Analysis of the Spatial and Temporal Distribution Pattern of Carbon Emissions from Ships In Ports

Abstract

Against the backdrop of the “Dual Carbon” goal proposed during the 14th Five-Year Plan period, China’s infrastructure development is advancing toward green and low-carbon patterns, in which ship carbon emissions have become a major environmental issue. As an effective tool for recording ship activities, Automatic Identification System (AIS) data provides a reliable means to accurately quantify carbon emissions. Based on AIS data, this paper adopted a grid method combined with the STEAM model to estimate ship carbon emissions in a port area by zone, and introduced the Spearman correlation coefficient to analyze the spatio-temporal distribution of ship carbon emissions from four aspects: time, space, ship navigation status, and traffic flow factors. The results show that the total CO₂ emissions of Qingdao Port in September 2022 were estimated to be 47705.62 tons, with oil tankers accounting for 38.6% of total emissions. In terms of navigation status, carbon emissions from moored ships accounted for more than half of the total emissions in the studied waters of Qingdao Port. Temporally, different ship types exhibited consistent hourly variation patterns, with high-emission periods concentrated from 22:00 to 06:00. Spatially, the highest carbon emissions were mainly concentrated in Qianwan Port Area, main fairways, anchorages, and some terminal zones. Regarding traffic flow factors, carbon emissions showed moderate-to-high positive correlations with ship density for different vessel types, and low positive or no correlations with ship speed. This study thoroughly analyzes the carbon emission characteristics of ships in Qingdao Port waters. The proposed method can be further applied to ports and waterways, providing valuable references for understanding ship carbon emissions and formulating targeted mitigation measures, as well as theoretical support for port regulatory policies on ship carbon emissions.