Statistical Algorithms for Sustainability Measurement and Decision Making

Abstract

Sustainability assertions are holistic in nature because they represent commentaries on the impacts of process and products on three dimensions of sustainability: environmental, economic, and societal. A large number of indicators (or metrics) may be used to observe the sustainability behavior of a process or product system. Because of the complex way these indicators interact with each other in influencing system performance, it is useful to construct a holistic measure to observe sustainability performance. The Euclidean distance, composed of the indicator values representing a system, was introduced as such a measure, and has been called the sustainability footprint. In this chapter detailed computations are shown on a test system to illustrate how the sustainability footprint is calculated and how it is used to compare among competing alternatives of a system in terms of sustainability. This method based on Euclidean distance is compared with other proposed methods for indicator aggregation, such as Vector Space Theory, Canberra distance, zCanberra distance, and Mahalanobis distance. In addition, two other objectives are achieved. First, by applying the principal component analysis, the redundant indicators are identified, and second, the rank order of the indicators in terms of their contribution to sustainability is calculated. This information will be helpful in improving sustainability performance at the redesign stage based on the relative contributions of the indicators and their controllability.

“The straight line, a respectable optical illusion which ruins many a man.”

—Victor Hugo, Les Misérables

“The majority merely disagreed with other people's proposals, and, as so often happens in these disasters, the best course always seemed the one for which it was now too late.”

—Tacitus