The scale of PCF and Scope 3.1 calculations in the chemical industry has grown significantly. What was once a manual task for a handful of products now involves automated calculations across hundreds or thousands of product lines. The biggest challenge in scaling is identifying the right emission factors for all purchased raw materials and mapping them efficiently to your internal data.

Spreadsheets, generic name matching and costly consultancies don’t scale. But the opposite instinct doesn’t work either: modeling every chemical in maximum detail is rarely worth the cost, and generic proxies everywhere compromise your results. In practice, not every data gap deserves the same effort. What matters is how important a chemical is for your result and what the data has to prove.

In this webinar, we show how efficient mapping and targeted modeling fit together. We introduce a systematic framework for identifying suitable emission factors, assessing remaining data gaps, and determining the appropriate level of data quality based on their relevance and the purpose of your assessment. This means using existing emission factors where available, applying suitable proxies where sufficient, and focusing detailed modeling on the gaps that matter most. Whether you apply the framework in-house or with our support, you can allocate your modeling efforts efficiently and build a dataset that is fit for purpose.

Join us to learn how efficient emission factor mapping and targeted modeling can improve data coverage, reduce manual effort, and support reliable PCF and Scope 3.1 calculations at scale.