Weyand, Steffi (2023)
Methodology development for upscaling in prospective LCA: The case of perovskite solar cells as emerging functional material-based energy technology.
Technische Universität Darmstadt
doi: 10.26083/tuprints-00024546
Ph.D. Thesis, Primary publication, Publisher's Version
Abstract
This cumulative dissertation developed a novel upscaling methodology for prospective life cycle assessment (LCA) to project environmental performances of emerging technologies from a current to future development stage using perovskite solar cells (PSC) as a case study for an emerging functional material (FunMat)-based energy technology. Beyond the case study, upscaling in LCA is essential to assist research groups, technology developers, planners, and policymakers prioritize responsible research activities proactively and prevent unintended consequences early in innovations. This methodology development was carried out in three publications and consisted of four steps: First, a meta-analysis was conducted to understand and define the upscaling challenges in LCAs of PSC and further emerging photovoltaic technologies (PVs) (Publication 1). Second, an upscaling scheme called UpFunMatLCA was developed for generating upscaling scenarios in prospective LCA. The upscaling scenarios were modeled qualitatively and quantitatively using upscaling mechanisms and modules as predefined development pathways (Publication 2). Third, a PSC-LCI-database evolved from applying and validating UpFunMatLCA in the case study of evaluating the environmental sustainability of PSCs upscaled from lab samples to commercial deployment as PV modules. Last, the environmental break-even time (e-BET) was introduced as a novel indicator for interpreting when the upscaled PSC's environmental performance achieves benefits over current commercial benchmarks (Publication 3). The results highlight that the PSC’s environmental performance cannot be adequately demonstrated from previous LCA studies compared to other emerging PVs and commercial benchmarks. The PSC’s high environmental impacts were attributed to high processing energies of inefficient laboratory (lab) equipment resulting from a low technology maturity. Upscaling scenarios provide a method to integrate technology development into prospective LCA by projecting potential development pathways. PSC’s development pathways include combinations of the three technological mechanisms during upscaling: A) process learning, B) material learning, and C) external developments. Process learning is the key mechanism for upscaling processing energies from lab to fabrication in industrial manufacturing factories (fab) as the main contributor to energy-related impacts like global warming. Material-related impacts like resource use require including additional material learning in the assessment. Upscaling in prospective LCA does not provide definitive environmental impacts but strives to generate realistic scenarios based on current knowledge to drive the future environmental sustainability of emerging technologies. The developed methodology pioneers upscaling in prospective LCA by combining a specific technology group's theoretical and practical methods. It represents, thus, an essential template for other technology groups to transfer similar upscaling methods for increasing and supporting the comprehensiveness of the LCA results on emerging technologies compared to commercial benchmarks.
Item Type: | Ph.D. Thesis | ||||
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Erschienen: | 2023 | ||||
Creators: | Weyand, Steffi | ||||
Type of entry: | Primary publication | ||||
Title: | Methodology development for upscaling in prospective LCA: The case of perovskite solar cells as emerging functional material-based energy technology | ||||
Language: | English | ||||
Referees: | Schebek, Prof. Dr. Liselotte ; Rohde, Prof. Dr. Clemens ; Sonnemann, Prof. Dr. Guido | ||||
Date: | 27 October 2023 | ||||
Place of Publication: | Darmstadt | ||||
Collation: | 126 Seiten in verschiedenen Zählungen | ||||
Refereed: | 10 July 2023 | ||||
DOI: | 10.26083/tuprints-00024546 | ||||
URL / URN: | https://tuprints.ulb.tu-darmstadt.de/24546 | ||||
Abstract: | This cumulative dissertation developed a novel upscaling methodology for prospective life cycle assessment (LCA) to project environmental performances of emerging technologies from a current to future development stage using perovskite solar cells (PSC) as a case study for an emerging functional material (FunMat)-based energy technology. Beyond the case study, upscaling in LCA is essential to assist research groups, technology developers, planners, and policymakers prioritize responsible research activities proactively and prevent unintended consequences early in innovations. This methodology development was carried out in three publications and consisted of four steps: First, a meta-analysis was conducted to understand and define the upscaling challenges in LCAs of PSC and further emerging photovoltaic technologies (PVs) (Publication 1). Second, an upscaling scheme called UpFunMatLCA was developed for generating upscaling scenarios in prospective LCA. The upscaling scenarios were modeled qualitatively and quantitatively using upscaling mechanisms and modules as predefined development pathways (Publication 2). Third, a PSC-LCI-database evolved from applying and validating UpFunMatLCA in the case study of evaluating the environmental sustainability of PSCs upscaled from lab samples to commercial deployment as PV modules. Last, the environmental break-even time (e-BET) was introduced as a novel indicator for interpreting when the upscaled PSC's environmental performance achieves benefits over current commercial benchmarks (Publication 3). The results highlight that the PSC’s environmental performance cannot be adequately demonstrated from previous LCA studies compared to other emerging PVs and commercial benchmarks. The PSC’s high environmental impacts were attributed to high processing energies of inefficient laboratory (lab) equipment resulting from a low technology maturity. Upscaling scenarios provide a method to integrate technology development into prospective LCA by projecting potential development pathways. PSC’s development pathways include combinations of the three technological mechanisms during upscaling: A) process learning, B) material learning, and C) external developments. Process learning is the key mechanism for upscaling processing energies from lab to fabrication in industrial manufacturing factories (fab) as the main contributor to energy-related impacts like global warming. Material-related impacts like resource use require including additional material learning in the assessment. Upscaling in prospective LCA does not provide definitive environmental impacts but strives to generate realistic scenarios based on current knowledge to drive the future environmental sustainability of emerging technologies. The developed methodology pioneers upscaling in prospective LCA by combining a specific technology group's theoretical and practical methods. It represents, thus, an essential template for other technology groups to transfer similar upscaling methods for increasing and supporting the comprehensiveness of the LCA results on emerging technologies compared to commercial benchmarks. |
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Status: | Publisher's Version | ||||
URN: | urn:nbn:de:tuda-tuprints-245460 | ||||
Classification DDC: | 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering 600 Technology, medicine, applied sciences > 624 Civil engineering and environmental protection engineering |
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Divisions: | 13 Department of Civil and Environmental Engineering Sciences 13 Department of Civil and Environmental Engineering Sciences > Institute IWAR 13 Department of Civil and Environmental Engineering Sciences > Institute IWAR > Material Flow Management and Resource Economy Exzellenzinitiative Exzellenzinitiative > Graduate Schools Exzellenzinitiative > Graduate Schools > Graduate School of Energy Science and Engineering (ESE) |
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Date Deposited: | 27 Oct 2023 11:59 | ||||
Last Modified: | 30 Oct 2023 07:29 | ||||
PPN: | |||||
Referees: | Schebek, Prof. Dr. Liselotte ; Rohde, Prof. Dr. Clemens ; Sonnemann, Prof. Dr. Guido | ||||
Refereed / Verteidigung / mdl. Prüfung: | 10 July 2023 | ||||
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