Forest restoration plays a vital role in combating climate change by enhancing long-term carbon sequestration and supporting biodiversity.
Yet a central challenge remains: where exactly can restoration efforts be implemented most effectively? Climate conditions, soil suitability, land-use pressures, settlement patterns, infrastructure, and political stability all influence whether a location can sustain successful restoration—especially when projects aim to generate verified carbon credits.
As part of an initiative of Tree Global and in collaboration with Nicholas Evans from Muwale, I developed a spatial analysis tool that helps identify the most promising areas for forest restoration in selected regions across Central Africa. The tool integrates diverse climatological, political, infrastructural, and environmental datasets and evaluates them through a transparent, rule-based workflow. Built on QGIS and making extensive use of its Model Builder capabilities, the tool enables users to:
- Screen large regions for eligibility under carbon-credit standards
- Exclude areas unsuitable due to conflict, land use, or accessibility
- Score and rank remaining landscapes according to restoration potential
- Adjust parameters interactively to match national or project-level needs
The developed system is able to support governments, NGOs, and restoration practitioners in focusing resources where they can achieve the highest climate impact—and create durable, high-quality carbon projects.