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PRospective EnvironMental Impact asSEment (premise): A streamlined approach to producing databases for prospective life cycle assessment using integrated assessment models

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  • Sacchi, R.
  • Terlouw, T.
  • Siala, K.
  • Dirnaichner, A.
  • Bauer, C.
  • Cox, B.
  • Mutel, C.
  • Daioglou, V.
  • Luderer, G.

Abstract

Prospective Life Cycle Assessment (pLCA) is useful to evaluate the environmental performance of current and emerging technologies in the future. Yet, as energy systems and industries are rapidly shifting towards cleaner means of production, pLCA requires an inventory database that encapsulates the expected changes in technologies and the environment at a given point in time, following specific socio-techno-economic pathways. To this end, this study introduces premise, a tool to streamline the generation of prospective inventory databases for pLCA by integrating scenarios generated by Integrated Assessment Models (IAM). More precisely, premise applies a number of transformations on energy-intensive activities found in the inventory database ecoinvent according to projections provided by the IAM. Unsurprisingly, the study shows that, within a given socio-economic narrative, the climate change mitigation target chosen affects the performance of nearly all activities in the database. This is illustrated by focusing on the effects observed on a few activities, such as systems for direct air capture of CO2, lithium-ion batteries, electricity and clinker production as well as freight transport by road, in relation to the applied sector-based transformation and the chosen climate change mitigation target. This work also discusses the limitations and challenges faced when coupling IAM and LCA databases and what improvements are to be brought in to further facilitate the development of pLCA.

Suggested Citation

  • Sacchi, R. & Terlouw, T. & Siala, K. & Dirnaichner, A. & Bauer, C. & Cox, B. & Mutel, C. & Daioglou, V. & Luderer, G., 2022. "PRospective EnvironMental Impact asSEment (premise): A streamlined approach to producing databases for prospective life cycle assessment using integrated assessment models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    • Handle: RePEc:eee:rensus:v:160:y:2022:i:c:s136403212200226x
    DOI: 10.1016/j.rser.2022.112311
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    Cited by:

    1. Chen Chris Gong & Falko Ueckerdt & Christoph Bertram & Yuxin Yin & David Bantje & Robert Pietzcker & Johanna Hoppe & Michaja Pehl & Gunnar Luderer, 2023. "Robust CO2-abatement from early end-use electrification under uncertain power transition speed in China's netzero transition," Papers 2312.04332, arXiv.org.
    2. Maes, Ben & Sacchi, Romain & Steubing, Bernhard & Pizzol, Massimo & Audenaert, Amaryllis & Craeye, Bart & Buyle, Matthias, 2023. "Prospective consequential life cycle assessment: Identifying the future marginal suppliers using integrated assessment models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    3. Martin, Nick & Talens-Peiró, Laura & Villalba-Méndez, Gara & Nebot-Medina, Rafael & Madrid-López, Cristina, 2023. "An energy future beyond climate neutrality: Comprehensive evaluations of transition pathways," Applied Energy, Elsevier, vol. 331(C).
    4. Ilyes Tegani & Okba Kraa & Haitham S. Ramadan & Mohamed Yacine Ayad, 2023. "Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory," Energies, MDPI, vol. 16(13), pages 1-23, June.
    5. van den Oever, A.E.M. & Costa, D. & Messagie, M., 2023. "Prospective life cycle assessment of alternatively fueled heavy-duty trucks," Applied Energy, Elsevier, vol. 336(C).
    6. Sacchi, R. & Bauer, C. & Cox, B. & Mutel, C., 2022. "When, where and how can the electrification of passenger cars reduce greenhouse gas emissions?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    7. Moritz Ostermann & Julian Grenz & Marcel Triebus & Felipe Cerdas & Thorsten Marten & Thomas Tröster & Christoph Herrmann, 2023. "Integrating Prospective Scenarios in Life Cycle Engineering: Case Study of Lightweight Structures," Energies, MDPI, vol. 16(8), pages 1-24, April.

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