The risk of ship–bridge collisions should be evaluated using advanced models to consider different anti-collision and bridge-protection measures. This study aimed to propose a method to evaluate the effectiveness of active and passive safety measures in preventing ship–bridge collision. A novel ship–bridge collision probability formulation taking into consideration different safety measures was proposed. The model was applied at Jintang Bridge in China where the surrounding vessel traffic is ultra-crowded. We calculated the collision probability between the bridge and passing traffic using automatic identification system (AIS) data, Monte Carlo simulation, and Bayesian networks. Results under four different safety measures (i.e., active measures, passive measures, both measures and none) were analyzed and compared. The analysis concluded that both active and passive safety measures are effective in reducing the ship–bridge collision probability. Active measures, if deployed properly, can provide protection at an equivalent level than passive measures against collision risks. However, passive measures, such as setting arresting cables, are necessary in cases where the response time of the active measures is long. The proposed method and the results obtained from the case study may be useful for robust and systematic effectiveness evaluation of safety measures in other cases worldwide.

This research aims to identify and analyze the significant risk factors contributing to accidents involving fishing vessels, a crucial step towards enhancing safety and promoting sustainable practices in the fishing industry. Using a data-driven Bayesian network (BN) model that incorporates feature selection through the random forest (RF) method, we explore these key factors and their interconnected relationships. A review of past academic studies and accident investigation reports from the Fishery Mutual Insurance Association (FMIA) revealed 17 such factors. We then used the random forest model to rank these factors by importance, selecting 11 critical ones to build the Bayesian network model. The data-driven Bayesian network (BN) model is further utilized to delve deeper into the central factors influencing fishing vessel accidents. Upon validation, the study results show that incorporating the random forest feature selection method enhances the simplicity, reliability, and precision of the BN model. This finding is supported by a thorough performance evaluation and scenario analysis.

Ship pollution accidents can cause serious harm to marine ecosystems and economic development. This study proposes a ship pollution accident analysis method based on a knowledge graph to solve the problem that complex accident information is challenging to present clearly. Based on the information of 411 ship pollution accidents along the coast of China, the Word2vec’s word vector models, BERT–BiLSTM–CRF model and BiLSTM–CRF model, were applied to extract entities and relations, and the Neo4j graph database was used for knowledge graph data storage and visualization. Furthermore, the case information retrieval and cause correlation of ship pollution accidents were analyzed by a knowledge graph. This method established 3928 valid entities and 5793 valid relationships, and the extraction accuracy of the entities and relationships was 79.45% and 82.47%, respectively. In addition, through visualization and Cypher language queries, we can clearly understand the logical relationship between accidents and causes and quickly retrieve relevant information. Using the centrality algorithm, we can analyze the degree of influence between accident causes and put forward targeted measures based on the relevant causes, which will help improve accident prevention and emergency response capabilities and strengthen marine environmental protection.

The International Maritime Organization (IMO) has been progressively implementing stricter regulations on ship carbon emissions, leading to many vessels adopting the virtual arrival (VA) method to reduce their carbon footprint. However, the effectiveness of the traditional VA method often varies in busy ports with complex traffic organization scenarios. To address this, our study presents a novel, comprehensive model that integrates vessel scheduling with the VA approach. This model is designed to achieve a dual objective: reducing carbon emissions through virtual arrival while simultaneously minimizing vessel waiting times. In addition to these goals, it incorporates essential aspects of safety, efficiency, and fairness in port management, utilizing the NSGA-2 algorithm to find optimal solutions. This model has been tested and validated through a case study at Ningbo-Zhoushan port, employing its dataset. The results demonstrate that our innovative model and algorithm significantly outperform traditional scheduling methods, such as First-Come-First-Serve (FCFS) and Virtual-Arrival Last-Serve (VALS), particularly in terms of operational efficiency and reduction in vessel carbon emissions.

With the rapid development of international trade and the fast growth of port freight volumes, the risk of ship pollution in marine areas has significantly increased. Frequent ship pollution accidents seriously endanger marine ecosystems and are extremely unfavorable to economic development and marine environmental protection. In this study, we investigate the results of emergency responses after ship pollution accidents and analyze the factors affecting a successful emergency response. A Bayesian network model of the emergency response results of ship pollution is established, and the emergency response results of ship pollution accidents in the example of Zhoushan Port are analyzed. Based on the analysis, suggestions for strengthening the emergency preparedness of Zhoushan Port are proposed. We believe that the results of this are beneficial for improving ship pollution risk management and decision making, and that they have practical significance for marine environmental protection.

Currently, enterprises are paying more and more attention to green innovation in order to reduce the negative impact on the environment and achieve sustainable development. Different from evaluating the green innovation ability of enterprises, how to evaluate a person’s green innovation ability from the perspective of open innovation is a new direction. This study aims at proposing a novel framework to evaluate college students’ green innovation ability. First, a set of criteria to evaluate college students’ green innovation ability is determined by experts using the panel consensus approach. Second, the best worst method (BWM) is used to calculate the weights of criteria. Lastly, modified fuzzy technique for order of preference by similarity to ideal solution technique (TOPSIS) technique is adopted to rank college students’ green innovation ability. Moreover, a case study is presented to verify the validity of the proposed method. The results provide implications for academic, industry, and policymakers. Specifically, through the evaluation of college students’ green innovation ability, universities can find the inadequacies in culturing students’ green innovation ability and strengthen education in the aspects corresponding to the criteria with high global weights. Companies can select the talented internship students with green innovation ability as employees and should realize that it is good for them to cultivate college students with universities. Policymakers of the education department can trace the quality of education in college senior students’ green innovation ability every year. In addition, they should pay more attention to establish policies regarding those criteria with high global weights.