Presenter/Author Information

Georg Geisler
Stefanie Hellweg
Konrad Hungerbühler

Keywords

decision-making, life cycle assessment, pesticides, significance, uncertainty

Start Date

1-7-2004 12:00 AM

Description

Uncertainty assessment in LCA enables the evaluation of the significance of results, which is important for providing sound decision-support. In this work, an LCA was performed on two plant-growth regulators considering various sources of uncertainty: In the LCI, uncertainties of imprecise measurements of elementary flows, temporal and spatial variation, and different production processes were assessed. In the characterisation phase, the uncertainties of substance properties and the composition of sum-parameters were considered. These uncertainties were expressed as probability distributions and assessed via stochastic modelling (Monte-Carlo Simulation). For most LCI- and LCIA-data, generic uncertainty ranges were used. Uncertainties due to assumptions on the production efficiency were reflected by a best-case and a worst-case scenario. Contributions to variance of all uncertain input parameters were calculated. One plant-growth regulator was defined as significantly better than the other, if the impact score was lower in 90% of the simulations. The results showed that differences in median impact scores of a factor of 1.6 were sufficient in the impact categories global warming, acidification, and eutrophication for a significant distinction of the products. The applied doses and the elementary flows of basic-chemical production and energy supply had the highest contribution to variance in these impact categories. By contrast, dispersions are large concerning the toxicity impact categories and the photooxidant creation potential. This can be mainly attributed to the high contribution to variance of sum-parameters and characterisation factors. The implications of these uncertainties on the decision-making process are discussed. Moreover, tentative rules of thumb for estimating the significance of results are put forward. Finally, a format is proposed how complex results of uncertainty assessments may be presented for decision-support.

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Jul 1st, 12:00 AM

Uncertainties in LCA of Plant-Growth Regulators and Implications on Decision-Making

Uncertainty assessment in LCA enables the evaluation of the significance of results, which is important for providing sound decision-support. In this work, an LCA was performed on two plant-growth regulators considering various sources of uncertainty: In the LCI, uncertainties of imprecise measurements of elementary flows, temporal and spatial variation, and different production processes were assessed. In the characterisation phase, the uncertainties of substance properties and the composition of sum-parameters were considered. These uncertainties were expressed as probability distributions and assessed via stochastic modelling (Monte-Carlo Simulation). For most LCI- and LCIA-data, generic uncertainty ranges were used. Uncertainties due to assumptions on the production efficiency were reflected by a best-case and a worst-case scenario. Contributions to variance of all uncertain input parameters were calculated. One plant-growth regulator was defined as significantly better than the other, if the impact score was lower in 90% of the simulations. The results showed that differences in median impact scores of a factor of 1.6 were sufficient in the impact categories global warming, acidification, and eutrophication for a significant distinction of the products. The applied doses and the elementary flows of basic-chemical production and energy supply had the highest contribution to variance in these impact categories. By contrast, dispersions are large concerning the toxicity impact categories and the photooxidant creation potential. This can be mainly attributed to the high contribution to variance of sum-parameters and characterisation factors. The implications of these uncertainties on the decision-making process are discussed. Moreover, tentative rules of thumb for estimating the significance of results are put forward. Finally, a format is proposed how complex results of uncertainty assessments may be presented for decision-support.