This report is the consolidated work of an interdisciplinary course project in CEE494/598, CON598, and SOS598, Urban Infrastructure Anatomy and Sustainable Development. In Fall 2012, the course at Arizona State University used sustainability research frameworks and life-cycle assessment methods to evaluate the comprehensive benefits and costs when transit-oriented development is…
This report is the consolidated work of an interdisciplinary course project in CEE494/598, CON598, and SOS598, Urban Infrastructure Anatomy and Sustainable Development. In Fall 2012, the course at Arizona State University used sustainability research frameworks and life-cycle assessment methods to evaluate the comprehensive benefits and costs when transit-oriented development is infilled along the proposed light rail transit line expansion. In each case, and in every variation of possible future scenarios, there were distinct life-cycle benefits from both developing in more dense urban structures and reducing automobile travel in the process.
Comparative life cycle assessment (LCA) evaluates the relative performance of multiple products, services, or technologies with the purpose of selecting the least impactful alternative. Nevertheless, characterized results are seldom conclusive. When one alternative performs best in some aspects, it may also performs worse in others. These tradeoffs among different impact…
Comparative life cycle assessment (LCA) evaluates the relative performance of multiple products, services, or technologies with the purpose of selecting the least impactful alternative. Nevertheless, characterized results are seldom conclusive. When one alternative performs best in some aspects, it may also performs worse in others. These tradeoffs among different impact categories make it difficult to identify environmentally preferable alternatives. To help reconcile this dilemma, LCA analysts have the option to apply normalization and weighting to generate comparisons based upon a single score. However, these approaches can be misleading because they suffer from problems of reference dataset incompletion, linear and fully compensatory aggregation, masking of salient tradeoffs, weight insensitivity and difficulties incorporating uncertainty in performance assessment and weights. Consequently, most LCA studies truncate impacts assessment at characterization, which leaves decision-makers to confront highly uncertain multi-criteria problems without the aid of analytic guideposts. This study introduces Stochastic Multi attribute Analysis (SMAA), a novel approach to normalization and weighting of characterized life-cycle inventory data for use in comparative Life Cycle Assessment (LCA). The proposed method avoids the bias introduced by external normalization references, and is capable of exploring high uncertainty in both the input parameters and weights.
