Scope 3 emissions are, for most organizations, the biggest and most complex chunk of their carbon footprint. Unlike scope 1 and 2 emissions, which come from sources you directly control or pay for, scope 3 covers everything happening upstream and downstream in your value chain. That’s suppliers, logistics, product use, end-of-life disposal, business travel, and more. Measuring them accurately requires a lot of data, and knowing exactly what data you need and where to find it is half the battle.
Whether you’re reporting under the CSRD, responding to a CDP questionnaire, or setting science-based targets through SBTi, getting your scope 3 data collection right is what separates a credible report from a rough estimate. Here’s a practical breakdown of what you actually need.
The 15 scope 3 categories and their data requirements
The GHG Protocol divides scope 3 emissions into 15 categories, split between upstream activities (related to your supply chain and inputs) and downstream activities (related to what happens after your product leaves your hands). Each category has its own data logic.
Upstream categories (1-8)
- Category 1 – Purchased goods and services: You’ll need spend data or quantity data on everything you buy, along with supplier-specific emission factors or industry averages.
- Category 2 – Capital goods: Similar to category 1, but focused on long-lived assets like machinery or buildings. Spend-based or physical quantity data works here.
- Category 3 – Fuel and energy-related activities: This covers upstream emissions from producing the fuels and electricity you use. You’ll need your actual energy consumption figures from scope 1 and 2.
- Category 4 – Upstream transportation and distribution: Distance traveled, weight of goods, and mode of transport are the key inputs here.
- Category 5 – Waste generated in operations: Volume of waste by type (landfill, recycling, incineration) is what you’re after.
- Category 6 – Business travel: Distance traveled by employees across flight, rail, and road, ideally broken down by class of travel.
- Category 7 – Employee commuting: Number of employees, commute distances, and transport modes. Survey data is often the only way to get this.
- Category 8 – Upstream leased assets: Energy consumption data for any assets you lease but don’t operate directly.
Downstream categories (9-15)
- Category 9 – Downstream transportation and distribution: Similar to category 4, but for the leg of the journey after your product leaves your facility.
- Category 10 – Processing of sold products: Relevant if your products are intermediate goods. You’ll need data on how customers process them further.
- Category 11 – Use of sold products: Lifetime energy consumption of your products in use. This is often the largest category for manufacturers of electronics or appliances.
- Category 12 – End-of-life treatment of sold products: Estimated volumes of products reaching end-of-life, and how they’re disposed of.
- Category 13 – Downstream leased assets: Energy use data for assets you own but lease out to others.
- Category 14 – Franchises: Energy and operational data from franchise locations you don’t directly control.
- Category 15 – Investments: Equity share or project finance data for financial institutions and investors.
Across all 15 categories, the data you need falls into two broad types: activity data (what you actually did, like kilometers traveled or kilograms purchased) and emission factors (how much carbon that activity produces per unit). Getting both right is what drives accuracy. And that brings us to a distinction that matters a lot in practice.
Primary vs. secondary data: what’s the difference?
Not all scope 3 data is created equal. Primary data comes directly from the source, meaning your suppliers, logistics partners, or facilities provide their actual emissions figures. Secondary data, on the other hand, uses industry averages, emission factor databases, or spend-based proxies to estimate emissions when direct data isn’t available.
Primary data is more accurate and more credible, but it’s also harder to collect. It requires suppliers to measure and share their own emissions, which many haven’t done yet. Secondary data is much easier to work with, but it introduces uncertainty and can mask meaningful differences between suppliers. A supplier running on renewable energy looks identical to one running on coal if you’re using the same industry-average emission factor for both.
The GHG Protocol encourages organizations to prioritize primary data for their most significant emission categories. That means identifying where in your value chain the biggest emissions sit, and focusing your data collection efforts there first rather than trying to get primary data for everything at once.
Where to source scope 3 emissions data
Finding reliable carbon emissions data requires tapping into several different sources, and the right one depends on the category you’re measuring.
- Supplier questionnaires and engagement: Directly asking suppliers for their product-level or company-level emission data is the gold standard for category 1. Platforms like CDP’s supply chain program facilitate this at scale.
- Internal financial and operational systems: Your procurement, finance, and logistics data are rich sources of activity data. Spend figures, purchase volumes, and logistics records all feed into scope 3 calculations.
- Emission factor databases: Publicly available databases such as the IPCC, Ecoinvent, and national government databases provide emission factors for hundreds of activities and materials. These are the backbone of secondary data approaches.
- Life cycle assessment (LCA) data: For product-level emissions, especially in categories 1, 11, and 12, LCA data provides detailed cradle-to-gate or cradle-to-grave figures. LCA specialists are a distinct type of sustainability expert who focuses specifically on this kind of analysis.
- Industry associations and sector databases: Many industries have developed sector-specific emission factors that are more accurate than generic averages.
- Employee surveys: For category 7 (commuting), surveys are often the only practical way to collect mode and distance data at scale.
In practice, most organizations use a combination of all of these. The key is to document your sources clearly, because data provenance matters when you’re reporting externally or being audited. A well-structured data collection process also makes year-on-year comparisons much more reliable.
Common data gaps and how to handle them
Even with the best intentions, gaps are almost inevitable in scope 3 data collection. Suppliers don’t respond to questionnaires, internal systems weren’t built with carbon accounting in mind, and some value chain activities are simply hard to trace. The question isn’t whether you’ll have gaps, but how you handle them.
The most practical approach is to use secondary data as a placeholder while you work to fill gaps with primary data over time. Spend-based emission factors (which estimate emissions based on how much money you spent in a given category) are a common fallback for category 1 when supplier data isn’t available. They’re not precise, but they’re better than leaving a category blank.
It’s also worth being transparent in your reporting about where estimates were used and why. Frameworks like the CSRD and SBTi don’t expect perfection in year one. What they do expect is a credible methodology, clear documentation, and a plan to improve data quality over time. Acknowledging data limitations honestly is a sign of a mature reporting process, not a weakness.
How data quality affects your scope 3 reporting
Data quality has a direct impact on the credibility and usefulness of your scope 3 report. Poor data doesn’t just produce inaccurate numbers; it can undermine your ability to identify where to focus emissions reduction efforts, set meaningful targets, and demonstrate progress year over year.
The GHG Protocol uses a data quality scoring system that looks at factors like technological representativeness (does the emission factor reflect your actual technology?), geographical representativeness (does it reflect your region?), and temporal representativeness (how recent is it?). Higher-quality data scores better across all three dimensions.
For organizations reporting under the CSRD or aligning with SBTi, data quality also affects how your targets and progress claims hold up to scrutiny. Auditors and third-party verifiers will look closely at your methodology, and weak data quality in high-emission categories raises red flags. Investing in better data for your most material categories is one of the highest-impact things you can do to strengthen your overall sustainability reporting.
It’s also worth noting that data quality improvement is a journey. Most organizations start with a spend-based approach for the majority of categories and gradually shift toward activity-based and then supplier-specific data as their processes mature. Building that roadmap early means you’re not scrambling when reporting requirements tighten.
Ready to get your scope 3 data under control?
Measuring scope 3 emissions is genuinely complex, and the data requirements alone can feel overwhelming when you’re starting out. But with the right expertise in your corner, it becomes a much more manageable process.
At Dazzle, we connect organizations with pre-screened sustainability experts who specialize in exactly this kind of work, whether that’s a scope 3 emissions specialist, an LCA expert, or a CSRD reporting consultant. Our matching process is built for flexibility, so whether you need someone for a focused data collection project or ongoing support, we can find the right fit. And because our experts are ready to go, you can start working with the right person within 48 hours. If you’re ready to take the guesswork out of your value chain emissions data, get in touch with our team.
If you’re interested in learning more, contact our team of experts today.
