Daniel Inman

Life cycle environmental impacts of selected U.S. ethanol production and use pathways in 2022.

Projected life cycle greenhouse gas (GHG) emissions and net energy value (NEV) of high-ethanol blend fuel (E85) used to propel a passenger car in the United States are evaluated using attributional life cycle assessment. Input data represent national-average conditions projected to 2022 for ethanol produced from corn grain, corn stover, wheat straw, switchgrass, and forest residues. Three conversion technologies are assessed: advanced dry mill (corn grain), biochemical (switchgrass, corn stover, wheat straw), and thermochemical (forest residues). A reference case is compared against results from Monte Carlo uncertainty analysis. For this case, one kilometer traveled on E85 from the feedstock-to-ethanol pathways evaluated has 43%-57% lower GHG emissions than a car operated on conventional U.S. gasoline (base year 2005). Differences in NEV cluster by conversion technology rather than by feedstock. The reference case estimates of GHG and NEV skew to the tails of the estimated frequency distributions. Though not as optimistic as the reference case, the projected median GHG and NEV for all feedstock-to-E85 pathways evaluated offer significant improvement over conventional U.S. gasoline. Sensitivity analysis suggests that inputs to the feedstock production phase are the most influential parameters for GHG and NEV. Results from this study can be used to help focus research and development efforts.

Modeling biofuel expansion effects on land use change dynamics

Increasing demand for crop-based biofuels, in addition to other human drivers of land use, induces direct and indirect land use changes (LUC). Our system dynamics tool is intended to complement existing LUC modeling approaches and to improve the understanding of global LUC drivers and dynamics by allowing examination of global LUC under diverse scenarios and varying model assumptions. We report on a small subset of such analyses. This model provides insights into the drivers and dynamic interactions of LUC (e.g., dietary choices and biofuel policy) and is not intended to assert improvement in numerical results relative to other works. Demand for food commodities are mostly met in high food and high crop-based biofuel demand scenarios, but cropland must expand substantially. Meeting roughly 25% of global transportation fuel demand by 2050 with biofuels requires >2 times the land used to meet food demands under a presumed 40% increase in per capita food demand. In comparison, the high food demand scenario requires greater pastureland for meat production, leading to larger overall expansion into forest and grassland. Our results indicate that, in all scenarios, there is a potential for supply shortfalls, and associated upward pressure on prices, of food commodities requiring higher land use intensity (e.g., beef) which biofuels could exacerbate.

Understanding the Developing Cellulosic Biofuels Industry through Dynamic Modeling

The purpose of this chapter is to discuss a system dynamics model called the Biomass Scenario Model (BSM), which is being developed by the U.S. Department of Energy as a tool to better understand the interaction of complex policies and their potential effects on the burgeoning cellulosic biofuels industry in the United States. The model has also recently been expanded to include advanced conversion technologies and biofuels (i.e., conversion pathways that yield biomass-based gasoline, diesel, jet fuel, and butanol), but we focus on cellulosic ethanol conversion pathways here. The BSM uses a system dynamics modeling approach (Bush et al., 2008) built on the STELLA software platform.

Life Cycle Assessment of the Energy Independence and Security Act of 2007: Ethanol - Global Warming Potential and Environmental Emissions

The objective of this study is to use life cycle assessment (LCA) to evaluate the global warming potential (GWP), water use, and net energy value (NEV) associated with the EISA-mandated 16 bgy cellulosic biofuels target, which is assumed in this study to be met by cellulosic-based ethanol, and the EISA-mandated 15 bgy conventional corn ethanol target. Specifically, this study compares, on a per-kilometer-driven basis, the GWP, water use, and NEV for the year 2022 for several biomass feedstocks.

Biomass Scenario Model Scenario Library: Definitions, Construction, and Description

Understanding the development of the biofuels industry in the United States is important to policymakers and industry. The Biomass Scenario Model (BSM) is a system dynamics model of the biomass-to-biofuels system that can be used to explore policy effects on biofuels development. Because of the complexity of the model, as well as the wide range of possible future conditions that affect biofuels industry development, we have not developed a single reference case but instead developed a set of specific scenarios that provide various contexts for our analyses. The purpose of this report is to describe the scenarios that comprise the BSM scenario library. At present, we have the following policy-focused scenarios in our library: minimal policies, ethanol-focused policies, equal access to policies, output-focused policies, technological diversity focused, and the point-of-production- focused. This report describes each scenario, its policy settings, and general insights gained through use of the scenarios in analytic studies.

Supply Chain Sustainability Analysis of Three Biofuel Pathways

The Department of Energy’s (DOE) Bioenergy Technologies Office (BETO) collaborates with industrial, agricultural, and non-profit partners to develop and deploy biofuels and other biologically-derived products. As part of this effort, BETO and its national laboratory teams conduct in-depth techno-economic assessments (TEA) of technologies to produce biofuels as part state of technology (SOT) analyses. An SOT assesses progress within and across relevant technology areas based on actual experimental results relative to technical targets and cost goals from design cases and includes technical, economic, and environmental criteria as available. Overall assessments of biofuel pathways begin with feedstock production and the logistics of transporting the feedstock from the farm or plantation to the conversion facility or biorefinery. The conversion process itself is modeled in detail as part of the SOT analysis. The teams then develop an estimate of the biofuel minimum selling price (MSP) and assess the cost competitiveness of the biofuel with conventional fuels such as gasoline.

Estimating biofuel feedstock water footprints using system dynamics

Increased biofuel production has prompted concerns about the environmental tradeoffs of biofuels compared to petroleum-based fuels. Biofuel production in general, and feedstock production in particular, is under increased scrutiny. Water footprinting (measuring direct and indirect water use) has been proposed as one measure to evaluate water use in the context of concerns about depleting rural water supplies through activities such as irrigation for large-scale agriculture. Water footprinting literature has often been limited in one or more key aspects: complete assessment across multiple water stocks (e.g., vadose zone, surface, and ground water stocks), geographical resolution of data, consistent representation of many feedstocks, and flexibility to perform scenario analysis. We developed a model called BioSpatial H2O using a system dynamics modeling and database framework. BioSpatial H2O could be used to consistently evaluate the complete water footprints of multiple biomass feedstocks at high geospatial resolutions. BioSpatial H2O has the flexibility to perform simultaneous scenario analysis of current and potential future crops under alternative yield and climate conditions. In this proof-of-concept paper, we modeled corn grain (Zea mays L.) and soybeans (Glycine max) under current conditions as illustrative results. BioSpatial H2O links to a unique database that houses annual spatially explicit climate, soil, and plant physiological data. Parameters from the database are used as inputs to our system dynamics model for estimating annual crop water requirements using daily time steps. Based on our review of the literature, estimated green water footprints are comparable to other modeled results, suggesting that BioSpatial H2O is computationally sound for future scenario analysis. Our modeling framework builds on previous water use analyses to provide a platform for scenario-based assessment. BioSpatial H2Os system dynamics is a flexible and user-friendly interface for on-demand, spatially explicit, water use scenario analysis for many US agricultural crops. Built-in controls permit users to quickly make modifications to the model assumptions, such as those affecting yield, and to see the implications of those results in real time. BioSpatial H2Os dynamic capabilities and adjustable climate data allow for analyses of water use and management scenarios to inform current and potential future bioenergy policies. The model could also be adapted for scenario analysis of alternative climatic conditions and comparison of multiple crops. The results of such an analysis would help identify risks associated with water use competition among feedstocks in certain regions. Results could also inform research and development efforts that seek to reduce water-related risks of biofuel pathways.

A case study to examine the imputation of missing data to improve clustering analysis of building electrical demand

Building performance data are widely used for daily operation, improving building efficiency, identifying and diagnosing performance problems, and commissioning. In this study, the authors explore the use of missing data imputation and clustering on an electrical demand dataset. The objective was to compare four approaches of data imputation and clustering analysis. Results of this study suggest that using multiple imputation to fill in missing data prior to performing clustering analysis results in more informative clusters. Commonly used methods to fill in missing data lead to changes in cluster membership that are not suggestive of a change in the buildings performance, but instead is a result of the choice of imputation method used. Practical application : The authors demonstrate, through the use of a case study, the application of a statistically sound method for filling in missing data in large buildings performance datasets. The methods used in this analysis are available through the open-source programming language R and are straight forward to implement. The approach demonstrated in this case study could aid buildings analysts with fault detection and continuous commissioning of large commercial buildings.