Your uncertainty score reflects the overall reliability of your GHG inventory. It ranges from Level 1 (Poor) to Level 5 (Excellent), depending mainly on the share of emissions computed with activity-based data versus a monetary (expense-based) approach, and on the quality of the emission factors used.

Level 1 – Poor: A first step towards a comprehensive GHG inventory. A vast majority of your GHG emissions were computed using an expense-based approach. This is a good way to identify your company's most significant sources of emissions. These emissions now need to be specified by using activity data.Level 2 – Fair: You have completed your first activity-based studies. A small portion of GHG emissions were specified using an activity-based approach. However, the majority of emissions are still estimated using a monetary approach. To continue improving your GHG inventory, you need to increase the proportion of activity data.Level 3 – Standard: You have achieved a reliable GHG inventory. Your emissions are estimated using a mix of expense and activity-based studies. You've also used a majority of EF that are provided by renowned databases. To continue improving the precision of your GHG inventory, add new activity studies and improve the quality and granularity of the data you share.Level 4 – Good: You have achieved a high-quality GHG inventory. Your emissions are estimated using a satisfying proportion of activity-based studies. You've also used a majority of EF that are provided by renowned databases. The activity data you've used is granular and precise. You can now focus on moving from an expense-based approach to an activity one for the remaining GHG emissions that are still computed using monetary data.Level 5 – Excellent: You have achieved a prime GHG inventory. You mostly use activity-based studies to compute your company's GHG emissions. The emission factors you used come from renowned databases, and the activity data you share is precise and granular.
Note: if you see data with the type "unknown", the uncertainty score is not as reliable and will likely be underestimated.

More details on how uncertainties are estimated are provided in the article "How are uncertainties computed?".