Japan, Republic of Korea, ASEAN with the Asian region, and make these economies participate in GVC in a more regional way, thus promoting the restructuring of GVCs to a regional direction, especially the deepening development of Asian regional value chains. Our research provides important practical significance for China to accelerate the construction of a new development pattern of domestic and international double cycles with the help of RCEP to cope with the increasing uncertainty of the world economy.

Cooperating with technology companies is an important mean for regional banks to develop fintech. Using the manually collected data on the cooperation between each commercial bank and technology companies, this paper assembles a panel dataset of 334 regional banks from 2011 to 2019 as a sample to construct a PSM-DID model, and empirically examines the economic outcomes of the cooperative development of fintech on regional banks. We find that although the cooperation can promote the bank’s loan growth, it will also reduce the bank’s return on assets and increase the non-performing loan ratio. These findings hold with numerous robustness checks and different attempts to mitigate endogeneity concerns. Furthermore, the evidence on the comparison of the effects of cooperation on retail loans and corporate loans and the comparison between the outcome of business cooperation and that of technical cooperation, suggests that there appears principal-agent problem in business cooperation about retail lending. The negative effects of principal-agent problem arising from the cooperation are more pronounced for the technology company (acting as an agent) operating its own loan business or having cooperated with multiple banks simultaneously, and for the bank (acting as a principal) with weak fintech capability or low power in cooperation. This paper not only contributes to the relevant research on the principal-agent problem of financial intermediaries in a unique context of technology-driven financial innovations, but also provides an important reference for scientifically evaluating the economic consequences of commercial banks’ mode choice of developing fintech.

Operational risk is one of the vital factors that affect the survival resilience and sustained development of enterprises. This paper builds on an interdisciplinary perspective of finance and geographics and uses the combined panel data of Chinese A-share listed companies and financial permit information to empirically test the effect of bank branch geographic expansion on corporate operational risk. The results show that branch geographic expansion can mitigate bank-company information asymmetry by reducing bank-company distance and intensifying bank competition, thereby mitigating corporate financial constraints, enhancing bank debt governance, and consequently reducing corporate operational risks. A residual decomposition analysis is carried out based on different risk-taking incentives of enterprises and finds that bank branch geographic expansion mainly reduces operational risks driven by speculation incentives and passive risk-taking but promotes risk-taking driven by innovation incentives. Further analysis finds that, bank branch geographic expansion exerts a stronger impact on firms that are newly established or belong to hi-tech industries. Bank branch geographic expansion and loose monetary policy works a synergistic effect in terms of reducing corporate operational risks. The effect of bank branch geographic expansion turns out to be stronger among firms that are less disciplined by capital market or located in regions with less-developed infrastructure or digital finance. Our findings provide important policy implications from the perspective of physical branch distribution of commercial banks as regards reducing corporate operational risks, ameliorating business environment and building economic resilience.

The impacts of climate change are exerting increasingly pronounced effects on economic activities and production processes. Faced with escalating climate risks, enterprises demonstrate stronger managerial incentives for earnings manipulation. This study aims to examine the effect of climate risk on corporate real earnings management and elucidate the underlying transmission mechanisms. Based on the data from Chinese listed companies covering the period 2011 – 2022, our empirical analysis reveals that climate risk significantly intensifies the degree of real earnings management. We identify three primary channels through which climate risk operates:financing constraints, environmental uncertainty, and market supervision. The heterogeneity analysis further demonstrates that non-state-owned enterprises, firms operating in heavily polluting industries, and those exhibiting high fixed asset growth rates exhibit more pronounced financial manipulation behaviors when they confronted with climate risks. This research deepens our understanding of how climate risk shapes accounting strategies among Chinese firms and offers important policy implications for refining China’s climate change response framework.

Listing live hog futures provides a hedging tool to resist and manage market risks directly. This paper investigates the hedging function of live hog futures by starting with the conversion of hedging mode, analyzing the performance of cross hedging before listing live hog futures, and direct hedging after the listing, aiming to answer the question of are live hog futures an effective hedging tool. According to the results, before the listing of live hog futures, using corn or soybean meal futures to carry out cross-hedging reduces the risk of hog price by 2%, with an excess return of 0.02%; after the listing of hog futures, using live hog futures to carry out direct hedging reduces the risk of hog price by 13%, with an excess return of 0.04%. Overall, the live hog futures are an effective hedging tool, which plays an important role in reducing risk, preserving profit and stabilising production. It is recommended that the government enhance the connectivity between futures and the spot market, the Dalian Commodity Exchange optimize contract rules, and hedgers trade rationally to promote the performance of the hog futures and serve the national strategy of securing supply and stabilizing prices.

Photovoltaic (PV) modules have been ushered in the “retirement tide”, and recycling retired PV modules is an important means to use resources and environmental protection efficiently. This paper discusses the PV module recycling supply chain composed of PV manufacturers and PV recyclers. It explores the optimal recycling strategy of retired PV modules using the noncooperative-cooperative biform game models. On this basis, with the help of the numerical simulation method, we analyze the influence of consumer recycling price sensitivity coefficient, competition intensity, and other factors on the optimal strategy. The study shows that: 1) The recycling price, recycling volume, enterprise recycling profit, and processing technology level all show a positive relationship with consumer recycling price sensitivity coefficient, government subsidy strength, and recycling value, and they all show an inverse relationship with recycling competition intensity, commissioned processing cost and processing cost. 2) The proportion of R&D costs borne by manufacturers shows an inverse relationship with the consumer recycling price sensitivity factor, the strength of government subsidies, and the recycling value, and a positive relationship with the intensity of recycling competition, commissioned treatment costs, and treatment costs. This study aims to provide a reference for the selection of recycling strategies for retired PV modules and the formulation of recycling subsidy policies.

Against the backdrop of the reform in scientific and technological evaluation under the guiding principle of “breaking the five unicorns and establishing new standards”, peer review plays a pivotal role. Especially when confronted with large-scale evaluation tasks, the strategy of grouped evaluation has gradually emerged as the core approach of peer review. However, due to the varying scoring scales among experts, issues such as ensuring fairness within groups, achieving equitable comparisons among groups, and accurately assessing the credibility of experts have gradually emerged as challenges. To address these challenges, this paper systematically investigates strategies for aggregating expert panel scores using data standardization processing methods, comparing and analyzing the maximum-minimum value algorithm, centralization algorithm, Z-score standardization algorithm, and the newly proposed mean normalization algorithm. Through mathematical statistics and empirical analysis, this paper delves into the effectiveness and underlying issues of existing data standardization strategies in the context of both intra-group and inter-group evaluation in scientific and technological evaluation. The research findings reveal that different algorithms exhibit distinct advantages in eliminating differences in expert scoring scales, enhancing evaluation fairness, and improving accuracy. This provides specific and targeted guidance for optimizing algorithms and methods. The paper offers theoretical support for the rational selection and application of data standardization algorithms, further promoting the continuous optimization and improvement of the scientific and technological evaluation system.

A multi-choice cooperative game is a game in which players have more than two levels of participation, wherein a player’s payoff is a vector, instead of a real number, assigning him a payoff for every participation level. This dimensional expansion leads to many extensions of the traditional Shapley value to the multi-choice situation. In this paper, the multi-choice Shapley values are surveyed within a unified framework. They are divided into five classes: Permutations based, Harsanyi dividends based, player-level pairs’ marginal contributions based, and multi-linear extensions based. Every value is defined explicitly, and the axiomatizations of the values are also concerned. Specially attention is paid to five types of axioms: Traditional ones, strong monotonicity, balanced contribution, potential function, and reduced game consistency. Some future research directions are proposed at the end of the paper.

The debate on whether there is an idiosyncratic volatility anomaly (IVA) in the stock market has received widespread attention and remains unresolved. This paper distinguishes between daily idiosyncratic volatility and monthly idiosyncratic volatility to retest the existence of IVA to explain this controversy. Based on the sample of Chinese A-share listed companies, we find that the debate on the existence of IVA stems from the inconsistent sampling frequency of empirical data, the daily IVA is significant, while the monthly IVA is insignificant, the reason for this disagreement is the information attenuation of historical samples. The daily IVA is largely explained by the return reversal of a subset of overpriced stocks with high idiosyncratic volatility, and the equity overpricing factor plays an important moderating role in daily IVA. The heterogeneity test results show that the daily IVA is more significant in stock portfolios with lower institutional investor shareholding ratio, larger retail investors share, lower short selling and analyst attention. The key reason for stock overpricing and daily IVA is the unbalanced transaction size of margin trading caused by strict short-sale constraints in Chinese capital market. The findings above can help people understand the academic debate about the existence of IVA, and also deepen investors’ understanding of the pricing anomalies in China.

The process of economic globalization and financial integration has not only made the links between international financial markets increasingly close, but also led to more complex risk linkages among global financial markets especially under tail events. Therefore, it is essential to study the risk contagion relationship in global financial markets to ensure the healthy development of regional finance. In this paper, we propose a nonlinear tail risk measure — CoEVaR$^{\mathrm {SIM}}$ by combining the single-index expectile model and conditional expected value-at-risk (CoEVaR). Based on our risk measure, we construct a dynamic risk spillover network of global stock markets using 30 important global stock market indices from 2009 to 2023, measure the systematic risk of global stock markets and explore the dynamic characteristics of the tail risk spillover structure. The empirical results show that: 1) From the perspective of risk measurement, the systematic risk index constructed by our risk measure has time-varying characteristics, which is very sensitive to tail risk events, and significantly increases during the crisis period. 2) From the perspective of the evolution of risk network, the risk trends in the Americas, Europe, and Asia are relatively consistent during low-risk periods. While during high-risk periods, the stock markets of the Europe and North America regions spillover risks to other regions’ stock markets. 3) In terms of event analysis, the COVID-19 epidemic in 2020 exacerbated the inter-regional risk spillovers in the global stock market. The Russian-Ukrainian war in 2022 dramatically altered the structure of risk spillovers in the global stock market, with the Russian stock market being the only center of risk exporter. This study can inform macro policy makers, multinational financial investment institutions and the prevention of offshore risks.