As a very important passenger transportation model in the era of sharing economy, the online ride-hailing (ORH) has also caused new traffic management issues while improving resource allocation. Although regulations and policies have imposed macro-level supervision on the ORH market, they have not prevented some drivers from cheating on platforms’ subsidies and jeopardizing passengers’ safeties at the source. In order to realize the voluntary and sustainable ORH supervision, and enable relevant participants to actively supervise, report and comply with rules, this paper constructs an evolutionary game model among the platform, passengers and drivers. Based on the bounded rationality and expected benefits of the participants, the main factors determining the optimal strategies are analyzed. At the same time, the evolution path and the equilibrium state of the three game groups are studied by numerical simulation. The results show that important factors of realizing the benign supervision of ORH include minimizing the reporting costs of passengers, making penalties for drivers who violate the rules far greater than the illicit incomes, realizing the platform supervision costs less than the sum of penalty incomes and positive social effects. In addition, improving rewards for reporting can promote the continuity of passengers’ participation but increase the possibility of false reports. Therefore, the platform needs to consider the cost of identifying false information when designing the reward amount.

As one of the most important grain protection policies in China, the minimum purchase price policy prevents the fluctuation of grain output and protects the interests of farmers by regulating the prices of major grain varieties. For developing countries with a shortage of agricultural resources, represented by China, an in-depth study on the implementation effect and public satisfaction of this policy is of great significance for promoting the sustainable development of the grain industry. Based on the interest demands of the government, farmers, grain enterprises and consumers, this paper constructs a policy satisfaction evaluation model based on the Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation. The research shows that the implementation effect of this policy has promoted the sustainable development of China’s grain in four aspects: improving farmers’ enthusiasm for planting, optimizing the structure of supply and demand, reducing the adverse impact of disasters, and ensuring the steady increase of output. However, due to the differences in natural resources and folk customs, the implementation effect of this policy varies in different regions.

In the decision-making process, it often happens that decision makers hesitate between several possible preference values, so the multiattribute decision-making (MADM) problem of hesitant triangle fuzzy elements (HTFEs) has been widely studied. In related research works, different operators are used to fuse information, and the weighting model is used to represent the degree of difference between information fusion on various indicators, but the mutual influence between information is often not considered. In this sense, the purpose of this paper is to study the MADM problem of the hesitant triangular fuzzy power average (HTFPA) operator. First, the hesitant triangular fuzzy power-weighted average operator (HTFPWA) and the hesitant triangular fuzzy power-weighted geometric (HTFPWG) operator are given, their properties are analyzed and special cases are discussed. Then, a MADM method based on the HTFPWA operator and the HTFPWG operator is developed, and an example of selecting futures products is used to illustrate the results of applying the proposed method to practical problems. Finally, the effectiveness and feasibility of the HTFPA operator are verified by comparative analysis with existing methods.

Port operators must now reduce emissions without weakening the reliability of cargo-handling and logistics services. Two load groups are especially important in this setting: vessels connected to shore-side facilities during berthing and heavy-duty vehicles working inside the terminal area. Their energy-use patterns shape both dispatch stability and the carbon intensity of the port energy system. This paper therefore proposes an integrated port energy management model that jointly schedules wind power, photovoltaic generation, hydrogen production and storage, shore power, conventional purchases, berthed-vessel demand, and low-carbon heavy-duty transport demand. The model combines price-based demand response with a tiered carbon-trading penalty so that flexible electricity consumption and emission costs are reflected in the dispatch decision. Numerical simulations show that the joint use of demand response and the carbon-penalty mechanism lowers total economic dispatch cost by about $11.05\%$ and reduces carbon emissions by $24.52\%$. The results indicate that coordinated renewable-energy and logistics-aware scheduling can improve the economic and environmental performance of port operations.

The rapid expansion of economic globalization and trade has led to a sharp increase in the shipping investment risks currently faced by cities around the world. This study aims to explore the risk warning mechanism of shipping input under public health events to establish an effective risk warning method. This would enable the rapid identification of potential risk inputs and the implementation of targeted prevention and control measures to ensure public health and safety. This study investigates the mechanisms of both intra-regional and cross-border risk transmission within shipping networks. It establishes a transmission dynamics model (termed the SEIR-SEI model) incorporating climatic, economic, and health factors to analyze the potential inherent risks of regional shipping nodes. It uses the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model, modified by the entropy weight method, to calculate the importance of nodes in the shipping network. This comprehensive approach considers the network’s clustering coefficient, betweenness centrality, closeness centrality, degree centrality, and eigenvector centrality. To validate the practicality of the model, this study selects shipping data with Shanghai, China, as the destination node to conduct simulation computations of different risk propagation chains. The findings demonstrate that overall risk transmission is determined by the joint influence of a node’s inherent risks and propagation probabilities. This study not only clarifies the process of cross-border transmission of public health events through the shipping network between cities of different countries, but also provides insights for the application of shipping input risk assessment systems, enriching the academic research on logistics network propagation.