Creutzig, F. et al. The underestimated potential of solar energy to mitigate climate change. Nat. Energy 2, 17140 (2017).

Article 

Google Scholar
 

Jacobson, M. Z. et al. 100% clean and renewable wind, water, and sunlight all-sector energy roadmaps for 139 countries of the world. Joule 1, 108–121 (2017).

Article 

Google Scholar
 

Luderer, G. et al. Assessment of wind and solar power in global low-carbon energy scenarios: an introduction. Energy Econ. 64, 542–551 (2017).

Article 

Google Scholar
 

Dale, S. (analysis). BP Statistical Review of World Energy 2021. BP p.l.c. (2021). Available at: https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2021-full-report.pdf?.

Haegel, N. M. et al. Terawatt-scale photovoltaics: transform global energy. Science 364, 836–838 (2019).

Article 
CAS 

Google Scholar
 

Hernandez, R. R. et al. Environmental impacts of utility-scale solar energy. Renew. Sustain. Energy Rev. 29, 766–779 (2014).

Article 

Google Scholar
 

MacKay, D. J. C. Solar energy in the context of energy use, energy transportation and energy storage. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 371, 20110431 (2013).


Google Scholar
 

Smil, V. Power Density Primer: Understanding the Spatial Dimension of the Unfolding Transition to Renewable Electricity Generation (Part I – Definitions) (2010). Available at: https://moodle2.units.it/pluginfile.php/553999/mod_folder/content/0/smil-article-power-density-primer.pdf?.

Turney, D. & Fthenakis, V. Environmental impacts from the installation and operation of large-scale solar power plants. Renew. Sustain. Energy Rev. 15, 3261–3270 (2011).

Article 

Google Scholar
 

Jordaan, S. M., Lee, J., McClung, M. R. & Moran, M. D. Quantifying the ecosystem services values of electricity generation in the US Chihuahuan Desert: a life cycle perspective. J. Ind. Ecol. 25, 1089–1101 (2021).

Article 

Google Scholar
 

Fthenakis, V. & Kim, H. C. Land use and electricity generation: a life-cycle analysis. Renew. Sustain. Energy Rev. 13, 1465–1474 (2009).

Article 

Google Scholar
 

Bukhary, S., Ahmad, S. & Batista, J. Analyzing land and water requirements for solar deployment in the Southwestern United States. Renew. Sustain. Energy Rev. 82, 3288–3305 (2018).

Article 

Google Scholar
 

Capellán-Pérez, I., de Castro, C. & Arto, I. Assessing vulnerabilities and limits in the transition to renewable energies: land requirements under 100% solar energy scenarios. Renew. Sustain. Energy Rev. 77, 760–782 (2017).

Article 

Google Scholar
 

Hernandez, R. R., Hoffacker, M. K. & Field, C. B. Land-use efficiency of big solar. Environ. Sci. Technol. 48, 1315–1323 (2014).

Article 
CAS 

Google Scholar
 

Majumdar, D. & Pasqualetti, M. J. Analysis of land availability for utility-scale power plants and assessment of solar photovoltaic development in the state of Arizona, USA. Renew. Energy 134, 1213–1231 (2019).

Article 

Google Scholar
 

Mauro, G. & Lughi, V. Mapping land use impact of photovoltaic farms via crowdsourcing in the Province of Lecce (Southeastern Italy). Sol. Energy 155, 434–444 (2017).

Article 

Google Scholar
 

Wu, X. et al. Unveiling land footprint of solar power: a pilot solar tower project in China. J. Environ. Manag. 280, 111741 (2021).

Article 

Google Scholar
 

Hernandez, R. R., Jordaan, S. M., Kaldunski, B. & Kumar, N. Aligning climate change and sustainable development goals with an innovation systems roadmap for renewable power. Front. Sustain. 1, 583090 (2020).

Article 

Google Scholar
 

Hernandez, R. R., Cagle, A. E., Grodsky, S. M., Exley, G. & Jordaan, S. M. Comments on: Land use for United States power generation: a critical review of existing metrics with suggestions for going forward (Renewable and Sustainable Energy Reviews 2021; 143: 110911). Renew. Sustain. Energy Rev. 166, 112526 (2022).

Article 

Google Scholar
 

Wachs, E. & Engel, B. Land use for United States power generation: a critical review of existing metrics with suggestions for going forward. Renew. Sustain. Energy Rev. 143, 110911 (2021).

Article 

Google Scholar
 

Cagle, A. E. et al. Standardized metrics to quantify solar energy-land relationships: a global systematic review. Front. Sustain. 3, 1035705 (2023).

Article 

Google Scholar
 

Fritsche, U. et al. Energy and Land Use – Global Land Outlook Working Paper (UNCCD, 2017). https://doi.org/10.13140/RG.2.2.24905.44648.

IINAS. Selected Results from GEMIS 4.95: Electricity Generation (IINAS, 2017).

Trainor, A. M., McDonald, R. I. & Fargione, J. Energy sprawl is the largest driver of land use change in United States. PLOS ONE 11, e0162269 (2016).

Article 

Google Scholar
 

Groesbeck, J. G. & Pearce, J. M. Coal with carbon capture and sequestration is not as land use efficient as solar photovoltaic technology for climate neutral electricity production. Sci. Rep. 8, 13476 (2018).

Article 

Google Scholar
 

Jordaan, S. M. The land use footprint of energy extraction in Alberta. PhD thesis, University of Calgary (2010) https://doi.org/10.11575/PRISM/3520.

Ong, S., Campbell, C., Denholm, P., Margolis, R. & Heath, G. Land-Use Requirements for Solar Power Plants in the United States. NREL/TP-6A20-56290, 1086349, https://doi.org/10.2172/1086349 (2013).

Jordaan, S. M. et al. Understanding the life cycle surface land requirements of natural gas-fired electricity. Nat. Energy 2, 804–812 (2017).

Article 

Google Scholar
 

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

Article 
CAS 

Google Scholar
 

Carr, N. B., Fancher, T., Freeman, A. T. & Battles Manley, H. Surface area of solar arrays in the conterminous United States. U.S. Geological Survey data release (2016). Available at: https://doi.org/10.5066/F79S1P57.

Fujita K. S. et al. United States Large-Scale Solar Photovoltaic Database (ver. 2.0, August 2024). U.S. Geological Survey https://doi.org/10.5066/P9IA3TUS (2024).

Bolinger, M. & Bolinger, G. Land requirements for utility-scale PV: an empirical update on power and energy density. IEEE J. Photovolt. 12, 589–594 (2022).

Article 

Google Scholar
 

Yu, J., Wang, Z., Majumdar, A. & Rajagopal, R. DeepSolar: a machine learning framework to efficiently construct a solar deployment database in the United States. Joule 2, 2605–2617 (2018).

Article 

Google Scholar
 

Wang, Z., Arlt, M.-L., Zanocco, C., Majumdar, A. & Rajagopal, R. DeepSolar++: understanding residential solar adoption trajectories with computer vision and technology diffusion models. Joule 6, 2611–2625 (2022).

Article 

Google Scholar
 

Malof, J. M., Bradbury, K., Collins, L. M. & Newell, R. G. Automatic detection of solar photovoltaic arrays in high resolution aerial imagery. Appl. Energy 183, 229–240 (2016).

Article 

Google Scholar
 

Hou, X. et al. SolarNet: a deep learning framework to map solar plants in China from satellite imagery. In ICLR 2020 Workshop Tackling Clim. Change Mach. Learn (ICLR, 2020).

Camilo, J., Wang, R., Collins, L. M., Bradbury, K. & Malof, J. M. Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery. Preprint at https://doi.org/10.48550/ARXIV.1801.04018 (2018).

IEA. Renewables 2019. https://www.iea.org/reports/renewables-2019 (2019).

Hoffacker, M. K., Allen, M. F. & Hernandez, R. R. Land-sparing opportunities for solar energy development in agricultural landscapes: a case study of the Great Central Valley, CA, United States. Environ. Sci. Technol. 51, 14472–14482 (2017).

Article 
CAS 

Google Scholar
 

Cagle, A. E. et al. The land sparing, water surface use efficiency, and water surface transformation of floating photovoltaic solar energy installations. Sustainability 12, 8154 (2020).

Article 
CAS 

Google Scholar
 

Maguire, K., Tanner, S. J., Winikoff, J. B., Williams, R., & United States. Department of Agriculture. Economic Research Service. Utility-Scale Solar and Wind Development in Rural Areas: Land Cover Change (2009-20). https://handle.nal.usda.gov/10113/8374829https://doi.org/10.32747/2024.8374829.ers (2024).

Leccisi, E., Raugei, M. & Fthenakis, V. The energy and environmental performance of ground-mounted photovoltaic systems—a timely update. Energies 9, 622 (2016).

Article 

Google Scholar
 

Kurnik, J., Jankovec, M., Brecl, K. & Topic, M. Outdoor testing of PV module temperature and performance under different mounting and operational conditions. Sol. Energy Mater. Sol. Cells 95, 373–376 (2011).

Article 
CAS 

Google Scholar
 

Jensen, A. R., Sifnaios, I., Furbo, S. & Dragsted, J. Self-shading of two-axis tracking solar collectors: impact of field layout, latitude, and aperture shape. Sol. Energy 236, 215–224 (2022).

Article 

Google Scholar
 

Hernandez, R. R. et al. Techno–ecological synergies of solar energy for global sustainability. Nat. Sustain. 2, 560–568 (2019).

Article 

Google Scholar
 

Stowell, D. et al. A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK. Sci. Data 7, 394 (2020).

Article 

Google Scholar
 

Dunnett, S., Sorichetta, A., Taylor, G. & Eigenbrod, F. Harmonised global datasets of wind and solar farm locations and power. Sci. Data 7, 130 (2020).

Article 

Google Scholar
 

Turkovska, O. et al. Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning. One Earth 7, 1741–1759 (2024).

Article 

Google Scholar
 

Jordaan, S. M., Combs, C. & Guenther, E. Life cycle assessment of electricity generation: a systematic review of spatiotemporal methods. Adv. Appl. Energy 3, 100058 (2021).

Article 

Google Scholar
 

Lovering, J., Swain, M., Blomqvist, L. & Hernandez, R. R. Land-use intensity of electricity production and tomorrow’s energy landscape. PLOS ONE 17, e0270155 (2022).

Article 
CAS 

Google Scholar
 

Daniela-Abigail, H.-L. et al. Life cycle assessment of photovoltaic panels including transportation and two end-of-life scenarios: Shaping a sustainable future for renewable energy. Renew. Energy Focus 51, 100649 (2024).

Article 

Google Scholar
 

Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (eds, Navab, N., Hornegger, J., Wells, W. M. & Frangi, A. F.) 9351 234–241 (Springer International Publishing, 2015).

Dai, T. et al. Land resources for wind energy development requires regionalized characterizations. Environ. Sci. Technol. 58, 5014–5023 (2024).

Article 
CAS 

Google Scholar
 

Koellner, T. & Scholz, R. W. Assessment of land use impacts on the natural environment: Part 2: generic characterization factors for local species diversity in Central Europe. Int. J. Life Cycle Assess. 13, 32–48 (2008).


Google Scholar
 

Koellner, T. et al. UNEP-SETAC guideline on global land use impact assessment on biodiversity and ecosystem services in LCA. Int. J. Life Cycle Assess. 18, 1188–1202 (2013).

Article 

Google Scholar
 

De Baan, L., Alkemade, R. & Koellner, T. Land use impacts on biodiversity in LCA: a global approach. Int. J. Life Cycle Assess. 18, 1216–1230 (2013).

Article 

Google Scholar