4.2 - Activity data collection method
How to collect the activity data needed for accounting?
This sub-section details what activity data is, how to collect it and how the different types of activity data can be used.
As a reminder, the emissions from each emission source of the organisation are estimated as follows:
Emission from a source = Activity data x Emission factor = Result ± Uncertainty
Activity Data
Activity data is the data that accounts for the various physical flows of the organisation. A mass of raw material, the number of kilometres travelled by the various people involved, the quantities of energy consumed by the organisation are examples of activity data.
For a given emission source, the activity data and the denominator of the chosen emission factor must be expressed in the same unit. In general, it is advisable to collect the data that allows the most precise emission factor to be used, and to convert it if necessary. If only one type of data is available, the appropriate emission factor must then be chosen.
Activity data (km) x Emission factor (kgCO₂e/km) = Result (kgCO₂e) ± Uncertainty (kgCO₂e)
Data Collection
The collection of this activity data is often the most time-consuming phase for an Initial-level approach. It is indeed necessary to identify, for each emission source, whether and in what form the data exists within the organisation, and who holds it. If certain data is not available, data collection improvement actions must be part of the transition plan, in order to build monitoring of this data in anticipation of the next Bilan Carbone®. If collection has been planned upstream and improved following the first assessment, it can prove faster (particularly for a Standard Assessment). An organisation carrying out an Advanced Assessment will have implemented a real-time data collection dashboard.
To optimise current and future data collection, the organisation must:
Create a data collection matrix, which precisely defines the data to be collected, drawing on the identification of emission sources, the perimeters established previously and the data already present within the organisation.
Assign a focal point for each emission category, or for each type of activity (depending on the structure of the company), who is responsible for collecting data for that category from the various data holders.
Document and retain all data collected, including in particular their source, the units and uncertainty associated, the associated documents (for example invoices, delivery notes, internal reports) and any other information deemed relevant, in order to ensure that this data is traceable and comparable from year to year.
Balance collection efforts: Collecting very precise data for a non-significant emission source is often costly in terms of time. It is important to take care of collection for significant emission sources. Focusing on action and reducing significant emissions is at the heart of the Bilan Carbone®.
In the long run, it is recommended that the organisation equip itself with a system that monitors data collection, which will enable it to report certain indicators, conduct future Bilan Carbone® approaches more quickly, and ensure monitoring of its transition plan. However, as developed below, the automated collection of financial activity data will not achieve these three objectives, as this data will call upon spend-based emission factors.
🔎 To express the Bilan Carbone® with an "analytical" reading, in line with analytical carbon accounting, activity data must be collected by analytical axis, and therefore broken down by responsibility (suppliers, clients, sites, etc.). Accounting codes allow for comprehensive coverage of the emission sources borne by the organisation. The associated data sources are thus linked to each accounting code (for example a purchase export for accounting code 601321), then to the analytical axes (for example a purchase export broken down by supplier).
Data sources must integrate the analytical axes identified as relevant for the analysis of results. The absence of analytical axes in a data source needed to divide emissions according to that analytical axis may justify abandoning that analytical axis. Adding the analytical axis to the data source will be an improvement action for the approach.
Data collection matrix
For each emission source, this matrix must indicate the Bilan Carbone® emission category and subcategory concerned, the label, the value, the unit, the type, the source and the uncertainty (with a single characteristic) of the activity data.
🔎 In analytical carbon accounting, the matrix indicates for each activity data point the associated accounting code and analytical axis.
This matrix will also serve to list the emission factors that will be attached to this activity data, by indicating the label of the desired emission factor, the label of the emission factor actually used, its value, its unit, its uncertainty (all characteristics), its type and its source.
⏳[WIP] An example data collection matrix will be made available shortly.
The Different Types of Activity Data
The four main types of activity data are detailed below. They are described from the most reliable (real data) to the least reliable (approximate data).
Real activity data
Real activity data can represent a physical or financial reality.
🔎 These real activity data are called primary activity data by the regulatory GHG report method.
Physical activity data
These are physical data available to the organisation in raw form and with a high degree of accuracy, either because they precisely track the physical flows involved, or because they are able to trace them retrospectively. For example, the mass of raw material used (e.g. 200 tonnes of new stainless steel per year) or the number of kWh of electricity consumed (shown on the invoice) are real data that are easily collectable by and for organisations.
Physical activity data are the data with the highest reliability for carrying out a Bilan Carbone®. It is advisable to use this type of activity data as much as possible.
The organisation must nonetheless take a critical look at the statistic used to qualify the associated uncertainty.
Financial activity data
These are data available to the organisation with a very high degree of accuracy, as financial accounting is highly regulated. For example, the cost of electricity for the organisation is information that will always be available. This data is therefore associated with very low uncertainty.
On the other hand, it remains strongly inadvisable to generalise the use of this data, and this for two reasons:
This financial activity data is associated with spend-based emission factors, which themselves carry very high uncertainty.
This activity data does not allow for a physical analysis of what is happening within the organisation, which prevents the development and implementation of a relevant transition plan. Indeed, the only action that can be associated with financial activity data is to reduce the organisation's spending, which is limited and sometimes even counterproductive (buying better often means buying more expensively).
Extrapolated activity data
These are data derived from an extrapolation of other physical, statistical or approximate data. This type of data is regularly used during Bilan Carbone® approaches, particularly for travel and food emission categories, through the sending of a questionnaire to the organisation's staff. A certain number of people respond to the questionnaire, and the results are extrapolated to all staff. The organisation may also use extrapolations when certain data for the current year is not yet available (for example using an electricity invoice from a previous year), or when it uses activity data from sectors, sites or geographical locations that are not representative of its situation, by applying a correction factor. Note that if non-representative data is used as-is and not corrected, this data is not extrapolated data but approximate data.
This data is generally fairly reliable, as it is adapted specifically to the organisation. However, it is preferable to use real data.
The assumption used for extrapolation must be documented within the data collection matrix.
Data derived from computer simulations or Artificial Intelligence (AI) models is also considered to be extrapolations. The organisation should then judge the level of acceptability of these simulations or models.
Extrapolations can be of several types. For example, a temporal extrapolation (using an invoice from year N-2) will not have the same consequences as a geographical extrapolation (using data valid in rural areas for urban sites), or an extrapolation to a population (40% of people respond to a questionnaire, and the results are applied to all staff). The organisation should judge the level of acceptability of the extrapolation, as some incongruous extrapolations can be far less precise than the use of statistical data.
Once the extrapolation has been carried out, the organisation must take a critical look to qualify the associated uncertainty. The organisation can thus balance its efforts: if the extrapolation represents a significant share of the Bilan Carbone®, either the extrapolation must be precise, or the organisation must endeavour to collect real activity data. Conversely, if the extrapolation represents an extremely small share of the Bilan Carbone®, a moderately precise extrapolation may be acceptable.
🔎 These activity data are also called extrapolated activity data by the regulatory GHG report method.
Statistical activity data
These are data derived from more or less targeted statistics, for example for average home-to-work travel distances: the average may cover France, or the service sector in France, or even at the level of a particular municipality.
This data is generally associated with significant uncertainties, even if this varies depending on the dataset (France, municipality, sector) on which the statistic is based, the source of the data, and the date of the statistics used. It is advisable to limit the use of this type of data as much as possible, and to use it only for emission sources where data would be completely missing, or for non-significant emission sources. For example, for a supplier or partner who does not respond to the organisation's requests.
The organisation must take a critical look at the statistic used to qualify the associated uncertainty.
🔎 This statistical activity data is called secondary activity data by the regulatory GHG report method.
Approximate activity data
These are data that are not representative of the situation within the organisation, but which are nonetheless used in cases where the organisation is unable to propose an extrapolation adapted to the organisation or to access physical or statistical data.
This data is associated with very high uncertainties. It is advisable to limit the use of this type of data as much as possible, and to use it only for emission sources where data would be completely missing, or for non-significant emission sources.
🔎 These activity data are also called approximate activity data by the regulatory GHG report method.
Summary of the different types of activity data
Real
kWh of electricity consumed on site, shown on the invoice
Very low
Extrapolated
kWh of electricity consumed on site, extrapolated from invoices for the first six months of the year
Variable depending on the quality of the extrapolation
Statistical
kWh of electricity consumed on site, obtained from the French average electricity consumption per m² in the service sector
Medium
Approximate
kWh of electricity consumed by another organisation in the same sector, uncorrected
High
Requirements relating to data collection
Here are the various requirements to be met in terms of activity data collection for each of the 3 maturity levels.
Initial Level: criterion K1
All data collection methods are accepted, provided that the most precise data is always prioritised.
The organisation must build a solid documentation process that will enable data collection to be improved for future Bilan Carbone® exercises. If done precisely, the data collection matrix can form an integral part of this documentation.
The organisation must precisely identify data that would not be sufficiently precise or accessible, and make it more reliable before the next Bilan Carbone® exercise.
Standard or Advanced Level: criteria K2 and K3
Any extrapolated, statistical or approximate data must be framed and subject to extensive documentation that justifies:
The need for its use in place of physical data (specifying why physical data is inaccessible or unsatisfactory)
The associated assumptions and the methodology used to obtain the data
Where relevant, the tools used to obtain the data
In the case of an Advanced Assessment, it is strongly recommended that the organisation attempt to the greatest possible extent to exclude the use of any extrapolated, statistical or approximate data.
The organisation must build a solid documentation process that will enable data collection to be improved for future Bilan Carbone® exercises. If done precisely, the data collection matrix can form an integral part of this documentation.
Furthermore, the organisation must implement actions that will improve data quality and facilitate access to real data, for example by supporting its suppliers in making the data they transmit more reliable.
The organisation must also implement data monitoring systems within itself, and in particular an emissions dashboard for significant emissions. These monitoring systems can be automated and periodic (monthly, annual, etc.), depending on the difficulty of collection and the GHG quantities associated with this data. Activities associated with highly emissive activities must be covered first by these monitoring systems. Less emissive activities will be covered progressively as the organisation gains maturity on the climate issue.
Such processes enable the organisation to develop a "climate culture" within itself, which promotes the implementation of reduction actions and monitoring of their implementation.
Do you have a comprehension question? Consult the FAQ. The method is living and therefore subject to change (clarifications, additions): find the change log here.
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