Representing and visualizing data uncertainty in input-output life cycle assessment models

Abstract

Uncertainty assessment is crucial to the reliability of decisions made based on results of life cycle assessment (LCA) models. However, the most popular uncertainty analysis method in LCA models defines uncertainty as quantified empirical judgment of the sources of the inventory data, which lacks the deviations due to the measurements of the data. We create a modified range method that leverages an array of publicly available data to represent the uncertainty of the actual values used in input-output models as ranges. Using these ranges, we propagate and visualize results from the uncertainties. We demonstrate the utility of the method using the Economic Input-Output Life Cycle Assessment (EIO-LCA) model and focus on the uncertainty of estimated energy consumption. Results show that for energy consumption values in the model, average uncertainty ranges are within ±40% with some outliers. Our method screens based on the magnitude of impacts and the relative uncertainty. Improved uncertainty assessment supports various types of decisions, such as product comparisons, hotspot analysis, and overall energy analyses. We used three case studies to demonstrate the implementations of our method. This method can be extended to additional types of flows, beyond energy, and to process-matrix-based LCA models.