A systematic approach to assess the environmental impact of emerging technologies: A case study for the GHG footprint of CIGS solar photovoltaic laminate

Abstract

Estimating the environmental impact of emerging technologies at different stages of development is uncertain but necessary to guide investment, research, and development. Here, we propose a systematic procedure to assess the future impacts of emerging technologies. In the technology development stage (technology readiness level < 9), the recommended experience mechanisms to take into account are (a) process changes, (b) size scaling effects, and (c) process synergies. These developments can be based on previous experience with similar technologies or quantified through regression or engineering dimension calculations. In the industrial development phase, (d) industrial learning, based on experience curves or roadmaps, and (e) external developments should be included. External developments, such as changes in the electricity mix can be included with information from integrated assessment models. We show the applicability of our approach with the greenhouse gas (GHG) footprint evaluation for the production of copper indium gallium (di)selenide (CIGS) photovoltaic laminate. We found that the GHG footprint per kilowatt peak of produced CIGS laminate is expected to decrease by 83% going from pilot to mature industrial scale production with the largest decrease being due to expected process changes. The feasibility of applying our approach in practice would greatly benefit from the development of a database containing information on size scaling and experience rates for a wide variety of materials, products, and technologies.

Non-linearity in the Life Cycle Assessment of Scalable and Emerging Technologies

“…What the present work suggests is that the uncertainties introduced by non-linear effects can be substantial and should be explicitly considered in the life cycle assessment of technologies that are emerging and not yet operating at scale. These findings confirm previous research on the uncertainties in the LCA of emerging technologies (Lacirignola et al, 2017;van der Hulst et al, 2020).…”

“…The approach indeed has limitations as it is reasonable to assume that when upscaling a specific activity, some inputs would be replaced by others, for example when changing the material composition of specific components of the technology or shifting from one source of energy to another. This has been observed in previous studies, for example by Blanco et al (2020) andvan der Hulst et al (2020). The simulation performed here was not able to take this sort of model-uncertainty into account and the conclusions provided here should be interpreted considering such limitation.…”

Scheme for generating upscaling scenarios of emerging functional materials based energy technologies in prospective LCA (UpFunMatLCA)

“…The general framework of van der Hulst et al. (2020) is specified regarding selecting upscaling mechanisms and modeling these mechanisms using the introduced upscaling modules, particularly for emerging FunMats. In this context, we decided to distinguish between material and process learning mechanisms instead of using a chronological distinction of technology maturity from low to high.…”

Life Cycle and Techno-Economic Assessment Templates for Emerging Carbon Management Technologies

“…There are numerous other valuable studies on approaches to prospective assessment of emerging technologies (Gavankar et al, 2015;Miller and Keoleian, 2015;Sharp and Miller, 2016;Lacirignola et al, 2017;Cucurachi et al, 2018;Thomassen et al, 2019;Bergerson et al, 2020;Hung et al, 2020;Thonemann et al, 2020;van der Hulst et al, 2020). However, many include advanced probabilistic analysis, specific advice for particular technological pathways, or the use of advanced software applications, all of which are outside the scope of this study.…”