The green logistics market is set to hit USD 2.1 trillion by 2034. Driven by regulation, sustainability goals, and growing awareness of environmental issues, green logistics is now mainstream. So, to stay competitive, many logistics companies will implement carbon tracking for the first time. But what do they need to calculate logistics emissions?

In this blog, we’ll explore:

• What inputs are required by different methodologies for calculating shipment emissions
• The disparity in input accuracy
• How Lune’s emissions intelligence can overcome this challenge

Different methodology, different inputs

What data is available to a logistics service provider ultimately dictate the methodology used for calculating shipment emissions. Each methodology results in a different degree of accuracy.

To calculate shipment emissions, there are three main methodologies that carbon calculators, partners, or intelligence can leverage:

1. Primary data (Greatest accuracy, least accessible)
2. Fuel modelling (Very accurate and accessible)
3. Averages (Accurate and most accessible)

All three methodologies are outlined by the Global Logistics Emissions Council (GLEC) so can be used for voluntary reporting, compliance, and informing effective logistics emissions reductions.

Primary data

The most accurate way to calculate transport emissions is using primary data, i.e. by measuring the actual fuel consumed. This would then be distributed proportionately across shipments.

Calculations using this methodology would receive an A+ on Lune’s data quality score. Top marks!

To use this methodology, logistics companies would have to know the actual fuel consumed on a particular journey or leg. However, primary data is inaccessible to most, as many carriers don’t share it, meaning it exists in siloes. Which is why this methodology is often out of reach.

Modelling fuel consumption

If you can’t use actual data, you can model it. Modelling fuel consumption uses detailed data to accurately replicate how much fuel was burned for a particular journey or leg.

This would receive an A- on Lune’s data quality score, so it can be used to inform emissions reductions, compliance, and voluntary reporting.

The methodology for modelling fuel consumption is very specific to the transport mode. For example, modelling fuel consumption for road transportation must account for traffic situations, road gradients, engine emission standards. Whereas modelling fuel consumption for cargo ships must account for the changing energy outputs of both the main and auxiliary engines throughout the voyage. Yet, modelling airfreight emissions requires in-depth data around take-off vs. cruising.

Lune customers can access the precision afforded by this methodology through sharing the following inputs:

• Transport identifier (e.g. vessel IMO, flight number, or truck type etc.)
• Origin and destination
• Load

Calculating speed

To model fuel consumption, we need to gauge the vessel, plane, or truck speed. This can be done by dividing the distance travelled by the time. Lune infers this by utilising origin, destination, and transport identification to track distances and surface arrival and departure times.

How is load expressed?

This is how heavy your specific shipment is. It is used to allocate the correct amount of emissions from the overall journey. For example, a truck makes multiple deliveries for multiple companies in one trip.

This can be expressed as TEU, tonnes, grams… any unit of weight. In the above example, that’s tonnes.

Using transport, industry, or generic averages

The most commonly used formula for calculating transport emissions is: Emission factor × distance × load = Total shipment emissions.

To estimate shipment emissions, using this method, you only need three basic inputs:

• Load
• Origin and destination
• Mode

The vast majority of logistics providers have this data in some capacity. For example, you may have the shipment origin and destination, you may know the transport mode for each leg, and you have the TEU.

What’s a transport mode emission factor

To calculate freight emissions, a transport mode is required to select the right emissions factor.

Emission factors are standardised values used to calculate the amount of carbon emissions a particular activity produces. For logistics, they are generally expressed as “gCO₂e / t*km”. This is the grams of CO₂e emitted per shipment tonne per kilometre travelled. Sounds complicated!

GLEC has produced a whole database of these emission factors. The database includes vessel-specific factors, continent-specific trucks, and even generic transport modes.

This means if you don’t know the exact IMO number of the vessel that transported your shipment, you can still use the emission factor for an industry average container ship.

Distance travelled

Finally, you need to know how far the shipment travelled. The most accurate way is to track the transport, then you know exactly how many kilometres it’s done. However, tracked data is uncommon.

Most companies know the shipment's origin and destination. Using these two points, you can estimate the shortest practical route between two points, known as the shortest feasible distance. Or for aviation, it’s common to use great circle distance. This is the shortest distance between two points on the surface of a sphere (the Earth), measured on the sphere.

The importance of accurate inputs

High-level inputs result in high-level outputs. Accurate inputs result in accurate outputs. For example, using the emissions factor for the actual vessel and the actual distance travelled results in more precise logistics emissions data.

The accuracy of your data impacts the effectiveness of the actions it informs. For example, using industry averages is suitable for compliance, but ineffective for reducing emissions, as important details are lost.

Read: How accurate is logistics emissions data?

In an ideal world, all emission estimates would done using primary data. However, this data in generally inaccessible. We need accurate alternatives.

That’s where Lune’s emissions intelligence (EI) comes in. It means logistics providers can estimate emissions with greater precision without extra data collection. It eliminates barriers created by data gaps, empowering organisations to achieve sustainability goals with confidence.

To learn more about what’s driving greener supply chains, download our guide.