Land-use change and greenhouse gas emissions from corn and cellulosic ethanol

Dunn, Jennifer; Mueller, Steffen; Kwon, Hoyoung; Wang, Michael Q.

doi:10.1186/1754-6834-6-51

Cited by 133 publications

(96 citation statements)

References 27 publications

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“…The largest contribution of these is from ethanol production (e.g., consumption of process chemicals and enzymes, imported energy) at 13 g-CO 2 eq/MJ, followed by stover collection and transport at 11 g-CO 2 eq/MJ. Corn grain ethanol and corn stover ethanol LUC GHG emissions are 7.6 and −0.6 g-CO 2 eq/MJ, respectively, based on previous analysis [27]. Also presented in Fig.…”

Section: Baseline Ghg Emissionsmentioning

confidence: 56%

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Policy Implications of Allocation Methods in the Life Cycle Analysis of Integrated Corn and Corn Stover Ethanol Production

et al. 2015

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A biorefinery may produce multiple fuels from more than one feedstock. The ability of these fuels to qualify as one of the four types of biofuels under the US Renewable Fuel Standard and to achieve a low carbon intensity score under California's Low Carbon Fuel Standard can be strongly influenced by the approach taken to their life cycle analysis (LCA). For example, in facilities that may co-produce corn grain and corn stover ethanol, the ethanol production processes can share the combined heat and power (CHP) that is produced from the lignin and liquid residues from stover ethanol production. We examine different LCA approaches to corn grain and stover ethanol production considering different approaches to CHP treatment. In the baseline scenario, CHP meets the energy demands of stover ethanol production first, with additional heat and electricity generated sent to grain ethanol production. The resulting greenhouse gas (GHG) emissions for grain and stover ethanol are 57 and 25 g-CO 2 eq/MJ, respectively, corresponding to a 40 and 74 % reduction compared to the GHG emissions of gasoline. We illustrate that emissions depend on allocation of burdens of CHP production and corn farming, along with the facility capacities. Co-product handling techniques can strongly influence LCA results and should therefore be transparently documented.

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Section: Baseline Ghg Emissionsmentioning

confidence: 56%

“…We include land-use change (LUC) GHG emissions. We describe our development of LUC GHG emissions that are used in this analysis in a separate publication [27].…”

Section: Goal and Scopementioning

confidence: 99%

Policy Implications of Allocation Methods in the Life Cycle Analysis of Integrated Corn and Corn Stover Ethanol Production

et al. 2015

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“…Developing countries have more disposable income allowing for increased meat consumption, which requires more grain to feed livestock. At the same time, biofuel production is expected to increase (Edgerton, 2009) and compete for available agricultural land (Dunn et al, 2013). To meet present and future needs, productivity per unit area (i.e., grain yield) must be increased.…”

mentioning

confidence: 99%

Survey of Plant Density Tolerance in U.S. Maize Germplasm

2014

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A broad range of U.S. maize (Zea mays L.) germplasm was evaluated for plant density tolerance (PDT) to identify potential sources of favorable alleles and to obtain a better understanding the underlying genetics involved. Thirty‐two hybrids created using a set of inbreds representing parentage of key heterotic subgroups were evaluated at plant densities ranging from 47,000 (19,000 plants per acre [ppA]) to 133,000 plants ha−1 (54,000 ppA). Forty‐eight phenotypic traits from five categories (photosynthetic capability, plant architecture, growth responses, source–sink relationship, and general stress tolerance) as well as grain yield were evaluated in three environments that differed for levels of soil moisture availability. The relationship between plant density and grain yield was assessed for each hybrid, with a wide range of responses observed. Five hybrids showed substantial tolerance to plant densities ≥116,000 plants ha−1 based on grain yield performance. Phenotypic trait correlations revealed a subset of traits associated with grain yield across plant densities, with all five categories of traits implicated directly; the subset included leaf angle, upper stem diameter, leaf area required to produce a gram of grain, kernel rows per ear, days to canopy closure, barrenness, kernels per plant, kernel length, leaf number, upper leaf area, staygreen, zipper effect, kernels per row, and anthesis–silking interval. Analysis of gene action for grain yield across plant densities emphasized the prominence of additivity, the increasing importance of nonadditivity as plant density and environmental stress levels increased, and genotype by environment interaction. This work paves the way for further characterization of PDT through quantitative trait locus mapping and candidate gene approaches.

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“…Wang et al (2012) documented GREET ethanol pathway key parameters and estimated WTW GHG emissions of ethanol from corn and from corn stover, including direct and indirect land use change impacts. Detailed modeling of land use changes of ethanol pathways is documented in Dunn et al (2013).…”

Section: Ethanol Productionmentioning

confidence: 99%

Environmental, Economic, and Scalability Considerations and Trends of Selected Fuel Economy-Enhancing Biomass-Derived Blendstocks

Dunn

Biddy

Jones

et al. 2017

ACS Sustainable Chem. Eng.

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Twenty-four biomass-derived compounds and mixtures, identified based on their physical properties, which could be blended into fuels to improve spark ignition engine fuel economy, were assessed for their economic, technology readiness, and environmental viability. These bio-blendstocks were modeled to be produced biochemically, thermochemically, or through hybrid processes. To carry out the assessment, 17 metrics were developed for which each bio-blendstock was determined to be favorable, neutral, or unfavorable. Cellulosic ethanol was included as a reference case. Overall economic and, to some extent, environmental viability is driven by projected yields for each of these processes. The metrics used in this analysis methodology highlight the near-term potential to achieve these targeted yield estimates when considering data quality and current technical readiness for these conversion strategies. Key knowledge gaps included the degree of purity needed for use as a bio-blendstock. Less stringent purification requirements for fuels could cut processing costs and environmental impacts. Additionally, more information is needed on the blending behavior of many of these bio-blendstocks with gasoline to support the technology readiness evaluation. Overall, the technology to produce many of these blendstocks from biomass is emerging, and as it matures, these assessments must be revisited. Importantly, considering economic, environmental, and technology readiness factors, in addition to physical properties of blendstocks that could be used to boost engine efficiency and fuel economy, in the early stages of project research and development can help spotlight those most likely to be viable in the near term.

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Land-use change and greenhouse gas emissions from corn and cellulosic ethanol

Cited by 133 publications

References 27 publications

Policy Implications of Allocation Methods in the Life Cycle Analysis of Integrated Corn and Corn Stover Ethanol Production

Policy Implications of Allocation Methods in the Life Cycle Analysis of Integrated Corn and Corn Stover Ethanol Production

Survey of Plant Density Tolerance in U.S. Maize Germplasm

Environmental, Economic, and Scalability Considerations and Trends of Selected Fuel Economy-Enhancing Biomass-Derived Blendstocks

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