Objective Blue-green space is considered as an important ecological facility to optimize the thermal environment, whose positive effects on thermal environment optimization have gained widespread attention. Previous research has paid less attention to the construction of comprehensive indicators such as the scale, shape and layout of blue-green space, the spatial heterogeneity of the impact of blue-green space on the thermal environment, and the research unit of block, which makes it difficult to implement grounded optimization strategy for blue-green space as a response to thermal mitigation regulation. Thoroughly exploring the multi-dimensional impact of blue-green space on the thermal environment is beneficial for climate-adaptive urban development.
Methods This research takes the central urban area of Tianjin as the research area. The intense development and high-density construction in Tianjin have led to the fragmentation of blue-green space and the continuous deterioration of the thermal environment, making central Tianjin an area in urgent need of ecological transformation. Based on Landsat 8 remote sensing imagery and ENVI for land surface temperature (LST) inversion, the average of multiple datasets is utilized as the indicator to characterize the thermal environment. High-precision identification of blue-green space at a 2 m resolution is achieved through Google Earth images and eCognition 8.9 software. On this basis, combined with OpenStreetMap road data, over 300 blocks are delineated as the basic research units. Integrating landscape ecology and morphological analysis (morphological spatial pattern analysis, MSPA) based on ArcGIS Pro 3.0, Fragstats 4.2, and Guidos Toolbox 2.9 software, multi-dimensional evaluation indices of blue-green space at the block scale are calculated from the perspectives of “ scale − shape − layout” . Finally, a multiscale geographically weighted regression (MGWR) model is introduced to conduct the statistical analysis.
Results 1) The results show that the blue-green space in the central urban area of Tianjin exhibit a distribution characteristic of “four corridors and multiple points”, whereas the LST shows a distinct pattern of being “high in the center and low in the periphery”. The distribution of blue-green space in the central urban area of Tianjin is consistent with the low value of LST, and the scale, shape and layout of such blue-green space, as well as LST itself, all demonstrate spatial aggregation. 2) The core indicators of the impact of blue-green space on LST include the proportion of green space, LPI, COHESION, SHAPE_MN, the proportion of core layout, the proportion of branch layout, and the proportion of edge layout. Different indicators of blue-green space vary to a certain degree in terms of the scale of effects on the thermal environment. The SHAPE_MN index and the proportion of edge layout have smaller effect scales, exhibiting significant spatial heterogeneity. In contrast, the proportion of green space and that of core layout have larger effect scales, with a more gradual spatial heterogeneity in their impact. 3) Among the aforesaid indicators of blue-green space, the proportion of green space, the proportion of core layout, and COHESION index have a significant negative impact on the thermal environment. In contrast, the proportion of branch layout and that of edge layout have a significant positive effect. The intensity of impact varies among the indicators, with the average impact strength of the proportion of core layout being the highest, while that of SHAPE_MN being the lowest and unstable. Finally, based on empirical results, the research proposes an optimization scheme for blue-green space to improve the thermal environment. The scheme involves dividing the urban area into responsive zones based on the multi-scale spatial heterogeneity of the indicators of blue-green space, and optimizing the scale, shape and layout indicators of blue-green space at the block level according to their respective impact strength. The three-level optimal zoning of blue-green space is delimited, and precise optimization methods are proposed respectively for the scale, shape and layout of blue-green space. Specifically, in terms of the scale of blue-green space, it is supposed to take advantage of every opportunity to increase greenery and bluey; in terms of the shape of blue-green space, it is supposed to optimize the shape based on decentralized connection; and in terms of the layout of blue-green space, it is supposed to integrate fragmented blue-green spaces into an interconnected network of blue-green spaces. The research results may provide a theoretical reference for the planning of blue-green space at the block scale from the perspective of thermal environment optimization.
Conclusion The research offers a comprehensive insight into the multi-dimensional and spatially heterogeneous impacts of blue-green space on the thermal environment within urban blocks. It underscores the potential of blue-green space in contributing to climate-adaptive urban development and provides targeted recommendations for the planning and management thereof. These include optimizing the scale, form, and layout of blue-green space to enhance their thermal mitigation capabilities. The findings may serve as a theoretical foundation for climate adaptation strategies in high-density urban areas and the fine management of urban blocks, advocating for a systematic integration of blue-green space into urban planning framework. Future research may separately assess blue and green spaces from the dimensions of scale, shape and layout and quantify their interactions to further explore the effects of blue-green space on the thermal environment. Additionally, with the improvement in data availability, a further research with high spatiotemporal ductility may be conducted across multiple time series and climatic zones.
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