Carbon neutrality is of great significance to the sustainable development of human society, and carbon neutrality technology and ecological carbon sequestration are two important factors affecting carbon neutrality capacity. In this paper, we develop an economic growth model that takes into account both factors, while also considering the deadline for carbon neutrality. By the theory of optimal control, we obtain closed-form formulas for optimal consumption, investment, capital stock, and carbon neutrality capacity. Based on theoretical and numerical analysis, several policy recommendations are proposed. Specifically, countries need to set carbon-neutral targets that match their own endowments and target capital stocks. Countries or regions within the same country should choose different technical levels of carbon-neutral investment according to their different stages. Unlike usual expectations, the path of carbon neutralization capacity may decrease with the elasticity of output to investment. As the deadline approaches, investment strategies may be abnormal.
In this article, we use text analysis to extract implicit information such as specialization of economic governance from government work reports, providing a new explanation for the sources of deviation in local economic growth goals. The results are that the specialization of economic governance can bring economic growth exceeding expectations, which is reflected in the fact that the actual economic growth rate exceeds the expected goals announced in the government work report. This is related to the effective allocation of resource elements, and is also motivated by factors such as “promotion championships”. With the transformation of local government performance evaluation system, the impact of economic governance specialization on the deviation of economic growth goals has decreased. However, in cities with different regions or administrative levels, professional officials are effective in promoting economic growth. Furthermore, if there are too many prospects for the future, weak execution ability, and lower innovation as well as higher compliance with previous policies in the local government’s economic governance, that may reduce the impact of specialization in economic governance on the deviation of economic growth targets, which is not conducive to achieving economic growth exceeding expectations. This study has reference significance for better leveraging the role of the government in resource allocation as well as in economic growth.
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 during2014–2021, and geographically and temporally weighted LASSO regression model to identify their key driving factors. The results obtained show that the synergy degrees of pollution and carbon emission reductions in China’s 30provinces show an upward trend with a range between 0.11 and 0.71, which also shows significant spatiotemporal characteristics with the spatial trend of “northeast-southwest”, the spatial pattern of “hot in the south and cold in the north”, and the temporal evolution of “increasing hot spots and decreasing cold spots”. Temperature, humidity, water resource utilization, energy intensity, energy structure, common wealth, environmental protection investment, and artificial intelligence technology are identified as the key drivers of the synergy of pollution and carbon emission reductions in China. Our findings not only help deeply understand pollution and carbon emission reductions, but also help improve the provincial targeted policies for pollution and carbon emission reductions in China.
Improving the green and low-carbon development mechanism is a concrete embodiment to implement the concept of “lucid waters and lush mountains are invaluable assets”, and upgrading China’s current carbon trading system is the major enabler for enhancing this mechanism. The present study links “peak carbon emissions” with carbon pricing mechanisms, and derives the economic and welfare effects of three carbon pricing policies under an identical cap on total emissions. Furthermore, the study increases the relevance and applicability of the research conclusions by treating carbon prices as endogenous variable. The CGE simulation results demonstrate that a hybrid policy which is comprised of carbon taxes and carbon trading market outperforms single-policy scenarios in terms of economic output and social impact, for example, its negative impact on GDP is less than 0.034percentage points by period 10 (base year 2020), which is the lowest in all scenarios, thus contributing to a win-win situation for the environment and economy in China. Mechanism analysis shows that the hybrid policy not only eases the pressure on key emission-reduction industries but also reduces the simulated carbon price from 113.73 CNY/ton to 57.81 CNY/ton in period 10, achieving the dual effects of “pressure-easing and production-increasing”. Moreover, the hybrid policy could increase the share of renewable energy consumption to 32.26% in later periods, thereby to some extent facilitating the decarbonization and zero-carbonization of China’s power system. On the other hand, welfare analysis reveals that under a single carbon tax scenario, the social welfare in period 15 would decrease by 0.65 percentage points compared to the baseline scenario, with the least negative impact. Therefore, we think that it is necessary to clarify the attribute positioning of the carbon tax in the following carbon pricing policy design so as to maximize the incentive effects of the carbon market.
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.
