Sylvera Carbon Ratings

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Join us for the Carbon Markets Summit on June 30, 2022.

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Join us for a virtual panel on “Carbon Markets in 2022: The Impact of Soaring Demand”
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Confidence

Pick the best carbon offsets with the most accurate ratings and actionable insights available. This means you won’t waste money on bad investments and put your reputation at risk as a result.

Choice

Get a view of the whole market. Compare quality & price to find the highest rated credits for the best value. You choose how to buy credits – direct to a project developer or via broker or exchange.

Efficiency

Save time & money on due diligence. Move quickly with a lean team. Our experts spend an average of 60 hours collecting, analyzing and distilling data for every project, so you don’t have to.

Our Edge

The Sylvera difference

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Independence

We don't sell carbon credits. We never have. We also aren’t paid by developers to rate projects.

From the very start, we aimed to avoid conflicts of interest to ensure our ratings are unbiased and reliable.

Unparalleled depth & accuracy

We build robust and bespoke ratings frameworks and production systems for each project type.

‍We ensure consistency across frameworks and reporting, so you can easily compare value across the whole market.

‍Our ratings are not generated by algorithms alone, but by a team of experts analyzing a variety of quantitative and qualitative data, who then distill it into detailed reports.

Science & technology expertise

We have a large and growing team of experts who hold advanced degrees, working across our Multi-Scale Lidar, Geographic Information System (GIS), Ratings and Machine Learning disciplines.

Independence

We don't sell carbon credits, and we aren’t paid by developers to rate carbon projects.

This means we avoid conflicts of interest, and you can trust that our ratings and reports are unbiased.

We use lidar scanners to measure biomass at scale. We’ve laser scanned over 1 million tropical rainforest trees in Gabon and Peru, and will be building on this dataset as we conduct more research trips.

Our estimates of the above-ground biomass and carbon stored in forests are up to 15x more accurate than conventional estimates derived from methods known as allometrics.

We use this first-of-its-kind data to train advanced machine learning models, which use Earth observation data to estimate carbon stocks from space, in near real-time and with great accuracy.