A hybrid Data Quality Indicator and statistical method for improving uncertainty analysis in LCA of a small off-grid wind turbine

Matthew Ozoemena, Wai Ming Cheung, Reaz Hasan, Philip Hackney

Research output: Contribution to conferencePaperpeer-review

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Abstract

In Life Cycle Assessment (LCA) uncertainty analysis has been recommended when choosing sustainable products. Both Data Quality Indicator and statistical methods are used to estimate data uncertainties in LCA. Neither of these alone is however adequate enough to address the challenges in LCA of a complex system due to data scarcity and large quantity of material types. This paper applies a hybrid stochastic method, combining the statistical and Data Quality Indicator methods by using a pre-screening process based on Monte Carlo rank-order correlation sensitivity analysis, to improve the uncertainty estimate in wind turbine LCA with data limitations. In the presented case study which performed the stochastic estimation of CO2 emissions, similar results from the hybrid method were observed compared to the pure Data Quality Indicator method. Summarily, the presented hybrid method can be used as a possible alternative for evaluating deterministic LCA results like CO2 emissions, when results that are more reliable are desired with limited availability of data.
Original languageEnglish
Publication statusPublished - 18 Jun 2014
EventARCOM Doctoral Workshop on Sustainable Urban Retrofit and Technologies - London South Bank University
Duration: 18 Jun 2014 → …
http://epc.ac.uk/events/international-conference-on-manufacturing-research-2013/

Workshop

WorkshopARCOM Doctoral Workshop on Sustainable Urban Retrofit and Technologies
Period18/06/14 → …
Internet address

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