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Title: Application of a variance-based sensitivity analysis method to the Biomass Scenario Learning Model

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. Here in this paper 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.
 [1] ;  [1] ; ORCiD logo [1] ;  [2] ;  [3]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  3. National Renewable Energy Lab. (NREL), Golden, CO (United States); Dartmouth College, Hanover, NH (United States)
Publication Date:
Report Number(s):
Journal ID: ISSN 0883-7066
Grant/Contract Number:
AC36-08GO28308; AC36-08-GO28308
Published Article
Journal Name:
System Dynamics Review
Additional Journal Information:
Journal Volume: 33; Journal Issue: 3-4; Journal ID: ISSN 0883-7066
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (EE-3B)
Country of Publication:
United States
09 BIOMASS FUELS; variance based sensitivity analysis; statistical program; learning curve; experience curve; learning; biofuel; biomass
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1460765; OSTI ID: 1464323