LCA has become an important method to study environmental impacts of human activities. Still, there are several methodological issues in LCA that can adversely affect the results reliability.... Show moreLCA has become an important method to study environmental impacts of human activities. Still, there are several methodological issues in LCA that can adversely affect the results reliability. Three of these issues relate to a) allocation, b) the representation of the time dimension and c) the interpretation of results in LCA. Uncertainties play a fundamental and underlying role for these issues. It is widely-agreed that correctly dealing with these different uncertainty sources is a vital step towards increasing the usefulness and reliability of LCA results. Practical ways to deal with uncertainty are needed. The aim of this thesis is to deepen the understanding of the uncertainty dimension of current LCA. By means of addressing different sources of uncertainty not yet addressed, with new methods, a clearer picture of the implications of different sources of uncertainty in LCA is provided. This thesis departed from broad domains of uncertainty including risk, uncertainty as conventionally described, ignorance and indeterminacies. The selected sources of uncertainty are in the domains of risk and conventional uncertainty i.e. those due to incomplete scientific knowledge and that are to some extent quantifiable. This does not mean that all can be known or quantified as ignorance and indeterminacies exist. Show less
Mendoza Beltran, M.A.; Heijungs, R.; Guinée, J.B.; Tukker, A. 2015
Purpose Despite efforts to treat uncertainty due to methodological choices in life cycle assessment (LCA) such as standardization, one-at-a-time (OAT) sensitivity analysis, and analytical and... Show morePurpose Despite efforts to treat uncertainty due to methodological choices in life cycle assessment (LCA) such as standardization, one-at-a-time (OAT) sensitivity analysis, and analytical and statistical methods, no method exists that propagate this source of uncertainty for all relevant processes simultaneously with data uncertainty through LCA. This study aims to develop, implement, and test such a method, for the particular example of the choice of partitioning methods for allocation in LCA, to be used in LCA calculations and software. Methods Monte Carlo simulations were used jointly with the CMLCA software for propagating into distributions of LCA results, uncertainty due to the choice of allocation method together with uncertainty of unit process data. In this study, a methodological preference is assigned to each partitioning method, applicable to multi-functional processes in the system. The allocation methods are sampled per process according to these preferences. A case study on rapeseed oil focusing on three greenhouse gas (GHG) emissions and their global warming impacts is presented to illustrate the method developed. The results of the developed method are compared with those for the same case similarly quantifying uncertainty of unit process data but accompanied by separate scenarios for the different partitioning choices. Results and discussion The median of the inventory flows (emissions) for separate scenarios varies due to the partitioning choices and unit process data uncertainties. Inventory variations are reflected in the global warming results. Results for the approach of this study vary with the methodological preference assigned to the different allocation methods per multi-functional process and with the continuous distribution of unit process data. The method proved feasible and implementable. However, absolute uncertainties only further increased. Therefore, it should be further researched to reflect relative uncertainties, more relevant for comparative LCAs. Conclusions Propagation of uncertainties due to the choice of partitioning methods and to unit process data into LCA results is enabled by the proposed method, while capturing variability due to both sources. It is a practical proposal to tackle unresolved debates about partitioning choices increasing robustness and transparency of LCA results. Assigning a methodological preference to each allocation method of multi-functional processes in the system enables pseudo-statistical propagation of uncertainty due to allocation. Involving stakeholders in determining these methodological preferences allows for participatory approaches. Eventually, this method could be expanded to also cover other ways of dealing with allocation and to other methodological choices in LCA. Show less
