Belian offers a science‑backed platform that generates credible baseline estimates for forest carbon projects by applying rigorous causal inference to model counterfactual land‑use outcomes. Leveraging a foundation AI model trained on satellite and ground data and calibrated to local Bornean forest conditions, it delivers high‑resolution, site‑specific baselines that can be integrated into project documentation and audited by regulators or verifiers.
Funding
Funding not disclosed
Founders
Product
Problem
Carbon offset projects struggle to prove real climate impact because they cannot reliably estimate the counterfactual—what would have happened to the forest without the conservation intervention. This uncertainty undermines market credibility and can lead to over‑crediting.
Solution
Belian provides a science‑backed platform that generates credible baseline estimates for forest carbon projects. By applying rigorous causal inference methods, the system models the counterfactual outcomes of land‑use change, enabling precise measurement of avoided emissions or restoration gains. The platform leverages a large‑scale AI foundation model trained on satellite and ground data, calibrated to local forest conditions, to produce high‑resolution, site‑specific baselines. Results are delivered through a scalable workflow that integrates with existing project documentation and can be audited by regulators or third‑party verifiers. This approach gives project developers, NGOs, and carbon market participants a transparent, data‑driven basis for credit issuance.
Target Audience
Primary customers are forest carbon project developers, NGOs, and verification bodies that need robust baseline calculations for emissions avoidance or restoration credits.
Features
- Causal inference engine that quantifies counterfactual forest carbon trajectories using statistical methods
- AI foundation model trained on global remote‑sensing datasets, fine‑tuned to local Bornean forest characteristics
- Integration of forest carbon expertise to incorporate field measurements, land‑use history, and ecological variables
- Locally calibrated baseline outputs that reflect site‑specific conditions and management practices
- Scalable processing pipeline capable of handling large project portfolios and high‑resolution satellite imagery
- Exportable reports and data packages compatible with carbon standard verification requirements