Offshore wind power is experiencing fast development in the last years and the global installed capacity is expected to be multiplied by four in the next decade. The demand for environmental assessments of offshore wind farms, mainly using Life Cycle Assessment (LCA), is quite recent and also rapidly increasing. However, access to data to conduct these LCA might be limited because of confidentiality, time constraint, data scarcity for projects at early stages of development. Some potential users of LCA results (eg public stakeholders or design engineers) might also not have access to this data. In this work, an approach is proposed to generate site-specific Life Cycle Inventories (LCI) and mass balance of offshore wind electricity production rapidly as a limited set of input data is required and in a flexible way. It is flexible thanks to the possibility to adapt the required data used as inputs for multiple user profiles. A parameterized LCI model in python language for an offshore wind farm has been developed with a cradle-to grave approach. The model’s user enters input data about the farm to be modeled. The model can be used at three different levels depending on which data is at the user’s disposal. At the “easiest” level of use, the user provides 20 parameters’ value on the modeled farm such as nominal capacity of turbines, water depth, foundation type... The results generated consider site characteristics such as turbines and foundations size or technological choices. At intermediate and expert levels, the user enters more input data, such as mass of turbine’s main parts. The possibility to use the model at these three levels enables the valorization of specific data, whenever available to get more specific results, but makes it possible to get rough estimates even for cases where data is scarce. The steps to develop the model were: 1 identifying the main site-specific parameters that affects LCA results 2 generating a parameterized LCI model based on a limited set of parameters by using LCIs from literature and aggregated mass data 3 designing the model to introduce several levels of use to be adaptable according to the input data at the user’s disposal This case study shows that parameterized LCA models can be structured with different levels of use. Such implementation valorizes available data from existing industrial projects to meet the need of rapid, flexible screening LCA for future projects. --- This work has been conducted in the frame of the project LIF-OWI [2021-2023] which was financed by ADEME, France Energies Marines and ANR (n° ANR-10-IEED-0006-34).