Average-Data Method
The average-data method estimates Scope 3 emissions using industry-average emission factors per physical unit of product or material — such as kgCO₂e per tonne of steel, per tonne of cement, or per kg of packaging material — when supplier-specific data is unavailable.
What is Average-Data Method?
The average-data method occupies the middle ground in the GHG Protocol's Scope 3 data quality hierarchy — more accurate than spend-based estimation (which uses financial proxies) but less specific than supplier-specific data. It uses physical quantities of goods or materials purchased, combined with industry-average emission factors expressed per unit of material.
For example, if an organisation purchases 1,000 tonnes of structural steel, the average-data method would multiply this by the industry-average embodied carbon factor for steel (approximately 1.5–2.5 tCO₂e per tonne, depending on production route and geography). This is more accurate than a spend-based calculation because it uses physical quantity rather than price, but less accurate than a supplier-specific EPD that reflects the actual carbon intensity of the specific steelmaker.
Average-data emission factors are available from several sources. The DEFRA dataset includes material-level factors for common goods. Lifecycle assessment databases such as ecoinvent provide cradle-to-gate factors for thousands of materials and products. Industry associations publish sector-specific benchmarks (e.g., the World Steel Association, the Global Cement and Concrete Association). The ICE Database (Inventory of Carbon and Energy), maintained by the University of Bath, is widely used in the UK construction sector for embodied carbon of building materials.
The average-data method is particularly useful when you know what you bought (in physical units) but not from which specific supplier or production process. It is the standard approach for construction materials (steel, concrete, timber, glass, insulation), agricultural commodities (tonnes of wheat, dairy, meat), chemical feedstocks, and other physically quantifiable inputs.
The main limitation is that averages obscure the variation between suppliers. Two steel suppliers may have vastly different carbon intensities depending on whether they use electric arc furnace (recycled) or basic oxygen furnace (primary) production. Average factors blend both into a single number. As data quality improves, organisations should aim to replace average factors with supplier-specific data for their most material procurement categories.
Practical Examples
A housebuilder estimates embodied carbon in its developments using the ICE Database: tonnes of concrete, steel, timber, brick, and insulation × average kgCO₂e per tonne for each material, producing a whole-building carbon estimate.
A food retailer uses DEFRA average emission factors per tonne of commodity (beef, dairy, poultry, fresh produce) to estimate Category 1 emissions from its product range, based on purchasing volumes from its supply chain.
A pharmaceutical company uses ecoinvent lifecycle factors per kg of active pharmaceutical ingredient to estimate the embodied emissions of its raw material inputs across 200 product lines.
How Climatise Helps
Climatise applies average-data factors from DEFRA, ecoinvent, and sector databases when you upload procurement data with physical quantities. The platform maps materials to the correct factors and flags where supplier-specific data could improve accuracy — helping you build a data quality improvement plan.
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