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.
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
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.
Competitive games of strategic interaction play an important role in dynamically changing environments. Firms’ strategic choices shape the state of the environment in which they operate, and changes in the state of the environment in turn affect firms’ strategic choices, resulting in a dynamic and systematic process of strategy evolution. In this paper, carbon neutral technological innovation in industrial firms is viewed as a strategic choice, which is influenced by the state of the environment and government policies. Through the environmental state equation and the behavioral dynamics equation of the enterprises, a synergistic evolution system of the enterprise’s carbon neutral technology strategy choice and the environmental state is constructed, and the theoretical model is applied to the actual case of the iron and steel industry to validate the theoretical results of the model, and the evolution characteristics of the synergistic system and the effect of the governmental mechanism are analyzed. The results of this study show that only appropriate government subsidies can motivate enterprises to implement carbon-neutral technological innovations, and that the simultaneous implementation of carbon-neutral technological innovation subsidies and a cap-and-trade mechanism can effectively enhance the level of emission reduction of enterprises, promote the low-carbon transformation of the industrial sector, and at the same time contribute to the sustained improvement of environmental quality.
In the process of China’s progress towards achieving “carbon peak and carbon neutrality”, green finance has become an important driving force for enterprises to promote green innovation, and it has become an urgent practical problem how to lead enterprises to co-create green value. Based on the open innovation theory, this study uses the difference-in-differences model to evaluate the impact of Green Credit Guidelines on corporate green value co-creation, and further excavates the mechanism and boundary conditions of the relationship. Based on the data of listed companies in China from 2009 to 2020, the empirical test results show that:
1) Green Credit Guidelines has a significant positive impact on green value co-creation. 2) The two dimensions of green collaborative innovation (breadth and depth) play a mediating role between Green Credit Guidelines and green value co-creation, respectively. 3) Environmental leadership positively moderates the relationship between Green Credit Guidelines and green value co-creation; CEO openness positively moderates the relationship between Green Credit Guidelines and green collaborative innovation. In addition, the research conclusion is still valid by conducting the endogenous and robustness tests. Overall, this study not only constructs the theoretical framework for green value co-creation between firms and external partners and explores the channels through which green credit policy enhances green value, but also expands the theoretical boundaries of research on corporate green value co-creation, which provides a theoretical reference for testing the effectiveness of green credit policy in China and promoting firms to actively engage in green value co-creation.
Enterprises are confronted with a multitude of risks concurrently, and the interaction and cross-contagion of various risks have exerted a notable impact on technological innovation. Based on the sample of non-financial enterprises in China’s A-share market, this paper measures the systematic risk of firms according to the dynamic hybrid
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.
Based on the upper echelons theory, rising climate policy uncertainty affects management’s perception on future development, thereby influencing corporate investment decisions. According to the real option theory and growth option theory, this study investigates whether climate policy uncertainty impacts corporate investments via management perception, based on textual analysis of MDA (management’s discussion and analysis)sections in annual reports of Chinese non-financial listed companies from 2007 to 2023. We construct two dimensions of management perception indicators: risk perception, tone, and text complexity — for empirical analysis. The findings reveal that increased climate policy uncertainty significantly raises corporate financial and green investments while suppressing fixed asset investments, and these effects exhibiting long-term persistence. Mechanism analysis demonstrates that heightened climate policy uncertainty amplifies management’s perception of uncertainty and negative tone, and elevates annual report text complexity, ultimately shaping investment behavior. Furthermore, the rising climate policy uncertainty will suppress excessive investment, intensify underinvestment, and increase the default risk and stock price volatility risk of enterprises.
In the past decade, the joint institutional investors in China’s capital market are developing and growing at an extraordinary speed, and the influence of joint institutional investors’ banding behavior on corporate behavior is increasingly significant. Based on the data of Chinese listed companies from 2010 to 2020, this paper innovatively constructs the network of common institutional investors and examines the impact of common institutional investors’ banding behavior on listed companies’ fulfillment of social responsibility. The study finds that the grouping of common institutional investors has a positive impact on the CSR performance of listed companies. The conclusion is still valid after the robustness test. The mechanism research finds that the joint institutional investors’ banding promotes enterprises to fulfill their social responsibilities by exerting information effect and governance effect. This paper expands the research on the effect of joint institutional investors’ banding on corporate social responsibility, and provides new evidence for informal institutions to enhance corporate social responsibility.
This paper examines the relationship between litigation risk and financial distress using the data from A-share listed companies in the Shanghai and Shenzhen stock markets from 2003 to 2019. The study finds that litigation risk significantly exacerbates financial distress. The mechanical analysis shows that litigation risk increases the likelihood of financial distress by intensifying financing constraints and reducing profitability from the perspective of capital allocation path. The reputation insurance mechanism analysis reveals that corporate social responsibility has a significant negative moderating effect on the relationship between litigation risk and financial distress. Further research finds that this distress effect caused by litigation risk is more pronounced in companies that are in the growth and decline stages, have more severe information asymmetry, and have a lower proportion of female directors. Additionally, compared to securities litigation, operational litigation has a more severe impact on financial distress. This paper provides a theoretical basis for understanding the relationship between litigation risk and financial distress and offers empirical evidence for reducing corporate financial risk and promoting the healthy development of the market.
The governance effectiveness of the board of directors will have an important impact on the management’s behavior and the synchronization of stock prices. This paper selects the data of Chinese A-share listed companies from 2007 to 2021 as a sample to explore the impact of the board faultlines on the share price synchronization and the intermediary effect of accounting information quality. The results show that the higher the strength of the board faultlines, the higher the stock price synchronization, which indicates that the board faultlines have reduced information efficiency. The mechanism test results show that the board faultlines enhance the stock price synchronization by reducing the accounting information quality of enterprises. The conclusion is still valid after a series of robustness tests. Further research shows that there is a negative relationship between the board bio-faultlines and the stock price synchronization, and a positive relationship between the board task-faultlines and the stock price synchronization. The level of corporate financialization and external supervision both negatively moderate the positive relationship between the board faultlines and the stock price synchronization. The research conclusion of this paper deepens and enriches the research on the economic consequences of the board faultlines, and also provides practical inspiration for listed companies to improve the supervision mechanism of the board of directors, improve the quality of accounting information and enhance the efficiency of resource allocation.
Cracking the constraints of water resources and innovating water management for grain production are major challenges that must be addressed to enhance the comprehensive production capacity of grain and ensure food security. Ensuring water and food security is a major challenge in building harmonious coexistence between humans and nature. Therefore, this article designs a quasi-natural experiment around the pilot policy of water rights trading, collects panel data of 30 provinces (autonomous regions, munilipalities) in China from 2010 to 2021, and constructs a difference-in-difference model to examine the impact of water rights trading policy on the comprehensive grain production capacity. The results reveal that: ① Water rights pilot policy can significantly improve the comprehensive grain production capacity. ② Heterogeneity analysis shows that the grain yield increase effect of water rights trading policies is more significant in areas with high water resource endowments, central and western regions, while it is significant in both main and non-main grain producing areas.③ Mechanism analysis reveals that the water rights pilot policy enhances the comprehensive grain production capacity by increasing the construction of agricultural water conservancy facilities, adjusting the planting structure towards grain production, and adopting water-saving irrigation technologies. Moreover, large-scale operation can enhance the grain yield increasing effect of the water rights pilot policy. These conclusions can provide important references for promoting and improving China’s water rights pilot policy and enhancing the comprehensive grain production capacity.
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.
The selection of direction vectors is a key research focuses in multiplicative directional distance function (MDDF). The non-uniqueness of vector selection often implies the non-uniqueness of performance evaluation results. Therefore, the challenge for decision-makers lies in the endogenous selection of direction vectors for MDDF. To address this issue, this paper proposes a new optimal endogenous direction setting method based on piecewise Cobb-Douglas technology, which fully endogenizes the direction vector of MDDF. This method ensures the Pareto efficiency of MDDF and demonstrates that MDDF can be represented by the absolute distance in Euclidean space. By unifying the relative distance of MDDF with the absolute distance in Euclidean space, it provides a new perspective for explaining the economic implications of MDDF. Firstly, the L1-norm normalized endogenous direction vector is determined preliminarily by combining the definition of MDDF. Secondly, considering that the MDDF determined by the L1-norm optimal endogenous direction cannot compare the technical inefficiency between decision-making units, the L2-norm normalized optimal endogenous direction is obtained by further processing the L1-norm optimal endogenous direction. Then, the corresponding mathematical programming model is constructed to determine the MDDF based on the L2-norm optimal endogenous direction, and its geometric interpretation, economic characteristics, and graphical illustration are provided. Finally, the proposed method is applied to the evaluation of operational efficiency of Chinese transport sector to demonstrate the rationality and effectiveness of the optimal endogenous direction.
House prices have distinctive economic attributes. An accurate house prices forecasting is significant. To capture real-time predictive information and improve forecasting accuracy, this paper proposes a short-term house price forecasting approach with multi-source heterogeneous information and data traits, Firstly, Weibo, Baidu and stock market are collected to construct the multi-source heterogeneous dataset. Subsequently, the memory-trait and mutual-trait of house prices series are analyzed by data-trait-driven method to guide the division of sample interval and model selection. Lastly, statistical models and machine learning models are employed to verify the effectiveness of the proposed approach. The average housing prices across 70 large and medium-sized cities in China are selected as the sample data. Empirical results show that the proposed approach can achieve better prediction accuracy at the short-term forecasting, and the multi-source heterogeneous information can significantly improve the prediction performance of house prices. This provides theoretical support for scientific decision-making of management departments.
Innovating supply chain financing services and alleviating the financing challenges faced by small and medium-sized emission control enterprises are crucial foundations for achieving carbon peak and neutrality targets. To revitalize carbon assets, this study innovatively proposes a new pledge financing mode for carbon assets based on blockchain; its evolutionary path and impact mechanisms of key factors are systematically analyzed with a complex network evolution game model. Both theoretical models and simulations based on real-world cases suggest that when the cost of blockchain is controlled within a certain range: 1) Under the financing model driven by blockchain technology, there is a substantial enhancement in the capability to withstand market and credit risks. It can quickly evolve to the Pareto optimal state where financial institutions provide loans and emission-control enterprises make repayments under conditions of high collateralization, high carbon price volatility, and low default costs. Under the current mode, if there is a decrease in carbon prices at the end of the period and a high pledge rate, it will lead to financial institutions being unwilling to provide loans; even a substantial increase in default costs cannot effectively constrain the default behavior of emission-control enterprises. 2) The blockchain model not only significantly reduces the financing costs for financial institutions and emission-control enterprises but also greatly increases the financing returns for both parties, creating a “win-win” result; in the early stages of the project, the income of financial institutions and emission-control enterprises can be stabilized at a relatively high level. While financial institutions may have slight profits after a long period of evolution under the existing model, they consistently operate at a loss during their early evolutionary stages. This study investigates the improvements brought by blockchain technology to the pledged financing of carbon assets in the supply chain. The findings provide theoretical support for the innovative development of blockchain-enabled carbon finance.
Existing origin-destination (OD) demand estimation studies mainly focus on “functional” model research, lacking effective theoretical analysis. They primarily utilize the same error metric without properly distinguishing the characteristics of different input data. Unlike existing studies, we adopt the
In the context of manufacturer investing in quality and retailer investing in retail service, this paper investigates the encroachment and information sharing decisions in a two-level supply chain consisting of a single manufacturer and a single brick-and-mortar retailer. Four dynamic game models based on different encroachment and information sharing choices are constructed and then the Bayesian Nash equilibrium solutions are obtained. By comparing the firms’ ex ante profits, the equilibrium decisions about the encroachment and information sharing are derived, and the impact of the model parameters on the equilibrium is also examined through sensitivity analysis. The main results of this paper are as follows. First, when the quality sensitivity coefficient is high, the retailer will proactively share information. If the manufacturer chooses to encroach, the retailer’s willingness to share information decreases (increases) with the service sensitivity coefficient when the channel substitutability is high (low), and first decreases and then increases with the service sensitivity coefficient when the channel substitutability is medium. Second, the manufacturer will choose to encroach when the encroachment cost is low. The manufacturer’s willingness to encroach increases with the quality sensitivity coefficient, and increases with the service sensitivity coefficient when both the service sensitivity coefficient and the quality sensitivity coefficient are low. Third, the manufacturer can induce the retailer to share information by designing a compensation payment incentive contract under certain conditions, which can lead to the Pareto improvements in their respective profit levels.
Under the preposition warehouse mode, the O2O (offline to online) orders have the characteristics of immediacy and small batch. Generally, the more frequent the delivery, the higher the distribution cost but the lower the delay cost. Consequently, there is a tradeoff between distribution cost and delay cost for the manager to make delivery decisions minimizing the total cost. The real difficulty lies in the fact that the manager cannot accurately predict the demand information of future O2O orders, and can only make real-time dynamic decisions on whether to deliver and which orders to be delivered based on the previous order information and delivery effect. In this paper, using online algorithm and competitive analysis, an online decision-making model is established for O2O order instant delivery problem with capacity limit under the preposition warehouse mode. An asymptotic lower bound of 2 is derived for this online problem and an online delivery strategy with competitive ratio of 3 is designed. The present online optimization model and strategy can not only be directly used in the practice of O2O order delivery under the preposition warehouse mode, but also have some reference value for the related problems such as make-to-order and purchase-to-order.
The dynamic randomness of the arrival of numerous small batch orders in the precooling service platform brings about real-time disturbances to the operation process of precooling services. It significantly enhances the difficulty of platform operation management, requiring a reoptimization of the precooling operation. This study focuses on the resequencing optimization problem of precooling operations for fresh fruits and vegetables with instant orders on the trusteeship platform, and considers two kinds of precooling facilities including fixed precooling warehouses and mobile precooling vehicles. A resequencing framework of precooling operations for instant orders is first proposed, followed by a resequencing model based on rolling horizon method to minimize operational costs while ensuring service responsiveness. Secondly, integrating the stochastic information of order arrivals, a resequencing algorithm is proposed based on vehicle proactive response, including three stages of facility quantity evaluation, instant response, and periodic sequencing. This approach effectively facilitates proactive decision-making and enhances the overall optimality of the sequencing solution. Finally, nine order scenarios are designed based on practical precooling operations in numerical experiments, and the proposed resequencing approach is compared to classic resequencing models and algorithms. The findings verify that the proposed approach significantly reduces service response time without compromising cost objectives. Moreover, we analyze the impact of order arrival rates and distribution characteristics on the precooling operation sequencing solutions. Our observations can provide effective decision support for trusteeship platforms in enhancing service responsiveness of instant orders.
To solve global complicated optimization problems, the plant rhizome growth-based optimization algorithm (PRGO) is proposed inspired by the structure of plant rhizomes and the way they absorb nutrients. In the algorithm, the plant rhizomes are divided into two categories, the taproot and the fibrous root. The rhizomes of taproot plants absorb nutrients from the soil and rely mainly on the fiber roots distributed on the main and lateral roots, while the rhizomes of the fibrous root plants diffuse into the soil to absorb nutrients mainly dependent on the fiber roots that distributed on the indeterminate roots. In this algorithm, the fiber roots distributed on the rhizomes are considered as the trial solutions of the algorithm, and the mathematical models of the fiber roots growth in soil simulate the optimization process of the algorithm. In addition, the growth process of the taproot plants is associated with the global exploration search, and the growth process of the fibrous root plants relates to the local exploitation search. The global asymptotic convergence of the algorithm is proved by applying Markov’s correlation theory, and the simulation results using the CEC2014 and CEC2017 test sets show that the proposed algorithm outperforms the other comparative algorithms in terms of optimization accuracy and convergence speed for the most of the test functions.
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.
