ALBERT

Description: Purpose This study aims to compare the life cycle greenhouse gas (GHG) emissions of two cellulosic bioenergy pathways (i.e., bioethanol and bioelectricity) using different references and functional units. It also aims to address uncertainties associated with a comparative life cycle analysis (LCA) for the two bioenergy pathways. Methods We develop a stochastic, comparative life cycle GHG analysis model for a switchgrass-based bioenergy system. Life cycle GHG offsets of the biofuel and bioelectricity pathways for cellulosic bioenergy are compared. The reference system for bioethanol is the equivalent amount of gasoline to provide the same transportation utility (e.g., vehicle driving for certain distance) as bioethanol does. We use multiple reference systems for bioelectricity, including the average US grid, regional grid in the USA according to the North American Electric Reliability Corporation (NERC), and average coal-fired power generation, on the basis of providing the same transportation utility. The functional unit is one unit of energy content (MJ). GHG offsets of bioethanol and bioelectricity relative to reference systems are compared in both grams carbon dioxide equivalents per hectare of land per year (g CO 2 -eq/ha-yr) and grams carbon dioxide equivalents per vehicle kilometer traveled (g CO 2 -eq/km). For the latter, we include vehicle cycle to make the comparison meaningful. To address uncertainty and variability, we derive life cycle GHG emissions based on probability distributions of individual parameters representing various unit processes in the life cycle of bioenergy pathways. Results and discussion Our results show the choice of reference system and functional unit significantly changes the competition between switchgrass-based bioethanol and bioelectricity. In particular, our results show that the bioethanol pathway produces more life cycle GHG emissions than the bioelectricity pathway on a per unit energy content or a per unit area of crop land basis. However, the bioethanol pathway can offer more GHG offsets than the bioelectricity pathway on a per vehicle kilometer traveled basis when using bioethanol and bioelectricity for vehicle operation. Given the current energy mix of regional grids, bioethanol can potentially offset more GHG emissions than bioelectricity in all grid regions of the USA. Conclusions The reference and functional unit can change bioenergy pathway choices. The comparative LCA of bioenergy systems is most useful for decision support only when it is spatially explicit to address regional specifics and differences. The difference of GHG offsets from bioethanol and bioelectricity will change as the grid evolves. When the grids get cleaner over time, the favorability of bioethanol for GHG offsets increases.