Carbon Emission Accounting and Emission Reduction Path of Container Terminal Under Low-Carbon Perspective

Accurate carbon emission estimation across all operational stages of container terminals is essential for advancing low-carbon development in the transportation sector and designing effective emission reduction pathways. This study develops a two-layer carbon accounting framework that integrates vessel berthing–waiting and terminal operations, tailored to the operational characteristics of Shanghai Port container terminals. The Ship Traffic Emission Assessment Model (STEAM) is applied to estimate emissions during berthing, while a bottom-up method is employed for mobile-mode container handling operations. Targeted mitigation strategies—such as shore power adoption, operational optimization, and “oil-to-electricity” or “oil-to-gas” transitions—are evaluated through comparative analysis. Results show that vessels generate substantial emissions during erthing, which can be significantly reduced (by over 60%) through shore power usage. In terminal operations, internal transport trucks have the highest emissions, followed by straddle carriers, container tractors, and forklifts; in stacking, tire cranes dominate emissions. Comprehensive comparisons indicate that “oil-to-electricity” can reduce total emissions by approximately 39%, while “oil-to-gas” can achieve reductions of about 73%. These findings provide technical and policy insights for supporting the green transformation of container terminals under the national dual-carbon strategy.

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Publication Details

Journal
Atmosphere
Published
2025-10-03
DOI
https://doi.org/10.3390/atmos16101158
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
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article

Carbon Emission Accounting and Emission Reduction Path of Container Terminal Under Low-Carbon Perspective

Atmosphere
Genomics and Phylogenetic Studies
article

Carbon Emission Accounting and Emission Reduction Path of Container Terminal Under Low-Carbon Perspective

article en

Abstract

Accurate carbon emission estimation across all operational stages of container terminals is essential for advancing low-carbon development in the transportation sector and designing effective emission reduction pathways. This study develops a two-layer carbon accounting framework that integrates vessel berthing–waiting and terminal operations, tailored to the operational characteristics of Shanghai Port container terminals. The Ship Traffic Emission Assessment Model (STEAM) is applied to estimate emissions during berthing, while a bottom-up method is employed for mobile-mode container handling operations. Targeted mitigation strategies—such as shore power adoption, operational optimization, and “oil-to-electricity” or “oil-to-gas” transitions—are evaluated through comparative analysis. Results show that vessels generate substantial emissions during erthing, which can be significantly reduced (by over 60%) through shore power usage. In terminal operations, internal transport trucks have the highest emissions, followed by straddle carriers, container tractors, and forklifts; in stacking, tire cranes dominate emissions. Comprehensive comparisons indicate that “oil-to-electricity” can reduce total emissions by approximately 39%, while “oil-to-gas” can achieve reductions of about 73%. These findings provide technical and policy insights for supporting the green transformation of container terminals under the national dual-carbon strategy.

AtmosphereVol. 16(10)
University of Science and Technology of China (CN), Chinese Academy of Sciences (CN), Hefei Institutes of Physical Science (CN), Institute of Intelligent Machines (CN), State Key Laboratory of Transducer Technology (CN), Beijing Municipal Ecological and Environmental Monitoring Center (CN), Chinese Research Academy of Environmental Sciences (CN)
Openalex Percentile: Top 47%
Genomics and Phylogenetic Studies
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Carbon Emission Accounting and Emission Reduction Path of Container Terminal Under Low-Carbon Perspective · Atmosphere (2025) | TGRS Research Map | TGRS