Businesses are under increasing pressure to reduce their environmental impact. A new whitepaper from AllChiefs, a firm specialising in sustainable logistics, explores the value of primary data in accelerating the decarbonization of logistics, diving into the definitions, challenges, market trends, and practical recommendations for cargo owners to move forward.
Logistics companies have long struggled to calculate emissions. Many companies estimate their carbon footprint using generic emission factors like origin, destination, transport mode, and freight volume. While this can be a good starting point, it often fails to show the full picture.
For this reason, many companies have been seeking to make their emissions calculations more precise by incorporating additional factors, such as load factors, vehicle types, or carrier-specific emissions intensity.
Cargo owners are increasingly recognising the value of using 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,” said 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 categorised 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 using methods that combine primary data and key emission-related factors, and default data, which consists of pre-calculated, average emission values, typically based on industry averages or databases, and often tailored by transport mode and geography.
For example, in road logistics, using primary data can reveal significant differences between estimated and actual emissions, which can be up to three times higher or half as much, due to factors such as 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 optimises costs.
Access to more reliable data on fuel consumption, load factors, and route efficiency allows for performance comparison among logistics partners and helps companies choose carriers with lower emissions. A good overview of data also helps improve operational efficiency through better planning and better truck fill rates, which help 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 because calculations rely on static default emissions.
Challenges in data collection and sharing
Nevertheless, making a shift towards primary data is no simple task. Many small and medium-sized logistics providers lack the digital infrastructure and the resources needed 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 standardising primary data exchange.
There are also technical complications: A multitude of different data formats, low data quality, the need to upskill personnel, and different companies using 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.
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 make logistics emissions data sharing easier.
Globally, tools such as IATA’s CO2 Connect for Cargo use 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,” said Tanke. “Though the shift may seem complex, the goal should not be perfect accuracy from the get-go, but having data that is good enough to drive effective decarbonization.
Sourced from Consultancy.eu