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.

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.

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

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

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 during 2014-2021, 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 30 provinces 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.

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

Based on the fund flow data of inter-sectoral financial transactions from 1992 to 2020, this paper measured the risk ripple effect of share default from a macro perspective and analyzed its evolutionary characteristics. Finally, combined with major events, this paper built an inter- sectoral fund association chart to analyze the impact of major events on the risk ripple effect of share default. We yield the following results. First, the total risk ripple effect of share default shows the characteristics of constant fluctuation from 1992 to 2020, and its fluctuation is strongly correlated with major events. There is heterogeneity in the ripple effect of total share default risk in each institutional sector, which is mainly related to the functions of institutional sector. Second, the direct risk ripple effect of share default reflects the evolution characteristics of the institutional sector structure of fund source in China's stock market. The indirect risk ripple effect of share default decreases gradually with the increase of contagion frequency, and the indirect risk ripple effect of various institutional sectors is heterogeneous. Thirdly, from the perspective of association and link structure evolution of institutional sector, different major events have a heterogeneous impact on the fund association relationship between China's financial institutions and other institutional sectors. Clarifying the risk ripple effect of share default among sectors and analyzing its evolution characteristics is valuable for the smooth circulation of national economy.

In order to identify the mechanism by which regulatory pressure from local emergency management departments and service empowerment from work safety service institutions drive the spread of standardized work safety behavior among industrial park enterprises, based on the theories of spreading dynamics and evolutionary game theory, this study constructs an SEIR evolutionary game model to explore the strategic choices between local emergency management departments and work safety service institutions. It analyzes the process of the spread of standardized work safety behavior among industrial park enterprises under different behavioral decisions of the two entities. Furthermore, it conducts multi-scenario simulation and analysis to investigate the process and patterns of system evolution towards a benign and stable state. The results indicate that the diffusion threshold of compliant work safety behavior in industrial park enterprises can predict the evolutionary trend of such behavior within the system. The interactive behavior of “strict regulation” by local emergency management departments and “high-quality service” by service institutions is more conducive to the spread of compliant work safety behavior in industrial park enterprises. Under this strategy combination, strengthening the regulatory measures of local emergency management departments can maximally promote the spread of compliant work safety behavior in industrial park enterprises. Augmenting the regulatory capacity and efficiency of local emergency management departments, formulating attractive and deterrent reward and punishment policies to guide high-quality service provision by work safety service institutions, and stimulating proactive compliant work safety behavior by industrial park enterprises are all conducive to the formation of integrated, coordinated, and mutually constrained mechanisms for work safety governance in the park.

Emergency supplies security is an important part of national emergency management system and offers a critical support for preventing and resolving major risks. But less attention is paid to the suffering of victims, which results in a lack of humanization in the emergency supplies procurement and stockpiling, and even sparks social panic. In view of this, the implementation effect of emergency supplies prepositioning and procurement is defined as the reduced suffering of beneficiaries, and a model of emergency supplies stockpiling and purchasing considering the suffering of the victims under government commissioning is proposed. We compare the optimal decisions of the government and the supplier when the suffering of those affected by disasters is taken into consideration and when it is not. Moreover, several conditions for emergency supply chain coordination are proposed, and the option price's range that both improves the supplier's profit and reduces the suffering of the beneficiaries is obtained, by comparing the supplier's profit and the total social benefit under the model of emergency supplies stockpiling and procurement under government entrustment with those under the government single stockpiling model. Then a model of emergency supplies stockpiling and procurement is constructed considering spot market procurement in the model extension. The results show that the introduction of spot market procurement into the model reduces the risk of the government's emergency supplies inventory. Finally, numerical simulations show that: The supplier's expected profit is most sensitive to the fluctuation of the probability of sudden disaster occurrence; When fluctuating downward from the base point, the government's expected profit is most sensitive to fluctuations in the option exercise price, while when fluctuating upward from the base point, the government's expected profit is most sensitive to fluctuations in the probability of a sudden disaster. The efficiency of emergency stockpiling and procurement will be improved by spot market procurement only when the option price determined by government-enterprise negotiation is in the appropriate range. The proposed model is closer to our country's “people-oriented” concept of emergency response. The related conclusions provide theoretical support for making more accurate emergency supplies procurement and prepositioning strategies as well as coordination strategies.

The coordinated development of thermal power and renewable energy is the key to continuously promote the low-carbon transformation of electric power. In order to take into account the low-carbon nature of the power system and the sustainability of collaborative operation mode, this paper constructs a wind-solar-thermal power operation optimization model under the coupling of electricity-carbon market on the basis of considering the current endowment distribution of power generation resources and the market trading environment. This paper discusses the impact of market coupling implementation on operation results and the effect of market coupling, and carries out scenario analysis of the changes of electricity and carbon market situation. The results show that the optimization model not only promotes energy saving and emission reduction, but also increases the proportion of renewable energy, while the adjustment of parameters such as carbon price and ancillary service cost can further guide the redistribution of power generation benefits. This helps to enhance the enthusiasm of all subjects to participate in collaborative operation and achieve a balance between low-carbon and sustainable. It shows that the proposed optimization strategy is more suitable for the safe and stable transition stage under the low-carbon transformation of electric power.

This work focuses on the pure electric vehicle travel scenario with travelers of two classes: On-route charging travelers and non-on-route charging travelers. Considering the interests of both travel users and charging station operators, an optimal charging and subsidy policy model for the electric vehicle transportation system is established, which takes account of impact of three factors: Travel time, charging service level, and charging distribution balance. Revenue neutrality based congestion toll and subsidy policy for roads and charging stations is investigated to manage the travel and charging behavior of electric vehicle users, and we theoretically prove the existence of congestion toll and subsidy schemes. By introducing gap functions, the original problem is transformed into an equivalent unconstrained optimization problem, and a gradient based solving algorithm is proposed. Finally, the effectiveness of the model, algorithm, and tax neutral management schemes is verified through numerical examples. The results show that compared to the user equilibrium scenario, on-route charging users of the same OD in the system optimal scenario is distributed among long and short path more evenly, the fact of which indirectly alleviates the uneven distribution of on-route charging users at various charging stations; in the system optimal scenario, as the degree of constraint on the charging distribution equilibrium increases, on the one hand, it leads to higher external costs corresponding to the tolls charged to congested charging stations, on the other hand, it leads to an increase in the total charging time, a decrease in the total queuing time, and an increase in the total travel time of the system; The proposed tax neutral management scheme compensates for congestion tolls and subsidies on roads and charging stations, achieving zero total tax revenue and avoiding large fiscal transfers.

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.

To prevent the excessively high ratio of product subsidies, some regions set restriction on it. Because of this, it is common for agricultural machinery enterprises to cheat subsidies by dishonest behaviors. Therefore, it is necessary to analyze its underlying mechanisms and affecting factors from the perspective of market returns. Will the agricultural machinery enterprises be rewarded for their honest behaviors in the market competition? This paper analyzes the best market returns that the two agricultural machinery enterprises will get in market competition when individually choosing the strategy of honesty or dishonesty by constructing a simultaneous game pricing model for duopoly competition, and investigates the evolution process and influencing factors of their choice of strategies based on evolutionary game model. The results show that: High subsidy intensity and low subsidy ratio restriction may result in the loss of market returns to the honest enterprise, and the extent of the losses is not only related to subsidies, but also influenced by the preference cost of farmers and government regulation. Only when its intensity for both enterprises exceeding the threshold will the regulatory has a reward and punishment effect of “rewarding honesty and punishing violations”, and has a positive effect on promoting them to choose the strategy of honesty, otherwise they will choose the strategy of dishonesty. But too strict supervision will not significantly improve the promotion effect, it will further aggravate the punishment cost shifting behavior of enterprises, resulting in the increase of purchase cost for farmers. In addition, any enterprise gains higher returns through violations with the increase of product price and sales. Therefore, taking this market feature as the basis for identifying the risk of violation into the regulatory content will help government to improve the efficiency of supervision. The above results provide the decision-making reference for government in the optimization of supervision mechanism to regulate enterprises behavior.

There is a significant spatial propagation characteristic of agricultural product price fluctuations, and the national purchase and storage strategy plays an important role in alleviating the spatial diffusion of abnormal price fluctuations of important agricultural products. Based on the perspective of coupled network cascade failure, this paper simulates the cascading failure process of the double-layer network under different attack scenarios and the impact of the implementation of the storage strategy on the price fluctuation and diffusion of agricultural products. The results show that: 1) Compared with random attacks, the collapse threshold of the double-layer network in the case of deliberate attack is lower and faster, and the collapse speed of the agricultural product price fluctuation diffusion network is always faster than that of the agricultural product storage network. 2) The double-layer network is more vulnerable when the price rises and the storage is released, while the double-layer network is more robust when the price falls and the storage is collected, and the heterogeneity of the impact of the attack strategy on the two-layer network in two different scenarios is mainly reflected in the middle and late stages of cascading failure. 3) The protection of key nodes with high topology degree and high inventory capacity of hybrid protection can effectively improve the vulnerability of the double-layer network, alleviate the “failure” of the agricultural product market, and have a significant positive impact on ensuring supply and price stability. This study will provide a theoretical reference and decision-making basis for further optimizing the agricultural product procurement and storage network and its implementation policies, and scientifically regulating the abnormal fluctuation of agricultural product prices.

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.

In the actual online advertising inventory delivery problem, the number of users visiting the website cannot be accurately predicted, resulting in uncertainty in the supply of exposure to the target group; at the same time, uncertain covariate information will also have an impact on the user's searching and browsing behaviors, which in turn affects the accuracy of the online advertising inventory allocation. To address these issues, this paper proposes an online advertising inventory allocation model that simultaneously considers uncertain covariate information and the supply of exposures to target demographics. Building upon existing research, the model constructs a distribution uncertainty set based on historical data, comprising both the probabilities of uncertain covariate scenarios and the moment information of the corresponding exposure supply. Furthermore, a joint stochastic chance-constrained model is developed based on this uncertainty set to ensure robustness under worst-case scenarios. The proposed model is solved using an algorithm that utilizes existing optimization software for rapid iteration. Finally, simulation analysis is conducted to validate the effectiveness of the model and algorithm.

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

In this paper, a learning-driven fruit fly optimization algorithm (LDFOA) is proposed to solve the permutation flow shop scheduling problem. Firstly, in order to improve the diversity of the population, the mixed strategy was used to initialize the position of the fruit fly in the solution space. Secondly, in the smell stage, four perturbation operators are constructed to further expand the search range of fruit fly individuals. In the visual stage, the feature information of elite fruit flies is collected to establish a probability model, and the individual realizes the evolution of fruit flies through continuous learning from the probability model. In addition, the idea of iterative greedy algorithm is introduced to perform local search for the best individual, so that the fruit fly is directed to more promising regions. Finally, it used Rec and Taillard test sets to test the performance of the algorithm and compared with the current algorithm with the best effect to solve the permutation flow shop scheduling problem. The results show that LDFOA algorithm has stronger optimization ability.

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.

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.

With the development of digitalization, networking and intelligence in social and economic activities, the connections between firms are becoming close, and the impact of related risks on the financial distress of firms is increasing. Existing research usually uses social network analysis methods to quantify topological structures and related risk impacts, but those methods are not applicable to heterogeneous networks containing different types of entities and relations. In particular, the related risk paths are long and the quantification of higher-order related risks propagated through indirect paths faces challenges. To end this, we design a framework for predicting financial distress of firms by incorporating higher-order related risk features. In the framework, we propose an unsupervised heterogeneous graph representation learning model to construct higher-order related risk features and develop an explainable method to mine higher-order related risk paths. Experimental evaluations demonstrate the superior predictive power of the unsupervised heterogeneous graph representation learning model over benchmark methods for financial distress prediction. In addition, the experimental results show that there are two types of higher-order related risk paths that help predict the financial distress of firms.

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.

Climate policy is the key to addressing climate change and realizing carbon neutrality. The assessment of climate policy is filled with uncertainties affecting economy, climate and energy through parameter uncertainty and model uncertainty. We develop a robust assessment framework based on the expected output from IAM for climate policies performance by incorporating the integrate assessment models (IAMs) and distributionlly robust optimization to address the impacts of model uncertainty and joint parameters uncertainties simultaneously on the assessment of climate policy. Besides, Wasserstein ambiguity set is utilized to demonstrate information of uncertain parameters. The results show that: 1) The stricter mitigation efforts, the less impacts of uncertainty on climate performance but the larger economic costs. 2) Uncertainty requires stronger mitigation efforts for the realization of carbon neutrality. 3) The worst-case distribution from the expected output of IAM has less effects on the expected net present value and expected global warming but brings great effects on the risk of future abrupt climate damage. 4) The temperature increase performance of climate policies is relatively robust across IAMs. Our work is the first study that develops a framework for assessing climate policies on the realization of carbon neutrality under uncertainty by integrating distributionally robust optimization and general climate-economic model, which provides a conservative and robust assessment for climate policy.

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.

Preventing and resolving financial risks and promoting enterprises to "transform from virtual to real" is an inevitable requirement for China's manufacturing industry to realize high-quality development and implement economic structural transformation. Based on the research perspective of intelligent manufacturing, this paper takes the pilot demonstration project of intelligent manufacturing implemented by the Ministry of Industry and Information Technology as a quasi-natural experiment, and adopts a multi-period double-difference research method to systematically investigate whether intelligent manufacturing can effectively promote enterprises to "transfer from virtual to real". The results of the study show that the implementation of intelligent manufacturing significantly reduces the financialization level of enterprises by suppressing the profit-seeking and reservoir motives of enterprises, and the above results are more obvious in enterprises that are not state-owned, have higher minimum wage standards in their respective regions, adopt diversified development modes, and are farther away from the banks and enterprises. The analysis of economic consequences reveals that the implementation of intelligent manufacturing can not only promote the enterprise "de-virtualization", but also promote the enterprise "to the real", and alleviate the crowding out effect of financialization on the enterprise's real investment. This paper further expands the research literature on intelligent manufacturing and enterprise financialization by examining the impact of the implementation of intelligent manufacturing on enterprise financialization, which is of reference value for promoting the transformation and upgrading of manufacturing enterprises and realizing high-quality development.

The industry 4.0 technology makes the cooperation of manufacturing enterprises and logistics enterprises more convenient, accelerating the integration of the two industries. Logistics enterprises or manufacturing logistics departments join in the procurement and production of manufacturers, so as to achieve the deep strategic cooperation and "you have me, I have you". However, it is not clear how Industry 4.0 technology affect the integration of the two industries. This study uses qualitative comparative analysis (QCA) and multiple regression, and proposes the influence path of Industry 4.0 technologies including big data, Internet of Things, artificial intelligence, blockchain, augmented reality and 5G on the integration of the two industries from both qualitative and quantitative perspectives, based on the empirical research on Typical Cases of Deep Integration and Innovation Development of Logistics and Manufacturing Industry (2021), and finds that: From the overall path, enterprises can integrate from the innovation, cost and efficiency, such as using the dual factors of process and assets to drive innovation, using the three factors of mode, process and assets to drive cost optimization, and using technology to achieve efficiency optimization. From the perspective of technology, the single application of blockchain, augmented reality technologies with low popularity can improve enterprise efficiency, while more mature technologies require overlapping applications, such as the Internet of Things, big data, artificial intelligence, 5G. Smaller enterprises apply industry 4.0 technology more obvious effects for the integration, but the Internet of Things and big data show opposite characteristics for different enterprises, when the enterprises use them, larger manufacturing enterprises have more significant effects, while smaller logistics enterprises are more obvious.

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.

More and more manufacturers are not only selling products through traditional retail channels, but also establishing channels directly targeting consumers to sell products. Faced with the threat of direct channels, retailers can respond to channel competition by switching to external suppliers. Considering the dual channel management problem of manufacturers when retailers hold external choices, a secondary supply chain consisting of one manufacturer and two retailers holding different external choices is constructed. The wholesale price decisions of manufacturers and the switching supplier decisions of retailers are explored, and the impact of outside options by retailers on the manufacturer's optimal channel strategy is analyzed. Research has shown that manufacturers' wholesale price decisions are constrained by retailers' external choices, but adopting customized wholesale price strategies can weaken the constraints of retailers' external choices; Abandoning some or retaining all retailers may become the optimal channel strategy for manufacturers, and abandoning some retailers when the efficiency of direct sales channels is low can improve the profitability of manufacturers; When retailers have limited external selection advantages, closing inefficient direct sales channels may become the optimal channel strategy for manufacturers.

By considering the inability to independently manufacture and remanufacture core components, suppliers are introduced to provide core components to downstream firms. This paper develops a supply chain model consisting of one supplier, one manufacturer and one remanufacturer. In the supply chain, both the manufacturer and the remanufacturer purchase key components from the supplier to produce final products. We examine the outsourcing and authorization remanufacturing modes. Subsequently, we analyze and compare the equilibrium solutions and corresponding consumer surplus, environmental impact and social welfare under the two remanufacturing modes considering the supplier-led and the manufacturer-led scenarios. The results show that, under the supplier-led scenario, the supplier keeps the procurement prices of core components under the two remanufacturing modes unchanged. Under the manufacturer-led scenario, the supplier changes the procurement price for the remanufacturer; in any scenario, compared to the authorization remanufacturing mode, the outsourcing remanufacturing mode leads to higher collection rate, supply chain profit, consumer surplus, and social welfare, but lower environmental impact. Therefore, the outsourcing remanufacturing mode should be adopted between the manufacturer and the remanufacturer; however, under the outsourcing remanufacturing mode, when the collection scale coefficient is relatively large, the manufacturer should be encouraged to be the leader in the supply chain; on the contrary, the supplier should be the leader in the supply chain.

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.

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.

In the context of technology patent licensing, the game models for the supply chain of new energy vehicles have been established based on four management strategies: no introduction, individual introduction by suppliers/manufacturers, and mutual introduction of professional managers. The aim is to comprehensively explore the impact of introducing professional managers on innovation decision-making within companies. Additionally, we have investigated the question of whether supply chain members should introduce professional managers from various perspectives. The study reveals a negative correlation between the innovation level of companies and incentive levels. Enterprises that introduce professional managers experience an increase in profits as their self-set incentive levels increase, but a decrease as the incentive levels set by the other party increase. From the standpoint of enhancing innovation levels and expanding market demand, the decision to have both supply chain members introduce professional managers is considered the most prudent choice. However, a comprehensive consideration of various interests indicates that both parties may opt for two extreme scenarios: Either not introducing professional managers at all or introducing them mutually. From the standpoint of enhancing innovation levels and expanding market demand, the decision to have both supply chain members introduce professional managers is considered the most prudent choice. But, a comprehensive consideration of various interests indicates that both parties may opt for two extreme scenarios: Either not introducing professional managers at all or introducing them mutually. However, the profit optimization of supply chain members can be realized by designing benefit-cost-sharing contract under the mutual introduction of professional managers. This indicates that when all supply chain members introduce professional managers, setting up a proper coordination mechanism is helpful to promote enterprise innovation and maintain the stable operation of supply chain.

In the process of guaranteed delivery of display advertising, the publisher needs to design allocation strategies in advance based on contracts signed with advertisers, delivering impressions to various campaigns according to specified proportions. However, the existence of multi-level demand contracts in reality and the significant uncertainty in impression supply present substantial challenges to this process. To address this issue, this paper proposes a distributionally robust chance-constrained programming model, where the determination of impression demands is regarded as a decision-making process. Adopting a conservative approximation approach, the model is transformed into an easy-to-solve mixed-integer second-order cone programming (MISOCP) model. Besides, considering the problem in which the risk level is a decision variable, a feasible MISOCP model is also designed. Extensive out-of-sample testing, compared with the benchmark model, validates the robust performance of the proposed model and solution method.

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.

The complex computational rules associated with multivariate interval-valued time series (ITS) pose challenges for conventional point-valued methods. This paper treats multivariate interval-valued variable as elements in a high-dimensional random set space and employs the principles of decomposition-ensemble forecasting to construct a MITSSD-FC-IVAR model. Firstly, this paper defines a unit directional discrete weighted inner product and provides formulas for the operations of multivariate ITS distance and variance. Second, this paper defines and calculates the spectral characteristics of multivariate ITS, introducing the multivariate ITS spectral decomposition (MITSSD) method to decompose the original multivariate ITS into multiple sub-sequences with specific frequency components. Finally, to accommodate the frequency domain properties of ITS, this paper builds a Fourier coefficient-interval vector autoregressive (FC-IVAR) model to forecast the sub-sequences of multivariate ITS and ensemble them into the original multivariate ITS predictions. Empirical findings using multivariate energy assets and weather conditions data show that the model proposed in this paper outperforms various benchmark models.

In robust parameter design, the quality of data and model parameter uncertainty often affect the modeling and the acquisition of optimal parameters. In addition, batch effects need to be considered in the actual product manufacturing process. To address the above problems, a robust method has been proposed that can handle batch effects in products or processes and is insensitive to outliers. This method is a multi-response modeling and optimization approach based on Bayesian quantile mixture models. The quantile mixture model is first incorporated into a Bayesian framework. Bayes' theorem is then used to derive the posterior distributions of the model parameters and the Markov chain Monte Carlo algorithm is used to calculate the estimates of the parameters. Next, the multivariate expected loss function is constructed. Finally, the expected quality loss function is minimized to find the optimal parameter settings and the Euclidean distance of the predicted responses between batches is calculated. The simulation example and 3D printing example show that the proposed method is resistant to outliers and reduces the impact of model parameter uncertainty on modeling and optimization.

Sequential accelerated life tests (SALT) can update prior information of model coefficients via collecting lifetime data under different stress levels, and then get better experimental strategy. SALT has been applied in industries successfully, but this method has its drawback. If the model is unknown, then the analysis results may deviate from the true values. To address above-mentioned problem, we propose a SALT for planning and analyzing accelerated lifetime experiment considering model uncertainty. First, we get lifetime data from high stress level, and update the prior information of model coefficients; next, the SALT plan is optimized based on the prior information of candidate models under the criterion that incorporates both asymptotic prediction variance and squared bias; finally, a Bayesian model averaging (BMA) framework is used to derive the posterior model and the posterior distribution for the low lifetime quantile. The proposed method can get the optimal solution of SALT through update prior information sequentially. Furthermore, the method is robust. It can avoid the uncertainty of results caused by a single specified distribution model.

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.

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.