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

JOURNAL OF CLEANER PRODUCTION

卷:239

Review

Micolier, Alice^1,2  Loubet, Philippe³  Taillandier, Franck⁴  Sonnemann, Guido² 

[1] Univ Bordeaux, CNRS, UMR 5295, I2M, F-33405 Talence, France

[2] Univ Bordeaux, CNRS, UMR 5255, ISM, F-33405 Talence, France

[3] ENSCBP INP Bordeaux, UMR 5255, F-33607 Pessac, France

[4] Aix Marseille Univ, RECOVER, IRSTEA, Aix En Provence, France

关键词: Model coupling;    Consequential LCA;    Use phase;    Human behaviour;    Consumption and production;   

DOI  :  10.1016/j.jclepro.2019.118123

来源: Elsevier

【 摘 要 】

Life 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 socio-economic 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 behavior-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. (C) 2019 Elsevier Ltd. All rights reserved.

【 授权许可】

Free