Solar Footprints in CaliforniaThis GIS dataset consists of polygons that represent the footprints of solar powered electric generation facilities and related infrastructure in California called Solar Footprints. The location of solar footprints was identified using other existing solar footprint datasets from various sources along with imagery interpretation. CEC staff reviewed footprints identified with imagery and digitized polygons to match the visual extent of each facility. Previous datasets of existing solar footprints used to locate solar facilities include: GIS Layers: (1) California Solar Footprints, (2) UC Berkeley Solar Points, (3) Kruitwagen et al. 2021, (4) BLM Renewable Project Facilities, (5) Quarterly Fuel and Energy Report (QFER)Imagery Datasets: Esri World Imagery, USGS National Agriculture Imagery Program (NAIP), 2020 SENTINEL 2 Satellite Imagery, 2023Solar facilities with large footprints such as parking lot solar, large rooftop solar, and ground solar were included in the solar footprint dataset. Small scale solar (approximately less than 0.5 acre) and residential footprints were not included. No other data was used in the production of these shapes. Definitions for the solar facilities identified via imagery are subjective and described as follows: Rooftop Solar: Solar arrays located on rooftops of large buildings. Parking lot Solar: Solar panels on parking lots roughly larger than 1 acre, or clusters of solar panels in adjacent parking lots. Ground Solar: Solar panels located on ground roughly larger than 1 acre, or large clusters of smaller scale footprints. Once all footprints identified by the above criteria were digitized for all California counties, the features were visually classified into ground, parking and rooftop categories. The features were also classified into rural and urban types using the 42 U.S. Code § 1490 definition for rural. In addition, the distance to the closest substation and the percentile category of this distance (e.g. 0-25th percentile, 25th-50th percentile) was also calculated. The coverage provided by this data set should not be assumed to be a complete accounting of solar footprints in California. Rather, this dataset represents an attempt to improve upon existing solar feature datasets and to update the inventory of "large" solar footprints via imagery, especially in recent years since previous datasets were published. This procedure produced a total solar project footprint of 150,250 acres. Attempts to classify these footprints and isolate the large utility-scale projects from the smaller rooftop solar projects identified in the data set is difficult. The data was gathered based on imagery, and project information that could link multiple adjacent solar footprints under one larger project is not known. However, partitioning all solar footprints that are at least partly outside of the techno-economic exclusions and greater than 7 acres yields a total footprint size of 133,493 acres. These can be approximated as utility-scale footprints. Metadata: (1) CBI Solar FootprintsAbstract: Conservation Biology Institute (CBI) created this dataset of solar footprints in California after it was found that no such dataset was publicly available at the time (Dec 2015-Jan 2016). This dataset is used to help identify where current ground based, mostly utility scale, solar facilities are being constructed and will be used in a larger landscape intactness model to help guide future development of renewable energy projects. The process of digitizing these footprints first began by utilizing an excel file from the California Energy Commission with lat/long coordinates of some of the older and bigger locations. After projecting those points and locating the facilities utilizing NAIP 2014 imagery, the developed area around each facility was digitized. While interpreting imagery, there were some instances where a fenced perimeter was clearly seen and was slightly larger than the actual footprint. For those cases the footprint followed the fenced perimeter since it limits wildlife movement through the area. In other instances, it was clear that the top soil had been scraped of any vegetation, even outside of the primary facility footprint. These footprints included the areas that were scraped within the fencing since, especially in desert systems, it has been near permanently altered. Other sources that guided the search for solar facilities included the Energy Justice Map, developed by the Energy Justice Network which can be found here:https://www.energyjustice.net/map/searchobject.php?gsMapsize=large&giCurrentpageiFacilityid;=1&gsTable;=facility&gsSearchtype;=advancedThe Solar Energy Industries Association’s “Project Location Map” which can be found here: https://www.seia.org/map/majorprojectsmap.phpalso assisted in locating newer facilities along with the "Power Plants" shapefile, updated in December 16th, 2015, downloaded from the U.S. Energy Information Administration located here:https://www.eia.gov/maps/layer_info-m.cfmThere were some facilities that were stumbled upon while searching for others, most of these are smaller scale sites located near farm infrastructure. Other sites were located by contacting counties that had solar developments within the county. Still, others were located by sleuthing around for proposals and company websites that had images of the completed facility. These helped to locate the most recently developed sites and these sites were digitized based on landmarks such as ditches, trees, roads and other permanent structures.Metadata: (2) UC Berkeley Solar PointsUC Berkeley report containing point location for energy facilities across the United States.2022_utility-scale_solar_data_update.xlsm (live.com)Metadata: (3) Kruitwagen et al. 2021Abstract: Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 2009. Energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 2040. Geospatial data describing the energy system are required to manage generation intermittency, mitigate climate change risks, and identify trade-offs with biodiversity, conservation and land protection priorities caused by the land-use and land-cover change necessary for PV deployment. Currently available inventories of solar generating capacity cannot fully address these needs. Here we provide a global inventory of commercial-, industrial- and utility-scale PV installations (that is, PV generating stations in excess of 10 kilowatts nameplate capacity) by using a longitudinal corpus of remote sensing imagery, machine learning and a large cloud computation infrastructure. We locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423 gigawatts (−75/+77 gigawatts) at the end of 2018. Enrichment of our dataset with estimates of facility installation date, historic land-cover classification and proximity to vulnerable areas allows us to show that most of the PV solar energy facilities are sited on cropland, followed by arid lands and grassland. Our inventory could aid PV delivery aligned with the Sustainable Development GoalsEnergy Resource Land Use Planning - Kruitwagen_etal_Nature.pdf - All Documents (sharepoint.com)Metadata: (4) BLM Renewable ProjectTo 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. This feature class denotes "verified" renewable energy projects at the California State BLM Office, displayed in GIS. The term "Verified" refers to the GIS data being constructed at the California State Office, using the actual application/maps with legal descriptions obtained from the renewable energy company. https://www.blm.gov/wo/st/en/prog/energy/renewable_energy https://www.blm.gov/style/medialib/blm/wo/MINERALS_REALTY_AND_RESOURCE_PROTECTION_/energy/solar_and_wind.Par.70101.File.dat/Public%20Webinar%20Dec%203%202014%20-%20Solar%20and%20Wind%20Regulations.pdfBLM CA Renewable Energy Projects | BLM GBP Hub (arcgis.com)Metadata: (5) Quarterly Fuel and Energy Report (QFER) California Power Plants - Overview (arcgis.com)

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.

The Global Solar Power Tracker is a worldwide dataset of utility-scale solar PV facilities. It includes solar farm phases with capacities of 20 megawatts (MW) or more (10 MW or more in Arabic-speaking countries). A solar project phase is generally defined as a group of one or more solar units that are installed under one permit, one power purchase agreement, and typically come online at the same time. The Global Solar Power Tracker catalogs every solar farm phase at these capacity thresholds of any status, including operating, announced, under development, under construction, shelved, cancelled, mothballed, or retired. Each solar farm included in the tracker is linked to a wiki page on the GEM wiki.

Architecture

Global Energy Monitor’s Global Solar Power Tracker uses a two-level system for organizing information, consisting of both a database and wiki pages with further information. The database tracks individual solar farm phases and includes information such as project owner, status, and location. A wiki page for each solar farm is created within the Global Energy Monitor wiki. The database and wiki pages are updated annually.

Status Categories

• Announced: Proposed projects that have been described in corporate or government plans but have not yet taken concrete steps such as applying for permits.
• Development: Projects that are actively moving forward in seeking governmental approvals, land rights, or financing.
• Construction: Site preparation and equipment installation are underway.
• Operating: The project has been formally commissioned; commercial operation has begun.
• Shelved: Suspension of operation has been announced, or no progress has been observed for at least two years.
• Cancelled: A cancellation announcement has been made, or no progress has been observed for at least four years.
• Retired: The project has been decommissioned.
• Mothballed: The project is disused, but not dismantled.

Research Process

The Global Solar Power Tracker data set draws on various public data sources, including: - Government data on individual power solar farms (such as India Central Electricity Authority’s “Plant Wise Details of All India Renewable Energy Projects” and the U.S. EIA 860 Electric Generator Inventory), country energy and resource plans, and government websites tracking solar farm permits and applications; - Reports by power companies (both state-owned and private); - News and media reports; - Local non-governmental organizations tracking solar farms or permits.

Wiki Pages

For each solar farm, a wiki page is created on Global Energy Monitor’s wiki. Under standard wiki convention, all information is linked to a publicly-accessible published reference, such as a news article, company or government report, or a regulatory permit. In order to ensure data integrity in the open-access wiki environment, Global Energy Monitor researchers review all edits of project wiki pages.

Mapping

To allow easy public access to the results, Global Energy Monitor worked with GreenInfo Network to develop a map-based and table-based interface using the Leaflet Open-Source JavaScript library. In the case of exact coordinates, locations have been visually determined using Google Maps, Google Earth, Wikimapia, or OpenStreetMap. For proposed projects, exact locations, if available, are from permit applications, or company or government documentation. If the location of a solar farm or proposal is not known, Global Energy Monitor identifies the most accurate location possible based on available information.

Average of the hourly Direct Normal Irradiance (DNI) over 17 years (1998-2014). Data extracted from the National Solar Radiation Database (NSRDB) developed using the Physical Solar Model (PSM) by National Renewable Energy Laboratory ("NREL"), Alliance for Sustainable Energy, LLC, U.S. Department of Energy ("DOE"). The current version of the National Solar Radiation Database (NSRDB) (v2.0.1) was developed using the Physical Solar Model (PSM), and offers users the solar resource datasets from 1998 to 2014). The NSRDB comprises 30-minute solar and meteorological data for approximately 2 million 0.038-degree latitude by 0.038-degree longitude surface pixels (nominally 4 km2). The area covered is bordered by longitudes 25° W on the east and 175° W on the west, and by latitudes -20° S on the south and 60° N on the north. The solar radiation values represent the resource available to solar energy systems. The AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) model uses half-hourly radiance images in visible and infrared channels from the GOES series of geostationary weather satellites, a climatological albedo database and mixing ratio, temperature and pressure profiles from Modern Era-Retrospective Analysis (MERRA) to generate cloud masking and cloud properties. Cloud properties generated using PATMOS-x are used in fast radiative transfer models along with aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary sources to estimate Direct Normal Irradiance (DNI) and Global Horizontal Irradiance (GHI). A daily AOD is retrieved by combining information from the MODIS and MISR satellites and ground-based AERONET stations. Water vapor and other inputs are obtained from MERRA. For clear sky scenes the direct normal irradiance (DNI) and GHI are computed using the REST2 radiative transfer model. For cloud scenes identified by the cloud mask, Fast All-sky Radiation Model for Solar applications (FARMS) is used to compute the GHI. The DNI for cloud scenes is then computed using the DISC model. The data in this layer is an average of the hourly GHI over 17 years (1998-2014). NOTE: The Geographical Information System (GIS) data and maps for solar resources for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) were developed by the U.S. National Renewable Energy Laboratory (NREL) and provided for Canada as an estimate. At present, neither the NREL data, nor the Physical Solar Model (PSM) on which the NREL data is based, have been either assessed or validated for the particular Canadian weather applications. A Canadian GHI map developed by the department of Natural Resources Canada (NRCan) is based on the State University of New York (SUNY) model and has been assessed and validated for the particular Canadian weather applications. The Canadian GHI map is available at http://atlas.gc.ca/cerp-rpep/en/.

description: 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 United States of America lower 48 states. 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 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. Units are in kilowatt hours per meter squared per day. OtherCitation 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. Marion, William and Stephen Wilcox, 1994: "Solar Radiation Data Manual for Flat-plate and Concentrating Collectors". NREL/TP-463-5607, National Renewable Energy Laboratory, 1617 Cole Boulevard, Golden, CO 80401. ### 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.; abstract: 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 United States of America lower 48 states. 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 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. Units are in kilowatt hours per meter squared per day. OtherCitation 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. Marion, William and Stephen Wilcox, 1994: "Solar Radiation Data Manual for Flat-plate and Concentrating Collectors". NREL/TP-463-5607, National Renewable Energy Laboratory, 1617 Cole Boulevard, Golden, CO 80401. ### 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

In collaboration with the Mississippi Department of Wildlife, Fisheries, and Parks Natural Heritage Program, the U.S. Fish and Wildlife Service (Service) developed the Mississippi Solar Siting Tool to provide stakeholders the general guidance necessary to reduce potential adverse impacts to sensitive habitats and species in Mississippi when siting proposed solar energy projects. The purpose of the map is to assist solar energy developers in screening environmentally sensitive areas compared to areas where lower environmental impacts are anticipated. The decision framework is similar to that described in the Service’s 2012 Land-Based Wind Energy Guidelines (Land-Based Wind Energy Guidelines), particularly during Tiers 1 (Preliminary Site Evaluation) and 2 (Site Characterization); whereas Tiers 3-5 involve field studies to predict and monitor impacts. Environmental risks include direct impacts (e.g., from construction or clearing, loss, fragmentation, or degradation of habitat, displacement or behavioral changes), and indirect impacts (e.g., increased predator populations). The assigned risk categories and corresponding colors in the map represent the Service’s estimation of the relative environmental risk to species of concern and sensitive habitats within an area. Regardless of the environmental risk associated with a particular area, solar developers should coordinate with the Service and other appropriate Federal and State agencies and follow guidelines to inform the siting and development of any proposed solar energy project.

This map shows how each census tract measures with respect to both solar potential and household solar usage.Out of the top 20 census tracts that have a high and consistent potential for solar, yet have low current usage rates, 11 of them were in the Victorville/Hesperia area of San Bernardino County, California. Another seven tracts were on the border of Los Angeles and Kern counties, spanning the cities of Lancaster, Mojave, and California City. The top tract is in the Yucca Valley area. These are places that might benefit from some incentive programs that offset or spread out the initial investment.Other related items:ArcGIS blog article explaining our processaccompanying feature layer to this web map (also referenced below)"Top 20" Geolist appSources: U.S. Census Bureau's American Community Survey (ACS) 2014-2018 5-year estimates, Table(s) B25040, B25117, as well as Esri analysis of data obtained from the “Global Solar Atlas 2.0", a free, web-based application is developed and operated by the company Solargis s.r.o. on behalf of the World Bank Group, utilizing Solargis data, with funding provided by the Energy Sector Management Assistance Program (ESMAP).

This dataset is based on solar interconnection data drawn from the publicly posted inventories of New York State’s electric utilities. This dataset represents the most comprehensive source of installed distributed solar projects, including projects that did not receive State funding, for all of New York State since 2000. This dataset does not include utility-scale projects that participate in the NYISO wholesale market. The interactive map at https://www.nyserda.ny.gov/All-Programs/Programs/NY-Sun/Solar-Data-Maps/Statewide-Projects provides information on Statewide Distributed Solar Projects since 2000 by county. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

These files contain U.S. national map packages (ESRI ArcMap 10.5) for 2000 and 2020, including:

1. Heating season length and intensity (monthly heating degree-days evaluated from a base temperature of 18.3 ºC);
2. Residential heating energy needs (monthly kWh per household);
3. Solar heating resources on 10m² surfaces of optimal tilt (monthly Wh);
4. Optimal south-facing tilt values for solar heat collection (angular degrees above horizontal);
5. Net solar heating resources on 10m² optimally-tilted collector surfaces (NSHR₁₀) (monthly MWh per household and sums over 10km x 10km sectors);
6. Proportions of the NSHR₁₀ provided by diffuse radiation (monthly percentage by location);
7. Collector areas needed to intercept solar radiation equal to household heating needs (monthly m²);
8. Median absolute deviations in the NSHR₁₀ obtained with twelve consecutive years of solar radiation data (annual MWh and percentage).

Please see associated publication (Rempel et al. 2020) for source data and methodological details.

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 solar development.Attributes:Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 areaUrban areas: defined by the U.S. Census.8Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool9Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping ToolMajor highways: available from ESRI Living Atlas10Airports: 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' (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 ToolActive mines: Active Mines and Mineral Processing Plants in the United States in 200311Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center or installation.Table 1 Solar Steeply sloped areas >10o Population density >100/km2 Capacity factor <20% Urban areas <500 m Water bodies <250 m Railways <30 m Major highways <125 m Airports <1000 m Active mines <1000 m Military Lands <1000m 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 cycles.Footnotes:[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/CreditsTitle: Techno-economic screening criteria for utility-scale solar photovoltaic energy installations for Integrated Resource PlanningPurpose for creation: These exclusion criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning.Keywords: solar, photovoltaic, resource potential, techno-economic, PV, IRPExtent: western states of the contiguous U.S.Use LimitationsThe 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: PublicContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.gov Oluwafemi Sawyerr femi@ethree.com

The annual average direct normal solar resources by 12-Digit Hydrologic Unit (HUC) was estimated from maps produced by the National Renewable Energy Laboratory for the U.S. Department of Energy (February 2009). The original data was from 10km, satellite modeled dataset (SUNY/NREL, 2007) representing data from 1998-2005. The 10km data was converted to 30m grid cells, and then zonal statistics were estimated for a final value of average kWh/m2/day for each 12-digit HUC. For more information about the original dataset please refer to the National Renewable Energy Laboratory (NREL) website at www.nrel.gov/gis/data_solar.html. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

GIS data for India's direct normal irradiance (DNI) and global horizontal irradiance. Provides 10-kilometer (km) solar resource maps and data for India. The 10-km hourly solar resource data were developed using weather satellite (METEOSAT) measurements incorporated into a site-time specific solar modeling approach developed at the U.S. State University of New York at Albany. The data is made publicly available in geographic information system (GIS) format (shape files etc). The new maps and data were released in June 2013. The new data expands the time period of analysis from 2002-2007 to 2002-2011 and incorporates enhanced aerosols information to improve direct normal irradiance (DNI). These products were developed by the U.S. National Renewable Energy Laboratory (NREL) in cooperation with India's Ministry of New and Renewable Energy, through funding from the U.S. Department of Energy and U.S. Department of State.

The html maps are provided as supplementary information for the publication titled Parcel Scale Assessment of Rooftop Solar Technical Potential (NREL/PR-7A40-80780). The maps contain information on rooftop solar technical potential at the block scale for the city of Orlando in Florida. The rooftop solar technical potential information is based on data from two different datasets. The first dataset is LiDAR data for the city of Orlando obtained from the Orlando Utilities Commission (OUC) (Koebrich et al. 2021). The second dataset is a national parcel dataset (HIFLD 2020) which contains descriptive data and geometries for parcels in the U.S. Parcel scale data from both these datasets have been processed and aggregated to block scale to produce these html maps. The first html map (block scale developable roof area for Orlando) contains the developable roof area for solar. The second html map (block scale rooftop solar technical potential for Orlando) contains the rooftop solar technical potential in units of kilowatts as well as additional information on the most common building use type and the most common building occupancy type for the block. These html maps are provided to demonstrate the proof-of-concept analysis conducted for Orlando.

This dataset identifies counties and municipalities that had a solar energy ban or moratorium as of April 2022. A TIF data file and a PNG map of the data are provided, showing areas where solar energy bans or moratoriums exist across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

The geospatial data reflected in the protected area layer mostly pertain to natural and wilderness areas where development of utility-scale renewable energy is prohibited and were heavily based on RETI 1.0 blackout areas.1 The protected area layer is distinguished for solar PV technology by the BLM greater sage grouse habitat management area which provides separate exclusion areas for the different technology types. Tables 1 and 2 below lists the data sources and precise selection query for each dataset, if applicable, that make up the protected area layer.Table 1: Datasets used in the Protected Area Layer

Dataset

Example Designations

Citation or hyperlink

PAD-US (CBI Edition)

National Parks, GAP Status 1 and 2, State Parks, Open Spaces, Natural Areas

“PAD-US (CBI Edition) Version 2.1b, California”. Conservation Biology Institute. 2016. https://databasin.org/datasets/64538491f43e42ba83e26b849f2cad28.

Conservation Easements

California Conservation Easement Database (CCED), 2022a. 2022. www.CALands.org. Accessed December 2022.

Inventoried Roadless Areas

“Inventoried Roadless Areas.” US Forest Service. Dec 12, 2022. https://www.fs.usda.gov/detail/roadless/2001roadlessrule/maps/?cid=stelprdb5382437

BLM National Landscape Conservation System

Wilderness Areas, Wilderness Study Areas, National Monuments, National Conservation Lands, Conservation Lands of the California Desert, Scenic Rivers

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-areas

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-study-areas

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-national-monuments-nca-forest-reserves-other-poly/

Greater Sage Grouse Habitat Conservation Areas (BLM)

For solar technology: BLM_Managm IN (‘PHMA’, ‘GHMA’, ‘OHMA’) For wind technology: BLMP_Managm = ‘PHMA’

“Nevada and Northeastern California Greater Sage-Grouse Approved Resource Management Plan Amendment.” US Department of the Interior Bureau of Land Management Nevada State Office. 2015. https://eplanning.blm.gov/public_projects/lup/103343/143707/176908/NVCA_Approved_RMP_Amendment.pdf

Other BLM Protected Areas

Areas of Critical Environmental Concern (ACECs), Recreation Areas (SRMA, ERMA, OHV Designated Areas), including Vinagre Wash Special Recreation Management Area, National Scenic Areas, including Alabama Hills National Scenic Area

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-off-highway-vehicle-designations

https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-areas-of-critical-environmental-concern

BLM, personal communication, November 2, 2022.

Mono Basin NFSA

https://pcta.maps.arcgis.com/home/item.html?id=cf1495f8e09940989995c06f9e290f6b#overview

Terrestrial 30x30 Conserved Areas

Gap Status 1 and 2

CA Nature. 30x30 Conserved Areas, Terrestrial. 2021. https://www.californianature.ca.gov/datasets/CAnature::30x30-conserved-areas-terrestrial/ Accessed September 2022.

CPAD

Open Spaces and Parks under city or county level

California Protected Areas Database (CPAD), 2022b. 2022. https://www.calands.org/cpad/. Accessed February 22, 2023.

USFS Special Interest Management Areas

Research Natural Areas, Recreation Areas, National Recreational Trail, Experimental Forest, Scenic Area

https://data-usfs.hub.arcgis.com/datasets/usfs::special-interest-management-areas-feature-layer/about

Proposed Protected Area

Molok Luyuk Extension (Berryessa Mtn NM Expansion)

CalWild, personal communication, January 19, 2023.

Table 2: Query Definition for Components of Protected Areas Dataset SQL Query PAD-US (CBI Edition) p_des_tp IN ('Wild, Scenic and Recreation River', 'Area of Critical Environmental Concern', 'Ecological Reserve', 'National Conservation Area', 'National Historic Site', 'National Historical Park', 'National Monument', 'National Park General Public Land', 'National Preserve', 'National Recreation Area', 'National Scenic Area', 'National Seashore', 'Wilderness Study Area', 'Wilderness Area', 'Wildlife Management Area', 'State Wildlife Management Area', 'State Park', 'State Recreation Area', 'State Nature Preserve/Reserve', 'State Natural Area', 'State Ecological Reserve', 'State Cultural/Historic Area', 'State Beach', 'Special Management Area', 'National Wildlife Refuge', 'Natural Area', 'Nature Preserve', 'Research Natural Area') Or s_des_tp IN ('Natioanal Monument', 'National Monument', 'National Park General Public Land', 'National Preserve', 'National Recreation Area', 'National Scenic Area', 'National Seashore', 'National Conservation Area', 'Area of Critical Environmental Concern', 'National Wildlife Refuge', 'State Park', 'State Wildlife Area', 'State Wildlife Management Area', 'State Wildlife Refuge', 'State Ecological Reserve', 'Wild, Scenic and Recreation River', 'Wilderness Area', 'Wildlife Management Area') Or t_des_tp IN ('National Monument', 'National Park General Public Land', 'National Recreation Area', 'Area of Critical Environmental Concern', 'National Conservation Area', 'State Wildlife Management Area', 'Wild, Scenic and Recreation River', 'Wildlife Management Area') Or p_loc_ds IN ('Ecological Reserve', 'Research and Educational Land') Or gap_sts IN ('1', '2') Or own_type = 'Private Conservation Land' Or (own_type = 'Local Land' And (p_des_tp LIKE '%"Open Space"%' Or p_des_tp LIKE '%Park%' Or p_des_tp LIKE '%Recreation Area%' Or p_des_tp LIKE '%Natural Area%')) Or (p_des_tp = 'Other State Land' And (p_loc_ds IN ('State Vehicular Recreation Area', 'BLM Resource Management Area', 'Resource Management Area') And gap_sts <> '2')) CPAD AGNCY_LEV IN ('City', 'County') And ACCESS_TYP = 'Open Access' And (UNIT_NAME LIKE '%Park%' OR UNIT_NAME LIKE '%Open Space%' OR UNIT_NAME LIKE '%park%' OR UNIT_NAME LIKE '%Recreation Area%' OR UNIT_NAME LIKE '%Natural Area%' OR GAP2_acres > 0 OR GAP1_acres >0) Greater Sage- Grouse Habitat Conservation Areas (BLM) For Solar Technology: BLM_Managm IN (‘PHMA’, ‘GHMA’, ‘OHMA’) For Wind Technology: BLM_Managm = ‘PHMA’ This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.For a complete description of the creation of this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.[1] Final RETI Phase 2A report, available at https://ww2.energy.ca.gov/2009publications/RETI-1000-2009-001/RETI-1000-2009-001-F-REV2.PDF.

Change Log: Version 1.1 (January 22, 2024 10:29 AM) Layer revised to allow for gaps to remain when combining all components of the protected area layer.

This data represents the underlying data in the Puerto Rico Solar Map. As of June 2020, the data has no longer been updated and is considered archived. This data has been authorized for public use. Want to connect with the team behind the data? Please email us at RA@directrelief.org.

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This dataset includes information on completed and pipeline (not yet installed) solar electric projects supported by the New York State Energy Research and Development Authority (NYSERDA). Blank cells represent data that were not required or are not currently available. Contractor data is provided for completed projects only, except for Community Distributed Generation projects. Pipeline projects are subject to change. The interactive map at https://data.ny.gov/Energy-Environment/Solar-Electric-Programs-Reported-by-NYSERDA-Beginn/3x8r-34rs provides information on solar photovoltaic (PV) installations supported by NYSERDA throughout New York State since 2000 by county, region, or statewide. Updated monthly, the graphs show the number of projects, expected production, total capacity, and annual trends.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

The SolarSMART Social Energy Atlas 2020 Georgia Interview dataset provides results from interviews with 2018 residents of the State of Georgia above the age of 18 on their perceptions of photovoltaic solar energy. The interview includes the following: demographic information for each respondent, their location in the state (by ZIP Code); the type of house they reside within; whether they have or have not adopted solar energy (and if so, how did they adopt solar energy); audio recordings of a semi-structured interview between the informants and representatives from the project who were often agents from the UGA Cooperative Extension Service; and the outputs from that interview that also included draw-a-map responses to where they perceived people adopted solar technologies in the United States and the State of Georgia. Respondents were from 25 of the 159 counties of the State of Georgia, with representation of both urban and rural residents. In total, the dataset includes 25 variables currently coded (with over 170 identified. Transcriptions of the interviews are also provided in the dataset alongside graphics for each informant that can be used in presentations and other derivatives involving this dataset in the future. This dataset inspired the SolarSMART 2020 Georgia Survey dataset (DOI: 10.5281/zenodo.4540819).

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information