How to save 40% carbon emissions, by enriching your carbon data

Imagine slashing the carbon emissions of your carriers or fleet by up to an astonishing 40%. This isn't just a lofty goal; it's an achievable reality. In this enlightening blog post, we're going to guide you through a transformative approach to measuring and managing carbon emissions in logistics. Moving beyond the realm of rough averages and generic calculations, we delve into strategies that promise not only heightened accuracy but also significant reductions in your environmental footprint. Whether you're a logistics manager, a fleet owner, or simply invested in sustainable practices, the insights shared here will reshape the way you think about and tackle carbon emissions. Join us on this journey towards a more sustainable and efficient future in logistics.

Fleetenergies Team
Fleetenergies Team
June 14, 2024
How to save 40% carbon emissions, by enriching your carbon data

Harnessing Data in Carbon Emissions Accounting

In the realm of logistics, whether you're managing a fleet of trucks as a carrier or contracting transport services as a shipper, pinpointing the carbon footprint of your operations is crucial. However, estimating carbon emissions isn't straightforward and demands precision. Let's explore this concept through the lens of a specific client road shipment, calculated using four distinct data sources.

1. Baseline Emissions Assessment Using Default Factors

The most basic approach involves statistical estimates, particularly when specific vehicle data is unavailable. To align with GLEC (Global Logistics Emissions Council) standards, identifying transport mode and generic road vehicle types is a minimum requirement. Once the vehicles are categorized, average emission figures for each type provide a foundational, albeit rough, estimate of emissions. When we delve into the Baseline Emissions Assessment using Default Factors, a critical aspect emerges: the tendency towards overcalculation of emissions. This phenomenon is intrinsically linked to the method's lack of precision and how it impacts the estimation of carbon emissions.

2. Enhanced Accuracy with Consumption-Enriched Data

A step up in precision, this method involves supplementing precise vehicle type or categories data with average consumption figures. Enhanced further by incorporating load specifics, this approach yields more refined estimates, especially for full truck loads as opposed to partial ones. This method represents a significant leap in the accuracy of emissions estimation compared to the baseline method. This model is particularly potent due to the depth and diversity of the data it utilizes, primarily sourced from Fleetenergies' extensive experience in monitoring fuel consumption. Fleetenergies can pinpoint the fuel consumption for each transport activity with remarkable precision. Since fuel consumption is directly proportional to carbon emissions, this method offers a much more accurate estimation of emissions.

3. Leveraging Connected Vehicle Data

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. This approach is further enhanced by Fleetenergies' capability to monitor and actively reduce fuel consumption using advanced Fuel Monitoring Systems. The integration of these systems can lead to a significant reduction in fuel consumption, with potential reductions of up to 15%.

4. Payload Accounting for Utmost Precision

The apex of accuracy in carbon emissions measurement accounts for the payload. This technique allows for the allocation of emissions to specific shipment parts, enhancing the granularity of the carbon footprint analysis.

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

Baseline Emissions with Default Factors Consumption-Enriched Data Connected Vehicles Data Payload Accounting
t CO2e 2640 2222 1971 1478
g CO2e/t-km 135 113.6 100.8 75.6
Difference -15.9% -25.4% -44%

The Synergy of Diverse Data and AI

The most precise, and consequently lowest, carbon emission figures are obtained by amalgamating various data sources, enriched through artificial intelligence. This process involves integrating OEM data, historical consumption records, sensor feedback from telematics, real-time connected vehicle data, and payload specifics. Through AI, we can discern patterns and obtain a comprehensive view of emissions, eliminating the need for conservative safety margins and overestimations in reporting.

By meticulously combining these insights with live shipment data, we offer not just an account of emissions but a pathway to sustainable operational excellence.

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