VM0045 Improved Forest Management with Dynamic Matched Baselines

Updated 2026-11-02 · 3 min read · By the akar.earth specialist team

VM0045 explained: how Verra's IFM methodology uses matched national forest inventory plots as a dynamic baseline, and whether it can work in Indonesia.

VM0045 is Verra’s improved forest management (IFM) methodology that measures the baseline dynamically, by comparing the project forest with matched control plots drawn from a national forest inventory. Credits reflect how much more carbon the project forest holds than similar forests managed under business as usual over the same period.

It is one of the clearest examples of the shift from forecast baselines to measured, ex-post baselines in forest carbon. That shift also shapes VM0047 for ARR and VM0048 for REDD+.

The idea behind dynamic matched baselines

Traditional IFM methods forecast what a landowner would have harvested. The forecast is fixed at validation. If timber markets fall, or a drought slows growth, the baseline no longer reflects reality, yet credits keep flowing.

VM0045 avoids this. It uses a large set of inventory plots from outside the project. For each verification period, it selects control plots that match the project on characteristics such as forest type, site conditions and initial stocking. The change in carbon on those control plots becomes the baseline. The project earns credits only for carbon gains above what matched forests achieved.

How VM0045 works step by step

1. Confirm applicability. The project must be an IFM activity on land that remains forest. The required inventory data must exist for the region. Read the current applicability conditions on the Verra website.
2. Inventory the project. Install and measure project plots to the methodology’s design. Plot design must be compatible with the national inventory so that comparisons are fair. See plot sampling design.
3. Define matching variables. Choose the characteristics used to match project plots with control plots, within the rules the methodology sets.
4. Select control plots. At each verification, match project plots to inventory plots with repeated measurements.
5. Calculate baseline and project change. Compare carbon stock change on project plots with matched control plots over the same interval.
6. Deduct leakage and uncertainty. Reduced harvest can shift harvest elsewhere. Matching quality and sample size drive uncertainty deductions.
7. Apply the buffer. The AFOLU non-permanence risk tool sets the buffer contribution.

Can VM0045 work in Indonesia and Southeast Asia?

The binding constraint is data. VM0045 was built around a national inventory with many permanent plots, repeated measurements, open data and consistent protocols. Indonesia does run a national forest inventory through the forestry ministry, with permanent sample plots across forest types. Whether that data is accessible, frequent enough, dense enough and compatible with VM0045’s matching rules is a question to resolve with the ministry and Verra before any investment.

Other practical issues:

• Forest heterogeneity. Indonesian lowland dipterocarp, peat swamp, montane and mangrove forests differ sharply. Matching needs enough control plots in each stratum.
• Disturbance history. Many logged-over forests have uneven damage. Control plots must reflect similar histories.
• Tenure. IFM in Indonesia usually involves PBPH licence holders. Read PBPH licences.

Where national data falls short, a project could consider VM0010 for logging-to-protection cases, while accepting more scrutiny of a static baseline. The lessons of VM0045 still apply: build your own long-term plot network and use remote sensing to track comparable forest outside the boundary. That evidence strengthens any baseline.

Pitfalls verifiers flag

• Plot protocol mismatch between project plots and inventory plots, such as different minimum diameters or plot sizes.
• Poor matching that pairs project plots with control plots that differ in productivity or disturbance.
• Small effective sample sizes, which inflate uncertainty.
• Inconsistent remeasurement intervals between project and control data.
• Unclear leakage treatment where the owner manages other forests.

Data questions to settle before choosing VM0045

Before you commit, answer these with documents, not assumptions:

• Who holds the inventory data, and can the project legally use it for crediting?
• How many plots with at least two measurements exist in the project’s forest types?
• What minimum diameter, plot size and measurement protocol does the inventory use?
• How often are plots remeasured, and will that continue through the crediting period?
• Can matching variables be measured the same way on project plots?

If any answer is weak, model the project under an alternative methodology too.

Where to go next

See baseline setting for how dynamic and static baselines compare across methodologies, and improved forest management for IFM options in Indonesia. Our field data campaigns design permanent plot networks that can stand up to matched-baseline comparison. The standards hub lists every methodology we cover.