This expert analysis explores a new era in natural solutions to climate change, represented by the methodology Verra VM0047. This framework for afforestation, forest restoration and vegetation restoration (ARR) projects represents a fundamental shift from static to dynamic. dynamic modelling of carbon baselines, thereby increasing the integrity of the voluntary carbon market (VCM).

1. Evolution of ARR methodologies

The VM0047 methodology, published in October 2023, replaces the older Clean Development Mechanism (CDM) procedures, namely AR-ACM0003 and AR-AMS0007. The main drawback of the previous methods was the use of static baselines, which assumed zero natural regeneration without project intervention, which often did not correspond to reality and led to criticism regarding the integrity of the credits.

VM0047 is the first methodology at VCM that mandatorily implements dynamic performance benchmarks (Performance Benchmarks) derived from Remote Sensing data. This approach allows for a more accurate capture of the „business-as-usual“ scenario by comparing project areas with control areas outside the project in real time.

2. Two pillars of carbon quantification

VM0047 defines two different approaches to quantifying carbon sequestration, adapted to different types of activities:

• Area-based approach: It is intended for projects that convert land use to forest or increase carbon stocks in existing forests. It requires the use of a dynamic benchmark and is suitable for areas larger than 1 ha with planting densities above 50 units per hectare.
• Census-based approach: Ideal for smaller projects or dispersed plantings such as urban forestry, agroforestry or windbreaks. Zero leakage is assumed and the baseline is set to zero if conservative criteria are met. This approach requires accurate recording of each planted unit.

3. Technological Innovation: Stocking Index

The key tool of the dynamic baseline is Stocking Index (SI). This is a remote sensing metric (e.g. NDVI, NDFI or canopy height from LiDAR) that has been shown to correlate with aboveground biomass.

The process works as follows:

1. Within a radius of 100 km from the project, control surfaces with similar biophysical and socio-political characteristics.
2. Using algorithms (e.g. k-nearest neighbor), project areas are paired with control areas.
3. The development of SI over time is monitored. If biomass also grows in the control areas (natural regeneration), the project receives credits only for the increase that exceeds this baseline. This mechanism directly solves the problem additionality (complementarity).

4. Updates in version VM0047 v1.1

In May 2025, the Verra organization released a revised version 1.1, which brings significant improvements for project developers:

• Extension to degraded forests: Projects are now also allowed on forested lands if they have not been used for commercial logging in the past 10 years.
• Flexibility in biomass removal: Allows for the removal of invasive species during site preparation, if this biomass is properly accounted for in the project's emissions.
• Simplified SI validation: Now, it is sufficient to demonstrate the correlation of SI with biomass either through published studies, or by direct measurement in the region (previously both methods were required).

5. Assisted Natural Regeneration (ANR)

Assisted natural regeneration (ANR) represents a spectrum of approaches between active planting and passive restoration. It includes activities such as fire prevention, fencing against grazing, ring-weeding, and support for existing seedlings.

Advantages of ANR over plantations:

• Cost-effectiveness: ANR uses natural processes, which drastically reduces the cost of seedlings and logistics.
• Biodiversity and resilience: Studies show that ANR areas can sequester 3 to 4 times more carbon than monoculture plantations of the same age (e.g. examples from China).
• Carbon potential: In Niger, the FMNR (farmer-managed natural regeneration) method helped sequester 1.4 Mg of carbon per hectare per year.

Despite its benefits, version 1.0 had limitations that made it difficult for ANRs to engage, particularly in the inventory approach that required only direct planting. Version 1.1 and the inclusion of degraded forests partially address these obstacles.

6. Case study: Project Gamma, Mongolia

This project is a clear example of the application of VM0047 in practice. The goal is to restore 30,000 ha of deforested land in Selenge province.

• Focus: Pure carbon sequestration and biodiversity, no planned logging.
• Types: Native species such as Scots pine, Siberian larch, poplar and elm.
• Funding: The expected yield is 3.9 million tons of CO2e over 40 years.
• Implementation: It uses an area-based approach, with land classified as "special purpose areas", which prevents its use other than afforestation.

7. Criticism and challenges to integrity

Although VM0047 is seen as a step forward, experts like Elias Ayrey warn of ongoing risks:

• „Big Timber“ and commercial plantations: There is a concern that the methodology still allows industrial companies to obtain credits for planting monocultures that they would have done without carbon financing (additionality issue).
• Data transparency: Critics are demanding that project developers be required to publish maps of project and control areas to prevent manipulation of results ("gerrymandering").
• Verification intervals: Some say the five-year verification cycle may be too long to detect fires or illegal logging in time, while modern satellites allow for almost continuous monitoring.

A proposed solution for improving quality is, for example, the obligation to set aside at least 30 % areas for the restoration of native forests without economic use.

The Verra VM0047 methodology brings the necessary scientific rigor and technological modernization to the ARR sector. Use dynamic baselines a Remote Sensing data increases investor confidence and ensures that carbon credits represent a real reduction in the amount of CO2 in the atmosphere. However, the correct setting of the Stocking Index and the transparency of the entire verification process are key to the success of projects. JRi&CO2AI