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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Integrating Carbon Capture, Utilization, & Sequestration into Chemical Pulp Mills

The U.S. pulp and paper industry presents a unique and largely untapped opportunity for large- scale carbon dioxide removal (CDR). Unlike most industrial sectors, pulp mills rely heavily on biomass, meaning that much of their carbon emissions originate from atmospheric CO₂ that was recently captured by plants. If this biogenic CO₂ can be captured and permanently stored, pulp mills can be transformed from carbon emitters into net carbon removal facilities. This project was motivated by that opportunity and aimed to develop and evaluate integrated, low-cost strategies for capturing, utilizing, and sequestering CO₂ within existing chemical pulping operations. The scope of this work focused on four complementary innovations designed to integrate seamlessly into kraft pulp mill infrastructure: (1) in situ CO₂ capture within the recovery cycle, (2) oxy-fuel retrofitting of the rotary lime kiln to produce a high-purity CO₂ stream, (3) ex situ CO₂ capture and mineralization using pulp mill residues (dregs, grits, and lime mud), and (4) beneficial reuse of these residues as mineral carbonate fertilizers. The project combined process modeling, laboratory experimentation, life cycle assessment (LCA), and field trials to evaluate the technical feasibility, economic viability, and environmental impact of these approaches. The results demonstrate that pulp mills can serve as effective platforms for carbon removal when equipped with integrated carbon capture systems. Process modeling showed that combining sodium spiking with oxy-fuel calcination significantly enhances CO₂ capture efficiency while reducing costs by up to 31% compared to conventional configurations. Experimental work further revealed that calcination behavior in high-CO₂ environments differs substantially from traditional systems, leading to the development of a new kinetic model that predicts reaction rates under these conditions. This model provides essential design guidance for next-generation decarbonized lime kilns. In parallel, the project demonstrated that alkaline mineral residues generated during pulping operations can be repurposed as a sustainable alternative to agricultural lime. Across a wide range of soils in the southeastern United States, these materials performed equivalently to commercial lime in adjusting soil pH while offering lower greenhouse gas emissions and reduced cost. Field and greenhouse studies confirmed that crop and tree growth responses were comparable, supporting their viability as a drop-in replacement. This co-product pathway provides a practical utilization strategy that offsets costs and improves overall system economics. A major contribution of this project is the first comprehensive life cycle assessment of carbon removal in pulp and paper systems across multiple system boundaries. Results show that retrofitted mills can achieve carbon removal efficiencies ranging from 12% to 92%, depending on how the system is defined. This finding highlights a critical issue in carbon accounting: reported performance is highly sensitive to methodological choices. By explicitly quantifying these differences, this work provides valuable guidance for policymakers, carbon registries, and project developers working to standardize carbon removal metrics. From a commercialization perspective, the technologies investigated in this project are well- aligned with existing industrial infrastructure, minimizing the need for entirely new facilities. 3 DE-EE0009413 Industry engagement throughout the project—including collaboration with pulp and paper companies, equipment manufacturers, and carbon removal developers—has accelerated the transition from research to deployment. Notably, a commercial developer is actively pursuing carbon capture projects at pulp mills in the southeastern United States and has cited this research as a contributing foundation. The emergence of voluntary carbon markets and long-term offtake agreements further strengthens the business case for implementation. The broader public benefits of this work are significant. By enabling large-scale carbon removal using existing industrial systems, this approach offers a near-term pathway to reduce atmospheric CO₂ concentrations while supporting domestic manufacturing and rural economies. The reuse of industrial residues as fertilizers reduces reliance on mined materials, lowers costs for farmers, and decreases environmental impacts associated with conventional lime production. In addition, the project has supported workforce development by training graduate students and researchers in carbon capture technologies, helping to build capacity in a critical area of national interest. In conclusion, this project demonstrates that integrated carbon capture, utilization, and sequestration in pulp mills is both technically feasible and economically promising. By combining process innovation, experimental validation, and systems-level analysis, the work advances the understanding of how biomass-based industries can contribute to climate mitigation. The findings provide a strong foundation for commercial deployment and offer a scalable solution for transforming a major U.S. industry into a source of durable carbon removal.

09 BIOMASS FUELS↗
DOE OSTI2023

Pathways for Negative-Emissions Hydrogen: Opportunities and R&D Needs

The Carbon-Negative Hydrogen Workshop, hosted by NREL in Golden, Colorado, focused on developing a shared understanding of the importance of and opportunities for generation and use of carbon-negative H2 in industrial decarbonization. Attendees helped define research needs, spot barriers to commercialization, and identify technical challenges, opportunities, and knowledge gaps for this emerging technology area. Workshop discussion themes included energy justice in early-stage research, R&D pathways for negative-emissions H2, biomass gasification to carbon-negative H2, and policy and carbon accounting considerations.

biomass↗
DOE OSTI2025

A Comparative Analysis of Waste-as-a-Feedstock Accounting Methods in Life Cycle Assessments

Global waste generation is a ubiquitous challenge, driving a paradigm shift towards viewing waste as a valuable resource for a circular economy across diverse sectors. While innovative waste-to-resource pathways are crucial, rigorous Life Cycle Assessment (LCA) is essential to ensure the pathways are an important part of current practices. However, LCA application to waste valorization varies, leading to incomparable results due to differing methodological choices. This paper examines three key nuances in waste-as-resource LCAs: the zero-burden assumption, the biogenic carbon neutrality assumption, and the benchmark assumption for emissions avoidance. Using a waste gasification to hydrogen case study, we demonstrate how these methodological decisions impact LCA outcomes. Our findings reveal that waste composition significantly influences the results and highlight challenges associated with biogenic carbon accounting under various system boundary assumptions. Emissions avoidance accounting requires multi-functional unit perspectives and robust benchmark selection. This paper clarifies these accounting approaches, empirically illustrates their influence, and discusses broad implications for accurate sustainability assessment, emphasizing the critical role of transparent LCA choices for effective policy and investment in circular economy solutions.

LCA method↗
DOE OSTI2024

Spatially Varying Costs of GHG Abatement with Alternative Cellulosic Feedstocks for Sustainable Aviation Fuels

Cellulosic biomass-based sustainable aviation fuels (SAFs) can be produced from various feedstocks. The breakeven price and carbon intensity of these feedstock-to-SAF pathways are likely to differ across feedstocks and across spatial locations due to differences in feedstock attributes, productivity, opportunity costs of land for feedstock production, soil carbon effects, and feedstock composition. We integrate feedstock to fuel supply chain economics and life-cycle carbon accounting using the same system boundary to quantify and compare the spatially varying greenhouse gas (GHG) intensities and costs of GHG abatement with SAFs derived from four feedstocks (switchgrass, miscanthus, energy sorghum, and corn stover) at 4 km resolution across the U.S. rainfed region. We show that the optimal feedstock for each location differs depending on whether the incentive is to lower breakeven price, carbon intensity, or cost of carbon abatement with biomass or to have high biomass production per unit land. The cost of abating GHG emissions with SAF ranges from $\$181$ Mg –1 CO 2 e to more than $\$444$ Mg –1 CO 2 e and is lowest with miscanthus in the Midwest, switchgrass in the south, and energy sorghum in a relatively small region in the Great Plains. While corn stover-based SAF has the lowest breakeven price per gallon, it has the highest cost of abatement due to its relatively high GHG intensity. Furthermore, our findings imply that different types of policies, such as volumetric targets, tax credits, and low carbon fuel standards, will differ in the mix of feedstocks they incentivize and locations where they are produced in the U.S. rainfed region.

09 BIOMASS FUELS↗
DOE OSTI2024

Data for Spatially Varying Costs of GHG Abatement with Alternative Cellulosic Feedstocks for Sustainable Aviation Fuels

Cellulosic biomass-based sustainable aviation fuels (SAFs) can be produced from various feedstocks. The breakeven price and carbon intensity of these feedstock-to-SAF pathways are likely to differ across feedstocks and across spatial locations due to differences in feedstock attributes, productivity, opportunity costs of land for feedstock production, soil carbon effects, and feedstock composition. We integrate feedstock to fuel supply chain economics and life-cycle carbon accounting using the same system boundary to quantify and compare the spatially varying greenhouse gas (GHG) intensities and costs of GHG abatement with SAFs derived from four feedstocks (switchgrass, miscanthus, energy sorghum, and corn stover) at 4 km resolution across the U.S. rainfed region. We show that the optimal feedstock for each location differs depending on whether the incentive is to lower breakeven price, carbon intensity, or cost of carbon abatement with biomass or to have high biomass production per unit land. The cost of abating GHG emissions with SAF ranges from $181 Mg−1 CO2e to more than $444 Mg−1 CO2e and is lowest with miscanthus in the Midwest, switchgrass in the south, and energy sorghum in a relatively small region in the Great Plains. While corn stover-based SAF has the lowest breakeven price per gallon, it has the highest cost of abatement due to its relatively high GHG intensity. Our findings imply that different types of policies, such as volumetric targets, tax credits, and low carbon fuel standards, will differ in the mix of feedstocks they incentivize and locations where they are produced in the U.S. rainfed region. Note: Column V in TableS7_DayCentSimulatedYield.csv should be labelled Corn Stover CoSo-NT-50% Max.

Geospatial↗
DOE OSTI2023

The Effect of Rapid Development on Soil CO2 Efflux in a Cellulosic Biofuel Stand

As awareness of climate change increases, the need for carbon neutral fuel sources is growing. Lignocellulosic biofuel derived from pine trees has been suggested as one potential energy source; however, it requires more research before its efficacy for climate change mitigation can be determined. Due to the large share of forest carbon held in soils and the extensive area of pine plantations in the southeast U.S., a better understanding of plantation soil carbon dynamics is critical for biofuel carbon accounting. This study evaluated the effects of canopy development and productivity on soil CO2 efflux, a proxy for soil respiration (Rs), in an intensively managed loblolly pine (Pinus taeda) stand over a period from May 2015 to December 2019. We found that leaf area index (LAI) and gross ecosystem production (GEP), as well as meteorological variables, had significant effects on Rs, but that both overall Rs and soil carbon pools did not increase over the course of the study. We thus hypothesize that GEP and LAI had intra-annual effects on Rs, and that the lack of change in Rs is the result of an increase in autotrophic respiration (Ra) that offset a decrease in decomposition of the previous stand’s organic matter.

09 BIOMASS FUELS↗
DOE OSTI2025

Blue Carbon Stocks Along the Pacific Coast of North America Are Mainly Driven by Local Rather Than Regional Factors

Coastal wetlands, including seagrass meadows, emergent marshes, mangroves, and temperate tidal swamps, can efficiently sequester and store large quantities of sediment organic carbon (SOC). However, SOC stocks may vary by ecosystem type and along environmental or climate gradients at different scales. Quantifying such variability is needed to improve blue carbon accounting, conservation effectiveness, and restoration planning. We analyzed SOC stocks in 1,284 sediment cores along >6,500 km of the Pacific coast of North America that included large environmental gradients and multiple ecosystem types. Tidal wetlands with woody vegetation (mangroves and swamps) had the highest mean stocks to 1 m depth (357 and 355 Mg ha −1 , respectively), 45% higher than marshes (245 Mg ha −1 ), and more than 500% higher than seagrass (68 Mg ha −1 ). Unvegetated tideflats, though not often considered a blue carbon ecosystem, had noteworthy stocks (148 Mg ha −1 ). Stocks increased with tidal elevation and with fine (<63 μm) sediment content in several ecosystems. Stocks also varied by dominant plant species within individual ecosystem types. At larger scales, marsh stocks were lowest in the Sonoran Desert region of Mexico, and swamp stocks differed among climate zones; otherwise stocks showed little correlation with ecoregion or latitude. More variability in SOC occurred among ecosystem types, and at smaller spatial scales (such as individual estuaries), than across regional climate gradients. These patterns can inform coastal conservation and restoration priorities across scales where preserving stored carbon and enhancing sequestration helps avert greenhouse gas emissions and maintains other vital ecosystem services.

emergent marsh↗
DOE OSTI2025

A tale of two towers: comparing NEON and AmeriFlux data streams at Bartlett Experimental Forest

Long-term ecological data are essential for detecting impacts of climate change and other global change factors, and for making informed predictions about future change. However, long-term measurements are rarely replicated at the site level, which raises questions about their representativeness. We used a multiscale approach to evaluate the agreement of parallel observations from AmeriFlux and NEON (National Ecological Observatory Network) towers at Bartlett Experimental Forest, New Hampshire, USA. The two towers are separated by a horizontal distance of 93 m. Here, we focused our analysis on standard meteorological variables; fluxes of CO 2 , sensible heat, and latent heat measured by eddy covariance; and phenology derived from PhenoCam imagery. Results suggest excellent agreement between AmeriFlux and NEON in meteorology and phenology, and good agreement in fluxes at the half-hourly scale. However, large disagreements in CO 2 and latent heat fluxes occurred at the annual scale, with implications especially for the forest carbon balance. The AmeriFlux tower measurements indicate a site that is close to carbon-neutral (-8 ± 65 g C m -2 y -1 , mean ± 1 SD), whereas the NEON tower measurements indicate a forest that is a carbon sink (-137 ± 10 g C m -2 y -1 ). Causes of this disagreement may include measurement height (26 m vs. 35 m), which resulted in different flux footprints being measured by the two towers, and differences in the flux measurement systems. Our results suggest the need for caution when attempting to merge long-term flux data from two different measurement platforms, and when using measurements from any one measurement platform to inform decision-making on issues related to carbon accounting or natural climate solutions.

Carbon cycle↗
DOE OSTI2026

Forest aboveground biomass estimation through integration of sentinel-2 and PALSAR-2 time series: assessing models trained on GEDI and field inventory benchmarks

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico’s National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI’s reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (≥200 Mg/ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESA-CCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCI-BIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m −2 ) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m −2 (mean ≈ 350 g m −2 ), while predicted values ranged from 0 to ∼4000 g m −2 (mean ≈ 275 g m −2 ), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m −2 and R 2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.

Climate change↗
DOE OSTI2022

Methane and Carbon Dioxide Fluxes in a Temperate Tidal Salt Marsh: Comparisons Between Plot and Ecosystem Measurements

Tidal wetlands are comprised of complex interdependent pathways where measurements of carbon exchange are often scale dependent. Common data collection methods (i.e., chambers and eddy covariance) are inherently constrained to different spatial and temporal scales which could generate biased information for applications of carbon accounting, identifying functional relationships and predicting future responses to climate change. Consequently, it is needed to systematically evaluate measurements derived from multiple approaches to identify differences and how techniques complement each other to reconcile interpretations. For our study, to accomplish this, we tested ecosystem-scale eddy covariance with plot-scale chamber measurements within a temperate salt marsh. We found good agreement (R 2 = 0.71–0.95) when comparing measurements of CH 4 emissions and CO 2 exchange but this agreement was dependent upon canopy phenology with discrepancies mainly arising during senescence and dormancy phenophases. The environmental drivers for CH 4 and CO 2 fluxes were mostly preserved across different measurement techniques, but the number of drivers increases while their individual strength decreases at the ecosystem scale. Empirical upscaling models parameterized with chamber measurements overestimated annual net ecosystem exchange (NEE; 108%) and gross primary production (GPP; 12%) while underestimating ecosystem respiration (Reco; 14%) and CH 4 emissions (69%) compared to eddy covariance measurements. Our results suggest that the environmental complexity of CH 4 and CO 2 fluxes in salt marshes may be underestimated by chamber-based measurements, and highlights how different techniques are complementary while considering limitations at each level of measurement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Empirically-calibrated H100 node power models for accurate AI training energy estimation

Accurately quantifying the energy use of artificial intelligence (AI) training is critical for infrastructure planning, carbon accounting, and sustainable data center operation, but few studies have directly measured the power consumption of production workloads on contemporary hardware. By combining empirical measurements from Brookhaven National Laboratory during AI training on 8-graphics-processing-unit H100 systems with open-source benchmarking data, we develop statistical models relating computational intensity to node-level power consumption. We measure the gap between manufacturer-rated thermal design power (TDP) and actual power demand during AI training. Our analysis reveals that even computationally intensive workloads operate at only 76% of the 10.2 kW TDP rating. Our architecture-specific model, calibrated to floating-point operations, predicts energy consumption with 11.4% mean absolute percentage error, significantly outperforming TDP-based approaches (27%–37% error). We identified distinct power signatures between transformer and convolutional neural network architectures, with transformers showing characteristic fluctuations that may impact grid stability. These results provide a measurement-grounded basis for improving AI training energy estimates, enabling more reliable infrastructure sizing, cost projections, and environmental impact assessments.

Newkirk, Alex C↗
DOE OSTI2021

Adding Tree Rings to North America's National Forest Inventories: An Essential Tool to Guide Drawdown of Atmospheric CO 2

Tree-ring time series provide long-term, annually resolved information on the growth of trees. When sampled in a systematic context, tree-ring data can be scaled to estimate the forest carbon capture and storage of landscapes, biomes, and—ultimately—the globe. A systematic effort to sample tree rings in national forest inventories would yield unprecedented temporal and spatial resolution of forest carbon dynamics and help resolve key scientific uncertainties, which we highlight in terms of evidence for forest greening (enhanced growth) versus browning (reduced growth, increased mortality). We describe jump-starting a tree-ring collection across the continent of North America, given the commitments of Canada, the United States, and Mexico to visit forest inventory plots, along with existing legacy collections. Failing to do so would be a missed opportunity to help chart an evidence-based path toward meeting national commitments to reduce net greenhouse gas emissions, urgently needed for climate stabilization and repair.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Soils' dirty little secret: Depth–based comparisons can be inadequate for quantifying changes in soil organic carbon and other mineral soil properties

Quantifying changes in soil organic carbon (SOC) stocks and other soil properties is essential for understanding how soils will respond to land management practices and global change. Although they are widely used, comparisons of SOC stocks at fixed depth (FD) intervals are subject to errors when changes in bulk density or soil organic matter occur. The equivalent soil mass (ESM) method has been recommended in lieu of FD for assessing changes in SOC stocks in mineral soils, but ESM remains underutilized for SOC stocks and has rarely been used for other soil properties. In this paper, we draw attention to the limitations of the FD method and demonstrate the advantages of the ESM approach. We provide illustrations to show that the FD approach is susceptible to errors not only for quantifying SOC stocks but also for soil mass-based properties such as SOC mass percent, C:N mass ratio, and δ 13 C. Here we describe the ESM approach and show how it mitigates the FD method limitations. Using bulk density change simulations applied to an empirical dataset from bioenergy cropping systems, we show that the ESM method provides consistently lower errors than FD when quantifying changes in SOC stocks and other soil properties. To simplify the use of ESM, we detail how the method can be integrated into sampling schemes, and we provide an example R computer script that can perform ESM calculations on large datasets. We encourage future studies, whether temporal or comparative, to utilize sampling methods that are amenable to the ESM approach. Overall, we agree with previous recommendations that ESM should be the standard method for evaluating SOC stock changes in mineral soils, but we further suggest that ESM may also be preferred for comparisons of other soil properties including mass percentages, elemental mass ratios, and stable isotope composition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

From Depletion to Restoration: Lessons From Long‐Term Monitoring of Carbon Gains and Losses in Cropping Systems

As global atmospheric CO 2 rapidly approaches a key tipping point, there is an urgent need to implement strategies to reverse this pattern. A generally accepted understanding of carbon (C) in agricultural fields includes: (H1) substantial C loss occurs when natural vegetation is converted to crops, (H2) soils typically reach a steady-state C concentration under contemporary practices, and (H3) improved management or crop selection can enhance soil C stocks over time. Significant variability exists, but studies consistently show large C losses from agricultural ecosystems, supporting H1. Although steady-state C levels (H2) are commonly assumed, measuring C gains or losses in mature agroecosystems is challenging. Efforts to increase soil C storage (H3) have limited data due to the diversity of potential practices, compounded by substantial variability in soil C measurements. Here, long-term (7–17 year) ecosystem C flux data from diverse cropping systems revealed that conventionally tilled annual row crops (maize and soybean) act as significant long-term atmospheric C sources, challenging H2. Furthermore, conservation tillage practices reduced C losses compared with conventional tillage but showed minimal evidence for long-term ecosystem C storage, even after 20+ years. This indicates that no-till practices reduce C losses but imply that no soil C is added, challenging H3. By contrast, perennial Miscanthus × giganteus, Panicum virgatum, and restored tallgrass prairie systems store C at the ecosystem scale more effectively than minimally tilled annual row crops. Analysis over multiple years demonstrates significant ecosystem C storage with perennial crops, varying by species, starting in the first year of transition. These findings, although focused on one region, suggest that the assumptions of steady-state C levels and increased storage from conservation practices do not universally apply and that significant changes to agroecosystems are required to increase C storage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A Biochar Classification System and Associated Test Methods

In this chapter, a biochar classification system related to its use as soil amendment is proposed. This document builds upon previous work: “Standardized product definition and product testing guidelines for biochar that is used in soil” (IBI, 2015) (aka IBI Biochar Standards) and “Guidelines for biochar production for a sustainable production of biochar: European Biochar Certificate” (EBC, 2012-2022) (aka EBC Biochar Standards). It should be noted that the EBC guidelines constitute the basis for biochar certification through the independent government accredited inspection agency, Bio.Inspecta AG. The present document constrains its scope to materials with properties that satisfy the criteria for biochar as defined by either the IBI Biochar Standards or the EBC Standards (Annex I), as required by the current methodologies developed for accounting carbon credits associated with the biochar use in the voluntary market (e.g., Verra, Puro.Earth). The classification system envisions enabling stakeholders and commercial entities to (i) identify the most suitable biochar to fulfil the requirements for a particular soil and/or land-use, (ii) distinguish the application of biochar for specific niches (e.g., soilless agriculture), and (iii) assess the climate-impact value based on biochar properties, production efficiency, and alternative feedstock uses. It is based on the best current knowledge and will need to be improved as new data and knowledge become available in the scientific literature.

biochar, climate change impacts, soil amendment, c↗
DOE OSTI2026

Data for Greenhouse Gas Accounting Procedures in Low Carbon Fuel Policies Overlook the Spatial Variability of Miscanthus-Derived Sustainable Aviation Fuel

Low carbon fuel policies such as the U.S. Renewable Fuel Standard (RFS), Canada Clean Fuel Regulations (CFR), and California Low Carbon Fuel Standard (LCFS) as well as the 45Z tax credit are intended to reduce greenhouse gas (GHG) emissions from transportation. Cellulosic feedstocks, optimized biorefineries, and favorable farming locations can significantly reduce biofuel carbon intensity (CI). Despite advances in field-to-fuel GHG monitoring and flexibility in resource allocation within biorefineries (e.g., governing net electricity production), rigid CI accounting procedures in current policies may limit CI responsiveness across candidate sites and processing facilities. This work examines a hypothetical biomass-to-sustainable aviation fuel (SAF) pathway using miscanthus and alcohol-to-jet (i) to demonstrate how GHG accounting requirements drive estimates of biofuel CIs and (ii) to explore potential CI and financial implications of scenario-specific life cycle assessment (LCA). Results demonstrate that GHG accounting using the CFR/LCFS can reasonably account for distinct levels of net electricity production by a biorefinery, but only the CFR yields similar CI sensitivity to spatially explicit factors (feedstock CI, grid electricity CI) as scenario-specific LCA: most GHG accounting frameworks do not capture CI variation across candidate sites in the United States. Ultimately, this work demonstrates the importance of LCA methodological specifications in low carbon fuel policies and tax credits.

Miscanthus↗
DOE OSTI2023

Carbon Storage Monitoring, Verification, and Accounting (MVA)

Fact sheet about carbon storage monitoring, verification, and accounting (MVA). Includes background on MVA, the Energy & Environmental Research Center, and Plains CO 2 Reduction (PCOR) Partnership Initiative plans for MVA strategy development.

01 COAL, LIGNITE, AND PEAT↗