To address the limitations of traditional accounting methods in fully reflecting the spatial distribution and dynamic evolution of port carbon emissions,this study conducts a carbon emission footprint analysis based on spatiotemporal data field theory.Taking a large port in northern China as the research object,a theoretical framework of spatiotemporal data fields is introduced to construct a port carbon emission potential field model.By integrating emission data from different port areas across spatial and temporal dimensions,and applying methods such as potential value superposition and spatiotemporal distance transformation,spatiotemporal mapping,trend forecasting,and emission reduction evaluation can be achieved.The results show that port carbon emissions exhibit significant spatial heterogeneity and temporal persistence.Specifically,the production area accounts for 40%-45% of emissions,with an annual growth rate of 2.64%.emissions in the dock area are closely related to berthing density and decreased by 10%-15% after the widespread adoption of shore power systems.Through the comparison of model prediction and actual data,it is verified that under differentiated emission reduction measures,the annual growth rate of port emissions dropped from 4.28% to 2.15%,with an annual reduction potential of 447 t.The model can provide theoretical support for dynamic monitoring and predictive analysis of carbon emissions in complex systems,and can contributes to the low-carbon transformation of the port industry and the construction of green and smart ports.