Estimating Missing Unit Process Data in Life Cycle Assessment Using a Similarity-Based Approach

In life cycle assessment (LCA), collecting
unit process data from
the empirical sources (i.e., meter readings, operation logs/journals)
is often costly and time-consuming. We propose a new computational
approach to estimate missing unit process data solely relying on limited
known data based on a similarity-based link prediction method. The
intuition is that similar processes in a unit process network tend
to have similar material/energy inputs and waste/emission outputs.
We use the ecoinvent 3.1 unit process data sets to test our method
in four steps: (1) dividing the data sets into a training set and
a test set; (2) randomly removing certain numbers of data in the test
set indicated as missing; (3) using similarity-weighted means of various
numbers of most similar processes in the training set to estimate
the missing data in the test set; and (4) comparing estimated data
with the original values to determine the performance of the estimation.
The results show that missing data can be accurately estimated when
less than 5% data are missing in one process. The estimation performance
decreases as the percentage of missing data increases. This study
provides a new approach to compile unit process data and demonstrates
a promising potential of using computational approaches for LCA data
compilation.