Trustworthy measurement and reporting of carbon stocks are essential to functioning and high-integrity carbon markets. But uncertainty is inevitable: no methodology is completely accurate. So, how accurate are carbon estimates for nature-based projects? What are the sources of uncertainty? Finally, how is Treeconomy working to address uncertainty? This is the first in our “Understanding Uncertainty” blog series from the scientific team behind Treeconomy’s industry-leading carbon measurement, monitoring, and reporting capability. Today, we take a closer look at the inherent variability of trees and the relationships we use to measure them. 

The nature-based voluntary carbon market has been criticised in recent years for erroneous carbon estimates and grossly exaggerated project benefits. 

Investors, developers, and buyers require confidence in carbon estimates for nature-based projects, yet digital measurement, reporting, and verification (dMRV) providers rarely include uncertainties in estimates, undermining trust and transparency. 

To be as transparent as possible, Treeconomy now endeavours to estimate the range of carbon stored in not only the overall project but also for each individual measured tree and the probabilities associated with our estimates. This means we can estimate that a biomass or carbon value will fall within a given range a certain percent of the time. 

But is it even possible to know how accurate a prediction of carbon is in the first place?

To directly measure tree biomass and the carbon stored within it requires felling or uprooting a living tree, then drying and weighing it with heavy machinery. This is referred to as “destructive sampling.” Obviously, this is not a realistic practice across an entire project: the process is slow, costly, and highly labour-intensive. Moreover, extensive felling is contrary to the goals of most reforestation projects. Instead, tree weight (or biomass) is conventionally estimated by relating it to dimensions that are easier to measure, like height and trunk diameter. Foresters have been able to validate these relationships using trees they have chopped down and weighed themselves. The relationship between living things and their growth is a field of science of its own called allometry.

Allometry is not only useful for estimating the biomass of a tree, but also for predicting the other dimensions of the tree, like height or diameter. For example, for a spruce tree with a height of 10 metres, an allometric equation might predict the diameter of the same tree to be 15 cm, or vice-versa. 

This is valuable to remote sensing-based efforts of carbon measurement. While aerial surveying can be employed to reliably measure tree height, direct measurement of tree diameter from the sky is not presently commercially viable for monitoring at scale. However, we can use allometry to estimate a tree’s diameter from its aerially measured height. If we know these two things, we have the ingredients we need to make an educated guess at the tree’s overall biomass and, therefore, the carbon stored within.

Check out our Ackron project case study series for more info on conventional carbon measurement techniques and the value of remote sensing technology.

Note the overall relationship between height and diameter in the above plot, meaning that as height increases, a proportional increase in diameter occurs. We can therefore use this model to estimate diameter in scenarios when only the height for Norway spruce is known. 

These models relate a height to a single diameter value, but two trees of the same height can have different diameters (and therefore different overall sizes), so what do we do about the possibility of the different carbon outcomes that can occur downstream? In reality, a tree of a given height will not always have the exact same diameter and is very likely to vary – possibly by a lot. An allometric equation might under or over-predict the diameter, leading to a much lower or higher mass and carbon stock estimate. It’s, therefore, important to account for those potential differences in our carbon measurement methodology.

In afforestation projects, the variability in diameter for a given tree height is meaningful because these variations can contribute to potentially significant carbon underestimation or overestimation. 

Both are problematic: carbon underestimation means projects may under-issue credits and not be rewarded for the true and total value of their climate impact contributions. In the case of carbon overestimation, credit buyers are over-promised and may purchase a greater number of carbon units than have actually been realised by the project. Overestimation in particular can erode public and buyer trust and confidence in nature-based carbon removal schemes. 

By providing ranges of estimates and their probabilities, customers and the public are given a robust and scientifically honest range of outcomes, which acknowledges natural variability and recognises that “uncertainty is a certainty”: no estimate is perfect. Presenting honest uncertainty, rather than false certainty, can actually inspire confidence and support carbon market stakeholders in making more thoughtful, risk-aware decisions. Importantly, presenting uncertainty can help to safeguard against criticisms of inaccuracy and greenwashing on the part of carbon buyers.

These ranges can be thought of as a buffer around each carbon prediction, where, along with our best guess prediction, we also provide an estimate for what limits are possible as a result of allometric variability. For example, we can provide an absolute minimum/maximum observed diameter for a given tree height from our measurement databases. However, we can also provide a lower and upper limit based on the percentage of diameter measurements at a distance from the average diameter. The latter measurement is more useful since it’s more descriptive of “typical” trees for a given height and species. 

In doing so, we can offer a central estimate as well as a worst- and best-case scenario. The stronger our predictions, the narrower the gap between all those numbers will be: understanding uncertainty and then working to reduce it is a major driver of our research as we strive to provide confidence to carbon developers, buyers, and investors. 

Consider the graph below. For a tree of 15m in height, notice how the possible diameters tend to “pile up” around the middle of the graph, corresponding to a diameter of about 19 cm. Our actual diameter prediction will fall somewhere here, but we will also provide measurements consistent with the edges of this range.

Given the variation demonstrated in just a single tree, project carbon totals for forests comprising tens to hundreds of thousands of trees can be materially affected by the sum of this variation. We account for this across the entire project by calculating the total project carbon in all trees and then repeating this exercise many times using different plausible diameter combinations based on the height of each tree in the project. We can then take the total carbon from each of these “runs” and graph them out to see the range of total project carbon. From here, we can provide full-project ranges similar to those we present for individual trees, but now we have the whole picture and the effect on total carbon estimates.

We can, therefore, conclude that for a theoretical monoculture project site consisting of 1000 Norway spruce trees, we expect the amount of carbon storage to be about 742 tCO2e (the average). This number can theoretically be as low as 682 tCO₂e and as high as 794 tCO₂e. However, since those extreme values are so unlikely, they’re not particularly helpful, and we can further constrain this range and say 68% of the time, the minimum and maximum possible carbon values will fall between 708 and 774 tCO₂e. 

Delivering results in this manner is far more honest and insightful than offering a single predicted value. It provides different outcomes for different scenarios, and considers the possibility of site-specific variables that can influence results much better than a single estimation can. Also, we can see the variation is not very extreme, with the minimum and maximum outcomes being within 10% of the average prediction.

Treeconomy offers best-in-class natural capital baselining and carbon measurement services with a multi-award-winning scientific team and our Sherwood nature-based project monitoring platform. 

Our dedicated commercial team works to facilitate confident investment into a growing portfolio of fully-monitored nature projects on our marketplace.

We are trusted by landowners, natural capital investors, government agencies, carbon standards, and corporate carbon buyers. 

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