Sometimes a product in Pickler shows a higher impact or higher eco-score than you expected — either on its own or compared to another product.
The good news: Pickler already prevents many of the common LCA inconsistencies. Everything is calculated with Fast-Track LCA, our verified methodology that ensures results are consistent and comparable.
This means you don’t need to worry about differences in methodology, boundaries, allocation rules, database versions, or modelling assumptions — all of this is built-in and applied consistently across every product.
Still, unexpected results can happen. They usually come from four areas:
Expectation management: your gut feeling or eco-claims suggest a different outcome.
Incorrect data input: the data input in your Pickler product doesn’t match with your internal data.
Materials mapped to the wrong IDEMAT sources
A high share of default values instead of primary data
This guide helps you understand why a result may look unexpected.
Step 1: understand an LCA result versus gut feeling or eco-claims
When an LCA result in Pickler doesn’t match your expectation, it usually doesn’t mean anything is wrong; it can just means the outcome is different from what you expected.
You might feel your product should score better because it’s recyclable, biobased, mono-material, FSC-certified, or contains recycled content.
Or you may simply “know” from experience or another source that material X should be better than material Y.
These signals are useful, but they each describe only one characteristic of a product. An LCA measures the entire lifecycle, so it can show a different picture than what eco-claims or intuition suggest.
This is completely normal — an LCA often highlights impacts that labels do not cover.
Step 2: Validate Your Data Input
Unexpected results can also come from simple data mismatches. Check whether the product in Pickler truly matches the product in real life.
Double-check your data input, and pay extra attention to these:
Materials: Are the correct materials selected?
Weights: Are they correct and not estimated?
Transport: Are distances and transport modes realistic?
Small corrections here often explain large differences in impact.
Step 3: Validate Your IDEMAT Mapping
Each material you enter is mapped to a corresponding IDEMAT dataset. If a result seems off, confirm that the right source was chosen. This also counts for other data you map to IDEMAT, such as location data.
You can review what you mapped your data to on the Mapping page or by clicking the source tag in the product form, passport, or comparison.
See the Full Story Behind Your Result
Pickler is fully transparent, so you’re free to explore the underlying sources for any material or process. You can do this by clickint the source icon you find in product passports, comparisons and in the product form. If something still feels unclear, our team is here to help.
Step 4: Check Your Data Quality (Primary vs. Defaults)
Your footprint is based on a mix of:
Primary data — exact data from your supply chain
Secondary data — market average data from IDEMAT
Default values — conservative placeholders when data is missing
You can find the data quality for every product in Pickler. A high share of defaults means your result reflects market averages, not your exact situation. This isn’t wrong — but it may not match your expectations.
For example, when you don’t enter transport distance, Pickler will take a default of 1000km by truck and 20.000 container ship. This may not reflect your situation and can cause a higher impact.
Replacing defaults with more primary data often lowers the footprint and increases accuracy.
Still unsure about your result?
If this guide didn’t answer your question or you still feel something in the result doesn’t add up, we’re here to help.
You can reach us anytime through the chat in Pickler or by emailing [email protected]. We’re happy to review your setup and walk through the result with you.