No Supplier Data? Here’s How to Choose the Right Emission Factors for Scope 3.1

Collecting primary emissions data from suppliers is the gold standard for Scope 3.1 reporting. But what happens when suppliers can’t, or won’t, provide data?

This is the reality for most organisations. Many suppliers lack the tools, awareness, or motivation to share reliable emissions information. That’s when secondary data and emission factors become essential.

However, using the wrong factor, whether too generic or regionally inaccurate, can distort your carbon footprint and undermine credibility.

In this guide, we’ll break down a practical, tiered approach to selecting the right emission factors when supplier data is missing, so you can stay accurate, transparent, and audit-ready.

Why Emission Factor Selection Matters

When supplier-specific data is unavailable, choosing the right emission factor can mean the difference between credible insights and misleading estimates. The wrong choice might:

• Inflate or underreport your actual Scope 3.1 footprint
• Reduce your ability to meet ESG disclosure standards
• Undermine trust with investors, auditors, and customers

Your fallback should not be “any available factor”, but the most accurate and appropriate one available for your procurement context.

A Tiered Strategy for Choosing the Right Factor

Here’s a practical guide to choosing the right emission factors when supplier data is unavailable:

Match Granularity in Procurement Data

If you know what you bought (e.g., “aluminum sheets” or “steel fasteners”), you can use product-specific emission factors from lifecycle databases like:

• ecoinvent
• DEFRA (UK)
• ADEME (France)

If you only have spend-level data, use EEIO (economic input-output) databases like:

• USEEIO (US-based)
• EXIOBASE (global)

2. Combine Spend- and Activity-Based Methods

• You don’t have to choose just one method.
• Use spend-based data to identify high-emission categories, then apply process- or activity-based factors (e.g., kg CO₂e per kg of material) to items where more detail is available.
• This hybrid model increases precision while keeping the process scalable across your supplier base.

3. Localise Where Possible

Emission factors vary by geography due to:

• Grid electricity mix
• Manufacturing technology
• Transportation methods

Example: Producing steel in China can emit 2x the CO₂e compared to the same product made in France.

4.Prioritise Fresh, Transparent Sources

Emission factors become outdated as industries decarbonise. Avoid using legacy data from:

• Outdated LCA studies
• Undisclosed methodologies
• "Black box" proprietary datasets

Instead, favor transparent, frequently updated sources that disclose:

• Data collection method
• Base year

5. Understand Uncertainty Trade-offs
Product-specific factors from ecoinvent typically have ±25-50% uncertainty, while spend-based USEEIO factors can range ±50-200%. Document these ranges - they're critical for credible reporting and audit readiness.

6. Build a Confidence Register

Track uncertainty levels for each major spending category. Focus improvement efforts on high-impact, high-uncertainty categories first. A steel supplier with ±150% uncertainty deserves more attention than office supplies with ±100% uncertainty.

7. Document All Assumptions

   If you’re estimating emissions, assumptions are inevitable. But they must be documented. Clearly record:

• Why a specific factor was chosen
• What proxies were used
• Any regional or sector-based adjustments

This is key for:

• Auditability
• Future refinement
• Regulator confidence

 A good audit trail protects your credibility, even when exact data is missing.

Example: Hybrid Factor Mapping in Action

A global consumer goods brand lacked supplier data for packaging vendors in Southeast Asia.

Here’s how they tackled it:

• Used spend data to identify high-impact vendors
• Collected basic material specs (e.g., “PET bottle, 30g”)
• Applied region-specific product-based factors from ecoinvent and ADEME
• Documented all assumptions in a central repository

Result: They avoided overestimating emissions by 17%, compared to generic global averages.

Next Steps for Smarter Scope 3.1 Estimation

1. Audit your procurement granularity, what do you actually know?
2. Build a factor selection matrix (by category, data type, geography) Combine spend + process data where possible
3. Use tools like Mavarick to auto-match emission factor
4. Maintain a live assumptions log for every estimation.

Want to simplify Scope 3.1 supplier reporting?

Conclusion

Just because supplier data is missing doesn’t mean your Scope 3.1 report has to be inaccurate.

With a structured approach to choosing emission factors, based on procurement detail, regional relevance, and data transparency, you can build estimates that are both defensible and decision-useful.

Remember: Transparency about uncertainty builds credibility, not undermines it. Stakeholders expect emission estimates to have ranges - what they don't expect is pretending perfect precision when using proxy data.

FAQs

What’s the best database for emission factors?

It depends. ecoinvent, DEFRA, and ADEME are strong for product-level data; USEEIO and EXIOBASE are good for spend-based estimates..

Can I mix spend- and weight-based emission factors?

Yes, in fact, combining them (a hybrid approach) often improves accuracy, especially for high-impact purchases.

Do I need supplier permission to use regional emission factors?

No. Regional factors are based on average industry data. They’re used as a proxy when supplier-specific data is unavailable.

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