Uncertainty analysis of life cycle greenhouse gas emissions from petroleum-based fuels and impacts on low carbon fuel policies.
The climate change impacts of U.S. petroleum-based fuels consumption have contributed to the development of legislation supporting the introduction of low carbon alternatives, such as biofuels. However, the potential greenhouse gas (GHG) emissions reductions estimated for these policies using life cycle assessment methods are predominantly based on deterministic approaches that do not account for any uncertainty in outcomes. This may lead to unreliable and expensive decision making. In this study, the uncertainty in life cycle GHG emissions associated with petroleum-based fuels consumed in the U.S. is determined using a process-based framework and statistical modeling methods. Probability distributions fitted to available data were used to represent uncertain parameters in the life cycle model. Where data were not readily available, a partial least-squares (PLS) regression model based on existing data was developed. This was used in conjunction with probability mixture models to select appropriate distributions for specific life cycle stages. Finally, a Monte Carlo simulation was performed to generate sample output distributions. As an example of results from using these methods, the uncertainty range in life cycle GHG emissions from gasoline was shown to be 13%-higher than the typical 10% minimum emissions reductions targets specified by low carbon fuel policies.
Civil and Environmental Engineering Department, and Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213-3890, USA.
Aranya
A
Venkatesh
Paulina
P
Jaramillo
W Michael
WM
Griffin
H Scott
HS
Matthews
D000397:Air Pollution / Q000032:analysis / Q000331:legislation & jurisprudence / Q000706:statistics & numerical data*; D002244:Carbon / Q000032:analysis*; D058572:Carbon Footprint; D004784:Environmental Monitoring; D058735:Environmental Policy; D019652:Extraction and Processing Industry / Q000706:statistics & numerical data; D017752:Greenhouse Effect; D016018:Least-Squares Analysis; D009010:Monte Carlo Method; D010578:Petroleum / Q000032:analysis / Q000706:statistics & numerical data*; D012044:Regression Analysis; D014186:Transportation / Q000706:statistics & numerical data; D035501:Uncertainty; D001335:Vehicle Emissions / Q000032:analysis
D010578:Petroleum; D001335:Vehicle Emissions; D002244:Carbon