A product carbon footprint (PCF) measures the total greenhouse gas (GHG) emissions generated across a product’s lifecycle, from raw material extraction to manufacturing, use, and disposal.
While PCF calculations are often presented as accurate numbers, that precision is frequently misleading. In reality, they are impacted by boundary decisions, data gaps, and assumptions. All this makes them less exact than they appear.
Therefore, it can be challenging for businesses to rely on PCF for reporting and decision-making. In short: PCF is a useful but imperfect metric, its accuracy depends heavily on boundaries, data quality, and assumptions.
Businesses often adopt a structured PCF methodology and supply chain approach to improve reliability. However, due to Scope 3 data gaps, broader data limitations, and necessary assumptions, results can vary significantly.
Defining PCF Boundaries: Where the Calculation Begins and Ends
Many businesses adopt a cradle-to-gate approach to calculate emissions. Though this simplifies the analysis, significant parts of the product’s journey are not included here. This typically excludes transportation, product usage, and end-of-life disposal or recycling.
For example, in the case of a steel beam used in construction, cradle-to-gate emissions would include iron ore mining, steelmaking, and fabrication at the mill, but would exclude transportation to the construction site, installation, and even recycling or disposal.
This is why companies need to carefully set their PCF boundaries.
Why do boundary decisions matter?
For businesses, setting boundaries in PCF means deciding where to draw the line. Should they include only procurement and manufacturing, or go beyond? There is no universally applied standard in practice, and this is where inconsistency begins.
If the boundary is too narrow, important emission sources may be missed. And if it is too broad without relevant data, results lose reliability. As a result, two companies can report materially different emissions for the same product, simply because they define boundaries differently.
In a study published in the International Journal of Life Cycle Assessment, it was found that boundary selection can significantly influence LCA results, even when evaluating identical products.
Supply Chain Data Challenges in PCF
A significant part of a product’s carbon footprint comes from the supply chain. It includes raw material procurement, manufacturing and logistics, however, collecting accurate data is challenging as it is spread across multiple sources.
In reality, most companies face limited visibility beyond Tier 1 suppliers, with Tier 2 and Tier 3 data often unavailable or inconsistent.
As a result, businesses rely on:
- Primary data: Collected directly from the suppliers and manufacturers.
- Secondary data: Based on industry averages.
In a study published in the Journal of Cleaner Production, researchers highlighted that data quality and availability are among the largest contributors to uncertainty in Scope 3 emissions calculations. In reality, most companies face limited visibility beyond Tier 1 suppliers, with Tier 2 and Tier 3 data often unavailable or inconsistent.
Assumptions in PCF Calculations
Even with defined boundaries and available data, PCF calculations depend on assumptions, such as transport distance, product life, and disposal pathways. While it gives a rough estimate, it brings inconsistency. Even small variations in these assumptions can significantly change the final footprint.
In a study published in Environmental Science & Technology, it was observed that methodological assumptions in lifecycle assessments can lead to wide variability in carbon footprint outcomes. These assumptions are rarely standardized across companies, making comparisons difficult even when similar methodologies are used.
The Industry Challenge: False Precision in PCF
Despite these limitations, PCF results are often communicated as precise, comparable numbers. This creates a problem of “false precision”, where outputs appear accurate, but the underlying data and assumptions introduce uncertainty.
This can lead to:
- Misguided sustainability strategies
- Inaccurate product comparisons
- Overconfidence in reported emissions
- Potential compliance and credibility risks
Conclusion
PCF is often treated as a precise number, but in reality, it is shaped by boundaries, data quality, and assumptions. Treating it as an exact value can create false confidence and lead to poor decision-making.
Businesses should instead view PCF as a decision-support tool; one that improves with better data, clearer boundaries, and consistent methodologies. Companies that recognize these limitations are better positioned to make informed decisions, avoid false precision, and build more credible sustainability strategies.
Platforms like Fitsol help businesses tackle scattered supply chain data, improve visibility across vendors, and apply structured methodologies, bringing greater accuracy and confidence to carbon footprint measurement.
FAQs
What are scope 3 emissions?
Scope 3 emissions are indirect emissions across a company’s supply chain—from suppliers to product use and disposal. Because the data comes from multiple vendors and stages, it’s often fragmented and hard to measure accurately.
Why do PCF results vary?
PCF results are influenced by various factors like how a business defines its boundaries, data quality, sources, and assumptions. Even small differences in these inputs can lead to significantly different results.
Why do companies use cradle-to-gate instead of a full lifecycle?
The cradle-to-gate approach is widely used because it covers emissions from raw material sourcing to manufacturing and requires relatively less data. A full lifecycle (cradle-to-grave) approach is more comprehensive but significantly more complex and data-intensive, often increasing uncertainty when reliable data is unavailable.