Assessing the sustainability of emerging technologies: A probabilistic LCA method applied to advanced photovoltaics. (20th June 2020)

• Record Type: Journal Article
• Authors: Blanco, Carlos F.; Cucurachi, Stefano; Guinée, Jeroen B.; Vijver, Martina G.; Peijnenburg, Willie J.G.M.; Trattnig, Roman; Heijungs, Reinout
• Is Part Of: Journal of cleaner production. Volume 259(2020)
• Publication Date: 2020-06-20
• Subjects: Life cycle assessment -- Uncertainty -- Global sensitivity analysis -- Emerging technologies -- LCA -- Sustainability assessment; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.120968
• Languages: English
• ISSNs: 0959-6526

Abstract

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. Highlights: Emerging technologies have undetermined product systems and are difficult to assess in LCA. A probabilistic method is proposed to incorporate this uncertainty. More robust comparative assessments can be made with incumbent technologies. Sensitivity indices rank the contribution of this uncertainty to variance in impact score.