We use Product Carbon Footprints (PCFs) to improve the accuracy of upstream emissions calculations. To ensure transparency and consistency, we evaluate the quality of PCF sources before import. This guide outlines the steps and criteria we use to assess PCF quality.

Guide to evaluating PCF quality

We have a framework for evaluating a PCF source and giving it a 1-5 score for quality. This guide explains what the framework covers and how it is used for scoring.

Introduction to PACT PCFs

Our approach to PCFs is aligned with the Partnership for Carbon Transparency (PACT). PACT has developed methodologies for calculating consistent PCFs, and being able to share them in a standardised way.

In terms of data quality, the PACT approach introduces:

Methodological consistency
Metrics for scoring data quality and primary data usage
Directions for third party assurance

💡 Given the above, we therefore accept any PCF receive in PACT format as at least meeting our minimum quality criteria.

Building on PACT and data source quality

The PACT methodology and data model tracks certain information relating to datasource quality. This includes methodology, primary data share, and assurance status. We use the data available in the PACT data model as the foundation for our datasource quality framework.

We then build on this by asking for further information that gives deeper insight into source quality. This leads us to ask additional information on:

Methodology note - technical documentation on the PCF methodology. This gives full transparency of the calculation approach.
PCF source - the organisation that calculated the PCF. This highlights the experience and reliability of the calculating organisation
Input data - what type of business data was used to calculate the PCF, e.g. product-specific activity data or proxy estimates. This demonstrates the accuracy of calculations.
Allocation rules - a description of the approach used to allocate emissions amongst multiple products. This demonstrates what level of the PACT hierarchy for allocation approaches was used.
Lifecycle emissions - GHG emission values for the contributing emission processes in the PCF. More granular information here makes it easier to validate emission values and the methodological approach.

This additional information feeds directly into the data quality indicators we assess in our quality framework below.

💡 We therefore built a scoring framework that builds upon the PACT approach to assess emission datasource quality

Data quality framework for PCF sources

We score the quality of a PCF source on a 1-5 scale. This is done by assessing three categories:

Source reliability - how trusted the PCF data source is
Source methodology - how transparent and aligned with best practice the methodology used to calculate the PCF is
Source data - how high quality the type of data used in the PCF calculation is

Each individual category is also scored 1-5 and weighted equally in the overall quality score.

Source reliability

Reliability assesses the assurance status of the PCF, and the type of organisation that originally calculated the PCF.

💡 The Source reliability score is taken as the sum of the score from assurance and PCF source as per the table below, with a max score of 5.

Score	Assurance	PCF Source
3	Reasonable assurance	Government body Certified platform Leading LCA consultancy Peer-reviewed academic work
2	Limited assurance Reasonable assurance of partial LCA	Non-certified platform Recognised LCA consultancy In-house LCA team
1	Limited assurance of partial LCA	In-house calculated with tool Unverified LCA consultant
0	No assurance given	Unnamed source

Source methodology

Methodology assesses how 3 key features of a PCF study approach align with best practice:

System boundary is what is included in the study
Lifecycles is the level of granularity to which the PCF can be broken down
Allocation is how emissions are shared across multiple product systems

💡 The Source methodology score is taken as the minimum tier which is reached across the three methodological indicators.

Score	System boundary	Lifecycles	Allocation
5	No exclusions OR Technological note gives thorough justification for medium exclusions (<5%), e.g. by sensitivity analysis.	Technological note gives numerical breakdown and complete description of lifecycle emissions processes.	Industry best practice Product Category Rule applied. OR Technological note details full allocation method.
4	Low exclusions (<1%) justified.	Breakdowns in PCF value given, and detailed explanation of what emissions processes are included.	Product category or sector specific rule appliedOr Allocation avoided by system design or irrelevance.
3	Low unjustified exclusions (<1%)ORJustified Medium (<5%) exclusions.	Breakdowns in PCF value given, and limited explanation of what emissions processes are included.	Allocation approach is consistent with industry standard.
2	Medium exclusions (<5%), not justified.	No breakdown in PCF value, and limited explanation of what emissions processes are included.	Allocation approach is inconsistent with industry standard.
1	High exclusions (≥5%)	Confirm that 3 PACT lifecycles are included	Allocation approach defined

Source data

Data assesses how accurate and reliable the input data used for PCF calculations is. The data type describes the business data used to multiply with emission factors. The PACT indicators come from the PACT data model, and are also used to assess data source reliability.

💡 The Data type is used to score, unless its unknown in which case the score is determined by the PACT indicators.

Score	Data type	PACT Data Quality Indicators	PACT Primary Data share
5	Product-specific activity data	Completeness/Reliability = 1/1	75%
4	Activity data	Completeness/Reliability = 1/2	50%
3	Allocated corporate footprint	Completeness/Reliability = 2/2	25%
2	Spend data	Completeness/Reliability = 2/3	10%
1	Estimated data	Completeness/Reliability = 3/3	0%

How we apply the framework in practice

All the information we require to assess the data source quality of PCFs we get through the PACT data model or Altruistiq’s own Supplier Engagement tool.

We therefore take the following steps to score PCF source quality:

Receive PCF data and all associated data quality fields through supplier engagement.
We conduct an automated validation on the GHG emissions, to confirm it’s within the expected distribution of values for its product class.
The indicators for Source reliability and Source data can be scored automatically based on the score thresholds in the framework tables above.
The indicator for Source methodology is scored by a member of the Altruistiq Research team for subjective decisions on Lifecycles and Allocation.
The final PCF source data quality score is given as the average of all three indicators.
Add the PCF and its new data source quality score to our platform
Confirm the improvement in data quality scores when replacing a standard secondary emission factor with the PCF

Summary

We have constructed a framework that assesses the data quality of PCF sources. This gives clear scoring thresholds on the Source, Methodology and Data to show how PCF data can be improved over time. These scores are built on the PACT framework, which gives the basis for standardised PCFs.

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