Solar energy, as a low-carbon energy source, plays a critical role in the global energy transition. While existing studies have examined the impact of SF development on land surface temperature (LST), inconsistent conclusions underscore an urgent need to systematically reveal the spatial heterogeneity of these impacts and their driving factors. Therefore, this study takes 1266 solar farms (SFs) in China as research objects. Based on the Google Earth Engine platform and Landsat satellite data, we revealed the patterns of LST impacts from SF construction and operation. Combined with machine learning methods, we systematically elucidated the spatial heterogeneity characteristics of SF on LST, and simulated the potential future impacts of SF development on LST at the grid scale across China. The results showed that SF development overall caused a significant increase in LST of 0.809 °C (p < 0.001); however, spatial differentiation was highly significant. Specifically, warming dominated in eastern humid zones, cropland land cover types, and small-scale SFs, while cooling prevailed in northwest arid zones, barren land cover types, and large-scale SFs. Macro-scale geoclimatic factors (e.g., air pressure, solar radiation) exhibited the most prominent regulatory effects on LST, followed by ecological and layout factors (e.g., NDVI, SF area). In addition, one of the key findings was that ignoring the construction year of different SFs overestimated their impact on LST, which was being reported for the first time. The research framework can provide methodological extensions for research in similar regions, and the findings can provide theoretical support for the sustainable development of solar energy.
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