Multicriteria Decision Analysis and Life Cycle Assessment

Abstract

Assessment of environmental impact is one of the crucial steps in life-cycle assessment (LCA). Current LCA tools typically compute an overall environmental score using a linear-weighted aggregation of normalized inventory data relating to relative performance in impact categories such as global warming, stratospheric ozone depletion, or eutrophication. However, uncertainty associated with quantification of weights is, in general, very high. Moreover, where multiple stakeholder groups are engaged in a particular problem, there may be several different sets of weights that result in disparate scores or ranking. In some cases, the final results may seem entirely dependent upon the relative importance of weights and/or level of data uncertainty. Therefore, we propose to couple life-cycle impact assessment tools with stochastic multiattribute acceptability analysis (SMAA), which is a multicriteria decision analysis (MCDA) technique for exploring uncertain weight spaces. This paper briefly reviews the current state of the art for impact assessment in LCA and compares results using the U.S. Environmental Protection Agency’s TRACI model with the SMAA approach for transportation energy alternatives with uncertain preference information. In both cases, life-cycle inventories are compiled from Argonne National Labs’ GREET model. In the typical life-cycle impact assessment (LCIA), case results are based on the total environmental score, allowing dissimilar impacts to be added together, which correlates rank to the highest normalized impact. However, the SMAA approach balances the criteria more evenly, resulting in a different preference ordering. The difference between the two methods is partly due to stochastic versus point representation of weights. Data normalization, which converts incommensurate impact units to dimensionless quantities for the purpose of aggregation, greatly influences the results.