+3 votes
419 views

Hello OpenLCA community, 

I would like to tell you that trying to find a method to use a single score for ACV in OpenLCA I found a difference between the normalization values of ReCiPe (H) Endpoint (LCIA Methods 2.4.0) and the values published by RIVM in their official normalization values file.

I would like to know please why this difference exists and also how I could get a single score from the OpenLCA data structure.

Thank you very much in advance

in openLCA by (370 points)

1 Answer

+1 vote
by (160 points)

Thank you for this feedback, indeed we have investigated and updated the ReCiPe normalization values. Please find them in v2.4.2 of the openLCA LCIA Method package here.

Sarah Serafini

by (370 points)
Dear Sarah, thank you for your reply. Regarding this, is there any documentation with the changes that have been made? from what I have compared in Excel, you have done the direct replacement of the RIVM factors in the normalisation column, but there are very small percentual differences that I don't know if they are due to some additional calculation that you have done. The second thing is that the weighted results have no units (pt, mpt), so it is not well understood how the single score is arrived at and the method behind it. But I'll ask in a better thread. Thanks very much!
by (160 points)
Dear Felipe,

You can find a blog post [here](https://www.openlca.org/openlca-lcia-method-package-2-4-2-update-released/) with the documentation. Regarding the ReCiPe normalization values, I have updated them to match the official ones, without further manipulations. The slight percentage differences you notice are due to an issue with the version of Pandas I used. This version parses float data as 32-bit floats when working with CSV files, causing minor rounding of some values. However, these values remain safe to use. In the next method package release, we will update them to the non-rounded version.

Best regards,
Sarah
by (370 points)
Dear Sarah, thank you for your detailed explanation.
Best regards,
...