The most transparent and in-depth carbon credit ratings available
Sylvera’s ratings assess the likelihood that the credits issued by a carbon project have delivered on their claims of avoiding or removing one metric ton of carbon dioxide or other greenhouse gasses.
We develop specific frameworks for each category of carbon project. This is the only way to accurately assess quality across fundamentally different activities. If you evaluate a REDD+ project and Renewables project with a generic framework, the results will be inaccurate.
All frameworks are peer-reviewed by a committee of experts and carbon market stakeholders (including project developers and registries) to ensure scientific consensus. Then we publish our frameworks so buyers understand exactly what we test and how we do it.
When evaluating projects, we build meticulous models for carbon, strength of baseline, and financial additionality, using our independent and proprietary data.
Here’s our process for rating nature-based projects at a glance:
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The Sylvera difference
Project-type-specific frameworks
We believe one-size-fits all frameworks produce unreliable results.
That’s why we build meticulous frameworks for every project category to accurately test project design, carbon accounting, and climate impact claims.
Unparalleled rigor and accuracy
We extract, clean, and organize data from project design documentation (PDD) and every monitoring report.
We meticulously build carbon, strength of baseline and financial additionality models from the ground up.
Independent Data Validation
Our expert analysts leverage advanced machine learning (ML) technology, verified, independent data, and proprietary field data to test the accuracy of credit issuances and claims.
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Carbon
Our carbon score verifies whether a project is accurately reporting on its activities, which directly translates to its overall avoided emissions or removals of CO2, and other GHGs.
For AFOLU projects, we confirm the planting of trees and the protection against deforestation by comparing data provided by the project developers with our own measurements using remote sensing data and our proprietary machine learning (ML) models.
For Renewables projects, Sylvera compares reported generation with third-party, independent generation data from grid operators, energy regulators, and offtakers.
Additionality
We examine whether emissions reductions or removals above and beyond what would have occurred in the “business as usual” scenario have materialized as a direct result of revenue from carbon offsets.
Additionality also assesses the likelihood and severity of over-crediting risk that emanates from inflated counterfactual baseline claims.
Permanence
Combining remote sensing data, climate modeling and project documentation, we evaluate whether the GHG emissions avoided or removed by the project are likely to be maintained for an atmospherically significant period of time.
Co-Benefits
We assess the scope and relative impact of project activities on local biodiversity and communities - which are linked to UN Sustainable Development Goals (SDGs).
The co-benefits score does not feed into the Sylvera Rating, as co-benefits do not have a direct bearing on the climate impact of carbon credits.
Our machine-learning models train and run on multiple types of satellite data
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Synthetic Aperture Radar (SAR) Satellite
E.g. ALOS PALSAR, Sentinel-1
LiDAR Satellites
E.g. GEDI
Optical Satellites
E.g. Landsat-7, Landsat-8, Sentinel-2
Multi-Scale LiDAR
E.g. proprietary terrestrial and UAV lidar data
Unrivaled machine learning capabilities ensure accuracy
Sylvera uses machine learning (ML) and multiple types of satellite data to identify specific features of forests and land cover. We train proprietary ML models in specific biomes and geographies to produce accurate carbon estimates for different project types.
For example, when assessing forest growth in an afforestation, reforestation, and revegetation (ARR) project, we use ML to estimate the canopy height of trees in the project area (PA). To do this, we train a model to identify forest canopy height by feeding it tens of thousands of labeled data points. Then we run our models on the PA to estimate the canopy height. By running the model over the same area with data from multiple years, we can see changes over time in the forest area.
Every project receives an assessment and distinct rating
Our carbon credit ratings give buyers the confidence to invest in high-quality projects that deliver real climate impact.
Trusted carbon data for real climate action.
Sylvera helps organizations and governments get on track to net zero. To learn more about our products, contact us.
