This study quantifies and compares the life cycle greenhouse gas (GHG) emissions of renewable diesel (RD), sustainable aviation fuel (SAF), and biodiesel (BD) produced from two U.S. canola production systems: 1) emerging intermediate winter canola, typically grown in double- or relay-cropping systems between the growing seasons of main crops, and 2) main canola, mostly spring canola but also including winter canola, which are grown as primary crops occupying the field for a full growing season. Using the Research and Development version of the Greenhouse gases, Regulated Emissions, and Energy use in Technologies (R&D GREET) model and the most up-to-date life cycle inventory data-field trial data for intermediate winter canola (>37,000 acres) and recent national survey data for spring canola-this life cycle analysis (LCA) estimates the direct emissions from canola cultivation and harvest, the conversion of canola into fuels, fuel transportation, and combustion. In addition, we account for market-mediated emissions associated with a scenario of 0.5 billion gallons per year of spring canola-based biofuels, including induced land use change (ILUC), induced other crop (nonfeedstock) production changes, and induced livestock production changes. For intermediate winter canola, these market-mediated effects were not modeled, as ILUC is expected to be negligible due to its integration into existing rotations, and data are currently insufficient to reliably quantify other market-mediated changes. The estimated life cycle direct emissions of RD/SAF derived from intermediate winter canola and main spring canola are about 32 and 33 g of CO2-equivalent per megajoule of fuel (g CO(2)e/MJ), respectively. Corresponding emissions for BD from intermediate winter canola and main spring canola are about 30 and 31 g of CO(2)e/MJ, respectively. Farming is the dominant emissions source for both canola systems, with intermediate winter canola and main spring canola emitting about 19 and 20 g of CO(2)e/MJ, respectively. ILUC and other induced changes increase emissions of main spring canola-derived RD/SAF and BD by about 18 and 17 g of CO(2)e/MJ, respectively. These results indicate that the GHG emissions of biofuels produced from the two canola systems may differ substantially due to the different land use dynamics of the systems.

Dedicated energy crops are promising feedstocks to make biofuels including jet fuels. This study applies life cycle analysis (LCA) to estimate direct well-to-wake (WTW) greenhouse gas (GHG) emissions (g CO2e/MJ) for jet fuel derived from five energy crops-biomass sorghum, miscanthus, switchgrass, poplar, and willow-via Fischer-Tropsch-to-Jet (FTJ) and Ethanol-to-Jet (ETJ) pathways. The WTW boundary includes direct emissions from biomass production, fuel production, and fuel combustion. The R&D GREET model is expanded to conduct the LCA, using national average biomass yields and farming inputs from the 2023 Billion-Ton Study. In addition, this study estimates emissions from market-mediated effects, including induced land-use change, induced other crop (non-feedstock) production changes, and induced livestock production changes using global economic and emissions factor models. On a per-dry U.S. ton basis, cultivation and harvest emissions are lowest for willow (51,565 g CO2e) and highest for biomass sorghum (104,488 g CO2e). Per-acre results show similarly high emissions for sorghum and lowest values for poplar and willow. Direct WTW emissions are substantially lower for FTJ (biomass sorghum: 5.5; miscanthus: 10.3; switchgrass: 11.7; poplar: 11.9; and willow: 8.7 g CO2e/MJ) than ETJ (33.2; 33.8; 34.8; 36.2; and 31.7 g CO2e/MJ, respectively). When market-mediated emissions are included, miscanthus exhibits the lowest total emissions across energy crop pathways. Although results are sensitive to modeling assumptions, they indicate that high-yielding perennial and woody crops, particularly when planted on marginal land, could significantly reduce WTW emissions for bio-jet fuels by combining low direct emissions with soil carbon gains and favorable market-mediated effects.

Sustainable aviation fuel (SAF) is crucial for improving energy security, enhancing domestic production, and reducing carbon emissions in the aviation sector. Among various SAF production technologies, the ethanol-to-jet (ETJ) pathway is a promising option due to its economic viability and technological maturity. This study integrates a techno-economic analysis (TEA) and a life cycle analysis (LCA) to evaluate emissions reduction strategies for SAF production via the ETJ pathway, considering use of ethanol derived from both corn grain and corn stover. Conventional corn grain-derived ETJ fuel reduces greenhouse gas (GHG) emissions by 22 % compared to fossil jet fuel, with potential reductions of 26 %-96 % when incorporating renewable energy sources, with a 6 %-32 % increase in the minimum fuel selling price (MFSP). Corn stover-derived ETJ achieves a 77 % GHG reduction but with higher MFSPs compared to corn grain ETJ. Carbon capture and storage (CCS without considering the cost for piping and sequestration, only compression) reduces the emissions of corn grain-derived ETJ by up to 32 gCO2e/MJ and enables negative emissions for corn stover-derived ETJ, with MFSP increases ranging from 1 % to 22 %. While carbon capture and utilization (CCU) increase ethanol yield by 47 %, it raises MFSPs by 54 % due to high electricity demand. Sustainable farming practices provide only limited carbon intensity (CI) reductions individually but do offer cumulative benefits when combined. These findings highlight the trade-offs between cost and environmental impact, providing insights to optimize SAF production strategies and support aviation sector goals for emissions reduction.

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Consistent methods are essential for generating country and region-specific estimates of greenhouse gas (GHG) emissions used for reporting and policymaking. The estimates of direct N2O emissions from U.S. agricultural soils have primarily relied on the use of emission factors (EFs, Tier-1) and process-based models (Tier-3). However, Tier-1 estimates are relatively crude while Tier-3 calculations can be costly. This work addressed this gap by developing a Tier-2, regression-based approach by leveraging a meta-database containing 1883 field N2O observations together with environmental and management covariates from 139 studies. Our results estimated higher monthly soil N2O emissions (N2Om, kg N/ha) during the growing season (0.38) than the fallow period (0.15), highlighting the importance of considering measurement periods when utilizing meta-databases for analyzing N2O drivers. Significantly different N2Om were found for tillage practices (conventional > no-till: 0.42 > 0.27), fertilizer type (liquid > solid manure: 0.55 > 0.32), and soil texture (fine > coarse: 0.36 > 0.22). The comparisons of the influence of crop type and rotation, water management, and soil order on N2O emissions are complicated by regional data availability and interactions among different factors. Additionally, the finding that N2O emissions reported based on area (N2Om), N input rate (EF), or yield can alter treatment rankings underscores the need to establish transparent criteria for rewarding or discouraging regionally-based management practices using N2O metrics. Finally, we show how General Linear Models (GLMs) can be used to estimate country and regional Tier-2 N2Om using a suite of covariates. Our GLMs identified tillage, water management, N input type and rate, soil properties, and elevation as the most influential covariates for the conterminous U.S. The limited accuracy of regional-scale GLMs, however, suggests the need to further improve the quality and availability of GHG and covariate data through concerted efforts in data collection.

Offshore macroalgae production offers the potential to provide valuable biomass for food, energy, and higher value products without the use of land or freshwater while using excess nutrients and carbon dioxide. To realize this potential, the Macroalgae Research Inspiring Novel Energy Resources program of the Advanced Research Projects Agency-Energy has initiated projects to develop advanced cultivation technologies that enable the costand energy-efficient production of macroalgal biomass. This study addresses the life-cycle greenhouse gas emissions and energy return on investment for five U.S. offshore macroalgae production systems designed for deployment at the thousand-hectare scale using a detailed module developed within the GREET life-cycle analysis model for this study. The carbon intensity of macroalgae production system designs, expressed as kg of carbon dioxide equivalent per dry metric ton of algae harvested, vary widely from 49 to 220 and confirm that biomass productivity has the highest degree of sensitivity across the model parameters tested. Regardless of the system designs, the upstream and combustion emissions from fuel use are the key contributor (over 45 %) to carbon intensity, indicating that the use of low-carbon fuels (e.g., renewable diesel) could further reduce greenhouse gas emissions. Further studies need to specify the market opportunity and specific product slates for macroalgae to provide a complete picture of the environmental impacts of macroalgal feedstock.

Achieving the United States' target of net-zero greenhouse gas emissions by 2050 will require technological transformations and energy sector migration.

Soil organic carbon (SOC) sequestration through sustainable farming offers great opportunities for agricultural and economy-wide decarbonization and advances the discussion about feedstock certification under bioenergy regulations to incentivize farmers supplying low-carbon feedstocks for biofuels and bioproducts. This situation has prompted researchers to utilize process-based simulation models to quantify SOC changes and their impacts on the carbon intensities of feedstock production. The premise behind such applications, though, is that models continue to be calibrated and validated with databases that cover various soil/climate/farming conditions.The present work demonstrates the use of SOC stock data from large-scale soil databases to evaluate regional simulations of historical changes in SOC stocks and, further, to estimate parameters via an inverse modeling approach. We employed a parameterized version of CENTURY (pCENTURY) to simulate baseline SOC stocks in counties in the U.S. Midwest where two databases—the Unified North American Soil Map and the Rapid Carbon Assessment—record reference SOC stocks at single time points over the period of 1970s ~ 2010s. We first conducted long-term “spin-up” simulations to generate the baseline SOC stocks in topsoils (0–30 cm depth) and subsoils (30–100 cm depth) with default parameter values and given model inputs. For comparison, we inversely modeled reference SOC stocks to adjust the edaphic coefficients that control SOC decompositions, and finally we simulated calibrated baselines. Overall, the baseline SOC stocks simulated over modern agricultural periods were comparable to but smaller than either of the reference SOC stocks without calibration. However, in about one-fourth of the 156 counties examined, references were much larger than baselines (>20 Mg C/ha), regardless of soil depth. On the counties, inverse modeling of the baselines resulted in larger SOC loss over the 30-yr projection period, which led to either lower C sequestrations in topsoils or higher C emissions in subsoils, compared to those calculated from uncalibrated baselines. However, when SOC changes induced by a new farming practice were calculated relative to baselines, additional SOC sequestration due to the alternative practice was not significantly affected by model calibration. This finding provides insights into determining the baseline SOC stocks from model simulations and calibrations and calculating the additionality for SOC in a consistent way.

Data from : A Global Meta-analysis of Cover Crop Response on Soil Carbon Storage Within a Corn Production System

By influencing soil organic carbon (SOC), cover crops play a key role in shaping soil health and hence the system's long-term sustainability. However, the magnitude by which cover crops impacts SOC depends on multiple factors, including soil type, climate, crop rotation, tillage type, cover crop growth, and years under management. To elucidate how these multiple factors influence the relative impact of cover crops on SOC, we conducted a meta-analysis on the impacts of cover crops within rotations that included corn (Zea mays L.) on SOC accumulation. Information on climatic conditions, soil characteristics, management, and cover crop performance was extracted, resulting in 198 paired comparisons from 61 peer-reviewed studies. Over the course of each study, cover crops on average increased SOC by 7.3% (95% CI, 4.9%-9.6%). Furthermore, the impact of cover crop-induced increases in percent change SOC was evaluated across soil textures, cover crop types, crop rotations, biomass amounts, cover crop durations, tillage practices, and climatic zones. Our results suggest that current cover crop-based corn production systems are sequestering 5.5 million Mg of SOC per year in the United States and have the potential to sequester 175 million Mg SOC per year globally. These findings can be used to improve carbon footprint calculations and develop science-based policy recommendations. Taken altogether, cover cropping is a promising strategy to sequester atmospheric C and hence make corn production systems more resilient to changing climates.

built in MS Excel. Land change area data is from Purdue University's Global Trade Analysis Project (GTAP) model, a computable general equilibrium (CGE) economic model. Section 2 describes the GTAP data CCLUB uses and how these data were modified to reflect shrubland transitions. Feedstock- and spatially-explicit below-ground carbon content data for the United States were generated with a surrogate model for CENTURY's soil organic carbon sub-model (Kwon and Hudson 2010) as described in Section 3. CENTURY is a soil organic matter model developed by Parton et al. (1987). The previous CCLUB version used more coarse domestic carbon emission factors. Above-ground non-soil carbon content data for forest ecosystems was sourced from the USDA/NCIAS Carbon Online Estimator (COLE) as explained in Section 4. We discuss emission factors used for calculation of international greenhouse gas (GHG) emissions in Section 5. Temporal issues associated with modeling LUC emissions are the topic of Section 6. Finally, in Section 7 we provide a step-by-step guide to using CCLUB and obtaining results.

Brazil is the second-largest ethanol producer in the world, primarily using sugar cane as feedstock. To foster biofuel production, the Brazilian government implemented a national biofuel policy, known as RenovaBio, in which greenhouse gas (GHG) emission reduction credits are provided to biofuel producers based on the carbon intensities (CI) of the fuels they produce. In this study, we configured the GREET model to evaluate life cycle GHG emissions of Brazilian sugar cane ethanol, using data from 67 individual sugar cane mills submitted to RenovaBio in 2019/2020. The average CI per megajoule of sugar cane ethanol produced in Brazil for use in the U.S. was estimated to be 35.2 g of CO2 equivalent, a 62% reduction from U.S. petroleum gasoline blendstock without considering the impacts of land use change. The three major GHG sources were on-field N2O emissions (24.3%), sugar cane farming energy use (24.2%), and sugar cane ethanol transport (19.3%). With the probability density functions for key input parameters derived from individual mill data, we performed stochastic simulations with the GREET model to estimate the variations in sugar cane ethanol CI and confirmed that despite the larger variations in sugar cane ethanol CI, the fuel provided a robust GHG reduction benefit compared to gasoline blendstock.

Direct emissions of soil nitrous oxide during a growing season (N2Ogs) can be quantified with process-based models considering interactions between management, climate, and soil moisture when key data are available. We used an adapted “parameterized CENTURY/DAYCENT-model” (pCENTURY) calibrated with crop growth and soil organic matter decay coefficients at the county-level for the estimation of N2Ogs in the United States Corn Belt. Model estimated N2O-emissions from corn-based biofuels scenarios considering crop rotation, fertilizer inputs, tillage, and weather were compared against meta-summary of field observations from 55 studies. Both model and meta-summary ranked N2Ogs-emissions to be corn > wheat > soybean phase while model likely underestimated cover crop N2Ogs-emissions. The N2Ogs-emissions and the associated emission factors (EFs) were modeled and summarized to be greater after anhydrous ammonia than urea application and from conventional tilled than non-tilled fields. Modeled and observed N2Ogs-emissions after organic and inorganic fertilizer amendment did not differ due to high variability associated with the treatments. However, the organic fertilizer associated EFs were greater according to meta-summary data because of N input rates. Regionalized weather scenarios indicate hotspots for N2Ogs-emissions can occur where crop N uptake is limited during dry years and in eastern states also during normal or wet seasons. The pCENTURY-derived N2Ogs EFs (0.91 ± 0.19%) for counties investigated were only slightly lower than literature (1.07 ± 0.57%) or Tier-1 (1%) values. Our preliminary evaluation of regional soil moisture estimates showed reasonable agreement between monthly soil moisture estimates and the North American Soil Moisture Dataset during the growing season, but overestimation of soil moisture in winter-spring can influence the estimates of annual N2O emissions so future work is needed to calibrate soil moisture-associated model parameters. Our work provided scenario-based estimates of climate and management impacts on soil N2Ogs-emissions together with valuable spatial insights into EFs that will be improved by more accurate information of fertilizer inputs and more temporally refined model evaluation.