Document Type

Theses, Ph.D

Rights

This item is available under a Creative Commons License for non-commercial use only

Disciplines

2. ENGINEERING AND TECHNOLOGY, Civil engineering, Architecture engineering, Construction engineering, Municipal and structural engineering

Publication Details

A thesis submitted to Technological University Dublinin fulfilment of the requirements for the degree of Doctor of Philosophy

Abstract

Life Cycle Assessment (LCA) quantifies the potential environmental impact of a product system throughout its life cycle from raw material extraction, production, manufacture, use and maintenance through to final disposal. The results from LCA studies are often used to support decision-making processes andpolicy development. LCA is conducted in four iterative steps, beingGoal and Scope definition, Life Cycle InventoryAnalysis (LCI), Life Cycle Impact Assessment (LCIA), andInterpretation. The guidelines for each step are provided in the International Organization for Standardization (ISO) standards, ISO 14040:2006 and 14044:2006. Uncertainty arises in all steps of an LCA,yet the propagation and reporting of these uncertainties is not mandatory for ISO compliance and is often not donein LCA case studies. There have been significant research efforts to improve uncertainty classification and quantification in LCA, particularly focusing on the LCI step. However, astructured uncertainty management method forall steps in anLCA is still needed. The intent of this research is to improve uncertainty reportingin LCA case studies through the development and demonstration of a structured uncertainty management methodthat can readily be integrated into the international standardsfor LCA. The case study chosen to demonstrate the uncertainty management method was a construction project in Ireland, focusing on climate change.For this case study, the data and uncertainties for the LCI step were compiled in Excel. The uncertainties werepropagated,and the potential impact was calculatedusing an open source software for statistical programming, RStudio. Code was also written in RStudio to identify and rank the input uncertainties that contributedthe most to the totaloutput uncertainty. These

DOI

https://doi.org/10.21427/0ENP-BS68


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