Policy implications of uncertainty in modeled life-cycle greenhouse gas emissions of biofuels.
Biofuels have received legislative support recently in California's Low-Carbon Fuel Standard and the Federal Energy Independence and Security Act. Both present new fuel types, but neither provides methodological guidelines for dealing with the inherent uncertainty in evaluating their potential life-cycle greenhouse gas emissions. Emissions reductions are based on point estimates only. This work demonstrates the use of Monte Carlo simulation to estimate life-cycle emissions distributions from ethanol and butanol from corn or switchgrass. Life-cycle emissions distributions for each feedstock and fuel pairing modeled span an order of magnitude or more. Using a streamlined life-cycle assessment, corn ethanol emissions range from 50 to 250 g CO(2)e/MJ, for example, and each feedstock-fuel pathway studied shows some probability of greater emissions than a distribution for gasoline. Potential GHG emissions reductions from displacing fossil fuels with biofuels are difficult to forecast given this high degree of uncertainty in life-cycle emissions. This uncertainty is driven by the importance and uncertainty of indirect land use change emissions. Incorporating uncertainty in the decision making process can illuminate the risks of policy failure (e.g., increased emissions), and a calculated risk of failure due to uncertainty can be used to inform more appropriate reduction targets in future biofuel policies.
Department of Civil & Environmental Engineering, and Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, USA.
Kimberley A
KA
Mullins
W Michael
WM
Griffin
H Scott
HS
Matthews
D000397:Air Pollution / Q000032:analysis / Q000331:legislation & jurisprudence / Q000706:statistics & numerical data*; D056804:Biofuels / Q000032:analysis / Q000706:statistics & numerical data*; D000440:Butanols / Q000032:analysis; D002140:California; D058572:Carbon Footprint; D004784:Environmental Monitoring; D058735:Environmental Policy; D000431:Ethanol / Q000032:analysis; D017752:Greenhouse Effect; D008962:Models, Theoretical; D009010:Monte Carlo Method; D035501:Uncertainty
D056804:Biofuels; D000440:Butanols; D000431:Ethanol