Make a decision in a sustainable development perspective requires to assess its economic, social and environmental benefits, and thus its sustainability. The environmental life cycle assessment (LCA) is one of the tools to assess sustainability as it aims to estimate the potential environmental impacts of goods, services, and decisions. Its main strengths are: (i) to quantify environmental impacts all along the value chain (life cycle perspective: from extraction of raw materials, their transformation, use and end of life), and (ii) to convert the emissions and withdrawals from the environment (inventory) into impacts on climate change, human health and ecosystem quality thanks to characterization factors (CFs). LCA makes it possible to identify avenues for the reduction of environmental impacts with a systemic perspective. Complementary to the attributional approach traditionally used in LCA, consequential LCA allows quantifying the environmental consequences of a decision, like the implementation of a public policy or the development of new technologies. Its use could be very relevant to enhance impact assessments performed before implementing a public policy. However, as with every assessment tool, one of the main limitations of LCA is the uncertainty of its results which may be high and is still too rarely assessed. As a tool for decision-making support, special attention should be paid to uncertainty reduction in LCA, in order to reduce the overall uncertainty in decision-making. Regionalization is one way to reduce uncertainty due to spatial variability in LCA. It refers to the enhancement of the geographical representativeness of LCA results. It can be integrated into LCA using many approaches at the different stages of LCA, especially thanks to inventory regionalization or inventory spatialization for LCA practitioners. However, existing approaches are now misidentified, and their relevant use conditions should be clarified. In addition, integrating regionalization may induce additional workload for the LCA practitioner, especially for data collection and modeling. Therefore, a methodology to prioritize data collection efforts for regionalization in LCA should be proposed to reduce the spatial uncertainty of the LCA results. This prioritization should aim to optimize the practitioner’s efforts by focusing on data that mostly contributes to uncertainty, i.e. the most sensitive, thus that has the highest potential for uncertainty reduction. The few existing methodologies to prioritize data collection efforts in LCA are ill-adapted to the LCA structure and the validity of their prioritization may be challenged. Besides, no methodology to prioritize regionalization efforts in LCA exists. To address those limitations, the main purpose of this research project is to develop a methodological framework to prioritize the efforts for uncertainty reduction in LCA through the operationalization of regionalization, and ultimately enhance the decision-making. Four objectives are thus devised: (1) develop a framework to structure and operationalize the use of existing approaches for regionalization in LCA, (2) develop a methodology to prioritize data collection in LCA for the reduction of spatial uncertainty to prioritize regionalization efforts in LCA, (3) apply the developed frameworks to prioritize the regionalization efforts to case studies in attributional LCA, (4) apply the methodology to prioritize the regionalization efforts to a case study in consequential LCA to assess the environmental consequences of a public policy in the transportation sector in France. The responses to those objectives have generated the following contributions. The first contribution is a critical review of existing approaches and recommendations to integrate the spatial dimension in LCA. Then it was applied to guide an organization for the agri-food sector to spatialize its internal LCA database on the short-term. Secondly, a methodology to prioritize the regionalization efforts in LCA was proposed to identify the main contributors to the spatial uncertainty, accounting for uncertainty from inventory and CFs. To do so, global sensitivity analyses are performed using Sobol indices that account for interactions between variables in the LCA model. This iterative methodology is designed for LCA practitioners and LCA database developers and allows to prioritize step by step: (1) the scenarios in the LCA study where the uncertainty should be reduced, (2) the most sensitive impact categories on which prioritizing the inventory data collection, (3) between inventory regionalization or spatialization, (4) the most sensitive input variables to be regionalized or spatialized in priority. Next, this methodology was used to perform meta-analyses on the regionalization needs of two economic sectors. Therefore, the LCA practitioner would no longer need to evaluate by himself the needs for a new study but would reuse precomputed recommendations for the associated sector. The results for the meta-analyses suggest the importance of (i) the contribution of the spatial variability of CFs to the results uncertainty which justifies using regionalized CFs, (ii) using global sensitivity analysis instead of impact contribution analysis to prioritize data collection in LCA. Finally, the methodology to prioritize the regionalization efforts was applied to a case study in consequential LCA from partial equilibrium economic modeling. This case study aims to assess the environmental consequences by 2050 of alternative transportation scenarios from the implementation of the energy transition law in France. This application highlights the important contribution of the uncertainty from the partial equilibrium economic modeling to the results uncertainty in consequential LCA. The main limitations of this project are associated with the operationalization of the methodology to prioritize the regionalization efforts in LCA: limited consideration of spatial correlations between LCA variables, important computational time when performing global sensitivity analysis with a high number of input variables, the non-implementation of global sensitivity analysis and regionalized impact methods in most of LCA software, the lack of available data and tools to regionalize and spatialize the inventory. Those limitations could be addressed in the future thanks to mutual efforts for the different stakeholders in the LCA community. Note, however, that democratizing the consideration of uncertainty and regionalization in LCA requires primarily an implementation of the associated methods in LCA software. In a broader way, this research project contributes to inform a bit more the LCA community by: (i) enhancing the consideration of regionalization in LCA, (ii) exploring the operationalization of regionalized impact methods, especially for the regionalized impact methodology IMPACT World+, (iii) exploring the links between uncertainty and regionalization to help prioritizing regionalization efforts, (iv) enhancing the consideration and the reduction of uncertainty in LCA, especially in consequential LCA from partial equilibrium economic modeling. Uncertainty is no longer seen here as a failure but is used as a tool to target the uncertainty reduction by using sensitivity analysis.