# 4.2 - Method for collecting activity data

Source: Midjourney

This subsection details what an activity data is, how to collect them and how the different types of activity data can be used. {% hint style="info" %} As a reminder, emissions from each emission source of the organisation are estimated as follows: Emission from a source = [Activity data](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4-accounting/4.2-method-for-collecting-activity-data) x [emission factor](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4-accounting/4.3-method-for-selecting-emission-factors) = [result](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4-accounting/4.5-emission-profile) ± [uncertainty](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4-accounting/4.4-method-for-estimating-uncertainties) {% endhint %} ## Activity data Activity data are the data that report on the various physical flows of the organisation. A mass of raw material, the number of kilometres travelled by the different 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 if necessary to convert it. If only one type of data is available, the appropriate emission factor should then be chosen. {% hint style="info" %} [Activity data](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4-accounting/4.2-method-for-collecting-activity-data) (**km**) x [emission factor](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4-accounting/4.3-method-for-selecting-emission-factors) **(kgCO₂e/km**) = [result](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4-accounting/4.5-emission-profile) (**kgCO₂e**) ± [uncertainty](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4-accounting/4.4-method-for-estimating-uncertainties) (**kgCO₂e**) {% endhint %} ## Collection of activity data Collecting these activity data is often the most time-consuming phase for a beginner-level approach. It is 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 are not available, [Process improvement action](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/5-transition-plan/5.2-construction-of-the-action-plan#les-differentes-categories-dactions) 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 the collection was thought out upstream and improved following the first assessment, it can prove faster (notably for an intermediate Bilan). An organisation carrying out an advanced Bilan will have put in place a data collection dashboard on an ongoing basis. To optimise present and future data collections, the organisation shall: * Create a [Data collection matrix](#matrice-de-collecte-des-donnees), which defines **precisely** the data to be collected, based on the[identification of emission sources](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/2-scope-of-the-approach/2.4-operational-boundary#identification-des-sources-demissions), the [boundaries](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/2-scope-of-the-approach/2-introduction-to-scope-identification) elaborated previously 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 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 the[uncertainty ](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4-accounting/4.4-method-for-estimating-uncertainties)associated, related documents (for example invoices, delivery notes, internal reports) and any other information deemed relevant, to ensure that these data are traceable and comparable from year to year. * Balance data 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 when collecting data 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 equips itself with a system that monitors data collection, which will allow it to surface certain indicators, to carry out future Bilan Carbone® approaches more quickly, and to ensure the [monitoring of its transition plan](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/5-transition-plan/5.5-monitoring-and-governance-of-the-transition-plan). Be careful however, as developed below, the automated collection of [accounting activity data](#les-donnees-dactivite-comptables) will not allow these three objectives to be achieved, because these data will call upon emission factors in [Spend-based emission factors](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4.3-method-for-selecting-emission-factors#fe-en-ratios-monetaires). > :mag\_right: *To express the Bilan Carbone® with an "analytical" reading, in coherence with the* [*carbon accounting framework*](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/appendices/bibliography#guides-pratiques-de-comptabilisation)*,* *activity data must be collected by analytical dimension, and therefore allocated by responsibility (suppliers, customers, sites, etc.). Accounting codes make it possible to be exhaustive on the* [*Supported emissions*](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/2-scope-of-the-approach/2.4-operational-boundary#exigences-relatives-a-la-cartographie-des-sources-demissions) *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*](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/appendices/glossary#a) *(for example an export of purchases broken down by suppliers).* > > *Data sources must incorporate the analytical dimensions identified as relevant for the analysis of results. The absence of analytical dimensions in a data source necessary to split emissions according to that analytical dimension can be a justification for abandoning that analytical dimension. The addition of the analytical dimension in the data source will be a* [*Process improvement action*](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/5-transition-plan/5.2-construction-of-the-action-plan#les-differentes-categories-dactions) *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](#recapitulatif-des-differents-types-de-donnees-dactivite), the source and [the uncertainty ](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4-accounting/4.4-method-for-estimating-uncertainties)(with a single characteristic) of the activity data. > :mag\_right: *In* [*carbon accounting framework*](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/appendices/bibliography#guides-pratiques-de-comptabilisation)*, the matrix indicates for each activity data, 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](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4.3-method-for-selecting-emission-factors#les-differents-types-de-facteurs-demission) and its source. > ⏳\[[WIP](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/foreword#structures-des-informations-specifiques)] An example of a data collection matrix will be made available soon. ## 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 (approximated data). ### Actual activity data Actual activity data can represent a [physical](#les-donnees-dactivite-physiques) or [accounting](#les-donnees-dactivite-comptables). > :mag\_right: *These actual activity data are called primary activity data by the method of the* [*Regulatory GHG Assessment*](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/appendices/bibliography#autres-standards-normes-et-reglementations-de-comptabilisation-des-emissions-de-ges)*.* #### 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 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 take a critical view of the statistic used [**to qualify the associated uncertainty**](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4-accounting/4.4-method-for-estimating-uncertainties/4.4.1-why-uncertainties)**.** #### 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 a very low uncertainty. However, it is strongly discouraged to generalise the use of these data, and this for two reasons: * These accounting activity data are associated with [emission factors in spend-based ratios](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4.3-method-for-selecting-emission-factors#fe-en-ratios-monetaires), 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 even 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 approximated data. This type of data is regularly used during Bilan Carbone® approaches, 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 all employees. 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 not representative of its situation, by applying a corrective factor. Careful, if non-representative data are used as-is and **uncorrected**, these data are not extrapolated data but [approximated](#les-donnees-dactivite-approchees). These data are generally quite reliable, as specifically adapted to the organisation. However, it is preferable to use actual data. The hypothesis used to extrapolate must be documented within the [Data collection matrix](#matrice-de-collecte-des-donnees). {% hint style="info" %} Data resulting from computer simulations or Artificial Intelligence (AI) models are also considered extrapolations. It is then for the organisation to judge the level of acceptability of these simulations or models. {% endhint %} {% hint style="info" %} 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 for 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 for the organisation to judge the acceptability level of the extrapolation, some incongruous extrapolations being much more imprecise than the use of statistical data. {% endhint %} Once the extrapolation has been carried out, the organisation shall take a critical view [**to qualify the associated uncertainty**](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4-accounting/4.4-method-for-estimating-uncertainties/4.4.1-why-uncertainties). The organisation can thus **balance its collection efforts** : if the extrapolation represents a significant part of the Bilan Carbone®, either the extrapolation must be precise, or the organisation must endeavour to collect actual activity data. Conversely, if the extrapolation represents an extremely small part of the Bilan Carbone®, a moderately precise extrapolation may be acceptable. > :mag\_right: *These activity data are also called extrapolated activity data by the method of the* [*Regulatory GHG Assessment*](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/appendices/bibliography#autres-standards-normes-et-reglementations-de-comptabilisation-des-emissions-de-ges)*.* ### Statistical activity data These are data that come from more or less targeted statistics, for example for average distances of commuting: the average may cover France, or the tertiary sector in France, or a specific municipality. These data are generally associated with significant uncertainties, even if 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 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 view of the statistic used [**to qualify the associated uncertainty**](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/4-accounting/4.4-method-for-estimating-uncertainties/4.4.1-why-uncertainties)**.** > :mag\_right: *These statistical activity data are called secondary activity data by the method of the* [*Regulatory GHG Assessment*](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/appendices/bibliography#autres-standards-normes-et-reglementations-de-comptabilisation-des-emissions-de-ges)*.* ### Approximated activity data These are data that **are not representative** of the situation within the organisation, but which are nevertheless used in cases where the organisation is not able 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 totally missing, or for non-significant emission sources. > :mag\_right: *These activity data are also called approximated activity data by the method of the* [*Regulatory GHG Assessment*](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/appendices/bibliography#autres-standards-normes-et-reglementations-de-comptabilisation-des-emissions-de-ges)*.* ### Summary of the different types of activity data
Type of ADExampleAssociated uncertainty
ActualkWh of electricity consumed on site, indicated on the invoiceVery low
ExtrapolatedkWh of electricity consumed on site, extrapolated from invoices for the first six months of the yearVariable depending on the quality of the extrapolation
StatisticalkWh of electricity consumed on site, obtained from the French average of electricity consumption per m² in the tertiary sectorAverage
ApproximatedkWh of electricity consumed by another organisation in the same sector, uncorrectedHigh
## 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](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/1-framework-for-the-approach/1.1-define-your-bilan-carbone-r-maturity-level).
Beginner level: criterion K1 All data collection methods are accepted, provided that the most precise data are always prioritised. The organisation shall build a robust documentation process that will improve data collection for future Bilan Carbone® exercises. If carried out precisely, the data collection matrix can become an integral part of this documentation. The organisation shall precisely identify the data that would not be sufficiently precise or accessible, and make them reliable before the next Bilan Carbone® exercise.
Intermediate level or Advanced level: criteria K2 and K3 Any extrapolated, statistical or approximated data shall be framed and be subject to extensive documentation justifying: * The necessity of its use instead of physical data (specifying why 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 Bilan, it is strongly recommended that the organisation attempts as much as possible to exclude the use of any extrapolated, statistical or approximated data. The organisation shall build a robust documentation process that will improve data collection for future Bilan Carbone® exercises. If carried out precisely, the data collection matrix can become an integral part of this documentation. Furthermore, the organisation shall put in place [actions ](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/5-transition-plan/5.2-construction-of-the-action-plan)that will improve data quality, facilitate access to actual data, for example by supporting its suppliers to make the data they transmit more reliable. The organisation shall also put in place [data monitoring systems](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/5-transition-plan/5.5-monitoring-and-governance-of-the-transition-plan#tableau-de-bord-renommer-dispositif-de-suivi) within itself, notably a dashboard of significant emissions. These monitoring systems can be automated and periodic (monthly, yearly, etc.), depending on the difficulty of collection and the GHG masses associated with these data. Data associated with highly emitting activities must be prioritised by these monitoring systems. Less emitting activities will be covered as the organisation gains maturity on the carbon issue. Such processes enable the organisation to develop a "climate culture" internally, which favours [the implementation of reduction actions](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/5-transition-plan/5.4-implementation-of-the-transition-plan) and the [monitoring of their implementation](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/5-transition-plan/5.5-monitoring-and-governance-of-the-transition-plan).
*** *Do you have a question of understanding?* [*Consult the FAQ*](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/appendices/faq)*. The method is living and therefore likely to evolve (clarifications, additions): find the* [*monitoring of changes here*](https://www.bilancarbone-methode.com/methode-bilan-carbone-r-en/foreword/history-and-tracking-of-changes)*.*