Ming Lei

A system of incentives can be established to encourage several parties to unite as a community of interest and become jointly committed to the platform economic governance. The platform economy involves progressively more complex subjects of interest and relationships, which are not the typical principal–agent one-time cooperative relationship. This study investigates the problem of regulatory incentives in the platform economy, specifically focusing on the relationship between the government, platform enterprises, and merchants. It analyzes this issue under conditions of asymmetric information by constructing and solving a dual principal–agent model. The findings indicate the following: (1) the government’s incentives and regulatory mechanisms can be considered as interchangeable to some extent, with decisions made by evaluating their respective costs; (2) the government’s optimal incentives and regulations ultimately shape the self-regulatory behavior of merchants through platform enterprises; and (3) the optimal level of incentives for both the government and the platform enterprise is influenced by factors such as the ability coefficient, the social transformation coefficient, and the merchants’ reliance on the platform enterprise. Additionally, the optimal effort level of the platform enterprise and the merchants increases with higher levels of the regulatory effort, risk sensitivity coefficient, and ability coefficient. A win–win scenario and a long-term, stable cooperative partnership can be reached by the three parties under the ideal incentive intensity. The study’s conclusions can serve as a theoretical foundation and support for the creation of incentive contracts for platform economy regulation.

At the 75th session of the United Nations General Assembly, China proposed the ambitious goal of achieving carbon peaking by 2030 and carbon neutrality by 2060. To investigate the impact of emission reduction behaviors of upstream and downstream enterprises in the supply chain, this paper focuses on the influencing factors of the supply chain enterprises’ emission reduction decision-making. The study aims to explore the emission reduction behaviors of these enterprises in the context of China’s carbon trading market. Using the theory of system dynamics, an evolutionary game model was developed and simulated using AnyLogic software. The simulation analyzed the effects of carbon price, subsidies, and punishment strategies on the emission reduction decisions of supply chain enterprises, providing insights into their behavioral impact. The results demonstrate that punishment, subsidy intensity, and carbon price changes all influence the emission reduction decisions of upstream and downstream enterprises. Suppliers are more sensitive to carbon price, while manufacturers are more sensitive to subsidy intensity. Additionally, the closer the equilibrium carbon price, subsidy, and market are, the shorter the time for the emission reduction probability of both enterprises to stabilize. Therefore, it is recommended that supply chain companies increase their awareness of environmental responsibility and enthusiasm for green innovation, actively respond to the carbon trading system, improve their internal subsidy system, and promote green technology innovation.

As a social problem involving a wide range of objects, targeted poverty alleviation governance needs to clearly define stakeholders and identify their behaviour choices, so as to seek a multiagent collaborative governance strategy, and strive to jointly promote the realization of a targeted poverty alleviation goals in an atmosphere to win-win cooperation and benefit sharing. By constructing a three-subject evolutionary game model of local government, social organization, and poverty group in the process of targeted poverty alleviation, this paper discusses the influence of their behavioural decisions on multisubject collaborative governance of targeted poverty alleviation and selects samples to carry out simulation experiments on the model. The results show that, first, superior government support has little effect on the evolution of tripartite competition, and the conclusion is inconsistent with the general cognition. However, the enhancement of support from local governments and social organizations can effectively promote the transformation of the willingness of the poor groups to cooperate. Second, a modest increase in the punishment of social organizations and poor groups can have a binding effect on the behavioural strategy choices of both sides and ultimately promote the process of targeted poverty alleviation. Third, the increase in the benefits of social organizations and poor groups can significantly improve the willingness of both sides to cooperate, so the “endogenous driving force” of the poor groups should be enhanced to achieve the Pareto optimal state of targeted poverty alleviation.

Environmental monitoring is an important tool for environmental protection supervision and management. Environmental monitoring can help us effectively understand and master the degree of environmental pollution, and provide data support for putting forward environmental protection measures. Scientific layout and reasonable level of environmental monitoring network design is an essential cornerstone for environmental monitoring, and a significant measure to promote the industry and green sustainable development. This paper systematically analyzed its requirements of monitoring stations in the highway traffic environment monitoring network. First of all, the paper analyzed the influencing factors of regional monitoring stations in the Xinjiang transportation environment monitoring network by referring to the idea of planning the distribution points of the national transportation environment monitoring network, and determines the weight of them by using the analytic hierarchy process (AHP), which lays a foundation for the subsequent selection and determination of environmental monitoring stations. Secondly, the advantage order of ecological monitoring objects’ importance degree was synthetically sorted by the fuzzy comprehensive evaluation method. Finally, the ranking results of the environmental monitoring objects were integrated to determine the number of traffic environmental monitoring stations that need to be built, and the layout of the highway traffic environment monitoring network in Xinjiang was proposed.

As product variety is an important feature for modern enterprises, multi-product demand forecasting is essential to support order decision-making and inventory management. However, these well-established forecasting approaches for multi-dimensional time series, such as Vector Autoregression (VAR) or dynamic factor model (DFM), all cannot deal very well with time series with high or ultra-high dimensionality, especially when the time series are short. Considering that besides the demand trends in historical data, that of associated products (including highly correlated ones or ones having significantly causality) can also provide rich information for prediction, we propose new forecasting approaches for multiple products in this study. The demand of associated products is treated as predictors to add in AR model to improve its prediction accuracy. If there are many time series associated with the object, we introduce two schemes to simplify variables to avoid over-fitting. Then procurement data from a grid company in China is applied to test forecasting performance of the proposed approaches. The empirical results reveal that compared with four conventional models, namely single exponential smoothing (SES), autoregression (AR), VAR and DFM respectively, the new approaches perform better in terms of forecasting errors and inventory simulation performance. They can provide more effective guidance for actual operational activities.

China has been an emerging investor in global energy industry. Different from Chinese investments in traditional energy sectors which seek natural resources and energy security for China, Chinese overseas investments in the alternative energy sector will benefit the recipient countries by facilitating clean energy utilization and alternative energy products export. In this paper, the determinants of Chinese outward direct investment (ODI) pattern in global alternative energy sector is investigated using a project-level data set. With a Probit regression model, it is found that younger Chinese firms are more prone to Greenfield investments rather than Merge and Acquisition (M&A), while underdeveloped and risky regions are not favorable for Greenfield investments. This work combines project-level data with quantitative analysis and makes a contribution to understanding Chinese ODI landscape in global alternative energy sector.