PCF or LCA? 7 Common Pitfalls When Confusing the Two

PCF or LCA? 7 Common Pitfalls When Confusing the Two

PCF and LCA are two different tools that help companies make better decisions about their products. Both work on measurement logic. Both require data. Both are powerful when used correctly. The problem starts when these two tools are presented as the same thing. This confusion leads to errors in project design, unnecessary costs in data collection, and misleading results in reporting.

In this article, we examine the seven most common mistakes in PCF and LCA projects. We offer practical recommendations for each pitfall.

Pitfall 1: Assuming They're the Same Thing

The first mistake is treating PCF and LCA as synonymous. PCF (Product Carbon Footprint) measures a product's climate impact. The result is usually expressed as a single number: X kg CO2e. LCA (Life Cycle Assessment) evaluates multiple environmental impacts. These impacts include global warming potential (GWP), acidification, eutrophication, ozone layer depletion, water use, and resource depletion.

PCF can be thought of as a subset of LCA. But in practice, the purposes are different. PCF is usually prepared for customer or supply chain requirements. LCA is used for product development, EPD (Environmental Product Declaration) preparation, or strategic decision-making.

ISO 14067 is the reference standard for PCF calculation. ISO 14040 and ISO 14044 define the LCA framework. Knowing the requirements of these standards is critical for properly defining project scope.

Pitfall 2: Drawing Wrong Boundaries

System boundary defines where the calculation begins and ends. This decision directly affects what the result represents.

Cradle-to-gate: Covers stages from raw material extraction to when the product leaves the factory gate. Provides a production-focused view. Often preferred in B2B relationships because the buyer incorporates the product into their own processes.

Cradle-to-grave: Covers the entire life cycle. Includes raw material extraction, production, distribution, use phase, and end-of-life. More meaningful for consumer products.

Gate-to-gate: Covers only processes within the production facility. Provides a partial picture and is generally used as supplier data.

When boundaries are drawn incorrectly, results are incomplete or create unnecessary detail burden. Calculating cradle-to-grave when the customer wants cradle-to-gate wastes both time and money.

Pitfall 3: Skipping the Functional Unit

The functional unit defines what is being measured and forms the basis for comparison. Different functional units for the same product produce completely different results.

Consider a paint product, for example. "1 kg of paint" and "the amount of paint needed to cover 1 m² of surface" produce different results. One paint may be denser but require less usage. Functional unit selection reveals this difference.

If the functional unit is not clearly defined in a PCF study, customer comparison becomes difficult. In LCA, functional unit selection affects all impact categories. ISO 14044 requires the functional unit to be clear, measurable, and comparable.

Pitfall 4: Ignoring Data Quality

In PCF and LCA, data quality determines the reliability of the result. Data is evaluated in two main categories:

Primary data: Measurement data from the company's own operations. Energy bills, production records, raw material consumption data fall into this category. It is the most reliable data type.

Secondary data: Industry averages or literature values from databases (Ecoinvent, GaBi, DEFRA). Used when primary data is not available, but uncertainty is higher.

Data quality assessment is based on geographic compatibility, temporal compatibility, technological compatibility, and completeness criteria. If this assessment is not documented, problems arise during verification. Auditors expect to see data sources and quality assessments.

Pitfall 5: Neglecting Contribution Analysis

Which stage or material produces the most emissions? This question forms the basis of the reduction plan. Contribution analysis breaks down the total impact into its components.

For example, a contribution analysis for a textile product might look like this: raw material (cotton) 40%, dyeing and finishing 25%, garment making 10%, distribution 5%, use (washing) 15%, end-of-life 5%. This breakdown shows where to focus for reduction.

If a PCF or LCA study does not include contribution analysis, the number remains decorative. It doesn't produce decisions. Saying "our product's carbon is X kg CO2e" without contribution analysis is like giving directions without a map.

Pitfall 6: Confusing Reporting Formats

PCF results are usually shared as a single CO2e value. This value is calculated by converting all greenhouse gases to CO2 equivalent based on global warming potential.

LCA results include multiple impact categories. Global warming potential (GWP) is just one of them. Acidification is expressed as SO2 equivalent, eutrophication as PO4 equivalent. Each category represents a different environmental impact.

Sending an LCA when the customer expects a PCF, or vice versa, creates communication breakdown. Clarifying expectations at the start of the project prevents this problem.

Pitfall 7: Not Distinguishing Internal and External Use

PCF and LCA studies can serve different purposes:

External use: Customer requests, CBAM declarations, EPD preparation, marketing communications. Third-party verification may be required in this case. Methodology and results may be publicly available.

Internal use: Product development, supplier evaluation, strategy development. Verification may not be mandatory. Detail level and format may be more flexible.

If the purpose is not clearly defined, the study is produced in the wrong format and wrong detail level. A study prepared for external use requires more comprehensive documentation and verification.

Conclusion

PCF and LCA are powerful tools when used correctly. The difference starts with clearly defining the purpose. Boundaries, functional unit, data quality, and output format are shaped according to this purpose. Confusing the two leads to waste of time and resources.

Before starting a project, answer these questions: Who is this study for? According to which standard will it be done? Which boundaries apply? What data is available? How will the result be used? Giving clear answers to these questions is the most effective way to avoid the seven pitfalls.