New insights into the environmental performance of perovskite-on-silicon tandem solar cells – a life cycle assessment of industrially manufactured modules

Roffeis, Martin; Kirner, Simon; Goldschmidt, Jan-Christoph; Stannowski, Bernd; Perez, Laura Miranda; Case, Chris; Finkbeiner, Matthias

doi:10.1039/d2se00096b

Cited by 16 publications

(12 citation statements)

References 20 publications

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“…The harmonized value of Roffeis et al ( 2022) is with the 32 kg CO 2 -eq/m 2 PSC module only slightly higher than GFabM+ML with the 29 kg CO 2 -eq/ m 2 PSC module or lower with the 36 kg CO 2 -eq/m 2 PSC module, when excluding the industrial learning since this was not considered in the first LCA study of fab manufactured PSC modules. 26 Applying UpFunMatLCA is a good way to anticipate environmental impacts before knowledge from the fab-scale is available. The process learning modules support a structured and systematic way to upscale the processing energy from the lab to the fab.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…Comparing the results with the real fab data shows that GFabM+ML is the best fit (Figure ). The harmonized value of Roffeis et al (2022) is with the 32 kg CO 2 -eq/m 2 PSC module only slightly higher than GFabM+ML with the 29 kg CO 2 -eq/m 2 PSC module or lower with the 36 kg CO 2 -eq/m 2 PSC module, when excluding the industrial learning since this was not considered in the first LCA study of fab manufactured PSC modules …”

Section: Resultsmentioning

confidence: 99%

“…23−25 Only one study, including real data from an industrial manufacturing line, is available. 26 However, it considers PSC-silicon tandems to face the same challenges as silicon PVs. The benefits or drawbacks of worldwide deployment of PSC modules have still not been assessed.…”

Section: ■ Introductionmentioning

confidence: 99%

“…UpFunMatLCA implements upscaling systematically and comprehensively by modeling prospective technology development using three upscaling mechanisms: (1) process learning, upscaling of the manufacturing demand; (2) material learning, anticipating prospective materials and linked processes; and (3) external developments to model temporal changes of the background system. However, previous PSC’s LCAs exclude upscaling as described above or include upscaling not comprehensibly or systematically. − Only one study, including real data from an industrial manufacturing line, is available . However, it considers PSC-silicon tandems to face the same challenges as silicon PVs.…”

Section: Introductionmentioning

confidence: 99%

“…We introduce a novel interpretation indicator called environmental break-even time to define when the upscaled PSC’s environmental performance achieves benefits over current commercial benchmarks for the evaluation of climate-friendliness and resource efficiency. Finally, we assess the effects of different upscaling scenarios on climate-friendliness and resource efficiency and discuss how well environmental impacts can be anticipated in advance by comparing the upscaled values with the first LCA study using real fab data.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Are Perovskite Solar Cells an Environmentally Sustainable Emerging Energy Technology? Upscaling from Lab to Fab in Life Cycle Assessment

Weyand,

Kawajiri,

Mortan

et al. 2023

ACS Sustainable Chem. Eng.

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Evaluating the environmental sustainability of perovskite solar cells (PSC) as an emerging functional material (FunMat) requires upscaling scenarios to assess environmental impacts adequately and detect possible risks before commercialization. Previous studies took life cycle assessment (LCA) to anticipate the environmental performance of prospective PSC applications and compared it to commercial benchmarks. However, these comparisons are biased, since upscaling has not been addressed at all or comprehensively. We evaluate the climate-friendliness and resource efficiency of PSC modules upscaled using UpFunMatLCAa scheme to generate systematically upscaling scenarios in prospective LCA. Upscaling manufacturing demand is performed using three-stage process learning: (1) technological learning, (2) size scaling, and (3) industrial learning. The upscaled PSC modules are deployed worldwide, including country-specific irradiation and PSC’s temperature effect. The novel indicator, the environmental break-even time, indicates when the upscaled PSC’s environmental impacts offset country-specific electricity mixes. If PSC modules achieve five years lifetime, they will be applied as climate-friendly energy technology in almost all worldwide countries. Demonstrating resource efficiency required the inclusion of additional material learning. UpFunMatLCA does not provide definitive environmental impacts but strives to generate realistic scenarios based on current knowledge to drive the future environmental sustainability of PSC applications.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%