Different types of uncertainty

Question

Dear community,

I have several doubts about the different types of uncertainty that can be accounted for in openLCA:

1) In a process (tab "General Information"), we can define the pedigree matrix. To what type of data does it refer to?

2) In the "Inputs/outputs" tab, for each flow we can fill in the "uncertainty" and "Data quality" columns. To what type of uncertainty does the "uncertainty" column refer to? How does it relate to the basic uncertainty that can be specified along with the additional uncertainty associated with the quality of the data?

Thanks in advance for your help.

Best Regards,

Answer 1

Pedigrees can be established for the entire process data quality as well as for individual flow data quality. The pedigree you choose will dictate how you assign values in each case. Data quality for individual flows may be quite variable, but the process pedigree is designed to capture the overall net "quality" of the process.

Uncertainty is likely related to the data quality but is really capturing the actual numeric range of an individual datapoint. You can use data quality to assign uncertainty if you'd like (i.e, the worse the quality of the data more likely the broader your uncertainty will be).

Comments

Dear byong,

Thank you for your explanation.

Based on your explanation can I conclude that the consideration of uncertainty at process and flow levels are mutually exclusive? Or in other words, if I choose one, I should not choose the other?

Thanks in advance for your answer.

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Thanks to jmos and byong!

I have the same question. Do I need to choose uncertainty in the process only or can I specify it for the process as well as for each of its flow?

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To both of these comments, the uncertainty of processes and flows represent different things and both can be valuable, but can be difficult and time consuming to assess. You can run results without data quality or uncertainty at all so neither is inherently required. Both are forms of metadata and/or for sensitivity analysis.

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Thank you, dear byong!

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Thank you byoung.