Building a Programmatic Carbon Engine for Nature-based Forestry Projects

Carbon modelling sits at the core of how nature-based forestry projects are designed, evaluated, and scaled.

High-integrity NbS projects depend on robust carbon models that can guide landscape selection, species design, and investment decisions. Yet much of the sector still relies on manual, spreadsheet-based workflows that are difficult to scale, compare, or audit. As projects grow in size and complexity, these approaches introduce inconsistencies, operational bottlenecks, and knowledge silos that limit both accuracy and speed.

In this piece, we outline how Thryve is building toward a programmatic carbon modelling engine for forestry projects - one that brings structure, consistency, and transparency to carbon estimation across ARR, IFM, and REDD+ project types. By progressively moving from manual models to reusable, Python-based workflows, we aim to support better decision-making for investors, project teams, and landowners, while laying the groundwork for deeper integration with field planning, spatial data, and long-term project operations.

Read on to understand why programmatic carbon modelling matters for scaling high-integrity NbS projects and how Thryve is approaching
this transition.