The food system, one of very complicated systems, has a challenge of applying Life Cycle Assessments (LCAs), which is just the selection of impact categories & indicators to represent sustainability.
We have investigated the representativeness & predictability of various impact indictors in the food sector, based on a correlation analysis of 9 Life Cycle Impact Assessment(LCIA) methods and 129 impact categories.
The analysis results show that on the one hand strong correlations are observed between the investigated LCIA methods or their impact categories/indictors, which reveals representativeness & predictability between the impact indicators and may reduce the number of the existing impact indicators. On the other hand, the results show that close correlations are not found between them, but it might lead to the emergence of a LCIA method from combining impact indictors.
Moreover, the results indicate that, since some of the LCIA methods (e.g. CML-IA baseline) have close correlations with 8 methods and their indicator combinations do not show dependencies, such LCIA methods could represent the whole impact categories, whereas since some of the LCIA methods (e.g. EPS 2015d) are not observed close correlations with other methods and their indicator combinations show considerable dependencies, such LCIA methods might not represent the whole impact categories providing an inefficient analysis.
Research findings above were published in "The International Journal of Life Cycle Assessment" under the title of "Correlation analysis of life cycle impact assessment methods and their impact categories in the food sector: representativeness and predictability of impact indicators" (https://doi.org/10.1007/s11367-023-02214-5).
