Towards a Simplified LCA Tool: Minimising the Life Cycle Environmental Impact at the Early Stages of Building Design

Abstract

Minimising the direct and indirect environmental impact of buildings over their life cycle has become a growing concern worldwide. Life Cycle Assessment (LCA) has been effective in addressing this goal. However, it is constrained by several factors. First, little-detailed information to calculate LCA is available at the early stages of building design when the most important decisions are made. Second, the LCA method is too complex to apply at this stage, both in terms of the knowledge required to operate the conventional calculation software and for the inputs. Streamlining the LCA application at the early design phase has been hampered by the lack of reliable, available, comparable and consistent information on the life cycle environmental performance of buildings. Most of the previous LCA studies are based on a quantification method that requires the use of complex tools and an enormous amount of data and is best employed when the building is complete. The time and expense involved in this type of analysis make it unfeasible for small or individual projects. This thesis is the first step toward the development of a tool that allows designers to employ LCA in the early design stages of a building. It aims to allow designers to more easily apply the logic of LCA by minimising data requirements and identifying the most effective parameters that promise to make the most difference. A review of relevant literature has further identified the external criteria that are most closely associated with the effectiveness of LCA, namely: climate, location, building type and building lifetime. These parameters have been used to define a system boundary for generating results that reflect the characteristics of a specific building. Common rules have been extracted from the results of previous LCA studies of building envelopes and classified in relation to the most effective external criteria. The process shows that the results of quantitative LCA provide a ready-to-use database.