PCF is not boring, it is your approach.

“We need a Product Carbon Footprint (PCF),” says the investor or the brand, and the turnaround is often expected to be immediate. A PCF is a quantified estimate of greenhouse gas emissions associated with a product. In many cases, teams start soliciting proposals before they have a clear grasp of system boundaries, data needs, functional units, or what the PCF will be used for, and the work moves forward on assumptions.

Timelines are rarely discussed with the same seriousness as the number itself. With pressure to deliver what an investor has requested or what a brand wants to publish, the exercise is pushed into “quick output” mode, and the purpose of a PCF gets undermined.

A footprint intended to be reliable becomes shaped by urgency rather than method, and confidence becomes the first casualty in a PCF. This is when the work turns dull and drab, not because PCF lacks meaning, but because it is treated as a number to be delivered rather than a tool to guide decisions and action.

When the brief is simply “send us the PCF”, the work naturally narrows to calculation and formatting. Teams focus on filling data gaps quickly, choosing emission factors that are available, and making assumptions that allow the spreadsheet to close.

The output may look clean, but it often carries low confidence because the foundations, such as boundary clarity, functional unit definition, allocation logic, and data traceability, have not been built with enough care for PCF. In that mode, the PCF becomes a compliance artefact that answers the external request and then stops.

It does not identify which hotspot matters most, which supplier data is missing, which process step needs improvement, or which design choice would cut emissions meaningfully, even though that is what PCF should enable. It also becomes difficult to repeat year on year without changing methods again, which makes trend tracking and performance management weak.

A PCF becomes genuinely useful only when it is treated as a decision pack, not a single number. It should tell you where emissions sit, how confident you are in the result, what assumptions matter most, and what the next actions are.

Those next actions could be supplier engagement, process optimisation, material substitution, energy interventions, or packaging redesign, and the PCF should make the case for what happens next. It should also make it clear which actions are quick wins and which need investment decisions, such as changes in equipment, energy sourcing, or product specifications. Without that shift, PCF stays dull because it produces activity, not progress.

So here is the question that matters more than any spreadsheet for PCF.

Are we just measuring PCF… or measuring and changing?

What a PCF actually is, and why that matters
A PCF is a quantified estimate of greenhouse gas emissions associated with a product, typically expressed as kg CO2e per functional unit. It is rooted in life cycle thinking and Life Cycle Assessment (LCA) principles, and it is often aligned with standards such as ISO 14067 and the GHG Protocol Product Standard.

That word, estimate, is not a weakness. It is a reminder that PCF is a model built on choices, and those choices should be visible to the people using the result.

Choices about system boundaries (cradle to gate, cradle to grave) and choices about functional units (what you are measuring, and in what form) shape the PCF before any calculation begins. Choices about allocation (how you split emissions across co-products) and data sources (primary supplier data versus secondary databases) can materially change a PCF.

Choices about time and geography (what year, what region, what grid factors) can move the result even when the product has not changed, and a PCF that hides these assumptions invites misuse. That is how a neat number turns into misleading certainty.

This happens in very practical ways: boundaries stop at cradle to gate instead of cradle to grave, recycling is assumed at end of life, or supplier-specific data is replaced with industry averages, and the PCF still gets presented as a headline. None of these are inherently wrong choices, but when they are not stated, comparison becomes unfair and communication becomes risky.

This is also where external communication goes wrong. When a PCF is used for product claims, comparisons, or buyer negotiations, the missing detail becomes a liability, because questions about boundary, allocation, and data quality will surface sooner or later.

When teams acknowledge and document these choices, the PCF changes character. It becomes a decision tool rather than a number, because the method is made visible.

Decision-ready PCFs make the method visible: the functional unit is clear, the system boundary is explicit, allocation logic is recorded, data hierarchy is shown, and key assumptions are tested. With that transparency, the PCF becomes usable for hotspot identification, supplier engagement, and year-on-year tracking.

A decision-ready PCF also makes governance practical. It clarifies who signs off boundaries, who owns supplier data requests, how emission factors are selected, and how changes are documented, so the organisation can repeat the work without reinventing the method each cycle.

In manufacturing, a good PCF translates into specific levers across sectors. Automotive teams often focus on metal intensity, scrap, machining energy, and paint shops; textiles teams often see hotspots in wet processing and heat; jewellery teams can target yields and refined metal sourcing; metals teams can address furnace fuels and recycled content; FMCG teams can prioritise packaging and distribution, and PCF helps prioritise what to fix first.

In short, the credibility of a PCF does not come from producing a number. It comes from being honest about the choices behind the number, and using that clarity to change decisions.

Why PCF feels painful for most teams
Most organisations assume that the real deal is carbon accounting. The reality is more mundane and more brutal: the hard part is data discipline, and this is where GHG Accounting and PCF collide in practice.

PCF sits at the intersection of product engineering, procurement, finance, operations, logistics, sustainability, and sometimes marketing. Each function holds a slice of the data, and no one slice is complete, so the work becomes a coordination challenge.

A useful step is to treat PCF data as an operational dataset with owners, cadence, and checks. When activity data capture becomes part of procurement and production routines, rather than a once-a-year scramble, the quality improves and the workload drops.

A common issue is that data collection is an add-on exercise, not an inherent part of the work, even where companies have implemented EMS or other standards, and PCF projects inherit that fragmentation. Activity data may exist, but it is often scattered across spreadsheets, emails, ERPs, and invoices.

Units and conversions are another pain point. Litres versus kilograms, wet weight versus dry weight, net weight versus gross weight, and other conversion choices can quietly distort a PCF, especially when different sites and suppliers follow different conventions.

Time boundaries also create noise: one supplier provides last year’s average while another provides quarterly data, and the PCF becomes hard to defend when asked what changed and why. Supplier data can also be missing or unusable, pushing teams to rely on proxies and generic emission factors.

Assumptions for conversions and methods can be carried out by consultants, but assumptions for activity data crunched under pressure are then forgotten, which makes a PCF difficult to defend later. This is why the practitioner’s truth is simple: calculation is not the hard part, the data discipline behind PCF is.

It also helps to connect product work to Scope 1, Scope 2, and Scope 3 early. For most products, relevant activity data spans site fuels and electricity, supplier processes, transport, packaging, use phase, and end of life, and PCF will only be robust if those interfaces are managed.