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LCA of emerging technologies: addressing high uncertainty on inputs' variability when performing global sensitivity analysis

Abstract : In the life cycle assessment (LCA) context, global sensitivity analysis (GSA) has been identified by several authors as a relevant practice to enhance the understanding of the model's structure and ensure reliability and credibility of the LCA results. GSA allows establishing a ranking among the input parameters, according to their influence on the variability of the output. Such feature is of high interest in particular when aiming at defining parameterized LCA models. When performing a GSA, the description of the variability of each input parameter may affect the results. This aspect is critical when studying new products or emerging technologies, where data regarding the model inputs are very uncertain and may cause misleading GSA outcomes, such as inappropriate input rankings. A systematic assessment of this sensitivity issue is now proposed. We develop a methodology to analyze the sensitivity of the GSA results (i.e. the stability of the ranking of the inputs) with respect to the description of such inputs of the model (i.e. the definition of their inherent variability). With this research, we aim at enriching the debate on the application of GSA to LCAs affected by high uncertainties. We illustrate its application with a case study, aiming at the elaboration of a simple model expressing the life cycle greenhouse gas emissions of enhanced geothermal systems (EGS) as a function of few key parameters. Our methodology allows identifying the key inputs of the LCA model, taking into account the uncertainty related to their description.
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Soumis le : mercredi 16 novembre 2016 - 16:20:12
Dernière modification le : mercredi 14 octobre 2020 - 03:41:14
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Martino Lacirignola, Philippe Blanc, Robin Girard, Paula Perez-Lopez, Isabelle Blanc. LCA of emerging technologies: addressing high uncertainty on inputs' variability when performing global sensitivity analysis. Science of the Total Environment, Elsevier, 2017, 578, pp.268-280. ⟨10.1016/j.scitotenv.2016.10.066⟩. ⟨hal-01398080⟩



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