@article{Lesage:15254,
      recid = {15254},
      author = {Lesage, Pascal and Mutel, Chris and Schenker, Urs and  Margni, Manuele},
      title = {Are there infinitely many trucks in the technosphere, or  exactly one? How independent sampling of instances of unit  processes affects uncertainty analysis in LCA},
      publisher = {Springer},
      journal = {The International Journal of Life Cycle Assessment},
      address = {Berlin, Germany. 2019-02},
      number = {ARTICLE},
      pages = {13 p.},
      note = {MARGNI, Manuele est un chercheur à la HEI-VS, HES-SO  depuis 2020.},
      abstract = {Product systems use the same unit process models to  represent distinct but similar activities. This notably  applies to activities in cyclic dependency relationships  (or “feedback loops”) that are required an infinite number  of times in a product system. The study aims to test the  sensitivity of uncertainty results on the assumption made  concerning these different instances of the same  activities. The default assumption assumes homogeneous  production, and the same parameter values are sampled for  all instances (e.g., there is one truck). The alternative  assumption is that every instance is distinct, and  parameter values are independently sampled for different  instances of unit processes (e.g., there are infinitely  many trucks). Intuitively, sampling the same values for  each instance of a unit process should result in more  uncertain results. The results of uncertainty analyses  carried out under either assumption are compared. To  simulate models where each instance of a unit process is  independent, we convert network models to acyclic LCI  models (tree models). This is done three times: (1) for a  very simple product system, to explain the methodology; (2)  for a sample product system from the ecoinvent database,  for illustrative purposes; and (3) for thousands of product  systems from ecoinvent databases. The uncertainty of  network models is indeed greater than that of corresponding  tree models. This is shown mathematically for the  analytical approximation method to uncertainty propagation  and is observed for Monte Carlo simulations with very large  numbers of iterations. However, the magnitude of the  difference in indicators of dispersion is, for the  ecoinvent product systems, often less than a factor of 1.5.  In few extreme cases, indicators of dispersion are  different by a factor of 4. Monte Carlo simulations with  smaller numbers of iterations sometimes give the opposite  result. Given the small magnitude of the difference, we  believe that breaking away from the default approach is  generally not warranted. Indeed, (1) the alternative  approach is not more robust, (2) the current default  approach is conservative, and (3) there are more pressing  challenges for the LCA community to meet. This being said,  the study focused on ecoinvent, which should normally be  used as a background database. The difference in dispersion  between the two approaches may be important in some  contexts, and calculating the uncertainty of tree models as  a sensitivity analysis could be useful.},
      url = {http://arodes.hes-so.ch/record/15254},
      doi = {https://doi.org/10.1007/s11367-018-1519-8},
}