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Urban vegetation is widely regarded as a key strategy for mitigating urban heat, but its cooling performance is not constant and varies across climatic zones and urban structures. Most existing evidence derives from single-city or single-climate studies, leaving the differences in vegetation cooling with respect to building density and background climate insufficiently quantified globally. This study examined vegetation cooling efficiency (CE), defined as the absolute slope of the relationship between NDVI and normalized land surface temperature (LST), across 20 major cities spanning tropical, arid, temperate, and continental climate zones during 2000–2020. We combined city-level regression, built-up fraction stratification, and interpretable machine learning to quantify vegetation CE and its variation across climate and urban density gradients. CE varied roughly eightfold across cities (≈0.07–0.60), with the strongest responses in arid and continental cities such as Dubai and Almaty. Increasing built-up fraction systematically weakened the NDVI–LST relationship, turning near-neutral or slightly positive in the most compact temperate cores (80%–100% built-up). The machine learning model reproduced these patterns (out-of-sample R2 = 0.757), identifying NDVI and evapotranspiration as dominant drivers. These findings indicate that vegetation cooling is strongly context-dependent, underscoring the need for climate-specific and morphology-based perspectives on urban greening rather than generalized evaluations.
Hu et al. (Wed,) studied this question.