From tree-wise monitoring to action: a digital forest carbon twin framework for forest carbon MRV and climate-smart forest management

Abstract

Forests are increasingly expected to deliver climate mitigation together with productivity, resilience, and biodiversity outcomes, yet forest carbon monitoring, reporting, and verification (MRV) remain constrained by fragmented data workflows, weak traceability, and limited links between measurement and management action. This conceptual framework paper defines the Digital Forest Carbon Twin (DFCT) as a carbon-specialized digital twin whose minimum operational condition is a persistent tree-wise state that is repeatedly updated by observations and models. From this state, carbon accounting, uncertainty propagation, verification evidence, and decision support are generated through one versioned architecture. We propose a six-layer framework covering data acquisition, processing and integration, tree-wise state and carbon, MRV, decision support, and stakeholder/governance interfaces. The framework is operationalized through an auditor-facing verification workflow, a protocol-harmonization strategy, and a maturity pathway from pilot implementation to a scaled platform. An illustrative pilot in the City of Joensuu, Finland, demonstrates how ground laser scanning, UAV data, field measurements, tree-object reconstruction, and web visualization can be connected within one implementation pathway; the pilot is presented as evidence of integration feasibility rather than as a completed validation of carbon-accounting accuracy. Comparison with conventional inventories, remote-sensing workflows, MRV systems, generic digital twins, and decision-support systems shows that the defining DFCT features are persistent tree identities, versioned state updating, explicit uncertainty, reproducible lineage, and shared monitoring-to-action logic. The framework provides a practical research agenda for interoperable, verifier-ready, and climate-smart forest management systems.