4.2 - Activity data collection method
How to collect the activity data necessary for accounting?
This subsection details what an activity data is, how to collect it and how the different types of activity data can be used.
As a reminder, 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 are the data that account for the different physical flows of the organisation. A mass of raw material, the number of kilometres travelled by 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 recommended to collect the data that allows the use of the most precise emission factor, and convert it if necessary. If only one type of data is available, the appropriate emission factor must be chosen.
Activity data (km) x emission factor (kgCO2e/km) = result (kgCO2e) ± uncertainty (kgCO2e)
Activity data collection
Collecting this activity data is often the most time-consuming phase for a Beginner level approach. It is indeed necessary to identify, for each emission source, whether and in which form the data exists within the organisation, and who holds it. If some data are not available, some Process improvement action will need to be part of the transition plan, in order to build monitoring of these data in anticipation of the next Bilan Carbone®. If data collection was planned upstream and improved following the first assessment, it can prove faster (notably for an Intermediate level assessment). An organisation carrying out an Advanced level Bilan Carbone® will have put in place 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, relying on theidentification of emission sources, the boundaries previously developed and the data already present within the organisation.
Assign a coordinator 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 keep all collected data, including notably their source, units and theuncertainty associated, the accompanying documents (for example invoices, delivery notes, internal reports) and any other information deemed relevant, to ensure that these data are traceable and comparable year on year.
Balance collection efforts: Collecting a very precise data point for a non-significant emission source is often time-consuming. It is important to ensure careful collection for significant emission sources. Focusing on action and the reduction of significant emissions is at the heart of the Bilan Carbone®.
In the long run, it is recommended that the organisation implements a system that monitors data collection, which will allow it to raise certain indicators, to carry out future Bilan Carbone® processes more quickly, and to ensure the monitoring of its Transition Plan. However, note, as developed below, that automated collection of accounting activity data will not allow these three objectives to be achieved, because these data will rely on emission factors in Spend-based emission factors.
🔎 To express the Bilan Carbone® with an "analytical" reading, in coherence with the Carbon accounting analytical framework, activity data must be collected by analytical axis, and therefore broken down by responsibility (suppliers, customers, 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 must integrate the analytical axes identified as relevant for the analysis of results. The absence of analytical axes in a data source necessary for dividing emissions according to that analytical axis may justify abandoning that analytical axis. Adding the analytical axis to the data source will be a Process improvement action of the approach.
Data collection matrix
For each emission source, this matrix must 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 Carbon accounting analytical framework, the matrix indicates for each activity data point the accounting code and the associated analytical axis.
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 shortly.
The different types of activity data
The four main types of activity data are detailed below. They are described from the most reliable (actual data) to the least reliable (approximate data).
Actual activity data
Actual activity data can represent a physical or accounting.
🔎 These actual activity data are called primary activity data by the method of the Regulatory GHG Assessment.
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 involved physical flows, or because they can 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 actual 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 recommended to use this type of activity data as much as possible.
The organisation shall nevertheless adopt a critical view on 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 is strongly discouraged to generalise the use of these data, for two reasons:
These accounting activity data are associated with emission factors in Spend-based emission factors, which themselves are very uncertain
These activity data do not allow 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 expenditures, which is limited and sometimes counterproductive (buying better is often synonymous with buying more expensive)
Extrapolated activity data
These are data resulting from an extrapolation from other physical, statistical or approximate data. This type of data is regularly used in Bilan Carbone® processes, notably for travel and catering categories, via the sending of a questionnaire to the organisation's employees. A certain number of people respond to the questionnaire, and the results are extrapolated to the entire employee population. The organisation can also use extrapolations when some data for the current year are not yet available (for example using an electricity invoice from a previous year), or when it uses activity data from sectors, sites or geographic locations that are not representative of its situation, applying a corrective factor. Note that if non-representative data are used as-is and uncorrected, 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 actual data.
The assumption used to extrapolate must be documented within the Data collection matrix.
Data resulting from computer simulations or Artificial Intelligence (AI) models are also considered extrapolations. The organisation must then assess the acceptability level 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 geographic 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 the entire employee population). The organisation must judge the acceptability level of the extrapolation, as some incongruous extrapolations can be much more imprecise than using statistical data.
Once the extrapolation has been carried out, the organisation shall take a critical view to qualify the associated uncertainty. The organisation can thus balance its collection efforts: if the extrapolation represents a significant share of the Bilan Carbone®, either the extrapolation must be precise, or the organisation must seek to collect actual 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 method of the Regulatory GHG Assessment.
Statistical activity data
These are data derived from more or less targeted statistics, for example for average home–work travel distances: the average may cover France, or the tertiary sector in France, or the scale of a particular municipality.
These data are generally associated with significant uncertainties, although this varies depending on the dataset (France, municipality, sector) on which the statistic is performed, the source of the data, and the date of the statistics used. It is recommended to limit the use of this type of data as much as possible, and to use them 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 shall adopt a critical view on the statistic used to qualify the associated uncertainty.
🔎 These statistical activity data are called secondary activity data by the method of the Regulatory GHG Assessment.
Approximate activity data
These are data that are not representative of the situation within the organisation, but which are nonetheless used when the organisation is unable to provide an extrapolation adapted to the organisation or to access physical or statistical data.
These data are associated with very large uncertainties. It is recommended to limit the use of this type of data as much as possible, and to use them 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 method of the Regulatory GHG Assessment.
Summary of the different types of activity data
Actual
kWh of electricity consumed on site, indicated on the invoice
Very low
Extrapolated
kWh of electricity consumed on site, extrapolated from invoices over 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 tertiary sector
Average
Approximate
kWh of electricity consumed by another organisation in the same sector, uncorrected
High
Requirements related to data collection
Here are different requirements to be achieved in terms of activity data collection for each of the 3 maturity levels.
Beginner level: criterion K1
All data collection methods are accepted, provided that the most precise data are always prioritised.
The organisation shall build a solid documentation process that will improve data collection for subsequent Bilan Carbone® exercises. If created precisely, the data collection matrix can be an integral part of this documentation.
The organisation shall identify precisely the data that would not be sufficiently accurate or accessible, and secure them before the next Bilan Carbone® exercise.
Intermediate or Advanced level: criteria K2 and K3
Any extrapolated, statistical or approximate data must be framed and subject to extensive documentation that justifies:
The necessity of its use instead of physical data (specifying in what way the physical data is 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 level Bilan Carbone®, it is strongly recommended that the organisation endeavours as much as possible to exclude the use of any extrapolated, statistical or approximate data.
The organisation shall build a solid documentation process that will improve data collection for subsequent Bilan Carbone® exercises. If created precisely, the data collection matrix can be an integral part of this documentation.
In addition, the organisation shall implement actions that will improve data quality, facilitate access to actual data, for example by supporting its suppliers to secure the data provided by them.
The organisation shall also implement data monitoring systems within itself, including notably a dashboard of significant emissions. These monitoring systems can be automated and periodic (monthly, annual, etc.), depending on the difficulty of collection and the GHG masses associated with these data. Data associated with highly emitting activities must be covered first by these monitoring systems. Less emitting activities will be covered as the organisation gains maturity on carbon issues.
Such processes allow the organisation to develop a "climate culture" internally, which favours the implementation of reduction actions and the monitoring of their implementation.
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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