Using AI to unlock forest carbon markets in Tanzania
A blog by the Frontier Tech Hub
Tanzania is home to Africa’s third-largest forest cover, yet it also faces one of the highest deforestation rates globally. With nearly three-quarters of national greenhouse gas emissions linked to forest loss, improving how deforestation is monitored is an essential environmental policy. It is also central to unlocking climate finance through forest carbon markets. Our pilot “Using AI to scale access to forest carbon markets” explored whether artificial intelligence could help address a persistent challenge: generating useful forest carbon data that can be used by Tanzanian stakeholders.
Digital forest monitoring tools already exist, but many rely on machine-learning models trained on data from very different geographies, such as the Amazon or Southeast Asia. When applied in Tanzania, these models often perform poorly.
This pilot focused on the Rufiji Delta, home to East Africa’s largest mangrove forest, and tested whether open-source AI models could be adapted to Tanzania’s unique forest ecosystems. The answer was clear: adaptation is essential. Models required significant retraining using locally relevant satellite data, and progress was slowed by a lack of reliable “ground truth” data to validate deforestation on the ground. Despite these constraints, the team succeeded in developing a model that could accurately detect deforestation events in the Tanzanian context, ultimately outperforming generic open-source alternatives.
Building AI talent alongside technology
A defining feature of the pilot was its focus on people as much as platforms. Rather than importing expertise, the project invested in mobilising local AI talent through “AI Chapters”: peer learning groups combining online training, mentorship, and real-world challenges. This resulted in the creation of the aforementioned forest monitoring tool specific to Tanzania.
Participants ranged from experienced developers to complete beginners. Through structured learning and hands-on projects, teams built practical AI solutions. These included solutions for forest monitoring, visual imagery for crop disease detection, and even a chatbot for mental health support. The approach demonstrated that locally rooted, collaborative learning models can rapidly translate technical skills into applied solutions.
From prototype to impact
The AI tool developed through the pilot provides a critical capability: detecting where and when deforestation occurs. This functionality is relevant across multiple use cases, from national forest monitoring to emerging regulatory requirements around deforestation-free supply chains.
However, the pilot also highlighted a key limitation. On its own, deforestation detection is not enough. To support carbon market access, the tool would need to be embedded within broader digital Monitoring, Reporting and Verification (DMRV) platforms that handle carbon accounting, reporting, and verification. Rather than competing with existing platforms, the most promising pathway to impact may be integration, particularly if the tool’s superior accuracy in Tanzanian forests can be robustly demonstrated.
This pilot reinforces a recurring lesson in frontier technology: impact depends on local relevance. AI systems trained elsewhere rarely transfer seamlessly, and sustainable solutions require investment in local skills, institutions, and long-term ownership.
By pairing talent development with applied experimentation, this work offers a glimpse of how AI could support Tanzania’s forest sector as part of a wider ecosystem working toward climate, conservation, and economic goals.
