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Global Sensitivity Analysis of Background Life Cycle Inventories.

Aleksandra Kim^1,2, Christopher L Mutel¹, Andreas Froemelt^2,3,4

¹Laboratory for Energy Systems Analysis, Paul Scherrer Institute, 5232 Villigen, Switzerland.

Environmental Science & Technology

|April 12, 2022

Summary

This study introduces a new Global Sensitivity Analysis (GSA) protocol for Life Cycle Assessment (LCA) models. It accurately identifies key environmental impact drivers, reducing data collection needs for robust assessments.

Keywords:

Brightway Swiss household food consumption global sensitivity analysis life cycle assessment supply chain traversal uncertainty reduction

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Area of Science:

Environmental Science
Life Cycle Assessment (LCA)
Sustainability

Background:

Life Cycle Assessment (LCA) studies quantify environmental impacts but often struggle with uncertainty propagation.
Global Sensitivity Analysis (GSA) is used to identify uncertainty drivers in LCA, but is computationally expensive for complete models.
Existing GSA methods face challenges with high-dimensional LCA models and often make simplifying assumptions.

Purpose of the Study:

To propose a novel Global Sensitivity Analysis (GSA) protocol specifically designed for large-scale Life Cycle Assessment (LCA) problems.
To overcome the limitations of existing GSA methods by not assuming model linearity or complexity.
To provide a validated and computationally feasible approach for comprehensive GSA in LCA.

Main Methods:

Development of a new GSA protocol applicable to high-dimensional LCA models.
Extensive validation of the proposed GSA protocol's results.
Comparison of the new protocol against an existing GSA method using filtering and ranking metrics.

Main Results:

The new GSA protocol yields more accurate results compared to existing methods.
It effectively filters non-influential and ranks influential uncertainty drivers in LCA models.
Demonstrated application to Swiss household food consumption highlights practical benefits.

Conclusions:

The proposed GSA protocol enhances the understanding of complex LCA models.
It leads to more robust estimations of environmental impacts with reduced data collection efforts.
Open-source Python package implementations are available, promoting wider adoption.