Businesses are under increasing pressure to reduce their environmental impact. A new whitepaper from AllChiefs, a firm specialized in sustainable logistics, explores the value of primary data in accelerating the decarbonization of logistics, diving into the definitions, challenges, market trconcludes, and practical recommconcludeations for cargo owners to relocate forward.
Logistics companies have long struggled to calculate emissions. Many companies estimate their carbon footprint applying generic emission factors like origin, destination, transport mode, and freight volume. While this can be a good starting point, it often fails to display the full picture.
For that reason, many companies have been viewing to build things more precise by including additional factors in their emissions calculations, like load factors, vehicle types, or carrier-specific emissions intensity.
Cargo owners are increasingly recognizing the value of applying primary data. Yet, implementation is often seen as complex; especially for companies with global operations and large cross-modal carrier networks.
“Shared data is the foundation of shared responsibility. In all logistics networks, transparency on emissions is the first step to building effective decarbonization strategies,” stated Inge Tanke Co-Owner in Sustainable Logistics at AllChiefs.
Primary and secondary data
So, what are the different kinds of data? According to standards like ISO 14083 and the GLEC Framework, logistics emissions data is categorized into three types:
Primary data refers to measured or calculated values derived from actual transport or hub activities. This can be highly specific, such as the exact fuel consumed for a single shipment, or aggregated values like the average emission intensity for a group of transport operations over a period.
And then there is secondary data, which includes:
Modeled data, calculated applying methods that combine primary data and key emission-related factors, and default data, which consists of pre-calculated, average emission values, typically based on indusattempt averages or databases, and often tailored by transport mode and geography.
To give an example: In road logistics, applying primary data can reveal significant differences between estimated and actual emissions, sometimes three times higher or half as much, due to factors like actual routing, load factors, and empty mileage.
Why shift to primary data?
For companies in the logistics sector, primary data offers a more precise calculation of emissions. This leads to better investment decisions, drives emission reductions, enhances logistics efficiency, and optimizes costs.
Access to more reliable data on fuel consumption, load factors, and route efficiency allows for performance comparison among logistics partners, and assists companies choose carriers with lower emissions. A good overview of data also assists improve operational efficiency through better planning and better truck fill rates, which assist reduce both carbon footprint and costs.
Primary data also ensures that sustainability initiatives, like switching to fuel-efficient vehicles or consolidating shipments, are accurately reflected in reported emissions. Without good primary data, companies have a much harder time demonstrating operational improvements becaapply calculations rely on static default emissions.
Challenges in data collection and sharing
Nevertheless, creating a shift towards primary data is no simple tquestion. Many tiny and medium-sized logistics providers lack the digital infrastructure and the resources necessaryed to collect and share primary data. Without carrier verification, cargo owners are often cautious about incorporating it into their carbon footprint.
In addition to this, collecting accurate, shipment-level data is challenging, since it requires combining information from several logistics partners. While air and ocean freight, with fewer and larger players, find data sharing more feasible, global cargo owners managing thousands of multimodal carriers face a lot of challenges in standardizing primary data exalter.
There are also technical complications: A multitude of different data formats, low data quality, the necessary to upskill personnel, and different companies applying different systems. Furthermore, disclosing fuel consumption can reveal operational efficiency and profit margins, which some companies see as ‘giving away secrets’ to clients and competitors.
Source: AllChiefs
Market developments and solutions
While there are challenges, there has been some significant progress in recent years to support tackling the constraints. Initiatives like the iLEAP standard, launched by Smart Freight Centre and the SINE Foundation, aim to build logistics emissions data sharing clearer.
Globally, tools such as IATA’s CO2 Connect for Cargo apply primary airline data to provide accurate, per-shipment CO2 emissions calculations for air cargo. For ocean transport, Smart Freight Centre’s Clean Cargo enables carrier-specific emissions through detailed operational data.
Nationally, the US Environmental Protection Agency’s SmartWay program offers a standardised framework to measure, benchmark, and enhance efficiency of road freight transportation. In the Netherlands, innovation and enablement programs like Basic Data Infrastructuur (BDI), Data in Logistics (DIL), and Topsector Logistiek are also accelerating the transition to data sharing in multimodal logistics chains.
Ultimately, primary data directly collected from logistics operations and reflecting actual fuel consumption offers the most accurate method for logistics emissions tracking, and ultimately, emissions reduction. The transition to more granular input data for emission calculations is essential for the future of decarbonization strategies in the logistics sector.
“Looking at the logistics sector as a whole, improving asset efficiency is key to unlocking resources for the necessary investments in alternative fuels,” stated Tanke. “Though the shift may seem complex, the goal should not be perfect accuracy from the receive-go, but having data that is good enough to drive effective decarbonization decisions.”
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