In the context of carbon emission reduction, this study constructs a bi-level planning model for port microgrid investment and deployment, with the government as the upper-level decision-maker and the port area as the lower-level follower. The model incorporates the interests of the port area, including berthed vessels, and aims to maximize environmental benefits while minimizing the total cost of the port area over the planning period. Using the Column and Constraint Generation (CCG) algorithm, the optimal investment and operation strategy for the port area during the planning horizon is derived. The study analyzes the deployment of the port microgrid system under varying incentive budgets and evaluates the resulting environmental benefits, comparing the effectiveness of different incentive strategies. The results demonstrate that a hybrid incentive strategy can significantly enhance investment motivation in port microgrid systems, thereby effectively fostering innovation in the energy structure of the port region and accelerating the emission reduction process.

To address the safety and economic requirements for ships navigating the complex ice environments of Arctic waters, this paper proposes a multi-objective improved Sparrow Search Algorithm (SSA) to optimize both wind resistance and ice resistance. The Risk Index Outcome (RIO), calculated by the Polar Operational Limit Assessment Risk Indexing System (POLARIS), and the safe water depth threshold are adopted as constraints to ensure navigation safety and mitigate the impact of resistance on navigation efficiency along Arctic routes. First, meteorological and ice data for the Arctic route are processed, and a grid environment map is constructed according to ship type. Second, safe navigable areas are identified, and a multi-objective function model is established. Finally, the improved sparrow search algorithm is applied to optimize the route and is compared with other typical path planning algorithms to verify the effectiveness and feasibility of the proposed method. The results indicate that the optimal path generated by the improved sparrow search algorithm, based on the multi-objective model of wind and ice resistance, can significantly reduce ship resistance during navigation-achieving a reduction of up to 10.9%. Moreover, there is no significant difference in path length or running time compared with other algorithms. This study provides an economical and reliable optimization solution for ship navigation in Arctic routes.

This study employs the MARIS model and a convective diffusion model to simulate the diffusion of nuclear wastewater released from Japan. Based on the simulation results, it proposes optimized methods for ballast water exchange to prevent the direct discharge of radioactive ballast water into ports, thereby mitigating potential threats to the ecological environment. The research focuses on the Fukushima nuclear incident and the subsequent continuous release of 1.3 million tons of nuclear wastewater into the ocean. Results indicate that radioactive substances are mainly concentrated in the surface layer of the ocean, with detectable enrichment of radioactive elements such as cesium in seawater and aquatic organisms near the Fukushima nuclear power plant. Consequently, ships operating near eastern Japanese ports are taking in ballast water contaminated with radioactive materials, including cesium-134 and cesium-137. Using a convective diffusion module, the study simulates the variation in radioactive substance concentrations during ballast water exchange at different distances, providing theoretical support for optimizing exchange strategies. The findings show that performing a secondary ballast water exchange more than 20 nautical miles from Japan's coast can reduce radioactive substance concentrations in ballast water to one ten-thousandth of the pre-exchange levels. The conclusions of this study can assist maritime regulatory authorities in formulating effective management measures, thereby contributing to the protection of marine ecosystems.

To achieve carbon neutrality, governments and enterprises are accelerating the decarbonization process in the shipping industry. This paper focuses on the Low-Carbon Maritime Supply Chain (LMSC), considering both government policies and consumers' green preferences. A two-stage Stackelberg game model between shipping companies and freight forwarders is developed to determine optimal pricing and carbon emission reduction strategies, while examining the impacts of carbon taxes, government subsidies, and consumers' green preferences on decision-making. The analysis yields three main findings: First, cooperation between freight forwarders and shipping companies maximizes overall profits for the LMSC; however, to achieve higher emission reduction levels, both parties should co-lead the supply chain. Second, government subsidies and enhanced low-carbon awareness produce dual effects: while they help reduce emissions and increase corporate profits, they may also raise TEU market prices for consumers under certain conditions. Third, although carbon taxes significantly improve the low-carbon performance of the LMSC, they reduce profits for all participants and increase TEU market prices.

To effectively identify vessels using fuel with excessive sulfur content, a reverse calculation method for determining fuel sulfur content was developed based on emission and diffusion characteristics. A Gaussian puff compensation model was applied to estimate the emission source strength of vessels from monitored SO₂ concentrations at designated points. In addition, a computational model for vessel fuel consumption was established using key vessel parameters, including the power and fuel consumption rates of main and auxiliary engines. The proposed method demonstrated superior performance compared to the mainstream carbon balance method in detecting vessels with non-compliant sulfur content, achieving detection and false detection rates of 86.60% and 2.06%, respectively. Over a 30-day continuous monitoring period, the fuel sulfur content of 2,743 vessels was successfully determined, representing an effective detection rate of 82.72%. Among these, 131 vessels were identified as potentially exceeding sulfur limits. Subsequent verification confirmed that 111 vessels used non-compliant fuel, resulting in an assessment accuracy of 84.73%. These findings demonstrate the method's capability to enable real-time monitoring of fuel sulfur content without requiring CO₂ concentration data.

Accurate forecasting of port container throughput is of great significance for port operators and government administrations in making scientific decisions. Existing forecasting methods, however, often pay insufficient attention to short-calendar-time PCT and exhibit limited accuracy in handling nonlinear and non-stationary fluctuation series. This paper takes the container throughput of Shanghai Port as the research object and proposes a novel deep learning model based on secondary decomposition using CCVMD and STL. Using the correlation coefficient as a reference, variational mode decomposition is first applied to the original time series. Subsequently, a secondary decomposition divides the data into seasonal, trend, and residual components. An algorithm-optimized long short-term memory neural network is then employed to predict each component separately, and the final prediction results are aggregated. Experimental results show that the combined decomposition model with data preprocessing significantly outperforms other models in PCT forecasting. The proposed model achieves a mean absolute percentage error of 0.021 703, a root mean square error percentage of 0.026 852, and a mean absolute error percentage of 0.022 14, indicating superior overall performance compared to 12 benchmark models and several models from prior studies. Furthermore, the secondary decomposition approach demonstrates enhanced reliability in tracking extreme values, removing and reducing noise, and improving interpretability.

As an important component of the waterway transportation system, Ro-Ro passenger ship transportation plays a significant role in inland river, coastal, and even cross-strait transport services. In recent years, collisions involving Ro-Ro passenger ships have occurred from time to time. To mitigate the losses caused by such accidents, this paper proposes an emergency decision-making model for Ro-Ro passenger ship collisions based on a fuzzy Bayesian network. The identified emergency decision variables for RoPax ship collisions are fuzzified by introducing fuzzy logic. Combined with improved IF-THEN rules, confidence rule bases are established and then converted into a conditional probability table, thereby constructing a complete Bayesian network inference structure. Ultimately, the optimal emergency decision scheme is determined through utility value evaluation. The results demonstrate that the proposed emergency decision-making model is effective and feasible, aligning with practical application requirements. This study provides ship decision-makers with a reference basis for emergency response in the event of a RoPax ship collision.

The arrival and handling times of vessels are subject to significant uncertainty. Triangular fuzzy numbers, characterized by upper and lower bounds and a most likely value, provide an effective means of representing such imprecise information. In this context, this paper first establishes a fuzzy integer programming model for berth allocation at container terminals, aiming to minimize the total departure delay time of vessels. An improved Multi-Verse Optimizer (MVO) algorithm is then proposed to solve the model, incorporating solution repair and breakout strategies. Comparative analysis shows that, in contrast to deterministic berth allocation schemes, the proposed fuzzy berth allocation approach demonstrates notable advantages in reducing total departure delay time and exhibits greater effectiveness in handling uncertainty. Moreover, the improved MVO algorithm achieves solution speed improvements of 59.9%, 44%, and 26.1% in small, medium, and large scale experiments, respectively, compared to the standard multi-verse optimizer. These results indicate that the proposed algorithm can efficiently solve the fuzzy integer programming model for berth allocation and offers valuable decision-making support for addressing berth allocation problems under fuzzy uncertainty.

To study the pitching stabilization performance of super-large ships under severe sea conditions, this paper takes the tanker "KVLCC2" as the research object. A weighting matrix is utilized to stabilize its transfer function model in Mathematica, and the stability of the model is verified using the root trajectory shaping method. Subsequently, a simplified first-order closed-loop gain-shaping algorithm is applied to design the robust controller. In addition, a dual nonlinear feedback control algorithm is proposed to be incorporated into the control system to further enhance its pitching stabilization performance. To validate the effectiveness of the dual nonlinear feedback control system for pitching stabilization, wind scale of 7 and 8 wind and wave models along with perturbation links are introduced into the system for simulation experiments. The experimental results demonstrate that even with a time lag constant of 0.15, the dual nonlinear feedback control system effectively improves the ship's pitching stabilization performance under rough sea conditions. The proposed dual nonlinear feedback control system can provide technical support for the smooth and efficient navigation of super-large ships in varying sea conditions.

From the aspects of navigation service, reservation navigation, green and low carbon, the service level index system of inland navigation hub under the reservation mode of 6 first-level indicators and 22 second-level indicators is constructed. Secondly, the game comprehensive weighting method is used to combine the qualitative weights and quantitative weights determined by the interval two-tuple linguistic method and the CRITIC method respectively. Then, based on the matter-element extension theory, the service level evaluation model of inland navigation hub under the reservation mode is constructed. Finally, taking the Three Gorges navigation hub of the Yangtze River as an example, the empirical analysis is carried out to verify the scientificity and feasibility of the model. The research conclusion has a good reference value for improving the service level of inland navigation hub.