The results of your GHG inventory are not completely accurate. Quantifying these uncertainties isn't easy, as not all emission factor databases provide an uncertainty and there is a lack of consistency between the databases that do provide one.
Uncertainty Sources
There are several sources of uncertainty.
Inherent uncertainty linked to the computation of an emission factor
Lack of granularity of existing emission factors
Lack of consistency between similar emission factors provided by different sources
Incorrect selection of an emission factor
Granularity and precision of activity data (primary and secondary data)
Methodological choices
What is currently included in the uncertainties that are displayed?
Currently, only the uncertainty linked to the emission factor is included in the computation of uncertainties.
Activity data uncertainties (for instance: uncertainty on the total distance traveled by the vehicle fleet) will be included soon.
How are emission factors uncertainties computed?
As not all emission factor databases provide an uncertainty and there is a lack of consistency between the databases that do provide one, Greenly has implemented an internal process to link all emission factors to an uncertainty.
There are 6 levels of uncertainty: 5%, 15%, 30%, 50%, 65% and 80%. The level of uncertainty is selected depending on the type of emission factor and on an emission factor confidence score.
Confidence score (between 0 and 1)
Greenly Monetary Emission Factors
These ratios are computed by dividing emission factors provided by renowned databases (e.g. Ecoinvent) by the average price of the product, service or activity.
The confidence score is then selected depending on the bias of primary data that is used to compute the ratio and the variance of the category (e.g. a very specific sector with highly homogeneous products with have a low variance).Input-output Monetary Emission Factors
These ratios are computed using input-output tables for a given sector and country.
The confidence score is then selected depending on the variance of the sector and on the relevance of the sector for the Greenly category.Company Specific Monetary Emission Factors
These ratios are computed by dividing the GHG inventory of a company by its revenue. Sectoral averages can also be computed.
The confidence score is then selected depending on the reliability of the revenue and on the reliability, transparency and completeness of the GHG inventory.Activity Emissions Factors
These ratios are provided by external sources.
The confidence score is selected depending on the reliability of the source. For example, a renowned and trustworthy database will have the highest score and a non peer-reviewed article a low score.
Uncertainty
Once the confidence score is computed for an emission factor, the uncertainty level is selected depending on the type of emission factor and the confidence score.
💡 Examples
An activity emission factor with a confidence score of 1 will have an uncertainty of 5%
A Greenly monetary emission factor with a confidence score of 0 will have an uncertainty of 80%
How are aggregated uncertainties computed?
Each activity in your GHG inventory is associated with an uncertainty, derived from the uncertainty of its emission factor.
To compute the aggregated uncertainty at a higher level, whether for a visualisation category, a regulatory category, a scope, or the total footprint, a weighted average is used, where each activity's uncertainty is weighted by its share of total emissions:
Aggregated uncertainty = (Emissions₁ × Uncertainty₁ + Emissions₂ × Uncertainty₂ + …) / (Emissions₁ + Emissions₂ + …)
This means that activities with larger emissions have more influence on the aggregated uncertainty. An activity representing a small share of emissions will have little impact on the group's uncertainty, even if its individual uncertainty is high.
💡 Example — A Scope 3 with two activities:
Activity A: 900 tCO₂e, uncertainty 5%
Activity B: 100 tCO₂e, uncertainty 50%
Aggregated uncertainty = (900 × 5% + 100 × 50%) / (900 + 100) = (45 + 50) / 1000 = 9.5%
Why not the Root-Sum-of-Squares (RSS) method?
An alternative approach used in some standards (such as the IPCC Guidelines for national GHG inventories) is the root-sum-of-squares (RSS) method, which combines uncertainties as follows:
Aggregated uncertainty = √((Emissions₁ × Uncertainty₁)² + (Emissions₂ × Uncertainty₂)²+ …) / (Emissions₁ + Emissions₂ + …)
Using the same example, RSS would give: √(45² + 50²) / 1000 = √4525 / 1000 ≈ 6.7%, a lower result than the weighted average (9.5%).
RSS assumes that individual uncertainties are statistically independent and will partially cancel each other out, producing a more optimistic combined uncertainty. The weighted average makes no such assumption; it treats uncertainties as fully additive, which is more conservative and more appropriate given that:
Emission factor uncertainties at Greenly are discrete levels (5%, 15%, 30%, 50%, 65%, 80%), not statistical distributions derived from rigorous measurements
The independence assumption behind RSS is difficult to verify in practice, as many activities share similar emission factors or data sources
A more conservative figure is more honest when communicating uncertainty ranges