Klappir’s spend-based emissions calculations for Purchased Goods & Services (Scope 3 Category 1) are based on emission factors derived from EXIOBASE 3.8, a globally recognised multi-regional environmentally-extended input-output (EEIO) database covering 44 countries plus a Rest-of-World aggregate, licensed CC-BY-SA 4.0.database.
The methodology estimates emissions based on:
spend amount + currency + purchasing category
Emission factors are mapped to UNSPSC purchasing categories and normalised across multiple currencies and reporting years.
What are spend-based calculations used for?
Spend-based calculations are designed for procurement data where physical quantity information is unavailable.
Typical examples include:
consulting services
software and SaaS
office purchases
professional services
general supplier spend
They are less accurate and reliable for purchases where activity data is available.
Data Types on the Klappir Platform | Description | Best practice |
Activity Data | Direct from source: e.g., 500 liters of diesel, 1200 kWh of electricity | Preferred for accuracy and transparency |
Precalculated Data | Already converted into CO₂ values (e.g., 2 tonnes of CO₂ from a trip) | Use when source data is unavailable |
Spend-based Data | Estimated emissions based on how much money was spent e.g., 1000 euros on diesel might generate a rough emissions figure using an average euro to tonne CO2 equivalent conversion factor. | It's only used when nothing else is available as it is broad and not specific. |
Best practice: Importing Spend - Purchased Goods & Services :
The monetary values you import should exclude VAT.
Always prioritise importing activity data if you have access to it (this gives you much more advanced analytical capacities).
When classifying your data for import, we recommend you always input values at the most granular level (Commodity level).
Follow the same import workflow as any other data type. Head to Data>Import>Data>"Spend - Purchased Goods & Services"
Once imported, head to Data>Insights>Purchased Goods and Services to view your data and verify that it matches with your source data. You can use Insights and KPIs to take a deeper look and analyse it.
Methodology Overview
Where are the coefficients coming from?
The primary source is EXIOBASE 3.8, a multi-regional environmentally-extended input-output (EEIO) database covering 44 countries plus a Rest-of-World aggregate, licensed CC-BY-SA 4.0. The base year for the underlying coefficient is 2019 — the last year with real (non-nowcast) CO₂ fossil data in EXIOBASE 3.8.
How do we process them?
For each (UNSPSC family, year, currency) cell we:
Take the 2019 EXIOBASE base coefficient in EUR for the matched product.
Convert to the target currency at the 2019 base-year EUR cross rate (ECB reference rates). FX-first preserves the multi-regional economic structure encoded in EXIOBASE.
Re-price from 2019 to the target year using the target-country GDP deflator (World Bank WDI series
NY.GDP.DEFL.ZS, indexed 2019 = 100).
This approach improves consistency across years and currencies while reducing distortion caused by exchange-rate and purchasing-power volatility.
Important considerations
Spend-based models estimate emissions using sector-level economic averages rather than product-specific lifecycle data analysis (LCA).
Results are therefore best suited for high-level emissions estimation when activity-based data in unavailable.
It is not recommended to use spend-based calculations for:
product-level carbon claims
supplier comparisons
tracking actual decarbonization improvements
calculating emissions where activity data exists
large difference between the basic prices and purchase prices
# | Limitation | Impact | Direction of bias |
1 | EXIOBASE 3 coefficients are at basic prices; customer spend is at purchaser prices. | Coefficients slightly overestimate emission intensity per unit of spend. Best practice would be for users to exclude VAT when importing data. | Conservative (overestimate), typically 10–25% depending on sector. |
2 | 2025 normalization factors (deflator and FX) use 2024 actuals. | 2025 coefficients are mis-calibrated to the extent 2025 inflation or FX rates diverged from 2024. | Direction depends on inflation differential; expected to be small for most currencies. |
3 | 2019 EXIOBASE 3.8 intensities are applied to all reporting years (2015–2025) via GDP-deflated spend. | Year-to-year changes in your footprint reflect changes in spending, not real-world progress (or backsliding) in supplier industries. | Emissions are likely overstated for years after 2019, since many industries have become cleaner since then. |
4 | Economy-wide GDP deflator used rather than sector-level price indices. | Single deflator applied to all sectors in a given currency area; does not capture sector-specific price dynamics. | Uncertain; may over- or under-adjust depending on sector. |
5 | All EXIOBASE coefficients use the DE (Germany) regional variant as the base, regardless of target currency or likely production origin. | Regional differences in production technology and carbon intensity are not captured. Emission intensity of electricity generation, steel, or agriculture, for example, varies substantially by country. | Uncertain; DE production intensity may over- or underestimate emission intensity relative to actual production origin depending on category and currency area. |