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

机译:基于代理的建模可以在多大程度上增强生命周期评估?基于文献综述的答案

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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.
机译:生命周期评估(LCA)已证明其对与商品和服务生产相关的整个价值链建模的价值。但是,由于社会经济环境的不确定性,对消费体系(例如产品的使用阶段)进行建模仍然具有挑战性。基于代理的模型(ABM)可以通过改进LCA中的消费系统建模来减少这些不确定性。到目前为止,尚无关于ABM如何对LCA中的行为驱动建模做出贡献的系统研究。本文旨在通过审查结合了这两种工具的所有论文来弥补这一差距。重点研究了18个案例研究,根据LCA国际标准四个阶段得出的标准进行了分析。还选择了特定于基于代理的模型和两个工具的耦合的标准,例如耦合的类型和程度。结果表明,为了将具有过多不确定性的前景系统建模,由行为驱动的使用阶段,局部可变性,新兴技术,ABM已与LCA耦合,无法探索情景并支持结果建模。前景库存数据主要是在使用阶段从ABM收集的。通过这次审查,我们确定了在每个LCA阶段ABM的潜在好处:(i)场景探索,(ii)前景清单数据收集,(iii)时间和/或空间动力学模拟,以及(iv)数据解释和交流。此外,还提供了在目标和范围阶段如何选择耦合类型和耦合程度的方法学指导。最后,已经确定了具有挑战性的LCA研究领域,可以从基于代理的方法中受益,包括清单和影响评估阶段的行为驱动动态。 (C)2019 Elsevier Ltd.保留所有权利。

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