Life Cycle Assessment (LCA) has been widely applied over the last three decades as a standardized tool for the comprehensive environmental impact assessment of products and processes. While LCAs have traditionally been based on available data from existing processes at pilot or large scale, it is estimated that 80% of environmental impacts of a process are linked to decisions at the design phase. It is therefore worth to develop tools that allow adapting the current LCA framework for the application to early-stage schemes and emerging technologies. Several difficulties hinder LCA practitioners from conducting such studies. In particular, emerging technologies tend to differ significantly from the existing processes they aim to substitute and may have unknown future applications. They present a wider data gap linked to the lack of information on the life cycle phases. Moreover, many of these systems are still at the laboratory stage, which involves substantial differences compared to industrial scale procedures. As a result, LCAs of emerging technologies are subject to an increased level of uncertainty that needs to be estimated to contribute to the reliability and credibility of the results. Global sensitivity analysis (GSA) has been proposed by several authors as a tool to evaluate the global uncertainty of LCA results and the influence of each variable input on the total variability of the model output. However, the GSA results and their corresponding parameter ranking depend on the description of each input’s variability, namely the corresponding probability distribution used by the practitioner to model the range of values that an input may assume. In this study, we propose a protocol to evaluate the effect of the choices in the selection of inputs’ distribution functions on GSA results and provide recommendations for LCA practitioners.