Rogers Kristin, Seager Thomas P
Ecological Science and Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, Indiana 47907, USA.
Environ Sci Technol. 2009 Mar 15;43(6):1718-23. doi: 10.1021/es801123h.
Life cycle impact assessment (LCIA) involves weighing trade-offs between multiple and incommensurate criteria. Current state-of-the-art LCIA tools typically compute an overall environmental score using a linear-weighted aggregation of characterized inventory data that has been normalized relative to total industry, regional, or national emissions. However, current normalization practices risk masking impacts that may be significant within the context of the decision, albeit small relative to the reference data (e.g., total U.S. emissions). Additionally, uncertainty associated with quantification of weights is generally very high. Partly for these reasons, many LCA studies truncate impact assessment at the inventory characterization step, rather than completing normalization and weighting steps. This paper describes a novel approach called stochastic multiattribute life cycle impact assessment (SMA-LCIA) that combines an outranking approach to normalization with stochastic exploration of weight spaces-avoiding some of the drawbacks of current LCIA methods. To illustrate the new approach, SMA-LCIA is compared with a typical LCIA method for crop-based, fossil-based, and electric fuels using the Greenhouse gas Regulated Emissions and Energy Use in Transportation (GREET) model for inventory data and the Tool for the Reduction and Assessment of Chemical and other Environmental Impacts (TRACI) model for data characterization. In contrast to the typical LCIA case, in which results are dominated by fossil fuel depletion and global warming considerations regardless of criteria weights, the SMA-LCIA approach results in a rank ordering that is more sensitive to decisionmaker preferences. The principal advantage of the SMA-LCIA method is the ability to facilitate exploration and construction of context-specific criteria preferences by simultaneously representing multiple weights spaces and the sensitivity of the rank ordering to uncertain stakeholder values.
生命周期影响评估（LCIA）涉及权衡多个不可通约的标准之间的利弊。当前最先进的LCIA工具通常使用已相对于行业总量、区域或国家排放进行归一化的特征化清单数据的线性加权聚合来计算总体环境得分。然而，当前的归一化做法可能会掩盖在决策背景下可能很重要的影响，尽管相对于参考数据（例如美国总排放量）而言较小。此外，与权重量化相关的不确定性通常非常高。部分由于这些原因，许多生命周期评估（LCA）研究在清单特征化步骤就截断影响评估，而不是完成归一化和加权步骤。本文描述了一种名为随机多属性生命周期影响评估（SMA-LCIA）的新方法，该方法将一种优势排序归一化方法与权重空间的随机探索相结合，避免了当前LCIA方法的一些缺点。为了说明这种新方法，使用温室气体排放与交通能源使用（GREET）模型获取清单数据，并使用化学及其他环境影响减少与评估工具（TRACI）模型进行数据特征化，将SMA-LCIA与基于作物、化石燃料和电力燃料的典型LCIA方法进行比较。与典型的LCIA情况不同，在典型LCIA中，无论标准权重如何，结果都主要由化石燃料消耗和全球变暖因素主导，而SMA-LCIA方法产生的排序对决策者偏好更为敏感。SMA-LCIA方法的主要优点是能够通过同时表示多个权重空间以及排序对不确定的利益相关者价值观的敏感性，促进对特定背景标准偏好的探索和构建。
