the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
High-resolution global shipping emission inventory by Shipping Emission Inventory Model (SEIM)
Abstract. The high-resolution ship emission inventory serves as a crucial dataset for various disciplines including atmospheric science, marine science, environmental management, etc. Here, we present a global high spatiotemporal resolution ship emission inventory at a resolution of 0.1° × 0.1° for the years 2013, 2016–2021, generated by the state-of-the-art Shipping Emission Inventory Model (SEIMv2.2). Initially, the annual 30 billion Automatic Identification System (AIS) data underwent extensive cleaning to ensure data validity and accuracy in temporal and spatial distribution. Subsequently, integrating real-time vessel positions and speeds from AIS data with static technical parameters, emission factors, and other computational parameters, SEIM simulated ship emissions on a ship-by-ship, signal-by-signal basis. Finally, the results were aggregated and analyzed. In 2021, the ship activity dataset established based on AIS data covered 109.3 thousand vessels globally (101.4 thousand vessels reported by the United Nations Conference on Trade and Development). Concerning the major air pollutants and greenhouse gases, global ships emitted 847.2 million tons of CO2, 2.3 million tons of SO2, 16.1 million tons of NOx, 791.2 kilo tons of CO, 737.3 kilo tons of HC, 415.5 kilo tons of primary PM2.5, 61.6 kilo tons of BC, 210.3 kilo tons of CH4, 45.1 kilo tons of N2O in 2021, accounting for 3.2 % of SO2, 14.2 % of NOx, and 2.3 % of CO2 emissions from all global anthropogenic sources, based on the Community Emissions Data System (CEDS). Due to the implementation of fuel-switching policies, global ship emissions of SO2 and primary PM2.5 saw a significant reduction of 81.3 % and 76.5 % in 2021 compared to 2019, respectively. According to the inventory results, the composition of vessel types contributing to global ship emissions remained relatively stable through the years, with container ships consistently contributing ~ 30 % of global ship emissions. Regarding vessel age distribution, the emission contribution of vessels built before 2000 (without Tier standard) has been declining, dropping to 10.2 % in 2021, suggesting that even a complete phase-out of these vessels would have limited potential for reducing NOx emissions in the short term. On the other hand, the emission contribution of vessels built after 2016 (meeting Tier III standard) kept increasing, reaching 13.3 % in 2021. Temporally, global ship emissions exhibited minimal daily fluctuations. Spatially, high-resolution emission characteristics of different vessel types were delineated. Patterns of ship emission contributions by different types of vessels vary among maritime regions, with container ships predominant in the North and South Pacific, bulk carriers predominant in the South Atlantic, and oil tankers prevalent in the Arabian Sea. The distribution characteristics of ship emissions and intensity also vary significantly across different maritime regions. Our dataset, which is accessible at https://zenodo.org/records/11069531 (Wen et al., 2024), provides daily breakdown by vessel type and age is available for broad research purposes, and it will provide a solid data foundation for fine-scale scientific research and shipping emission mitigation.
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RC1: 'Comment on essd-2024-258', Sijia Dong, 12 Aug 2024
I really enjoy reading this well-written and highly interesting manuscript presented by Yi et al. The authors use a Shipping Emission Inventory Model to carefully analyze and generate a powerful set of global emission inventory. The analytical process was thoroughly stepped through in the manuscript, and the data was well-presented. These figures and data would be useful to policy-makers and technology-developers, in addition to scientists in atmospheric and ocean sciences. I’d recommend publication with minor revision.
One small suggestion I have for the authors is that, the definitions of some terms could be better clarified so that the general audience who are not so familiar with the field can understand more easily. For example, I am not 100% sure what AIS signal means – does this mean how many ships there are in this dataset? If so, the authors can simply say this in the captions of Table 1 and Figure 2, and also define this term in the text. Also, please define HC, BC, and other abbreviations that appear in the manuscript.
In addition, figures could have higher resolutions. Right now, it is hard to read the legends. Figure 3i, ‘N2O’ does not have the subscript.
Sijia Dong
Citation: https://doi.org/10.5194/essd-2024-258-RC1 - AC1: 'Reply on RC1', Huan Liu, 12 Oct 2024
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RC2: 'Comment on essd-2024-258', Anonymous Referee #2, 30 Sep 2024
This manuscript is a study that established the global ship emissions inventories from 2013, 2016 to 2021 using the SEIMv2.2 model, providing a comprehensive analysis of the patterns of spatiotemporal variations in ship emissions throughout the years. The authors conducted a thorough cleaning of the global AIS data, correcting for spatial drift, excessively long-time intervals, and data misalignment, and employed compression techniques to make the data computable. This extraordinary work is quite challenging, but it ultimately ensures the quality of the data, allowing for a broad analysis of ship emissions from multiple angles, including the composition of ship types, age distribution, temporal changes, spatial variations, and analyses across different intersecting dimensions. The article's visualization of the data results is also very clear and intuitive. The dataset it provides could be useful for future scholars in the fields of atmosphere, ocean, and environment. Overall, this is a good paper that deserves to be published in ESSD. Nevertheless, some minor issues must be clarified.
First, in the analysis of temporal changes, the authors could include some discussion on international policies, particularly how recent fuel-switching regulations impact changes in international shipping emissions, especially within Emission Control Areas (ECA).
Second, could the authors add a paragraph discussing on the uncertainties and limitations of the model in the conclusion section? This could include a discussion on the accuracy of AIS data, the uncertainties in emission factors, and potential future work in these areas.
Finally, in the conclusion section, it would be beneficial if the authors could emphasize the global impacts revealed by the spatial heterogeneity of emissions structure and intensity shown in the high-resolution ship emission inventories. For example, how might this spatial variation affect environmental impacts, or what insights could it provide for emission reduction strategies?
Citation: https://doi.org/10.5194/essd-2024-258-RC2 - AC2: 'Reply on RC2', Huan Liu, 12 Oct 2024
Status: closed
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RC1: 'Comment on essd-2024-258', Sijia Dong, 12 Aug 2024
I really enjoy reading this well-written and highly interesting manuscript presented by Yi et al. The authors use a Shipping Emission Inventory Model to carefully analyze and generate a powerful set of global emission inventory. The analytical process was thoroughly stepped through in the manuscript, and the data was well-presented. These figures and data would be useful to policy-makers and technology-developers, in addition to scientists in atmospheric and ocean sciences. I’d recommend publication with minor revision.
One small suggestion I have for the authors is that, the definitions of some terms could be better clarified so that the general audience who are not so familiar with the field can understand more easily. For example, I am not 100% sure what AIS signal means – does this mean how many ships there are in this dataset? If so, the authors can simply say this in the captions of Table 1 and Figure 2, and also define this term in the text. Also, please define HC, BC, and other abbreviations that appear in the manuscript.
In addition, figures could have higher resolutions. Right now, it is hard to read the legends. Figure 3i, ‘N2O’ does not have the subscript.
Sijia Dong
Citation: https://doi.org/10.5194/essd-2024-258-RC1 - AC1: 'Reply on RC1', Huan Liu, 12 Oct 2024
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RC2: 'Comment on essd-2024-258', Anonymous Referee #2, 30 Sep 2024
This manuscript is a study that established the global ship emissions inventories from 2013, 2016 to 2021 using the SEIMv2.2 model, providing a comprehensive analysis of the patterns of spatiotemporal variations in ship emissions throughout the years. The authors conducted a thorough cleaning of the global AIS data, correcting for spatial drift, excessively long-time intervals, and data misalignment, and employed compression techniques to make the data computable. This extraordinary work is quite challenging, but it ultimately ensures the quality of the data, allowing for a broad analysis of ship emissions from multiple angles, including the composition of ship types, age distribution, temporal changes, spatial variations, and analyses across different intersecting dimensions. The article's visualization of the data results is also very clear and intuitive. The dataset it provides could be useful for future scholars in the fields of atmosphere, ocean, and environment. Overall, this is a good paper that deserves to be published in ESSD. Nevertheless, some minor issues must be clarified.
First, in the analysis of temporal changes, the authors could include some discussion on international policies, particularly how recent fuel-switching regulations impact changes in international shipping emissions, especially within Emission Control Areas (ECA).
Second, could the authors add a paragraph discussing on the uncertainties and limitations of the model in the conclusion section? This could include a discussion on the accuracy of AIS data, the uncertainties in emission factors, and potential future work in these areas.
Finally, in the conclusion section, it would be beneficial if the authors could emphasize the global impacts revealed by the spatial heterogeneity of emissions structure and intensity shown in the high-resolution ship emission inventories. For example, how might this spatial variation affect environmental impacts, or what insights could it provide for emission reduction strategies?
Citation: https://doi.org/10.5194/essd-2024-258-RC2 - AC2: 'Reply on RC2', Huan Liu, 12 Oct 2024
Data sets
Global shipping emissions for the years 2013 and 2016-2021 Wen Yi et al. https://zenodo.org/records/11069531
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