THE FIRST two parts of this series established two things. First, that mandatory climate related financial disclosures are bringing logistics emissions under a level of scrutiny the sector hasn't previously experienced. Second, that the most common methodology used to estimate those emissions — spend-based proxies — can produce results that diverge from operational reality by as much as 70 per cent.
The natural follow up question is: what does good measurement actually look like? Not in theory, but in the practical reality of Australian supply chains where data is fragmented, carriers are numerous and no two freight networks are structured the same way.
The answer is less complicated than the industry assumes — and the results are more consequential than most expect.
The shift from spend-based estimation to activity-based measurement sounds like it demands a wholesale data transformation. It doesn't. What it requires is connecting data that supply chain teams already work with every day to an emissions modelling framework that can make use of it.
The core inputs are straightforward: consignment volumes and weights, origin and destination pairs, transport mode for each leg, carrier identity and — where available — vehicle type and load factor. This is standard operational data. It lives in transport management systems, carrier invoices, shipping instructions and freight forwarder reports. Most supply chains generate it as a by product of moving freight.
The difference is that under a spend-based approach, none of this matters. A dollar figure and a sector average emission factor do all the work. Under an activity-based approach, each of these data points contributes to a more precise and — critically — more useful picture of where emissions actually sit across a logistics network.
This doesn't mean perfection is the starting point. Activity-based measurement operates on a spectrum. Even a partial shift — replacing spend proxies with basic shipment level data on distances, modes and weights — delivers a materially different result. The GLEC Framework and ISO 14083 both provide structured methodologies for exactly this, accommodating different levels of data availability while progressively improving accuracy.
The point isn't to let perfect be the enemy of good. It's to stop letting a rough proxy be the permanent substitute for available data.
The most striking outcome of moving to activity-based measurement isn't that the headline emissions number changes — although it usually does. It's that the distribution changes. And with it, the entire picture of where reduction opportunities sit.
In work with an ASX listed beverage producer, the transition from spend-based estimation to activity-based analysis revealed that the company's reported logistics emissions were significantly overstated. The spend-based methodology had been allocating emissions-based on freight costs that bore little relationship to the underlying transport task. When actual shipment data — volumes, routes, modes and carrier profiles — was applied through GLEC aligned modelling, a 70 per cent discrepancy emerged.
That wasn't a 70 per cent emissions reduction. It was a 70 per cent correction in reporting accuracy. But the commercial impact was immediate: the company had been purchasing carbon credits against an inflated baseline. Accurate measurement delivered direct cost savings while simultaneously improving the credibility of their climate reporting.
A different pattern emerged with a major construction materials importer. Here, the issue wasn't overall overstatement — it was misallocation. Spend-based estimates had distributed emissions relatively evenly across the company's international supply lines. Lane level activity analysis told a different story. Specific trade lanes — driven by vessel routing, transhipment patterns and port handling — were producing disproportionately high emissions relative to volume. This insight enabled targeted carrier procurement strategies that maintained service levels while reducing emissions intensity on the lanes that mattered most.
In a third engagement, vessel level performance analysis for a global fashion brand revealed up to 30 per cent variability in emissions intensity between different vessels operating on the same trade lane. Same origin, same destination, same cargo type — but materially different carbon outcomes depending on which vessel carried the freight. Without shipment level data linked to specific vessel performance, that variability was invisible. With it, informed carrier selection and verified biofuel insetting pathways became possible.
These aren't edge cases. They're what happens routinely when the measurement approach matches the complexity of the logistics task.
There is a useful distinction emerging in this space between data that satisfies a compliance requirement and data that informs an operational decision.
Compliance data gives you a number to put in a sustainability report. It meets the minimum threshold for disclosure. For many organisations today, that's what spend-based estimation delivers — a defensible estimate that ticks the reporting box.
Decision grade data gives you a lever. It tells you which lanes are emissions intensive and why. It shows you whether a modal shift from road to rail on a specific corridor would deliver a meaningful reduction or a marginal one. It quantifies the difference between carriers on the same route. It makes the business case for consolidation, network redesign, or fleet specification changes in terms that procurement and operations teams can act on.
The gap between these two grades of data is where most of the unrealised value in logistics decarbonisation currently sits. And as mandatory disclosure regimes mature and audit scrutiny increases — which they will — the market's tolerance for compliance grade only data will narrow.
The shortcomings of compliance grade logistics emissions data are now well understood and the methodologies and tools to do better already exist. At some point, continuing to deliver spend-based proxies to clients — when activity-based alternatives are available and materially more accurate — stops being a pragmatic compromise and starts looking like a professional choice not to ask harder questions of the data.
Organisations that invest now in building the operational data foundations for decision grade emissions measurement won't just be better positioned for reporting. They'll be better positioned to reduce costs, improve carrier relationships and demonstrate credible progress against targets that investors and customers are increasingly tracking.
Once the measurement foundation is in place, the conversation shifts from reporting to action — and specifically, to the commercial reality of the operational changes that better data reveals. Fleet transitions, modal shifts and procurement strategies stop being theoretical sustainability initiatives and start being evidence-based business decisions.
That commercial dimension — including the practical economics of emerging technologies like battery electric vehicles in freight operations — is where this series turns next.
This article appeared in the April | May 2026 edition of DCN Magazine