Ratings

Bottom-up Approach

The most in-depth ratings available today

We meticulously build carbon, strength of baseline and financial additionality models from the ground up with independent and proprietary data. We believe this bottom-up approach is an essential part of providing quality due diligence, and it’s what sets us apart. We take the same fundamental approach across all our frameworks (REDD+, ARR, IFM, and renewables, among others) so that you can reliably compare credits and make investment decisions with greater confidence.

Following our rigorous process, Sylvera takes between 60-120 hours to develop, test and QA ratings for every individual project. We re-rate projects when new monitoring and verification reports are published. We also update our machine learning data on a quarterly basis.

Our process for rating Agriculture, Forestry and Other Land Use (AFOLU) projects at a glance:

Ratings Methodology

REDD+ Framework

For each project type, Sylvera develops a proprietary framework to assess carbon credit quality. This paper outlines our overall framework for REDD+ projects, and highlights how we apply it to AUD and APD projects.

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Ratings Methodology

ARR Framework

For each project type, Sylvera develops a proprietary framework to assess carbon credit quality. This paper outlines our framework for ARR projects.

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Ratings Methodology

Renewables (RES) Framework

For each project type, Sylvera develops a proprietary framework to assess carbon credit quality. This paper outlines our framework for Renewables (RES) projects.

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White Paper

Sylvera Carbon Credit Ratings: Frameworks & Processes

How do we create a Sylvera carbon credit rating, what types of assessments do we carry out and what makes our carbon credit rating system accurate and reliable?

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Fact Sheet

How Sylvera uses machine learning

Learn how Sylvera utilizes machine learning (ML) and multiple types of satellite data to identify specific features of forests and land cover.

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Sylvera Rating System

Ratings bring transparency to carbon credit quality

Each rating is derived from the holistic analysis of a project's carbon performance, additionality and permanence.

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).
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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.

Carbon

Cutting-edge technology

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.

Partial Assessments

Sylvera only issues a rating when our stringent data requirements are fulfilled

When we don’t have access to all the key data required to evaluate the carbon performance, additionality and permanence of a project, we cannot produce a Complete Sylvera rating. Instead, we issue a report - with the label/grade P for “Pending” - based on the best data available today. When new data is issued and when it satisfies all our criteria for rigorous analysis, we will reexamine the project and issue a Complete Sylvera rating. Pending grades can be applied to both issuing and pre-issuing carbon projects.

Neutral

Based on data that is available, Sylvera has not identified anything which would negatively impact the subscores.

N/A

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Concerned

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Explore

Explore projects by certifications, UN Sustainable Development Goals, co-benefits and more. Shortlist your favorites and download factsheets to compare multiple projects.

Complete carbon credit ratings you can trust

The most in-depth ratings available today

Ratings bring transparency to carbon credit quality

We utilize multiple types of satellite data to train and run our machine learning models.

Unrivaled machine learning capabilities ensure accuracy

Every project receives an assessment and distinct rating

Sylvera only issues a rating when our stringent data requirements are fulfilled

Building confidence in carbon markets