Sourcing data is one of the biggest challenges when it comes to a company's carbon footprint. It is important to consider when deciding the scope of business activities to include within the carbon footprint, and when deciding how to report activity data in the Log emission categories. Moreover, collecting data is challenging because the company needs to coordinate with its suppliers and customers as well as with internal departments of the company.
Why is it important to collect data with sufficient quality?
Ensure that the inventory appropriately reflects GHG emissions
The first step in data sourcing is to select the activity categories that should be of a high or low priority in order to focus data collection efforts on the former. Here are some criteria to base the screening process on:
Generally, significant activity categories should receive the most precise data sourcing treatment.
Cozero Logs offer activity data entry options. In general, a hierarchy of data entry options are offered. These are below, and listed from high to low data accuracy.
Primary data – Primary data is the most accurate because it’s the raw data that is directly measured or collected from a facility or direct supplier. For instance: Raw consumption data directly from your suppliers
Secondary data – Secondary data is not directly collected or measured but rather sourced from third-party databases. specific activities, mostly estimations. For instance: industry-average data (e.g. published database, statistics) or proxy data. Secondary data are generally less accurate than primary data as they are estimations or averages of data but they are useful when primary data is unavailable or of poor quality. For instance: Estimated distance traveled based on industry-average data
Spend data – Spend data is data related to the expenditures on goods and services purchased from external suppliers and then multiplied by the corresponding emission factors (e.g. kg CO2e per €). These emission factors are based on environmentally extended input-output (EEIO) models. Spend data is generally the least accurate since EEIO data is clustered into large product categories and limited geographic regions.
Users should choose the method that is the most appropriate to the data available to them, to their business goals and the significance of the emissions of the category.