DOI: 10.1007/s11367-019-01674-y
论文题名: Extending sensitivity analysis using regression to effectively disseminate life cycle assessment results
作者: Di Lullo G. ; Gemechu E. ; Oni A.O. ; Kumar A.
刊名: International Journal of Life Cycle Assessment
ISSN: 9483349
出版年: 2020
卷: 25, 期: 2 语种: 英语
英文关键词: GHG
; Interpretation
; Life cycle assessment
; Morris
; Regression
; RUST
; Sensitivity
; Sobol
; Uncertainty
Scopus关键词: petroleum
; analytical error
; Article
; confidentiality
; greenhouse gas
; life cycle assessment
; linear regression analysis
; mathematical analysis
; measurement accuracy
; priority journal
; regression analysis
; sensitivity analysis
; uncertainty
英文摘要: Purpose: Bottom-up–based life cycle assessment (LCA) approaches are used to assess the greenhouse gas emissions of various products such as transportation fuels. Bottom-up spreadsheet–based models include numerous calculations and assumed values that are uncertain. Currently, most LCAs provide point estimates with a simple one-at-a-time sensitivity analysis, which provides limited insight into how the model assumptions affect the results. Additionally, the LCA models are generally presented with a limited number of scenarios to avoid overwhelming the reader; however, this limits the usefulness of the work, as each reader will be interested in different scenarios. The goal of this work is to use a global sensitivity and regression to provide as much information to the reader as possible in an easily digestible form. Methods: The Morris and Sobol global sensitivity methods are examined to determine if they can accurately identify the key inputs that have the largest effect on overall output variance. A multiparameter linear regression is then used to simplify the model into a single equation. Rstudio and Excel VBA are used to create an easy-to-use template called the Regression, Uncertainty, and Sensitivity Tool (RUST) that can be inserted into any Excel-based LCA model. This method is applied to the previously published FUNdamental ENgineering PrinciplEs-based ModeL for Estimation of GreenHouse Gases in Conventional Crude Oils and Oil Sands (FUNNEL-GHG-CCO/OS) as an example case. Results and discussion: Both the Morris and Sobol methods can identify the key parameters, but the Morris method requires less than 1/100th as many model evaluations. Of the model’s 65 parameters, 14 key parameters were identified. The corresponding regression model was found to have an accuracy of ± 0.5 g CO2 eq/MJ 90% of the time and a maximum error of + 3 and − 1 g CO2 eq/MJ. Conclusions: This work found that the Morris method can be used to screen key parameters and that a stepwise multiparameter linear regression approach can be used to develop a simplified version of the model. The developed RUST Excel workbook can be used to perform the sensitivity and regression analysis of any Excel-based LCA models. The regression model can then be easily published, it does not require a large effort to make a user friendly version of the model, and it conceals confidential data if necessary. The simplified model makes it easy for policy markets to investigate how changes in critical parameters affect the LCA results without having to learn how to use the full complex model. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159931
Appears in Collections: 气候变化与战略
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作者单位: Department of Mechanical Engineering, 10-263 Donadeo Innovation Centre for Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Recommended Citation:
Di Lullo G.,Gemechu E.,Oni A.O.,et al. Extending sensitivity analysis using regression to effectively disseminate life cycle assessment results[J]. International Journal of Life Cycle Assessment,2020-01-01,25(2)