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What is the ADEME “Monetary Approach”? (A friendly guide)

  • Writer: Antonio Anguiano
    Antonio Anguiano
  • Oct 7
  • 3 min read

1) The core idea : Physical vs Monetary approach (ADEME)


ADEME provides two complementary ways to turn your activity into emissions: physical factors (convert kWh, km, liters, kg → kgCO₂e) and monetary factors (convert € spent, HT → kgCO₂e using ratios expressed in kgCO₂e/k€). The GHG Protocol also recognizes this split (activity/average-data vs. spend-based).

Approach

What it uses

Best for

Overview

Physical (activity-based)

Meters & quantities (kWh, L, km, kg)

Electricity, fuels, refrigerants, freight, well-instrumented processes

Higher accuracy, needs more efforts and time to collect data.

Expensive

Monetary (spend-based)

€ HT per purchase category × ADEME ratio (kgCO₂e/k€)

Broad coverage, quick Scope 3 baselines (purchased goods & services, capital goods)

Fast, scalable, affordable, good first pass

Monetary ratios come from environmentally extended input–output (EEIO) models that link sectors’ economic flows to their environmental impacts

2) What ADEME’s monetary factors are (in plain English)


ADEME publishes average carbon intensities per euro for many purchase categories (e.g., office supplies, IT, business travel). Technically, these factors come from environmentally extended input–output (EEIO) models that link economic flows to environmental impacts. You convert kgCO₂e/k€ to kgCO₂e/€, multiply by your spend (HT), and sum.


Formula:

EF (kgCO₂e/€) = EF (kgCO₂e/k€) / 1000
Emissions (kgCO₂e) = SpendHT (€) × EF (kgCO₂e/€)

3) What it’s best for


  • Screening & baselines: quick, comparable first pass across all your spend—especially Scope 3: Purchased Goods & Services and Capital Goods.

  • Services where you rarely have physical data (kWh, liters, tonne-km).

  • Company-wide coverage from a single accounting export (FEC) using ADEME’s Base Carbone/Base Empreinte datasets.



4) What it’s not for


  • High-precision cases where you have activity data (electricity in kWh, fuel in liters, freight in tonne-km). Use physical factors there—more accurate than price-based averages.

  • Situations highly sensitive to inflation or product mix differences; monetary ratios are averages and carry larger uncertainty.



5) How the calculation works (step by step)


  1. Input your spend (net of VAT) from the French accounting export FEC and map each account/line to an ADEME monetary category.

  2. Pick the factor for that category (unit: kgCO₂e per k€ HT) and convert:

    EF_kg_per_eur = EF_kg_per_keuro / 1000 Emissions_kg = Amount_HT_EUR × EF_kg_per_eur

  3. Assign Scope & GHG category based on the factor family (e.g., Purchased Goods & Services = Scope 3).

  4. Aggregate by Scope, category, account, supplier, and period.

  5. Document factor source, version, geography (France by default), and year.


Example:

You spent €1,000 HT on office supplies.

The ADEME factor says 250 kgCO₂e/k€.→ 250/1000 = 0.25 kgCO₂e/€ → 1,000 × 0.25 = 250 kgCO₂e.



6) Do’s and Don’ts (checklist)


Do

  • Use HT (net of VAT) amounts.

  • Keep geography & year consistent with the factor you choose (ADEME factors are published for France with versioning).

  • Prefer physical data when you have it (electricity, fuel, freight).

  • Tag each line with a quality level (specific match vs. generic fallback).


Don’t

  • Count stocks (class 3)—use the corresponding purchase accounts (class 6) to avoid double counting.

  • Mix gross (TTC) and net (HT) amounts.

  • Forget currency conversion and price inflation effects when comparing years. Monetary ratios are sensitive to them.



7) Where the factors live


  • ADEME Base Carbone / Base Empreinte (public datasets & docs). Data.gouv.fr+1

  • ADEME’s “Ratios monétaires” page explains the definition, units, and intended uses.

  • For method alignment with GHG Protocol (especially Scope 3 Category 1: Purchased Goods & Services), see the official guidance. ghgprotocol.org+1


8) Bottom line


The ADEME monetary approach turns spend → emissions using sector averages. It’s the best starting point for a whole-company footprint and for spotting hot spots quickly. As your data matures, switch high-impact areas to activity-based (physical) methods to sharpen accuracy—without losing the speed and coverage that monetary factors provide.

 
 
 

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