Climatiq
source

ADEME

494 Factors494 Factor
156 Regions156 Region
9 Activities9 Activity

About ADEME

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 ADEME website
DATASET

Climate TRACE Emissions Inventory

PropertyValue
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 URLURL
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

PropertyValue
Type of license
Creative Commons Attribution 4.0 International (CC BY 4.0)
License URLURL

Data quality

PropertyValue
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

PropertyValue
IPCC AR method
  • AR6
  • For some activities, this source reports only CO2 values.
LCA boundary
  • cradle_to_gate
  • upstream
  • well_to_tank
Scope applicability
  • 3.1
  • 3.2
  • 3.3
Emissions breakdown
  • CH4
  • CO2
  • N2O
  • Total (CO2e, AR6)
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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