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Methodology development for upscaling in prospective LCA: The case of perovskite solar cells as emerging functional material-based energy technology

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
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.

Alternative Abstract:
Alternative abstract Language

In dieser kumulativen Dissertation wurde eine neuartige Methodik für das Upscaling in der prospektiven Ökobilanzierung (LCA) anhand eines Fallbeispiels entwickelt, um die Umweltverträglichkeit innovativer Technologien vom aktuellen auf ein zukünftiges Entwicklungsstadium abzuschätzen. Als Fallbeispiel wurden Perowskit-Solarzellen (PSC) als innovative Energietechnologie auf der Basis von Funktionsmaterialien (FunMat) untersucht. Das Upscaling in der Ökobilanz soll Forschungsgruppen, Technologieentwicklern, Planern und politischen Entscheidungsträgern dabei unterstützen, verantwortungsvolle Forschungsaktivitäten zu priorisieren und proaktiv voranzutreiben, um unbeabsichtigte Folgen bei Innovationen frühzeitig zu erkennen und zu vermeiden. Die Methodenentwicklung erfolgte in drei Publikationen und anhand der vier folgenden Schritte: Erstens wurde eine Meta-Analyse durchgeführt, um die Herausforderungen des Upscalings von Ökobilanzen für PSC und weiterer innovativer Photovoltaik-Technologien zu verstehen und zu definieren (Publikation 1). Zweitens wurde ein Upscaling-Schema namens UpFunMatLCA entwickelt, um Upscaling-Szenarien für prospektive Ökobilanzen zu erstellen. Die Upscaling-Szenarien wurden qualitativ und quantitativ mittels sogenannter Upscaling-Mechanismen und -Module modelliert (Publikation 2). Drittens wurde für das Fallbeispiel eine Sachbilanz-Datenbank (PSC-LCI-Datenbank) entwickelt. Diese ermöglicht die Anwendung und Validierung von UpFunMatLCA zur Hochskalierung der Umweltverträglichkeit von PSCs von Laborsamples zum kommerziellen Einsatz als PV-Module. Zuletzt wurde die Ökologische Break-Even-Zeit (e-BET) als neuer Indikator zur Interpretation von Ökobilanzergebnissen eingeführt. E-BET legt einen Schwellenwert fest, ab wann die Umweltleistung der innovativen Technologie, hier der PSCs, Vorteile gegenüber den derzeitigen kommerziellen Benchmarks erzielt (Publikation 3). Die Ergebnisse zeigen, dass die Umweltleistung von PSC im Vergleich zu anderen innovativen PVs und kommerziellen Benchmarks nicht ausreichend durch bisherige LCA-Studien untersucht wurde. Die hohen Umweltauswirkungen der PSC wurden auf die hohe Verarbeitungsenergie ineffizienter Laborgeräte zurückgeführt, die aus einem geringen Technologiereifegrad der PSC resultiert. Upscaling-Szenarien bieten eine Methode zur Integration der Technologieentwicklung in prospektive LCA durch Projektion möglicher Entwicklungspfade. Die Entwicklungspfade von PSC umfassen Kombinationen der drei technologischen Mechanismen während des Upscalings: A) Prozesslernen, B) Materiallernen und C) externe Entwicklungen. Prozesslernen ist der Schlüsselmechanismus für die Hochskalierung von Verarbeitungsenergien vom Labor bis zur Fertigung in industriellen Produktionsfabriken (from lab to fab), die am meisten zu energiebezogenen Umweltauswirkungen wie der globalen Erwärmung beitragen. Materialbezogene Umweltauswirkungen wie die Ressourcennutzung erfordern die Einbeziehung von zusätzlichem Materiallernen in die Bewertung. Das Upscaling in der prospektiven Ökobilanz liefert keine endgültigen Ergebnisse, sondern zielt darauf ab, realistische Szenarien auf der Grundlage des aktuellen Wissensstandes zu erstellen, um die künftige Umweltverträglichkeit innovativer Technologien zu fördern. Die entwickelte Methodik leistet Pionierarbeit beim Upscaling in der prospektiven Ökobilanzierung, indem sie die theoretischen und praktischen Methoden einer bestimmten Technologiegruppe kombiniert. Sie stellt somit eine wesentliche Vorlage für andere Technologiegruppen dar, um ähnliche Methoden zur Verbesserung und Unterstützung der Ökobilanzergebnisse neuer Technologien im Vergleich zu kommerziellen Benchmarks abzuleiten.

German
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
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)
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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