文献类型：期刊文献

英文题名：Spatio-Temporal Characteristics of Ship Carbon Emissions in Port of New York and New Jersey Based on AIS Data

作者：Lin, Weixiong[1];Wang, Nini[2];Yin, Jianchuan[1,3,4]

机构：[1]Guangdong Ocean Univ, Coll Naval Architecture & Shipping, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Coll Math & Comp, Zhanjiang 524088, Peoples R China;[3]Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China;[4]Guangdong Prov Engn Res Ctr Ship Intelligence & Sa, Zhanjiang 524088, Peoples R China

年份：2025

卷号：13

期号：11

外文期刊名：JOURNAL OF MARINE SCIENCE AND ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:001624018400001)、、EI(收录号：20254819604374)、Scopus(收录号：2-s2.0-105022932285)、WOS

基金：This work was supported by the National Natural Science Foundation of China under Grants 52271361 and 52231014, the Special Projects of Key Areas for Colleges and Universities in Guangdong Province under Grant 2021ZDZX1008, the Natural Science Foundation of Guangdong Province of China under Grant 2023A1515010684.

语种：英文

外文关键词：ship carbon emissions; port of New York and New Jersey; activity-weighted method; AIS data; container ships

外文摘要：Shipping is a major source of carbon emissions and faces an urgent need for decarbonization. Research on vessel carbon emissions not only characterizes regional emission patterns but also provides critical evidence for targeted mitigation policies and optimized maritime management. This study quantifies vessel carbon emissions in the Port of New York and New Jersey from February to November 2023 using Automatic Identification System (AIS) data combined with the STEAM model. An activity-weighted spatial allocation method was applied to distribute emissions across 100 m x 100 m grids. Emission characteristics were analyzed across four dimensions: vessel type, operational state, temporal variation, and spatial distribution. Results show that total emissions during the study period reached approximately 136,701.8 t, with container ships contributing 62.3% of the total. Berthing operations were identified as the dominant emission source, accounting for 73.4% of total emissions, followed by tugboats and cargo vessels. Temporally, emissions peaked in October (10.8%) and were lowest in February (8.8%), reflecting variations in trade intensity and seasonal weather conditions. Spatially, emissions exhibited strong clustering around terminal berths. A sensitivity analysis was performed to assess the robustness of the emission estimates. When the load factor (LF) varied by +/- 10%, total emissions changed by only +/- 1.85%, indicating that the results are highly stable and robust. This limited variation arises from the dominance of berthing operations with relatively steady auxiliary loads and the application of the constraint LF <= 1, which prevents unrealistic overloading. These findings offer indicative insights that can inform port-level emission management and serve as a reference for future low-carbon policy development.