Climate TRACE
About Climate TRACE
Climate TRACE (Tracking Real-Time Atmospheric Carbon Emissions) is a collaborative initiative aimed at providing precise, real-time tracking of global greenhouse gas emissions using artificial intelligence, satellite image processing and other remote sensing technologies along with academic research and publicly available data.
Visit Climate TRACE websiteClimate TRACE Emissions Inventory
| Property | Value |
|---|---|
| Description | Climate TRACE (Tracking Real-Time Atmospheric Carbon Emissions) is a collaborative initiative aimed at providing precise, real-time tracking of global greenhouse gas emissions using artificial intelligence, satellite image processing and other remote sensing technologies along with academic research and publicly available data. |
| Source type | Non-governmental |
| Original dataset URL | URL |
| Year released | 2022 |
| Geography | Multiple regions |
| Sector | |
| Type of data | Activity-based |
| Emission results | CO2 - CH4 |
| Data Transformation | The source reports emissions in TCO2e/Tproduct, which have been converted to kgCO2e/kg to ensure consistency in the database. |
License
| Property | Value |
|---|---|
| Type of license | Creative Commons Attribution 4.0 International (CC BY 4.0) |
| License URL | URL |
Data quality
| Property | Value |
|---|---|
| Data quality assurance | Vetted by Climatiq. For further information on data quality assurance, see: https://www.climatiq.io/methodology#Data-Quality |
| Quality flag(s) | Partial factor For some emission factors, the source reports emission in CO2 only or CH4 only and does not include the impact of other GHGs. |
Methodology
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|---|---|
| IPCC AR method |
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| LCA boundary |
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| Scope applicability |
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| Emissions breakdown |
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| Methodology description | Climate TRACE (Tracking Real-time Atmospheric Carbon Emissions) is a global coalition of AI specialists, data scientists, researchers, and non-governmental organisations that independently tracks human-caused GHG emissions to specific sources. Emissions are modelled in three steps: (1) remotely observing asset activity — such as steam plumes from power plant smokestacks, infrared heat signatures from industrial facilities, or a ship's course and speed; (2) associating emissions factors with those assets and their activities; and (3) estimating absolute emissions over a given timeframe. Climate TRACE AI and machine learning algorithms are trained on satellite imagery paired with ground truth data from on-site sensors, then applied to similar facilities worldwide to produce independent emissions estimates without relying on self-reported figures. Climate TRACE Where satellite-based modelling is not applicable, non-satellite statistical models and meta-modelling based on other inventories are used. The dataset is the first to provide globally comprehensive emissions estimates for individual sources across all anthropogenic emitting sectors, spanning January 2021 to the present with a two-month reporting lag and monthly updates. For full details, see the Climate TRACE methodology documentation: https://climatetrace.org/approach and sector-specific methods on GitHub: https://www.google.com/url?q=https://github.com/climatetracecoalition&sa=D&source=editors&ust=1778066895626061&usg=AOvVaw3ufIUVckKZ6tx9Ym5jupxu. |
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Featured Emission Factors in Climate TRACE
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