Cucurachi S, Borgonovo E, Heijungs R
Institute of Environmental Sciences (CML), Leiden University, P.O. Box 9518,2300, RA, Leiden, The Netherlands.
Bren School of Environmental Sciences and Management, University of California, Santa Barbara, USA.
Risk Anal. 2016 Feb;36(2):357-77. doi: 10.1111/risa.12443. Epub 2015 Nov 23.
The life cycle assessment (LCA) framework has established itself as the leading tool for the assessment of the environmental impact of products. Several works have established the need of integrating the LCA and risk analysis methodologies, due to the several common aspects. One of the ways to reach such integration is through guaranteeing that uncertainties in LCA modeling are carefully treated. It has been claimed that more attention should be paid to quantifying the uncertainties present in the various phases of LCA. Though the topic has been attracting increasing attention of practitioners and experts in LCA, there is still a lack of understanding and a limited use of the available statistical tools. In this work, we introduce a protocol to conduct global sensitivity analysis in LCA. The article focuses on the life cycle impact assessment (LCIA), and particularly on the relevance of global techniques for the development of trustable impact assessment models. We use a novel characterization model developed for the quantification of the impacts of noise on humans as a test case. We show that global SA is fundamental to guarantee that the modeler has a complete understanding of: (i) the structure of the model and (ii) the importance of uncertain model inputs and the interaction among them.
生命周期评估(LCA)框架已成为评估产品环境影响的主要工具。由于存在几个共同方面,一些研究已经确定了整合LCA和风险分析方法的必要性。实现这种整合的一种方法是确保在LCA建模中仔细处理不确定性。有人声称,应该更加关注量化LCA各个阶段中存在的不确定性。尽管该主题一直吸引着LCA从业者和专家越来越多的关注,但对可用统计工具仍缺乏理解且使用有限。在这项工作中,我们介绍了一种在LCA中进行全局敏感性分析的协议。本文重点关注生命周期影响评估(LCIA),特别是全局技术对于开发可靠影响评估模型的相关性。我们使用一个为量化噪声对人类的影响而开发的新型特征化模型作为测试案例。我们表明,全局敏感性分析对于确保建模者全面理解以下内容至关重要:(i)模型的结构;(ii)不确定模型输入的重要性及其之间的相互作用。