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.
Highlights
Life cycle assessment (LCA) is widely considered as the most relevant methodology to assess the environmental performances of products and processes over their life cycle and is currently applied to different industrial sectors (Jacquemin et al, 2012; Moomaw et al, 2011)
Starting from the global sensitivity analysis (GSA) protocol presented by Cucurachi et al (2016), we propose a methodology that relies on the reiteration of several GSA calculations under different hypothesis regarding the description of the input parameters
Global sensitivity analysis is a powerful tool to study the influence of the different parameters of complex models and to establish a ranking among them, in order to identify the ones that are most influent on the variability of the output
Summary
Life cycle assessment (LCA) is widely considered as the most relevant methodology to assess the environmental performances of products and processes over their life cycle and is currently applied to different industrial sectors (Jacquemin et al, 2012; Moomaw et al, 2011). In the LCA context, global sensitivity analysis (GSA) has been recently identified by several authors as a relevant practice to address several issues: (i) to study the combined influence of the different input parameters (Padey et al, 2013), (ii) to assess the robustness of the results (Wei et al, 2015), (iii) to enhance the understanding of the structure of the model (Cucurachi et al, 2016) (iv) to ensure transparency, reliability and credibility of LCA practices (Bisinella et al, 2016) and (v) to contribute to the decision-making process (Andrianandraina et al, 2015). GSA allows establishing a ranking among the input parameters and identifying the most influential on the variability of the output of the model. GSA techniques support the execution of LCAs and facilitate its interpretation, promoting an enhanced decision making process (Cucurachi et al, 2016)
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