Why spend-based emission factors are a starting point, not a destination, and what it takes to move beyond them.
For most shipowners, Scope 3 Category 1 (Purchased Goods and Services) is the largest unaddressed source of supply-chain emissions, and the one with the weakest data behind it.
Most operators today calculate it the same way: spend with a supplier multiplied by a sector-average emission factor from an Environmentally Extended Input-Output (EEIO) database. The method is accepted under the GHG Protocol and is a fair starting point. The problem is that the uncertainty it carries is becoming a material reporting risk.
For maritime procurement categories, the uncertainty band sits at ±40 to ±80 percent. That is a wide range to set a reduction target against, and it is no longer good enough for the buyers and regulators that ESG-conscious shipowners are now answering to.
Three pressures are converging on the same outcome.
CSRD and ESRS require in-scope companies to report Scope 3 emissions with increasing specificity from 2025 onward, including a credible improvement plan for data quality. CBAM creates precedent and political momentum for supply-chain carbon accounting beyond the products it directly covers. And large cargo owners are embedding Scope 3 supplier data requests into procurement and tendering. Spend-based estimates are no longer considered sufficient by leading buyers.
The 2024 GHG Protocol consultation itself included submissions arguing that the spend-based method should be phased out as a primary calculation approach. Whatever the final outcome, the direction of travel is clear: supplier-specific verified data.
The structural problems compound on each other. A single EEIO factor covers an entire sector, so a pump, a crane, and a piece of safety gear all share the same number. There is no geographic resolution, so a Chinese yard and a Norwegian one appear identical, despite very different grid carbon intensities. There is no product differentiation, so a low-carbon variant and a conventional one look the same on paper. And input-output tables update infrequently, so inflation and currency movement inflate spend figures without changing physical volume.
Take a concrete example. A shipowner spends USD 1.86m with a marine paints supplier. The EEIO factor for the paints sector is 0.59 kg CO₂ per USD. The estimate comes out at roughly 1,099 t CO₂e. That number says nothing about whether the paint is water-based or solvent-based, produced with renewable energy, or carrying a third-party verified Environmental Product Declaration. The supplier could halve the actual footprint of their product and your reported number would not move.
We recently analysed a single fiscal year of procurement data from a mid-sized European shipowner: USD 46.3m of OPEX across 362 active suppliers, six procurement categories. The estimated Category 1 footprint came to 8,057 t CO₂e using EEIO factors and IPCC AR5 GWPs.
Two findings stood out.
First, the emission risk is concentrated. Five suppliers (maritime catering, marine paints, two drydock facilities in Asia, and ship supplies) accounted for roughly 50 percent of the total Category 1 footprint. Targeted supplier engagement is therefore tractable, not overwhelming.
Second, primary data is scarce. We researched the 20 largest suppliers by estimated emission volume. Only two had publicly available product carbon footprint data as of March 2026. The remaining eighteen had corporate sustainability reporting, ESG dashboards, and net-zero commitments, but no product-level numbers that could replace the spend-based estimate. This is not a criticism of those suppliers. Most have not been asked with sufficient urgency, or supported with the right tools.
When we modelled the directional shift from EEIO to primary data across the largest categories, the change was material. Provisions tightened by 30 to 50 percent. Stores by 20 to 40 percent. Technical by 25 to 45 percent. Drydocking by 30 to 60 percent. The honest consequence is that the 8,057 t CO₂e baseline is more likely closer to 4,000 to 5,600 t once primary data lands. That difference affects target credibility, intensity metrics, and your standing in charterer assessments.
Engaging fifteen to twenty suppliers can realistically cover 70 to 80 percent of the emission footprint with primary data within twelve to eighteen months. The remaining tail can continue on spend-based estimates with a documented improvement plan, which is exactly what ESRS expects.
ReFlow contributes to the IMEF methodology under the IMPA SAVE program, the maritime industry's effort to align responsible procurement and environmental data on a shared activity-based standard. Working within IMEF means the data you collect is portable across buyers, not built for a single client.
Before committing to a supplier engagement programme, the highest-leverage action is to understand where your data is most uncertain and which supplier relationships to prioritise first.
We offer a free, confidential 30-minute Scope 3.1 hotspot review. We sit with your sustainability and procurement leads, walk through your Category 1 spend distribution, identify the suppliers that determine your baseline, and outline a defensible transition plan in line with CSRD expectations. One export from your finance system is all we need.