This is our third post in a series of articles covering emissions from freight transport. This post explains when and where it’s necessary to optimize freight emissions calculations for accuracy and some of the crucial factors that can influence the precision of emissions estimates.
Check out the other posts in this series to learn about the various levels of complexity when calculating emissions from freight, how to measure freight transport emissions, and more.
When it comes to freight transport emissions, the type of vehicle, fuel, and load makes a considerable difference to the estimated carbon footprint for that journey. As a simple example, a cargo ship can emit as little as 3.5kg of CO2e to transport 1 tonne 1000 kilometers, while air freight is more like 2,300kg—a difference of more than 600x. Emissions estimates can be simple or complex—the challenge is to optimize the time and resources spent obtaining data and doing the calculations to get the level of accuracy you need. This leaves logistics managers and sustainability teams with a tough choice: do they rely on broad industry averages that miss key details, or rather attempt granular calculations that may be infeasible at scale?
Reliable emissions estimates help organizations make informed decisions about their transport choices and increase accuracy in reporting. This is particularly important as regulations around emissions reporting become stricter and stakeholders demand more precise carbon accounting. So, how can you aim for the highest accuracy while working within your limitations?
While getting a precise overview of emissions is desirable, this is usually not scalable for large companies or third-party logistics (3PL) providers who need to track thousands of logistics operations or may not have access to shipment data. There are, however, some important factors that can influence the accuracy of emissions estimates significantly:
Fuel combustion underpins all freight emissions, which vary largely based on fuel source. Below you can see common fuel types and their well-to-wheel emission factors (including the production of fuels as well as the combustion) for road freight on a typical truck (12-20 tonnes), taken from the Climatiq database:
Mapping out the route is often more complex than simply calculating the distance from start to end location. For example, if a shipment goes from Istanbul to Berlin via sea, it typically involves trucking to Istanbul's port, sea freight to a major European port like Hamburg, and then either rail or road transport to Berlin. Plus, calculations must include electricity consumption from any warehouses or ports used along the way. Vehicles also often stop en route rather than going directly from point A to point B, creating disparity between estimated versus actual emissions.
A truck running at 50% capacity has about 70% higher per-tonne emissions compared to running full. Load factors can be included within the emission factors themselves, otherwise they should be apportioned appropriately across shipments along with empty runs.
Certain goods must be transported via a “cold chain”, the term used to describe temperature-controlled freight transport. Refrigerated vehicles typically emit more emissions than standard vehicles due to the energy needed for cooling. In addition, the fridges onboard use fluorinated refrigerants that are potent greenhouse gases and therefore have a global warming effect in the event of leakage. Emission factors specially tailored to refrigerated vehicles account for this increased environmental impact.
Transport data comes in multiple formats and units. A transport provider, for example, may provide fuel consumption for a single leg of the journey, while a transport management system (TMS) provides data in kilometers traveled for that same journey. Standardizing data across shipments and sources is time intensive but a crucial step to ensure accurate and reliable emissions estimates.
In the most basic format, freight emissions calculations require two inputs: an emission factor and a quantifiable activity. However, there are a vast number of emission factors for freight transport activities, and without prior knowledge it can be hard to decide which one is the most fitting. For example, when searching the Climatiq database for the term “cargo ship”, 48 emission factors are found.
To decide which of these 48 factors is most suitable, extra information about the shipment is needed. Emission factors are often specific to vehicle type, size, load, and fuel source, which can help you decide which one is most relevant. For example, the image below shows how a smaller, refrigerated vehicle with average load is more polluting per tonne-km than a larger vehicle with a heavier load:
A small feeder vessel serving regional routes has very different emissions characteristics compared to a large container vessel crossing entire oceans. Using an emission factor tailored to the vehicle type, size, load, and fuel source will increase accuracy.
If data on any of these elements is missing, it’s possible to use a generic emission factor. This can, however, have a significant impact on the accuracy of the emissions estimates. Consider this example comparing emission factors for various maritime vessels from the UK Government’s 2024 dataset:
The relative difference in estimated emissions is stark, and is amplified when the activity (in this case, 20,000 tonne-km) increases.
Though high precision is desirable, for the sake of practicality, accuracy must sometimes be sacrificed—if the intended use case allows. Note that accuracy may also be limited by the availability of shipment data. Below is a selection of potential objectives in measuring freight emissions, categorized by the necessary level of shipment and emission factor data specificity:
Approach:
Use default emission factors provided by frameworks such as the Global Logistics Emissions Council (GLEC) / ISO 14083 or the GHG Protocol. Estimates might be based on average load factors or shipment weights.
Advantages:
Limitations:
Approach:
Instead of relying on average emission factors, organizations can increase accuracy by using tailored emission factors and/or detailed shipment data on transportation modes and cargo weights. Data sources may include transportation management systems (TMS), shipment records, or 3PL providers.
Advantages:
Limitations:
Approach:
Incorporates extensive data on variables such as vehicle type, fuel type, shipment-specific route details, and empty runs to maximize the accuracy of estimates and ensure suitability for decision-making purposes.
Advantages:
Limitations:
For companies struggling with accuracy due to limited data or resources, calculation engines provide a reliable solution. These tools supplement shipment data with the necessary information to automate calculations and generate accurate emissions estimates, even with limited inputs. Businesses can integrate these solutions in various ways—as stand-alone tools, plugins for existing logistics software, or APIs that connect directly with transport management systems (TMS). Climatiq’s API, for example, uses basic shipment data such as start and end location alongside emission factors from GLEC to create compliant and reliable emissions estimates.
As regulations tighten and stakeholder scrutiny intensifies, the need for accurate emissions calculations is becoming increasingly critical. Addressing the complexity of transport emissions is not a matter of if but when. Automating these calculations provides a scalable and efficient solution, enabling both improved precision and the ability to handle large volumes of data with ease.
Looking to start calculating transport emissions? You can learn more about the Climatiq freight emissions API here, or try out the interactive demo here.
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