VSME Primary vs. Secondary Data: Practical Examples for Suppliers

Why practical examples, alongside the explainer

Our article on primary vs. secondary data explains what the distinction is and why it matters to your client's auditor. This article shows how large the difference actually turns out to be in practice, with three calculation examples from different sectors.

One thing upfront: the figures below are illustrative, not official emission factors. verified.supply deliberately does not build in fixed sector benchmarks — in the wizard's calculation helper, you enter your own activity data, your own factor, and that factor's source (for example from co2emissiefactoren.nl or a supplier invoice). The examples below show the principle, not a prescribed number.

Example 1 — Transport: your own fuel consumption vs. a generic tonne-km factor

A haulier drives 45,000 km a year with an 18-tonne truck for a regular client. Without their own figures, the client falls back on a generic sector factor per tonne-kilometre — for instance (illustrative) 0.12 kg CO2e per tonne-km, multiplied by the weight carried and the distance. For 45,000 km with an average load of 12 tonnes, that comes to roughly 64.8 tonnes CO2e a year.

If the haulier instead provides their own fuel consumption — say 16,500 litres of diesel a year, taken directly from their own fuel card records — the calculation helper works out emissions using a recognised diesel factor (for example 2.68 kg CO2e per litre, source: co2emissiefactoren.nl). That comes to roughly 44.2 tonnes CO2e: notably lower than the generic estimate, because the latter is conservatively rounded up to cover a worst-case scenario.

For the client, this makes a direct difference: the same route now counts as primary data instead of an estimate, and the real figure is measurably lower than the generic assumption — exactly the kind of improvement a CSRD report needs to show year on year.

Example 2 — Energy: your own energy contract vs. the national residual grid mix

A manufacturing company uses 120,000 kWh of electricity a year. Without further information, the client uses the national residual grid mix for scope 2 location-based (B3.7) — for instance (illustrative) 0.33 kg CO2e per kWh, equal to roughly 39.6 tonnes CO2e.

If the supplier has their own energy contract with Guarantees of Origin (for example 100% wind power), the location-based figure (B3.7) stays unchanged — it is, after all, based on the national grid — but a market-based figure (X1.2) is added that does reflect the supplier's own purchase, and moves towards zero for 100% renewable electricity backed by valid GoOs.

This is exactly why the VS Standard asks for both: B3.7 (location-based) shows what the grid delivers on average, X1.2 (market-based) shows what you actually bought. A supplier who only reports B3.7 misses the chance to make their own sustainable energy choice visible.

Example 3 — Raw materials: a product-specific factor vs. a spend-based estimate

A metal processor purchases 8 tonnes of steel a year, worth €24,000. Without product data, the client falls back on a spend-based estimate: purchase amount × a generic sector factor per euro spent — for instance (illustrative) 0.85 kg CO2e per euro, equal to roughly 20.4 tonnes CO2e.

If the metal processor instead provides their own purchase invoice with a product-specific CO2 factor from the steel producer (for example from an Environmental Product Declaration), the result depends on the production process: recycled steel from an electric arc furnace typically has a considerably lower factor per tonne than steel from a blast furnace using primary iron ore — in this example (illustrative) 1.1 tonnes CO2e per tonne of steel, so 8.8 tonnes CO2e in total.

The gap between the spend-based estimate (20.4 tonnes) and the product-specific primary data (8.8 tonnes) is more than double in this example — and that gap is exactly what a spend-based method structurally cannot show: two steel purchases of the same value can have a very different footprint depending on the production process.

What this means for your client

In all three examples, something fundamental changes: the data no longer counts as secondary (an estimate that comes out the same for every comparable supplier), but as primary — traceable to your own activity, with a source and a year. That is exactly the quality percentage your client is assessed on under ESRS E1.

It also affects the outcome: in two of the three examples above, the real emissions come out lower than the generic estimate, because sector averages are often conservatively rounded up. Primary data is therefore not just more accurate — in practice it regularly paints a more favourable picture than the estimate your client would otherwise have had to use.

How to record this in your own VS profile

Every numeric field in the verified.supply wizard has a "Calculate" mode alongside direct entry: you enter your activity data (litres of diesel, kWh, tonnes of material), your own conversion factor, and that factor's source. The calculation helper then does only the multiplication — activity × factor = value — and automatically sets `is_estimated` to the right value.

The full calculation, including source and year, appears in the Supplier Evidence PDF and the machine-readable export your client receives. That way, your client's auditor can see at a glance that this is primary data, and exactly how it was derived.