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,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 development,Attributes:,,

,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 development,Attributes:,,

,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 development,Attributes:,,

,This version of the base exclusions incorporates the latest draft version of the Protected Area Layer (Solar) and the techno-economic exclusion layer. The base exclusions form the fundamental exclusion set that the additional planning priorities of the Core Land Use Screen is added to. The merged footprints of these primary two layers are modified by the Greater Sage-Grouse Habitat Management Areas, the Bureau of Land Management Desert Renewable Energy Conservation Plan (DRECP) Land Use Plan Amendment (LUPA) Development Focus Areas (DFAs), Variance Process Lands (VPL), and the General Public Lands (GPL), as well as the 2024 BLM Western Solar Plan. These areas allow for renewable energy applications and are therefore exempt (erased) from the base exclusions layer, even if the protected area layer or techno-economic exclusion layer identified the area as an exclusion. The DFAs are partitioned by technology type so that only the DFAs that allow solar energy are applied in this modification.,The area of California remaining after removing the base exclusions is called the resource potential basemap. It forms the starting point (or base) used in renewable resource estimation and defines where environmental and land-use datasets can be applied in exploring implications of renewable resource development.,Note: Existing project footprints are also included as part of the base exclusions.,More information is found in the CEC presentation from the August 19 webinar, as well as the 2023 Land Use Screens Staff Report in the CEC Energy Planning Library. ,

,This version of the base exclusions incorporates the latest draft version of the Protected Area Layer (Solar) and the techno-economic exclusion layer. The base exclusions form the fundamental exclusion set that the additional planning priorities of the Core Land Use Screen is added to. The merged footprints of these primary two layers are modified by the Greater Sage-Grouse Habitat Management Areas, the Bureau of Land Management Desert Renewable Energy Conservation Plan (DRECP) Land Use Plan Amendment (LUPA) Development Focus Areas (DFAs), Variance Process Lands (VPL), and the General Public Lands (GPL), as well as the 2024 BLM Western Solar Plan. These areas allow for renewable energy applications and are therefore exempt (erased) from the base exclusions layer, even if the protected area layer or techno-economic exclusion layer identified the area as an exclusion. The DFAs are partitioned by technology type so that only the DFAs that allow solar energy are applied in this modification.,The area of California remaining after removing the base exclusions is called the resource potential basemap. It forms the starting point (or base) used in renewable resource estimation and defines where environmental and land-use datasets can be applied in exploring implications of renewable resource development.,Note: Existing project footprints are also included as part of the base exclusions.,More information is found in the CEC presentation from the August 19 webinar, as well as the 2023 Land Use Screens Staff Report in the CEC Energy Planning Library. ,

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,Solar Footprints in California,This 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, 2023,Solar 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 Footprints,Abstract: 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

,Solar Footprints in California,This 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, 2023,Solar 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 Footprints,Abstract: 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