4.4.2 - How to determine them?
Qualitative and quantitative method for estimating the uncertainties of a Bilan Carbone®.
Uncertainties within a Bilan Carbone® take two different forms. The first is qualitative, meaning that characteristics are defined for activity data and emission factors, then rated qualitatively. The second is quantitative, stems from the qualitative information and leads to the determination of a 95% confidence interval.
Output formats
Taking uncertainties into account results in three different but correlated forms of results:
A qualitative uncertainty on GHG emissions, ranging from "Very high" to "Very low"
A 95% confidence interval, that is an interval [Lower Bound; Upper Bound] in which the result has a 95% chance of lying
A percentage value, which means that the result is potentially
% higher than its value: 
These three forms of results are complementary and make it possible to present uncertainties to audiences with different levels of understanding of these issues.
Qualitative determination
The qualitative determination of uncertainties for an emission source involves completing two matrices, one for the emission factor and the other for the activity data. To fill in these two matrices, the organisation shall assign a quality rating (Very good, Good, Average, Poor, Very poor) to each of the five characteristics of the activity data or of the emission factor. The meaning of the different characteristics is detailed below.
Technical representativeness
Very good
Geographical representativeness
Good
Temporal representativeness
Average
Completeness
Poor
Reliability
Very poor
The five characteristics used are as follows:
Technical representativeness: This characteristic assesses whether the value is truly representative of the current state of techniques and technologies used. EF: Assesses whether technological advances have occurred since the creation of the emission factor. AD: Assesses whether the activity data comes from a different or obsolete technology.
Geographical representativeness: This characteristic assesses whether the value is well suited to the geographical location of the emission source. EF: Assesses whether the emission factor applies to a territory different from that of the emission source. AD: Assesses whether the activity data comes from another territory.
Temporal representativeness: This characteristic assesses whether the value is up to date for the emission source. EF: Assesses whether the emission factor is sufficiently up to date, whether its validity period has been exceeded, or whether changes have occurred since the emission factor was created. AD: Assesses whether the activity data used comes from a year different from the year of the assessment.
Completeness: This characteristic assesses whether the value is statistically representative of the emission source. EF: Assesses whether the emission factor was developed from a representative dataset. AD: Assesses whether the activity data comes from statistical averages or extrapolations based on an insufficient number of data points.
Reliability: This characteristic determines whether the method of collecting the activity data and the source from which the emission factor originates are reliable. EF: Assesses whether the emission factor comes from an unqualified or unreliable source. AD: Assesses whether the activity data is based on a rough or even speculative estimate rather than a precise measurement.
For an emission factor:
If the emission factor (EF) comes from a database that uses uncertainties, the matrix shall be taken from or completed using the information from the database.
🔎 For the Base Empreinte®, for example, users can directly reuse the ratings associated with the EF characteristics in the database. These ratings assess the EF's ability to represent what it claims to be.
However, the organisation may use an emission factor that is not fully adapted to the emission source considered. Thus, for organisations wishing to be more precise in their treatment of uncertainties, it may be relevant to adjust and re-evaluate the ratings of the various characteristics originating from databases.
Example: The emission factor for a Spanish orange comes from a very high-quality LCA, and all its ratings show "Very good". An organisation using this emission factor for oranges from another country should ideally downgrade the rating associated with the "Geographical representativeness" characteristic.
If the emission factor does not come from a database using uncertainties, the matrix shall be established from the documentation provided on the EF. If no documentation is available, the ratings associated with the EF in the matrix shall be low.
⏳[WIP] A practical example of completeness (qualitative and quantitative) of the uncertainty will be available soon, in the annex.
Guidance for completing the matrix for an emission factor is given below.
🌐 English version of this image.
For an activity data:
For activity data (AD), the organisation shall assign a rating to the activity data used.
Example: The organisation's data is a number of kWh taken from the twelve invoices associated with the chosen temporal boundary. The organisation assigns the "Very good" rating to this activity data.
However, for organisations wishing to be more precise in their treatment of uncertainties, it is possible, as with emission factors, to assign a rating to each of the five characteristics of the matrix.
⏳[WIP] A practical example of completeness (qualitative and quantitative) of the uncertainty will be available soon, in the annex.
🔎 Completed uncertainty matrices for typical collection methods will be available soon on the General Carbon Plan.
Guidance for completing the matrix for an activity data is given below.
🌐 English version of this image.
Quantitative determination
The quantitative determination of uncertainties requires no additional effort from the organisation. Bilan Carbone® tools use the qualitative determination of uncertainties to automatically produce this quantitative determination. For this quantitative approach, it is assumed that the distribution laws of uncertainties are log-normal.
The matrices obtained via the qualitative determination serve as the basis for the quantitative determination. Coefficients are associated with each quality for each characteristic according to the matrix below:
Technical representativeness
Very poor
Poor
Average
Good
Very good
U1 = 2.00 U1 = 1.50
U1 = 1.20
U1 = 1.10* U1 = 1.00 *Coefficient assigned by the ABC
Geographical representativeness
Very poor
Poor
Average
Good
Very good
U2 = 1.10 U2 = 1.05*
U2 = 1.02
U2 = 1.01 U2 = 1.00 *Coefficient assigned by the ABC
Temporal representativeness
Very poor
Poor
Average
Good
Very good
U3 = 1.50 U3 = 1.20
U3 = 1.10
U3 = 1.03 U3 = 1.00
Completeness
Very poor
Poor
Average
Good
Very good
U4 = 1.20 U4 = 1.10
U4 = 1.05
U4 = 1.02 U4 = 1.00
Reliability
Very poor
Poor
Average
Good
Very good
U5 = 1.50 U5 = 1.20
U5 = 1.10
U5 = 1.05 U5 = 1.00
The geometric standard deviations (GSD) associated with the activity data and the emission factor are then calculated using the following formula:
These two geometric standard deviations are then combined to obtain a standard deviation for the emission source, using the following formula:
Using this geometric standard deviation, the organisation obtains the 95% confidence interval for the emission source, which is as follows:
The lower value of this interval is called the "Lower Bound", and the upper value is called the "Upper Bound". It is then possible to propagate these uncertainties associated with the different emission sources to obtain uncertainties on an emission subcategory, an emission category or on the entire assessment.
For a given emission source, its sensitivity is defined as its weight in the total assessment. Let E1 be the value of an emission source and T the total value of the assessment, the sensitivity S1 of emission source E1 is:
Let E1 and E2 be the values of two emission sources, S1 and S2 their respective sensitivities and GSD(E1) and GSD(E2) their respective geometric standard deviations, the geometric standard deviation on E1 + E2 is:
The 95% confidence interval is then obtained using the same method as previously.
⏳[WIP] A practical example of completeness (qualitative and quantitative) of the uncertainty will be available soon, in the annex.
Requirements related to uncertainties
Here are different requirements to be met in terms of taking uncertainties into account for each of the 3 maturity levels.
Beginner level: criterion M1
The organisation shall qualify and quantify uncertainties for all direct emissions and significant indirect emissions of the Bilan Carbone® (as a reminder, this represents at least 80% of emissions).
Qualitative determination of uncertainty can be carried out:
by assigning a single quality rating (Very good, Good, Average, Poor, Very poor) to each activity data concerned. This overall rating is therefore representative and consistent with all 5 characteristics.
by using as-is the ratings of emission factors from databases.
The quantitative determination of uncertainty is automatically calculated from the qualitative determination.
Intermediate level: criterion M2
The organisation shall qualify and quantify uncertainties for all direct emissions and significant indirect emissions of the Bilan Carbone® (as a reminder, this represents at least 80% of emissions).
Qualitative determination of uncertainty can be carried out:
by assigning a single quality rating (Very good, Good, Average, Poor, Very poor) to each activity data concerned. It is however recommended to associate a rating to each of the 5 characteristics (technical, geographical, temporal representativeness, completeness and reliability), i.e. 5 ratings for each activity data responsible for the most significant of the assessment.
emissions recommended by using as-is the ratings of emission factors from databases. It is however
The quantitative determination of uncertainty is automatically calculated from the qualitative determination.
to adjust those ratings when the actual use is not exactly within the scope of application intended by the database.
Advanced level: criterion M3
The organisation shall qualify and quantify uncertainties for all emissions of the Bilan Carbone®.
The organisation shall systematically use the five characteristics for activity data and modify the ratings attributed to emission factors by databases when this is relevant. The qualitative determination of uncertainty shall
systematically be carried out: by assigning a quality rating (Very good, Good, Average, Poor, Very poor) to each of the 5 characteristics
(technical, geographical, temporal representativeness, completeness and reliability), for each activity data used. by adjusting the ratings
The quantitative determination of uncertainty is automatically calculated from the qualitative determination.
Do you have a comprehension question? Consult the FAQ. The method is living and therefore likely to evolve (clarifications, additions): find the track of changes here.
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