Calculating your emissions with Avarni involves three essential steps: preparing your data, importing it, and finally, reviewing your emission entries.
This user guide will walk you through the process of reviewing your emission entries, offering helpful tips and tricks for navigating the Review Screen.
Content:
Reviewing emission entries
After your data has been imported, Avarni will generate the results reflecting your Scope 1, 2 or 3 emissions in the Emissions Hub. Once the data has been processed, follow these steps to review your emission entries.
📌 Note: Most uploads will take a few minutes to an hour to be processed. However, for larger files, it may take up to 4 hours.
1. Click on Import Data on the left hand menu and then locate the file that you would like to review and click View:
2. You will now see the Review activity screen (in the Emissions Hub) where you can review the automatically assigned emission factors and adjust if needed.
📌 Note: We suggest to always review your records and the assigned emission factors. While we are focused on ensuring emission factors are mapped accurately, there may be occasional oversights in capturing all the details and varying levels of accuracy dependant on the quality of the data that has been imported.
3. To start the review process, we would recommend to group emission entries. This feature is useful when reviewing large datasets. We suggest first trying to group the data by Account, Description or Vendor, and reviewing your data from there.
Grouping your data will help make the review process more efficient by condensing your dataset down to its unique rows. For example, if you group your data based on Account, all rows that contain the same Account name will be grouped together, transforming a dataset of thousands of rows potentially down to a few hundred. Refer to this link for a more in-depth example.
4. To change the emission factor mapping, select the arrow under Factor:
5. Type in keywords to locate the applicable emission factor.
Text above: This is the emission factor. For example: Chemicals (screenshot above).
Text below: This is the emission factor source. For example: Australia - EXIOBASE (screenshot above).
📌 Note: Please be aware that we are unable to guide you on the most suitable emission factor for your data. Seeking guidance from a consultant may be useful.
💡 Tip: To search for multiple fields of emission factors, separate the search with a semi colon (;). For example: beverages;2019. If the factor does not appear, check your filters to see if the emission factor source is included in your filter.
6. If you have made any changes to the emission factor, the button Pending will automatically change to Confirmed:
7. If you are happy with the emission factor that has been mapped, select Pending or the tick icon to change the status of that row to Confirmed.
💡 Tip: To bulk update the status, click on the checkbox on the left under All Records:
8. To view all of your emission entries (from previous uploads or manually entered), navigate to Emissions Hub.
💡 Tip: Avarni retains the emission factor mappings from previous uploads when the Activity Class, Description, and Supplier's details are identical, and when the status is changed to Reviewed, Flagged or Excluded.
For example: If you previously uploaded a data set where the Activity Class was 'Insurance', the Description was 'Prepaid Insurance' and the Supplier was 'BUPA', with the emission factor mapped to 'Insurance', and have changed the status to Reviewed, Flagged or Excluded, any future uploads matching the same Activity Class, Supplier Name and Description will automatically recall this mapping.
Review screen tips and tricks
📗 Review status: There are three different statuses for each line item:
Confirm: Confirm the mapping of the emission factor to the line item is correct. For example: Air ticket expenses has been matched to business travel .
Flagged: Unknown line items that require further analysis. For example: Line item is missing data or cannot identify the correct emission factor to map.
Excluded: Line items that are not required and can be removed in the final conversation. For example: Tax payments or salary payments.
📗 Supplier name: Clicking on a supplier's name will open up a google search tab, allowing you to search up the supplier's name within a matter of seconds.
📗 Running percentage: A running percentage bar (refer to the top right bar) that allows you to track your progress throughout the data review exercise. In the example below, it shows the percentage that has been excluded (by hovering over the red), the percentage reviewed, net reviewed, the amount of entries, and the net total.
📌 Note: The running percentage bar will not appear if you navigate directly to the Emissions Hub. This will only appear if you navigate via Import Data > View.
📗 Useful features:
Search field (highlighted yellow): Allows you to search for key words.
Set emissions sorting (highlighted purple): Sort emissions by different factors such as Activity Class, Description, Scope, GHG Category, Contry, Supplier, Last updated at, Status and much more.
Filter emission entries (highlighted red): Allows you to filter according to different fields. For example: If you would like to only view data against Country = Australia, you can filter through here.
Change table’s density (highlighted blue): Allows you to change the density of the table.
Show/Hide columns (highlighted as green): Allows you to show and hide the columns. For example: If the Input Unit is not being used during the Review Process, you can simply hide it.
Toggle full screen (highlighted as orange): Allows you to enlarge the screen.
Download as excel (highlighted as pink): Allows you to download the information as an excel file.
📗 Group emission entries: This feature is useful when reviewing many rows of data. It allows you to group the entries into Activity Class, Description, Vendor or Country.
We recommend first trying to group the data into Activity Class and Description and reviewing your data from there.
This will help eliminate thousands of rows down to hundreds or less rows for you to review:
For example: The below has a count of 84165 data when we view as ‘All Records’:
However, after Grouping it by ‘Activity Class’, we can see that the count has reduced to 8, which will allow you to review your data much quicker:
