Sometimes only 20 model inputs out of few hundreds of thousands drive most of the uncertainty in life cycle assessment models
This new ESD-PSI study published in the Environmental Science & Technology journal has introduced a novel global sensitivity analysis (GSA) protocol that can aid in increasing robustness of life cycle assessment (LCA) results by improving data quality of only a tiny fraction of datasets. On the example of impacts from Swiss household consumption, we show how to efficiently conduct GSA for complete background LCA models, and consistently validate results.
In recent years many Life Cycle Assessment (LCA) studies have been conducted to quantify the environmental performance of products and services. Some of these studies propagated numerical uncertainties in underlying data to LCA results, and several applied Global Sensitivity Analysis (GSA) to some parts of the LCA model to determine its main uncertainty drivers. However, only a few studies have tackled GSA of complete LCA models, due to the high computational cost of such analysis and lack of appropriate methods for very high-dimensional models.
This study proposes a new GSA protocol suitable for large LCA problems that, unlike existing approaches, does not make assumptions on model linearity and complexity, and includes extensive validation of GSA results.
We illustrate the benefits of our protocol by comparing it with an existing method in terms of filtering of non-influential and ranking of influential uncertainty drivers, and include an application example of Swiss household food consumption.
We note that our protocol obtains more accurate GSA results, which leads to better understanding of LCA models, and less data collection efforts to achieve more robust estimation of environmental impacts. Implementations supporting this work are available as free and open source Python packages.
This collaborative study was carried out by Aleksandra Kim, Chris Mutel, Andreas Froemelt and Stefanie Hellweg - researchers from Paul Scherrer Institute, ETH Zurich, Future Cities Lab and the Swiss Federal Institute of Aquatic Science and Technology.
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