Short version: Avoiding overestimation means the GHG emissions avoided or the removals that are enhanced by a mitigation project are quantified conservatively relative to a realistic baseline.
Long version: To avoid overestimation of a crediting project’s effects, the emissions avoided, or removals enhanced, by crediting projects must not be exaggerated. Conservative quantification must ensure that it is unlikely too many credits will be issued to the project and must extend to both the project and baseline emissions. It typically demands that projects are monitored and that this data along with the quantified avoided emissions or enhanced removals are assured by accredited auditors before credit issuance. Overestimation can occur by inflating estimated baseline emissions and/or underestimating the project emissions including failures to account for a project’s indirect effects on GHG emissions (i.e., leakage).
Suppose that, for every 50 additional tonnes of CO2 emissions that are avoided by a crediting project, the project developer reports avoiding 100 tonnes, and 100 carbon credits are then issued to the project. Half of these credits would have no effect in mitigating climate change and using them instead of lowering internal emissions would make climate change worse. Overestimation of avoided GHG emissions can occur in several ways:
- Overestimating baseline emissions. The first – and most subtle – way GHG credits can be overestimated is if a project’s baseline emissions are overestimated.[1] Baseline emissions are the reference against which avoided GHG emissions are calculated, and are closely tied to additionality: they are the emissions that would have occurred in the absence of the expected revenue from selling issued credits.[2] Baselines are easier to determine for some types of projects than others. For a project that captures methane from a landfill and destroys it, the amount of methane that would have been emitted is generally the amount that is captured and destroyed plus methane that is not captured by the project (due to imperfect capture efficiency) as in the baseline scenario both sources of gas would have been emitted.[3] In contrast, there can be much greater uncertainty when estimating how many GHG emissions will be displaced on an electricity grid by a solar power project – leading to a greater risk of overestimation if estimation methods are not appropriately conservative.
- Underestimating actual emissions. Many kinds of carbon crediting projects avoid but do not eliminate GHG emissions. A project’s avoided GHG emissions are quantified by comparing the actual (i.e., ex post) emissions that occur after the project is implemented against its predicted (i.e., ex ante) baseline emissions.[4] In the same way that baseline emissions can be overestimated, actual project emissions can be underestimated – with both contributing to an overestimation of GHG emissions avoided by the project. For carbon removal projects, this source of overestimation risk can result from overestimating actual removals caused by the project. Exaggerated estimates of the actual impact of a project can arise through measurement error. For example, determining the increase in the amount of carbon stored in trees from one year to the next is subject to measurement uncertainty and sampling errors, which can sometimes overstate actual carbon storage. Many standards address this by discounting measured quantities wherever significant uncertainties arise.
- Failing to account for the indirect effects of a project on GHG emissions (aka “leakage”). To quantify avoided GHG emissions, actual project and baseline emissions are determined for all sources affected by a project. Often, however, a project will have both intended and unintended effects on GHG emissions. If quantification methods fail to account for GHG emission increases caused by the project at some sources (even indirectly), then the total avoided GHG emissions will be overestimated. Unintended increases in GHG emissions caused by a project outside of its recognized boundaries are referred to as “leakage”. The classic example is a forest preservation project that ends up shifting the production of timber and deforestation to other areas.
- Forward crediting. Although rare, carbon credits may be issued for avoided GHG emissions that a project developer expects to achieve in the future. Such “forward crediting” is usually problematic because it can lead to an over-issuance of carbon credits if a project fails to perform as expected. [5] It can also pose issues if future events (e.g., regulatory changes) lead to erroneous assumptions that inform the baseline emissions over the crediting period.
Finally, to control for these possible causes of overestimation, it is necessary to monitor and verify ex post a project’s performance.[6] Measurement and data collection procedures – and calculations or estimates derived from these data – should be scientifically sound and methodologically robust. Furthermore, project monitoring data should be rigorously verified. Verification entails assessing the veracity of data provided by project developers, often through an audit of selected data samples. Crediting project developers have an incentive to report data that maximizes the number of carbon credits they can sell. Verification helps to assure that reported data are accurate and do not overstate avoided GHG emission emissions.
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[1] For projects that enhance the removal of carbon, this baseline concern is flipped as the risk of overestimating the impact of a project would results from underestimating the baseline’s rate of carbon removal.
[2] Again, a common misconception is that the baseline for a project represents what would have happened “in the absence of the project.” However, it is essential to evaluate whether a proposed project is itself the baseline (i.e., is not additional), and therefore will avoid no emissions.
[3] Assuming that the project is additional and that the project itself does not affect the rate of methane generation at the landfill – for example, by creating a “bioreactor” landfill.
[4] For more information on the baseline concept and terminology see: https://ghginstitute.org/2022/03/14/what-is-a-baseline/
[5] See, for example, Offset Quality Initiative (2008).
[6] This process may include collecting and verifying data needed to estimate a project’s baseline emissions.