High data quality is essential for credible and fair use of Product Carbon Footprint (PCF) information.
Systembolaget applies a stepwise approach to PCF data. In the initial phase, reported data is used for learning, analysis, and method development, with the aim of strengthening data quality processes and building a robust foundation.
Any future strategic use—such as external communication or assortment steering—will only be considered once clear governance, transparent rules, and reliable quality processes are in place, and after suppliers have had the opportunity to review and improve their data.
What do we mean by “data quality” in PCF?
A PCF is based on three core elements: emission factors, activity data, and calculation methods and assumptions (such as system boundaries, cutoffs, and allocation choices). All of these may contain uncertainty compared to a “true” value.
In practice, data quality depends on:
- Relevance and timeliness (is the data up to date and appropriate for the product and geography?)
- Generic versus site-specific inputs (industry averages compared to measured or supplier specific data)
- Method consistency (aligned system boundaries, allocation methods, and assumptions across suppliers and categories)
- Completeness, including the Primary Data Share (PDS)—how much of the PCF is based on reported primary activity data rather than default values
Our focus is on robust and transparent quality assurance, supported by clear processes and ongoing improvement, to ensure that PCF data can be used in a consistent and comparable way over time.
How data quality is assessed – layered checks and continuous improvement
Data quality is supported through several reinforcing layers, combining automated controls, systematic screening, and collaborative follow-up.
- Platform input validation
The platform supports accurate reporting through input checks and feedback, including confirmation messages, error prompts, and validations that prevent invalid or unrealistic values. - CarbonCloud quality screening
CarbonCloud screens reported data across modules such as cultivation, production and ingredient composition, packaging and transport. The screening focuses on reasonableness and internal consistency, using typical values and logical boundaries.
Primary Data Share (PDS)
For each product, the platform shows how much of the PCF is based on reported primary data. A higher primary data share generally supports a more product specific footprint and better improvement work over time. - Primary Data Accuracy (PDA)
Systembolaget and CarbonCloud are jointly developing Primary Data Accuracy (PDA), combining beverage specific knowledge with data and system expertise. Rather than a simple pass/fail assessment, PDA highlights data points that may require closer review, based on defined rules such as mandatory fields, reasonable ranges, and category specific expectations. This approach will evolve as more data is collected and understanding increases.
Independent review and audits – building confidence responsibly
To further support trust and reliability, Systembolaget plans to conduct third-party audits of reported PCFs as part of quality assurance. If selected, suppliers may be asked to provide relevant supporting documentation. Any such requests will be clearly communicated and handled with respect for confidentiality and supplier effort.
Our long-term approach: trust, predictability, and collaboration
Systembolaget recognizes the importance of clear, gradual, and fair development of PCF use. In response, Systembolaget is strengthening collaboration with CarbonCloud on data collection and methodology, improving tools and guidance, and working towards clear quality frameworks—aligned with recognized principles such as PACT and PEFCR and supported by audits.
In short, the more relevant primary data that is collected across the value chain, the stronger the foundation for quality, comparability, and stability in PCF calculations—and the more useful PCF becomes as a tool for improvement over time.