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

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

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

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.

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

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.

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

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.

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.

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.

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

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.

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

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

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 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.

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.

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

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

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.

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

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.

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.

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

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.

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.

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.

Improving the green and low-carbon development mechanism is a concrete embodiment to implement the concept of “lucid waters and lush mountains are invaluable assets”, and upgrading China’s current carbon trading system is the major enabler for enhancing this mechanism. The present study links “peak carbon emissions” with carbon pricing mechanisms, and derives the economic and welfare effects of three carbon pricing policies under an identical cap on total emissions. Furthermore, the study increases the relevance and applicability of the research conclusions by treating carbon prices as endogenous variable. The CGE simulation results demonstrate that a hybrid policy which is comprised of carbon taxes and carbon trading market outperforms single-policy scenarios in terms of economic output and social impact, for example, its negative impact on GDP is less than 0.034percentage points by period 10 (base year 2020), which is the lowest in all scenarios, thus contributing to a win-win situation for the environment and economy in China. Mechanism analysis shows that the hybrid policy not only eases the pressure on key emission-reduction industries but also reduces the simulated carbon price from 113.73 CNY/ton to 57.81 CNY/ton in period 10, achieving the dual effects of “pressure-easing and production-increasing”. Moreover, the hybrid policy could increase the share of renewable energy consumption to 32.26% in later periods, thereby to some extent facilitating the decarbonization and zero-carbonization of China’s power system. On the other hand, welfare analysis reveals that under a single carbon tax scenario, the social welfare in period 15 would decrease by 0.65 percentage points compared to the baseline scenario, with the least negative impact. Therefore, we think that it is necessary to clarify the attribute positioning of the carbon tax in the following carbon pricing policy design so as to maximize the incentive effects of the carbon market.

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.

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.

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.

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.

Tourism related enterprises may bear operational risks due to significant fluctuations in tourism demand, especially after the outbreak of COVID-19, which is more pronounced in many regions. In order to more accurately describe the variability of tourism demand, this paper develops a multiscale interval decomposition ensemble framework for predicting it. Firstly, we propose a method for constructing tourist volume interval-valued time series (ITS), which derives the center and radius of ITS data based on the upper and lower limit time series. Secondly, using the bivariate empirical mode decomposition method to decompose the center and radius ITS, several decomposition component ITSs are obtained. Then, the kernel extreme learning machine optimized by particle swarm optimization (PSOKELM) is used to model and predict each decomposed component ITS. Finally, the predicted results of all decomposed component ITSs are simply added to generate center and radius forecasts, which are then converted into predicted the upper and lower limits of tourist volume ITS. Using the data of domestic and international tourist arrivals to Hawaii, an empirical study is conducted to validate the proposed method. The results show that compared with benchmark models, the proposed method has higher predictive accuracy and greater robustness, demonstrating its effectiveness in predicting the variability of tourism demand.

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.

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.

As an important manifestation of intelligent production, this article focuses on the signal effect of industrial robot introductions and explores how the introduction of industrial robots can promote export scales through signaling mechanism. Our research has shown that the introduction of industrial robots can promote export scales through the channel beyond productivity and product quality, suggesting a signaling effect of industrial robot adoption on export markets. Moreover, this promotion effect is not significant in domestic markets with lower levels of information asymmetry, indicating that the introduction of industrial robots also serves as a quality signal for exporting firms. We attribute the signaling effect of introducing industrial robots to two aspects: mitigating information asymmetry and improving the image of product quality. In addition, the signal effect of industrial robot introduction enables exporting firms to achieve export growth along the intensive margin, promoting both ordinary trade and intermediates trade at the product level. This study provides empirical evidence on the impact of industrial robot applications on export sales from the perspective of demand-side signaling, attributes to existing literature on the export-promoting effects of industrial robot adoption and provide practical references for implementation of industrial robot application strategies in the process of intelligent transformation in China’s manufacturing industry.

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.

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.