To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives.

Arctic shipping should not be understood as a sustainable transport outcome that will emerge automatically as sea ice declines. Rather, its long-term viability depends on whether stable operations can be achieved under persistent environmental and political uncertainties. Existing studies have examined Arctic shipping risks, governance arrangements, and route feasibility, but have paid limited attention to how cargo owners, shipping companies, and governments interact strategically under composite risk constraints. To address this gap, this paper develops a three-party evolutionary game model involving cargo owners, shipping companies, and the government under weather risk and political risk, and uses numerical simulation to examine system evolution, policy thresholds, and external shock responses. The results show that the system tends to converge toward a high-coordination equilibrium under low-risk conditions, whereas medium-risk conditions may lead to bistability and a low-coordination trap when government support remains below a critical threshold. Composite political shocks further amplify system vulnerability and weaken sustainable route operation. These findings suggest that the key challenge of Arctic shipping lies not in physical route accessibility alone, but in whether risk governance, market participation, and institutional support can jointly stabilize expectations and promote sustainable Arctic shipping operations.

Under the backdrop of data-driven maritime era and the shipping industry’s green and low-carbon transition, the China Shipowners’ Mutual Assurance Association is confronted with unclear legal status, insufficient operational capacity and delayed internationalization. Emerging risks including shore power, alternative fuels and emission control have posed systemic challenges to its traditional governance framework, with risk scenarios expanding from navigation to port berthing and liability identification shifting from single party to multi-party coordination. While digital tools like big data and AI are reshaping risk assessment and governance paradigms internationally, this paper adopts methodologies of legal characteristic analysis, comparative study of domestic and international practices, and case studies to clarify the core roles of Protection and Indemnity Associations (P&I Clubs) in areas such as accident investigation, oil pollution claims, and security provision. The findings indicate that international P&I clubs have formed mature governance and risk-sharing mechanisms, while Chinese counterparts are hampered by legislative gaps, traditional risk control models, incomplete global networks and limited international influence. To address these gaps and position the association to leverage future data-centric governance models, this study puts forward a four-pronged strategy based on institutional construction, capacity building and international expansion: formulating special legislation, advancing digital transformation and talent training, and participating in the formulation of technical standards and international rules to boost green port governance collaboration. This will help the association transform from passive adaptation to active leadership, so as to support the high-quality, data-aware, and sustainable development of China’s shipping industry.

Introduction: Cruise port operations are facing increasingly significant challenges in departure reliability amid frequent climate variability, port congestion, supply chain disruptions, and geopolitical risks. Unlike existing studies that mainly explain cruise delays from the perspectives of weather conditions, external shocks, or supply chain risks, this paper focuses on whether port turnaround planning itself may constitute an endogenous source of vulnerability. Methods: To identify this issue, this study uses the daily AIS data for the United States released by Marine Cadastre. These data are combined with planned berthing information, weather data, and vessel characteristics to construct a sample of 3,238 cruise ship–port–day observations. The study defines planned turnaround time as a time-based buffer resource in voyage-level operations. It then applies a fixed effects model, a threshold regression model, a half-hour interval model, mechanism tests, heterogeneity analysis, and moderation analysis to systematically examine the impact of planned turnaround time on departure reliability and its operational boundaries. Results: The results show a significant threshold-type nonlinear relationship between planned turnaround time and departure delay. An 8.5-hour threshold is identified as the main data-driven cutoff, while a meaningful buffering effect is mainly observed within 8 hours. The interval of 8.0–8.5 hours is better viewed as a transition range where marginal benefits begin to decline. Mechanism tests indicate that actual berthing service time is the most stable primary transmission channel of planned turnaround vulnerability, while port queuing pressure and berth occupancy pressure further modify the marginal effect of planned turnaround time. Discussion: This study moves the analysis of cruise departure delays from explanations based on exogenous shocks to the identification of endogenous vulnerability. By revealing that planned turnaround time itself can become a source of risk under specific threshold conditions, these findings complement existing resilience literature that has primarily focused on external drivers. It provides new micro-level evidence for understanding the resilience of cruise port operations, optimizing schedule planning, and improving port coordination efficiency.

Unmanned boat distance detection is an important foundation for autonomous navigation tasks of unmanned boats. Monocular vision ranging has the advantages of low hardware equipment requirements, simple deployment, and high efficiency of distance detection. Unmanned boats can sense the real-time navigational situation of waters through monocular vision ranging, providing data support for their autonomous navigation. This paper establishes a framework for unmanned boat distance detection. The framework extracts and recognizes the features of an unmanned boat through Yolov11m-pose and selects the key points of the ship for physical distance mapping. Using the camera calibration to obtain the pixel focal length, the main point coordinates and other parameters are obtained. The number of pixel points in the image key point to the image center pixel and the actual distance of the camera from the horizontal plane are combined with the focal length of the camera for triangular similarity conversion. These data are fused with the camera pitch angle and other parameters to obtain the final distance. At the same time, experimental verification of the key point detection model demonstrates that it fully meets the requirements for unmanned boat ranging tasks, as assessed by Precision, Recall, mAP50, mAP50-95 and other indicators. These indicators show that Yolov11m-pose has a better accuracy in the key point detection task with an unmanned boat. The verification experiments also illustrate the accuracy of the key point-based physical distance mapping compared with the traditional detection frame-based physical distance mapping, which was assessed by the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute error (MAE). The metrics show that key point-based unmanned boat distance mapping has greater accuracy in a variety of environmental situations, which verifies the effectiveness of this approach.