The traceability cliff
Most companies building a Scope 3 inventory discover the same pattern. Tier 1 suppliers — the ones you have contracts with, purchase orders for, and a procurement relationship with — are workable. You can send a questionnaire, request invoices, and in reasonable time get something that at least approximates real activity data. Go one tier further back, to your suppliers' suppliers, and the picture changes. Response rates drop, data formats stop being comparable, and in many cases nobody in your organization even has a documented list of who those companies are.
This isn't a software problem or a willpower problem. It's structural, and understanding why it happens is the first step to building a traceability strategy that doesn't collapse under its own ambition.
Why tier 1 is tractable
Tier 1 suppliers are tractable for three reasons that have nothing to do with emissions accounting specifically:
- You have leverage. A direct commercial relationship means you can make data provision a condition of doing business, or at least make the request hard to ignore.
- You have contact. Procurement teams already have relationship owners, renewal cycles, and communication channels in place. Asking for emissions data rides on existing infrastructure.
- You have visibility into volume. You know what you bought, how much, and when, which means even a spend-based estimate (the weakest rung on the GHG Protocol's data quality hierarchy) is calculable without the supplier doing anything at all.
This is why most Category 1 (Purchased Goods & Services) inventories start from spend-based or average-data estimates for tier 1 and gradually layer in supplier-specific primary data as engagement matures. It's a workable, if imperfect, starting point because the relationship already exists.
Where it breaks down past tier 1
None of those three conditions hold for tier 2 and beyond. A tier 1 component supplier may itself be sourcing raw materials, sub-assemblies, or packaging from a dozen upstream vendors that your company has never had direct contact with, doesn't have contracts with, and has no commercial leverage over. A few structural issues compound this:
- Leverage dilutes with distance. Your tier 1 supplier has its own competing pressures from its own customers, and emissions data requests from a customer two tiers removed usually rank low on its priority list.
- Sub-tier supplier lists are often proprietary or simply undocumented. Many tier 1 suppliers treat their own supplier base as a competitive asset and are reluctant to disclose it, independent of any emissions question.
- Raw material origin is frequently blended. Commodities like metals, chemicals, and agricultural inputs are routinely pooled across multiple origin sources before reaching tier 1, which makes attributing emissions intensity to a specific farm, mine, or mill close to impossible without dedicated origin-tracking systems.
- Capacity and literacy drop further back in the chain. A tier 1 supplier may have a sustainability team capable of producing a primary-data emissions figure. A tier 3 raw material processor in a different regulatory environment may not have anyone with the technical background to do so, even if willing.
The practical result is that most multi-tier Scope 3 inventories — whether the emissions sit in Category 1, Category 2 (Capital Goods), or Category 4 (Upstream Transportation & Distribution) — rely on industry-average emission factors for anything past tier 1, with primary data appearing only where a company has made a deliberate, resourced effort to go further.
What partial traceability actually looks like
Full tier-N primary data across an entire supplier base isn't a realistic near-term goal for most companies, and treating it as the bar for a credible inventory usually just produces paralysis. The more workable approach is partial traceability, applied deliberately rather than evenly.
Risk-based mapping. Rather than trying to trace every input with equal rigor, map categories of spend or materials by two variables: emissions intensity and exposure (to deforestation, high-carbon energy grids, or regulatory scrutiny like CBAM-covered commodities). Concentrate tracing effort on the small number of inputs that are both high-volume and high-risk — a steel or aluminium component, for instance, or an agricultural commodity with known land-use emissions exposure — rather than spreading effort evenly across a long supplier list where most items are immaterial.
Spot audits and targeted primary data collection. Instead of requesting supplier-specific data from every sub-tier vendor, select a representative sample — by region, by material type, or by known-risk category — and conduct deeper primary data collection or site-level audits on that subset. The results can then be used to refine average-data assumptions for the broader category, moving it up the data quality hierarchy without requiring universal coverage.
Hybrid data strategies. The GHG Protocol's hybrid method — combining supplier-specific data where available with average data elsewhere — is the realistic operating model for most multi-tier inventories, not a transitional phase to be outgrown. The goal isn't to eliminate average data entirely; it's to know precisely where it's being used, why, and what would improve it.
Why this is becoming non-optional
Traceability gaps used to be a disclosure quality issue. They're increasingly becoming an audit issue. CSRD's ESRS E1 requires Scope 3 disclosure under a double materiality lens, California's SB 253 phases in third-party assurance requirements for Scope 3 figures, and India's BRSR Core is tightening toward phased assurance as well. Assurance providers will ask not just what number you reported, but what data quality tier it sits on and how you justified it. A spend-based estimate for tier 1 purchased goods is a reasonable starting point today; it will be a harder position to defend once assurance becomes mandatory rather than voluntary.
The practical takeaway
Tier 1 data quality problems are solvable with engagement programs and better supplier onboarding. Tier N data quality problems are solvable only through deliberate prioritization — deciding which inputs matter enough to chase, and being honest in disclosures about which figures rest on primary data versus industry averages. Companies that get this right aren't the ones with the longest supplier questionnaires; they're the ones that can explain, category by category, exactly why their data looks the way it does.