Building a GHG Inventory with Incomplete Supplier Data: A Practical Guide

Building a greenhouse gas (GHG) inventory for Scope 3.1 – Purchased Goods and Services is a necessary but often daunting step in corporate climate action. While standards like the GHG Protocol emphasise supplier-specific data as the gold standard, most companies find themselves facing a familiar challenge: incomplete, inconsistent, or entirely missing supplier emissions data.

But a lack of perfect data does not mean you can’t move forward. In fact, regulators and voluntary reporting frameworks alike, including CDP, CSRD, and SBTi, recognise that proxies, estimates, and tiered data strategies are often necessary in early maturity phases. What matters is transparency, consistency, and continuous improvement.

In this guide, we’ll walk through a structured approach to building a credible, scalable Scope 3.1 inventory, even when supplier data is far from complete.

Why Waiting for Perfect Supplier Data Is a Mistake

It's common for sustainability teams to delay Scope 3.1 accounting efforts while they wait for supplier-specific data to improve. But this "data paralysis" can stall emissions transparency and compliance for years.

Reality check:

• Only 41% of suppliers can provide verified carbon data. The rest? Incomplete, inaccurate, or entirely missing. Even where emissions are reported, inconsistent boundaries or poor calculation methods make direct use risky
• Regulatory frameworks (e.g., CSRD) and voluntary platforms (e.g., CDP) explicitly allow the use of secondary data if supplier-specific data is unavailable

Instead of waiting, companies should use what they have—spend data, material records, and product categories, to build a working inventory with clear signals for future improvement.

Step-by-Step: Building a Scope 3.1 Inventory Without Full Primary Data

Step 1: Start with What You Control – Procurement Data

Your procurement systems are the foundation for Scope 3.1. Even without supplier emissions disclosures, you likely have access to:

• Purchase order history
• Supplier names and locations
• Spend or quantity purchased
• Product codes or descriptions
• Material specifications

From this, you can create a map of your emissions drivers, even before calculating the emissions.

Pro Tip: Enrich this data with HS codes or UNSPSC tags for better emission factor matching.

Step 2: Categorise Purchases Using a Logical Framework

Structure is everything. Grouping purchased goods and services into logical, standardised categories makes it easier to apply relevant emission factors. Examples:

• Raw Materials (e.g., steel, aluminum, plastics)
• Packaging Materials
• Electronics and Machinery
• Office Supplies and Services
• Outsourced Manufacturing

The GHG Protocol encourages this classification to align with emission factor databases and material flow logic.

Step 3: Match Activity Data to the Best Available Emission Factors

Here’s where secondary data fills the gap. Match procurement records to the most relevant emission factors using one of three approaches:

Spend-Based Estimation

Use EEIO (Environmentally Extended Input-Output) models like:

• USEEIO (U.S. Environmental Protection Agency)
• CEDA
• EXIOBASE (EU-funded global model)

Best for: Early-stage Scope 3.1 or low-granularity data.

Quantity-Based Estimation

Use LCI (Life Cycle Inventory) datasets such as:

• ecoinvent
• GaBi
• ADEME (France)

Best for: When you have material weights, volumes, or SKUs.

Industry Averages

For some sectors (e.g., packaging, automotive), trade associations and government agencies publish benchmark factors.

Example: The World Steel Association publishes average emissions intensity per tonne of steel, broken down by region.

Pro Tip: Always prioritise the most geographically and technologically relevant factors. For example, steel production in China has different emissions intensity than steel from Germany, so match your supplier locations to regional emission factors whenever possible.

Step 4: Use Tiered Data Quality Ratings

To ensure transparency and guide future improvements, assign a data quality score to each emissions estimate:

• High: Supplier-specific emissions with boundary clarity
• Medium: Quantity-based estimate from process-LCI or regional average
• Low: Spend-based estimate using national EEIO models

This tiering is encouraged by the GHG Protocol and is especially useful for auditors and internal reviews.

Use these scores to focus improvement efforts: high-volume, low-quality items should be your next target for supplier engagement. Focus quality improvements on categories representing >80% of your total spend or emissions, this is where better data will have the most impact on accuracy."

Step 5: Document Assumptions Transparently

Every estimate includes assumptions. These must be documented to ensure the inventory remains:

• Auditable
• Comparable year-on-year
• Improvement-ready

Include:

• Source and age of emission factors
• Boundaries applied (e.g., cradle-to-gate)
• Conversion factors used (e.g., $ to kg, or liters to tonnes)
• Any default or fallback logic used when data was missing

Quick Data Validation: Set up simple checks for obvious errors like negative emissions, unit mismatches, or factors that seem unreasonably high or low compared to industry norms. A basic sanity check can catch calculation errors before they compound across your entire inventory. Use central assumptions register and tie it to each line item or supplier group.ries into logical subcategories before estimation.

Recommended Emission Factor Sources

Keep sources updated and document versioning clearly in your inventory files.

Looking Ahead: Moving from Estimates to Primary Data

While secondary data allows companies to get started, primary data collection should remain a long-term goal, especially for:

• Strategic suppliers
• High-spend or high-emission categories
• Products included in low-carbon product or green claims

You can improve data quality over time by:

• Including emissions disclosure in supplier contracts or RFPs
• Using platforms like Mavarick or EcoVadis for supplier engagement
• Offering tools or templates to help suppliers calculate emissions more easily

A phased plan that targets 10–20% of emissions-heavy suppliers annually is often more realistic than full primary coverage.

Want to simplify Scope 3.1 supplier reporting?

Conclusion

Incomplete supplier data should never be a blocker to Scope 3.1 progress. With the right procurement foundation, structured categorisation, and reliable proxies, you can create a credible GHG inventory that evolves over time.

The key is to act, start where you are, be transparent about your assumptions, and build quality improvement into your roadmap. This approach not only meets reporting needs today but also sets the stage for increasing precision and supplier collaboration tomorrow.

FAQs

Is using spend-based estimates compliant with GHG Protocol?

Yes. The GHG Protocol allows for spend-based estimation where more granular data is unavailable, provided you document your sources and limitations

How do I know if my emission factor is “granular enough”?

A: Ideally, it should match both the product type and production region. For example, “primary aluminum, China, cradle-to-gate” is more precise than “generic metal products.”

What’s the biggest risk in using secondary data?

Applying generic or outdated emission factors without adjusting for region, process, or scale can lead to significant over- or underestimation.

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