Primary Data vs Secondary Data in Product Carbon Footprints: What's the Difference?

15 SEPTEMBER 2026

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13 MIN READ

Introduction

Send the same bill of materials to two different sustainability teams, and they can produce different Product Carbon Footprint results for the same product if they use different data sources, assumptions, or calculation choices. The product and manufacturing process may be identical. What changes is the data behind the calculation, including whether the team uses primary data specific to the processes in the product's life cycle or secondary data from sources such as life-cycle databases, industry averages, or proxy datasets.

This is not a minor technical detail. In a Product Carbon Footprint (PCF), the quality and representativeness of the underlying data can have a significant effect on the resulting PCF. Understanding the difference between primary and secondary data, and knowing when each is appropriate, can help sustainability, ESG, and procurement teams make better-informed PCF data decisions.

This blog explains what primary and secondary data mean in a PCF context, where each can be used, and how to select appropriate data for the processes included in your product carbon footprint.

What Is Primary Data in a Product Carbon Footprint?

Primary data is data collected from specific processes in the studied product's life cycle. It can include process activity data or direct emissions data and, under the GHG Protocol Product Standard, may be measured or modeled as long as the result is specific to the process being assessed. ISO 14067 defines primary data more specifically based on direct measurement or calculations based on direct measurements.

In a PCF, primary data can include:

Electricity or fuel consumption for a specific manufacturing process or site
Material quantities used in a specific production process, based on production or process records
Supplier-reported process or emissions data that is specific to the relevant product, component, process, or facility
Transport distances, modes, and other activity data specific to the product's actual transport processes
Waste and scrap quantities recorded for the relevant production process

ISO 14067 defines primary data as a quantified value of a process or activity obtained from direct measurement or a calculation based on direct measurements. The GHG Protocol Product Life Cycle Accounting and Reporting Standard defines primary data as data collected from specific processes in the studied product's life cycle and requires primary data for processes under the reporting company's ownership or control.

The main advantage of primary data is its process specificity. The tradeoff is the time and resources required to request, collect, assess, and maintain it. When primary data comes from suppliers or other value-chain partners, their cooperation may also be required.

What Is Secondary Data in a Product Carbon Footprint?

Secondary data is data that is not specific to the processes in the studied product's life cycle. It can come from life-cycle databases, industry averages, government statistics, published literature, financial data, proxy data, and other generic sources. These data can be used for processes where appropriate primary data is unavailable or unsuitable, subject to the requirements of the applicable accounting standard.

Common sources of secondary data in PCF work include life-cycle inventory databases, national emission factor publications, and industry association datasets. Under GHG Protocol guidance, secondary data includes industry-average data, financial data, proxy data, and other generic data. Proxy data can involve using information from a similar activity to estimate emissions for the activity being assessed.

ISO 14067 also recognizes secondary data from sources such as databases, published literature, default emission factors from national inventories, calculated data, estimates, and other representative data validated by competent authorities. It also includes data obtained from proxy processes or estimates within the broader definition of secondary data.

Secondary data is not automatically low quality. The key distinction is that it is not specific to the processes in the studied product's life cycle. Depending on the source, it may represent an industry average, regional factor, proxy process, or other non-process-specific estimate.

One advantage of secondary data is availability. It can help teams develop a PCF when supplier or process-specific data is unavailable. Secondary data may have lower process specificity or representativeness than suitable primary data, although high-quality secondary data can sometimes be preferable to lower-quality primary data.

Primary Data vs Secondary Data: Side-by-Side Comparison

Factor	Primary Data	Secondary Data
Source	Data specific to a process in the studied product's life cycle, obtained through measurement, calculation, modelling, or other process-specific records, consistent with the applicable standard's definition of primary data	Data that is not specific to the processes in the studied product's life cycle, such as life-cycle databases, industry averages, published literature, financial data, or proxy data
Specificity	Specific to the relevant process or activity in the product life cycle	Represents a broader, regional, industry-average, proxy, or otherwise non-process-specific source
Collection effort	Can require time and resources to collect, assess, maintain, and obtain from suppliers or other value-chain partners	Often more readily available, but still requires appropriate source selection and data-quality assessment
Representativeness	Can better represent the processes included in the product life cycle when the data is suitable and sufficiently complete	May have lower specificity for the product or process being assessed, depending on the source and its technological, geographical, and temporal representativeness
Typical use case	Processes under the reporting company's ownership or control, as well as relevant supplier or value-chain processes where process-specific data is available	Processes where appropriate primary data is unavailable or unsuitable, including situations where databases, proxy data, or other representative secondary sources are appropriate
Standards guidance	The GHG Protocol Product Life Cycle Accounting and Reporting Standard requires primary data for processes under the reporting company's ownership or control. ISO 14067 defines primary data based on process or activity-specific measurements or calculations based on direct measurements	Both frameworks allow the use of secondary data, subject to their respective requirements for data quality, representativeness, and appropriate application
Traceability	Can be traced to process-specific records, measurements, calculations, or supplier-provided information	Can be traced to the relevant database, publication, emission factor source, proxy, estimate, or other documented source

Why the Data Source Changes Your PCF Result

The quality and representativeness of the data used in a Product Carbon Footprint can affect the resulting PCF. Replacing a supplier-specific emission factor or process-specific dataset with a broader industry-average dataset can change the calculated result, even when the physical product itself has not changed.

This matters for three practical reasons.

Comparability

If one product in your portfolio is calculated using detailed primary supplier data while another relies more heavily on generic secondary data, differences in data quality and representativeness can make the two PCF results harder to compare. A difference in the reported footprint may reflect differences in the underlying data as well as differences in the products or processes themselves.

Defensibility

A PCF should clearly document its data sources, datasets, assumptions, and methodological choices so that customers, reviewers, or assurance providers can understand how the result was calculated. A PCF supported by appropriate primary data and documented records can provide greater traceability to the processes, activities, or supplier information used in the calculation.

Decision-making

If the goal is to identify where emissions can be reduced, secondary data can help identify potential hotspots and prioritize areas for further investigation. Primary data can then provide more process- or supplier-specific information to support decisions about where reduction efforts may have the greatest impact.

When to Use Primary Data vs Secondary Data

Neither data type is universally "better." The appropriate choice depends on the process being assessed, the availability and quality of the data, its contribution to the footprint, and the effort required to obtain more process-specific information.

Use primary data when:

The process is a significant contributor to the product's footprint and more specific data could materially improve the assessment
You own or control the process, such as electricity or fuel use in your own manufacturing operations
A supplier or other value-chain partner can provide data specific to the relevant product, component, process, or facility
The data will be used to support reduction decisions, supplier engagement, customer requests, or other applications where strong process-level traceability is important

Use secondary data when:

Suitable primary data is unavailable, inaccessible, unsuitable, or not feasible to collect, where the applicable accounting requirements permit the use of secondary data
The process is a relatively small contributor and collecting more specific data would provide limited additional value for the intended use of the PCF
You are conducting an initial screening assessment to identify potential hotspots before investing in more detailed data collection
A suitable secondary dataset has appropriate technological, geographical, and temporal representativeness for the process being assessed

The GHG Protocol Product Standard requires primary data for processes under the reporting company's ownership or control. For other processes, companies should select appropriate, sufficiently high-quality data based on availability, data quality, representativeness, and the purpose of the assessment. In practice, PCFs can therefore combine primary and secondary data across the product life cycle.

How Primary and Secondary Data Work Together in a Hybrid PCF

Many Product Carbon Footprints combine primary and secondary data, using process-specific information where available and appropriate and secondary data for processes where suitable primary data is unavailable or unsuitable, subject to the requirements of the applicable accounting standard.

A typical hybrid PCF for a manufactured product might look like this:

Raw material extraction and early-tier processing : Secondary data may be used when supplier- or process-specific data is unavailable, with the dataset selected for appropriate technological, geographical, and temporal representativeness.
Component manufacturing at a known supplier : Primary data can be used when the supplier provides data specific to the relevant product, component, process, or facility.
Your own assembly or production energy use : Primary activity data can be used for electricity, fuel, and other relevant energy consumption associated with the processes under your control.
Packaging materials : Primary data can be used for product- or process-specific packaging quantities where available, while appropriate secondary sources can be used for other required data.
Outbound transport : Primary transport activity data can be used when specific shipment or logistics information is available. Appropriate secondary transport data can be used when process-specific information is unavailable.
End-of-life treatment : Secondary data may be appropriate when product-specific disposal or recovery information is unavailable, using suitable regional or treatment-specific datasets.

The objective is to use appropriate data for each process and improve specificity where doing so provides meaningful value. Processes under the reporting company's ownership or control are particularly important because the GHG Protocol Product Life Cycle Accounting and Reporting Standard requires primary data for those processes.

Common Mistakes Teams Make When Choosing PCF Data

Treating all secondary data as equally acceptable : Not all secondary datasets have the same quality or representativeness. Check technological, geographical, temporal relevance, system boundaries, and source methodology before using them.
Chasing primary data for processes with limited impact : Spending weeks collecting highly detailed data for a process that contributes only a small share of the total footprint may not be the best use of resources. Data collection priorities should consider the process's contribution to the footprint, the intended use of the PCF, and the potential value of obtaining more specific data.
Not tracking which data was used and where : Without a clear record of the data source, data type, process covered, and relevant assumptions, it becomes much harder to explain, reproduce, or update the PCF later. It also makes it more difficult to identify where improved data could provide the most value.
Overlapping system boundaries and double counting : Double counting can occur when two datasets or calculations include overlapping portions of the same upstream process. Before combining data sources, teams should check what each dataset includes and whether its system boundary is compatible with the rest of the PCF.
Assuming secondary data is a one-time fix : Secondary datasets and emission factors can be updated over time. Teams should review the age, applicability, and continued representativeness of the data rather than assuming that a dataset selected once will remain appropriate indefinitely.
Skipping documentation of the source and version : Record the database or publication, dataset or emission factor, version or publication date, system boundary, and relevant assumptions. A general reference to "industry data" is rarely enough for reliable traceability.

How to Decide Which Data Type to Use for Each Part of the Product

A practical way to approach this decision is to evaluate each relevant material, process, and activity using three questions.

How much does this process contribute to the total footprint?

If a process is a relatively small contributor, using suitable secondary data may be proportionate to the purpose of the assessment. If it is a significant contributor, obtaining more specific primary data may provide greater value.

Can suitable primary data realistically be collected?

Some processes, particularly those several tiers upstream, may be difficult to obtain process-specific data for. Other data, such as energy consumption from your own manufacturing operations or process information from an engaged supplier, may be more accessible.

What is the data being used for?

An initial screening assessment can use appropriate secondary data to identify potential hotspots. A PCF used to support reduction decisions, supplier engagement, customer requests, or external reporting may benefit from stronger process-level data and traceability, depending on the intended use and applicable requirements.

Applying these questions across the materials, processes, and activities included in a PCF turns the primary-versus-secondary decision into a practical data-management exercise. It also highlights where improving data quality could have the greatest value.

Conclusion

Primary and secondary data are not competing options. They are different types of data that can be used together across a Product Carbon Footprint.

Primary data can provide greater process specificity where suitable data is available, particularly for processes under your control and relevant supplier or value-chain processes. Secondary data can provide useful coverage where appropriate primary data is unavailable, unsuitable, or difficult to collect, subject to the requirements of the applicable accounting standard.

The goal is not to collect primary data for every process. It is to use appropriate data for each part of the product life cycle while considering data quality, representativeness, availability, and the intended use of the PCF.

A strong PCF data strategy identifies where more specific data can materially improve the assessment, documents the sources used, and focuses data-collection efforts where they provide the greatest value.

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