How to break free from the Scope 3 data trap

The Scope 3 measurement world has trapped companies in a false choice: move fast with generalised data or go slowly with granular insights. Neither approach helps you decarbonise effectively.

You can grab quick estimates from industry databases, knowing the numbers are rough. Or you can invest time and money into a detailed life-cycle assessment (LCA), only to find that by the time it's published, your supply chain has already changed. Both approaches fail because they can't keep pace with the complexity and speed of bespoke supply chains, nor do they reveal the hidden inefficiencies that are driving up costs and emissions across a Scope 3 inventory.

Going too fast: the database trap

When companies lack supplier-specific data, they turn to databases, government benchmarks, and industry average emissions factors. Reported with multiple significant digits, these data tools promise an emissions intensity, while creating an illusion of control and false confidence.

This one-size-fits-all approach is fast, but rarely represents how products were made, leaving reports inaccurate and companies with no real way to decarbonise their complex supply chains. Industry averages can't tell you which suppliers are lower carbon (because they all look the same), they don’t tell you which ones are efficient and which use outdated processes. They can't account for specific geography, energy grids, or manufacturing methods. The uniformity this approach creates flattens out all of the variation that matters when you're trying to identify where to intervene.

You get numbers quickly but, while they are precise, more often than not, they are precisely wrong. Without accurate data, decision-making for meaningful action is impossible. Speed without insight isn't progress, it’s just poor reporting. We need to be better as an industry.

Going too slow: when precision prevents progress

LCAs on the other hand, have long been the gold standard of comprehensive, detailed emissions analysis. In practice, they take too long, are costly and can’t scale.

It takes months to generate a full LCA for just one product. While AI solutions claim to do produce analyses with similar detail in minutes: if it sounds too good to be true, it is. These types of data systems masquerade as accurate emissions measurements, without the virtue of using real supplier specific data or representative EFs. We’ll be publishing more of our scientific research about this soon.

The climate crisis and net-zero targets mean we need to move faster, and with confidence. Waiting for comprehensive analyses across 10-20 of your products isn't viable when you're racing against 2030 and 2050 deadlines selling 100s of products and buying 1,000s. An LCA still has a critically important role in fundamental emissions analysis, but we need far more dynamic measurement systems for Scope 3 as supply chains shift, suppliers change, and manufacturing processes evolve. LCAs build important institutional knowledge but offer a snapshot in time that goes out of date almost as soon as it's published.

Supply chains are dynamic systems, not static snapshots. Your measurement approach needs to keep pace with that.

In addition, with industrial average LCAs, the wrong emissions factors are often used, results can wildly over/under- estimate carbon footprints. Our research published in 2023 by Nature noted “LCA studies of common petrochemical products, including plastic bags, bottles and films, could be regularly inaccurate by up to 40% due to primary chemical production uncertainties and over 100% inaccurate if supply chains include uncommon production methods.” This way even meticulously conducted LCAs can misdirect your attempts to decarbonise.

The third way: just right

The real question isn't whether your Scope 3 approach is too fast or too slow. It's whether you're getting actionable insights that reduce emissions at the source and prove real progress.

Companies making real progress on Scope 3 aren't the ones with the most data or the most detailed reports. They're the ones moving once they have the minimum required accurate data to make a start. They understand that decarbonisation isn't about measuring everything: it's about identifying where you can make the biggest impact and moving quickly.

Neutreeno's approach is built on this. We understand how products are actually made and how that impacts your emissions. By reverse-engineering production processes, we identify how to alter a product so it's made in the most efficient and least emissive way possible. We focus on the handful of variables that drive the majority of your emissions, eliminating 90% of the data burden whilst maintaining accuracy.

This isn’t a silver bullet solution: it’s the tip of a hard-science-iceberg.

Our team of climate scientists, engineers and sustainability experts have mapped out thousands of manufacturing processes to understand how products are made. This makes our approach and methodologies completely different to business as usual.

We're redefining decarbonisation. Join us.

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