Yes. You have read that correctly. In this blog, we will explain how it is possible to save up to 40% on the carbon emissions of your carriers or your trucks if you are a carrier. You only have to change how you measure your carbon emissions. When you measure your carbon emissions, you don’t have to calculate based on averages, and you can be more accurate.

The Power of Data and Artificial Intelligence

Whether you are a carrier with a fleet of trucks or a producer with your fleet of trucks, you want to calculate the carbon emissions of your logistics operation accurately. There are different ways to calculate carbon emissions. For the sake of simplicity, we will use one specific shipment of one of our clients as an example and calculate its carbon emissions in four different ways.

Baseline and enriched baseline averages

To calculate your emissions, you can use statistical values if you don’t have actual data. For example, when you don’t know the vehicle types used to transport the goods. To be GLEC compliant, you need to consider the vehicle types at least. After categorising the vehicles for each trip, you can use average emission numbers per vehicle type. This gives you baseline calculations. It is the least accurate way to calculate carbon emissions.

A second, more accurate way to calculate carbon emissions is by enriching baseline consumption data. This is possible if you can categorise shipments per vehicle type and then add the average per vehicle type. This can be further enhanced by adding load information so you have vehicle and shipment weight. For full truck loads, this is easier than for partial loads.

Enriched consumption data

When you have detailed information on which truck transported what specific load. You can use the standard emissions data from OEMs enriched with actual consumption data from years of monitoring fuel consumption in trucks of all makes and models. Each vehicle gets assigned a specific consumption factor based on actual historical data. 

Connected vehicles

Connected vehicles collect a wide range of data on the performance of many vehicle parts. Because the vehicles are connected, this data can be shared in real-time. This can give you highly accurate actual data on the vehicle’s carbon emissions, making this way of collecting and reporting data even more accurate.

Payload accounting

The most accurate way of measuring and calculating carbon emissions also considers the payload. Using primary payload data to calculate emissions makes it possible to accurately assign carbon emissions to part of a shipment, further increasing the accuracy of carbon emission calculations.

We used these four ways of measuring and reporting carbon emissions to report on the total carbon emissions for one of our clients.

Because fleetenergies has 15 years of experience with measuring fuel consumption and advising drivers and transportation companies on more efficient use of vehicles and minimising fuel consumption, we can save additional carbon emissions by increasing the fuel efficiency of your fleet. This can mean up to 10% of additional emissions savings.

BaselineEnriched consumption dataConnected vehiclesPayload accounting
t CO2e 2640222219711478
g CO2e/t-km135113.6100.875.6

The Power of Data and Artificial Intelligence

The most accurate, and thus also the lowest, carbon emissions can be calculated and reported using a combination of different data sources and artificial intelligence to enrich that data.

The OEMs have data on their vehicles that they share. Telematics companies add sensors and additional data to that. Connecting vehicles gives accurate real-time data on the vehicle’s performance and, thus, the carbon emissions. We use artificial intelligence to enrich all collected data to get the most accurate results. By combining and harmonising OEM data, historical data, sensor data, connected vehicle data, and payload data, we can see patterns and a more comprehensive picture of carbon emissions. Combining those insights with real-time shipment data provides the most accurate carbon emission data—no need for safety margins and estimates leading to unnecessary high emissions reporting.

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