Materials

Scientific publication

Assessing the sustainability of emerging technologies: A probabilistic LCA method applied to advanced photovoltaics

Publication from Materials
Licht und Optische Technologien

C. F .Blanco, S. Cucurachi, J. B. Guinéea, M. G.Vijver, W. J.G.M. Peijnenburg, R. Trattnig, R. Heijungs

Journal of Cleaner Production Volume 259, 120968, https://doi.org/10.1016/j.jclepro.2020.120968, 6/2020

Abstract:

A key source of uncertainty in the environmental assessment of emerging technologies is the unpredictable manufacturing, use, and end-of-life pathways a technology can take as it progresses from lab to industrial scale. This uncertainty has sometimes been addressed in life cycle assessment (LCA) by performing scenario analysis. However, the scenario-based approach can be misleading if the probabilities of occurrence of each scenario are not incorporated. It also brings about a practical problem; considering all possible pathways, the number of scenarios can quickly become unmanageable. We present a modelling approach in which all possible pathways are modelled as a single product system with uncertain processes. These processes may or may not be selected once the technology reaches industrial scale according to given probabilities. An uncertainty analysis of such a system provides a single probability distribution for each impact score. This distribution accounts for uncertainty about the product system’s final configuration along with other sources of uncertainty. Furthermore, a global sensitivity analysis can identify whether the future selection of certain pathways over others will be of importance for the variance of the impact score. We illustrate the method with a case study of an emerging technology for front-side metallization of photovoltaic cells.

Keywords: Life cycle assessment Uncertainty Global sensitivity analysis Emerging technologies LCA Sustainability assessment

Url: https://www.sciencedirect.com/science/article/pii/S0959652620310155?casa_token=B2opNGGTCWoAAAAA:cun7wz8iVHlar0N4ncdsa1-4O0URwJRZ8m9vA_A0f9F4VpSoAGgPPsWhSA37l6mYXgBENoZiquNl