In Theory: Yes
If you follow pure LCA logic, each country can have a different waste treatment system. That means the end-of-life phase of a product may differ depending on where the product is disposed of.
As a result, the lifecycle impact of the same packaging could technically vary per destination country.
In Practice: Usually Not Necessary
In most real-world situations, you do not need to create a separate product or SKU for every country.
Especially within regions like Europe, differences in waste treatment systems are often relatively small. In those cases, it is more practical — and fully acceptable — to model countries together within a single product setup.
A Practical Approach: Use an End-of-Life Distribution
Instead of duplicating products, you can reflect regional differences using a distribution across end-of-life regions.
This means assigning percentages based on where products are actually sold and disposed of.
Example
If most products are sold in the Netherlands and the rest across Europe, you could model:
70% Netherlands
30% Europe
This captures the real-world flow of products without adding unnecessary complexity to your dataset.
When Does Separate Modelling Make Sense?
It only becomes valuable to model regions separately when the differences are:
Significant
Relevant to decision-making
Materially impacting the results
For example, if one market has a very different recycling infrastructure or disposal method, separate modelling may improve the accuracy of the assessment in a meaningful way.
Focus on Material Differences
This approach aligns with LCA best practice:
Focus on differences that materially impact results, and simplify where they don’t.
The goal of an LCA is not maximum complexity, but meaningful and decision-useful insights.
Reasonable Assumptions Are Acceptable
You do not need exact precision per country. It is completely acceptable to:
Group regions (for example: “Europe”)
Use estimates based on sales or distribution data
Apply reasonable assumptions where detailed data is unavailable
As long as the setup reflects reality reasonably well and the assumptions can be clearly explained, the model is considered valid and robust.