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Extending sensitivity analysis using regression to effectively disseminate life cycle assessment results



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.


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.


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.

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The authors thank Astrid Blodgett for editorial assistance.


NSERC/Cenovus/Alberta Innovates Associate Industrial Research Chair in Energy and Environmental Systems Engineering and the Cenovus Energy Endowed Chair in Environmental Engineering provided financial support for this project. Support was also given by the Natural Sciences and Engineering Research Council of Canada (NSERC) Postgraduate Scholarships - Doctoral Program. As a part of the University of Alberta’s Future Energy Systems (FES) research initiative, this research was made possible in part thanks to funding from the Canada First Research Excellence Fund (CFREF).

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Correspondence to Amit Kumar.

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Responsible editor: Serenella Sala

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Di Lullo, G., Gemechu, E., Oni, A.O. et al. Extending sensitivity analysis using regression to effectively disseminate life cycle assessment results. Int J Life Cycle Assess 25, 222–239 (2020).

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  • GHG
  • Interpretation
  • Life cycle assessment
  • Morris
  • Regression
  • RUST
  • Sensitivity
  • Sobol
  • Uncertainty