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Purpose Current estimations of the climate impact from indirect land use change (ILUC) caused by biofuels are heavily influenced by assumptions regarding the biofuel production period. The purpose of this paper is to propose a new method (baseline time accounting) that takes global land use dynamics into account that is consistent with the global warming potential, that is applicable to any phenomenon causing land use change, and that is independent of production period assumptions. Methods We consider ILUC in two forms. The first is called “accelerated expansion” and concerns ILUC in regions with an expanding agricultural area. The second is called “delayed reversion” and concerns ILUC in regions with a decreasing agricultural area. We use recent trends in international land use and projections of future land use change to assess how ILUC from biofuels will alter the development in global agricultural land use dynamics compared to the existing trend (i.e., the baseline development). We then use the definition of the global warming potential to determine the CO2 equivalence of the change in land use dynamics. Results and discussion We apply baseline time accounting to two existing ILUC studies in the literature. With current trends in global agricultural land use, the method significantly reduces the estimated climate impact in the previous ILUC studies (by more than half). Sensitivity analyses show that results are somewhat sensitive to assumptions regarding carbon sequestration and assumptions regarding postreversion ecosystems. Conclusions The global dynamic development in land use has important implications for the time accounting step when estimating the climate impact of ILUC caused by biofuel production or other issues affecting land use. Ignoring this may lead to erroneous conclusions about the actual climate impact of ILUC. Several land use projections indicate that the global agricultural area will keep expanding up to and beyond 2050. We therefore recommend to apply the baseline time accounting concept as an integrated part of future ILUC studies and to update the results on a regular basis.

The use of liquid biofuels has expanded over the past decade in response to policies such as the U.S. Renewable Fuel Standard (RFS) that promote their use for transportation. One rationale is the belief that biofuels are inherently carbon neutral, meaning that only production-related greenhouse gas (GHG) emissions need to be tallied when comparing them to fossil fuels. This assumption is embedded in the lifecycle analysis (LCA) modeling used to justify and administer such policies. LCA studies have often found that crop-based biofuels such as corn ethanol and biodiesel offer at least modest net GHG reductions relative to petroleum fuels. Data over the period of RFS expansion enable empirical assessment of net CO2 emission effects. This analysis evaluates the direct carbon exchanges (both emissions and uptake) between the atmosphere and the U.S. vehicle-fuel system (motor vehicles and the physical supply chain for motor fuels) over 2005–2013. While U.S. biofuel use rose from 0.37 to 1.34 EJ/yr over this period, additional carbon uptake on cropland was enough to offset only 37 % of the biofuel-related biogenic CO2 emissions. This result falsifies the assumption of a full offset made by LCA and other GHG accounting methods that assume biofuel carbon neutrality. Once estimates from the literature for process emissions and displacement effects including land-use change are considered, the conclusion is that U.S. biofuel use to date is associated with a net increase rather than a net decrease in CO2 emissions.

During commercial-scale dry-mill ethanol production from corn, as much as 6 % of the starch and all of the cellulose remain unconverted. In this study, two methods to improve ethanol production during commercial-scale corn ethanol production were tested that release and hydrolyze these unconverted carbohydrate fractions; controlled flow cavitation (CFC) and enzymatic cellulose hydrolysis. Corn slurry samples were collected from a 379 million liter per year ethanol plant in which a full-scale CFC unit was installed. Samples collected before and after the CFC unit, and after the jet cooker, were compared on three separate occasions. Results showed that CFC reduced the particle size, led to qualitative changes in cell structure, increased total sugars, and reduced total solids after liquefaction. It also led to significant increases in ethanol production and solids conversion during subsequent simultaneous saccharification and fermentation. The effects of CFC alone were greater than those of CFC plus jet cooking, possibly due to the formation of unfermentable products during jet cooking. On average, ethanol production from cavitated samples was 2.2 % greater than from uncavitated samples. Cellulase addition to uncavitated and cavitated samples led to significant 3.2 and 4.3 % increases in ethanol yield, respectively. The electrical energy used for CFC was 1/16th of that in the additional ethanol produced and the ethanol value was more than 38 times the cost of the additional electricity used. This indicates that CFC can both efficiently increase corn ethanol yields and reduce the amount of energy needed to produce it.

Background To reduce the environmental impacts of the aviation sector as air traffic grows steadily, the aviation industry has paid increasing attention to bio-based alternative jet fuels (AJFs), which may provide lower life-cycle petroleum consumption and greenhouse gas (GHG) emissions than petroleum jet fuel. This study presents well-to-wake (WTWa) results for four emerging AJFs: ethanol-to-jet (ETJ) from corn and corn stover, and sugar-to-jet (STJ) from corn stover via both biological and catalytic conversion. For the ETJ pathways, two plant designs were examined: integrated (processing corn or corn stover as feedstock) and distributed (processing ethanol as feedstock). Also, three H2 options for STJ via catalytic conversion are investigated: external H2 from natural gas (NG) steam methane reforming (SMR), in situ H2, and H2 from biomass gasification. Results Results demonstrate that the feedstock is a key factor in the WTWa GHG emissions of ETJ: corn- and corn stover-based ETJ are estimated to produce WTWa GHG emissions that are 16 and 73%, respectively, less than those of petroleum jet. As for the STJ pathways, this study shows that STJ via biological conversion could generate WTWa GHG emissions 59% below those of petroleum jet. STJ via catalytic conversion could reduce the WTWa GHG emissions by 28% with H2 from NG SMR or 71% with H2 from biomass gasification than those of petroleum jet. This study also examines the impacts of co-product handling methods, and shows that the WTWa GHG emissions of corn stover-based ETJ, when estimated with a displacement method, are lower by 11 g CO2e/MJ than those estimated with an energy allocation method. Conclusion Corn- and corn stover-based ETJ as well as corn stover-based STJ show potentials to reduce WTWa GHG emissions compared to petroleum jet. Particularly, WTWa GHG emissions of STJ via catalytic conversion depend highly on the hydrogen source. On the other hand, ETJ offers unique opportunities to exploit extensive existing corn ethanol plants and infrastructure, and to provide a boost to staggering ethanol demand, which is largely being used as gasoline blendstock. Electronic supplementary material The online version of this article (doi:10.1186/s13068-017-0698-z) contains supplementary material, which is available to authorized users.

Background Corn oil recovery and conversion to biodiesel has been widely adopted at corn ethanol plants recently. The US EPA has projected 2.6 billion liters of biodiesel will be produced from corn oil in 2022. Corn oil biodiesel may qualify for federal renewable identification number (RIN) credits under the Renewable Fuel Standard, as well as for low greenhouse gas (GHG) emission intensity credits under California’s Low Carbon Fuel Standard. Because multiple products [ethanol, biodiesel, and distiller’s grain with solubles (DGS)] are produced from one feedstock (corn), however, a careful co-product treatment approach is required to accurately estimate GHG intensities of both ethanol and corn oil biodiesel and to avoid double counting of benefits associated with corn oil biodiesel production. Results This study develops four co-product treatment methods: (1) displacement, (2) marginal, (3) hybrid allocation, and (4) process-level energy allocation. Life-cycle GHG emissions for corn oil biodiesel were more sensitive to the choice of co-product allocation method because significantly less corn oil biodiesel is produced than corn ethanol at a dry mill. Corn ethanol life-cycle GHG emissions with the displacement, marginal, and hybrid allocation approaches are similar (61, 62, and 59 g CO2e/MJ, respectively). Although corn ethanol and DGS share upstream farming and conversion burdens in both the hybrid and process-level energy allocation methods, DGS bears a higher burden in the latter because it has lower energy content per selling price as compared to corn ethanol. As a result, with the process-level allocation approach, ethanol’s life-cycle GHG emissions are lower at 46 g CO2e/MJ. Corn oil biodiesel life-cycle GHG emissions from the marginal, hybrid allocation, and process-level energy allocation methods were 14, 59, and 45 g CO2e/MJ, respectively. Sensitivity analyses were conducted to investigate the influence corn oil yield, soy biodiesel, and defatted DGS displacement credits, and energy consumption for corn oil production and corn oil biodiesel production. Conclusions This study’s results demonstrate that co-product treatment methodology strongly influences corn oil biodiesel life-cycle GHG emissions and can affect how this fuel is treated under the Renewable Fuel and Low Carbon Fuel Standards.

Background The greenhouse gas (GHG) emissions that may accompany land-use change (LUC) from increased biofuel feedstock production are a source of debate in the discussion of drawbacks and advantages of biofuels. Estimates of LUC GHG emissions focus mainly on corn ethanol and vary widely. Increasing the understanding of LUC GHG impacts associated with both corn and cellulosic ethanol will inform the on-going debate concerning their magnitudes and sources of variability. Results In our study, we estimate LUC GHG emissions for ethanol from four feedstocks: corn, corn stover, switchgrass, and miscanthus. We use new computable general equilibrium (CGE) results for worldwide LUC. U.S. domestic carbon emission factors are from state-level modelling with a surrogate CENTURY model and U.S. Forest Service data. This paper investigates the effect of several key domestic lands carbon content modelling parameters on LUC GHG emissions. International carbon emission factors are from the Woods Hole Research Center. LUC GHG emissions are calculated from these LUCs and carbon content data with Argonne National Laboratory’s Carbon Calculator for Land Use Change from Biofuels Production (CCLUB) model. Our results indicate that miscanthus and corn ethanol have the lowest (−10 g CO2e/MJ) and highest (7.6 g CO2e/MJ) LUC GHG emissions under base case modelling assumptions. The results for corn ethanol are lower than corresponding results from previous studies. Switchgrass ethanol base case results (2.8 g CO2e/MJ) were the most influenced by assumptions regarding converted forestlands and the fate of carbon in harvested wood products. They are greater than miscanthus LUC GHG emissions because switchgrass is a lower-yielding crop. Finally, LUC GHG emissions for corn stover are essentially negligible and insensitive to changes in model assumptions. Conclusions This research provides new insight into the influence of key carbon content modelling variables on LUC GHG emissions associated with the four bioethanol pathways we examined. Our results indicate that LUC GHG emissions may have a smaller contribution to the overall biofuel life cycle than previously thought. Additionally, they highlight the need for future advances in LUC GHG emissions estimation including improvements to CGE models and aboveground and belowground carbon content data.