Moja global’s Full Lands INtegration Tool (FLINT) runs cutting edge ecosystem models to estimate the carbon stored in forests, peatlands and soils. FLINT provides a comprehensive means for assessing the climate impacts of land-use change. The FLINT can support sustainable development goals by facilitating the widespread adoption of nature-based solutions (NbS). NbS are required to offset greenhouse gas (GHG) emissions, and to help society adapt to climate change effects already locked in.
Moja global supports the development of ecological models for policy makers. Our mission is to grow a globally representative community of scientists, analysts and decision makers who use the FLINT to measure nature-based GHG removals and lead our sustainable development.
|Taswira - a tool for visualising results from the FLINT Generic Carbon Budget Model that shows how critical ecological variables change over time and space. Shown here is the carbon stored in the Above-ground (AG) Biomass of forests in the southern Los Rios region of Chile.|
Modelling greenhouse gas emissions and removals from land-use, land-use change
The powerful FLINT, written in C++ for speed and scalability, integrates ecological modelling, environmental measurements and remote sensing data for modelling land-use and land- use change. FLINT models can be local, regional or global and have been successfully applied in Kenya, Indonesia, Chile, Canada and Australia.
Spatially explicit models that can report against multiple international standards
Land-use classification is the basis for accounting GHG emissions and removals under international and domestic policies. The moja global Reporting Tool meets the (2019) IPCC requirements for UNFCCC emissions inventories. Because FLINT models focus on fundamental ecosystem properties, the output of any FLINT model can be reclassified to meet all relevant (and potentially future) policy requirements.
Community of practice to support evidence-based policy decisions and national commitments
IUCN and moja global are partnering on an assessment high-impact forest landscape restoration opportunities that contribute to national climate and low-carbon development goals (NDCs). The potential NDC contributions will be calculated using the FLINT and meet the rules and standards for the Paris Agreement and regulatory carbon markets (e.g., including ICAO-CORSIA).
Collaborative solutions for predicting baseline deforestation rates with remote sensing and AI
Forecasting land-use change scenarios is an important part of policy development. moja global's Deforestation Team have trained a deep neural network to accurately predict potential forest conversion in tropical landscapes. These predictions can inform policy interventions to halt losses and help plan new forests.