Why Verified LCAs Still Aren't Comparable

A life cycle assessment (LCA) tallies a product’s environmental footprint from raw material extraction through disposal. Procurement teams, investors, and regulators expect to know by name, often before a supplier gets shortlisted or a claim gets reported. Third-party verification against ISO, the International Organization for Standardization, is supposed to settle whether that number can be trusted.

Two ISO-compliant, third-party verified LCAs can still be incomparable. Verification checks whether a study followed its own stated rules consistently. It says nothing about whether two studies used the same rules as each other. A verified LCA is internally sound but whether it lines up with the LCA sitting next to it is a separate question entirely, and verification was never built to answer it.

From Data Gap to Comparability Gap

Organizations working through life cycle impact tend to pass through the same three stages. First, LCA is not on their radar, as the need hasn’t surfaced, which means no studies exist for the product. Then comes a data-collection bottleneck: gathering the material, energy, and process inputs which an LCA needs is slow, and getting suppliers to hand over usable data is even slower. Clearing this bottleneck used to be treated as the finish line. It isn’t. Once the LCAs exist, a third stage shows up, and it’s the one that actually stalls decisions: the studies are sitting there, produced in good faith, and they still can’t be compared to each other. A supplier scorecard, a portfolio-wide reduction target, a claim that one option beats another: all of them assume the underlying numbers were built to matching assumptions. That assumption is usually untested.

Several Variables, One “Same” Product

Two studies of what looks like the same product can diverge from various variables: functional unit, system boundary, impact assessment method, dataset and its version, allocation rules, and geography or end-of-life assumptions. Move any one of those and the result shifts, independent of anything about the product itself. Neither study has to be wrong for the two numbers to be unusable side by side.

One example: a systematic review of electric truck life cycle studies found cradle-to-gate carbon footprints ranging from 23 to 313 tonnes of CO2 equivalent, a variation driven entirely by which system boundary was applied, not by any difference between the trucks themselves. Same category of vehicle, honest work in every underlying study, and a range that spans more than tenfold.

This reframes the useful question: while faced with two LCAs that disagree, the instinct is to ask which number is correct. However, the better question is whether both assessments were built under the same rules. If they weren’t, correctness isn’t even on the table yet. The two numbers are answers to different questions that happen to share a unit, and no amount of scrutinizing either one in isolation will make them line up. The scrutiny has to happen upstream, at the level of what each study assumed, not downstream at the level of the figure it produced.

Rules Solve Half the Problem

Product Category Rules, or PCRs, exist to close this gap. A PCR standardizes the assessment rules within a product category: it fixes the functional unit, the system boundary, and the allocation approach so two LCAs in that category finally answer one question instead of two adjacent ones. Where a PCR exists and both studies actually followed it, the comparability problem mostly disappears.

However, that is still just half the fix. The other half gets overlooked: format. A PCR governs how a study gets built. It says nothing about how the result gets transmitted to whoever has to use it next, and that handoff is usually where the context disappears. A raw number carries no methodology with it. A PDF buries the functional unit and boundary assumptions in an appendix nobody opens before the number gets copied into a spreadsheet. Both formats strip out exactly the information a reader would need to confirm the rules being actually followed, even when a perfectly good PCR was sitting behind the study the whole time.

The Missing Format Layer

A rulebook without a structured format to carry its outputs is unverifiable at the point of use. An Environmental Product Declaration, or EPD, is the standard document for reporting an LCA result, and a handful of structured, machine-readable formats now carry one, including ILCD+EPD, openEPD, and PACT/Pathfinder. Each carries the functional unit, boundary, and methodology alongside the figure itself, in a form which a system can read rather than a human having to dig for it. A procurement analyst, a supplier database, or another company’s own assessment tool can check that two numbers were built to matching rules before treating them as comparable. A bare number or a PDF can’t support that check, no matter how rigorous the underlying study was. The context needed to verify the rules is gone by the time the figure arrives at its destination, which means every recipient downstream is trusting the number on faith rather than confirming it.

What Carbon Data Is Still Missing

Carbon numbers don’t carry the intuitive trust that price does, and the gap isn’t about rigor. Price earned that trust over centuries, through shared currencies, standardized receipts, and accounting rules built specifically so a number could move between parties and still mean the same thing on arrival. Environmental data has neither piece of that infrastructure yet: no consistent methodological alignment across categories, and no standardized way to disclose a result once it’s calculated. Both gaps are solvable. Neither has been solved yet, which is why swings of this size keep showing up under a different product name.

The fix isn’t more audits stacked on top of the ones already in place. Think about a beer’s alcohol percentage, no third party re-verifies every bottle at the point of sale. It is trusted because of a measurement and disclosure system behind the label doing that work permanently, at the source. Carbon data needs an equivalent move: trust engineered into the format itself, not bolted on afterward through repeated certification of the same underlying number.

Fixing Both, Not One

Fixing only one half doesn’t solve the problem. It moves the failure downstream. A category with a solid PCR and no structured disclosure format still ships numbers that look incomparable, because nobody receiving the data can check that the rules behind it were followed. A structured format with no PCR behind it just moves the disagreement into a common container. The numbers become easy to compare and are still built on different assumptions, except now the mismatch is invisible instead of obvious.

Closing this gap from both sides means a few concrete shifts:

• Treating LCA figures as scenario-based results rather than absolute measurements.
• Developing PCRs for the product categories that carry the most weight in a portfolio.
• Demanding supplier data in structured exchange formats instead of PDFs or bare numbers.
• Building the internal capability to preserve methodological context as data changes hands, rather than letting it evaporate at each handoff.
• Training the people who are making purchasing and reporting decisions to ask about, functional unit and system boundary, before they ask about the number itself.

Comparability fails without shared rules. Trust fails without a shared format to carry them. Fix one and the other failure is just waiting downstream.