This write up summarizes the potential for biobased adhesives to be sourced from various material, specifically focusing on the following relevant factors:
1. Current biomass availability,
2. Market costs
3. Locations of industry/supply
4. Projections on how these materials will increase in availability according to their expected increased uses.
Biomass availability for Cross Laminated Timber (CLT) bio-based adhesives
Maggie Davis
September 18th, 2024
Purpose:
This write up summarizes the potential for biobased adhesives to be sourced from various material, specifically focusing on the following relevant factors:
1. Current biomass availability,
2. Market costs
3. Locations of industry/supply
4. Projections on how these materials will increase in availability according to their expected increased uses.
This project contributes to understanding and enhancing socioeconomic and environmental benefits of biofuels through modeling the effect of prices and policy incentives on fuel markets for “hard-to-decarbonize” transportation sectors. The main analytical tool used in this project is the BioTrans model, originally developed to assess and quantify the economic and energy security benefits of biofuels for light-duty vehicles and bioproducts. This project restructured and updated the BioTrans model to assess biofuels for the hard-to-decarbonize transportation sectors such as the aviation and shipping.
The BioTrans model is a market equilibrium model assessing the biofuel supply chain for a 30-year horizon with annual periods. It is a national (United States) model and has states as its spatial units. The model maximizes social surplus, which implies minimizing the costs, while meeting transportation fuel demands. While it takes transportation fuel markets into account endogenously, land allocation decisions and non-biofuel uses of biomass are considered exogenously. The model considers potential synergies or competition for the use of biomass among the different transportation segments as well as the competition between new biofuels and incumbent petroleum-based fuels.
The diagram in Figure 1 summarizes the main components included in BioTrans as of June 2024.
The biomass feedstocks and petroleum products in blue rectangles are those for which the model includes supply curves, and the transportation segments in red boxes are those for which the model includes demand curves. The intermediate activities reflect the steps required to convert biomass into biofuel, and the intermediate products are biofuels required for blending and retail. Each commodity must satisfy a material balance equation so that its sources and sinks match with each other.
The ability to explore the interaction of federal and state-level biofuel policies and their impact on the volume and mix of biofuels produced in the United States is one of the key attributes of the model. As of June 2024, BioTrans contains representations of the following biofuel-related policies and incentives:
Federal
- Renewable Fuel Standard
- Inflation Reduction Act (IRA) tax credits (Section 13201, Section 13202, Section 13203, Section 13704)
State
- California Low Carbon Fuel Standard
- Oregon Clean Fuel Program
- SAF tax credits
- Biodiesel and biomass-based diesel blending mandates
This International Feedstocks data portal supports the Global Biomass Resource Assessment,a multi-country government-led initiative dedicated to advancing the global transition to a sustainable bio-based economy. This product shares data assembled from citable sources around the globe, as reported for current biomass production as well as potential additional future production in some cases. Data were compiled into consistent classes based on the most recent reports received (ranging from 2018 to 2024).
The results from this new global sustainable supply assessment will allow scientists, policymakers, and industry leaders to explore potential sources of biomass as a foundation for a circular and sustainable global bioeconomy, supporting clean fuels, chemicals, materials and other products. The assessment was conducted by researchers at the U.S. Department of Energy (DOE) Oak Ridge National Laboratory (ORNL), with funding provided by the U.S. Department of State, and managed through DOE’s Bioenergy Technologies Office (BETO), on behalf of the CEM Biofuture Initiative and Mission Innovation. This data includes biomass resources available in many developing economies which often do not have fully advanced biomass industries. The assessment also aims to address the need for internationally accepted benchmarks quantifying sustainable biomass feedstock supplies that can be available to support a growing, circular and climate-smart bioeconomy.
The link below provides access to the data which can be filtered by country of interest and resource, as well as timeframe for the available biomass. The data are being shared based on the information received to date (references to sources are noted for each reported nation). We aim to improve and update this preliminary version of the data set in the future, based on user feedback. Please send suggestions for improvement and references to additional sources of data, or corrections to the reported data. [Data comments can be sent to biomass.updates@ornl.gov]
This data can be filtered by country and downloaded for further analysis. For example, the country of Uruguay is summarized below for available resources by year of production.
This dataset was compiled through an inventory and review of current sources of data and biomass resource assessments, including recent (since 2018) national, regional, and global assessments. See individual citations within the dataset.
This dataset contains data on forest production. The forestry products in this dataset includes hardwood, softwood, and mixed, and the dataset was obtained from the database of the 2023 Billion-Ton Report (Davis et al., 2024). The intended use is for the Feedstock Production Emissions to Air Model (FPEAM).
If you would also like access to this dataset, please use the "contact" button for a request to our research staff.
material_class: This indicates the label to denote land source or analysis source corresponding to the forest land chapter of the 2023 Billion-Ton Report.
subclass: This indicates the class of biomass.
resource_name: This indicates the type of resource.
diacls: This indicates the class of tree diameter. Class 1 has a diameter at breast height (DBH) that is greater than 11-inch, Class 2 has a DBH that is between 5- and 11-inch, and Class 3 has a DBH that is less than 5-inch.
owner: This indicates the owner such as public, private, and null or unknown.
fips: This is a geographically defined variable corresponding to counties provided as a 5-digit code.
county: This indicates the name of the county.
state: This indicates the name of the state.
County square Miles: This indicates the total area of the county in square miles including water.
scenario_name: This indicates the name of the scenario defined in the 2023 Billion-Ton Report. “mm” indicates mature market scenario, and it also contains “low”, “high”, and “emerging” scenarios.
scenario_price_offered: This indicates a price offered for a ton of biomass.
production: This indicates the total production of the forestry biomass in dry ton.
production_unit: This indicates the unit of production.
production_energy_content: This indicates the total energy contents in BTU which is production tons multiplied by BTU/ton.
energy_content_unit: This indicates the unit of energy content.
production_density_dtpersqmi: This indicates a production divided by the total area of the county.
harvest_costs: This indicates the cost of harvesting.
harvest_area: This indicates the total harvested area in acres.
harv_area_unit: This indicates the unit of harvested area.
lipid_based: This indicates either the biomass is based on lipid or not.
ash_percentage: This indicates the content of ash in percentage.
This dataset contains harvesting, chipping, and production cost data for forestland production by region and forest harvest system. The dataset supports Biomass from the forested land base analysis in the BT23 (Davis et al., 2024) and subsequent modeling using the Forest Sustainable and Economic Analysis Model (ForSEAM). The cost data was updated by Burton English and is in 2014 dollars and 2021 dollars. Data sources can be found in the accompanying PDF, ‘2021 Biomass Production Costs for the 2024 Billion Ton Analysis’, and the details can be found in the accompanying Microsoft Word file.
Cost_by_FHS_and_Region.csv is a comma-separated file that holds the following variables: Forest_Harvest_Systems: This is for different forest silvicultural and harvest methods.
Region: This is for different production cost zones of the CONUS as defined by ForSEAM.
Slope_over_40%: This is a binary variable to indicate if the slope exceeds 40%.
CutToLength: This is a binary variable to indicate if the cut-to-length method is used.
Cut: This indicates the forest management practice of either thinning or clearcutting.
Type: This indicates the type of forest.
Harverst_Production_Costs_2021$_per_bdt: The harvest costs of production per bone dry ton (bdt) in 2021 dollars.
Chipping_Costs_2021$_per_bdt: The costs of production to chip the biomass per bone dry ton (bdt) in 2021 dollars.
Total_Costs_2021$_per_bdt: The costs of production to harvest and chip the biomass per bone dry ton (bdt) in 2021 dollars.
Harverst_Production_Costs_2014$_per_bdt: The harvest costs of production per bone dry ton (bdt) in 2014 dollars.
Chipping_Costs_2014$_per_bdt: The costs of production to chip the biomass per bone dry ton (bdt) in 2014 dollars.
Total_Costs_2014$_per_bdt: The costs of production to harvest and chip the biomass per bone dry ton (bdt) in 2014 dollars.
Total_Forest_Production_Costs_2021$_per_bdt: The total production costs of forestland biomass per bone dry ton (bdt) in 2021 dollars.
Timber_Costs_2014$_per_dry_ton: The costs for timber per dry ton biomass in 2014 dollars.
Chipper_Costs_2014$_per_dry_ton: The costs for chipper per dry ton biomass in 2014 dollars.
Total_Costs_2014$_per_dry_ton: The costs for timber and chipper per dry ton biomass in 2014 dollars.
Cost_by_Region.csv is a comma-separated file that holds the following variables:
Region: This is for different production cost zones of the CONUS as defined by ForSEAM.
Slope_over_40%: This is a binary variable to indicate if the slope exceeds 40%.
CutToLength: This is a binary variable to indicate if the cut-to-length method is used.
Type: This indicates the type of forest.
Cut: This indicates the forest management practice of either thinning or clearcutting.
Forest_Production_Costs_2021$_per_bdt: The total production costs of forestland biomass per bone dry ton (bdt) in 2021 dollars.
Timber_Costs_2014$_per_dry_ton: The costs for timber per dry ton biomass in 2014 dollars.
Chipper_Costs_2014$_per_dry_ton: The costs for chipper per dry ton biomass in 2014 dollars.
Total_Costs_2014$_per_dry_ton: The costs for timber and chipper per dry ton biomass in 2014 dollars.
Cost_by_FHS.csv is a comma-separated file that holds the following variables:
Forest_Harvest_Systems: This is for different forest silvicultural and harvest methods.
Cut: This indicates the forest management practice of either thinning or clearcutting.
Harverst_Production_Costs_2021$_per_bdt: The harvest costs of production per bone dry ton (bdt) in 2021 dollars.
Chipping_Costs_2021$_per_bdt: The costs of production to chip the biomass per bone dry ton (bdt) in 2021 dollars.
Total_Costs_2021$_per_bdt: The costs of production to harvest and chip the biomass per bone dry ton (bdt) in 2021 dollars.
Harverst_Production_Costs_2014$_per_bdt: The harvest costs of production per bone dry ton (bdt) in 2014 dollars.
Chipping_Costs_2014$_per_bdt: The costs of production to chip the biomass per bone dry ton (bdt) in 2014 dollars.
Total_Costs_2014$_per_bdt: The costs of production to harvest and chip the biomass per bone dry ton (bdt) in 2014 dollars.
This dataset provides additional variables for modelers and other interested stakeholders for yield assumptions for modeled energy crops on agricultural land in the CONUS, as modeled by the POLYSYS model.
The yield unit was changed from lb/ac to dt/ac post-processing.
V0.1 changes include: tillage for subclass like 'energy crop' is now '[null]' and for subclass = 'Intermediate oilseeds' 'till' is now 'CT' (Conventional Tillage), format now uses pipe (|) delimiter.
This dataset can be cited as:
Hellwinckel, C., D. de la Torre Ugarte, H. Cook, M Davis, M. Langholtz. 2024. “A Customized Dataset for Yield from Agricultural Resources Modeled with POLYSYS as highlighted in Chapter 5: Biomass from Agriculture.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2350581.
The chapter relevant to this research can be cited as:
Hellwinckel, C., D. de la Torre Ugarte, J. L. Field, and M. Langholtz. 2024. “Chapter 5: Biomass from Agriculture.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316171.
Date: 20240523
State: Published
Title: A Customized Dataset for Yield from Agricultural Resources Modeled with POLYSYS as highlighted in Chapter 5: Biomass from Agriculture
Authors: Chad Hellwinckel , Daniel DeLaTorre Ugarte , Hope Cook, Maggie Davis , Matthew H Langholtz
Last_editor: Maggie R. Davis
Short_Description: This dataset provides additional variables for modelers and other interested stakeholders for yield assumptions for modeled energy crops on agricultural land in the CONUS, as modeled by the POLYSYS model.
Description: We present an updated estimate of potential biomass supplies from agricultural lands. The potential for farmers to respond to new markets for biomass has been assessed with the Policy Analysis System Model (POLYSYS) in previous versions of the billion-ton report (DOE 2017, 2016, 2011) and other studies (Oyedeji et al. 2021; Davis et al. 2020; Langholtz et al. 2019; Woodbury et al. 2018; Eaton, Langholtz, and Davis 2018; Langholtz et al. 2014; Langholtz et al. 2012; Jensen et al. 2007; De la Torre Ugarte and Ray 2000; Hellwinckel et al. 2015). Building on previous analyses, POLYSYS was used to update estimates of biomass supplies and prices from agricultural lands given environmental, land use, and technical constraints. The POLYSYS model, methods, and constraints are summarized in the chapter and detailed in the appendix. Changes from previous billion-ton reports include the use of the new 2023 USDA baseline, reporting of mature-market biomass supplies (see chapter Section 5.2: Methods Summary), oilseed supply estimates, and reporting of changes to carbon emissions and soil sequestration. For more information, please see https://bioenergykdf.ornl.gov/document/customized-dataset-yield-agricultural-resources-modeled-polysys
Keywords: Feedstock Production
Variables: Class,subclass,resource,fips,county,state_name,usdaregion,sqmi,till,model_name,scenario_name,prc_ofr,prod,harvest,yield_at_harvest,mean_annual_yield_at_harvest,produnit,yldunit,btu_ton,production_energy_content,energy_content_unit,prodens_dtpersqmi,landsrce,year,rotation
Variable descriptions:
Class is a assigned label to denote land source or analysis source corresponding to the agricultural chapter of 2023 Billion-Ton Report,subclass defines a class of biomass crops, resource shows the common name of the biomass crop type, fips is a geographic defined variable corresponding to counties provided as a 5-digit code, county as county name, state_name, usdaregion is a US Department of Agriculture defined region, sqmi is square miles for the county, till shows tillage class as nt = no till & rt = reduced tillage & ct = conventional tillage, model_name defines source of data from the POLYSYS model, scenario_name defined BT23 specific scenarios (see report for more information) as mm = mature market and low to high scenarios as well as an emerging scenario, prc_ofr as price offered from the POLYSYS model, prod as production with corresponding units, harvest as harvested acres corresponding to production levels, yield_at_harvest as the yield with corresponding units at the time of harvest, mean_annual_yield_at_harvest as the mean annual yield at this time of harvest with defined units, produnit as production units corresponding to “prod” column, yldunit as yield unit corresponding to yield_at_harvest and mean_annual_yield_at_harvest, btu_ton as British Thermal Unit (btu) per ton of resource, production_energy_content as calculated from btu_ton and prod, energy_content_unit as the units corresponding to production_energy_content, prodens_dtpersqmi as production density in dry tons per square mile as calculated from semi and prod, landsrce as the type of agricultural land defined at onset of simulation, year as year of harvest (selected) to correspond to scenario year, rotation as the defined rotation of relevant resources (oilseeds) modeled in POLYSYS
Publisher: Bioenergy Knowledge Discovery Framework (bioenergyKDF) Data Center
Publication Date: 2024-05-23
Type: plain text file (txt) with pipe (|) delimiter, as zipped (compressed)
Identifier: version 0.1 (v0.1)
DOI: https://doi.org/10.23720/BT2023/2282885
Relation: 2023 Billion-Ton Report, resource=“10.23720/BT2023/2282885” relation=“isVersionOf”
Description: Version 0.1: Corrected error within version 0.0 by changing data within variable ‘till’ to ‘[null]’ for ‘subclass’ of ‘Energy crops, herbaceous’ and ‘Energy crops, woody’ and ‘till’ is CT (Conventional Tillage) for ‘subclass’ of ‘Intermediate Oilseeds’
Source: agricultural_based_biomass.AgriResdJetCropProd_wHrvYldwAnnl
Language: English
Rights: CC0-1.0 license
What’s in the 2023 Billion-Ton Report?Oak Ridge National Laboratory’s Matthew Langholtz provides a short background summary for each of the resource classes and market scenarios explored in the 2023 Billion-Ton Report.
BioenergyKDF: How to access the dataOak Ridge National Laboratory’s Maggie Davis shares how to access the resources from the 2023 Billion-Ton Report using the Bioenergy KDF data portal.
2023 Billion-Ton Report: MacroalgaeAnne Otwell, a contractor in the Department of Energy's Bioenergy Technologies Office, highlights the findings about macroalgae from the 2023 Billion-Ton Report.
The U.S. Department of Energy Bioenergy Technology Office's (BETO's) 2023 Billion-Ton Report (BT23) is an assessment of renewable carbon resources potentially available in the United States. BT23 explores these resources in terms of quantity, price, geographical density and distribution, and market maturity. Resource quantities in this report are limited by specified economic and environmental sustainability constraints. Good practices are needed to ensure biomass production has positive environmental outcomes.
BT23 supports BETO's mission, particularly the 2023 Multi-Year Program Plan. To access 2023 Billion-Ton Report PDFs, appendices, and high-level messages, navigate to the 2023 Billion-Ton Report landing page at https://energy.gov/eere/bioenergy/2023-billion-ton-report-assessment-us… on the U.S. Department of Energy Bioenergy Technologies Office website.
Please cite the 2023 Billion-Ton Report as: U.S. Department of Energy. 2024. 2023 Billion‐Ton Report: An Assessment of U.S. Renewable Carbon Resources. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. ORNL/SPR-2024/3103. doi: 10.23720/BT2023/2316165.
a. Cotton gin trash and rice hulls, totaling 2.1 and 1.3 million tons per year in all scenarios, were omitted in error from the BT23 figures and initial data release. Data for these resources were added to the BT23 Agricultural Download on April 23rd, 2024. Methods are described in BT23 Appendix C, pages 6-7.
b. Orchard prunings, totaling 6 million tons per year in all scenarios, were classified as agricultural processing waste in the report, but are now classified as agricultural residues in the data. The combined changes from cotton gin trash, rice hulls, and orchard prunings cause agricultural residues to increase by 6 million tons per year and agricultural processing wastes to decrease by 2.5 million tons per year, as compared to values provided in Summary Table ES-1. This is less than a 1% change in the national results in all scenarios.
Constraints
Biomass resources in the 2023 Billion-Ton Report are presented as production capacity under specified environmental constraints, prices, and market scenarios. Modeling varies by resource class. For example:
Agricultural residue production capacity is limited to about 1/3 of national total by retention constraints for soil conservation.
Timberland resources are constrained such that total harvests are less than net growth, and sensitive areas are excluded.
Energy crop production capacity is modeled as producer response to biomass markets in addition to projected demands for food, feed, fiber, and export. More detail is provided this summary document and in the report.
Errata
Data for rice hulls and cotton gin trash are missing in the report, but have been added in the data portal (see data update information in “Featured Data Updates”)
In Table 1.5 on page 15, the phrase “except where cable systems are in use (Northwest United States)” is an error. Cable harvesting systems were modeled for conventional timber products, but biomass from logging residues from cable harvesting systems were not included in the analysis. This assumption to exclude logging residues from cable harvesting systems can be questioned, because cable harvesting systems produce piles of logging residues at collection.
In Figure ES-1 on page xix, labeling of microalgae and macroalgae in the top right of the figure are switched. The correct labeling should follow the symbology provided in the lower right of the figure, i.e. 169 million tons per year of microalgae at a weighted average price of $650 per ton, and 79 million tons per year of macroalgae at $500 per ton.
In Figure ES-6 on page xxviii, under “Remaining timberland (unharvested)”, the "/year" was included in error. This is because remaining timberland is a stock, not an annual rate of production. However, the “/year” is correct for the “Harvest for conventional forest products” and “Reference scenario (small-diameter trees)” categories in the same figure.
In Figure 5.11 on page 116, the primary and secondary y-axis scales are misaligned. The axes values should align with the horizontal lines.
In the text box on page 23, “During CO2 fermentation some of this recycled CO2 can be harnessed…” should instead say “During fermentation some of this recycled CO2 can be harnessed…”
The following errors in v0.1 of waste data were corrected in v1.0:
The wet waste and solid waste price data released were erroneously inflated 14%, and has been reduced to 0 to report as 2022$. The wastes summary in Table 3.1 remains unchanged as $2022.
A moisture content of 6% was assumed for waste paper, which was corrected to 5.5%, causing an increase of 447,000 dry tons of waste paper (i.e, 0.5% of waste paper).
Langholtz, Matthew H., Davis, Maggie, Hellwinckel, Chad, De La Torre Ugarte, Daniel, Efroymson, Rebecca, Jacobson, Ryan, Milbrandt, Anelia, Coleman, Andre, Davis, Ryan, Kline, Keith L., Badgett, Alex, Curran, Scott, Schmidt, Erik, Theiss, Timothy, Fried, Jeremy, English, Burton, Lambert, Lixia, Cook, Hope, Field, John, Abt, Robert, Parish, Esther, Rossi, David, Abt, Karen, Brandt, Craig, Saltiel, Troy, Davis, Kristen, Otwell, Anne, Clark, Robin, Miller, Lee, Brandeis, Consuelo, Oyedeji, Oluwafemi, Klein, Bruno, Wiatrowski, Matthew R., Hawkins, Troy, Ou, Longwen, Singh, Udayan, Zhang, Jingyi, Gao, Song, Snowden-Swan, Lesley, Valdez, Peter, Xu, Yiling, Zhu, Yunhua, De angelo, Julianne, Nepal, Prakash, Prestemon, Jeffery, Champion, Kathleen, Saenz, Benjamin, Harrison, Eliza, O dea, Claire, Cooney, Gregory, Hoffmann, Jeffrey, Shell, Michael, and Walker, Lee. 2023 Billion-Ton Report: An Assessment of U.S. Renewable Carbon Resources. United States: N. p., 2024. Web. doi:10.2172/2441098.
[Equivalent to: U.S. Department of Energy. 2024. 2023 Billion‐Ton Report: An Assessment of U.S. Renewable Carbon Resources. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. ORNL/SPR-2024/3103. doi: 10.23720/BT2023/2316165.]
Matthew H Langholtz , Maggie Davis , Chad Hellwinckel , Daniel DeLaTorre Ugarte , Rebecca Efroymson , Ryan Jacobson , Anelia Milbrandt , Andre Coleman , Ryan Davis , Keith L. Kline , et al.
Please cite as:
Hellwinckel, C., D. de la Torre Ugarte, J. L. Field, and M. Langholtz. 2024, Data from Biomass from the Agricultural Land Base, of Chapter 5 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (bioenergyKDF) Data Center, https://doi.org/10.23720/BT2023/2282885
We present an updated estimate of potential biomass supplies from agricultural lands. The potential for farmers to respond to new markets for biomass has been assessed with the Policy Analysis System Model (POLYSYS) in previous versions of the billion-ton report (DOE 2017, 2016, 2011) and other studies (Oyedeji et al. 2021; Davis et al. 2020; Langholtz et al. 2019; Woodbury et al. 2018; Eaton, Langholtz, and Davis 2018; Langholtz et al. 2014; Langholtz et al. 2012; Jensen et al. 2007; De la Torre Ugarte and Ray 2000; Hellwinckel et al. 2015). Building on previous analyses, POLYSYS was used to update estimates of biomass supplies and prices from agricultural lands given environmental, land use, and technical constraints. The POLYSYS model, methods, and constraints are summarized in the chapter and detailed in the appendix. Changes from previous billion-ton reports include the use of the new 2023 USDA baseline, reporting of mature-market biomass supplies (see chapter Section 5.2: Methods Summary), oilseed supply estimates, and reporting of changes to carbon emissions and soil sequestration.
Note: Oilseed Crops have rotations and therefore may have duplicate rows by resource with differing production units. Users may sum these production numbers for aggregated data.
Note 2: Cotton gin trash and Rice hulls are downloaded separately and were not included in visualizations by resource.
This dataset is authorized for use under CC0-1.0 license, provided the dataset is cited as follows:
Hellwinckel, C., D. de la Torre Ugarte, J. L. Field, and M. Langholtz. 2024, Data from Biomass from the Agricultural Land Base, of Chapter 5 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (bioenergyKDF) Data Center, https://doi.org/10.23720/BT2023/2282885
Text from the associated chapter of the 2023 Billion-Ton Report should be cited as:
Hellwinckel, C., D. de la Torre Ugarte, J. L. Field, and M. Langholtz. 2024. “Chapter 5: Biomass from Agriculture.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316171.
This dataset includes ForSEAM and BioSUM model output prepared for BT23 Chapter 4, as well as USDA-FS Forest Inventory Analysis datasets used to calculate waste biomass from the forested land base. Please access the data through the BT23 Data Portal or directly at https://bioenergykdf.ornl.gov/bt23-forestry-download
Please cite as:
Davis, M., L. Lambert, R. Jacobson, C. Brandeis, J. Fried, B. English. 2024, Modeled Output and Other Data from Biomass from the Forested Land Base, of Chapter 4 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (KDF) Data Center, https://doi.org/10.23720/BT2023/2281324
This dataset is authorized for use under CC0-1.0 license, provided the dataset is cited as follows:
Davis, M., L. Lambert, R. Jacobson, C. Brandeis, J. Fried, B. English. 2024, Modeled Output and Other Data from Biomass from the Forested Land Base, of Chapter 4 in the 2023 Billion-Ton Report. Version 0.0.1, Bioenergy Knowledge Discovery Framework (KDF) Data Center, https://doi.org/10.23720/BT2023/2281324
Text from the associated chapter of the 2023 Billion-Ton Report should be cited as:
Chapter 4: Biomass from the Forested Land Base Davis, M., L. Lambert, R. Jacobson, D. Rossi, C. Brandeis, J. Fried, B. English, et al. 2024. “Chapter 4: Biomass from the Forested Land Base.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316170.
