International audiencePolicy makers are nowadays debating about the future electricity mixes that should be deployed. The environmental impacts of electricity generation systems is one of the central issue for this debate. They have been widely assessed over the past decades, in particular with the LCA approach. Several literature reviews have shown the large variability associated with these results. It leads sometimes policy makers to consider LCA as an inconclusive method. Improving the understanding of the LCA results variability origins is a key issue to extend the use of LCA as a decision support tool. One approach to adress variability are sensitivity analysis (SA). However, when dealing with environmental impact assessment, most SAs remain at a local level or evaluate the variation of the input parameters one factor at a time. These approaches only partially reflect the LCA results variability, indeed, it does not consider the full range of input parameters interval and their probability distribution. To overcome these limitations, Global Sensitivity Analysis (GSA) approach has been developped in statistics. It enables apportioning the results variability of a model to its different input parameter variability, by varying all of them simultaneously according to their probability distributions. This link between result variability and parameter variability is quantitatively evaluated by the calculation of the so called Sobol indices. While it has been applied in only a few analyses in the field of environmental impact assessment, this statistical tool is yet to be embedded in LCA methodology. Thereby, this paper aims at proposing a method to implement GSA in the LCA field to adress the results variability issue related to energy pathways