Nature Communications · 2021 · 250 citations · 53 references
AgricultureEarth SciencesGeoinformaticsEnergy EconomicsGeospatial Artificial IntelligenceRenewable Energy SystemsEnergy GeographyGeospatial Machine LearningRenewable EnergyRenewable Energy Resource AssessmentSolar Photovoltaic SystemsSolar Radiation3D Geospatial AnalysisGeospatial AnalysisGeospatial Big DataHigh-resolution ModelingPhotovoltaic Resource AssessmentSolar Energy Resource AssessmentSolar Resource AssessmentSolar Resource Mapping
Abstract Rooftop solar photovoltaics currently account for 40% of the global solar photovoltaics installed capacity and one-fourth of the total renewable capacity additions in 2018. Yet, only limited information is available on its global potential and associated costs at a high spatiotemporal resolution. Here, we present a high-resolution global assessment of rooftop solar photovoltaics potential using big data, machine learning and geospatial analysis. We analyse 130 million km 2 of global land surface area to demarcate 0.2 million km 2 of rooftop area, which together represent 27 PWh yr −1 of electricity generation potential for costs between 40–280 $ MWh −1 . Out of this, 10 PWh yr −1 can be realised below 100 $ MWh −1 . The global potential is predominantly spread between Asia (47%), North America (20%) and Europe (13%). The cost of attaining the potential is lowest in India (66 $ MWh −1 ) and China (68 $ MWh −1 ), with USA (238 $ MWh −1 ) and UK (251 $ MWh −1 ) representing some of the costliest countries.
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