Overview
This guide covers the three steps to process miscellaneous services data: aligning with client-specific requirements, processing using the lightweight module, and reviewing the results for quality.
Key use cases
Processing miscellaneous services data for a new reporting year
Rebaselining historical data to apply updated emission factors
Reviewing flagged duplicates and data quality issues
1. Align with client requirements
Before processing, check whether the client has specific requests that would affect which module to use:
Supplier-specific or custom emission factors (outside Greenly's database): Favor supplier-specific EFs over this module.
2. Process the data
Only the lightweight module is available for miscellaneous services. If the client cannot complete it, keep the monetary approach.
The goal is to associate each service activity with the most suitable emission factor through the module's automated calculation.
⚠️ If working on a multi-year account, check the Rebaselining section below before starting.
Automatic processing (Lightweight module)
The calculation is done automatically. The module covers 5 types of services for which benchmarks can be established across companies. Benchmarks are updated annually using three years of data.How benchmarks are built:
Companies with an emission repartition too far from the norm are filtered out. This repartition also defines sub-types (e.g., companies with > 30% of emissions in TravelAndCommute vs. ~15% for others).
Companies with total emissions per employee too far from the norm are excluded.
For each service type, a median across benchmark companies is used to compute yearly impact per parent PC — avoiding undue influence from individual outliers.
When sub-types apply (e.g., low vs. high level of product purchase), an average within the sub-type is used.
All parent purchase categories use benchmark data except FoodAndDrinks, which is calculated based on number of days worked (one average meal per day, BEGES only).
The benchmarks are available in the analysis file.
Rebaselining
Rebaselining applies the latest emission factors to the base year and ensures categorization is consistent across years — enabling reliable year-on-year emission comparisons.
Before starting:
Check if a similar analysis exists for previous years.
Confirm with the deal owner / client whether rebaselining is planned.
Ensure module consistency across years — mixing module types between years makes emission comparisons unreliable.
⚠️ Do not proceed with rebaselining without aligning with the deal owner/client on specifics (custom emission factors, EF exclusions, PC overrides, etc.)
Manual rebaselining: A rebaseline can be done if the client fills in the template for previous year(s). Harmonize categorization across years as much as possible.
3. Review the data
After uploading, go to the "2. Review" section to check two things:
Duplicate expenses: The platform flags potential duplicates. Review them and mark as duplicates any expenses linked to activity data already uploaded in the module.
Data Quality checks: Activity data that deviates from expectations is flagged here. If no checks appear, everything is valid. Otherwise, review and make corrections as needed.
FAQs
What if the client cannot complete the lightweight module?
Keep the monetary approach instead. The lightweight module is the only activity-based option for miscellaneous services.