Qiang Zhang

This paper analyzes 441 of China's green port policies using text mining and quantitative evaluation methods to promote the sustainable development of China’s ports and accelerate the achievement of the “dual carbon” goal. The study first reviews the evolution of these policies, categorizing them into three stages: the embryonic stage, the startup stage, and the development stage. High-frequency words were extracted for each stage, followed by dimensionality reduction using Principal Component Analysis (PCA) and clustering analysis to classify the policies. The Latent Dirichlet Allocation (LDA) topic model was then applied to identify the main policy themes, and a Policy Modeling Consistency (PMC) index model was developed. The study evaluated 19 representative policies using cosine similarity and correlation coefficients. The analysis revealed several key issues: (1) the entity responsible for policy issuance is singular, with insufficient interdepartmental collaboration; (2) short-term policies are lacking, with weak responsiveness; (3) policies targeting shipping companies are underdeveloped; and (4) there is an imbalance in the use of policy instruments, with a focus on mandatory and hybrid tools. Based on these findings, the paper proposes several suggestions for improving green port development policies.

Transshipment hubs are important components of the global container shipping network. Nowadays, hybrid ports are emerging, handling both gateway and transshipment container traffic depending on their significant maritime connectivity. Effectively identifying transshipment hubs, including traditional transshipment hubs with high transshipment incidences and hybrid ports with sufficient transshipment capabilities, is crucial to gain a good understanding of container shipping networks. The method of reinforced structural holes (RSHs) has been introduced from the sociology to detect transshipment hubs at the global level, as it can fully consider the existence of separated cohesive port communities. The results show that the RSH-based approach is feasible to identify those hubs playing the role of bridges across different port communities worldwide, which is demonstrated from the perspective of maritime connectivity. The higher ranked hubs with higher RSH values generally have better maritime connections with ports from various port communities. Several policy implications have been further elaborated for relevant decision makers, such as liner companies and port operators.

Uncertainties in port handling efficiency can cause port delays in the liner shipping system. Furthermore, policies on carbon emission reduction, such as EEXI standards, restrict the potential for speed optimization in liner shipping operations. Traditional tactical planning speed optimization is unsuitable for operational-level decision making, leading to unreliable schedules. From a schedule-reliability and energy-efficiency perspective, we propose a real-time speed optimization method based on discrete hybrid automaton (DHA) and decentered model predictive control (DMPC). We use a dynamic adjustment of sailing speed to offset the disturbance caused by port handling efficiency uncertainties. First, we establish a DHA model that describes each ship’s hybrid dynamics of state switching between sailing and berthing; then, we develop a prediction model for the DMPC controller, which is analogous to the DHA model. The schedule is transferred into time–position coordinates as controller reference trajectories in the receding horizon speed optimization framework. We consider determining tracking errors, carbon emissions, and fuel consumption as our objectives, and we carry out engine power limitation (EPL) analysis for the sample ship, which turns the EEXI standards into constraints. We attain the recommended speed by solving a mixed-integer optimization. We carry out a case study, and our results indicate the effectiveness of our proposed DHA-DMPC scheme in lowering port delays and achieving the best trade-off between schedule reliability and energy efficiency. Additionally, we conduct further experiments to analyze the impacts of various carbon reduction policies on the performance levels of liner shipping operations.