We conducted a mobility chains, or well‐to‐wheels (WTW), analysis to assess the energy and emission benefits of cellulosic biomass for the U.S. transportation sector in the years 2015–2030. We estimated the life‐cycle energy consumption and emissions associated with biofuel production and use in light‐duty vehicle (LDV) technologies by using the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model. Analysis of biofuel production was based on ASPEN Plus model simulation of an advanced fermentation process to produce fuel ethanol/protein, a thermochemical process to produce Fischer‐Tropsch diesel (FTD) and dimethyl ether (DME), and a combined heat and power plant to co‐produce steam and electricity. Our study revealed that cellulosic biofuels as E85 (mixture of 85% ethanol and 15% gasoline by volume), FTD, and DME offer substantial savings in petroleum (66–93%) and fossil energy (65–88%) consumption on a per‐mile basis. Decreased fossil fuel use translates to 82–87% reductions in greenhouse gas emissions across all unblended cellulosic biofuels. In urban areas, our study shows net reductions for almost all criteria pollutants, with the exception of carbon monoxide (unchanged), for each of the biofuel production option examined. Conventional and hybrid electric vehicles, when fueled with E85, could reduce total sulfur oxide (SO_x) emissions to 39–43% of those generated by vehicles fueled with gasoline. By using bio‐FTD and bio‐DME in place of diesel, SO_x emissions are reduced to 46–58% of those generated by diesel‐fueled vehicles. Six different fuel production options were compared. This study strongly suggests that integrated heat and power co‐generation by means of gas turbine combined cycle is a crucial factor in the energy savings and emission reductions.

Since advances in the ABE (acetone‐butanol‐ethanol) fermentation process in recent years have led to significant increases in its productivity and yields, the production of butanol and its use in motor vehicles have become an option worth evaluating. This study estimates the potential life‐cycle energy and emission effects associated with using bio‐butanol as a transportation fuel. It employs a well‐to‐wheels (WTW) analysis tool: the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. The estimates of life‐cycle energy use and greenhouse gas (GHG) emissions are based on an Aspen Plus® simulation for a corn‐to‐butanol production process, which describes grain processing, fermentation, and product separation. Bio‐butanol‐related WTW activities include corn farming, corn transportation, butanol production, butanol transportation, and vehicle operation. In this study, we also analyzed the bio‐acetone that is coproduced with bio‐butanol as an alternative to petroleum‐based acetone. We then compared the results for bio‐butanol with those of conventional gasoline. Our study shows that driving vehicles fueled with corn‐based butanol produced by the current ABE fermentation process could result in substantial fossil energy savings (39%–56%) and avoid large percentage of the GHG emission burden, yielding a 32%–48% reduction relative to using conventional gasoline. On energy basis, a bushel of corn produces less liquid fuel from the ABE process than that from the corn ethanol dry mill process. The coproduction of a significant portion of acetone from the current ABE fermentation presents a challenge. A market analysis of acetone, as well as research and development on robust alternative technologies and processes that minimize acetone while increase the butanol yield, should be conducted.

Water consumption and water quality continue to be key factors affecting environmental sustainability in biofuel production. This review covers the findings from biofuel water analyses published over the past 2 years to underscore the progress made, and to highlight advancements in understanding the interactions among increased production and water demand, water resource availability, and potential changes in water quality. We focus on two key areas: water footprint assessment and watershed modeling. Results revealed that miscanthus-, switchgrass-, and forest wood-based biofuels all have promising blue and grey water footprints. Alternative water resources have been explored for algae production, and challenges remain. A most noticeable improvement in the analysis of life-cycle water consumption is the adoption of geospatial analysis and watershed modeling to generate a spatially explicit water footprint at a finer scale (e.g., multi-state region, state, and county scales) to address the impacts of land use change and climate on the water footprint in a landscape with a mixed biofuel feedstock.

Green water is vital for the terrestrial ecosystem, but water resource assessment often focuses on blue water. In this study, we estimated green water availability for major crops (i.e., corn, soybean, and wheat) and all other users (e.g., forest, grassland, and ecosystem services) at the county level in the United States. We estimated green water resources from effective rain (ER) using three different methods: Smith, U.S. Department of Agriculture—Soil Conservation Service (USDA-SCS), and the NHD plus V2 dataset. The analysis illustrates that, if green water meets all crop water demands, the fraction of green water resources available to all other users varies significantly across regions, from the Northern Plains (0.71) to the Southeast (0.98). At the county level, this fraction varies from 0.23 to 1.0. Green water resources estimated using the three different ER methods present diverse spatiotemporal distribution patterns across regions, which could affect green water availability estimates. The water availability index for green water (WAI_R) was measured taking into account crop water demand and green water resources aggregated at the county level. Beyond these parameters, WAI_R also depends on the precipitation pattern, crop type and spatially differentiated regions. In addition, seasonal analysis indicated that WAI_R is sensitive to the temporal boundary of the analysis.