How Do I Calculate a Product Carbon Footprint With Missing Supplier Data?

Your Product Carbon Footprint calculation is underway, but some of your suppliers have not provided the emissions data you need. Now you have a question: How do you calculate a Product Carbon Footprint with missing supplier data?

Supplier-specific data can improve the accuracy and representativeness of a PCF, but it may not always be available when a calculation needs to be completed. When supplier data is missing, manufacturers can use appropriate alternative data sources, such as secondary emission factor databases, industry-average data, proxy data, or other estimates, depending on the calculation requirements and available information.

The challenge is choosing an appropriate method without compromising the transparency or usefulness of the PCF. Each alternative data source has different levels of relevance, representativeness, and data quality, so the approach and assumptions should be documented clearly.

In this blog, we explain how to calculate a Product Carbon Footprint with missing supplier data, how to choose alternative data, assess data quality, document data gaps, and improve the calculation when better supplier information becomes available.

Supplier-specific emissions data can vary in availability, detail, and format. Common reasons include:

When supplier-specific data is unavailable, a PCF can use appropriate secondary data, proxy data, industry-average data, or estimates, depending on the calculation methodology and the information available. Recording the data source, methodology, assumptions, and relevant quality considerations helps make the calculation transparent and easier to update when better supplier information becomes available.

Yes. A Product Carbon Footprint can be calculated using a combination of primary and secondary data. When supplier-specific data is unavailable, appropriate alternatives can include secondary emission factors, industry-average data, proxy data, or estimates.

The key is to select data that is appropriate for the process being calculated and document the data source, methodology, assumptions, and relevant data-quality considerations. As better supplier information becomes available, the calculation can be reviewed and updated where appropriate.

When supplier-specific emissions data is unavailable, you can use other data sources to estimate the relevant emissions. The appropriate approach depends on the material or process being assessed, the data available, and the requirements of the calculation.

A Bill of Materials (BOM) can provide information about the materials and quantities used in a product, even when supplier-specific emissions data is unavailable. You can combine this information with an appropriate secondary emission factor for the relevant material or process to estimate its associated emissions.

Life Cycle Inventory databases provide secondary data for materials, processes, energy, and other life-cycle inputs. When supplier-specific data is unavailable, an appropriate dataset can be selected based on factors such as material or process type, geography, technology, and time period. The source and selection criteria should be documented.

Industry-average emission factors can be used when supplier-specific information is unavailable and a suitable average dataset is available for the relevant material, product, process, or industry category. The selected factor should be sufficiently representative of the activity being assessed and its intended use in the PCF.

Proxy data uses information from a similar material, process, or activity as a stand-in when specific data is unavailable. For example, data for a sufficiently similar material or production process may be considered as a proxy after assessing its relevance and representativeness. When proxy data is used, the basis for selecting it and any relevant limitations should be documented.

Spend-based methods estimate emissions using the economic value of purchased goods or services together with relevant secondary emission factors. This approach is recognized in GHG Protocol Scope 3 guidance for certain purchased-goods and services calculations when more detailed activity data is unavailable. For a product-level carbon footprint, however, its suitability depends on the calculation scope and methodology, so it should not automatically be treated as a universal substitute for material- or process-specific data.

The right approach depends on the information available for the material or process being assessed. Start by identifying what you know about the input, such as its material type, quantity, production process, geography, or purchase value. This helps determine which alternative data source can provide a reasonable representation of the activity.

For example, if your BOM provides the material type and weight, a relevant secondary emission factor may be suitable for estimating material emissions. If an exact dataset is unavailable, a proxy dataset may be considered when the underlying material or process is sufficiently representative. Where physical activity data is limited but purchase information is available, a spend-based method may be relevant for calculation contexts where that methodology is appropriate.

The selected method should also reflect the purpose and scope of the PCF. A data source that is appropriate for one material or process may not be suitable for another. Record why the selected data source was used and identify any relevant limitations.

As supplier-specific information becomes available, review the affected inputs and determine whether the calculation should be updated. This creates a practical process for improving data quality without requiring every input to have supplier-specific information from the beginning.

Once you have addressed missing data, you should assess the quality and representativeness of the data used in the calculation. The GHG Protocol Product Standard identifies five data-quality indicators: technological representativeness, geographical representativeness, temporal representativeness, completeness, and reliability.

For each significant input, consider:

A data-quality review can be performed at the input or process level rather than treating the entire PCF as having one uniform data-quality level.

For each significant input, review whether the emission factor matches the material or process, whether the geography is relevant, and whether the reference period is appropriate. Check whether the dataset adequately represents the activity being assessed and consider how the underlying data was collected and verified.

This review can also help identify where additional supplier information would provide the greatest improvement. For example, if an important material is currently represented by a broad secondary dataset, obtaining supplier-specific information may provide a more representative basis for the calculation.

The result of the review does not need to be a single overall score. The purpose is to understand where the data is well supported, where limitations exist, and which inputs may benefit from better information.

Data quality should be assessed in the context of the specific activity being calculated. An older dataset is not automatically lower quality, and a newer dataset is not automatically more representative. The relevance of the technology, geography, time period, completeness, and source reliability should all be considered together.

Documenting these data-quality considerations helps explain where supplier-specific data was used, where secondary or proxy data was used, and where further data improvement may be appropriate. The GHG Protocol also recommends documenting the approach and results of data-quality assessment and using the assessment to support improvements over time.

When supplier-specific data is unavailable, document the data sources, assumptions, and methods used to address the gaps. This helps reviewers understand how the PCF was calculated and where secondary, proxy, or estimated data was used.

Your documentation should include:

A consistent record can make missing-data decisions easier to review and update. For each relevant input, capture the material or process, data source, emission factor, unit, geography, reference period, and type of data used.

Also record any proxy selection, assumptions, adjustments, or limitations that affect how the data is interpreted. If supplier-specific information is expected to become available later, note the input that should be reviewed when that information is received.

Keeping these details together creates a clear record of how each data gap was handled and provides a practical starting point for future PCF updates.

Clear documentation creates a traceable link between the input data, emission factors, assumptions, calculation methods, and final PCF result. It also makes it easier to review the calculation and identify where better supplier-specific data could improve the assessment over time.

Missing supplier data can create several challenges during a PCF calculation. Avoiding these common issues can make the calculation easier to review and improve over time.

Carbalyze's platform, Caly, is designed to help manufacturers start a Product Carbon Footprint calculation using their Bill of Materials (BOM) while incorporating supplier-specific data where it is available.

Once you upload a BOM, Caly's AI-powered engine can automatically map materials to relevant emission factor datasets, reducing the need for manual emission factor lookup. Where supplier-specific data is available, Caly supports its use alongside relevant emission factors and supply-chain information. Where supplier-specific data is unavailable, the platform can use relevant emission factors and other available secondary data sources to support the calculation.

Caly is designed for non-LCA experts, helping simplify product carbon footprint analysis and reporting.

Reports generated through the platform are designed to align with recognized frameworks, including the GHG Protocol and ISO 14067. The workflow supports BOM-based analysis, material-to-emission-factor mapping, supplier data, and structured PCF reporting.

The goal is not to treat missing supplier data as if it does not exist. It is to provide a practical way to use available BOM data, supplier information, and relevant emission factors while identifying where better supplier-specific information can improve the calculation over time.

Missing supplier data does not have to prevent you from calculating a Product Carbon Footprint. When supplier-specific information is unavailable, appropriate secondary data, industry-average data, proxy data, or estimates can be used depending on the calculation requirements and available information.

The key is to select data that is relevant to the material or process being assessed, evaluate its quality and representativeness, and clearly document the sources, assumptions, and methods used. As better supplier-specific information becomes available, the relevant inputs can be reviewed and updated where appropriate.

A transparent approach to missing data helps make the PCF easier to review, improve, and maintain over time.