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Erschienen in:
29.03.2016 | UNCERTAINTIES IN LCA
A novel approach for uncertainty propagation applied to two different bio-waste management options
Erschienen in: The International Journal of Life Cycle Assessment | Ausgabe 10/2016
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Abstract
Purpose
A novel approach was used for quantifying uncertainty propagation in life cycle assessment (LCA). The approach was designed to be efficient and applicable in practice. The model was applied to a specific case study concerning alternative strategies for managing bio-waste: incineration versus anaerobic digestion followed by composting.
Methods
The uncertainty of each impact category was calculated starting from the variance (σ2) and geometric mean (μ) of the lognormal distribution of each input data. A procedure consisting of three mandatory steps and one facultative step was developed. Mandatory steps were calculation of the associated normal distribution for each input, calculation of the percentile curve for each input, and calculation of the percentile curve of the impact categories. The facultative step consisted in calculating the lognormal distribution of the impact categories if all the values of the percentile curve were >0.
Results and discussion
The uncertainty associated with the results of the anaerobic digestion and composting scenario was significantly higher than those associated with the incineration scenario. These results were confirmed by those obtained by Monte Carlo simulations. Environmental gains calculated for the scenario with incineration concerning acidification, global warming, terrestrial eutrophication, and photochemical ozone creation had a high level of probability (i.e., >90 %). On the contrary, the impact categories of the scenario with anaerobic digestion and composting had higher uncertainties.
Conclusions
The source of uncertainty in LCA analysis can be due to multiple factors. Among these, the variability of the values of the LCI can have a significant influence on the results of the study. LCA analysis based on the exploitation of geometric means and/or average values of inputs reported in LCI can lead to results affected by a low level of reliability. In particular, this aspect can play a relevant role for LCA-based decisions when different scenarios and options are compared. As in the case study reported in this work, neglecting the propagation of uncertainty can result in a relevant bias for obtaining a full informative impression of the problem analyzed.