Carbon Minds

Better PCF and Scope 3.1 coverage for your purchased raw materials, without chasing data for every substance. At hundreds or thousands of product lines, the hard part is finding the right emission factor for each raw material and mapping it to your internal data. Done by hand, that eats your team’s time. See how efficient mapping and targeted modeling fit together, so you can: → Improve data coverage → Reduce manual effort → Build a fit-for-purpose dataset, in-house or with support Tue, 10 Nov, 16:00 CET · Live Q&A

PCF and Scope 3.1 used to mean a handful of products. Now it’s hundreds or thousands of product lines. Has your emission factor mapping kept up? Spreadsheets, generic name matching and consultancy day-rates don’t scale. The longer they stay in place, the more manual effort and inconsistent factors pile up in your Scope 3.1 inventory. In this live webinar, learn how to: → Find suitable emission factors for purchased raw materials → Decide when a proxy is enough and when custom modeling pays off → Focus effort on the gaps that change your result 10 Nov, 16:00 CET · Live Q&A

Modeling every chemical in maximum detail is rarely worth the cost. Using generic proxies everywhere compromises your results. There’s a middle path: match data quality to how much each chemical matters and what your assessment has to prove. Join Dr. Arne Kätelhön and Guy Kobani of Carbon Minds for a framework that helps you: → Use existing emission factors where they exist → Apply proxies where they’re sufficient → Save detailed modeling for the gaps that matter most Live, 10 Nov 2026, 16:00 CET, with open Q&A.

If every data gap gets the same effort, your best modeling time goes to chemicals that barely move your result. Not every gap deserves the same work. What matters is how important a chemical is for your footprint and what the data has to prove. In this live session, Dr. Arne Kätelhön (Co-Founder & MD, Carbon Minds) shows a systematic way to: → Assess remaining data gaps by relevance → Choose between existing factors, proxies and custom modeling → Put modeling effort where better data changes the result 10 Nov, 16:00 CET · Bring your questions to the live Q&A.

Pharma supply chains trace back to base chemicals like propylene. Say your PCF uses one regional average for it. It looks fine. But some propylene plants in China use coal as feedstock. Their climate impact is very high. The average hides that. Your result can be ten times too high or too low. And you send it to your customer with your company's name on it. In 20 minutes, Leif Rohrbach, our Pharmaceutical Lead, shows you which data gaps move your result most, and when to ask your supplier for primary data. Watch on demand.

You want to send pharma PCFs to customers without rebuilding the method each time. Leif Rohrbach, our Pharmaceutical Lead, explains it with a kitchen. ISO 14067 is the kitchen. PAS 2090 tells you which pans to use in pharma. Your company methodology says which pan you use for which dish. Let a third party check how you apply that methodology. Then many PCFs can come from one certified process. Leif calls this Level 2. Without it, each PCF stays a single number nobody has checked. Watch on demand, 20 minutes.