Global Facility-Level Solar Photovoltaic Inventory with Energy Generation and Loss Estimates

Overview

This Zenodo release provides a global facility-level solar photovoltaic (solar PV) inventory with facility-scale energy generation and aerosol-related loss estimates, prepared alongside the manuscript: "Coal plants persist as a large barrier to the global solar energy transition" Nature Sustainability.

The dataset was generated using the framework described in the manuscript and Methods. In brief, a three-step workflow was used: (1) identify candidate PV facilities globally by combining existing inventories, crowd-sourced records, and a CNN-based scan of Sentinel-2 imagery; (2) extract precise panel footprints from confirmed sites using SAM-based segmentation; and (3) integrate the resulting footprints with MERRA-2 atmospheric reanalysis and a validated PV model to estimate facility-level generation and losses from clouds and aerosols.

The release includes PV facility footprints and core attributes:
PV_ID, latitude, longitude, country, year, area_m2.

In the main inventory files, year is the PV facility build/commissioning year (installation year), estimated from Sentinel-2 time-series classification as described in the manuscript Methods.

It contains two complementary data components:

• A global geospatial PV facility inventory (`.gpkg`, `.csv`, `.parquet`).
• Annual facility-level PV generation/loss tables (PV_facility_generation_year_YYYY.csv, currently 2017-2023).

Package Contents

•  `global_pv_facility_inventory.gpkg`
  • Layer: `global_pv_facility_inventory`
  • Geometry: `MultiPolygon`
  • CRS: `EPSG:4326` (WGS 84)
• `global_pv_facility_inventory.csv`
  • Attribute-only table (no geometry)
• `global_pv_facility_inventory.parquet`
  • GeoParquet (geometry + attributes)
• `Year-specific facility-level generation/loss tables (top-level CSV files)`
  • Generated to support the manuscript analysis of facility-level PV energy generation and losses.
  • For each year-specific file, analysis includes only facilities installed by that year; therefore facility counts differ across years.
  • Year-specific facility-level generation/loss tables:
    • `PV_facility_generation_year_2017.csv`
    • `PV_facility_generation_year_2018.csv`
    • `PV_facility_generation_year_2019.csv`
    • `PV_facility_generation_year_2020.csv`
    • `PV_facility_generation_year_2021.csv`
    •  `PV_facility_generation_year_2022.csv`
    •  `PV_facility_generation_year_2023.csv`
  • Each file includes:
    • Core facility columns: `PV_ID`, `latitude`, `longitude`, `country`, `year`, `area_m2`
    • `power_POA (kWh)`: power generation estimated from plane-of-array (POA) irradiance.
    • `power_POA_clr (kWh)`: POA-based power generation under clear-sky (cloud-free) conditions.
    • `power_POA_cln (kWh)`: POA-based power generation under clean-sky (aerosol-free) conditions.
    • `aerosol_loss (kWh)`: facility-level aerosol-related energy loss, computed as `power_POA (kWh) - power_POA_cln (kWh)`.

How to Use This Dataset (Technical)

• If you need geometry, use:
  • `global_pv_facility_inventory.gpkg` (GIS-friendly)
  • `global_pv_facility_inventory.parquet` (fast analytics with geometry)
• If you need tabular attributes only, use:
  • `global_pv_facility_inventory.csv`
• For energy generation/loss analysis, use:
  • `PV_facility_generation_year_YYYY.csv` (currently 2017-2023)
• Linkages:
  • `PV_ID` is the facility identifier across all files.
  • `year` supports year-specific filtering and aggregation.

How This Dataset Is Used in the Paper

• To map and quantify global facility-level PV deployment (location, footprint area, and installation year).
• To estimate facility-level PV generation from POA irradiance under:
  • all-sky conditions (`power_POA (kWh)`),
  • clear-sky conditions (`power_POA_clr (kWh)`),
  • clean-sky conditions (`power_POA_cln (kWh)`).
• To quantify aerosol-related generation loss at facility level (`aerosol_loss (kWh)`), then aggregate by geography/year for manuscript analysis.

Potential Reuse in Other Research

• National/regional assessments of aerosol impacts on PV generation.
• Benchmarking climate and air-quality penalties for existing PV fleets.
• Integration with grid, policy, or emissions datasets for energy-transition studies.
• Geospatial analyses linking PV siting patterns with environmental and socioeconomic variables.

Snapshot Statistics

• Facilities: 140,945
• Countries: 181
• Inventory years: 2017-2024
• Generation/loss tables: 2017-2023
• Latitude range: 41.61° S to 68.38° N

Contact

• Dr. Rui Song: (rui.song@physics.ox.ac.uk); or (rui.song90@gmail.com)