A Stochastic Approach to Model Dynamic Systems in Life Cycle Assessment
A Stochastic Approach to Model Dynamic Systems in Life Cycle Assessment
复制标题
DOI：
10.1111/j.1530-9290.2012.00531.x
复制
发表时间：
2013-06-01
影响因子：
5.9
通讯作者：
Alfaro, Jose
中科院分区：
文献类型：
--
作者：
Miller, Shelie A.;Moysey, Stephen;Alfaro, Jose
本文提出了一个框架，以评估新兴系统的生命周期评估（LCA）。目前的生命周期评价方法对已建立的系统是有效的;然而，缺乏数据往往会抑制对未来产品或过程的有力分析，而这些产品或过程可能从生命周期信息中获益最多。在许多情况下，系统的生命周期清单（LCI）可以根据其开发路径而变化。对新兴系统进行建模可以洞察可能的趋势，并更好地了解未来情景对LCA结果的影响。建议的框架使用贝叶斯概率模型的技术采用。该方法提出了一种独特的方法来模拟系统的演变，可以独立使用或在一个基于代理的模型（ABM）的上下文中。该框架将情景建模与生命周期数据相结合，分析决策模式随时间的变化对生命周期评价的影响，从而使生命周期评价更具鲁棒性和动态性。潜在的用途包括研究不断发展的城市不断变化的城市新陈代谢，了解可再生能源技术的发展，确定物质流在空间和时间上的变化，以及预测开发产品的工业网络。一个柳枝稷能源案例说明了这种方法。
This article presents a framework to evaluate emerging systems in life cycle assessment (LCA). Current LCA methods are effective for established systems; however, lack of data often inhibits robust analysis of future products or processes that may benefit the most from life cycle information. In many cases the life cycle inventory (LCI) of a system can change depending on its development pathway. Modeling emerging systems allows insights into probable trends and a greater understanding of the effect of future scenarios on LCA results. The proposed framework uses Bayesian probabilities to model technology adoption. The method presents a unique approach to modeling system evolution and can be used independently or within the context of an agent-based model (ABM). LCA can be made more robust and dynamic by using this framework to couple scenario modeling with life cycle data, analyzing the effect of decision-making patterns over time. Potential uses include examining the changing urban metabolism of growing cities, understanding the development of renewable energy technologies, identifying transformations in material flows over space and time, and forecasting industrial networks for developing products. A switchgrass-to-energy case demonstrates the approach.