Normalization in Comparative Life Cycle Assessment to Support Environmental Decision Making: Normalization in Comparative LCA
Normalization in Comparative Life Cycle Assessment to Support Environmental Decision Making: Normalization in Comparative LCA
复制标题
比较生命周期评估的标准化以支持环境决策：比较 LCA 的标准化
DOI：
10.1111/jiec.12549
复制
发表时间：
2017
影响因子：
5.9
通讯作者：
Prado, Valentina
中科院分区：
文献类型：
--
作者：
Cucurachi, Stefano;Seager, Thomas P.;Prado, Valentina
Although it is now widely accepted that the proper perspective for analysis of environmental decisions is the life cycle perspective (ISO 2006), existing practices of life cycle assessment (LCA) still lack connection to structured approaches for“... it is only by providing context through normalization that data can be made meaningful for decisions. That is, better data alone is insufficient for extracting meaning.” environmental decision making. As a consequence, comparative LCA studies typically leave decision makers to confront complex decision problems without the aid of the analytical tools necessary to make trade-offs clear.It is only in the normalization and weighting steps of LCA (ISO 2006) that decision-analytic requirements can be met. However, formal decision techniques are here often ignored. A typical practice of the normalization step consists of dividing characterized results of a product system by the characterized total emissions within a political or geographical boundary (eg, European Union, United States, or global) or industry for a certain period of time (eg, 1 year). This approach should allow understanding the magnitude of emissions that can be attributed to the production of a marginal functional unit, as compared to a reference system, and is often referred to as external normalization (Norris 2001). Although the results are intended to be reproducible and scientifically defensible, years of experience with external normalization have revealed several obstacles to these goals. In particular, numerous studies documented the practical difficulties of obtaining, maintaining, and repairing reliable external normalization reference data sets (see Pizzol et al.[2016], among others). However, even perfect information and complete normalization references would not overcome more-profound objections to external normalization. Recent examinations reveal
