![]() Int J Life Cycle Assess 11(4):222–228īlom I, Itard L, Meijer A (2010a) Environmental impact of dwellings in use: maintenance of facade components. J Ind Ecol 6:49–78īenetto E, Dujet C, Rousseaux P (2006) Fuzzy-sets approach to noise impact assessment. Int J Life Cycle Assess 17(3):362–371īare JC, Norris G, Pennington DW, McKone T (2003) TRACI: The tool for the reduction and assessment of chemical and other environmental impacts. Int J Life Cycle Assess 17(3):337–349Ītkas CB, Bilec MM (2012b) Service life prediction of residential interior finishes for life cycle assessment. Athena Sustainable Materials Institute, MerrickvilleĪtkas CB, Bilec MM (2012a) Impact of lifetime on US residential building LCA results. Build Environ 32:317–20Īthena Sustainable Materials Institute in collaboration with Morrison Hershfield (2006) Service life considerations in relation to green building rating systems: an exploratory study. Build Environ 32:321–329Īdalberth K (1997b) Energy use during the life cycle of buildings: a method. The factor analysis used in the study corroborates the conclusions of previous studies and provides a basis for future statistical analyses.Īdalberth K (1997a) Energy use during the life cycle of single-unit dwellings: examples. The data are determined to be suitable for further analysis using inferential univariate and multivariate statistical tests, a topic that is examined in part 2 of this study as reported by Grant et al. ![]() ![]() The descriptive statistics provide important information about the distribution of data in the study. Other variables in the factor analysis were less clearly or inconsistently aligned. This alignment corroborates previous findings. In each analysis, cumulative life cycle impact, major replacement, and major replacement (frequency) aligned as variables in a common factor. Factor analysis resulted in three factors for GWP and ECO, and two factors for ACID. Data distributions for ECO and ACID deviated slightly from normal, which was confirmed through Shapiro-Wilk and Kolmogorov-Smirnov tests. Results and discussionĭescriptive statistics show a relatively normal data distribution for GWP. All statistical operations were performed using SPSS software. For the factor analysis, data were disaggregated according to nine variables, including cumulative life cycle impact, major replacement, major replacement (frequency), minor replacement, major repairs, minor repairs, inspections 1 and 2, and total transportation ( N = 45, 405 data points). Descriptive statistics measured minimum, maximum, mean, standard deviation, skewness, and kurtosis. Three environmental indicators were used to characterize the data: global warming potential (GWP), atmospheric eco-toxicity (ECO), and atmospheric acidification (ACID) from the Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts (TRACI) assessment method. Nine building envelope combinations were analyzed according to five service life models ( N = 45, 45 data points). The current study also provides a pretext for subsequent analyses with inferential statistics. It is believed that the use of descriptive statistics and factor analysis provides a comprehensive summary of the data, and insight on internal validity, which improves interpretation of results. ![]() This study examines the utility of descriptive statistics and factor analysis in the interpretation of an LCA data set.
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