4.2 - Method for collecting activity data

How to collect the activity data necessary for accounting?

Source: Midjourney

This subsection details what activity data are, how to collect them, and how the different types of activity data can be used.

As a reminder, the emissions of each emission source of the organisation are estimated as follows:

Emission of a source = Activity data x GHG emission factor = result ± uncertainty

Activity data

Activity data are the data that reflect the organisation’s various physical flows. A mass of raw material, the number of kilometres travelled by the various people involved, and 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. As a general rule, it is advisable to collect the data that allow the use of the most accurate emission factor, and convert them if necessary. If only one type of data is available, then the appropriate emission factor must be chosen.

Activity data (km) x GHG emission factor (kgCO2e/km) = result (kgCO2e) ± uncertainty (kgCO2e)

Activity data collection

Collecting this activity data is often the most time-consuming phase for an Initial level approach. It is necessary to identify, for each emission source, whether and in what form the data exist within the organisation, and who holds them. If certain data are not available, process improvement actions for collection shall be part of the Transition Plan, in order to build monitoring of these data in anticipation of the next Bilan Carbone®. If collection has been planned upstream and improved following the first assessment, it can be faster (notably for a Standard Assessment). An organisation carrying out an Advanced Assessment will have set up a rolling data collection dashboard.

To optimise present and future data collections, the organisation shall:

• Create a Data collection matrix, which defines precisely the data to be gathered, based on theidentification of emission sources, the Boundaries previously developed, 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 company’s structure), who is responsible for collecting the data for that category from the various holders.

• Document and retain all data collected, including notably their source, the units and theuncertainty associated uncertainty, the associated documents (for example invoices, delivery notes, internal reports) and any other information deemed relevant, to ensure that these data are traceable and comparable year over year.

• Balance collection efforts: Collecting very precise data for a non-significant emission source is often time-consuming. It is important to take care with collection for significant emission sources. Focusing on action and the reduction of significant emissions is the core 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 allow it to surface certain indicators, to conduct future Bilan Carbone® approaches more quickly, and to ensure the monitoring of its Transition Plan. Nevertheless, as developed below, automated collection of accounting activity data will not make it possible to achieve these three objectives, because these data will rely on emission factors in spend-based emission factors.

🔎 To express the Bilan Carbone® with a so-called “analytical” reading, consistent with analytical carbon accounting, activity data should be collected by analytical dimension, and therefore broken down by responsibility (suppliers, clients, sites, etc.). Accounting codes make it possible to be exhaustive on the Supported emissions by the organisation. The associated data sources are thus linked to each accounting code (for example an export of purchases for accounting code 601321), then to the Analytical dimensions (for example an export of purchases broken down by suppliers).

Data sources shall incorporate the analytical dimensions identified as relevant for the analysis of results. The absence of analytical dimensions in a data source necessary for splitting emissions according to that analytical dimension may justify abandoning that analytical dimension. Adding the analytical dimension to the data source will be a Process improvement action of the approach.

Data collection matrix

For each emission source, this matrix shall indicate the Bilan Carbone® 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 accounting code and the associated analytical dimension.

This matrix will also be used to list the emission factors that will be attached to these activity data, 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 of a data collection matrix will be made available in September 2024.

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 accounting.

🔎 reality. These real activity data are called primary activity data by the French regulation on greenhouse gas emissions reporting (Regulatory GHG Assessment or BEGES-R).

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 follow the physical flows involved, or because they are able to be traced retrospectively. For example, the mass of raw material used (example: 200 tonnes of new stainless steel per year) or the number of kWh of electricity consumed (indicated on the invoice) are real data that are easily collected by and for organisations.

Physical activity data are the most reliable data for carrying out a Bilan Carbone®. It is advisable to use this type of activity data as much as possible.

Nevertheless, the organisation shall take a critical look at the statistic used to qualify the associated uncertainty.

Accounting activity data

These are data available to the organisation with a very high degree of accuracy, because financial accounting is highly regulated. For example, the cost of electricity for the organisation is information that will always be available. These data are therefore associated with very low uncertainty.

However, it remains strongly discouraged to generalise the use of these data, for two reasons:

• These accounting activity data are associated with spend-based emission factors, which are themselves very uncertain

• These activity data do 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 accounting activity data is to reduce the organisation’s expenditure, which is limited and even sometimes counterproductive (buying better often means buying more expensive)

Extrapolated activity data

These are data resulting from an extrapolation based on other physical, statistical, or approximate data. This type of data is regularly used during Bilan Carbone® approaches, notably for the commuting and food categories, via sending a questionnaire to the organisation’s employees. A certain number of people respond to the questionnaire, and the results are extrapolated to all employees. The organisation can also use extrapolations when certain data for the current year are not yet available (for example using an electricity bill from a previous year), or when it uses activity data from sectors, sites or geographic locations that are not representative of its situation, by applying a correction factor. Caution, if the non-representative data are used as is and not corrected, these data are not extrapolated data but approximate.

data. These data are generally fairly reliable, as they are specifically adapted to the organisation. However, it is preferable to use real data.

The assumption used for extrapolation shall be documented in the Data collection matrix.

Data originating from computer simulations or Artificial Intelligence (AI) models are also considered extrapolations. It is then up to the organisation to judge the level of acceptability of these simulations or models.

Extrapolations can be of several types. For example, a temporal extrapolation (using a bill from year Y-2) will not have the same consequences as a geographical extrapolation (using data valid in rural areas for sites in urban areas), or as an extrapolation to a population (40% of people respond to a questionnaire, and the results are applied to all employees). It is up to the organisation to judge the level of acceptability of the extrapolation, as certain incongruous extrapolations can be far more imprecise than using statistical data.

Once the extrapolation has been carried out, the organisation shall take a critical look to qualify the associated uncertainty. The organisation can thus balance its efforts of collection: if the extrapolation represents a significant share of the Bilan Carbone®, either this extrapolation must be accurate, or the organisation shall strive to collect real activity data. Conversely, if the extrapolation represents an extremely small share of the Bilan Carbone®, a moderately accurate extrapolation may be acceptable.

🔎 These activity data are also called extrapolated activity data by the French regulation on greenhouse gas emissions reporting (Regulatory GHG Assessment or BEGES-R).

Statistical activity data

These are data derived from more or less targeted statistics, for example for average distances of home-to-work travel: the average may cover France, or the tertiary sector in France, or even the scale of a certain municipality.

These data are generally associated with significant uncertainties, although this varies depending on the dataset (France, municipality, sector) on which the statistic is based, the data source, 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 them only for emission sources where data are totally missing, or for non-significant emission sources. For example, for a supplier or partner not responding to the organisation’s requests.

The organisation shall take a critical look at the statistic used to qualify the associated uncertainty.

🔎 These statistical activity data are called secondary activity data by the French regulation on greenhouse gas emissions reporting (Regulatory GHG Assessment or BEGES-R).

Approximate activity data

These are data that are not representative of the situation within the organisation, but which are nevertheless used when the organisation is not able to propose an extrapolation adapted to the organisation or to access physical or statistical data.

These data are associated with very large uncertainties. It is advisable to limit the use of this type of data as much as possible, and to use them only for emission sources where data are totally missing, or for non-significant emission sources.

🔎 These activity data are also called approximate activity data by the French regulation on greenhouse gas emissions reporting (Regulatory GHG Assessment or BEGES-R).

Summary of the different types of activity data

Type of AD	Example	Associated uncertainty
Real	kWh of electricity consumed on site, indicated 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 of electricity consumption per m² in the tertiary sector	Medium
Approximate	kWh of electricity consumed by another organisation in the same sector, not corrected	High

Requirements relating to data collection

Here are different 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 accurate data are always prioritised.

The organisation shall build a robust documentation process that will make it possible to improve data collection for upcoming Bilan Carbone® exercises. If carried out precisely, the data collection matrix may form an integral part of this documentation.

The organisation shall identify precisely the data that would not be sufficiently accurate or accessible, and make them more reliable before the next Bilan Carbone® exercise.

Standard or Advanced level: criteria K2 and K3

Any extrapolated, statistical, or approximate data shall be controlled and be the subject of extensive documentation that justifies:

• The necessity of its use instead of physical data (specifying how the physical data are inaccessible or unsatisfactory)

• The associated assumptions and the methodology used to obtain the data

• If relevant, the tools used to obtain the data

In the case of an Advanced Assessment, it is strongly recommended that the organisation strive as much as possible to exclude the use of any extrapolated, statistical, or approximate data.

The organisation shall build a robust documentation process that will make it possible to improve data collection for upcoming Bilan Carbone® exercises. If carried out precisely, the data collection matrix may form an integral part of this documentation.

In addition, the organisation shall implement actions that will improve data quality and facilitate access to real data, for example by supporting its suppliers to increase the reliability of the data provided by them.

The organisation shall also implement data monitoring systems within its operations, notably a dashboard of significant emissions. These monitoring systems may be automated and periodic (monthly, annual, etc.), depending on the difficulty of collection and the GHG volumes associated with these data. Data associated with highly emissive activities shall be covered first by these monitoring systems. Less emissive activities will be covered as the organisation gains maturity on carbon matters.

Such processes enable the organisation to develop a “climate culture” internally, which promotes the implementation of emission reduction actions and the monitoring of their implementation.

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