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

Carlos F. Blanco*, Stefano Cucurachi, Jeroen B. Guinée, Martina G. Vijver, Willie J.G.M. Peijnenburg, Roman Trattnig, Reinout Heijungs

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


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.

Original languageEnglish
Article number120968
Pages (from-to)1-8
Number of pages8
JournalJournal of Cleaner Production
Early online date7 Mar 2020
Publication statusPublished - 20 Jun 2020


  • Emerging technologies
  • Global sensitivity analysis
  • LCA
  • Life cycle assessment
  • Sustainability assessment
  • Uncertainty

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