Environmental Decision-making Using Life Cycle Impact Assessment And Stochastic Multiattribute Decision Analysis: A Case Study On Alternative Transportation Fuels

摘要

Life cycle impact assessment (LCIA) involves weighing tradeoffs 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 decision-maker 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.