Strategic evaluation of environmental metrics : making use of life cycle inventories

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 1998.Includes bibliographical references (p. 203-208).This thesis explores the consequences of accepting the fact that the application of LCA to design evaluation cannot be a purely objective process, and develops a method for evaluating design alternatives based upon their life cycle inventories, called "Explicit LCA" (XLCA). The utility of current LCA methods for comparing designs is limited for one of two reasons: ( 1) they are incomplete, in that they do not actually identify an overall best design, or (2) while they develop cardinal metrices of environmental performance, their results are difficult to support because the inevitable assumptions and judgments they employ are inaccessible. XL.CA is different in that it strives to rank alternatives based on a more transparent analysis. XL.CA employs a set of models with the following features: ( 1) A life cycle inventory model exposes the subjective elements of accounting for material recycling. (2) An impact model translates inventories into impacts based on a well-defined set of steps from release to environmental changes to impacts on human health, the economy, and the natural environment. (3) Impacts are evaluated using standard welfare analysis, yielding monetary metrics for total environmental damage. These models are intended to be applied by environmental specialists, who have the time and expertise to analyze the factors that are critical to the decision at hand. This thesis presents two case studies in which XLCA is used to compare designs for automotive body structures. The cases demonstrate how XLCA can rank designs from a discrete set or determine that designs are indistinguishable. The key to the value of this approach is that the robustness of rankings can be systematically tested. A "bottom-up" analysis involves delving into the models and exploring the implications of reasonable variations in model parameters on rankings. A "top-down" analysis involves validating the variations in model parameters against national emissions and monetary quantities. These cases suggest the following conclusions: First, the scientific and the subjective elements of the analysis need to be exposed so that users can test the implications of a range of judgments upon the analytical results. Second, an explicit method such as XL.CA can help product developers to rank alternatives with increased confidence.by Samuel Albert Newell.Ph.D