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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Social credit, as a core element of the institutional environment, is not only the internal foundation for building trust among market entities, but also an important guarantee for optimizing corporate financing channels and improving resource allocation efficiency. This article uses the exogenous impact of the pilot reform of the social credit system to examine the impact and mechanism of the construction of the social credit system on the cost of equity capital of enterprises. Research has found that the construction of a social credit system can significantly reduce the cost of equity capital for enterprises. After a series of robustness tests such as parallel trends, placebo, and considering heterogeneity treatment effects, the financing relief effect still holds. Mechanism testing shows that alleviating information asymmetry, curbing management’s self-interest behavior, and resolving operational risks are the key to reducing the cost of equity capital for enterprises in the construction of a social credit system. Heterogeneity testing shows that the impact of social credit system construction on the cost of equity capital of enterprises is more prominent in private enterprises, enterprises with high operational uncertainty, poor social credit atmosphere, and regions with low marketization level and high degree of credit fragmentation. In addition, the construction of the social credit system has brought about a decrease in the cost of equity capital for enterprises, which not only alleviates financing constraints but also further optimizes investment behavior and ultimately promotes high-quality development of enterprises. This article not only deepens the theoretical understanding of the logical relationship between social credit and the financing capacity of micro entities, but also provides important basis and policy inspiration for solidly promoting the construction of institutionalized credit system, improving the financing environment of enterprises, and promoting economic transformation and upgrading.

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.

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 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.

Multi-load automated guided vehicles can simultaneously handle multiple item containers, which not only increases the operation ability and flexibility of automated guided vehicles, but also brings more challenges to the operation optimization of automated guided vehicles. The collaborative optimization problem of storage allocation and vehicle routing in multi-load robotic storage and retrieval system is investigated. A nonlinear mixed 0-1 integer programming model with regard to minimizing the sum of the total replenishment distance and the total weighted items distance, together with the linearized model, are both proposed. By combining the characteristics of the problem that contains three decision making components: storage allocation, vehicle assignment and route sequencing, seven local search operators, adaptive choice strategy of the operators, global repair strategy of vehicle routing, and adaptive large neighborhood search algorithm are designed. The simulation comparison and statistical test results of four hundred instances demonstrate that the proposed adaptive large neighborhood search algorithm performs better than exact algorithm, genetic algorithm, simulated annealing algorithm and variable neighborhood search algorithm; compared with the aforementioned algorithms, the larger the problem scale, the more superior the proposed adaptive large neighborhood search algorithm; the comparison results with two-phase optimization indicate that collaborative optimization has better performance; further analysis indicates that choosing appropriate multi-load AGVs (e.g., set the maximum capacity of multi-load AGVs as 6$\sim $7) and replenishment strategies (e.g., set the out of stock proportion as 0.25$\sim $0.30) can help the companies to balance their costs and efficiency well.

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.

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.

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.

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.

Non-linear factors are often selectively ignored in the modification process of financial asset pricing models. This study employs feedforward neural network methods from machine learning, integrating prior classification information to construct three optimized multi-layer perceptron (MLP) models: The hybrid neuron MLP, single-category Hybrid MLP, and multi-category hybrid MLP. These models predict returns in the Chinese stock market and compare their effects with linear regression models based on ordinary least squares and partial least squares estimation methods (OLS and PLS), support vector machines (SVM), extreme gradient boosting(XGBoost), recurrent neural networks (RNN), long-short-term memory networks (LSTM), and the classical MLP. The research found that the optimized MLP models with prior information can effectively reveal the complex relationships between market anomalies and stock excess returns; they significantly outperform traditional models that lack prior information in terms of prediction performance and investment strategies, demonstrating superior out-of-sample prediction results and robustness, and enhancing investment returns. The multi-category hybrid MLP model exhibits the best predictive performance and investment results. The prior information-based optimized MLP models developed in this study succeed in capturing the non-linear pricing structure between market anomalies and stock excess returns, substantially contributing to and enriching the theory of financial asset pricing.

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.

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.

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 this paper we incorporate natural disaster shocks into a general equilibrium model with production and finance linkage among firms to analyze the propagation of natural disaster shocks in the economy. We then use the input-output tables provided by the National Bureau of Statistics of China to test the theoretical results. We find that: 1) Natural disaster shocks not only directly negatively affect the sales of affected firms but also indirectly impact upstream and downstream firms through production network linkages; 2) finance linkages play a crucial role in the propagation of natural disaster shocks, as affected firms adjust the supply and demand of trade credit under financial pressure, thereby influencing the financing environment and production efficiency across the supply chain; 3) product complementarities amplify the transmission effects of natural disaster shocks within production networks; 4) firms with low upstreamness, centrality in the network suffer relatively greater negative indirect impacts from natural disaster shocks.

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.

As an important way to gather talents, employee stock ownership plan(ESOP) has received close attention from the academic circle. This paper examines the industrial and regional peer effects of ESOP, the motives of the peer effect in ESOP. We find that there are significant industrial and regional peer effects in ESOP, and this conclusion is still valid after various robustness tests. Information motivation, competition motivation and risk aversion motivation are the potential reasons why the target company imitates the ESOP of peers. Industry followers tend to learn from leaders, and when information transparency is low, competition is high and executives are more risk-averse, the industry and regional peer effect of ESOP is more obvious. Further research shows that the peer effect is stronger in industries and regions where ordinary employees hold shares than in executives. Moreover, under the influence of collectivism culture and in non-state-owned enterprises, the peer effect of ESOP is more obvious, and non-state-owned enterprises will learn from state-owned enterprises’ employee stock ownership plans. This peer effect is also helpful to retain employees and increase market value. This paper provides a new explanation for the driving factors of the ESOP, the reasons for mutual imitation, and then verifies.

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.

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 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.

Achieving uncertainty mining and calibration of system data is a crucial prerequisite for enhancing the quality of data analysis and the accuracy of management decisions. This paper proposes a cloud calibration method (CCM) for decision analysis problems based on cloud model theory. The method employs cloud numerical characteristics extraction, cloud generator construction, and membership transformation to achieve normalized calibration from original indicators to membership degrees. By comparing the CCM with existing methods such as data normalization, standardization, and functional transformation calibration using case study data, it was found that CCM outperforms others in comprehensive model performance evaluation. Additionally, machine learning models such as random forests, logistic regression, and neural networks were constructed using 11 datasets of varying sizes and dimensionalities to further validate the superiority and robustness of the new method. The results provide theoretical guidance and methodological support for effectively enhancing the quality of data analysis and decision modeling.