Last week, we examined how projects and retirements are slotted into the ICVCM’s Internal Assessment methodologies: in other words, which projects and credits are likely to be approved for the CCPs.
This week, we look at the methodologies that have been excluded from CCP assessment by the standards, as well as those that have been flagged as ‘Very unlikely’ by ICVCM. (The organization’s list of methodologies can be found here.)
Overall, the number of projects affected slotted into these unenviable categories is small: only about 3% of projects we track in our database. However, this group is responsible for nearly a third of all retirements in the market: 285m tCO2e to date.
Part of the reason for this large sum is that the CCP assessment comes as Verra has launched a new methodology for REDD+ projects; it excluded previous methodologies from assessment. You can read Verra’s statement on the move here, and information on how the standard plans to transition projects to the new methodology here.
There are more than 850m outstanding (issued, but not retired) credits floating around from these projects, particularly from older vintages. This makes it important for discerning buyers to understand which credits may be ones to avoid, as they look to offset their emissions in a way that will not fail the CCP quality threshold.
Notably, prices for credits from the excluded and very unlikely projects have been similar to all others: indeed, until 2022, those projects held a sizable price premium:
Leaving aside the uncertainty around the Excluded methodology projects, only 44 companies have retired credits from companies that are within the ICVCM Very Unlikely category, accounting for about 20m tCO2e retired all time. Some firms have retired credits exclusively from projects that are unlikely to clear the CCP quality bar:
Whether these companies will choose to find new projects and re-retire credits remains to be seen. AlliedOffsets users can log into the dashboard here to find out more about CCP likelihoods, and to access more data.
