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Article

The Potential of Informal Green Space (IGS) in Enhancing Urban Green Space Accessibility and Optimization Strategies: A Case Study of Chengdu

by
Yu Zou
1,
Liwei Zhang
2,*,
Wen Huang
2 and
Jiao Chen
1
1
School of Architecture, Southwest Jiaotong University, Chengdu 611756, China
2
Sichuan Institute of Land and Spatial Planning, Chengdu 610081, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(7), 1313; https://doi.org/10.3390/land14071313
Submission received: 21 May 2025 / Revised: 15 June 2025 / Accepted: 16 June 2025 / Published: 20 June 2025
(This article belongs to the Topic Biophilic Cities and Communities: Human-Environment Interaction and Sustainable Governance)
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Abstract

:
The inequity in the distribution of green spaces in megacities has a detrimental effect on the physical and mental well-being of their inhabitants, highlighting the necessity for careful and strategic urban planning, along with appropriate regulatory interventions. Nevertheless, scholarly articles addressing the equity of access to urban green spaces primarily concentrate on urban parks, with limited studies examining the influence of alternative types of green spaces. This research initially recognized and categorized informal green spaces (IGS) located within the Third Ring Road of Chengdu, utilizing the UGS-1m dataset and area of interest (AOI) data, in accordance with a well-defined classification framework. Then, the G2SFCA method and Gini coefficient were employed to assess the impact of IGS on the green space accessibility, especially scenario analysis of open and shared use of green space. The findings indicate that (1) IGS in the narrow sense constitute 21.2% of the overall green spaces within the study area, resulting in a reduction of the Gini coefficient by 0.103; (2) IGS in the broad sense, including public affiliated green spaces, shows an even more positive effect on improving the equity of green space supply, with a reduction of the Gini coefficient by 0.28; (3) there exists great spatial disparity in accessibility improvement effect by different types of IGS, so public policies must be customized to reflect local circumstances, taking into account the practicality and associated costs of management and maintenance of various IGS as well as accessibility enhancement; (4) certain older residential areas may not be amenable to effective enhancement through the use of IGS alone, and these should then adopt a multidimensional greening strategy such as green-roof. The findings of this research offer valuable insights for the planning and management of green spaces in densely populated urban environments, thereby aiding in the development of more refined models for the development of “Garden Cities”.

1. Introduction

Urban green spaces have attracted considerable attention from academics due to their beneficial effects on human health and the environment [1,2,3]. The progression of research within this domain has transitioned from evaluating the accessibility of green spaces to examining the exposure to these areas [4,5]. This shift has been accompanied by the development of more sophisticated metrics for assessing green space equity, as well as heightened expectations regarding the scale and accuracy of the research subjects [6,7,8,9]. Existing studies on the equity of green space provision have focused primarily on urban parks as the object of analysis [10,11,12], using multi-source data such as electronic navigation maps, Open Street Map and national land surveys [13,14,15]. However, Feltynowski et al. [16] found significant differences in the conclusions drawn from these data sources. Indeed, from a multiscale exposure perspective, focusing exclusively on large urban public green spaces, such as city parks, does not fully capture the characteristics and patterns of green space exposure inequity [17]. This is particularly evident in older communities, where the provision of large parks is challenging and may not meet the convenience and sharing needs of residents [18,19,20]. The effective differentiation of green spaces is therefore recommended in order to provide more targeted guidance for urban green infrastructure planning and management.
Following the proposal of the concept of “Informal Green Space” (IGS) by Rupprecht [21], scholars have conducted extensive research on the positive effects of such space. The unique aesthetic needs of individuals for such space [22,23,24], and the significant reduction in anxiety levels [25,26,27] that results from contact with it, have been demonstrated. In addition, IGS has been regarded as a means by which to enhance the equity of urban green space exposure [28,29], whilst concomitantly promoting biodiversity and the enhancement of urban green infrastructure systems [30,31,32,33]. In an effort to identify and extract these spaces, some scholars have conducted micro-level case studies and typological research [34,35,36]. Nevertheless, there remains a paucity of consensus on the concept and its classification.
The definition and classification of IGS is a subject on which different scholars have divergent views, which may lead to significant differences in their research conclusions. Rupprecht [21] has proposed that the concepts of “publicness” and “lack of maintenance” could serve as criteria for delineating the extent to which a green space is considered informal, such as vacant lots, wastelands, brownfields, “leftover areas,” urban derelict places, and vertical greening. Feng et al. [37] integrated the previously mentioned criteria with China’s “Standard Classification of Urban Green Space” (CJJ/T85-2017) [38], delineating formal green spaces as “public parks, squares, green areas for environment protection, attached green spaces, and regional green spaces,” while IGS refer to areas that do not fall within the parameters of this official classification, encompassing vacant lots, brownfields, and other green spaces that are not subject to maintenance or management, thereby exhibiting a “natural” state. Nonetheless, the criteria pertaining to “publicness” and “maintenance” have been scrutinized by various scholars. For instance, Sikorska et al. [28] advanced the argument that IGS encompass both “public” open green spaces and also private gardens and residential greenspaces which are “non-public” and enclosed, forming a binary taxonomy of managed informal and unmanaged informal categories [39,40]. Zhou [41] further posits that, in the identification of IGS, the criteria for assessing “loose management” levels should be relaxed. Specifically, he emphasizes the categorization of attached green spaces with public accessibility or potential under IGS, a strategy that can address gaps in green service coverage in high-density urban areas. Additionally, Zhou [41] argues that IGS should be based on ‘space’ as a physical entity, distinguishing it from types such as vertical greening, roof gardens and balcony greening. This approach presents a more expansive definition of IGS recognition in comparison to the work of Rupprecht [21], situating it as a generalized form of IGS. In several empirical studies, attached green spaces have consequently been incorporated into the classification of informal green spaces. For instance, Zeng et al. [42] included attached green spaces within the category of informal green spaces and developed a sub-classification based on three criteria: whether they were intentionally designed in advance, their degree of public accessibility, and the adequacy of management practices. In a Harbin-based study, Chen et al. [43] also regarded inner residential open green spaces as informal green spaces. Biernacka et al. [35] adopt a more straightforward approach, characterizing all non-government-managed green spaces as informal green spaces, including private and community gardens, as well as attached green spaces affiliated with public and educational institutions. Consequently, the distinct categorization of green spaces into formal and informal might be quite difficult, contingent upon the varying criteria employed by different researchers. However, it is generally recommended that the definition and identification of IGS emphasize “land” as a physical entity, and that remote sensing imagery from an “overhead” perspective be utilized as a data source to facilitate effective control within urban planning and management systems.
As the availability of green space data improves, research on the identification and extraction of large-scale IGS at the macro scale, and its role in promoting accessibility and equity, has been gradually increasing. A body of research focusing on Japan and Australia has indicated that IGS have the capacity to engender a phenomenon referred to as “green gentrification” [44]. A further analytical study of Chicago has sought to compare the differential effects of formal and informal green spaces on the processes of gentrification and equity [45]. A recent empirical study of two Eastern European cities has demonstrated that IGS, located in close proximity to transportation and communication routes, as well as within the gaps between multi-family housing, are of equal importance to formal green spaces in terms of enhancing green space equity [28]. An analysis of Wuhan, China, has revealed that the accessibility distribution becomes more uniform after the inclusion of IGS, with an impressive contribution rate of 79.32% [29]. In an empirical study of Xuzhou’s Gulou District, 78.46% of IGS had varying degrees of impact on improving park green space equity, but with limited potential, while 29.98% of surface IGS made green space distribution more inequitable [20]. Nevertheless, the absence of a thorough investigation into the diversity of IGS types, coupled with the inadequate refinement of accessibility measurement units, may result in biased outcomes or an exaggeration of the role of informal green spaces in research. For instance, in the case studies of Chicago and Wuhan, all non-park green spaces were categorized as IGS, and their impact on green space equity was assessed. In the case studies of Eastern European cities, areas managed by public authorities for residential and recreational use were classified as formal green spaces, while others were divided into managed and unmanaged IGS. In the Xuzhou case study, vacant, low-utilized urban land with clear boundaries and spontaneous vegetation growth, undisturbed for a considerable time, was classified as IGS. Consequently, a more profound comprehension of the influence of IGS on urban green space accessibility and equity necessitates a more detailed typological analysis and comparative research of IGS at a more extensive level [46].
The present study adopts a case-study approach, focusing on Chengdu as a model city. It is hypothesized that the following objectives will be accomplished and the following questions will be answered by this study:
(1)
How do the spatial configurations and quantitative compositions relate to formal versus informal green spaces within Chengdu’s central urban district?
(2)
What is the magnitude of improvement contributions from distinct informal green space typologies to accessibility and equity, and what is the nature of their inter-categorical variations?
(3)
Following the integration of informal green spaces within urban planning frameworks, are there still communities exhibiting substandard accessibility levels?

2. Data

2.1. Study Area

This research focusses on the region within the boundaries of the Third Ring Road of Chengdu specifically, a significant thoroughfare established in 2002, and serves as the core area of the Urban Renewal Master Plan for Chengdu (Figure 1). Chengdu, which is widely recognized as the origin of the “Garden city” concept in China, has been designated as one of the inaugural urban renewal pilot cities by the Ministry of Housing and Urban-Rural Development of the People’s Republic of China. A thorough characterization and in-depth analysis of IGS in the built-up areas of Chengdu are imperative for the further optimization of the “Garden city” development model. Such analysis also facilitates the identification and exploration of disadvantaged residential areas, providing valuable references for urban renewal initiatives.

2.2. Data Source

The following data were utilized in the study: (1) UGS-1m green space data with 1 m accuracy, from Shi et al. [47], and over a time period encompassing the year 2020. The mean overall accuracy (OA) and F1 score were evaluated through its performance on samples collected from five different cities, achieving 87.56% and 74.86%, respectively, thus indicating the framework’s reliability and precision. (2) Area of interest (AOI) polygons, obtained from Baidu Maps in 2020, exhibiting a categorization analogous to that of points of interest (POI). AOI can represent the delineated boundaries of ownership or management entities, thereby facilitating the classification and extraction of green spaces according to the criteria of the presence of a defined management entity and the public character of that entity. (3) Road network data, sourced from the Open Street Map (www.openstreetmap.org) in 2020. (4) The river system in Chengdu, which was obtained from the publicly available national geographic information resources service catalog system (2021). (5) A building rooftop vector data set, as disseminated by the Nanjing Normal University Smart City Perception and Simulation Laboratory [48].

3. Method

3.1. Classification of Informal Green Space

The technical route is shown in Figure 2. To enhance the detailed effect analysis of IGS on accessibility to urban green spaces, this study introduces a classification system which divides urban green spaces into three primary categories and five subcategories (see Table 1), grounded in the concepts of “publicness” and “management”. Firstly, formal green spaces are characterized by strong public attributes and high maintenance standards, which are clearly designated as parks, squares, or in scenic areas. Secondly, attached green spaces under clear management entities and certain maintenance standards could be further classified based on “publicness” as residential green spaces, municipal infrastructure-related green spaces and semi-public attached green spaces. Consequently, residential green spaces are maintained to high standards; however, access is restricted to limited residents, functioning as “club products”. In contrast, municipal infrastructure-related green spaces, such as those located along roadways and riversides, are accessible to the general public but require targeted renewal efforts to improve their aesthetic and recreational value, despite being governed by institutional sectors. Additionally, semi-public green spaces, which are linked to commercial and public buildings, exhibit notable landscaping and a high degree of accessibility. Thirdly, areas that lack distinctly established management entities and are predominantly in a natural condition may be regarded as aligning more closely with the narrowly defined concept of IGS.
This research puts forward the argument that attached green spaces have the potential of being transformed as public green spaces such as mini-parks through the implementation of an “open sharing” management policy and renewal investment. Consequently, these spaces should be classified as informal green spaces in the broad sense. In the subsequent discussion, the term IGS will specifically refer to a narrowly defined category of informal green space.

3.2. Extraction of Informal Green Space

This research employs area of interest (AOI) polygons as a substitute for the challenging-to-acquire property rights boundaries which needs to be derived from real estate registration. Formal green spaces such as parks are already in the AOI dataset. The extraction of attached green space has been conducted in compliance with the “Standard for Classification of Urban Green Space” (CJJ/T85-2017) [38] and designated AOI types. This process involved clipping the UGS-1m green space data to delineate the attached green spaces within commercial office buildings, public facilities, residential areas, and other relevant categories. Linear attached green spaces, including those adjacent to roadways and rivers, are created by establishing buffer zones derived from road networks and river systems. Finally, for green spaces not belonging to any AOI, building vector data were integrated to classify them based on the proportion of green area within patches into two categories: “other attached green spaces” and “IGS in narrow sense”. Specifically, if the green area proportion within its designated area is less than 35%, indicating a building density greater than 65%, it is classified as other attached green spaces. Conversely, if the green area proportion exceeds 35%, it is designated as an informal green space in a narrow sense. A comprehensive classification standard is presented in Table 1.

3.3. Measurement of Accessibility to Green Spaces and Its Equity

A variety of methods have been employed to assess the accessibility and equity of urban green spaces, including buffer zone statistics utilizing Euclidean straight-line distance [50], minimum distance [51], or network distance considering actual travel network constraints [52,53]. Despite their apparent simplicity, these methods fail to address critical supply–demand dynamics, resulting in an incorrect distribution of service capacity across green spaces. In recent years, there has been a notable increase in the utilization of measurement methodologies that are grounded in the principles of supply and demand dynamics [11,54,55], with two representative methods being the two-step floating catchment area method (2SFCA) [11,14,56,57] and the Gaussian two-step floating catchment area method (G2SFCA) considering distance decay effects [58,59,60,61]. The conventional 2SFCA approach quantifies both green space capacity and resident demand. However, this methodology exhibits pronounced threshold effects. Specifically, it employs a fixed search radius, leading to an abrupt decline in accessibility at boundaries. This outcome contradicts actual distance decay patterns. The G2SFCA method is a solution to this problem. It incorporates the advantages of 2SFCA while incorporating a Gaussian function to continuously attenuate accessibility. This ensures that accessibility decreases non-linearly with distance rather than abruptly. This study utilizes the G2SFCA method to calculate green space accessibility for each residential community.
The measurement of accessibility consists of two steps. The first step entails the computation of service capacity of green spaces by creating a fishnet grid with a resolution of 30 m as an analyzing unit, considering supply (the area of green space within the grid) and demand (the population amount being served) ratio, as delineated in Equation (1). In Equation (1), R j is defined as the green space service capacity of the designated fishnet grid; S j is the green space area within the fishnet grid represents supply; D i is the estimated population amount being served, which represents demand by dividing the total floor area of the targeted residential AOI i within the grid influential zone by the per capita residential floor area in Chengdu; and G ( d i j ) is the distance decay coefficient based on the Gaussian equation, where the distance threshold is set to 1 km, referencing the 15-min living circle. Equation (2) presents the calculation formulation for G ( d i j ) , and d i j represents the distance between the targeted residential AOI and the fishnet grid. The subsequent phase entails the evaluation of accessibility, utilizing residential AOI as the unit of analysis. The green space accessibility of residential AOI i is measured by aggregating green space service capacity of grids with the search radius of d 0 , weighted by G ( d i j ) , as indicated in Equation (3).
R j = S j ∑ i ∈ { d i j < d 0 } k D i × G ( d i j )
G ( d i j ) = e − ( 1 2 ) × ( d i j d 0 ) 2 1 − e − 1 / 2 , d i j ≤ d 0 0 , d i j > d 0
α i = ∑ i ∈ { d i j ≤ d 0 } m R j × G ( d i j )
A total of 4028 residential AOIs were obtained within the study area. Among which, 48 AOIs were excluded from the analysis due to the absence of building data, as they were still under construction. Consequently, 3980 communities remained for evaluation. To assess the equity of green space accessibility for the residential AOIs, the Lorenz curve and Gini coefficient are conventionally employed. The Gini coefficient, in particular, is a useful metric for quantifying the degree of disparity in a distribution, with values approaching 1 indicating a more pronounced inequality. The Lorenz curve reflects the relationship between the cumulative proportion p of the residential AOI and the proportion L(p) of green space resources, sorting residential AOIs in ascending order by green space accessibility. In this context, the green space accessibility index was analogized as a resource similar to income.

4. Result and Discussion

4.1. The Configuration and Distribution of IGS

In terms of scale, the total area of green space within Chengdu’s Third Ring Road encompasses 51.41 km2. Of this area, formally designated green spaces comprise 3.02 km2, representing 5.6% of the overall green space; attached green spaces account for 37.49 km2 (72.9%) and narrowly defined IGS constitute 10.9 km2, equivalent to 21.2% of the total green space. This result indicates that IGS play a vital role in the provision of green space in older urban areas, even exceeding the contributions of formal green spaces such as urban parks. For attached green space (generally defined IGS), residential area attached green space accounts for 23.2%, public attached green space accounts for 40.9%, and semi-public attached green space only for 6.6% (Figure 3). Given the substantial variations in the “publicness” and “maintenance standards” among these different attached green spaces, it is more appropriate to study them as distinct types, separately from narrowly defined IGS. The mean patch size of narrowly defined IGS is 1576.6 m2, which is less than the World Health Organization’s definition of public green spaces with an area larger than 1 hm2, but it is close to a suitable size for small parks as defined in the “Standard for planning of urban green space” GB/T51346-2019 (0.2–0.4 hm2) [62]. The average patch size of semi-public attached green space is 405.5 m2 and riverside green space is 1795.2 m2, both smaller than the average patch size of narrowly defined IGS.
In order to verify the accuracy of the green space categorization with the actual situation, a field survey was conducted. The survey involved the sampling of ten sites within the study area. The results indicate that the green space types derived from the field survey are consistent with the classified green space types.
From a spatial analysis standpoint, the distribution of 6913 IGS patches is characterized by significant unevenness (Figure 4). This observation indicates that the influence of IGS on the accessibility of urban green spaces is regionally variable, warranting further quantitative assessment. A broad examination reveals that large-scale IGS are primarily situated along the peripheries of railway linear corridors. Furthermore, the presence of undeveloped land parcels, a consequence of cyclical land development processes, has contributed to the emergence of IGS. It is important to highlight that several of these IGS patches have yet to be integrated into the management frameworks of park green spaces. In terms of distribution density, the IGS within the study area exhibit two distinct patterns: a large-scale continuous distribution and a scattered fragmented distribution. It is also significant to note that the central area is marked by a high concentration of commercial facilities. However, there is a pressing need to enhance the integration of urban functions with green spaces in the development of commercial service facilities. Locations such as Tianfu Square, Chunxi Road, and their adjacent districts, which serve as commercial hubs, generate considerable pedestrian traffic and energy consumption. Nonetheless, the provision of green spaces in these areas remains insufficient, thereby hindering the advancement of low-carbon districts.

4.2. How IGS Affect the Accessibility and Equity of Urban Green Spaces

4.2.1. Overall Effect of IGS on the Equity of Green Space Accessibility

The current research aimed to evaluate the influence of informal green spaces (IGS) on the accessibility and equity of urban green spaces. To achieve this objective, the study measured and compared the accessibility of formal green spaces, generalized defined IGS, and narrowly defined IGS. Additionally, various categories of green spaces were incorporated sequentially to evaluate their contribution to accessibility equity, based on their “publicness” and “maintenance” levels (Table 2 and Figure 5).
(1) Formal green spaces. The accessibility of formal green spaces in residential communities within the study area exhibits considerable variation, as indicated by a Gini coefficient of 0.79. The average accessibility is merely 1.14 m2, whereas communities located in proximity to park green spaces can attain accessibility levels of up to 118 m2.
(2) Green spaces attached to residential areas. The residential AOIs analyzed in this study demonstrate notable disparities in the extent of green space coverage, which can be attributed to variables such as the year of construction, geographical location, and property management practices. The study utilizes formal green space as a benchmark and subsequently aggregate the per capita area of associated green space within each residential AOI. The average accessibility of green space thus increases to 5.54 m2, with the Gini coefficient reflecting a decrease to 0.63. Enhancing the quality of property management services and fostering resident engagement in the maintenance of shared green spaces, such as community gardens, is crucial for optimizing the effectiveness of urban green service delivery.
(3) Public attached green spaces. The integration of formal green spaces, alongside green spaces attached to residential areas, and the subsequent addition of publicly attached green spaces—also municipal infrastructure-related green spaces—have shown a larger improvement in terms of enhancing the accessibility of green spaces for residential AOIs, with an increase in the average accessibility of green space to 12.5 m2, coupled with a reduction in the Gini coefficient to 0.422. These findings highlight the significant influence of publicly accessible green spaces on the promotion of equitable distribution. Employing street view green visibility as a criterion for assessing the equity of green space supply represents a logical methodology in pertinent research.
(4) Semi-public attached green spaces exhibit a unique management identity; however, their accessibility is contingent upon the extent of “openness and sharing”. As a specific category of IGS, the influence of these areas on green space accessibility warrants an independent policy simulation assessment and should not be included in the overall analysis of green space accessibility.
(5) The narrowly defined IGS. IGS in the narrow sense has been demonstrated to enhance the average green space accessibility to 15.75 m2, resulting in a reduction of the Gini coefficient to 0.419. However, the additional enhancement in equity provided by IGS is minimal when the contributions of public attached green spaces are included in the analysis. Scenario analysis indicates that the average green space accessibility increases to 8.76 m2 and the Gini coefficient decreases from 0.63 to 0.527 when narrowly defined IGS were incorporated into a “formal green spaces + residential attached green spaces” combination.

4.2.2. Effect of Semi-Public Attached Green Spaces on the Equity of Green Space Accessibility

Semi-public attached green spaces have clearly identified management entities, which differentiates them from public attached green spaces. In alignment with the park city concept that promotes open sharing, many cities have proposed the transformation of quasi-public attached green spaces into pocket parks for public utilization, illustrated by the “Technical Standard for Construction of Opening and Sharing Unit Affiliated Green Spaces” in Shanghai, China as an example. The current study indicates that semi-public attached green spaces constitute only 6.6% of the total green space area. Nevertheless, the maintenance standards of green spaces in these areas are comparatively elevated, owing to their concentration in employment, consumption, and transit zones. This, in turn, contributes to the improved efficacy of urban green space service delivery.
The influence of “opening and sharing scenarios” on accessibility and equity was assessed for three distinct categories of quasi-public attached green spaces (Table 3). The findings indicate that open sharing of green space attached to commercial and business facilities and green space attached to administrative and public service facilities contributed significantly to enhancing green space equity, utilizing “formal green space + residential attached green spaces” as a benchmark and seeing a decrease in Gini coefficients of 0.016 and 0.04, respectively. Although their contribution may be considered relatively modest when compared with public attached green space, well-defined management ownership and significant pedestrian traffic demonstrate higher standards of maintenance and design in comparison to more narrowly defined IGS. This difference leads to lower costs associated with renewal and maintenance. Furthermore, the accessibility and communal use of these attached green spaces have proven to be an effective approach for drawing individuals and mitigating the challenges associated with uneven spatial utilization at various times throughout the day.

4.3. Strategies for Improving Green Space Accessibility Considering IGS

4.3.1. Spatial Differentiation of IGS Enhancement Effect and Vulnerable Residentials Identification

The uneven spatial distribution of IGS needs spatial variability in their contribution to enhance residential AOI’s green space accessibility. Figure 6b–d illustrate the spatial differentiation of residential AOIs in terms of accessibility enhancement by various types of green space. Residential AOIs exhibiting accessibility levels below the mean standard of 5.54 with “formal green spaces + residential attached green spaces” as benchmark were identified as disadvantaged residentials, amounting to 2756 AOIs (Figure 6a), which are predominantly concentrated in the old urban districts. Three circumstances could be revealed, including (1) “disadvantaged overlay residentials”, where both public and semi-public green spaces cast limited efficacy in enhancing access to green spaces as well as narrowly defined IGS. Chunxi Road commercial area, the historical urban district of Yingmenkou, and Liangjiaxiang Station area are characterized as concentrated regions of underprivileged residential AOIs. (2) “Advantaged overlay residentials”. IGS might further augment accessibility for those residential AOIs which are already privileged areas if located near formal green spaces and have more green spaces attached to residential areas, especially when these residential areas are near rivers, suburban regions, and educational or research institutions. (3) “Disadvantaged uplifting residentials”. IGS has been demonstrated to significantly improve accessibility in certain disadvantaged residential areas to enhance their green space accessibility.

4.3.2. Targeted Strategies to Enhance the Accessibility of Disadvantaged Residentials

As demonstrated in the preceding analysis, both generalized and narrowly defined IGS have been shown to play a crucial role in enhancing the green space accessibility of residential areas. It is important to acknowledge that the financial resources required to provide high-quality leisure and recreational services vary across different categories of green spaces. To improve the green space and provide equity among the 2756 underprivileged residentials, it is essential to adopt strategies considering local conditions. Stanford et al. [63] advanced a tripartite framework for governance, encompassing three distinct approaches: strategic non-intervention, formalization, and temporary utilization. This study proposes four distinct strategies and identifies suitable policy implementation residentials based on cost–benefit trade-off consideration of different types of green spaces, based on the principles of minimal additional maintenance costs and optimal service quality (Figure 7). (1) “Opening and sharing of Quasi-public Attached Green Space”. This strategy requires the consideration of safety oversight concerns raised by public institutions. Nevertheless, its viability is significantly enhanced by the inherently public character of the managing organizations. (2) “Upgrading Public Attached Green Space”. This strategy focuses on enhancing the accessibility, usability, perceptibility, aesthetic appeal, and consumability of specific green spaces located along roadside and riverside. Although the renovation expenses for these areas are slightly greater than those associated with quasi-public attached green spaces, this approach demonstrates the greatest feasibility for implementation. (3) “Transforming Informal Green Space”. This strategy is for residential areas where the efficacy of the preceding two strategies is inadequate. This initiative involves the revitalization of informal green spaces that lack management by designated authorities and exist in a natural condition, a process that requires a substantial investment. (4) “Multi-dimensional Greening”. This strategy is for disadvantaged overlay residential areas which encounter challenges in achieving substantial accessibility improvements through the initial three strategies. Therefore, it is essential to improve green service levels by employing strategies such as three-dimensional greening and rooftop greening.
However, it is imperative to acknowledge that informal green spaces, distinguished by their spontaneous vegetation and successional processes, demonstrate temporal variability in their vegetation patterns [39]. Furthermore, centrally located green spaces, particularly those of a narrow-sense configuration, undergo functional transitions that are induced by urban renewal processes [40]. Consequently, sustainable governance necessitates implementing persistent dynamic surveillance regimes and responsive adjustment protocols, where intervention intensity is modulated via ongoing socio-ecological dual-axis importance evaluations. Collectively, during informal green space renovations, green zones with intensive resident usage demand prioritized safety reinforcement, aesthetic enrichment, and recreational capacity boosting. However, vigilance against formalization-induced degradation of vegetation succession and biodiversity is critical. This can be achieved by integrating satellite remote sensing and UAV surveys to build temporally resolved databases. Ultimately, this will forge a resilient “surveillance-response-adaptation” iterative framework.

4.4. Limitation

The current study is constrained by several limitations that warrant further investigation in subsequent research endeavors. Firstly, informal green space (IGS) was extracted based on areas of interest (AOI) data as a substitution for management entities, which does not entirely correspond with the actual property owner information. The methodology employed for the extraction of public attached green spaces, such as those adjacent to roadways and waterways, utilized buffer zones as a screening mechanism, which diverges from the planning demarcations considering different control lines. Secondly, the measurement of green space accessibility was performed at the level of residential AOIs, with population estimates derived from building area metrics. Future research could benefit from the incorporation of multi-source data, such as mobile phone signaling or location-based services (LBS), to capture the spatiotemporal dynamics of green space accessibility more accurately.
In summary, the identification of communities that experience challenges in accessing green space and the formulation of strategies to address these challenges in this study are still in the exploratory phase. Thresholds for defining “disadvantaged” status and optimization schemes require further in-depth research. The implementation of regulatory strategies carries with it the potential for the induction of legal conflicts or governance disorder, precipitated by erroneous tenure identification. Such identification has the capacity to engender ambiguities in ownership, which, in turn, have the potential to paralyze policy enforcement. In the context of high-density cores, particularly, contentious negotiations among stakeholders can result in a state of strategic paralysis. Spatial constraints may render “integrated multi-dimensional greening” to peripheral mitigation measures, which are inadequate in addressing the fundamental issue of green space scarcity.

5. Conclusions

The current research takes Chengdu, a provincial capital in western China, as a case study to evaluate the extent and distribution of informal green spaces (IGS) and to examine their potential for enhancing accessibility and equity in green spaces within densely populated urban environments. Existing literature that presents various interpretations regarding the definition and categorization of IGS predominantly emphasize “publicness” and “management”. In light of these findings, a comprehensive classification of green spaces is proposed to refine the conventional binary categorization of “formal” and “informal”. The research indicates that different categories of green spaces, such as residential area attached green spaces, public attached green spaces, semi(quasi)-public attached green spaces, and narrowly defined informal green spaces account for 94.1% of the urban green spaces within the study area, while narrowly defined informal green spaces comprise 21.2% of this overall.
The Gaussian two-step floating catchment area method (G2SFCA) and Gini coefficient are conventionally employed to validate the beneficial effect of informal green spaces on accessibility and equity. Firstly, the effects of public attached green spaces or municipal infrastructure-attached green spaces are significant, as evidenced by a reduction in the Gini coefficient by 0.208. This observation is especially significant in relation to green spaces associated with roadways, which function as essential environments for the daily activities of residents. This has prompted the development of strategies aimed at enhancing the ecological service levels of streets and converting these regions from “traffic spaces” to “green spaces”. Secondly, semi(quasi)-public attached green spaces which incorporate commercial establishments, public administration, or public services play a significant role in enhancing the equity of green space distribution. This is evidenced by a decrease in the Gini coefficient of 0.016 and 0.04. Thirdly, narrowly defined informal green spaces demonstrate a significant potential to enhance the equity of green space service provision, resulting in a Gini coefficient reduction of 0.103.
The study highlights that both generalized and narrowly defined integrated green spaces may improve access to green areas in certain affluent residential neighborhoods. In light of the difficulties encountered in converting IGS into public spaces that provide significant recreational benefits—stemming from policy, management, and financial obstacles, it is advisable to prioritize regulatory strategies in regions with a high concentration of disadvantaged residential populations. The establishment of policy priorities and the subsequent development of four strategic approaches were proposed by two fundamental principles: minimum additional maintenance expenses and the enhancement of green space provision quality. The strategic framework includes the following four initiatives: “Opening and Sharing of Quasi-public Attached Green Spaces”, “Upgrading Public Attached Green Spaces”, “Transforming Informal Green Spaces”, and “Multi-dimensional Greening”. These strategies were executed in 2756 disadvantaged residential areas where accessibility levels fell below the average score of 5.54 with “formal green spaces + residential attached green spaces” as a benchmark.

Author Contributions

Conceptualization, Y.Z., J.C. and L.Z.; methodology, Y.Z., W.H. and L.Z.; validation, Y.Z.; investigation, Y.Z.; resources, Y.Z. and L.Z.; data curation, Y.Z.; writing—original draft preparation, Y.Z.; writing—review and editing, Y.Z., L.Z., W.H. and J.C.; visualization, Y.Z.; supervision, L.Z.; project administration, L.Z., W.H. and J.C.; funding acquisition, L.Z. and J.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China, grant number 52308080, and 52208079.

Data Availability Statement

Data will be available on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Scope of the study area.
Figure 1. Scope of the study area.
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Figure 2. The technical route.
Figure 2. The technical route.
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Figure 3. Detailed composition of urban green space (unit: km2).
Figure 3. Detailed composition of urban green space (unit: km2).
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Figure 4. Distribution of green spaces within the study area.
Figure 4. Distribution of green spaces within the study area.
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Figure 5. Lorenz curve for green space accessibility considering various combination scenarios.
Figure 5. Lorenz curve for green space accessibility considering various combination scenarios.
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Figure 6. Spatial differentiation of residential AOIs in terms of accessibility enhancement by various types of green space. (a) Formal green space + residential attached green space, (b) public attached green space, (c) semi(quasi)-public attached green space, and (d) narrowly defined IGS.
Figure 6. Spatial differentiation of residential AOIs in terms of accessibility enhancement by various types of green space. (a) Formal green space + residential attached green space, (b) public attached green space, (c) semi(quasi)-public attached green space, and (d) narrowly defined IGS.
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Figure 7. Distinct strategies and suitable policy implementation residentials.
Figure 7. Distinct strategies and suitable policy implementation residentials.
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Table 1. The classification and extraction method of urban green space.
Table 1. The classification and extraction method of urban green space.
ClassificationSubcategoriesExtraction Method
Formal green spaceUrban parksAcquired though AOI
Attached green spacesGreen spaces attached to residential area Clipped by residential AOI
Municipal infrastructure attached green spaces
(public attached green spaces)		Roadside green spacesGreen space for environmental protection of road extraction standard is defined according to the Technical Specification for Green Space Extraction of Urban Agglomeration (T/CI 026-2021) [49]. The buffer of the OSM road network used to clip green spaces is defined as follows: 40 m for primary and secondary roads, 20 m for tertiary roads, 10 m for residential roads.
Riverside green spacesIn accordance with the planning technical stipulate in Chengdu, the river has been configured with a 50-m-wide buffer zone, designated as the green control area. The green space within this buffer zone is designated as the ecological buffer green space of the river.
Semi-public attached green spacesGreen space attached to commercial and business facilities Clipped by AOI of hotels, markets, offices, etc.
Green space attached to administrative and public service facilitiesClipped by AOI of educational institutions, healthcare facilities, government offices, stadiums, and exhibition centers.
Green space attached to transportation facilitiesClipped by AOI of transportation hubs, such as railway stations, high-speed train stations, etc.
Green space attached to public utilities facilitiesClipped by AOI of filling stations, petrol stations, fire stations, etc.
Green space attached to industrial facilitiesClipped by AOI of industrial enterprises, industrial parks, etc.
Other attached green spacesIn cases where area of interest (AOI) attribution has not been explicitly defined, the existence of green space that is partially obscured by buildings and exhibits a green space ratio of less than 35% is categorized as “other attached green space”. In contrast, if the green space ratio surpasses 35%, it is classified as informal green space in the narrow sense.
IGS in narrow the sense
Table 2. Enhancement of accessibility and equity considering various combination scenarios.
Table 2. Enhancement of accessibility and equity considering various combination scenarios.
G0 = Formal Green SpaceG1 = G0 + Residential Attached Green SpacesG2 = G1 + Public Attached Green SpaceG3 = G2 + Narrowly Defined IGS
Gini coefficient0.7950.630.4220.419
Accessibility1.145.5412.515.75
Table 3. Enhancement of accessibility and equity considering open sharing scenarios.
Table 3. Enhancement of accessibility and equity considering open sharing scenarios.
G0 = Formal Green Space + Residential Attached Green SpacesG0 + Green Space Attached to Commercial and Business FacilitiesG0 + Green Space Attached to Transportation and Public Utilities FacilitiesG0 + Green Space Attached to Administrative and Public Service Facilities
Gini coefficient0.6300.6140.6290.590
Accessibility5.545.755.556.72
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Zou, Y.; Zhang, L.; Huang, W.; Chen, J. The Potential of Informal Green Space (IGS) in Enhancing Urban Green Space Accessibility and Optimization Strategies: A Case Study of Chengdu. Land 2025, 14, 1313. https://doi.org/10.3390/land14071313

AMA Style

Zou Y, Zhang L, Huang W, Chen J. The Potential of Informal Green Space (IGS) in Enhancing Urban Green Space Accessibility and Optimization Strategies: A Case Study of Chengdu. Land. 2025; 14(7):1313. https://doi.org/10.3390/land14071313

Chicago/Turabian Style

Zou, Yu, Liwei Zhang, Wen Huang, and Jiao Chen. 2025. "The Potential of Informal Green Space (IGS) in Enhancing Urban Green Space Accessibility and Optimization Strategies: A Case Study of Chengdu" Land 14, no. 7: 1313. https://doi.org/10.3390/land14071313

APA Style

Zou, Y., Zhang, L., Huang, W., & Chen, J. (2025). The Potential of Informal Green Space (IGS) in Enhancing Urban Green Space Accessibility and Optimization Strategies: A Case Study of Chengdu. Land, 14(7), 1313. https://doi.org/10.3390/land14071313

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