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A global inventory of photovoltaic solar energy generating units

Abstract

Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 20091. 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 20402,3. 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 needs1,2,3,4,5,6,7,8,9. 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 aridlands and grassland. Our inventory could aid PV delivery aligned with the Sustainable Development Goals.

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Fig. 1: Solar PV facilities are detected in remote sensing imagery with machine learning.
Fig. 2: Aggregated arrangement of the global dataset.
Fig. 3: Pre-existing land cover for new solar PV installations.

Data availability

The dataset is publicly hosted on Zenodo and is available at https://zenodo.org/record/5005868 or https://doi.org/10.5281/zenodo.5005868. It will also be visualized and available for download via the World Resources Institute Resource Watch, and the Descartes Labs platform. Source data are provided with this paper.

Code availability

The code repository is publicly hosted on Github at https://github.com/Lkruitwagen/solar-pv-global-inventory. The code release for this publication is version 1.0.0 and is also hosted on Zenodo at https://doi.org/10.5281/zenodo.5045001.

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Author information

Affiliations

Authors

Contributions

L.K. designed and implemented the machine learning pipeline, designed and implemented the dataset analysis, and wrote the paper draft. K.S. designed the machine learning pipeline and dataset analysis, implemented the SPOT6/7 branch of the machine learning pipeline and wrote the paper draft. J.F. and L.B. contributed to the acquisition of training data and analysis of the dataset. S.S. contributed to the deployment of the machine learning pipeline. C.H. contributed to the analysis of the dataset and wrote the paper draft.

Corresponding author

Correspondence to L. Kruitwagen.

Ethics declarations

Competing interests

K.S. and S.S. are employees and shareholders of Descartes Labs Inc., the company that builds and maintains the cloud computation infrastructure used to conduct this research. J.F. and L.B. are employees of the World Resources Institute, a not-for-profit organization which will host and publicly visualize a copy of our dataset.

Additional information

Peer review information Nature thanks Fei Teng, Stefano Ermon, Rebecca R. Hernandez, Lynn Kaack and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Supplementary Information

This file contains Supplementary Information, including Supplementary Figs. 1–13, Tables 1–10 and references.

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Kruitwagen, L., Story, K.T., Friedrich, J. et al. A global inventory of photovoltaic solar energy generating units. Nature 598, 604–610 (2021). https://doi.org/10.1038/s41586-021-03957-7

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