How can you be certain that logistics emissions data is accurate? After all, estimates are estimates — and they’re not all created equal. Different emission measurement methods, inputs, and variables all sway emission data quality.

Which is why Lune Labs has developed a “Data Quality Score”, which builds upon the Smart Freight Centres quality indicators. Now, logistics service providers (LSPs) can gauge the health of their logistics emissions data and confidently decide whether they can use it to inform supply chain decarbonisation, scope 3 emissions reporting, or other sustainability compliance requirements.

Why is understanding the accuracy of emissions data important?

Action. Reaching net zero is the ultimate goal for green logistics. Only high quality freight emissions data can be used to inform effective emissions reductions. Poor data risks misreporting, non-compliance, and ineffective reductions.

If shippers want to reduce emissions, they need to determine exactly where they’re coming from.

For example, if you were calculating logistics emissions using the emissions factor for a “generic container ship”, you’d never realise the majority of your transport emissions are being produced by a single carrier. Rather than spending ages decarbonising a whole fleet, you could have a much quicker impact by swapping carriers. A much easier green logistics win!

To discover these opportunities, you need good data.

Primary data is based on actual fuel consumption, which yields the most accurate result. However, it’s elusive. Getting this data is like searching for a container in a port without a terminal ID. You know it’s there, but have no clue how to find it.

Challenges of accurately calculating logistics emissions

Calculating freight emissions requires three data points: load, distance, and mode. Yet, logistics is inherently full of data holes. So, having primary data for all three inputs is incredibly challenging.

In fact, only 63% of frontline logistics employees have access to real-time operational data. Most shipment data is siloed in Excel spreadsheets. Therefore, the inputs required to calculate supply chain logistics emissions accurately are inaccessible in real time.

Using the GLEC Framework to overcome missing data

One method of plugging data gaps is by borrowing averages.

The GLEC framework, created by the Smart Freight Centre, outlines alternative emissions measurement methods and provides industry averages to calculate shipment greenhouse gas emissions. For example, if logistics companies don’t know exactly how far their air freight travelled, they can use “Great Circle Distance” or “Shortest Feasible Distance” to calculate shipment emissions.

Although the resulting estimates are less precise, they can be used for carbon emissions reporting, ESG compliance, and pinpointing emissions hotspots for further investigation. Lune’s logistics emissions calculations are also audited and accredited by the Smart Freight Centre, making them report-ready.

Using emissions intelligence to enhance data quality

Now, industry leaders are turning to emissions intelligence to overcome data accuracy challenges. Logistics emissions intelligence collects data from third-party sources to enhance accuracy.

For example, a logistics service provider can input the origin and destination of a shipment travelling via road. Emissions intelligence enhances this data by pooling information, such as terrain and real-time traffic conditions from Google Maps, to model fuel usage. The result is precise transport emissions data that can be used to inform effective decarbonisation.

If logistics service providers can gauge the accuracy of emissions data, they can determine how to use it. Yet, if different methods and inputs yield different results, how can they know what counts as accurate data?

Using Lune’s Data Quality Score to gauge the accuracy of emissions data

Lune’s data quality score (DQS) reflects the precision and reliability of logistics emissions data. Logistics service providers that have integrated Lune’s emission intelligence can transparently share data quality with their customers.

By seeing and understanding their DQS, users can:

• Gauge the health of their data
• Compare data from other providers
• Mitigate the risk of low quality data
• Identify which data can inform effective emissions reductions

How does Lune score data quality?

Logistics emissions data quality is affected by three main variables: distance, load, and mode specificity. These variables, therefore, influence Lune’s data quality score (DQS). Increasing the accuracy of any of these variables will increase data quality.

Download: Lune’s data quality scoring matrix

Lune ranks data quality on a scale of A (excellent) to D (poor).

• A (Excellent): High quality, primary data. Few assumptions or advanced modelling are used. Most reliable outcomes for reporting, compliance, and informing emission reductions.
• B (Good): Granular data used for modelling emissions data based on trusted and reliable assumptions. Acceptable for reporting, compliance, and pinpointing emission hotspots for further investigation.
• C (Fair): Industry averages utilised for calculations. Acceptable for reporting and compliance. Improvements encouraged.
• D (Poor): Low quality estimates due to poor granularity, invalid or missing data. Unacceptable for climate reporting or compliance.

Improving your logistics emissions data quality

High quality data unlocks green logistics. It informs more effective decarbonisation, ESG compliance, and sustainability reporting. Lune’s DQS gives the transparency users need to understand, improve, and action their logistics emissions data with confidence.

To set a course for higher quality data, request a demo.