[en] Renewable energy technologies, which are rapidly evolving since the beginning of 2000's, are expected to contribute significantly to the electricity mix in the future. Although renewable energy technologies have low environmental impacts during the operation phase, they may present non-negligible impacts in upstream and downstream processes related to manufacture and installation, or end-of-life. Life Cycle Assessment (LCA) has emerged in the last decades as one of the main environmental management methods for the support of both public decision makers and industrial stakeholders. LCA-based approaches are widely accepted and applied to estimate the environmental impacts of both individual renewable energy systems and electricity mixes as a whole. However, most of the available studies are based on average inventories and estimations with different levels of accuracy, which leads to significant uncertainties in the LCA results. The numerous assumptions impose high variability and uncertainties in published LCA studies, which makes it necessary to address them in order to ensure a successful development of the emerging energy sectors and support stakeholder decisions. Uncertainty analysis tools available in common LCA software do not allow identifying the individual effects of different uncertainty and variability sources, or applying advanced methods such as Global Sensitivity Analysis. INCER-ACV project (contract 1705C0045), funded by ADEME in the framework of the call'Sustainable Energy'(APR-ED 2017), aimed to strengthen quantification methods to take into account the effects of possible parameters' variation on the environmental impact of energy production technologies compared to average scenarios. The goal of this project was to develop a standard protocol to analyze the effects of variability and uncertainties of input data, related to operational parameters of energy production pathways, used to conduct an LCA to estimate the environmental performance, and to provide industrial stakeholders with the tools to implement the protocol. The developed project included the characterization of uncertainties based on the definition of statistical distribution functions associated with operational parameters (which allow modeling a system's behavior). These parameters were integrated into parameterized models to obtain modular inventories. The application of the protocol, based on two case studies involving renewable energy production systems, allowed, on the one hand, identifying the most influencing parameters responsible for the uncertainty of the LCA results and, on the other hand, exploiting these parameters to generate simplified mathematical models which can be used to obtain estimations of environmental impacts based on a reduced number of variables. This application is among the main recent advances in terms of methodological developments in LCA. The methods and tools developed within this project can be applied to other renewable energy sectors as well as to other sectors. In order to achieve these targets, a first case study was developed by the scientific partner of this project (ARMINES) to identify the required elements for the construction of a parameterized model. This model allowed identifying the needs for the practical implementation of a sensitivity analysis, so as to account for the effects of uncertainties and variability of data linked to foreground processes. The generalization of this development in the form of a protocol was validated by the industrial partner (ENGIE) with the use of a second case study to ensure the correct consideration of the needs of industrial decision-makers and the …