Although a number of life cycle analysis studies (LCAs) have been published, there is little consensus on the feasibility of producing algal biofuels sustainably. For example, the net energy ratio ( !"#$%& !" !"#$" !"#$%&' !!"#$%& !" !" !!"#$%&'( !"#!!"#"$%&'" !"#$%& !"#$%& ) from the reported studies ranges from about 0.02 to 3.33. Our work attempted to understand these discrepancies by separating results dependent on subsystem choice or assumed performance from those caused by lack of process knowledge or its variability. Each of the life cycle stage: algae cultivation, dewatering, oil extraction, and fuel processing are analyzed independently of the other to consider various process options in them. In addition to the process choices made in each stage, the outcomes of any LCA are subject to uncertainty and variability within the model. This study couples LCA with Monte Carlo simulation to propagate the uncertainties from parameters through the modeled processes to the final output. Especially for algal biofuel, implementing uncertainty analysis improves the transparency of the results, considering that no industrial-scale plant is in operation and the algal biofuels industry is in a very early stage with unknown or unreported performance. Thesis advisor: Prof. Jefferson Tester, Prof. Ruth Richardson ii