At the city scale, residential segregation driven by socioeconomic differentiation is a key driver exacerbating green space inequity. This section shifts the perspective to the urban interior, delving into green equity at the meso-scale of communities and streets. It first examines the impact of the much-discussed “open communities” policy on green equity. Using a Two-Step Floating Catchment Area (2SFCA) model that incorporates multiple travel modes—walking, cycling, and driving—the study simulates changes in green space accessibility before and after the opening of gated communities. A core, counterintuitive finding is that opening communities does not alleviate, but may instead exacerbate, green inequity. The underlying reason is that high-income communities are often spatially clustered and possess higher-quality internal greening. When these communities are opened, their high-quality private green resources are primarily shared by similarly affluent residents nearby, further widening the gap with low-income clusters that already lack green resources. In search of more optimal pathways to enhance equity, this chapter further expands the traditional green space assessment framework. By introducing the Green View Index—calculated from street-view imagery using deep learning—and constructing a more advanced Comprehensive Floating Catchment Area (CFCA) model, the study finds that incorporating street greenery into the assessment significantly reduces the measured level of green inequity. This is because many central urban areas, while lacking large parks, have lush, tree-lined streets that provide residents with a high level of visual green exposure, partially compensating for the lack of park accessibility. This reveals a new policy pathway: enhancing the quality of streetscapes is an effective means of promoting green equity. The chapter further points out that even in models integrating street greenery, a bias remains if quantity (e.g., area) is used as the sole metric for green space attractiveness. To address this, the research establishes a multidimensional evaluation system for the comprehensive service capacity (i.e., quality) of green spaces, which includes metrics like internal and external connectivity, diversity, and ecological service capacity. The results show that the level of inequity measured based on “quality” is significantly lower than that measured based on “quantity.” This implies that focusing solely on green space area overestimates the real-world inequity problem, and that improving the comprehensive service quality of existing parks is another key pathway to narrowing the service gap between different social classes. Finally, to provide concrete operational guidance for the “open communities” policy, the chapter proposes an optimization framework that integrates a genetic algorithm. With the objective of promoting equity, this framework can intelligently calculate an optimal sequence for which communities to open first, thereby providing urban managers with a scientific decision-making tool to strategically advance urban renewal in stages with limited resources.

Research Perspective on City-Scale Urban Green Space and Equity

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发表日期：

2026年

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Jiayu Wu

摘要

At the city scale, residential segregation driven by socioeconomic differentiation is a key driver exacerbating green space inequity. This section shifts the perspective to the urban interior, delving into green equity at the meso-scale of communities and streets. It first examines the impact of the much-discussed “open communities” policy on green equity. Using a Two-Step Floating Catchment Area (2SFCA) model that incorporates multiple travel modes—walking, cycling, and driving—the study simulates changes in green space accessibility before and after the opening of gated communities. A core, counterintuitive finding is that opening communities does not alleviate, but may instead exacerbate, green inequity. The underlying reason is that high-income communities are often spatially clustered and possess higher-quality internal greening. When these communities are opened, their high-quality private green resources are primarily shared by similarly affluent residents nearby, further widening the gap with low-income clusters that already lack green resources. In search of more optimal pathways to enhance equity, this chapter further expands the traditional green space assessment framework. By introducing the Green View Index—calculated from street-view imagery using deep learning—and constructing a more advanced Comprehensive Floating Catchment Area (CFCA) model, the study finds that incorporating street greenery into the assessment significantly reduces the measured level of green inequity. This is because many central urban areas, while lacking large parks, have lush, tree-lined streets that provide residents with a high level of visual green exposure, partially compensating for the lack of park accessibility. This reveals a new policy pathway: enhancing the quality of streetscapes is an effective means of promoting green equity. The chapter further points out that even in models integrating street greenery, a bias remains if quantity (e.g., area) is used as the sole metric for green space attractiveness. To address this, the research establishes a multidimensional evaluation system for the comprehensive service capacity (i.e., quality) of green spaces, which includes metrics like internal and external connectivity, diversity, and ecological service capacity. The results show that the level of inequity measured based on “quality” is significantly lower than that measured based on “quantity.” This implies that focusing solely on green space area overestimates the real-world inequity problem, and that improving the comprehensive service quality of existing parks is another key pathway to narrowing the service gap between different social classes. Finally, to provide concrete operational guidance for the “open communities” policy, the chapter proposes an optimization framework that integrates a genetic algorithm. With the objective of promoting equity, this framework can intelligently calculate an optimal sequence for which communities to open first, thereby providing urban managers with a scientific decision-making tool to strategically advance urban renewal in stages with limited resources.