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Dear Andreas, Michael, GreenDelta,

Congratulations on the new version of openLCA 1.6 and also updating the Monte Carlo feature. I can now confirm that the negative results previously encountered with openLCA 1.3/1.4/1.5 are no longer there.

I do have twp separate questions and they pertain to the sampling approach used in openLCA for its MC function:

(1) Can the sampling approach in openLCA be summarized as "partially independent" sampling as is clarified in Suh and Qin 2017? (https://link.springer.com/article/10.1007/s11367-017-1287-x)

(2) Is it possible to isolate sampling only for the foreground processes? (i.e. turning off all upstream sampling)

All the best,

Michael
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Hi Michael,

thank you for the congrats!

To your questions:
-> 1) We perform the simulation in openLCA in a way that would be classified as "fully dependent" in the article you mention: all uncertain data is drawn at the same time, and then a calculation is started. If a process appears in several "branches" of a supply chain several times, uncertain data is drawn for this process only once and then used in the calculation.
-> 2)"is it possible to isolate sampling only for the foreground processes? (i.e. turning off all upstream sampling)": unfortunately not but I agree this would be a nice feature.

Best wishes,
Andreas
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Dear Andreas,

Correct, I have already exported all the data and made the appropriate estimations of the log-transformed data.

For me, I was just curious whether openLCA might include an option to automatically report log-transformed statistics, given that inventory data and thus results may commonly follow log-normal distributions? No need to respond to that, just a suggested-feature to add from a dedicated user :)

All the best,

Michael
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