Why is Having an Appropriate Stocking Index Important for VM0047 Credit Issuance?

Watch our on-demand webinar with carbon standard experts to learn how to select an appropriate stocking index to ensure your project is not being under-credited. 

 

 

 

In forest carbon projects operating under Verra’s VM0047 methodology, the amount of CO₂ removal a project can claim depends on new tree growth – specifically, how much additional carbon is sequestered by new trees, adjusted for baseline conditions and leakage. 

The baseline under VM0047 is dynamic, and uses a stocking index (a proxy for biomass) across project and control plots to estimate how much tree growth is due to project implementation, versus what would have occurred in the absence of the project.

But here’s the catch: if you choose an incorrect or an especially noisy stocking index, a project could end up with zero carbon credits, even if it’s having an impact. Let’s break down how this works.

The Role of the Stocking Index in VM0047 Dynamic Baseline

Under VM0047, the baseline is measured regularly in near real-time by comparing the project area to similar nearby areas with the same ecological and legal characteristics (i.e. control plots). 

To derive the dynamic performance benchmark, we retrieve the temporal trend in the stocking index across project and control plots. A linear regression is then applied to the project and control plot time-series, and the slope of the project and control plot regressions are contrasted.

If there is a significant difference between slopes, the performance benchmark is calculated as the ratio of average change in stocking index in control plots to average change in stocking index in the project area.

Why is this Important?

A good index should be proven to track biomass (i) in and around the project area, evidenced by field measurements and (ii) in the scientific literature. In other words, it should accurately reflect project efficacy and crediting potential.

Below are a few examples of how the stocking index is used to evaluate project performance and determine credit issuance.

In Scenario 1, the stocking index shows the tree biomass in the project area is increasing compared to the regrowth in control plots. However, if there is high regrowth outside the project area (Scenario 2) or low tree growth inside the project area (Scenario 3), the project would not be able to claim as many credits.

In all these examples, the stocking index tracks closely with the biomass, accurately representing project activities.

However, some projects use stocking indices that are related to biomass but are too inconsistent or noisy to be reliable. Because of this, the index might not accurately track how biomass changes over time (Scenario 4). This increases the risk that the project’s tree growth looks the same as the control areas – even when it’s actually different. If that happens, the project’s baseline is set to 1, meaning it can’t claim any CO₂ removals and won’t earn carbon credits.

Scenario 1.

Figure 1. Bottom left shows the relationship between the stocking index and biomass. Top right shows the trend in biomass in the project area over time based on field measurements. Bottom right shows the temporal trend in the stocking index temporal trend across the project and control plots.

• The stocking index is well chosen and closely tracks biomass.
• Tree biomass in the project area is increasing.
• The project will receive accurate credits based on actual removals.

 

 

 

 

 

Scenario 2.

Figure 2. Bottom left shows the relationship between the stocking index and biomass. Top right shows the trend in biomass in the project area over time based on field measurements. Bottom right shows the temporal trend in the stocking index temporal trend across the project and control plots.

• The stocking index is well chosen and closely tracks biomass.
• Tree biomass in the project area is increasing.
• However, there is high regrowth outside the project area.
• As a result, the project will be credited accurately for removals achieved.
• Due to severe baseline discounting, the project will not be able to credit many credits.

 

 

 

Scenario 3.

Figure 3. Bottom left shows the relationship between the stocking index and biomass. Top right shows the trend in biomass in the project area over time based on field measurements. Bottom right shows the temporal trend in the stocking index temporal trend across the project and control plots.

• The stocking index is well chosen and closely tracks biomass.
• Tree biomass in the project area has not increased since project start.
• Due to low project impact, the project will not be able to claim many credits.
• However, it will be credited correctly respective to removals achieved.

 

 

 

 

Scenario 4.

Figure 4. Bottom left shows the relationship between the stocking index and biomass. Top right shows the trend in biomass in the project area over time based on field measurements. Bottom right shows the temporal trend in the stocking index temporal trend across the project and control plots.

• The stocking index is not well chosen, leading to a weak correlation with biomass.
• Despite good project performance, high noise in the data noise obscures the impact of removals.
• As a result, the project will be under-credited for actual removals achieved.

 

 

 

 

 

Takehome Message

Having a stocking index that predicts biomass well is integral to VM0047 projects. Without one, even a well-executed project risks being unable to claim CO₂ removal credits.

If you’re developing a project under VM0047, ensure your stocking index is scientifically validated and capable of accurately tracking biomass over time. 

At Space Intelligence, we specialise in helping projects select the most accurate and effective stocking indices. Get in touch with us at [email protected] to ensure your project meets the highest standards for carbon credit eligibility.