CDFW BIOS GIS Dataset, Contact: BLM Bureau of Land Management, Description: To identify renewable energy approved and pending lease areas on BLM administered lands. To provide information about solar and wind energy applications and completed projects within the State of California for analysis and display internally and externally.

Energy data and map are from the California Energy Commission and include utility scalepower plants. Plants of any type below 1 MW (e.g. residential solar) are not included.Hydroelectric plants of 30 MW and less are considered renewable energy sources inCalifornia. Hydroelectric plants over 30 MW are non-renewable. Counties without piesymbols had no utility scale renewable energy capacity for the year. Data is for 2022 and iscurrent as of November 2, 2023. Projection: NAD 1983 (2011) California (Teale) Albers(Meters). For more information, contact John Hingtgen at wgrshelp@energy.ca.gov orGordon Huang at qfergen@energy.ca.gov.

Abstract: Monthly and annual average solar resource potential for the lower 48 states of the United States of America.

Purpose: Provide information on the solar resource potential for the for the lower 48 states of the United States of America.

Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.

Other Citation Details: George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME.

License Info

This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

CDFW BIOS GIS Dataset, Contact: CEC California Energy Commission (CEC), Description: Locations of proposed Solar thermal energy projects for the Renewable Energy Transmission Initiative (RETI), from the California Energy Commission.

The Nature Conservancy supports the rapid expansion of renewable energy, and the ample wind and solar resources in the central U.S. offer the opportunity to provide clean, low-impact power for people and wildlife. Achieving the clean energy build-out necessary to meet our climate goals will require quadrupling our renewable energy capacity in the United States by 2050. Up to 75% of the nation’s new large renewable energy projects will occur in the central U.S., home to much of the nation’s most promising wind and solar resources. The new Site Renewables Right map identifies where renewable energy can be developed in the central U.S. while still conserving important wildlife habitats and natural areas at the same time. It’s a win-win. Site Renewables Right provides a way for companies and communities to engage in the right conversations to avoid project delays and impacts to the very same wildlife and natural areas we are trying to protect from climate change.

The map depicts the amount of renewable energy production (gigawatt hours) for each county of California. The state-wide total is also given. The table depicts the amount of renewable energy production for each energy type for every county and a total summation is given. All data is for 2018 and for utility-scale purposes.

The global market for solar resource assessment software is experiencing robust growth, driven by the increasing demand for renewable energy and the need for efficient solar power plant development. The market size in 2025 is estimated at $250 million, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors: the expanding solar energy sector globally, stringent government regulations promoting renewable energy adoption, advances in software capabilities offering more accurate and detailed assessments, and the decreasing costs of solar technology making it more accessible. The segment for paid, commercial applications currently dominates the market share, reflecting the preference of large-scale solar developers for sophisticated, feature-rich software solutions that ensure optimal project planning and profitability. However, the free and personal application segments are also showing promising growth, catering to smaller-scale projects, educational institutions, and individual users exploring solar energy options. Geographic expansion into developing economies with high solar irradiance presents significant opportunities for market expansion. The continued growth trajectory is expected to be influenced by factors such as technological advancements leading to improved prediction accuracy and integration with other renewable energy modeling tools. Increased investment in research and development within the sector, coupled with the expanding adoption of cloud-based software solutions, will contribute to market expansion. However, challenges such as the need for accurate and reliable meteorological data, the complexity of software usage for non-experts, and the potential for market saturation in certain regions might impede growth to some degree. Nevertheless, the long-term outlook for the solar resource assessment software market remains positive, with a substantial increase in market value projected throughout the forecast period, driven by the relentless push towards global decarbonization and the escalating adoption of sustainable energy solutions.

From Databasin: https://drecp.databasin.org/datasets/c61b0e256e494fc5b6958d6c3999a19a/Development Focus Areas (DFAs). Designation on BLM-administered lands within which solar, wind, and geothermal renewable energy development and associated activities are allowable uses and that have been determined to be of low or lower resource conflict. The intent is to incentivize and streamline such development in these areas.Variance Process Lands (VPLs). Designation on BLM-administered lands that are available for solar, wind, and geothermal renewable energy development. Renewable energy projects on VPLs have minimal streamlining and must comply with a specific set of CMAs. Renewable energy applications in VPLs will follow the variance process described in the Western Solar Plan ROD.

IMPORTANT NOTICE This item has moved to a new organization and entered Mature Support on February 3rd, 2025. This item is scheduled to be Retired and removed from ArcGIS Online on July 30th, 2025. We encourage you to switch to using the item on the new organization as soon as possible to avoid any disruptions within your workflows. If you have any questions, please feel free to leave a comment below or email our Living Atlas Curator (livingatlascurator@esri.ca) The new version of this item can be found here Stations containing prime movers, electric generators, and auxiliary equipment for converting mechanical, chemical into electric energy with an installed capacity of 1 Megawatt or more generated from renewable energy, including biomass, hydroelectric, pumped-storage hydroelectric, geothermal, solar, wind, and tidal.Mapping Resources implemented as part of the North American Cooperation on Energy Information (NACEI) between the Department of Energy of the United States of America, the Department of Natural Resources of Canada, and the Ministry of Energy of the United Mexican States.The participating Agencies and Institutions shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics, if available, are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time and may differ from other official information. The Agencies and Institutions participants give no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data.Maintenance and Update Frequency: As Needed For more information visit Renewable Energy Power Plants

Energy data and map are from the California Energy Commission and include utility scalepower plants. Plants of any type below 1 MW (e.g. residential solar) are not included. Valuesshown are as of the end of the year, which may not include plants that went out of servicewithin the year. Hydroelectric plants of 30 MW and less are considered renewable energysources in California. Hydroelectric plants over 30 MW are non-renewable. Counties withoutpie symbols had no utility scale renewable energy capacity for the year. Data is for 2023 andis current as of July 2, 2024. Projection: NAD 1983 (2011) California (Teale) Albers (Meters).For more information, contact John Hingtgen at john.hingtgen@energy.ca.gov.

Energy generation data and map are from the California Energy Commission and include utility scale power plants. Plants of any type below 1 MW (e.g. rooftop solar)are not included. Hydroelectric plants of 30 MW and less are considered renewable energy sources in California. Hydroelectric plants over 30 MW are non-renewable. Counties without pie symbols had no utility scale renewable electric generation for the year. Data is for 2020 and is current as of July 18, 2022. Projection: NAD 1983 (2011) California (Teale) Albers (Meters). For more information, contact Rebecca Vail at (916) 477-0738 or John Hingtgen at 916) 510-9747.

MetadataThese data represent the offshore wind leases within the U.S. Outer Continental Shelf (OCS) - federally managed waters within the U.S. Exclusive Economic Zone (EEZ) - managed by the Bureau of Ocean Energy Management. State leases are managed by individual state leasing authorities. The data show Individual blocks and sub-blocks, commercial, research, and right of way lease areas. Leases are considered provisional after auction, prior to signatures from BOEM and the Lessee.

The site suitability criteria included in the techno-economic land use screens are listed below. As this list is an update to previous cycles, tribal lands, prime farmland, and flood zones are not included as they are not technically infeasible for development. The techno-economic site suitability exclusion thresholds are presented in table 1. Distances indicate the minimum distance from each feature for commercial scale wind developmentAttributes: Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 area Urban areas: defined by the U.S. Census. Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping Tool Major highways: available from ESRI Living Atlas Airports: The Airports dataset including other aviation facilities as of July 13, 2018 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. Available from Argonne National Lab Energy Zone Mapping Tool Active mines: Active Mines and Mineral Processing Plants in the United States in 2003Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center, or installation. Table 1 Wind Steeply sloped areas >10o Population density >100/km2 Capacity factor <20% Urban areas <1000 m Water bodies <250 m Railways <250 m Major highways <125 m Airports <5000 m Active mines <1000 m Military Lands <3000m For more information about the processes and sources used to develop the screening criteria see sources 1-7 in the footnotes. Data updates occur as needed, corresponding to typical 3-year CPUC IRP planning cyclesFootnotes:[1] Lopez, A. et. al. “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” 2012. https://www.nrel.gov/docs/fy12osti/51946.pdf[2] https://greeningthegrid.org/Renewable-Energy-Zones-Toolkit/topics/social-environmental-and-other-impacts#ReadingListAndCaseStudies[3] Multi-Criteria Analysis for Renewable Energy (MapRE), University of California Santa Barbara. https://mapre.es.ucsb.edu/[4] Larson, E. et. al. “Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Interim Report.” Princeton University, 2020. https://environmenthalfcentury.princeton.edu/sites/g/files/toruqf331/files/2020-12/Princeton_NZA_Interim_Report_15_Dec_2020_FINAL.pdf.[5] Wu, G. et. al. “Low-Impact Land Use Pathways to Deep Decarbonization of Electricity.” Environmental Research Letters 15, no. 7 (July 10, 2020). https://doi.org/10.1088/1748-9326/ab87d1.[6] RETI Coordinating Committee, RETI Stakeholder Steering Committee. “Renewable Energy Transmission Initiative Phase 1B Final Report.” California Energy Commission, January 2009.[7] Pletka, Ryan, and Joshua Finn. “Western Renewable Energy Zones, Phase 1: QRA Identification Technical Report.” Black & Veatch and National Renewable Energy Laboratory, 2009. https://www.nrel.gov/docs/fy10osti/46877.pdf.[8]https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Urban+Areas[9]https://ezmt.anl.gov/[10]https://www.arcgis.com/home/item.html?id=fc870766a3994111bce4a083413988e4[11]https://mrdata.usgs.gov/mineplant/Credits Title: Techno-economic screening criteria for utility-scale wind energy installations for Integrated Resource Planning Purpose for creation: These site suitability criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning. Keywords: wind energy, resource potential, techno-economic, IRP Extent: western states of the contiguous U.S. Use Limitations The geospatial data created by the use of these techno-economic screens inform high-level estimates of technical renewable resource potential for electric system planning and should not be used, on their own, to guide siting of generation projects nor assess project-level impacts.Confidentiality: Public ContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.govOluwafemi Sawyerr femi@ethree.com

This dataset was developed by the National Renewable Energy Laboratory (NREL) for the U.S. Agency for International Development's (USAID) South Asia Regional Initiative for Energy Cooperation (SARI/E). The dataset contains Wind Power Density at 50-m Above Ground Level in the form of a GIS shapefile. The data were output in Geographic Information Systems (GIS) format and incorporated into a Geospatial Toolkit (GsT) which is provided in data resources. The GsT allows the user to examine the resource data in a geospatial context along with other key information relevant to renewable energy development, such as transportation networks, transmission corridors, existing power facilities, load centers, terrain conditions, and land use.

Estimate the potential for producing hydrogen from key renewable resources (onshore wind, solar photovoltaic, and biomass) by county for the United States. This study was conducted to estimate the potential for producing hydrogen from key renewable resources (onshore wind, solar photovoltaic, and biomass) by county in the United States and to create maps that allow the reader to easily visualize the results. To accomplish this objective, the authors analyzed renewable resource data both statistically and graphically utilizing a state-of-the-art Geographic Information System (GIS), a computer-based information system used to create and visualize geographic information.

Land-use and environmental exclusions were applied to represent the most viable resources across the country. While wind, solar, and biomass are considered major renewable resources, other renewable energy resources could also be used for hydrogen production, thus contributing to hydrogen development locally and regionally. These additional resources include offshore wind, concentrating solar power, geothermal, hydropower, photoelectrochemical, and photobiological resources.

This study found that approximately 1 billion metric tons of hydrogen could be produced annually from wind, solar, and biomass resources in the United States. The greatest potential for producing hydrogen from these key renewable resources is in the Great Plains region. In addition, this research suggests that renewable hydrogen has the potential to displace gasoline consumption in most states if and when a number of technical and scientific barriers can be overcome.

License Info

This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

This dataset provides existing generation and technical generation potential of variable renewable energies for 371 local authority districts in Great Britain. The renewables include rooftop photovoltaic, ground-mounted photovoltaic, and onshore wind turbines. The data also include levelized cost of electricity (LCOE), which economically assesses the technical potential. The onshore wind potentials are included in relation to sccenicness, i.e. visual landscape impact. Furthermore, the data reflect different scenarios for land use competition of onshore wind and ground-mounted PV. The compilation of the existing potentials, as well as the methods to determine the technical potentials can be found in the following study: XXX.

This is the planning boundary for the Desert Renewable Energy Conservation Plan (DRECP). This boundary was jointly developed and approved by the California Department of Fish and Wildlife and the California Energy Commission.

Energy generation data and map are from the California Energy Commission and include utility scale power plants. Plants of any type below 1 MW (e.g. residential solar) are not included. Hydroelectric plants of 30 MW and less are considered renewable energy sources in California. Hydroelectric plants over 30 MW are non-renewable. Counties without pie symbols had no utility scale renewable electric generation for the year. Data is for 2021 and is current as of July 19, 2022.Projection: NAD 1983 (2011) California (Teale) Albers (Meters). For more information, contact Rebecca Vail at (916) 477-0738 or John Hingtgen at 916) 510-9747.

Abstract: Monthly and annual average solar resource potential for Hawaii.

Purpose: Provide information on the solar resource potential for Hawaii. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented due south at an angle from horizontal equal to the latitude of the collector location.

Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. Units are in watt hours.

Other Citation Details:

George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME.

Maxwell, E, R. George and S. Wilcox, "A Climatological Solar Radiation Model", Proceedings of the 1998 Annual Conference, American Solar Energy Society, Albuquerque NM.

License Info

DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

This dataset contains information about the biomass resources generated by county in the United States. It includes the following feedstock categories: crop residues, forest residues, primary mill residues, secondary mill residues, and urban wood waste. The estimates are based on county-level statistics and/or point-source data gathered from the U.S. Department of Agriculture (USDA), USDA Forest Service, EPA and other organizations, which are further processed using relevant assumptions and conversions.