> For the complete documentation index, see [llms.txt](https://www.bilancarbone-methode.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.bilancarbone-methode.com/english/4-accounting/4.2-methode-de-collecte-des-donnees-dactivite.md). # 4.2 - Activity data collection method

Source: Midjourney

This sub-section details what activity data is, how to collect it and how the different types of activity data can be used. {% hint style="info" %} As a reminder, the emissions from each emission source of the organisation are estimated as follows: Emission from a source = [Activity data](/english/4-accounting/4.2-methode-de-collecte-des-donnees-dactivite.md) x [Emission factor](/english/4-accounting/4.3-methode-de-selection-des-facteurs-demission.md) = [Result](/english/4-accounting/4.5-profil-demission.md) ± [Uncertainty](https://github.com/ABC-TransitionBasCarbone/methode-bilan-carbone/blob/translated-en/4-comptabilisation/4.4-methode-destimation-des-incertitudes) {% endhint %} ## 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. {% hint style="info" %} [Activity data](/english/4-accounting/4.2-methode-de-collecte-des-donnees-dactivite.md) (**km**) x [Emission factor](/english/4-accounting/4.3-methode-de-selection-des-facteurs-demission.md) **(kgCO₂e/km**) = [Result](/english/4-accounting/4.5-profil-demission.md) (**kgCO₂e**) ± [Uncertainty](https://github.com/ABC-TransitionBasCarbone/methode-bilan-carbone/blob/translated-en/4-comptabilisation/4.4-methode-destimation-des-incertitudes) (**kgCO₂e**) {% endhint %} ## 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](/english/5-transition-plan/5.2-construction-du-plan-daction.md#les-differentes-categories-dactions) 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](#matrice-de-collecte-des-donnees), which **precisely** defines the data to be collected, drawing on the [identification of emission sources](/english/2-scope-of-the-approach/2.4-perimetre-operationnel.md#identification-des-sources-demissions), the [perimeters](/english/2-scope-of-the-approach/2-introduction-a-lidentification-des-perimetres.md) 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](https://github.com/ABC-TransitionBasCarbone/methode-bilan-carbone/blob/translated-en/4-comptabilisation/4.4-methode-destimation-des-incertitudes) 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](/english/5-transition-plan/5.5-suivi-et-pilotage-du-plan-de-transition.md). However, as developed below, the automated collection of [financial activity data](#les-donnees-dactivite-comptables) will not achieve these three objectives, as this data will call upon [spend-based emission factors](/english/4-accounting/4.3-methode-de-selection-des-facteurs-demission.md#fe-en-ratios-monetaires). > :mag\_right: *To express the Bilan Carbone® with an "analytical" reading, in line with* [*analytical carbon accounting*](/english/annexes/bibliographie.md#guides-pratiques-de-comptabilisation)*,* *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*](/english/2-scope-of-the-approach/2.4-perimetre-operationnel.md#exigences-relatives-a-la-cartographie-des-sources-demissions) *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*](/english/annexes/glossaire.md#a) *(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*](/english/5-transition-plan/5.2-construction-du-plan-daction.md#les-differentes-categories-dactions) *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](#recapitulatif-des-differents-types-de-donnees-dactivite), the source and the [uncertainty](https://github.com/ABC-TransitionBasCarbone/methode-bilan-carbone/blob/translated-en/4-comptabilisation/4.4-methode-destimation-des-incertitudes) (with a single characteristic) of the activity data. > :mag\_right: *In* [*analytical carbon accounting*](/english/annexes/bibliographie.md#guides-pratiques-de-comptabilisation)*, 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](/english/4-accounting/4.3-methode-de-selection-des-facteurs-demission.md#les-differents-types-de-facteurs-demission) and its source. > ⏳\[[WIP](/english/readme.md#structures-des-informations-specifiques)] 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](#les-donnees-dactivite-physiques) or [financial](#les-donnees-dactivite-comptables) reality. > :mag\_right: *These real activity data are called primary activity data by the* [*regulatory GHG report*](/english/annexes/bibliographie.md#autres-standards-normes-et-reglementations-de-comptabilisation-des-emissions-de-ges) *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**](/english/4-accounting/4.4-methode-destimation-des-incertitudes/4.4.1-pourquoi-des-incertitudes.md)**.** #### 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](/english/4-accounting/4.3-methode-de-selection-des-facteurs-demission.md#fe-en-ratios-monetaires), 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](#les-donnees-dactivite-approchees) 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](#matrice-de-collecte-des-donnees). {% hint style="info" %} 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. {% 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 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. {% endhint %} Once the extrapolation has been carried out, the organisation must take a critical look [**to qualify the associated uncertainty**](/english/4-accounting/4.4-methode-destimation-des-incertitudes/4.4.1-pourquoi-des-incertitudes.md). 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. > :mag\_right: *These activity data are also called extrapolated activity data by the* [*regulatory GHG report*](/english/annexes/bibliographie.md#autres-standards-normes-et-reglementations-de-comptabilisation-des-emissions-de-ges) *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**](/english/4-accounting/4.4-methode-destimation-des-incertitudes/4.4.1-pourquoi-des-incertitudes.md)**.** > :mag\_right: *This statistical activity data is called secondary activity data by the* [*regulatory GHG report*](/english/annexes/bibliographie.md#autres-standards-normes-et-reglementations-de-comptabilisation-des-emissions-de-ges) *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. > :mag\_right: *These activity data are also called approximate activity data by the* [*regulatory GHG report*](/english/annexes/bibliographie.md#autres-standards-normes-et-reglementations-de-comptabilisation-des-emissions-de-ges) *method.* ### Summary of the different types of activity data
Type of ADExampleAssociated uncertainty
RealkWh of electricity consumed on site, shown 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 electricity consumption per m² in the service sectorMedium
ApproximatekWh of electricity consumed by another organisation in the same sector, uncorrectedHigh
## 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](/english/1-scoping-the-approach/1.1-definir-son-niveau-de-maturite-bilan-carbone-r.md).
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](/english/5-transition-plan/5.2-construction-du-plan-daction.md) 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](/english/5-transition-plan/5.5-suivi-et-pilotage-du-plan-de-transition.md#tableau-de-bord-renommer-dispositif-de-suivi) 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](/english/5-transition-plan/5.4-mise-en-oeuvre-du-plan-de-transition.md) and [monitoring of their implementation](/english/5-transition-plan/5.5-suivi-et-pilotage-du-plan-de-transition.md).
*** *Do you have a comprehension question?* [*Consult the FAQ*](/english/annexes/faq.md)*. The method is living and therefore subject to change (clarifications, additions): find the* [*change log here*](/english/readme/historique-et-suivi-des-modifications.md)*.* --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://www.bilancarbone-methode.com/english/4-accounting/4.2-methode-de-collecte-des-donnees-dactivite.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.