Carbon credits are the ‘net’ in net zero

And that’s where Sylvera started. Our carbon credit ratings and detailed analytics help buyers invest in high-quality projects with real climate impact.

Bottom-up & Peer-reviewed

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:

Data Extraxtion, Shapefile Extraction & Machine learning - timeline
Machine Learning QA, Ratings production & Internal Review -timeline
Developer Engagement, Ratings Publication & Continous monetoring - timeline
circle wit AA in it
Data Extraction & Shapefile Extraction - timeline
Machine Learning & Machine Learning QA - timeline
Ratings Production & Internal Review - timeline
Developer Engagement & Ratings Publication - timeline
Continous Monetoring - End of timeline
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MARKET ANALYSIS

This is Sylvera’s second annual publication of The State of Carbon Credits report, focusing on how the market can move forward after a year of intense scrutiny. We highlight where quality lies in the market, supported by Sylvera project-level case studies and global data, including credit issuance, retirement and pricing information.

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Policy

Regulatory action may seem daunting, but well-designed interventions can help increase quality participation in impactful activities. This paper outlines an overview of impending climate regulations.

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Ratings Methodology

For each project type, Sylvera develops a proprietary framework to assess carbon credit quality. This paper outlines our ratings framework for Direct Air Capture & Storage projects.

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RATINGS METHODOLOGY

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

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RATINGS METHODOLOGY

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

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Ratings Methodology

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

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Ratings Methodology

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

For each project type, Sylvera develops a proprietary framework to assess carbon credit quality. This paper outlines our framework for Afforestation, Reforestation and Revegetation (ARR) projects.

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Ratings Methodology

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

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

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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Ratings Methodology

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

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Ratings Methodology

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

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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 accounting, additionality and permanence.

Carbon

Carbon score graph - System UI

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

Permanence score graph - System UI

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

Carbon score graph - System UI

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.

Carbon score chart
Permanence score graph - System UI
Carbon score graph - System UI

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

Cutting-edge technology

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.

Our carbon credit ratings give buyers the confidence to invest in high-quality projects that deliver real climate impact.

Sylvera helps organizations and governments get on track to net zero. To learn more about our products, contact us.

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