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Quantifying the trade-off between parameter and model structure uncertainty in life cycle impact assessment
Rosalie van Zelm1, Mark A J Huijbregts
1Department of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, P.O. Box 9010, 6500 GL, Nijmegen, The Netherlands. r.vanzelm@science.ru.nl
Environmental Science & Technology
|July 25, 2013
Summary
Quantifying trade-offs between parameter and model structure uncertainty is key for robust life cycle impact assessment. This study on maize production shows how to minimize overall uncertainty by carefully selecting models and excluding certain factors.
Area of Science:
- Environmental Science
- Life Cycle Assessment
- Ecotoxicology
Background:
- Quantitative uncertainty assessments are crucial for reliable life cycle impact assessment (LCA) practice.
- Understanding the interplay between parameter and model structure uncertainty is essential for improving LCA robustness.
- Pesticide application in agriculture poses risks to freshwater ecosystems, necessitating detailed impact assessments.
Purpose of the Study:
- To quantify the trade-off between parameter uncertainty and model structure uncertainty in LCA.
- To identify optimal model complexity for reducing overall uncertainty in environmental impact assessment.
- To provide guidance for enhancing the use of quantitative uncertainty assessments in LCA.
Main Methods:
- Probabilistic simulation (Monte Carlo) was used to quantify parameter uncertainty in pesticide emissions, chemical data, and degradation products.
- Discrete choice analysis assessed model structure uncertainties related to concentration-response models, damage models, and transformation products.
- A case study of maize production in The Netherlands focused on freshwater ecotoxicity from pesticide application.
Main Results:
- The linear concentration-response model is preferable for minimizing overall uncertainty.
- Excluding pesticide transformation products can help reduce uncertainty.
- The selection of the damage model has a minor influence on the overall uncertainty.
Conclusions:
- Quantifying the trade-off between parameter and model structure uncertainty aids in selecting appropriate model complexity.
- This approach enhances the reliability and applicability of life cycle impact assessment.
- Informed decisions regarding model components can lead to more accurate environmental impact evaluations.
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