Sensitivity and Uncertainty Analysis

LCA data quality issues were investigated by using case studies on products from starch-PVOH biopolymers and petrochemical alternatives. These case studies demonstrated that the parameters and assumptions in the database as well as the characterization and normalization methods need to be addressed in sensitivity analysis in order to draw robust LCA conclusions. This study also presents an approach to integrate statistical methods into LCA models for analyzing uncertainty in industrial and computer-simulated datasets. This chapter calibrated probabilities for the LCA outcomes for biopolymer products arising from uncertainty in the inventory and from data variation characteristics - this has enabled assigning confidence to the LCIA outcomes in specific impact categories for the biopolymer vs. petrochemical polymer comparisons undertaken. Uncertainty combined with the sensitivity analysis carried out has led to a transparent increase in confidence in the LCA findings. It is concluded that LCAs lacking explicit interpretation of the degree of uncertainty and sensitivities are of limited value as robust evidence for decision making or comparative assertions.

[1]  Stefanie Hellweg,et al.  Life-cycle inventory of waste solvent distillation: statistical analysis of empirical data. , 2005, Environmental science & technology.

[2]  Stefanie Hellweg,et al.  Using Standard Statistics to Consider Uncertainty in Industry-Based Life Cycle Inventory Databases (7 pp) , 2005 .

[3]  Michael Srocka,et al.  How to obtain a precise and representative estimate for parameters in LCA , 2008 .

[4]  Ming-Lung Hung,et al.  Quantifying system uncertainty of life cycle assessment based on Monte Carlo simulation , 2008 .

[5]  Changsheng Li,et al.  Modeling impacts of carbon sequestration on net greenhouse gas emissions from agricultural soils in China , 2009 .

[6]  Reiner Wassmann,et al.  Modeling greenhouse gas emissions from rice‐based production systems: Sensitivity and upscaling , 2004 .

[7]  Jeri Benson,et al.  The robustness of maximum likelihood and distribution-free estimators to non-normality in confirmatory factor analysis , 1994 .

[8]  M. Huijbregts,et al.  Normalisation figures for environmental life-cycle assessment: The Netherlands (1997/1998), Western Europe (1995) and the world (1990 and 1995) , 2003 .

[9]  Mark A. J. Huijbregts,et al.  Framework for modelling data uncertainty in life cycle inventories , 2001 .

[10]  Bo Pedersen Weidema,et al.  Data quality management for life cycle inventories—an example of using data quality indicators☆ , 1996 .

[11]  Andreas Ciroth,et al.  Uncertainty calculation in life cycle assessments , 2004 .

[12]  Anna Björklund,et al.  Survey of approaches to improve reliability in lca , 2002 .

[13]  David Evans,et al.  How LCA studies deal with uncertainty , 2002 .

[14]  Heather L MacLean,et al.  Life cycle evaluation of emerging lignocellulosic ethanol conversion technologies. , 2010, Bioresource technology.

[15]  Patricia L. Mokhtarian,et al.  Life cycle assessment of fuel cell vehicles a methodology example of input data treatment for future technologies , 2002 .