Global Carbon Check, a carbon registry , has unveiled its approach to using machine learning (ML) technology to rate afforestation projects based on project progress, accuracy of information, and project risk. Global Carbon Check aims to provide more accurate and detailed insights into the complex dynamics of ecosystems of the AFOLU projects, by combining data, process-based modelling, and machine learning techniques.

Why and How Machine Learning

Global Carbon Check has been focused on using machine learning to enhance its values towards Transparency and Credibility. By understanding the effects of different restoration strategies and risk factors on forest outcomes, the agency hopes to provide decision-makers with the tools they need to make informed choices about land management and restoration.

The Global Carbon Check utilises machine learning and multiple types of satellite data to estimate and identify specific features such as canopy height, canopy cover, above-ground biomass, natural risk modelling, and deforestation risk assessment. The carbon score for a project is determined by comparing the results of the agency's modelling approach with the project reports. If there are any discrepancies, the carbon score will be adjusted accordingly.

Rating scopes included under Machine Learning

The rating process uses its modeling approach and a quality control process to ensure accuracy in the carbon score. The outputs of their models undergo a thorough quality control process to ensure that the results of the modeling approach are representative of what is happening on the ground within the project area. The Machine Learning models also help to calculate the risk of natural disasters such as fire, drought, and disease.

With this information, Global Carbon Check can provide an accurate assessment of the project's risk and accuracy. The Global Carbon Check approach generates a rigorous and detailed assessment that goes beyond the requirements of the most widely used greenhouse gas crediting programs. Integrating machine learning enabled us to evaluate projects transparency and credibility at scale.

In conclusion, Global Carbon Check's adoption of machine learning technology is a significant step towards providing more accurate and detailed insights into the complex dynamics of ecosystems, including forests, agriculture, and grasslands. The technology will enable decision-makers, buyers and stakeholders with the tools they need to make informed choices about the project.