Quantifying Ocean-based Greenhouse Gas Emissions
Introduction
A Global Emissions Pilot Study by emLab and Global Fishing Watch
For the first time, we have mapped and estimated the emissions of all industrial vessels operating in the ocean. Our analysis covers 925,682 ocean-going vessels that broadcast their GPS positions using the Automatic Identification System (AIS). Additionally, we used satellite radar from Sentinel-1 to detect vessels not tracked by AIS by leveraging over 32 million vessel detections across more than 932,000 radar scenes. These “dark fleet” vessels provide a more comprehensive picture of human activity at sea, since they have typically remained undetected through traditional tracking systems until now. This pilot study focused on large industrial vessels, specifically those over 15 meters in length, and their activity from 2016 through present (and back to 2015 for AIS-broadcasting vessels). These new data improve our understanding of how much greenhouse gas (GHG) emissions are produced at sea and provide actionable information to willing actors seeking to reduce emissions. While our approach is still under active development, we hope to continue refining and expanding our model over the coming year and to eventually publish the work in a high-impact peer-reviewed scientific journal. Future work will focus on expanding coverage and improving accuracy of our total marine emissions, allowing us to further unlock and inform various types of policy and/or market-based solutions that could help reduce at-sea GHG emissions.
Key Findings:
Industrial vessels emitted approximately 1.3 billion tons of CO2 in 2024, accounting for about 3.6% of global fossil fuel emissions (using the 2023 estimate from (IEA) (2023)).
Maritime emissions are likely growing at more than three times the rate of global CO2 emissions. Between 2016 and 2023, global fossil fuel emissions increased by 7% ((IEA) (2023)), while emissions from maritime vessels grew by 24%.
Between 2016 and 2024, total emissions from maritime vessels grew by 33%.
While 87% of maritime emissions in 2024 came from vessels that broadcast their positions via AIS, 13% came from dark fleet vessels that do not broadcast AIS. Understandin dark fleet activity is crucial for estimating both the magnitude of total emissions and tracking changes over time. Emissions from dark vessels decreased by 22% between 2016 and 2024, whereas emissions from AIS-tracked vessels rose by 50% over the same time period. This shift is largely due to increased AIS adoption and advancements in AIS technology.
From 2016 to 2024, increases in emissions from AIS-tracked vessels were primarily concentrated in the Pacific waters of East and Southeast Asia, which accounted for nearly half of the overall increase. Similarly, decreases in dark fleet emissions were also concentrated in this region, contributing to over half of the total reduction.
Our analysis also revealed seasonal and event-driven variations in emissions. Emissions consistently dipped during major holidays, including Christmas, New Year’s Day, and Chinese New Year, and also fell during the COVID-19 pandemic. Furthermore, our analysis also shows changes in the geographical distribution of emissions that are likely driven by new policies and infrastructure development.
Our AIS-based emissions estimates align closely with other published sources: we estimate approximately 1.3 B MT CO2 in 2024, whereas the latest IMO estimates are 1.06 B MT; the latest EDGAR estimates are 910 MMT; and the latest OECD estimates are 862 MMT. Other published estimates do not include dark fleet vessels, making GFW’s dark fleet estimates the first of their kind.
Model improvement change log
September 2025 data delivery
Dark fleet emissions model
[Changed] Updated model_number to _v20250901
[Changed] Uses an improved matching score for determining whether each S1 vessel detection is broadcasting or non-broadcasting
July 2025 data delivery
AIS-based emissions model
[Changed] Updated model_number to _v20250701
[Changed] Expanded our vessel coverage to now include 925,682 vessels. Of these, 901,214 (97%) are ‘low-information’ vessels.
[Changed] Improved emissions model by incorporating emissions correction factors for low main engine loads, based on the IMO Fourth GHG Report Table 20 (engines operating at very low loads operate inefficiently, and emit more of certain pollutants)
[Changed] Improved emissions model for SOX and PM by accounting for the fact that starting on January 1, 2020, the IMO required lower sulfur content fuel (0.5%, instead of the higher 2.5% that was typical for HDO prior to 2020). For data
>= 2020-01-01, we therefore apply a new correction factor for SOX and PM to account for this lower sulfur content fuel.[Changed] Improved vessel characteristic model for inferring the main engine power, length, and gross tonnage of low-information vessels
[Changed] Improved vessel characteristic model for inferring the vessel class of low-information vessels
[Changed] Improved vessel characteristic model for inferring the subclass of low-information cargo vessels
[Changed] Developed new vessel characteristic model for inferring the subclass of low-information tanker vessels (i.e., chemical or oil, liquefied gas, or other liquid)
[Changed] Developed new vessel characteristic model for inferring the maximum speed design characteristic of low-information vessels (replacing our old method that simply used average values from Table 81 of the 4th IMO Report).
Dark fleet emissions model
[Changed] Updated model_number to _v20250701
[Changed] Uses latest AIS-broadcasting emissions estimates (AIS model version _v20250701) as the basis for non-broadcasting emissions estimates
[Changed] Uses an improved matching score for determining whether each S1 vessel detection is broadcasting or non-broadcasting
[Changed] Uses an optimized k-nearest-neighbor value of k for interpolating the dark-to-broadcasting ratios inside and outside the S1 footprint
February 2025 data delivery
AIS-based emissions model
No changes were made.
Dark fleet emissions model
- [Changed] Updated model_number to _v20250228
- [Changed] Improved filtering of sea ice for Sentinel-1 detections
- [Changed] Improved matching of Sentinel-1 detections to AIS vessels
December 2024 data delivery
Since the September 2024 data delivery, we have made several exciting improvements to our model that we would like to highlight for our December 2024 data delivery.
Improvements to both the AIS-based and dark fleet emissions models include:
Added three new pollutants: We have added PM2.5, PM10, and VOCs as modeled pollutants in our methodology. These pollutants are particularly important to consider when assessing the health and environmental impacts of shipping emissions.
Increased temporal coverage: We now provide monthly data from January 2015 through October 2024 for the AIS-based emissions model, and from January 2016 through October 2024 for the dark fleet model.
Improvements to the AIS-based emissions model include:
Improved vessel classification algorithm: For low information vessels (i.e., those that don’t have known registry information for vessel class), we have developed a new vessel type classification sub-model that can differentiate many vessels that were previously lumped together as an undifferentiated
cargovessel type into the specific categories ofbulk_carrier,container,general,refrigerated, andro_ro. These classes now align with the IMO cargo vessel class types, allowing us to more accurately assign auxiliary engine power, boiler power, and design speed for these vessel classes using the IMO methodology. This new model leverages a random forest that is trained on information on port visit patterns by by over 26,000 vessels with known IMO cargo vessel class types, and allows us to more accurately classify cargo vessel types by looking at the most common IMO known vessel types that use the same ports. The method achieves a classification accuracy of 86%.Improved emissions model for fishing vessels: We have improved our emissions model for fishing vessels by leveraging published relationships that adjust main engine load factors for trawlers and dredgers while they are fishing, and also places limits on main engine load factors for other types of fishing vessels.
Improved estimation of design speed: We have improved our vessel-level design speed estimates by using the a table published in the 4th IMO GHG report that provides design speeds for different vessel types and sizes.
Improved filtering of segment noise: We have improved our filtering of noisy segments to avoid unnecessarily removing vessel activity, which allows us to capture more emissions.
Improvements to the dark fleet emissions model include:
Better noise filtering: The new S1 dataset contains less noise and incorporates features that allow for more reliable filtering of detections. Further, the matching score threshold for determining whether the S1 detection is matched to an AIS-broadcasting vessel has been refined. These updates on the S1 dataset increases our confidence in identifying vessels and preventing the assignment of emissions to non-vessel objects.
Higher resolution vessel size extrapolation: We now use 10 length bins (instead of the previous 2) for extrapolating AIS-based emissions to dark fleet emissions. Bins are estimated separately for both fishing and non-fishing vessels, meaning that monthly dark fleet emissions can now be estimated across 10 different size bins for both fishing and non-fishing values. This allows us to more accurately estimate dark fleet emissions for vessels of different sizes, since the size distributions within each length bin are now more similar between the AIS vessel data and S1 detection data.
Refined method for filling in S1 gaps: Missing dark-to-AIS ratios were updated using a k-nearest-neighbor (KNN) approach instead of simply using a single global ratio, allowing us to assign values based on the closest pixels with available information. This method improves the accuracy of dark emissions estimates, better reflecting spatial and temporal heterogeneity outside the S1 footprint, and addressing gaps within the footprint.
Document overview
This document summarizes the GFW framework for estimating emissions (Figure 3.1), detailing the data and methods used to develop the AIS-based emissions model and the dark vessel emissions model, along with their validation and discussion.
It is composed of the following sections:
AIS-based emissions model
- Methods: Here we fully describe the data and primary model specification used to estimate emissions using the GFW AIS dataset. This describes the data processing pipeline of moving from the raw AIS message data all the way up to voyage-level emission estimates. We also discuss areas of potential future model refinement.
- Results: Here we include high-level results from our AIS-based emissions model.
- Model validation: Here we quantify the performance of our AIS-based emissions model against a number of validation datasets. We quantify performance both for our primary model specification (described in the Methods section) as well as a number of other comparison models that rely on alternative assumptions. The primary model specification is the best performing models across all considered models.
Dark fleet emissions model
- Methods: Here we fully describe the data and model used to estimate emissions for the ‘dark fleet’ using the GFW Sentinel-1 synthetic aperture radar dataset. This describes the entire data processing pipeline and modeling pipeline. We also discuss areas of potential future model refinement.
- Results: Here we include high-level results from our dark fleet emissions model. This includes looking at how much the dark fleet emissions estimates increase global emissions estimates beyond the AIS-based model.
- Model validation: Here we quantify the performance of our dark fleet emissions model using AIS data.
Data delivery
Here we describe the data delivery process for the Climate TRACE and OceanMind teams, including the bucket locations on Google Cloud Storage where each dataset is delivered.
Data processing infrastructure
For this project, we primarily use the R language and an infrastructure based on targets and renv packages to manage our data analysis workflows and ensure reproducibility. The targets package facilitated the creation of an automated pipeline, organizing tasks into a sequence of dependencies that refresh only when their upstream dependencies change, optimizing computational efficiency. Meanwhile, renv allows to manage package dependencies, ensuring that every instance of our analysis can be replicated across different environments. GFW datasets are stored on Google BigQuery, and we access and manipulate those datasets using SQL. All interim datasets are also stored in Google BigQuery. The final output datasets for Climate TRACE and OceanMind, in CSV format that matches their standardized data delivery schemas, are delivered via the Climate TRACE Google Cloud Storage bucket.