We collate this data over time, ingest it into our proprietary machine learning algorithms, and analyze how the project has performed against its stated aims.
Carbon credits can be an effective way to transition large companies to a carbon-neutral future, but when it comes to creating these credits, accountability is critical to ensure they are actually removing the amount of carbon they claim to be from the atmosphere. One organization working to confirm the legitimacy of these credits is UK-based startup Sylvera.
The startup uses satellite, radar, and lidar data-fuelled machine learning to assess the true carbon capture potential of carbon credit projects. They use multi-layered data to assess the reach and quality of these projects. Some of the key factors they look at are permanence, how long the project can last, co-benefits, how well does the project contributes to other sustainability goals, and risks, what potential effects could the project have on surrounding people and ecosystems.
Sylvera’s technology was developed in conjunction with researchers from scientists from the University of California Los Angeles, the NASA Jet Propulsion Laboratory, and University College London. Co-founder Sam Gill explains the process to TechCrunch: “We collate this data over time, ingest it into our proprietary machine learning algorithms, and analyze how the project has performed against its stated aims.”