The most transparent and in-depth ratings available
We develop specific frameworks for each project category, because it is the only way to accurately assess quality across fundamentally different categories of activities. Simply put, 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 & registries – to ensure scientific consensus. Then we publish our frameworks so buyers understand exactly what we test and how we do it.
When evaluating individual projects, we meticulously build carbon, strength of baseline and financial additionality models from the ground up with independent and proprietary data.
Our process for rating nature-based projects at a glance:
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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.
We utilize multiple types of satellite data to train and run our machine learning models.
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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 utilizes 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, which are used to achieve accurate carbon estimates for different project types.
For example, if we are trying to assess forest growth in an afforestation, reforestation and revegetation (ARR) project, we will 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 labelled data points. Then we run our models on 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 gives market participants confidence to invest in high quality projects that deliver real climate benefits.
Building confidence in carbon markets
Sylvera's carbon intelligence helps you deliver on your climate commitments. Contact us to learn more about our platform.
