Implications of Uncertainty and Variability in the Life Cycle Assessment of Pig Production Systems(7 pp)

Goal, Scope and Background Calculating LCA outcomes implies the use of parameters, models, choices and scenarios which introduce uncertainty, as they imperfectly account for the variability of both human and environmental systems. The analysis of the uncertainty of LCA results, and its reduction by an improved estimation of key parameters and through the improvement of the models used to convert emissions into regional impacts, such as eutrophication, are major issues for LCA. Methods In a case study of pig production systems, we propose a simple quantification of the uncertainty of LCA results (intra-system variability) and we explore the inter-system variability to produce more robust LCA outcomes. The quantification of the intra-system uncertainty takes into account the variability of the technical performance (crop yield, feed efficiency) and of emission factors (for NH3, N2O and NO3) and the influence of the functional unit (FU) (kg of pig versus hectare used). For farming systems, the inter-system variability is investigated through differentiating the production mode (conventional, quality label, organic (OA)), and the farmer practices (Good Agricultural Practice (GAP) versus Over Fertilised (OF)), while for natural systems, variability due to physical and climatic characteristics of catchments expected to modify nitrate fate is explored. Results and Conclusion For the eutrophication and climate change impact categories, the uncertainty associated with field emissions contributes more to the overall uncertainty than the uncertainty associated with emissions from livestock buildings, with crop yield and with feed efficiency. For acidification, the uncertainty of emissions from livestock buildings is the single most important contributor to the overall uncertainty. The influence of the FU on eutrophication results is very important when comparing systems with different degrees of intensification such as GAP and OA. Concerning the inter-system variability, differences in farmer practices have a larger effect on eutrophication than differences between production modes. Finally, the physical characteristics of the catchment and the climate strongly affect the results for eutrophication. In conclusion, in this case study, the main sources of uncertainty are in the estimation of emission factors, due both to the variability of environmental conditions and to lack of knowledge (emissions of N2O at the field level), but also in the model used for assessing regional impacts such as eutrophication. Recommendation and Perspective Suitable deterministic simulation models integrating the main controlling variables (environmental conditions, farmer practices, technology used) should be used to predict the emissions of a given system as well as their probabilistic distribution allowing the use of stochastic modelling. Finally, our simulations on eutrophication illustrate the necessity of integrating the fate of pollutants in models of impact assessment and highlight the important margin of improvement existing for the eutrophication impact assessment model.     

Characterisation and Normalisation Factors for Life Cycle Impact Assessment Mined Abiotic Resources Categories in South Africa: The manufacturing of catalytic converter exhaust systems as a case study (10 pp)

Goal and Background Current Life Cycle Impact Assessment (LCIA) procedures have demonstrated certain limitations in the South African manufacturing industry. The aim of this paper is to propose new characterisation and normalisation factors for classified mined abiotic resource depletion categories in the South African context. These factors should reflect the importance of mined resources as they relate to region-specific resource depletion. The method can also be applied to determine global factors. Methods The reserve base (as in 2001) of the most commonly produced minerals in South Africa is used as basis to determine characterisation factors for a non-renewable mineral resources category. The average production of these minerals from 1991 to 2000 is compared to economically Demonstrated and Demonstrated Marginal Reserves (and not ultimate reserves) to obtain the characterisation factors in equivalence units, with platinum as the reference mineral. Similarly, for a non-renewable energy resources category, coal is used in South Africa as equivalent unit as it is the most important fossil fuel for the country. Crude oil and natural gas resources are currently obtained from reserves elsewhere in the world and characterisation factors are therefore determined using global resources and production levels. The normalisation factors are based on the total economic reserves of key South African minerals and world non-renewable energy resources respectively. A case study of the manufacturing of an exhaust system for a standard sedan is used to compare LCIA results for mined abiotic resource categories that are based on current LCIA factors and the new South African factors. Results and Discussion The South African LCIA procedure differs from current methods in that it shows the importance of other mined resources, i.e. iron ore and crude oil, relative to PGMs and coal for the manufacturing life cycle of the exhaust system. With respect to PGMs, the current characterisation factors are based on the concentrations of the metals in the ores and the ultimate reserves, which are erroneous with respect to the actual availability of the mineral resources and the depletion burden placed on these minerals is consequently too high. Conclusions The South African LCIA procedure for mined abiotic resources depletion shows the significance of choosing a method, which is inline with the current situation in the mining industry and its limitations. Recommendations and Outlook It is proposed to similarly investigate the impacts of the use of other natural resource groups. Water, specifically, must receive attention in the characterisation phase of LCIAs in South African LCAs.