Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Deploying photovoltaic systems in global open-pit mines for a clean energy transition

Nature Sustainability volume 8, pages 1037–1047 (2025)Cite this article

Subjects

Abstract

Climate action requires rapid scaling of solar energy while minimizing land conflicts. Solar farms often compete with agriculture and ecosystems, but repurposing abandoned mines could offer a solution. We assess global open-pit mining sites as potential solar hubs, analysing their technical feasibility and deployment timelines under diverse future scenarios. Using a residual artificial neural network and energy demand projections, we find that these disturbed lands could host solar installations covering around 48,000 km2—ten times the global solar footprint in 2018. Their total generation potential (4,764 TWh yr−1) could meet projected 2050 global electricity needs. While Mediterranean countries show the highest readiness for mine-to-solar conversions, African nations lag despite having optimal sunlight owing to infrastructure and policy barriers. Our scenario analysis reveals that deployment timing and scale depend heavily on economic growth, clean energy costs and fossil fuel prices—with aggressive transitions requiring solar capacity exceeding current mine areas by 106%. This study provides a road map for strategically aligning solar expansion with post-mining land revitalization.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Global overview of open-pit mining patches and PV installations.
Fig. 2: Global potential for photovoltaic electricity generation on open-pit mining patches.
Fig. 3: Global probabilities of photovoltaic installation in open-pit mining patches.
Fig. 4: The spatiotemporal distribution of PV installations in global mining areas under different development scenarios.

Similar content being viewed by others

Data availability

The PV boundary data are available via Zenodo at https://zenodo.org/record/5005868. The mine boundary datasets are available from Maus et al.16 and Tang et al.45, respectively. The European Space Agency WorldCover datasets46 are available via Zenodo at https://doi.org/10.5281/zenodo.7254221 and can also be accessed through GEE. The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) are available at https://doi.org/10.5067/MODIS/MOD13Q1 (ref. 51) and can also be accessed through GEE. The terrain data from NASADEM52 are available at https://doi.org/10.5067/MEaSUREs/NASADEM/NASADEM_HGT.001. The river data from Natural Earth are available at https://www.naturalearthdata.com/. The water body data from ESRI WorldCover are available at https://esa-worldcover.org. The road data from the GRIP dataset are available at www.globio.info/download-grip-dataset. The temperature data are available via Global Solar Atlas at https://globalsolaratlas.info/download/world. The power plant data from the Global Power Plant Database are available at http://datasets.wri.org/dataset/globalpowerplantdatabase. The population data from LandScan are available at https://landscan.ornl.gov/. The gross GDP and electricity consumption data are available in Chen et al.53. The carbon emissions data are available at https://db.cger.nies.go.jp/ged/ja/. The global horizontal irradiation data, photovoltaic power potential (PVOUT) data, global irradiation for optimally tilted surface data, diffuse horizontal irradiation data and direct normal irradiation data are available at https://globalsolaratlas.info/download/world.

Code availability

The core code of this study consists of two components: (1) GEE-based JavaScript implementation for global PV and mining area point sampling, environmental variable extraction and conversion to the CSV format, which is available via GitHub at https://github.com/KechaoWangEstel/Mining_DL.git, and (2) local Python implementation for the construction, data creation, training, testing and validation of a RANN model, which is available via GitHub at https://github.com/KechaoWangEstel/MiningPV_GEE.git.

References

  1. Wang, K., Xiao, W., He, T. & Zhang, M. Remote sensing unveils the explosive growth of global offshore wind turbines. Renew. Sustain. Energy Rev. 191, 114186 (2024).

    Article  Google Scholar 

  2. Balta-Ozkan, N., Yildirim, J. & Connor, P. M. Regional distribution of photovoltaic deployment in the UK and its determinants: a spatial econometric approach. Energy Econ. 51, 417–429 (2015).

    Article  Google Scholar 

  3. Choi, Y. & Song, J. Review of photovoltaic and wind power systems utilized in the mining industry. Renew. Sustain. Energy Rev. 75, 1386–1391 (2017).

    Article  Google Scholar 

  4. Grodsky, S. M. & Hernandez, R. R. Reduced ecosystem services of desert plants from ground-mounted solar energy development. Nat. Sustain. 3, 1036–1043 (2020).

    Article  Google Scholar 

  5. Renewables 2021 Global Status Report (UN Environment Programme, 2021); http://www.unep.org/resources/report/renewables-2021-global-status-report

  6. International Energy Outlook (US Energy Information Administration, 2023); https://www.eia.gov/outlooks/ieo/narrative/index.php

  7. Gaeta, M., Nsangwe Businge, C. & Gelmini, A. Achieving net zero emissions in Italy by 2050: challenges and opportunities. Energies 15, 46 (2022).

    Article  CAS  Google Scholar 

  8. Zhang, X., Xu, M., Wang, S., Huang, Y. & Xie, Z. Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine. Earth Syst. Sci. Data 14, 3743–3755 (2022).

    Article  Google Scholar 

  9. Zainol Abidin, M. A., Mahyuddin, M. N. & Mohd Zainuri, M. A. A. Solar photovoltaic architecture and agronomic management in agrivoltaic system: a review. Sustainability 13, 7846 (2021).

    Article  Google Scholar 

  10. Zhang, N. et al. Booming solar energy is encroaching on cropland. Nat. Geosci. 16, 932–934 (2023).

    Article  CAS  Google Scholar 

  11. Hernandez, R. R., Hoffacker, M. K., Murphy-Mariscal, M. L., Wu, G. C. & Allen, M. F. Solar energy development impacts on land cover change and protected areas. Proc. Natl Acad. Sci. USA 112, 13579–13584 (2015).

    Article  CAS  Google Scholar 

  12. Sun, Y., Zhu, D., Li, Y., Wang, R. & Ma, R. Spatial modelling the location choice of large-scale solar photovoltaic power plants: application of interpretable machine learning techniques and the national inventory. Energy Convers. Manage. 289, 117198 (2023).

    Article  Google Scholar 

  13. Thormeyer, C., Sasse, J.-P. & Trutnevyte, E. Spatially-explicit models should consider real-world diffusion of renewable electricity: solar PV example in Switzerland. Renew. Energy 145, 363–374 (2020).

    Article  Google Scholar 

  14. Maus, V. & Werner, T. T. Impacts for half of the world’s mining areas are undocumented. Nature 625, 26–29 (2024).

    Article  CAS  Google Scholar 

  15. Zang, Y. et al. Identification of surface mining and assessment of ecological restoration effects using GEE and Sentinel-2 image data—a case study on Yangtze River watershed, China. Ecol. Eng. 212, 107525 (2025).

    Article  Google Scholar 

  16. Maus, V. et al. An update on global mining land use. Sci. Data 9, 433 (2022).

    Article  Google Scholar 

  17. Froese, R. & Schilling, J. The nexus of climate change, land use, and conflicts. Curr. Clim. Change Rep. 5, 24–35 (2019).

    Article  Google Scholar 

  18. Moomen, A. Strategies for managing large-scale mining sector land use conflicts in the global south. Resour. Policy 51, 85–93 (2017).

    Article  Google Scholar 

  19. Hilson, G. An overview of land use conflicts in mining communities. Land Use Policy 19, 65–73 (2002).

    Article  Google Scholar 

  20. Mining the Sun: Benefits of Solar Energy on Former Mine Sites (The Nature Conservancy, 2024); https://www.nature.org/en-us/what-we-do/our-priorities/tackle-climate-change/climate-change-stories/mining-the-sun-solar-energy-former-mine-sites/

  21. Kumar, A. et al. A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew. Sustain. Energy Rev. 69, 596–609 (2017).

    Article  Google Scholar 

  22. Hafeznia, H., Yousefi, H. & Razi Astaraei, F. A novel framework for the potential assessment of utility-scale photovoltaic solar energy, application to eastern Iran. Energy Convers. Manage. 151, 240–258 (2017).

    Article  Google Scholar 

  23. Castro, D. M. & Silv Parreiras, F. A review on multi-criteria decision-making for energy efficiency in automotive engineering. Appl. Comput. Inf. 17, 53–78 (2021).

    Google Scholar 

  24. Anam, M. Z., Bari, A. B. M. M., Paul, S. K., Ali, S. M. & Kabir, G. Modelling the drivers of solar energy development in an emerging economy: implications for sustainable development goals. Resour. Conserv. Recycl. Adv. 13, 200068 (2022).

    Google Scholar 

  25. Zambrano-Asanza, S., Quiros-Tortos, J. & Franco, J. F. Optimal site selection for photovoltaic power plants using a GIS-based multi-criteria decision making and spatial overlay with electric load. Renew. Sustain. Energy Rev. 143, 110853 (2021).

    Article  Google Scholar 

  26. Simsek, Y., Watts, D. & Escobar, R. Sustainability evaluation of concentrated solar power (CSP) projects under clean development mechanism (CDM) by using multi criteria decision method (MCDM). Renew. Sustain. Energy Rev. 93, 421–438 (2018).

    Article  Google Scholar 

  27. Müller, J. & Trutnevyte, E. Spatial projections of solar PV installations at subnational level: accuracy testing of regression models. Appl. Energy 265, 114747 (2020).

    Article  Google Scholar 

  28. Shao, M. et al. A review of multi-criteria decision making applications for renewable energy site selection. Renew. Energy 157, 377–403 (2020).

    Article  Google Scholar 

  29. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778 (IEEE, 2016).

  30. Maus, V. et al. A global-scale data set of mining areas. Sci. Data 7, 289 (2020).

    Article  Google Scholar 

  31. Tang, L., Tim, T. W., Xie, H. P., Yang, J. S. & Shi, Z. M. A global-scale spatial assessment and geodatabase of mine areas. Global Planet. Change 204, 103578 (2021).

    Article  Google Scholar 

  32. Kruitwagen, L. et al. A global inventory of photovoltaic solar energy generating units. Nature 598, 604–610 (2021).

    Article  CAS  Google Scholar 

  33. Xiao, W., Deng, X., He, T. & Guo, J. Using POI and time series Landsat data to identify and rebuilt surface mining, vegetation disturbance and land reclamation process based on Google Earth Engine. J. Environ. Manage. 327, 116920 (2023).

    Article  Google Scholar 

  34. Zhao, Z.-Y., Chen, Y.-L. & Li, H. What affects the development of renewable energy power generation projects in China: ISM analysis. Renew. Energy 131, 506–517 (2019).

    Article  Google Scholar 

  35. Paschalis, A., Bonetti, S. & Fatichi, S. Controls of ecohydrological grassland dynamics in agrivoltaic systems. Earths Future 13, e2024EF005183 (2025).

    Article  Google Scholar 

  36. Uldrijan, D., Kováčiková, M., Jakimiuk, A., Vaverková, M. D. & Winkler, J. Ecological effects of preferential vegetation composition developed on sites with photovoltaic power plants. Ecol. Eng. 168, 106274 (2021).

    Article  Google Scholar 

  37. Choi, C. S. et al. Environmental co-benefits of maintaining native vegetation with solar photovoltaic infrastructure. Earths Future 11, e2023EF003542 (2023).

    Article  Google Scholar 

  38. Tawalbeh, M. et al. Environmental impacts of solar photovoltaic systems: a critical review of recent progress and future outlook. Sci. Total Environ. 759, 143528 (2021).

    Article  CAS  Google Scholar 

  39. Boeing, A., Neda, M., Steinberg, S. & Batista, J. The impact of lower quality water on soiling removal from photovoltaic panels. Renew. Sustain. Energy Rev. 169, 112870 (2022).

    Article  CAS  Google Scholar 

  40. Yang, S., Zhang, Y., Tian, D., Liu, Z. & Ma, Z. Water-surface photovoltaic systems have affected water physical and chemical properties and biodiversity. Commun. Earth Environ. 5, 632 (2024).

    Article  Google Scholar 

  41. Zhang, H. et al. Green or not? Environmental challenges from photovoltaic technology. Environ. Pollut. 320, 121066 (2023).

    Article  CAS  Google Scholar 

  42. Watari, T. et al. Total material requirement for the global energy transition to 2050: a focus on transport and electricity. Resour. Conserv. Recycl. 148, 91–103 (2019).

    Article  Google Scholar 

  43. Watari, T., Nansai, K. & Nakajima, K. Review of critical metal dynamics to 2050 for 48 elements. Resour. Conserv. Recycl. 155, 104669 (2020).

    Article  Google Scholar 

  44. Song, S., Li, Q., Leslie, G. & Shen, Y. Water treatment methods in heavy metals removal during photovoltaic modules recycling: a review. Resour. Conserv. Recycl. 208, 107701 (2024).

    Article  CAS  Google Scholar 

  45. Tang, L. & Werner, T. T. Global mining footprint mapped from high-resolution satellite imagery. Commun. Earth Environ. 4, 134 (2023).

    Article  Google Scholar 

  46. Zanaga, D. et al. ESA WorldCover 10 m 2021 v200. Zenodo https://doi.org/10.5281/zenodo.7254221 (2022).

  47. Wang, K., He, T., Xiao, W. & Yang, R. Projections of future spatiotemporal urban 3D expansion in China under shared socioeconomic pathways. Landsc. Urban Plan. 247, 105043 (2024).

    Article  Google Scholar 

  48. Chen, G. et al. Global projections of future urban land expansion under shared socioeconomic pathways. Nat. Commun. 11, 537 (2020).

    Article  CAS  Google Scholar 

  49. Wang, A. et al. Predicting the impacts of urban land change on LST and carbon storage using InVEST, CA-ANN and WOA-LSTM models in Guangzhou, China. Earth Sci. Inform. 16, 437–454 (2023).

    Article  Google Scholar 

  50. Zhang, M. et al. Impact of urban expansion on land surface temperature and carbon emissions using machine learning algorithms in Wuhan, China. Urban Clim. 47, 101347 (2023).

    Article  Google Scholar 

  51. Didan, K. MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061. NASA Land Processes Distributed Active Archive Center https://doi.org/10.5067/MODIS/MOD13Q1.061 (2021).

  52. NASA JPL NASADEM Merged DEM Global 1 arc second V001. NASA Land Processes Distributed Active Archive Center https://doi.org/10.5067/MEASURES/NASADEM/NASADEM_HGT.001 (2020).

  53. Chen, J. et al. Global 1 km × 1 km gridded revised real gross domestic product and electricity consumption during 1992–2019 based on calibrated nighttime light data. Sci. Data 9, 202 (2022).

    Article  Google Scholar 

Download references

Acknowledgements

The study was supported by the National Key Research and Development Program (approval number 2023YFE0122300).

Author information

Authors and Affiliations

  1. School of Public Affairs, Zhejiang University, Hangzhou, China

    Kechao Wang, Jiatong Zhou, Runjia Yang, Suchen Xu & Wu Xiao

  2. School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China

    Zhenqi Hu

  3. College of Geosciences and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing, China

    Zhenqi Hu

  4. Inclusive and Smart Urban-Rural Governance Lab, Zhejiang University, Hangzhou, China

    Wu Xiao

  5. Land Academy for National Development, Zhejiang University, Hangzhou, China

    Wu Xiao

Authors
  1. Kechao Wang
    View author publications

    Search author on:PubMed Google Scholar

  2. Jiatong Zhou
    View author publications

    Search author on:PubMed Google Scholar

  3. Runjia Yang
    View author publications

    Search author on:PubMed Google Scholar

  4. Suchen Xu
    View author publications

    Search author on:PubMed Google Scholar

  5. Zhenqi Hu
    View author publications

    Search author on:PubMed Google Scholar

  6. Wu Xiao
    View author publications

    Search author on:PubMed Google Scholar

Contributions

W.X. and K.W. designed the study and planned the analysis. K.W. performed the experiments, analysed the data and wrote the original paper. K.W., J.Z., R.Y. and S.X. contributed in the paper revision. Z.H. contributed in the feasibility studies. All authors contributed in the interpretation of findings, provided revisions to the paper and approved the final paper.

Corresponding author

Correspondence to Wu Xiao.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Sustainability thanks Steven Grodsky, Sebastian Luckeneder and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Mining patches with inside or nearby PV distribution across nine different regions globally.

(a) The Sullivan Mine in Canada, with PV systems within. (b) The Chevron Questa Mine in the United States, with PV systems within. (c) The Rosebel Mine in Suriname, with PV systems within. (d) The Meuro Mine in Germany, which lacks PV systems within its boundaries but has a significant concentration of PV nearby. (e) The Thaba Mine in South Africa, with PV systems within. (f) The Shengli Mine in China, with PV systems within. (g) The Fushun West Mine in China, with PV systems within. (h) The Datian Mine in China. (i) The DeGrussa Mine in Australia, with PV systems within. Base map from Google satellite maps, Google Earth Engine (https://code.earthengine.google.com). Map data © 2025 Google. Imagery © 2025 NASA.

Extended Data Fig. 2 Architecture and details of Residual Artificial Neural Network (RANN).

The input of the RANN is a 1 × 16 vector, which consists of five physical geographical factors, six socio - economic factors, and five resources condition factors (as shown in the leftmost box with a yellow background). After passing through 50 residual blocks of size 1 × 1024 in the middle, it finally outputs a 1 × 1 vector, which represents the probability. Specifically, these 50 residual blocks in the middle use shortcut connections to avoid the information loss problem in the deep network (as shown in the middle box with a gray background). The output 1 × 1 vector is the probability of PV deployment for each mining patch.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3, Tables 1–4 and References.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, K., Zhou, J., Yang, R. et al. Deploying photovoltaic systems in global open-pit mines for a clean energy transition. Nat Sustain 8, 1037–1047 (2025). https://doi.org/10.1038/s41893-025-01594-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41893-025-01594-w

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing