How to Evaluate the Quality of Your PCF’s Data

23 July 2023

8 Carbon Data Quality Indicators for Reliable Product Carbon Footprints

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Why Carbon Data Quality Matters for Product Carbon Footprints

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The accuracy of a Product Carbon Footprint (PCF) depends on the quality of the underlying carbon data. Whether you're using secondary data, supplier-provided PCFs, or emission factors from external databases, low-quality data can lead to inaccurate results, poor business decisions, and challenges in meeting sustainability reporting requirements.

Not all carbon data is created equal. Different databases, suppliers, and studies apply varying methodologies, system boundaries, data sources, and quality standards. As a result, evaluating carbon data quality is a critical step in every Product Carbon Footprint assessment.

To improve transparency and comparability, the GHG Protocol Product Life Cycle Accounting and Reporting Standard defines five key indicators for assessing data quality. Building on nearly three decades of LCA expertise, methodological advancements, and industrial experience, sustamize extends this framework with three additional indicators to provide a more comprehensive evaluation of PCF data quality.

By systematically assessing the quality of secondary carbon data and supplier data, companies can improve the reliability, transparency, and credibility of their Product Carbon Footprint calculations while reducing uncertainty in climate reporting and regulatory compliance.

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The Five Carbon Data Quality Indicators Defined by the GHG Protocol

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The GHG Protocol's Product Life Cycle Accounting and Reporting Standard lists 5 indicators relevant to evaluate data quality, adapted from Weidema & Wesnaes (1996):

(1) Technology

(2) Time

(3) Geography

(4) Completeness

(5) Reliability

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Beyond the GHG Protocol: Three Additional Data Quality Indicators

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It is crucial to keep those indicators in mind when evaluating data's quality, to ensure transparency and detect possible inaccuracies. However, nearly three decades of LCA practice, methodological improvements and industrial know-how have brought sustamize to perfect this list by adding 3 additional key indicators: precision, relevance and bias.

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Carbon Data Quality Assessment Template

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Here's a table we use at sustamize for assessing the data quality of our Product Footprint Engine's databases. You can use this template to assess the quality of your secondary data and data provided by your suppliers:

Geography: it plays a vital role, as different regions may have distinct emission factors and energy mixes which can considerably affect the results.
Timeliness: it indicates the currency and relevance of the data. Outdated information may not represent the current state of commonly used technology, methodologies, raw materials and generally emissions associated with a product.
Technology: it refers to the technologies, machinery and tools used in the manufacturing processes, and wether the data reflects the reality of the ones currently and commonly used.
Completeness: it signals whether all relevant emission sources and scopes are accounted for for that sepcific data.
Reliability: it perhaps the most important one as it indicates the consistency and dependability of the data, wether it is based on measured data or on assumptions and if it has been verified or peer-reviwed. This indicator signals wether the data can be trusted for decision-making.
Precision: it gauges the level of detail and accuracy in emissions data. Precision is provided by the amount of information given in a study and wether the data is based on own measurements rather than on assumptions.
Relevance: it indicates how well the data aligns with the specific purpose of your PCF assessment.
Bias: it refers to potential reality distortions involved in the data such as Global North/ Global South commonly used technologies or manufacturing practices, authors' affiliations etc.

By applying a score from 1 to 5 to these indicators (1 being the best quality grade and 5 the worst) to each of the data sets such as prescribed by Weidema & Wesnaes (1996), you can investigate how and why the data lacks of quality and visualize where data quality can be improved.

A must for transparency and reproducibility of an assessment.

The good news is that updated secondary data from reliable databases such as from the Product Footprint Engine follow strict quality checks and requirements.

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Want to know more about reference data, the product footprint engine or the sustamizer?

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