Yes. The GHG Protocol Corporate Value Chain (Scope 3) Standard explicitly accepts spend-based and average-data methods, particularly for early-stage reporting or where primary data collection is impractical at scale. What auditors expect is that you document the method, disclose the data quality, and show a plan to replace averages with primary data in your material categories.
Scope 3 emissions reporting has a reputation for being the most data-hungry part of any carbon accounting exercise, and for good reason. You’re essentially trying to account for emissions that happen outside your own operations, across a supply chain that might span dozens of countries and hundreds of suppliers. So when a supplier can’t or won’t share their actual emissions data, a very reasonable question comes up: can you just use an industry average instead? The short answer is yes, often you can. But like most things in scope 3 data collection, the details matter quite a bit.
This article walks through when industry averages are genuinely acceptable, how to pick the right dataset, what accuracy trade-offs you’re accepting, and how to be transparent about it all in your disclosures. Whether you’re preparing a CSRD report or working toward an SBTi-aligned target, getting this right will save you a lot of headaches down the line.
When industry averages are an accepted fallback
Industry averages are widely accepted as a legitimate method when supplier-specific data simply isn’t available yet. The GHG Protocol’s Corporate Value Chain (Scope 3) Standard explicitly recognizes spend-based and average-data methods as valid approaches, particularly in early-stage reporting or where primary data collection is impractical at scale. So you’re not cutting corners by using them. You’re following established methodology.
That said, industry averages work best under certain conditions. They’re most appropriate when a spend or activity category is relatively homogeneous, meaning the emissions intensity across suppliers in that category doesn’t vary dramatically. They’re also a reasonable choice when a category is lower in materiality, or when you’re in the process of building out your supplier engagement program and haven’t yet reached the point where primary data is flowing in reliably. Using averages for a high-spend, high-emissions category where supplier practices vary widely is a different story, and that’s where the accuracy trade-offs become much more significant.
How to choose the right industry average dataset
Not all industry average datasets are created equal, and choosing the right one can make a meaningful difference to the credibility of your scope 3 figures. The most widely used sources include the Ecoinvent database, the UK Government’s DEFRA emission factors, the US EPA’s supply chain greenhouse gas emission factors, and sector-specific datasets published by industry associations. For companies reporting under the EU Taxonomy or CSRD, it’s also worth checking whether sector-specific guidance points to preferred data sources.
When evaluating a dataset, a few things are worth checking:
- Geographic relevance: An emission factor from one region may not reflect the energy mix or production methods in another. A European manufacturer and an Asian manufacturer in the same industry category can have very different actual emissions intensities.
- Recency: Older datasets may not reflect changes in technology, energy grids, or production practices. Aim for datasets updated within the last few years where possible.
- Scope boundary alignment: Make sure the dataset covers the same emissions scope you’re trying to account for. Some factors include only direct combustion; others include upstream energy production as well.
- Sector granularity: A broad “manufacturing” average is far less useful than a factor specific to, say, aluminum smelting or textile dyeing. The more granular, the better.
Getting the dataset selection right is genuinely foundational to the rest of your reporting. A geographically mismatched or outdated factor can introduce errors that are hard to detect and harder to explain to auditors or stakeholders. The combination of geographic fit, recency, and sector granularity is what separates a defensible estimate from a rough guess. Once you’ve locked in your sources, the next thing to think about is what level of accuracy you’re actually accepting.
Accuracy trade-offs and materiality thresholds
Using industry averages introduces uncertainty, and it’s important to be honest with yourself about how much. Average-data methods can produce estimates that are meaningfully different from actual supplier emissions, sometimes by a significant margin in either direction. This isn’t a reason to avoid them, but it is a reason to apply them thoughtfully.
Materiality is the key concept here. Most reporting frameworks ask you to focus your data quality improvement efforts on the categories that matter most to your overall emissions profile. If a particular category represents a small fraction of your total scope 3 footprint, using an industry average there is a perfectly reasonable long-term choice. If it’s one of your largest categories, the accuracy trade-off becomes more significant, and stakeholders, auditors, or frameworks like SBTi may expect you to work toward primary data over time.
A practical approach is to rank your scope 3 categories by estimated emissions contribution and flag the top tier for primary data collection priority. This gives you a structured way to defend your methodology choices: you’re using averages where they’re proportionate, and investing in better data where it genuinely moves the needle on accuracy.
Building a data improvement roadmap
Industry averages are a starting point, not a permanent solution for your most material categories. Building a roadmap to improve data quality over time is both a best practice and increasingly an expectation from reporting frameworks and investors alike.
A practical roadmap typically moves through a few stages. You start with spend-based or average-data methods to get your baseline in place. From there, you identify which supplier categories are most material and begin direct supplier engagement, asking for actual emissions data, energy consumption figures, or product-level carbon footprints. Over time, as supplier responses come in, you replace average estimates with primary data category by category.
The timeline for this doesn’t need to be aggressive across the board. Focusing supplier engagement efforts on your top three to five most material categories first is a much more manageable approach than trying to collect primary data from every supplier simultaneously. It also lets you demonstrate progress year over year, which matters for frameworks like CDP where data quality trends are visible to stakeholders. Scope 3 emissions reduction consultants and sustainability reporting specialists can be particularly useful here, since the process of designing a supplier engagement program and tracking data quality improvements is a distinct skill set from general sustainability strategy work.
Disclosure and transparency requirements
Whatever methods you use, transparency about them is non-negotiable. Reporting frameworks including CSRD and CDP require you to disclose the data sources and methods used for each scope 3 category, not just the final numbers. That means documenting which categories use industry averages, which datasets you drew from, and why those choices were made.
This isn’t just a compliance formality. Transparent methodology disclosure protects you when stakeholders or auditors dig into the numbers. It also makes it easier to track your own data quality improvements over time, since you have a clear baseline to compare against as primary data starts replacing estimates.
One thing to be careful about is presenting average-based estimates with a level of precision that implies they’re more accurate than they are. Rounding to an appropriate number of significant figures and noting the uncertainty range where possible signals methodological honesty. It’s also worth noting in your disclosures that your data improvement roadmap is actively working to reduce reliance on averages in material categories, which shows stakeholders that the current approach is intentional and time-bound rather than a permanent workaround.
Ready to sharpen your scope 3 approach?
Navigating scope 3 data collection, from choosing the right datasets to building a supplier engagement program to getting your disclosures right, takes a specific kind of expertise. It’s detailed, framework-specific work, and the right support can make a real difference in how defensible and useful your reporting ends up being.
At Dazzle, we match organizations with pre-screened sustainability freelancers who specialize in exactly this kind of work, whether that’s a scope 3 emissions reduction consultant, a CSRD reporting expert, or an LCA specialist. Our network of 150+ experts is available on a project or interim basis, and we can connect you with the right person within 48 hours. If you’d like to talk through what kind of support would fit your situation, we’d love to hear from you.
