See the data quality behind your purchases emissions

Understand how much of your purchases emissions are based on real data versus estimates, so you know exactly where to improve your carbon footprint.

What’s new

Your carbon footprint dashboard now includes a widget that measures the data quality behind your purchases emissions. It shows how much of your purchases footprint relies on real, activity based data versus estimated or spend based data.

Why it matters

Knowing where your data quality stands makes it easier to prioritize which suppliers or purchase categories to improve first. Instead of guessing where your footprint is less accurate, you can see it directly from your carbon footprint dashboard and act on it.

30-minute demo

See it with your data

We show you how this works on an example close to yours.

Request a demo

More updates in Carbon Footprint

See Carbon Footprint →
  1. District heat, steam and cooling get their own emissions category See heat, steam and cooling emissions calculated separately from electricity, with dedicated emission factors and Scope 2 and Scope 3 (category 3) accounting for more precise results.
  2. Download scope 1, 2 and 3 data in a single file Clients can now export all of their scope 1, 2 and 3 carbon footprint data in one Excel or CSV file instead of pulling separate exports per scope.
  3. Enter historical Scope 2 emissions with the location based method You can now log historical Scope 2 emissions using the location-based method, split into electricity and heat, steam and cooling, so your past data is as detailed as your current reporting.
30-minute demo

See it with your own data.

Thirty minutes with our team. We show you how your data would come in, and what you would get out.

See Dcycle in action
  • No commitment
  • With your data
  • In your language

What your session looks like

  1. 01 You tell us what you’re being asked forCustomers, auditors or banks: what data and by when. 0–5 min
  2. 02 We show how your data comes inInvoices, ERP, suppliers: on an example close to yours. 5–20 min
  3. 03 You leave with a planWhat to automate first and how much time you save. 20–30 min
With someone from the team who then supports you