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A global approach for sparse representation of uncertainty in Life Cycle Assessments of waste management systems

  • UNCERTAINTIES IN LCA
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An Erratum to this article was published on 15 February 2017

Abstract

Purpose

Identification of key inputs and their effect on results from Life Cycle Assessment (LCA) models is fundamental. Because parameter importance varies greatly between cases due to the interaction of sensitivity and uncertainty, these features should never be defined a priori. However, exhaustive parametrical uncertainty analyses may potentially be complicated and demanding, both with analytical and sampling methods. Therefore, we propose a systematic method for selection of critical parameters based on a simplified analytical formulation that unifies the concepts of sensitivity and uncertainty in a Global Sensitivity Analysis (GSA) framework.

Methods

The proposed analytical method based on the calculation of sensitivity coefficients (SC) is evaluated against Monte Carlo sampling on traditional uncertainty assessment procedures, both for individual parameters and for full parameter sets. Three full-scale waste management scenarios are modelled with the dedicated waste LCA model EASETECH and a full range of ILCD recommended impact categories. Common uncertainty ranges of 10 % are used for all parameters, which we assume to be normally distributed. The applicability of the concepts of additivity of variances and GSA is tested on results from both uncertainty propagation methods. Then, we examine the differences in discernibility analyses results carried out with varying numbers of sampling points and parameters.

Results and discussion

The proposed analytical method complies with the Monte Carlo results for all scenarios and impact categories, but offers substantially simpler mathematical formulation and shorter computation times. The coefficients of variation obtained with the analytical method and Monte Carlo differ only by 1 %, indicating that the analytical method provides a reliable representation of uncertainties and allows determination of whether a discernibility analysis is required. The additivity of variances and the GSA approach show that the uncertainty in results is determined by a limited set of important parameters. The results of the discernibility analysis based on these critical parameters vary only by 1 % from discernibility analyses based on the full set, but require significantly fewer Monte Carlo runs.

Conclusions

The proposed method and GSA framework provide a fast and valuable approximation for uncertainty quantification. Uncertainty can be represented sparsely by contextually identifying important parameters in a systematic manner. The proposed method integrates with existing step-wise approaches for uncertainty analysis by introducing a global importance analysis before uncertainty propagation.

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Acknowledgments

The authors wish to thank the anonymous referees for the very perceptive and insightful comments. Financial support is acknowledged from the Danish Research Council through the IRMAR project grant, as well as from the Technical University of Denmark.

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Authors and Affiliations

  1. Department of Environmental Engineering, Technical University of Denmark, Miljoevej, Building 113, 2800, Kongens Lyngby, Denmark

    Valentina Bisinella, Thomas Højlund Christensen & Thomas Fruergaard Astrup

  2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 321, 2800, Kongens Lyngby, Denmark

    Knut Conradsen

Authors
  1. Valentina Bisinella
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  2. Knut Conradsen
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  3. Thomas Højlund Christensen
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  4. Thomas Fruergaard Astrup
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Correspondence to Valentina Bisinella.

Additional information

Responsible editor: Adisa Azapagic

An erratum to this article is available at http://dx.doi.org/10.1007/s11367-016-1259-6.

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Bisinella, V., Conradsen, K., Christensen, T.H. et al. A global approach for sparse representation of uncertainty in Life Cycle Assessments of waste management systems. Int J Life Cycle Assess 21, 378–394 (2016). https://doi.org/10.1007/s11367-015-1014-4

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  • DOI: https://doi.org/10.1007/s11367-015-1014-4

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