Methods for global sensitivity analysis in life cycle assessment

• Published in: The International Journal of Life Cycle Assessment
• Main Authors: Groen, Evelyne A., Bokkers, Eddie A. M., Heijungs, Reinout, de Boer, Imke J. M.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2017
• Subjects: Correlation; Key issue analysis; Random balance design; Regression; Sensitivity analysis; Sobol' sensitivity index; Variance decomposition; /dk/atira/pure/sustainabledevelopmentgoals/responsible_consumption_and_production; name=SDG 12 - Responsible Consumption and Production; Northeast Atlantic
• Online Access: https://research.vu.nl/en/publications/440522b6-e160-4e61-878d-b2b570decb59; https://doi.org/10.1007/s11367-016-1217-3; https://hdl.handle.net/1871.1/440522b6-e160-4e61-878d-b2b570decb59; http://www.scopus.com/inward/record.url?scp=84997611121&partnerID=8YFLogxK; http://www.scopus.com/inward/citedby.url?scp=84997611121&partnerID=8YFLogxK

Purpose: Input parameters required to quantify environmental impact in life cycle assessment (LCA) can be uncertain due to e.g. temporal variability or unknowns about the true value of emission factors. Uncertainty of environmental impact can be analysed by means of a global sensitivity analysis to gain more insight into output variance. This study aimed to (1) give insight into and (2) compare methods for global sensitivity analysis in life cycle assessment, with a focus on the inventory stage. Methods: Five methods that quantify the contribution to output variance were evaluated: squared standardized regression coefficient, squared Spearman correlation coefficient, key issue analysis, Sobol’ indices and random balance design. To be able to compare the performance of global sensitivity methods, two case studies were constructed: one small hypothetical case study describing electricity production that is sensitive to a small change in the input parameters and a large case study describing a production system of a northeast Atlantic fishery. Input parameters with relative small and large input uncertainties were constructed. The comparison of the sensitivity methods was based on four aspects: (I) sampling design, (II) output variance, (III) explained variance and (IV) contribution to output variance of individual input parameters. Results and discussion: The evaluation of the sampling design (I) relates to the computational effort of a sensitivity method. Key issue analysis does not make use of sampling and was fastest, whereas the Sobol’ method had to generate two sampling matrices and, therefore, was slowest. The total output variance (II) resulted in approximately the same output variance for each method, except for key issue analysis, which underestimated the variance especially for high input uncertainties. The explained variance (III) and contribution to variance (IV) for small input uncertainties were optimally quantified by the squared standardized regression coefficients and the main Sobol’ index. For ...