Result variations due to dynamic life cycle assessment compared to result variations due to sensitivity analysis on static inventory data

International audience

anglais. Biomass is a promising raw material for both energy and chemical production and their decarbonisation. However, calculating the impact of biomass use on climate change is still controversial, notably in terms of how to account for differences in the dynamics of carbon storage by photosynthesis and releases at the end of life (ex: incineration). Applying dynamic life cycle assessment (LCA) is one of the answers, but it requires more data and increases the complexity of the calculation. To address its relevance, we have to answer if the variation in results induced by the dynamic characterisation of the impact on climate change is significant compared with the variations induced by the uncertainty in the inventory data. This question is explored through the present case study, including the biomass production (miscanthus or wood residues), the CO2 production by biomass fermentation, the conversion of the CO2 into a polypropylene bag and the bag incineration with carbon capture and storage. A sensitivity analysis is performed to compare the results depending on i) the type of modelling (dynamic or static), ii) the characterization method to evaluate climate change (absolute global warming potential, AGWP, or temperature change, AGTP) and iii) one key parameter (the amount of CO2 emitted by soil organic carbon change during miscanthus production). The results reveal that variations induced by the dynamic modelling compared to the uncertainty on static inventory data are significant in the case of wood residues production and more significant when using AGWP than AGTP. Using AGTP rather than AGWP to assess the impact on climate change would thus improve comparisons between systems even without distributing inventories over time. Moreover, it would be useful to develop a method for assessing the relevance of carrying out a dynamic LCA, by precisely defining the flows that will benefit from being spread out over time. This method should consider as input data some time parameters such as the duration of the inventory or the time of the first emissions. It is important to i) keep time to improve the static inventory and perform sensitivity analyses and ii) limit the loss of information due to the cut-off included in the algorithm used to calculate a complete dynamic LCA.