Estimation of Unit Process Data for Life Cycle Assessment Using a Decision Tree-Based Approach

Lacking unit process data is a major
challenge for developing life
cycle inventory (LCI) in life cycle assessment (LCA). Previously,
we developed a similarity-based approach to estimate missing unit
process data, which works only when less than 5% of the data are missing
in a unit process. In this study, we developed a more flexible machine
learning model to estimate missing unit process data as a complement
to our previous method. In particular, we adopted a decision tree-based
supervised learning approach to use an existing unit process dataset
(ecoinvent 3.1) to characterize the relationship between the known
information (predictors) and the missing one (response). The results
show that our model can successfully classify the zero and nonzero
flows with a very low misclassification rate (0.79% when 10% of the
data are missing). For nonzero flows, the model can accurately estimate
their values with an R2 over 0.7 when
less than 20% of data are missing in one unit process. Our method
can provide important data to complement primary LCI data for LCA
studies and demonstrates the promising applications of machine learning
techniques in LCA.