Spend-based emission estimates are a practical starting point for Scope 3 reporting, but they can diverge significantly from actual product carbon footprints.
Most companies begin their Scope 3 journey with spend-based emission calculations. The method is straightforward: take procurement spend per category, multiply by an industry-average emission factor (expressed in kg CO₂e per euro or dollar), and produce a total estimate. It is the approach recommended by the GHG Protocol as a screening method, and for good reason — it requires no supplier-specific data and can be applied across an entire procurement portfolio in a matter of days.
But spend-based estimates are proxies. They assume that emissions scale linearly with cost, and they rely on sector-average emission factors that smooth over enormous variation between suppliers, materials, production methods, and geographies. For initial screening and materiality assessment, this is acceptable. For decision-making, target-setting, or regulatory reporting under frameworks like CSRD, it is often not enough.
A product carbon footprint (PCF) quantifies the greenhouse gas emissions associated with a specific product across its lifecycle — from raw material extraction through manufacturing, transport, use, and end-of-life. It is calculated following ISO 14067 and typically uses primary data from the actual production process, supplemented by lifecycle inventory databases for background processes.
Unlike spend-based estimates, a PCF reflects what actually happened: which energy source was used, how efficient the production process was, where materials were sourced, and how the product was transported. Two products that cost the same can have very different carbon footprints — and two products with the same carbon footprint can have very different prices.
The gap between spend-based estimates and actual PCF data can be substantial. In practice, divergences of 30–70% are common, and in some cases the difference can be a factor of two or more. The main drivers of this divergence fall into a few categories.
Energy mix and production efficiency. Spend-based factors use industry averages that blend producers running on renewables with those running on coal. A supplier using hydropower in Scandinavia will have a fundamentally different emission profile than one using grid electricity in Southeast Asia — even for an identical product at a similar price point. The spend-based method cannot distinguish between them.
Material composition and sourcing. Average emission factors assume a typical material mix for a product category. In reality, two components may use different steel grades, different polymer types, or different levels of recycled content. A spend-based estimate treats a euro of "steel products" the same regardless of whether it is virgin carbon steel or recycled stainless steel. The actual PCFs can differ by an order of magnitude.
Price volatility and currency effects. Because spend-based methods tie emissions to monetary value, they are sensitive to price fluctuations that have nothing to do with environmental performance. Commodity price spikes, exchange rate movements, or volume discounts can all shift the calculated emissions without any change in actual production. A supplier who raises prices appears to have higher emissions; one who offers a discount appears to have lower emissions. This is an artefact of the method, not a reflection of reality.
Aggregation and category mapping. Spend-based emission factors are typically mapped to broad procurement categories (NACE codes, NAICS codes, or similar). A single category like "fabricated metal products" covers everything from precision machined components to structural steel beams. The actual emission intensities within such a category vary widely, but the spend-based factor applies a single average to all of them.
Scope and boundary differences. Spend-based factors may include or exclude certain lifecycle stages inconsistently. Some factors cover cradle-to-gate; others include end-of-life treatment or use-phase emissions. A PCF, when calculated properly under ISO 14067, defines its system boundary explicitly. Comparing a spend-based estimate with a PCF without understanding the boundary definitions can produce misleading results.
Scope 3, Category 1 (purchased goods and services) is typically the largest emission category for manufacturing and service companies — often representing 40–70% of the total carbon footprint. If spend-based estimates systematically over- or undercount these emissions, the consequences are significant.
Targets become unreliable. If your baseline is built on spend-based estimates and you later switch to supplier-specific PCF data, your reported emissions may shift dramatically — not because anything changed in reality, but because your measurement improved. This can undermine the credibility of reduction targets set under the Science Based Targets initiative or similar frameworks.
Reduction efforts get misdirected. Spend-based data tends to highlight high-spend categories, not high-emission categories. A company might focus its decarbonisation efforts on a procurement category that looks large in spend-based terms but is relatively low-emission when measured accurately — while overlooking a smaller-spend category with genuinely high embedded carbon.
Supplier engagement loses precision. Without product-level data, it is difficult to have meaningful conversations with suppliers about emission reductions. "Your category has a high emission factor" is a less useful starting point than "this specific product has a PCF of X, and here is where the hotspots are."
The GHG Protocol defines a hierarchy of data quality for Scope 3 calculations, moving from spend-based methods (lowest accuracy) through average-data methods to supplier-specific methods (highest accuracy). The practical question is how to move along this hierarchy efficiently.
Step 1: Screen and prioritise. Use spend-based estimates to identify which procurement categories are likely to be material. This is where the method adds genuine value — as a screening tool, not a final answer. Focus on the top 10–15 categories by estimated emissions, which typically cover 80% or more of Scope 3 Category 1.
Step 2: Request activity data from key suppliers. For the prioritised categories, move from spend-based to activity-based calculations. This means collecting physical quantities (tonnes of material, kWh of energy, tonne-kilometres of transport) rather than relying on monetary proxies. Activity-based calculations using average emission factors are already significantly more accurate than spend-based methods.
Step 3: Collect actual PCF data. For the highest-impact products and most strategic suppliers, request product carbon footprints calculated under ISO 14067 or shared via the PACT Pathfinder Framework. This is primary data — the gold standard for Scope 3 accounting. It takes more effort to collect, but it provides the accuracy needed for credible reporting and meaningful reduction targets.
Step 4: Build a hybrid model. In practice, most companies will use a combination of methods for different parts of their portfolio. Supplier-specific PCFs for the top contributors, activity-based calculations for the next tier, and spend-based estimates for the long tail. The key is to be transparent about which method is used where, and to progressively improve data quality over time.
Not all PCF data is created equal. When evaluating product carbon footprints from suppliers, check the following.
System boundary: Does the PCF cover cradle-to-gate, or does it include use-phase and end-of-life? Make sure the boundary matches your reporting needs. For Scope 3 Category 1, cradle-to-gate is typically what you need.
Primary data share: What percentage of the data is primary (measured) versus secondary (from databases)? A higher share of primary data generally means higher accuracy. The PACT Pathfinder Framework recommends disclosing the primary data share as a data quality indicator.
Emission factor sources: Which databases or emission factors were used for background processes? ecoinvent, GaBi, and DEFRA are common and generally well-regarded. Custom or undocumented factors should be questioned.
Verification: Has the PCF been independently verified or reviewed? ISO 14067 allows for critical review, and third-party verification adds credibility. For EPDs, third-party verification is mandatory.
Temporal and geographical representativeness: Does the data reflect current production conditions? A PCF based on 2019 data and European grid factors may not represent a product manufactured in 2025 in a different region.
Several regulatory developments are accelerating the shift from spend-based estimates to actual emission data.
The Corporate Sustainability Reporting Directive (CSRD) requires companies to report Scope 3 emissions and to describe the methodologies and data sources used. While spend-based estimates are acceptable as a starting point, the expectation is that data quality will improve over time. Auditors will increasingly scrutinise whether companies are making reasonable efforts to obtain more accurate data.
The Carbon Border Adjustment Mechanism (CBAM) requires actual emission data for covered products (steel, aluminium, cement, fertilisers, electricity, hydrogen). Default values can be used temporarily, but the direction is toward verified, product-specific data.
The EU Battery Regulation mandates product carbon footprints for batteries placed on the EU market, calculated according to specified methodology and verified by third parties.
Industry frameworks like the PACT Pathfinder (formerly WBCSD Partnership for Carbon Transparency) are building the technical infrastructure for exchanging verified PCF data between companies in a standardised format. This reduces the friction of collecting supplier-specific data and makes the transition from spend-based to primary data more practical.
For sustainability professionals working to improve the accuracy of their Scope 3 data, a measured approach works best.
Acknowledge the limitations of spend-based data openly — in reports, in internal discussions, and in target-setting. Do not treat spend-based estimates as precise measurements.
Prioritise data improvement where it matters most. Focus on the procurement categories and suppliers that represent the largest share of estimated emissions. A small number of suppliers typically drives a large share of Scope 3.
Make PCF data part of procurement criteria. When selecting or evaluating suppliers, include carbon data quality as a factor. Suppliers who can provide verified PCFs demonstrate both environmental maturity and data transparency.
Use digital tools and data exchange standards. Platforms that support PACT Pathfinder data exchange, automated data collection from suppliers, and integration with your carbon accounting system reduce the manual effort and improve consistency.
Plan for a multi-year transition. Moving from spend-based to supplier-specific data across an entire procurement portfolio is not a one-year project. Set a realistic roadmap with milestones — for example, covering 50% of Category 1 emissions with activity-based or supplier-specific data within two years, and 80% within four years.
Spend-based emission estimates serve a useful purpose as a starting point for Scope 3 carbon accounting. But they are estimates — built on averages and assumptions that can diverge substantially from reality. As regulatory requirements tighten and stakeholders demand more credible data, the transition toward actual product carbon footprints is both necessary and increasingly practical. The companies that invest in this transition now will have more accurate baselines, more targeted reduction strategies, and stronger credibility in their sustainability reporting.