This is the second part in our blog series where we highlight some of the questions and answers from our recent forecasting webinar. In part one, we shared some of the most pressing questions raised by participants and the insights that followed. Here, we continue the conversation by diving into the next set of questions, covering everything from demand forecasts and corporate buyer behavior to pricing dynamics and the evolving role of standards.
Didn't get the chance to join live? Watch the full webinar below:
"What is your take on the effects of GHG Protocol and SBTi on the demand for Article 6 credits?"
For Article 6 credits, and particularly 6.4 emission reductions, the first credits are set to come online by the end of this year. I think there will be a big shift in how the demand for those credits is assessed. The GHG Protocol and SBTi’s convergence on developing a harmonized standard is very much aligned with that pathway of thinking. However, if harmonization takes time, those credits risk being left in limbo, raising questions about how corporates will be able to receive and purchase them. That could present a real challenge. The key question is how we can accelerate harmonization while also bringing more demand signals online early. At the same time, compliance markets such as Vietnam, South Africa, and potentially Brazil are considering how to include 6.4 emission reductions within their ETS frameworks, which could provide an additional source of demand.
"Have you taken into account or stress tested the model for political risk?"
One of the things we have not done yet is formally stress test for political risk. We do have some of the frameworks available, but we haven’t applied them. Instead, our focus has been on where the market leads itself in terms of its composition, looking over time at how demand, supply, and price dynamics evolve. For example, we can examine policy scenarios where a country might decide not to allow certain project types to count toward its NDC. But when it comes to broader political or geopolitical scenarios, we have not conducted stress testing in that sense as of yet.
"Can you share any insights into how the market is reacting in response to the high-integrity CCP label? Do you foresee similar schemes coming out into the market?"
Yes, we’ve looked at that quite extensively. We’ve examined individual ratings providers and various metrics of quality in the market. Accreditation, the CCP label, and any other form of stamp of approval are all elements we can track. To do this, we’ve assigned a quality indicator and plotted it against price. In some cases, certain criteria clearly show a pricing impact, while in others the effect is much less evident. For example, when comparing historic afforestation projects with VM47 methodologies, we see pricing curves diverge, with the market clearly perceiving the latter as higher quality.
"In your forecast, demand looks quite flat. Why don’t you expect demand to rise, given stronger legislation, more net zero deadlines approaching, and growing offsetting needs?"
Our model includes a theoretical demand curve for credits that rises steeply toward 2030 and 2050. However, what you likely saw in the chart was demand after being adjusted for price sensitivity. This adjustment reflects patterns we’ve observed in the market, which generally follow a logistic curve around price and retirement volume, meaning that as prices rise, demand falls until the market clears. Historically, the VCM has suffered from poor-quality data, so while we do account for how quantity is affected by price changes, the exact magnitude is difficult to estimate with precision. Looking ahead, in a more regulated market, we would expect demand to show lower sensitivity to price.
"Lars described early on that increased demand leading to increased prices will lead to increased project viability (and presumably therefore more projects). So how does this tie in with there being a current significant oversupply that has built up during a period of perceived low demand/low pricing?"
We can’t deny that those historic credits are sitting in the market, but we also can’t ignore that most people don’t view their quality as comparable to credits being issued today. To account for this, we apply a discount based on type, geography, and vintage. A 2015 credit is not the same as a 2023 credit, though it still has value and can’t simply be overlooked.
Over time, and depending on how aggressively you discount, this oversupply becomes less of an issue, as some credits are bought and some are retired. If prices were to plummet, some developers would simply choose not to issue credits at all. Our approach reflects what we hear from market participants: this is not a market with a single price. Instead, you see a wide range of prices depending on credit type and geography.
"When considering corporate buyers' demand, do you model an increase of market participants? If so, how?"
Yes, our demand dataset is live and continually updated. The same applies to our corporate buyers dataset, where we integrate both emissions estimates and reported emissions as they come in. We then match those companies to their climate targets and rerun the analysis each time, ensuring that any increase in market participants is automatically reflected in the model.
"In nature-based projects as an investor, I’ve been baffled by how buyers clearly signal very different views on pricing. For example, oil and gas majors still make the lowest possible bids for offtake agreements, creating downward price pressure in a significant demand space, while tech buyers like Microsoft seem like an anomaly, leading to a split in natural carbon removal into two totally separate forecasting exercises. What are your thoughts on the standard deviation in pricing and ranges?"
This is not a market with one price. We don’t yet have robust forwards, and no two credits are exactly the same. That’s why you see so many different prices across credit types and geographies. Different geographies command different prices, and different sectors do as well. The interactions between these factors also shift over time. Our approach is to incorporate all of these dynamics into the analysis, including the overhang of existing supply, to better capture the wide range of pricing outcomes.