From estimates to action: Why bottom-up PCFs are key to Scope 3 decarbonization

The growing demand for PCFs

As scrutiny intensifies on Scope 3 emissions, organizations are under pressure to move beyond estimates and shift to data-backed reporting. Accessing Product Carbon Footprints (PCFs) for goods that an organization purchases, is essential for this. Not just for compliance, but for unlocking meaningful emissions reductions across complex supply chains.

Yet how these PCFs are calculated makes a big difference to how useful it is. With multiple methods available to choose, understanding the difference between them is critical in choosing the right tools for job at each stage of the decarbonization journey.

In this blog post, you’ll learn:

• What’s a top-down PCF?
• What’s a bottom-up PCF? 
• Why does the method matter? 
• How M2030 supports bottom-up PCFs at scale 
• Which approach should you choose?

What is a top-down PCF?

There are many ways to define "top-down" and "bottom-up" PCFs. Here we’ll define top-down PCFs as those created by buying organizations for the goods and materials that they buy, on behalf of their suppliers, often using life cycle assessment (LCA) software and in-house expertise.

There’s also an even simpler approach. Some organizations use spend-based or activity-based emission factors combined with industry averages to estimate emissions. These methods are generally seen as indicative at best. Even top-down PCFs built with LCA software may not be completely representative, especially if buyers don’t fully control product specifications or know which facilities products are manufactured in. 

Top-down methods are popular because they are quicker and easier. 

They make use of internal resources and avoid the need to engage suppliers directly for primary data. It’s a valuable starting point, but only that: a start.

Recently, AI-based models have emerged to speed up this process even further. These tools combine algorithms, benchmarks, and probabilistic models to generate emissions estimates at a scale that would have been impossible just a few years ago. They help fill critical data gaps, supporting Scope 3 reporting at a time when regulations like the EU CBAM are raising the requirements for product-level data.

However, despite their speed and innovation, these estimates still rely heavily on assumptions. They don’t replace the need for real supplier-provided data when it comes to making strategic sourcing decisions or building effective decarbonization plans.

When suppliers themselves use AI-based tools to generate PCFs and refine them with real data, the results can improve. This hybrid approach allows suppliers to quickly build reasonably accurate PCFs across their product portfolios.

But there’s still a trade-off. 

Suppliers using only automated tools often miss out on understanding the important concepts and methodologies that shape a PCF. They might be unable to challenge choices that have a significant impact on the PCF value of a product.

For example, allocation methods for multi-product processes can have a massive impact on the final footprint. Without a deeper understanding of these nuances, trust in the reported values will always be limited.

True confidence in the data comes when suppliers take ownership of their PCFs. Through understanding the nuances and assumptions that sit behind the values they report to customers and working closely with the calculations.

What is a bottom-up PCF?

In contrast, bottom-up PCFs are built using real, primary data from suppliers — information about energy use, raw materials, production processes, logistics, and more. These PCFs reflect the actual emissions embedded in a specific product, made in a specific way, in a specific location.

Bottom-up methods can offer high traceability, greater accuracy, and better insights for action.

Of course, gathering this level of detail takes more time. Suppliers need to establish data collection frameworks and, without the right tools, it can take weeks to months to generate a handful of PCFs. Tools that enable this methodology to scale, however, are incredibly valuable for suppliers and for their customers.

For suppliers, hands-on PCF building creates a sense of ownership: understanding the details of methods, nuances of the data they hold, the proxies and assumptions that are being made. This deepens their understanding of their product’s environmental impact and the potential decarbonization actions that can be taken to reduce these emissions. 

For buyers, bottom-up PCFs provide data that is more reliable and backed by primary data. This informs sourcing decisions, supplier collaboration, and enables measurable progress against reducing Scope 3 Category 1 emissions.

Why does the method matter?

Procurement teams play a central role in supply chain decarbonization. But only if they have access to the right data. PCFs that lack supplier specificity can result in flawed sourcing decisions, inflated costs, and reputational risks.

AI-generated estimates cannot replace this kind of specificity. 

They cannot easily reflect the impact of real supplier actions over time, and they cannot validate the impact of engagement programs.  

AI-based models have a role to play. They enable fast assessments and help define priority areas for action. But to move from estimates to emissions reductions, organizations need to evolve their approach. A hybrid model – one that uses AI for scoping and top-down for benchmarking then shifts to bottom-up engagement for priority suppliers – offers a pragmatic way forward. 

The key is timing. 

Supplier enablement takes time. Organizations that invest early, even through small pilot programs, will be far better prepared when corporate targets and science-based goals demand deeper, faster action. This brings suppliers and their customers together to understand what data is needed, in what format, and provides lessons to both on how to go about wider engagement activities that might be required in the future.

How Manufacture 2030 supports bottom-up PCFs at scale

Manufacture 2030’s PCF Module is designed to make bottom-up PCFs a scalable reality for procurement teams. The PCF Module provides the tools that suppliers need to start building reliable, relevant PCFs.

Through M2030’s platform, suppliers have access to:

• Workflows that simplify the PCF request and submission process
• A guided PCF Builder, based on the PACT Methodology, that walks suppliers through every step
• PCF Academy to help build their own skills and knowledge
• A library of decarbonization actions to inspire and support emissions reductions
• Access to partners offering financial and practical support for carbon reduction initiatives

With the PCF Module, procurement teams can pinpoint high-impact areas within product portfolios, equip suppliers to participate in emissions reduction efforts, and turn PCFs into decarbonization levers not just metrics.

What approach should you choose?

Estimates are just the beginning. Top-down PCFs offer speed. AI models offer insight. But it's bottom-up PCFs that deliver what procurement teams really need: supplier-specific, actionable data that drives real emissions reductions.

If you’re just getting started, top-down approaches are suitable for initial reporting requirements. But if decarbonization is your desired outcome, engage your suppliers and give them ownership to calculate PCFs.

To reach net zero, organizations must move beyond estimates. The decarbonization journey begins with understanding - but it ends with action.