top of page

New dynamic baseline for ARR projects - Explainer

The golden question on every carbon project developer and investor’s mind is: how can we accurately quantify our credit issuance? In other words, how do we know for sure, that the returns (by way of sale of carbon credits) will far exceed the enormous CAPEX and OPEX required over the life of a Nature-based carbon project?

The purpose of this blog article is to explain in layman's terms how the updated Verra methodology for Afforestation and Reforestation (ARR) projects may impact your credit issuance potential. The simplification of diagrams are meant to describe how the methodology works on a conceptual level and may differ on certain technical/project specifications.

Out with the old

To understand Verra’s latest ARR methodology, it’s helpful to first understand the previous methodology and some of its gaps. In the previous methodology AR-ACM0003 and AR-AMS0007, projects typically observed business as usual scenarios only on the project area itself and used it to set a “static” baseline that once set at the start of a project, does not change year over year. Since most project areas are already degraded to begin with, many of these static baselines were either set very low or to zero (0) as the old methodology assumed no natural revegetation in areas without carbon project intervention. Many criticised this method for not truly capturing "business as usual" scenarios over time.

Example of degraded land

To simply illustrate, let’s assume 1 unit of tree is 1 unit of carbon biomass observed:

afforestation methodology

As the VCM demanded higher integrity projects and more robust standards, the old methodology possessed a few issues.

  • The project area itself may not truly be indicative of “business as usual” natural vegetation growth. In short, we need more data points.

  • This static baseline, once set, does not change over time. This does not capture real time baselines, which can change dramatically year on year.

This led leading scientists to develop an even more robust methodology to capture these nuances.

In with the new VM0047.

Enter, VM0047, a methodology for quantifying carbon removals through Afforestation, Reforestation, and Revegetation (ARR) activities. In the new methodology, we see the introduction of two key concepts that aim to rectify some foundational gaps in the previous methodology. 

Performance Benchmark

Firstly, the concept of performance benchmarking is not a new one. Similar to how it’s used in different industries, a performance benchmark in the context of a carbon project is used as a way to measure the true ‘performance’ of an ARR project using remote sensing proxies such as vegetation indices, and canopy height derived from LiDAR. On a high level, the performance benchmark is determined by comparing the average rate of increase of remote sensing proxies between “project plots” (sample areas inside the project area) and “control plots” (sample areas outside the project area). Conceptually, these remote sensing proxies outside the project area should provide a more holistic view on how vegetation grows in a business as usual rather than just on a project area itself in previous methodologies.

To establish and calculate the performance benchmark, many control plots are selected within a 100 km buffer zone of the project area. Using State of the Art Remote Sensing and AI (machine learning), the control plots are selected using an advanced proprietary algorithm developed by our team. is able to select an appropriate set of control plots to use as a proxy to gauge how vegetation grows in areas similar to that of the project area at the start of the project.


The carbon sequestration of the trees on the control plots are then monitored and tracked over time using proxies. Performance benchmark is then used to determine a ‘discount’ that is applied to credit issuance to control for carbon contribution that is likely to be non additional or in other words, business as usual vegetation growth rather than contribution from project activities itself.

Using the same example from diagram 1/4 above, here’s how the performance benchmark may affect ARR project issuance differently going forward:


Static vs. Dynamic Baselining

One significant departure from past methodologies is the dynamic annual occurrence of this process. Instead of relying on a static baseline, the "business as usual" outcome requires periodic reassessment, leading to the application of a recalculated credit issuance discount, every year.


How does this impact your afforestation/reforestation project?

As of 28th December 2023, previous methodologies have been phased out and all ARR projects going forward will need to employ remote sensing and machine learning techniques to select control plots and establish the performance benchmarks.

The adoption of this technology will be a game-changer, profoundly impacting revenue streams for ARR projects by significantly enhancing the accuracy and reliability of carbon credits. This transition ensures the issuance of robust credits, boosting investor confidence. It's imperative for stakeholders to act swiftly and integrate these state-of-the-art techniques to stay competitive and compliant in the evolving carbon economy.

The Bottomline

At present, there aren't any successfully registered projects on VM0047 yet (though many are in the process of registration this year). However, considering the nature of control plots as proxies, credit issuance will likely, at best, remain at similar levels or at worst, drop significantly due the discounting from control plots. are carbon modelling specialists. It is our core business to deeply understand carbon methodologies before we can even begin to design algorithms and employ machine learning to automate highly complex modelling calculations. Reach out if you require carbon due diligence services or looking for a Technical Partner on your projects.


bottom of page