Ship Spatial-Temporal Carbon Emission Pattern Mining Via Spatial Grid Partitioning Model

As the zones with the highest density of vessel activity, port areas exhibit carbon emission characteristics that profoundly influence the environmental quality of coastal cities and the implementation of low-carbon management strategies. To achieve a refined assessment of ship carbon emissions, this study focuses on the San Pedro Bay Ports in the United States. Based on AIS data and a bottom-up emission estimation approach, this study employs spatial gridding and multi-scale analysis to characterize the spatiotemporal distribution of ship-related carbon emissions in the port area during 2024. The results reveal significant structural disparities, seasonal variations, and spatial clustering in ship emissions. Emission hotspots are closely associated with terminal operation zones, anchorage areas, and main shipping lanes, while temporal emission patterns are driven by cyclical trends in global trade and energy demand. This study provides a theoretical foundation for the fine-scale identification of carbon emissions in port areas. The findings offer scientific support for green port management and the formulation of differentiated emission reduction strategies, serving as a valuable reference for port emission control and regional low-carbon transitions.