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

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

Digital twin map physical entities through simulation modeling, significantly enhancing system reliability and reducing maintenance costs by utilizing data fusion, behavior simulation, optimization decision-making, and visualization through virtual-physical interaction. In intelligent operation and maintenance practices, common challenges include poor data quality, scarcity of abnormal samples, and unclear degradation processes. Digital twin technology offers a novel paradigm to address these issues. This paper systematically reviews simulation and modeling techniques within digital twin applications, summarizing recent research advancements in key areas such as anomaly detection, remaining useful life prediction, fault diagnosis, and operation and maintenance decision-making. Focusing on the demands for intelligent equipment operation and maintenance, we summarize the research findings and technical pathways related to digital twin-driven intelligent maintenance. Based on prior theoretical research and practical applications, we propose a four-level hierarchy for digital twin-driven intelligent operation and maintenance. Furthermore, we illustrate the application of digital twin-driven intelligent maintenance in real-world scenarios with a case study on naval equipment. Finally, considering current research and engineering practices, this paper proposes future research directions to provide insights and guidance for digital twin-driven intelligent maintenance across the equipment life cycle.

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

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

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

Under the background of the accelerated reconstruction of the global industrial chain and supply chain, the United States tries to implement the friend-shoring and near-shoring strategy to reduce the dependence of its industrial chain and supply chain on China. Economies such as Southeast Asia and Mexico have become the main destination of China's industrial transfer, which is bound to have a negative impact on the impact scope, profitability and risk resistance of China's industrial sector in the global value chain. This paper uses the trade data of intermediate goods from the multi-regional input-output (MRIO) database to construct the global production network model, and extract the real network (null model) and artificial network (counterfactual model) that reflect the backbone of the global value chain from different perspectives, respectively. On this basis, it analyzes the potential impact of the United States' trade policy towards China on the restructuring of the global production network and the relocation risk of China's industrial chain. The results show that the friend-shoring strategy of the United States relying on Altasia and the near-shoring strategy relying on the United States-Mexico-Canada Agreement, and Canada will lead to the partial decoupling of the industrial chain and supply chain in the global scope, and moreover, the friend-shoring strategy has intensified the trend of economic anti-globalization and the risk of relocation of China's industrial chain. Finally, this paper puts forward policy suggestions to improve the resilience and security level of China's industrial chain and supply chain under the background of the United States' de-risking China-reliant supply chains.

Different from the traditional financial sector, science and technology finance can effectively support scientific and technological activities, which is of great significance to enhance our country's independent innovation capacity and achieve high-quality economic development. This paper uses the entropy method to comprehensively evaluate the development level of science and technology finance from four dimensions: resources, funds, financing and output. Based on the panel data of 31 provinces from 2007 to 2021, a spatial econometric model is constructed to empirically test the impact of science and technology finance on technological innovation. It is found that sci-tech finance not only has a significant promoting effect on local technological innovation, but also has an obvious spatial spillover effect. If the spatial spillover effect is not considered, the impact of sci-tech finance on technological innovation will be underestimated. Further research shows that in the eastern region, the direct effect and spatial spillover effect of sci-tech finance on technological innovation are more significant, and sci-tech finance improves the level of regional technological innovation by easing the financing constraints of enterprises and optimizing the industrial structure. The research of this paper provides data support for evaluating the impact of science and technology finance, and also provides policy reference for exploring the path of technological innovation promotion.

How digital platforms enable enterprises to achieve disruptive innovation is a key concern for managers in the context of the platform economy. Building on the literature on platform ecosystem and dynamic capability, we explore the effect of digital platform capabilities on disruptive innovation. Leveraging data from 209 Chinese high-tech enterprises that have either developed their digital platforms or integrated with existing ones, we find that digital platform capabilities have a significantly positive impact on disruptive innovation. We further show that structural flexibility and organizational unlearning partially mediate the relationship between digital platform capabilities and disruptive innovation. Specifically, digital platforms empower shaping structural flexibility, dismantling rigid organizational routines, and identifying emerging niche markets targeted for disruptive innovation on the one hand. On the other hand, they facilitate organizational unlearning, breaking away from existing knowledge path dependencies, and acquiring complementary knowledge required for disruptive innovation. Additionally, structural flexibility has a significant positive impact on organizational unlearning, and both factors serve as chain mediators between digital platform capabilities and disruptive innovation. This study deepens our understanding of the formation mechanisms behind disruptive innovation in high-tech enterprises within the platform economy framework. It addresses the practical question of which capabilities high-tech enterprises need to cultivate for disruptive innovation from a micro perspective. These insights provide valuable theoretical guidance for enterprises seeking to leverage digital platforms for achieving disruptive innovation in the context of ongoing digital transformation.

New urbanization construction is an essential driver for expanding domestic demand and a critical measure to facilitate internal circulation. This paper evaluates the impact of new urbanization pilot projects on the consumption levels of the rural migrant using data from the China Migrants Dynamic Survey. The study reveals that, compared to non-pilot areas, the consumption levels of rural migrants in pilot areas have significantly improved, and a series of robustness checks support this conclusion. Mechanism analysis indicates that the pilot projects have increased income, enhanced access to public services, and strengthened a sense of identity, all of which contribute to the increased consumption levels of rural migrants. The pilot projects have a more substantial effect in regions with low dialect diversity and more effectively raise the consumption levels of employed and intra-provincial migrants. Additionally, the pilot projects have boosted both daily and housing consumption of migrants, with a more pronounced effect in counties and county-level cities. The findings provide theoretical explanation and empirical evidence for establishing a long-term mechanism to expand domestic demand.

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

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

Artificial intelligence innovation is an engine for the formation of new quality productivity, which not only generates substantial social wealth but also exerts profound influence on employment opportunities and household consumption patterns. The key to promoting high-quality economic development lies in addressing the realistic dilemma of insufficient household consumption through enhancing demand-side capacity and optimizing the product supply system with the aid of artificial intelligence innovation. Based on the artificial intelligence innovation task model, this paper establishes a theoretical framework for the impact of artificial intelligence innovation on household consumption, providing a comprehensive understanding of the channels through which artificial intelligence innovation influences household consumption. Additionally, numerical simulations are conducted to validate the proposed theoretical model. Furthermore, empirical tests are conducted on the theoretical model using input-output data from various provinces between 2012 and 2020, as well as patent data from the State Intellectual Property Office. The findings indicate that: 1) Artificial intelligence innovation stimulates household consumption, with the promotion effect observed across various categories of consumption. Among them, the impact on enjoyment consumption is particularly significant, serving as a driving force for enhancing the quality and expansion of household consumption. Heterogeneity analysis reveals that artificial intelligence innovation has a stronger promotion effect on household consumption in the eastern region, urban areas, and industries characterized by high-skilled factor intensity. 2) The analysis of supply paths reveals that artificial intelligence innovation enhances household consumption through enhanced productivity and the realization of product innovation. Moreover, product innovation serves as the primary driver for promoting this effect. 3) The demand path analysis reveals that the "job creation effect" induced by artificial intelligence innovation exerts a lesser impact on the skill premium compared to the inhibitory "catfish effect", thereby enhancing household consumption capacity and fostering consumption growth.

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

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

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.

With the development of power exchange technology, China's new energy vehicle industry proposes battery as a service under the separation of vehicle and battery, which allows consumers to rent batteries on demand. Against this background, this paper constructs a Stackelberg model composed of the new energy vehicle manufacturer and the battery supplier. Two battery service models are considered: Not providing battery rental service (N) and providing battery rental service (B). The results show that, when the body price is relatively high (low), the new energy vehicle manufacturer should choose (not) to provide the battery rental service, but it is harmful (beneficial) to the battery supplier. Therefore, when the body price is moderate, the battery rental service can achieve a win-win outcome for the new energy vehicle manufacturer and the battery supplier. Meanwhile, we reveal the impact of the battery rental service on the decision of the new energy vehicle supply chain and analyze the impact of the battery life level and the body expectancy life on the win-win outcome's realization conditions. We also provide useful managerial implications for both the new energy vehicle supply chain and the overall industry.

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.

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.

International trade is generally considered one of the engines of economic growth and has long attracted widespread attention from governments and academia. Searching a model with both a solid economic foundation and strong empirical results has been forefront goal in international trade research. This paper proposes a theoretical model of trade structure that can reveal the true trade relations between countries based on data such as trade volume. Based on a simple micro-optimization decision, the model derives two kinds of core indicator: the effective trade distance between countries and the trade potential of a country. These indicators respectively characterize the trade closeness between countries and their trade hierarchy. A smaller effective trade distance reflects a greater propensity for trade between two countries, while a greater trade potential indicates a greater propensity for exporting and a greater propensity for importing. Based on empirical evidence from trade data for 198 countries/regions in 2021, this paper obtains the following results: 1) Compared with widely used traditional models such as the gravity model, our model has superior explanatory power, with an adjusted coefficient of determination of 0.954. 2) The logarithm of the effective trade distance between countries exhibits a significant bimodal distribution. The results show that they can be divided into two categories. In one category, the logarithm of the effective trade distance shows a clear linear correlation with the logarithm of the geographical distance, indicating that trade between these countries, in addition to economic size, is primarily constrained by “natural” factors such as geographical distance. In the other category, the effective trade distance is uncorrelated with geographical distance, indicating that trade between these countries is also significantly influenced by “non-natural” factors such as tariffs. Thirdly, there are significant differences in the trade potential of major economies across regions. In North America, the United States has a trade potential slightly less than 1, while Canada and Mexico have trade potential slightly greater than 1. Germany and France exhibit an export bias, while the United Kingdom exhibits an import bias. The three East Asian countries — China, Japan, and South Korea — all have trade potentials around 1.4, indicating a strong export bias. The three South Asian countries — India, Bangladesh, and Pakistan — have trade potentials less than 1, indicating an import bias. The model proposed in this paper is a universal method for studying the complexity of socioeconomic relationships based on flow, and can be extended to studies of areas such as international migration and foreign direct investment.

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.

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.

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

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

The digital transformation of government is a major initiative to improve the government's governance capacity in the new era, which provides a new idea to promote "tax governance by numbers". In order to explore the micro tax governance effect of government digital transformation, this paper takes the establishment of Big Data Bureau as a quasi-natural experiment, takes listed companies from 2008 to 2020 as the research samples, and uses the DID model to test the micro tax avoidance governance effect of government digital transformation based on the A-S model and information asymmetry theory. The results showed that government digital transformation can effectively inhibit corporate tax avoidance, and its inhibition is mainly generated through joint tax audits, optimization of business environment and government-enterprise cooperation and sharing. Further research shows that government service-oriented Big Data Bureaus and regions with stronger tax collection and management are more conducive to the micro tax governance effect of government digital transformation, and that the tax avoidance governance effect of digital government is more pronounced for highly digitized enterprises. This paper can extend and deepen the governance effect of government digital transformation and provide theoretical basis for the government to further promote digital transformation and create a fair tax environment.

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

Based on the 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.

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.

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.

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.

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

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

The application of industrial robots has significantly advanced the process of intelligentization within the manufacturing sector, fundamentally transforming firm labor structures and providing critical opportunities for high-quality economic development. Utilizing data on robot applications in Chinese manufacturing firms from 2012 to 2022, we analyze the impact of industrial robot usage on the human capital structure of firms. Our findings indicate: 1) The application of industrial robots exerts substitution effects and creation effects, optimizing the human capital structure of firms. 2) Channel analysis indicates that industrial robot application optimizes the human capital structure through three pathways: enhancing safety production, improving innovation levels, and upgrading labor quality. 3) The heterogeneity test indicates that the use of industrial robots significantly enhances the upgrading of human capital structures in labor-intensive firms, firms experiencing rapid technological advancements, and regions with a more developed factor market. Against the backdrop of the national policy of "delayed retirement" aimed at mitigating the impacts of an aging population and rising labor costs, this study offers policy insights and empirical evidence to support firm quality enhancement and high-quality economic development.

Digital transformation is an important micro foundation of China's industrial upgrading, and the characteristics of CEO play a key role in the digital transformation of enterprises. Based on relevant data from listed companies spanning the years 2011 to 2021, this study constructs adaptive LASSO and QUBO models using machine learning to select trait variables. The goal is to grasp the main contradictions and systematically study the influence mechanism of some important CEO characteristics on digital transformation. According to the results of the machine learning model, this paper argues that the characteristics of old, female, financial experience, management experience are the main traits promoting digital transformation, and production experience is the main inhibiting trait. Mechanism study shows that age of CEO will affect his experience richness in a reversed U-shaped relationship and affect his risk tendency in a U-shaped relationship, this makes a reversed U-shaped relationship between CEO age and digital transformation. Moreover, financial experience and management experience promote digital transformation by alleviating information asymmetry and improving corporate governance, production experience increases the short-termism characteristic of the CEO, which inhibits digital transformation, gender will promote digital transformation by less risk tendency. In this paper, two machine learning models are used for variable selection, it provides a reference for solving the problem of subjective variable selection, this paper enriches the research on CEO traits and digital transformation and provides some enlightenment for the design and adjustment of company management.

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 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.

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.

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

Empowering green innovation with digital elements is a key path for enterprises to cultivate competitive advantages and achieve green upgrading. The accompanying question is whether digital transformation can effectively empower comprehensive iterative optimization of green innovation? Previous studies have only discussed the positive role of digital transformation in green innovation, but have overlooked the status and structural changes of innovation activities in different fields within green innovation during digital transformation, failing to clarify the current stage matching problem and internal mechanism between digitization and greenization. Based on this, this article classifies the green patent information retrieval of A-share listed companies from 2007 to 2022 according to the "International Patent Classification Green List", and summarizes the green innovation of management and design, production energy conservation, and end of pipe treatment according to application fields. Based on the resource allocation theory and technology consistency theory analysis, the asymmetric effect of digital transformation on green innovation is empirically examined. Research has found that digital transformation has significantly promoted green innovation in business management and design, but its empowering effect on green innovation in production energy conservation and end of pipe treatment is insufficient; compared to the application of digital business scenarios, the layout and development of digital underlying technologies have a more significant asymmetric effect on green innovation; from the perspective of R&D resource allocation, digital transformation promotes green innovation in business management and design, which is not only the result of adding new R&D resources, but also comes at the cost of squeezing out R&D resources for green innovation in other fields. Meanwhile, the tripartite governance factors of green innovation (market, government, and society) play an important role in the coordinated evolution of digitization and greening. The research conclusion of this article reflects the current situation of cultivating green competitive advantages through digital elements, that is, digital transformation mainly achieves the improvement of resource utilization efficiency in business processes through green upgrading in the field of information technology, and has a significant "local empowerment" effect. This discovery not only provides practical reference for the government to formulate more targeted policies and measures for the coordination of industrialization and informatization, but also provides practical basis for the optimization of enterprise innovation resource allocation and the formulation of innovation strategies.