The Importance of Data Scoring in Reporting: Enhancing Data Quality for Better Emissions Calculation

In today's world, where environmental sustainability is a significant concern, it is crucial for any business to have accurate and reliable data about their carbon emissions.

However, accurately reporting greenhouse gas (ghg) emissions is not an easy task. It requires collecting and analyzing large amounts of data from various sources, including transportation data, energy usage and production data. Often, it is not known to users which data is necessary for a sufficient ghg emission calculation. Additionally, provided data varies between different users in terms of the density of relevant data for emission calculation, which affects the quality of the emission statement. The validity of a ghg emission statement is highly dependent on the input data used for emission calculation.

To address this issue, we at gryn have introduced a DATA SCORE. Our Data Score is a feature that helps businesses to improve the quality of their emission calculation and reporting by providing a clear indication of the data density and value of the provided data.

ISO 14083 and the GLEC Framework classify input data needed for emission calculation by the terms “default”, ”modeled”, and “primary.”

• Default data?refers to industry average figures using standard assumptions of vehicle efficiency, load factor, and empty running.
• Modelled data combines shipment data with information on vehicles and fleets to model fuel use and emissions.
• Primary data refers to good quality actual data of fuel use or emissions. It is used by transport or logistic site operators to calculate their Scope 1 carbon emissions. Transport buyers should collect primary data from carriers for Scope 3 emissions calculation.

For transparent reporting, companies must state what kind of input data was used. Thus, we want to create visibility and transparency for our users about the value of their provided data. Our users should get incentivized to provide more relevant data for emission accounting, since effective reduction measures can only be derived from greenhouse gas statements based on high-quality data. Emissions calculated based on default data, for example, cannot be used for deriving emission reduction measures.

Our data score consists of three scores, available for the transport modes air, ocean, road, and rail:

• the Shipment Density Score considers data fields that are important for shipment identification, as well as data fields that can be used for data validation.
• the Sustainability Density Score considers data fields that have an impact on the emissions' calculation.
• the Level Score is based on the GLEC terms “Default,” “Modelled,” and “Primary” and includes two intermediate levels, “Default +” and “Modelled+,” to ensure finer gradations. To reach a certain level, defined fields need to be provided by the users.

The three scores are embedded in our Data Health Dashboard, which provides businesses with a clear overview of their data quality. It furthermore includes a simulation feature that allows users to simulate how their data scores would improve if they provided certain data fields. This helps users to identify the data fields that are most important for improving their data scores and enables them to take action to collect this data.

Overall, gryn's Data Score is an essential tool for businesses looking to improve their ghg emissions reporting. By providing a clear indication of the quality of the provided data, it helps businesses identify areas for improvement and take action to collect the necessary data. With the increasing pressure on businesses to be transparent about their environmental impact, our Data Score is an important step towards achieving a more sustainable future.