1. Align with the client's specific requests
👉 Clients might have specific requests for data processing → they will have an impact on which module format you should opt for.
🎯 Examples of common requests
Custom or supplier-specific EFs (outside of Greenly's database):
The client must have paid for their integration, and the EFs should have been validated by the R& M team
These EFs must be added to the EF Manager as company-specific EFs before you can retrieve their IDs
⚠️ If custom EFs are required, the End-to-end module cannot be used — you must use the Advanced (manual) module
Excluding specific emission categories (e.g., meals, home heating, air conditioning):
Can be configured from Admeenly → Activity Data → Activities, Buildings & Employees → Employees section → "Employees activities that should not be accounted for"
After saving, trigger "Recalculate employee survey impact" to apply changes
Skipping part of the survey (meals, commute, or remote questions):
Configurable from Admeenly → General tab → Employee Survey section (checkboxes to skip sections)
⚠️ This only prevents employees from being asked those questions — it does not remove related emissions. To remove emissions, use the exclusion setting above
2. Choose the module that best fits your needs
🈵 Manual processing (Advanced module)
Some clients will need a study that differs from the standard one because of the type of data they'll provide
Choose this option only if the End-to-end module cannot be used
Cases where the Advanced module must be used:The client cannot or will not distribute the questionnaire to its employees
The client only has pre-aggregated or differently-structured data that does not fit the questionnaire payload
Custom / supplier-specific EFs are required and must be handled outside the automated flow
⭕ 100% automatic processing (E2E module)
✅ To be used as much as possible — it allows the client to be fully autonomous from data collection to data processing
Employees fill in the survey app directly at employee.greenly.earth
By default, whenever a new profile is created, an automated module computes employee emissions and uses average data for employees who didn't answer
⚠️ Cases where this module cannot be used:The client cannot or will not distribute the questionnaire to its employees
The client only has pre-aggregated or differently-structured data
Custom / supplier-specific EFs are required
3. Data Processing
The goal of the employee survey study is to compute CO₂e emissions for each employee based on their working habits and to extrapolate emissions for employees who did not answer.
⚠️ If you are working on a multi-year account, don't forget to check the Rebaselining section.
🈵 Manual processing (Advanced module)
💡 When you do an analysis for a company, first check if a similar analysis has been done for previous years, and check with the deal owner/client if a rebaselining has been planned.
The analysis spreadsheet can be found here (FR version here)
Goals of the manual module:
Calculate employee emissions when the survey is not available/possible for the client
Differentiate between required data for GHG Protocol and BEGES
Easy to use, with automatic calculation
Possibility to extrapolate missing answers
⚠️ Please use the Employee Survey app (E2E module) as often as possible. Use this manual analysis only when strictly necessary.
⚠️ This module is an a ctivity module — results will not be displayed in the "Employee" section but in the specific module in Activity - Overview.
Step 1 — Remove average emissions from the automated employee module:
If you want to use the manual module, you must first exclude all emissions computed by the automated module:
Go to Admeenly → Activity Data → Activities, Buildings & Employees (top right corner)
Go to the "Employees" section (scroll down), click on the text input next to "Employees activities that should not be accounted for" and select all categories (Air Conditioning, Commute, Home Heating, Meals)
Save — emissions will be automatically recomputed. Check on the platform that the automated employee survey shows "< 1gCO2e". If not, click "Recalculate Employee Survey Impact" on the General tab.
You also need to skip the client's task associated with the automated employee survey:Go to your client's profile on the platform
Go to "Data < Employees" and click on "Skip module" (top right corner)
Provide an explanation (most likely "Other") and note that you'll be using the manual module
Step 2 — Compute emissions and upload them:
Ask your client to populate the template (downloadable from the module on the platform)
Download the analysis file and paste your client's data into the "TO COMPLETE" sheet
Verify the following:
A2: Total number of employees
Sheet'Parameters':
Methodology used (very important for EF selection)
Country of study (very important for GHG Protocol — use "UK" for European countries other than France, "US" for non-European countries)
Start and end date of the study
Check Column B: ensure clients selected from the dropdown only (no free-text transport modes)
Check Column D: flag any very high distances (commuting distances should be realistic)
Check "Days Worked" vs "Days Worked Remotely": employees sometimes confuse total days worked with days at the office
Check cell J3: it should show 100%. If not, use the extrapolation tool (menu bar → "Outil D'extrapolation" → "Générer les réponses manquantes";only available in Gsheet) to fill in the missing rows, then re-check
If everything is correct, there should be no Error Messages in the "Calculation" sheet
Download the "Import tab" sheet as CSV and upload it on Admeenly
⚠️ Map the "Override PC"column to"Purchase Category Override (id)"
⚠️ If the upload returns a "Processing Error", click "See Import Logs" to identify the issue (often a wrong unit or an outdated EF id)
⭕ Automatic processing (E2E module)
No processing is needed since this is a fully automated module.
Once employees have answered the survey, the platform automatically computes emissions. To force a recalculation at any time, go to Admeenly → General tab → Employee Survey → "Recalculate employee survey impact".
Employees who haven't answered are automatically extrapolated using global average emissions. To disable extrapolation, check the "Avoid Extrapolation" checkbox on the same tab and trigger a recalculation.
♻️ Rebaselining
The goal of rebaselining is to apply the latest version of EFs to the base year and to ensure that categorization is harmonized with the current year → This allows you to study the annual evolution of emissions.
💡 When you do an analysis for a company, first check if a similar analysis has been done for previous years, and check with the deal owner/client if a rebaselining has been planned. It's very important to ensure that, across years, similar modules were used. Having consistency in the modules used ensures that EFs and methodology are consistent over years, enabling emissions comparisons.
⚠️ Do not proceed to any rebaselining without aligning with the deal owner/client on specifics (custom EFs, EF exclusions that differ from the current year, PC overrides, etc.)
🅰️ Automatic rebaselining
For the E2E module, the platform takes into account previously done studies to adapt its calculations. You can simply re-upload the data and follow the module's process for an efficient rebaselining. No manual intervention needed unless custom EFs were used.
Ⓜ️ Manual rebaselining
👉 Avoid using this section unless the data you have to rebaseline is very limited.
🎯 Steps to follow:
Retrieve the original analysis file for the base year
Update the "Parameters" sheet with the correct methodology, country, and study dates
Re-run the calculation to apply updated EFs
Re-upload the corrected "Import tab" CSV via Admeenly
💡 Try to harmonize categorization throughout the years as much as you can
4. Review the data
Once data has been uploaded (Advanced module) or the survey has been completed by employees (E2E module), go to the "2. Review" section. There, you need to:
Review expenses flagged as potential duplicates
→ The platform will flag expenses categorized under COMMUTE_EMPLOYEES, EMPLOYEE_MEAL, HOME_HEATING, and HOME_AIR_CONDITIONING — since these are the categories most likely to overlap with spend-based expenses
→ Review them and flag the ones linked to activity data uploaded in the module as duplicatesReview Data Quality checks
→ Activity data that deviates from expectations will be flagged here
→ If no checks appear, everything is good. If any check shows up, review and make changes if necessary