Managing the amount of greenspace (i.e., increasing or decreasing greenspace coverage) and optimizing greenspace configuration (i.e., increasing or decreasing greenspace fragmentation) are cost-effective approaches to cooling the environment. The spatial variations in their impacts on the thermal environment, as well as their relative importance, are of great importance for greenspace planning and management but are far from thoroughly understood. Taking Changsha, China as an example, this study investigated the spatial variations of the impacts of greenspace amount (measured as a percent of greenspace) and greenspace fragmentation (measured by edge density of greenspace) on the Landsat-derived land surface temperature (LST) using geographically weighted regression (GWR), and also uncovered the spatial pattern of their relative importance. The results indicated that: (1) Greenspace amount showed significantly negative relationships with LST for 91.73% of the study area. (2) Both significantly positive and negative relationships were obtained between greenspace fragmentation and LST, covering 14.90% and 13.99% of the study area, respectively. (3) The negative relationship between greenspace fragmentation and LST is mainly located in the urban areas, while the positive relationship appeared in the rural areas. (4) Greenspace amount made a larger contribution to regulating LST than greenspace fragmentation in 93.31% of the study area, but the latter had stronger roles in about 6.69% of the study area, mainly in the city center. These findings suggest that spatially varied greenspace planning and management strategies should be adopted to improve the thermal environment.

Enhancing greenspace cooling efficiency (GCE) is a cost-effective nature-based solution to improve the urban thermal environment. The spatiotemporal patterns of GCE and their driving factors have been investigated mainly based on land surface temperature in a spatial comparison perspective. However, the diurnal change in GCE based on air temperature (AT) and its non-linear responses to meteorological factors are far from thoroughly understood. Taking the subtropical Chinese city of Changsha as an example, we quantified the hourly GCE based on AT in the hottest month of 2020, investigated its diurnal changes, and uncovered its non-linear responses to meteorological change using the Generalized Additive Model. The results showed that (1) the hourly GCE displayed a U-shaped temporal pattern with an average of 0.0128 °C%⁻¹. The nighttime GCE (0.0134 °C%⁻¹) was significantly higher than the daytime GCE (0.012 °C%⁻¹). (2) Meteorological factors (i.e., temperature, relative humidity, and wind speed) significantly and non-linearly impacted GCE. (3) The responses of GCE to changes in relative humidity and wind speed followed an inverted U-shaped pattern, with the maximum values appearing at a relative humidity of 70% and a wind speed of 6m/s, respectively. GCE responded to temperature change more complexly, i.e., a negative response (35 °C). These findings extend our understanding of the diurnal variations of GCE and the non-linear responses to meteorological change and can help effective urban greenspace planning and management in Changsha, China, and other cities with similar climates in an era of rapid climate change. For example, expanding greenspace coverage as well as optimizing greenspace spatial configuration should be a priority action in areas where the AT is higher than 35 °C currently and will be in the future.