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To what extent can agent-based modelling enhance a life cycle assessment? Answers based on a literature review

hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorMICOLIER, Alice
hal.structure.identifierInstitut des Sciences Moléculaires [ISM]
dc.contributor.authorLOUBET, Philippe
hal.structure.identifierRisques, Ecosystèmes, Vulnérabilité, Environnement, Résilience [RECOVER]
dc.contributor.authorTAILLANDIER, Franck
hal.structure.identifierInstitut des Sciences Moléculaires [ISM]
dc.contributor.authorSONNEMANN, Guido
dc.date.accessioned2021-05-14T09:39:35Z
dc.date.available2021-05-14T09:39:35Z
dc.date.issued2019-12
dc.identifier.issn0959-6526
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/76506
dc.description.abstractEnLife cycle assessment (LCA) has proven its worth in modelling the entire value chain associated with the production of goods and services. However, modelling the consumption system, such as the use phase of a product, remains challenging due to uncertainties in the socioeconomic context. Agent-based models (ABMs) can reduce these uncertainties by improving the consumption system modelling in LCA. So far, no systematic study is available on how ABM can contribute towards a behaviour-driven modelling in LCA. This paper aims at filing this gap by reviewing all papers coupling both tools. A focus is carried out on 18 case studies which are analysed according to criteria derived from the four phases of LCA international standards. Criteria specific to agent-based models and the coupling of both tools, such as the type and degree of coupling, have also been selected. The results show that ABMs have been coupled to LCA in order to model foreground systems with too many uncertainties arising from a behaviour-driven use phase, local variabilities, emerging technologies, to explore scenarios and to support consequential modelling. Foreground inventory data have been mainly collected from ABM at the use phase. From this review, we identified the potential benefits from ABM at each LCA phase: (i) scenario exploration, (ii) foreground inventory data collection, (iii) temporal and/or spatial dynamics simulation, and (iv) data interpretation and communication. Besides, methodological guidance is provided on how to choose the type and degree of coupling during the goal and scope phase. Finally, challenging LCA areas of research that could benefit from the agent-based approach to include behaviour-driven dynamics at the inventory and impact assessment phase have been identified.
dc.language.isoen
dc.publisherElsevier
dc.title.enTo what extent can agent-based modelling enhance a life cycle assessment? Answers based on a literature review
dc.typeArticle de revue
dc.identifier.doi10.1016/j.jclepro.2019.118123
dc.subject.halSciences de l'ingénieur [physics]/Génie civil
dc.subject.halSciences de l'ingénieur [physics]/Génie civil/Eco-conception
bordeaux.journalJournal of Cleaner Production
bordeaux.page118123
bordeaux.volume239
bordeaux.hal.laboratoriesInstitut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.institutionINRAE
bordeaux.institutionArts et Métiers
bordeaux.peerReviewedoui
hal.identifierhal-02358416
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02358416v1
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