Application of a Variance-Based Sensitivity Analysis Method to the Biomass Scenario Learning Model

Paige Jadun, Laura J. Vimmerstedt, Brian W. Bush, Daniel Inman, Steve Peterson

Research output: Contribution to journalArticlepeer-review

14 Scopus Citations


Variance-based sensitivity analysis can provide a comprehensive understanding of the input factors that drive model behavior, complementing more traditional system dynamics methods with quantitative metrics. This paper presents the methodology of a variance-based sensitivity analysis of the Biomass Scenario Learning Model, a published STELLA model of interactions among investment, production, and learning in an emerging competitive industry. We document the methodology requirements, interpretations, and constraints, and compute estimated sensitivity indices and their uncertainties. We show that application of variance-based sensitivity analysis to the model allows us to test for non-additivity, identify influential and interactive variables, and confirm model formulation. To enable use of this type of sensitivity analysis in other system dynamics models, we provide this study's R code, annotated to facilitate adaptation to other studies. A related paper describes application of these techniques to the much larger Biomass Scenario Model.

Original languageAmerican English
Pages (from-to)311-335
Number of pages25
JournalSystem Dynamics Review
Issue number3-4
StatePublished - 2017

Bibliographical note

Publisher Copyright:
Copyright © 2018 Alliance for Sustainable Energy, LLC. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

NREL Publication Number

  • NREL/JA-6A20-73821


  • biofuel
  • biomass
  • experience curve
  • learning
  • learning curve
  • statistical program
  • variance based sensitivity analysis


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