Objective In the context of accelerating urbanization and the escalating impacts of global warming, urban green spaces have become increasingly crucial for mitigating urban heat, as they offer shaded environments that significantly enhance the overall livability and comfort of urban environments. Consequently, the optimization of vegetation layout within urban green spaces has become a fundamental strategy for addressing climate change and improving the sustainable quality of life for urban residents. Currently, the assessment of green space shading in relevant norms at home and abroad is usually calculated based on the projected area of adult tree crowns. The result is a static indicator that cannot accurately reflect the three-dimensional morphological characteristics of the tree crown and the influence of the plant growth process on the shading effect. To accurately assess the shading area of green space, it is necessary to establish a dynamic assessment method for green space shading based on the three-dimensional growth model of plants and the real lighting environment. Such a method should reflect the three-dimensional elements of plant morphology and the dynamic nature of plant growth.

Methods Based on the allometric growth equations for different tree species and plant crown morphology models, parametric tree growth models and a shading area generation algorithm for the shading area of green space trees are established in the Python and Grasshopper environment. The area within the 4-hour isohel on the summer solstice for trees in a green space is defined as the shading area of the green space and used as an assessment indicator for the shading effect of the green space, which is typically calculated by the calculation method for the shading area of a building as specified in the Assessment Standard for Green Building (GB/T50378-2019). Additionally, taking Xiangyang Academy in Xiangyang City, Hubei Province, as an example, this research compares and explores the application characteristics of the shading algorithm, and generates a three-dimensional model for tree growth in and dynamic shading area of the selected site based on the aforesaid parametric generation algorithm to evaluate and analyze the shading range of the buildings and plants on the site.

Results Compared with the calculation method based on the vertical projection of the tree crown, the shading simulation and assessment method based on the generated three-dimensional model for the site has the following characteristics. (1) It can dynamically simulate the actual light changes. The shadow area of green spaces within the campus of Xiangyang Academy is closer to the real shading situation. For example, after the trees have grown for thirty years, the overall shadow area of the campus accounts for 67.61% of the total area of the campus’s open space, among which the shadow area of buildings accounts for 21.55%, the shading area of green spaces accounts for 51.92%, and the overlapping area accounts for 5.86%. The calculation result of the vertical projection area of the tree canopy is 45.48% less than the area of the tree canopy, and there is a certain offset distance in space. (2) This observation reflects the temporal changes in vegetation shading on the site; the area of green space shading increases from 69,106 m² to 269,086 m² as plants grow, and the ratio of open space area rises from 13.33% to 51.92%, indicating a trend of rapid increase followed by gradual stabilization. (3) It can reflect the difference in shading ability of different tree species in different growth periods. Fast-growing tree species such as Celtis sinensis Pers. and Magnolia denudata Desr. have obvious shading effects in the early stage while slow-growing large trees such as Cinnamomum camphora (L.) and Zelkova serrata (Thunb.) Makino have better shading effects in the later stage. (4) It can reflect the differences in the shading characteristics of different areas, such as the campus dormitory area and teaching area with high greening and building density; the superimposed effect of plants and building shading is obvious, while the shading range of the athletic area is subject to significant changes in the growth of plants, and the plants planted have better shading potential and growth trend. Campus roads are mainly planted with slow-growing trees on both sides, with the shading area improving slowly; the overall trend is stable.

Conclusion The shading algorithm based on the parametric growth model proposed in this research can generate more accurate dynamic shading range, reflecting the dynamic change of shading range during plant growth, and is consistent with the existing standards for building shading assessment. The algorithm can be used to predict and simulate the long-term shading effect in different periods, to quantify the shading capacity of multiple trees in different periods, and to evaluate the zoning in different scales of space. The research improves the accuracy and applicability of the assessment of shading areas in urban green spaces and combines parametric algorithms to build a reusable and automatic calculation process, which can be used as a reference for urban green space planning and climate-adaptive design.