Spend-Based vs. Activity-Based vs. Supplier-Specific: The Three Ways Manufacturers Calculate PCF — and Which Ones Actually Pass Audits

Two manufacturers in the same industry submit product carbon footprint (PCF) reports for the same product category to the same OEM customer. One report passes review within a week. The other is rejected and sent back for recalculation.

The difference is not the size of the number. It is how the number was built.

One used spend-based estimates. The other used supplier-specific activity data. Both used the same GHG Protocol framework. Only one produced a result the customer could verify.

The method used to calculate a PCF determines not just its accuracy — it determines its auditability, its usability for Scope 3 reporting, and increasingly its acceptance in procurement and compliance contexts. Choosing the wrong method does not just create inaccuracy. It creates a report that cannot do the job it was built for.

Before comparing the three primary methods, it is important to note what the GHG Protocol actually defines. The GHG Protocol offers four different methods for calculating emissions: spend-based, average-data method (activity-based), supplier-specific, and hybrid. Each method varies in terms of accuracy versus accessibility.

This blog focuses on the three that manufacturers most commonly encounter — spend-based, activity-based, and supplier-specific — and the hybrid approach that combines them. Understanding what each measures, what data each requires, and what each can and cannot support is the foundation of any credible Scope 3 Category 1 reporting strategy.

Spend-based calculations estimate emissions by multiplying the amount of money spent on a good or service by an emission factor expressed per unit of currency. These emission factors are typically derived from environmentally extended input-output (EEIO) models such as EXIOBASE.

In practice: take the purchase price of a steel component, multiply it by an industry-average emission factor for steel production per dollar of spend, and arrive at an estimated CO₂e figure.

Only procurement spend data — the financial value of what was purchased. No supplier engagement required. No material weights, no process data, no origin information. When using the spend-based approach, data points are in-house in procurement or supplier data systems.

The spend-based method is fast, cost-effective, and requires minimal data collection. It is accepted under the GHG Protocol and useful when supplier-specific or activity data is unavailable.

The GHG Protocol recommends spend-based for initial screening — as a starting point to identify which Scope 3 categories are material before investing in more precise data collection. Companies can strategically use the spend-based method as a starting point for carbon accounting when other data is unavailable. It sets a baseline from which improvements in data collection and reporting can be developed.

The spend-based approach lacks precision, is vulnerable to price fluctuations, and can obscure the real drivers of emissions. An emission estimate built on financial spend rather than physical quantities cannot track real emissions reductions — if a manufacturer switches to a lower-emission material at higher cost, the spend-based number goes up, even though actual emissions went down.

The spend-based emissions factors are subject to inflation and exchange rates. A component that costs more due to supply chain disruption generates a higher calculated emission figure with no change in the underlying material or process.

More critically for PCF compliance: spend-based data is harder to justify unless supported by robust emission factor documentation. Auditors prefer activity-based because it provides transparency and traceability.

A PCF built entirely on spend-based estimates cannot meet ISO 14067's requirement for data quality transparency and verifiability when audited. It also cannot support product-level comparisons, decarbonisation tracking, or supplier qualification — the three primary commercial uses of a PCF.

Relying solely on spend-based data means risking over- or underestimating real emissions. In a documented comparison of purchased services, the spend-based method overestimated emissions by almost 37% compared to activity-based data.

The activity-based approach uses measurable operational data, such as actual fuel use, energy consumption, or freight distances, to calculate emissions. Each activity is matched to an emissions factor specific to the unit of measure.

For PCF calculation in manufacturing, this means using physical quantities — kilograms of steel, kilowatt-hours of electricity, litres of process chemicals — multiplied by standardised emission factors from databases such as ecoinvent, DEFRA, or EPA. The average-data method calculates upstream emissions of goods and services from suppliers' activity data on the amount of materials, fuel, electricity used, distance transported, and waste generated from the production of goods and services.

Physical activity data from the Bill of Materials and production records — material weights, energy inputs, transport distances, and waste volumes. This requires more structured internal data than spend-based, but does not necessarily require direct supplier engagement for every input.

Activity-based is the standard method for Scope 1 and Scope 2 emissions and is the preferred approach for most Scope 3 categories where physical data is available. Activity-based is preferred for audit-proof reporting under ESRS E1, as well as frameworks like SBTi — which requires activity-based data to show real decarbonisation — ISSB, GRI, and EcoVadis, all of which push companies toward more granular, verifiable reporting.

For manufacturers calculating PCFs using BOM data, the activity-based method provides meaningful granularity. It enables hotspot identification, material substitution analysis, and year-on-year reduction tracking — none of which are possible with spend-based estimates.

The activity-based method uses industry-average emission factors, not supplier-specific verified data. The average-data and spend-based methods both use secondary data, i.e. industry average data.

This means the emission factor applied to a steel component reflects the industry average for steel production globally or regionally — not the verified performance of the specific supplier that produced it.

For PCFs that will be submitted to customers or used for product-level sustainability claims, this creates a ceiling on credibility. The report is more defensible than a spend-based PCF, but it still rests on estimates rather than verified primary data. The ability to identify, analyse, and monitor emissions reduction efforts related to purchased products is limited if a business uses spend-based accounting methods exclusively and the same limitation applies when activity-based methods rely entirely on secondary averages rather than supplier-specific inputs.

The supplier-specific method collects product-level cradle-to-gate GHG inventory data from goods or services suppliers. Instead of applying an industry-average emission factor to a purchased component, the manufacturer uses the verified PCF data provided directly by the supplier for that specific product.

The supplier-specific method collects granular, product-level cradle-to-gate GHG inventory data from suppliers. This involves obtaining detailed emission factors for every good or service the reporting company procures. As a result, this method is renowned for its precision, providing the most accurate emissions data directly relevant to the company's supply chain operations.

Verified PCF data from each relevant supplier, aligned with a recognised standard such as ISO 14067 or the GHG Protocol Product Standard. When collecting emission factors from suppliers, it is recommended that companies request a description of the methodologies used to quantify emissions, the data sources used, and any other relevant information including the percentage of the product inventory calculated using primary data. Preference should be given to verified data.

The supplier-specific method delivers the most precise and accurate supply chain emissions data, representing the gold standard in carbon accounting.

For PCFs intended for third-party verification under ISO 14067, submission to customers under PACT-aligned supply chain programmes, or use in SBTi target validation, supplier-specific data is the standard that procurement teams increasingly require. An increasing number of retailers, companies purchasing materials and components, regulators, and investors now expect suppliers to disclose product carbon emissions data — and verification helps demonstrate the reliability, consistency, and accuracy of a carbon footprint by confirming whether the GHG statement meets international standards.

Collecting verified supplier-specific PCF data at scale is resource-intensive. The supplier-specific method utilises operational data, which provides more accurate results, but also requires more effort to collect this information from suppliers and the rest of the value chain. For manufacturers with hundreds of active suppliers, full supplier-specific coverage for all components is rarely achievable in a first-year PCF programme.

This is exactly where the hybrid approach becomes the operationally correct answer.

The hybrid method uses a combination of supplier-specific activity data where available, and secondary data to fill the gaps. This is the method that the GHG Protocol recommends.

In practice, a well-structured hybrid approach prioritises supplier-specific data for the highest-emission components — typically the top 10 to 20 percent of BOM line items by emissions weight — and uses activity-based average data for the remainder. This concentrates data collection effort where it has the greatest impact on PCF accuracy while maintaining complete coverage across all inputs.

To combine supplier-specific and secondary data methods, the most suitable calculation approach should be selected for all material Scope 3 categories. Some categories can be assessed both with an activity-based and a spend-based approach.

Businesses should also set a recalculation policy, updating spend-based estimates with activity data as supplier engagement improves. CSRD requires companies to report in line with ESRS E1, which sets expectations for data quality, traceability, and auditability.

The title of this blog sets up a direct question: which calculation methods actually pass audits? The answer requires distinguishing between two different types of audit.

Regulatory compliance audits — such as those required under California SB 253, SEC climate disclosure frameworks, or CSRD — evaluate whether the methodology used is documented, consistent, and aligned with a recognised standard. Both spend-based and activity-based methods are permissible under GHG Protocol for Scope 3 reporting, provided the method is disclosed, the emission factor sources are documented, and the data quality is reported. Neither method is inherently non-compliant at the corporate Scope 3 level.

Product-level PCF verification audits — such as ISO 14067 third-party verification, PACT conformance reviews, or customer-required PCF assessments — operate under stricter data quality requirements. Verification confirms whether the greenhouse gas statement meets international standards, and data errors in PCF calculation are not uncommon. At this level:

Large customers increasingly require supplier-specific data, and the audit and verification frameworks underpinning that demand are already built and operational. Spend-based PCFs are a starting point, not a destination.

No single method is universally correct. The choice should be driven by three factors: the purpose of the PCF, the data available, and the standard the report needs to conform with.

The data quality journey matters. GHG Protocol explicitly encourages manufacturers to improve data quality over time — starting with spend-based for initial disclosure, building toward activity-based, and ultimately developing supplier-specific data for material categories. If changes in data quality result in significant differences in emissions estimates, companies are required to recalculate base year emissions applying the new data sources. Planning for that recalculation from the start avoids the disruption of rebuilding a PCF baseline when data quality improves.