Why distribution modeling matters
Transport often sets the pace in A4 and can tilt C2 when end-of-life hauling is material. It is also visible to reviewers because the math is straightforward and the choices are traceable. In the U.S., transportation produced about 28 percent of total greenhouse gas emissions in 2022, so auditors expect care even when the product is low carbon elsewhere (EPA, 2024) (EPA, 2024).
The minimum viable model
Start at the factory gate and map the legs most products actually travel. Use ocean or rail for the long linehaul if relevant, then truck from port to regional DC, then outbound to customers. For outbound, aggregate a recent calendar year of shipments by destination zip code and weight so you capture where products really land without touching every invoice.
Why this level is good enough
Emission intensity varies by mode far more than by minor route quirks. Typical heavy truck freight factors fall roughly between 60 and 150 g CO2e per tonne‑km, deep sea container shipping often sits below 20 g CO2e per tonne‑km, while air freight runs in the hundreds to more than a thousand g CO2e per tonne‑km (UK Government GHG Conversion Factors, 2024) (UK Gov, 2024). Getting the mode split right and the big distances right beats chasing every driveway.
What to pull from ERP in one afternoon
The fastest path is a single export that includes a full year, weights, and destinations. Keep it simple and repeatable.
- Origin facility ID, ship date, transport mode per leg
- DC ID and address, port codes where used
- Destination country and postal or zip code
- Net shipped weight or units plus packout weight where relevant
- Product family or SKU mapping for grouping
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Factory to port to DC, then to zip codes
Model the upstream legs as representative corridors, for example factory to nearest port, port to DC, with average distances that match where containers actually move. For outbound, bucket shipments by zip, then calculate distance from the DC to the population centroid of each zip. Weight the distances by shipped mass. Reviewers want to see that the top destinations by mass are included and that odd outliers do not drive the average.
Coverage targets that satisfy verifiers
Work to cover at least 80 to 90 percent of outbound mass with the zip‑level dataset, then apply a documented fallback for the tail. If a month is missing, scale based on adjacent months and state that choice in the LCA report. Use the most recent full calendar year so purchasing and logistics leaders recognize the numbers and can back them up quickly.
Special cases worth separating
Air shipments deserve their own line because their intensity dwarfs surface modes. Low volume but high urgency orders can materially skew A4 if lumped in with truck averages. Less than truckload and parcel legs can be grouped, yet call them out so a reviewer is not guessing why short hauls look higher per tonne‑km.
Where zip codes help beyond A4
Zip level outbound patterns improve assumptions for replacement parts in B modules when maintenance includes shipped components. They also sharpen C2 by anchoring likely haul distances from common customer regions to typical end‑of‑life facilities. If those flows are sparse, say so in the limitations and keep the assumption conservative rather than creative.
Data hygiene that saves weeks
Name things once and keep them stable. Lock field names, store the raw export, and record the SQL or BI query used. The verification Q&A goes faster when the reviewer can run the same pull and land in the same rows. We prefer boring here because boring is auditable.
A quick sense check
Run a back‑of‑the‑envelope: one tonne shipped 9,000 km by sea at low double digit g CO2e per tonne‑km is roughly the same order of magnitude as 900 km by truck at low triple digits per tonne‑km. That is why nailing the mode split and the long legs is worth more than mapping every cul‑de‑sac.
Make distribution a repeatable report, not a fire drill
Stand up one standard ERP or BI export per product family that hits the fields above and refresh it annually. Document corridors, show coverage, and keep a short log of any gaps. When distribution data is clean on entry, teams spend their time improving results rather than retrofitting assumptions during verification.
