An academic paper published in Communications Earth & Environment has introduced a systematic methodology that leverages deep learning and high-resolution aerial imagery to quantify the land required for utility-scale solar projects in the U.S. Western Interconnection.
The study aims to resolve previous inconsistencies in land-use estimates by analyzing 719 solar facilities, providing crucial data for future energy systems planning and lifecycle assessments.
The core of the study involved training a neural network to delineate the total project footprint—including access roads, fencing, and buffer zones—rather than just the immediate panel area, a limitation in earlier analyses. The resulting dataset covers over 13 GW of installed capacity and reveals vital statistics on land efficiency.
The mean capacity-based land use efficiency across the sample was quantified at 24.7 W per square meter, with significant variability across projects, found the study.
The study found dual-axis tracking systems surprisingly demonstrated lower project-level land use efficiency compared to fixed-rack or single-axis systems. The paper attributes this to the need for greater spacing between tracking panels to prevent shading and allow for full movement, resulting in a larger overall footprint.
The research also classified the land cover utilized by these projects. While 65% of the total installed capacity is now on “developed” land, historical data indicates that 38% of these sites were previously cultivated cropland.
These findings highlight opportunities for “land sparing” strategies. Developers can mitigate environmental impacts by prioritizing placement on already-disturbed surfaces, such as existing rooftops or brownfields.
The researchers said the prevalence of projects on former agricultural land also highlights the potential for expanding agrivoltaics, the dual use of land for both solar energy generation and farming operations, to optimize resource use. Find the full study and methodology here.
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