DOI: 10.5281/zenodo.4633707
title: Wind and Solar Candidate Project Areas for Princeton Net Zero America Study (v2)

description: For visualizing renewable energy Candidate Project Areas (CPAs) using GIS software. A growing number of pledges are being made by major corporations, municipalities, states, and national governments to reach net-zero emissions by 2050 or sooner. This dataset provides granular guidance on what getting to net-zero really requires and on actions needed to translate these pledges into tangible progress. This data set contains the GIS data for solar, land-based wind, and offshore wind Candidate Project Areas (CPAs) under base and constrained land use assumptions (BLUA, CLUA). Each record in this dataset represents a “Candidate Project Area” with attributes such as nameplate capacity, annual generation, model region, distance to transmission, etc. The Lawrence Berkeley National Lab MAPRE tools (https://mapre.lbl.gov/gis-tools/) were used to create this dataset, along with input assumptions adapted from Wu et al 2020 (Grace C Wu <em>et al</em> 2020 <em>Environ. Res. Lett.</em> 15 074044). A full description of the processes used to generate this dataset can be found in Annex D of the main NZA report. The main report and report annexes can be found at https://netzeroamerica.princeton.edu/. What's new in this version: Reverted to earlier version of CPA dataset, prior to removal of densely populated areas, and prior to removal of existing and planned facilities. CPAs now include areas with population density up to 100 person/km2, and they have an attribute indicating the population density. Users can apply their own population density filters and thresholds. Added attributes indicating the following, in separate columns for each CPA: Human Modification Index (HMI), prime farmland, land cover type, presence of existing facility, presence of planned facility Data sources: Population density: Rose, Amy N., McKee, Jacob J., Sims, Kelly M., Bright, Edward A, Reith, Andrew E., and Urban, Marie L. “LandScan 2019.” Oak Ridge National Laboratory, 2020. https://landscan.ornl.gov/landscan-datasets HMI: Theobald, David et al. “Detailed Temporal Mapping of Global Human Modification from 1990 to 2017.” Dryad, 2020. https://doi.org/10.5061/dryad.n5tb2rbs1. Prime farmland: “USA Soils Farmland Class.” USDA NRCS, Esri, October 1, 2019. https://landscape11.arcgis.com/arcgis/rest/services/USA_Soils_Farmland_Class/ImageServer. Land cover: NLCD 2016. https://www.mrlc.gov/data?f%5B0%5D=category%3Aland%20cover&f%5B1%5D=region%3Aconus Homer, Collin G., Dewitz, Jon A., Jin, Suming, Xian, George, Costello, C., Danielson, Patrick, Gass, L., et al. “Conterminous United States Land Cover Change Patterns 2001–2016 from the 2016 National Land Cover Database: ISPRS Journal of Photogrammetry and Remote Sensing, v. 162, p. 184–199, At.” ISPRS Journal of Photogrammetry and Remote Sensing, v. 162, p. 184–199, April 2020. https://doi.org/10.1016/j.isprsjprs.2020.02.019. Existing solar arrays: Carr, N.B., Fancher, T.S., Freeman, A.T., and Battles Manley, H.M. “Surface Area of Solar Arrays in the Conterminous United States: U.S. Geological Survey Data Release,” 2016. http://dx.doi.org/10.5066/F79S1P57. Existing wind turbines: Hoen, B.D., Diffendorfer, J.E., Rand, J.T., Kramer, L.A., Garrity, C.P., and Hunt, H.E. “United States Wind Turbine Database (Ver. 3.3, January 14, 2021).” U.S. Geological Survey, American Clean Power Association, and Lawrence Berkeley National Laboratory, 2018. https://doi.org/10.5066/F7TX3DN0. Planned wind and solar facilities: “EIA (Last) (2019). Preliminary Monthly Electric Generator Inventory (Based on Form EIA-860M as a Supplement to Form EIA-860).” U.S. Energy Information Administration (EIA), n.d. https://www.eia.gov/electricity/data/eia860m/.
description: Field definitions: OBJECTID This field is automatically created by ArcMap software. Ignore. Area_km2 Area of the Candidate Project Area (km2) eGrid_to_E Indicates which model region the candidate project area is located d_road_km Distance to the nearest road (km) (source: ArcGIS) m_slope Mean slope for the Candidate Project Area (degrees) (source: USGS 2011) m_popden Population density (person/km2) (source: Landscan) m_GHI Mean Annual Global Horizontal Irradiance (source: NREL NSRDB) m_cf Annual capacity factor (%) (source: analysis using LBNL MAPRE GIS tools) egen Annual Generation (MWh) (source: analysis using LBNL MAPRE GIS tools) incap Nameplate Capacity (MW ac) (source: analysis using LBNL MAPRE GIS tools) STATE_NAME Self-Explanatory STATE_ABBR Self-Explanatory Country Self-Explanatory sub_ID Feature ID of the nearest existing substation with voltage &gt;= 161 kV (source: HIFLD 2019) d_sub_161k Distance to the nearest existing substation with voltage &gt;= 161 kV (km) (source: HIFLD 2019) d_sub_load Distance from the nearest existing substation with voltage &gt;= 161 kV (km) to the nearest electric load center, defined as Census Bureau Metropolitan Statistical Area with population &gt;= 750,000 (source: U.S. Census Bureau 2016) Longitude Coordinates of centroid, (decimal degrees) Latitude Coordinates of centroid, (decimal degrees) Shape_Length This field is automatically created by ArcMap software. Ignore. Shape_Area This field is automatically created by ArcMap software. Ignore. Field values: Shape [point, polyline, polygon] eGrid_to_E [California, Desert_Southwest, Florida, Louisiana_and_Ozarks, Lower_Midwest , Mid_Atlantic_and_Great_Lakes, New_England , New_York, Pacific_Northwest , Rocky_Mountains, Southeast, Texas, Upper_Midwest, Utah_Nevada] State [all states except for Alaska and Hawaii, plus the District of Columbia]