Equity Evaluation of Park Green Space Based on SDG11: A Case Study of Jinan City, Shandong Province, China
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Resource
2.3. Method
2.3.1. Entropy Value Method
- 1.
- Data standardization
- 2.
- Weight quantification
- 3.
- Composite Score Calculation
2.3.2. Two-Step Floating Catchment Area
2.3.3. Lorenz Curve and Gini Coefficient
2.3.4. Coupling and Coordination Degree Model
2.4. Methodological Workflow
2.4.1. Acquisition and Classification of PGS
2.4.2. Demand Level Quantification
2.4.3. Supply Level Measurement
- 1.
- Quantity indicator
- 2.
- Quality indicator
- 3.
- Accessibility indicator
3. Results
3.1. PGS Demand Analysis
3.2. PGS Supply Analysis
3.2.1. Quantitative Indicator Analysis
3.2.2. Quality Indicator Analysis
3.2.3. Analysis of Accessibility Indicator
3.2.4. Comprehensive Supply Level Analysis
3.3. Analysis of Supply and Demand Coordination of PGS
3.4. Measuring the Fairness of PGS Based on Gini Coefficient
3.5. Optimization Strategies for Fairness in PGS
4. Discussion
4.1. Methodological Innovation in Green Space Equity Assessment
- Holistic measurement: This study is significantly different from previous explorations that focused only on spatial accessibility [15]. While traditional studies are often limited to static analysis of spatial elements, which makes it difficult to truly touch the actual needs and experiences of residents, our approach realizes the deep coupling between spatial patterns and residents’ real needs. On the one hand, we anchor the core indicators of Sustainable Development Goal 11 (SDG11) that are closely related to the quality of life of urban residents and incorporate key elements such as housing affordability and air quality into the assessment system, which directly reflect the basic needs of residents in terms of housing costs and health environment; on the other hand, we innovatively introduce geographic big data technology by crawling and integrating online open platforms of residents’ data. This way of obtaining information based on geographic big data breaks through the limitations of traditional research data with limited sample size and insufficient timeliness and is able to reflect in real time and comprehensively the differences in the spatial use habits and needs of different groups, providing a more persuasive empirical basis for understanding the interactive relationship between urban space and residents’ needs.
- Social sensitivity: This study identifies minors (under 18) and the elderly (over 65) as PGS-vulnerable groups because cities have physiological and structural differences that make them more vulnerable. It uses the Gini coefficient to show how these groups are vulnerable in the allocation of PGS resources, echoing Wang et al.’s [23] criticism of ‘neutral’ analyses that hide power dynamics. It connects demographic data to spatial patterns, addressing concerns related to earlier aggregate scores in terms of methodology. Showing the differences in PGS quality and amenities between the north and south of Jinan is in accordance with global results [7] that are not often mentioned in Chinese research [57]. The result shows how PGS distribution may be changed for vulnerable groups, which has an effect on Jinan’s 2030 plan. Such evidence makes equity-centered analysis possible.
4.2. Vulnerable Groups and Green Space Justice
4.3. Implications for Decision-Making
- Insights at the regional level: the complete findings of the park equity assessment enable the Jinan New Urbanization Plan (2021–2035) [56] to decide how to best use resources across different administrative entities.
- Improvement of spatial effects: Finding the best locations for areas with low accessibility and for parks (Figure 18) can help with small-scale renovations, such as planning new parks in the neighborhoods that need them most to meet the needs of their residents, a strategy that has been very effective in Shanghai [15] and Padua [12].
4.4. Unaddressed Dimensions and Future Work
- User behavior data: surveys or cell phone signaling data could record, for example, how often residents visit the park [15], which could be a component of future research.
- Multiple-use benefits: assessing co-benefits such as carbon sequestration or impacts on mental health [51] extends the equity conversation beyond use.
- Temporal dynamics: tracking the long-term impacts of policy changes or urban sprawl on green space equity [63].
5. Conclusions
- The innovative value of this framework lies in its departure from conventional single-indicator analyses. In this study, a comprehensive assessment model of PGS equity (both spatial equity and social distributive justice) is constructed by integrating the indicators of SDG11, the Coupled Coordination Degree Model (CCDM), the Lorenz curve, and Gini coefficients. This connects the fairness of public green space to the larger goal of sustainable urban development and is different from basic approaches that only look at accessibility.
- Explicitly focusing on minors (under 18) and the elderly (over 65) as vulnerable groups. This is because both groups experience mobility constraints, and minors require safe recreational environments for their development, while older adults benefit from nearby green spaces. Vibrant intra-urban disparities, such as lower PGS accessibility for vulnerable groups in northern districts like Shanghe compared to southern cores like Lixia, are concealed by Jinan’s citywide per capita green space of 12 m2 and Gini coefficient of 0.35.
- These research findings provide a reference for decision-makers. Based on the evaluation results, PGS resource allocation can be guided, with priority given to northern regions. K-means clustering analysis can determine the optimal location for building new parks in areas with low accessibility, ensuring that intervention measures are consistent across the system and tailored to local conditions.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
SDG | Sustainable development goal |
PGS | Park green space |
GIS | Geographic information systems |
PD | Patch density |
LPI | Largest patch index |
LSI | Landscape shape index |
AI | Aggregation index |
SPLIT | Splitting index |
MESH | Effective mesh size |
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Type | Data | Source |
---|---|---|
Statistical data | Statistical yearbook | http://jntj.jinan.gov.cn/art/2023/12/21/art_27523_4750235.html (accessed on 18 August 2025) |
National population census | https://www.stats.gov.cn/sj/pcsj/rkpc/d7c (accessed on 18 August 2025) | |
China urban construction statistical yearbook | https://www.mohurd.gov.cn/gongkai/fdzdgknr/sjfb/tjxx/jstjnj/index.html (accessed on 18 August 2025) | |
Spatial data | Land use data [24] | https://zenodo.org/record/8176941 (accessed on 21 August 2025) |
PM2.5 data [25] | https://data.tpdc.ac.cn/zh-hans/data/6168e75d-93ab-4e4a-b7ff-33152e49d0bf (accessed on 18 August 2025) | |
Web-based open data | OSM data | http://planet.openstreetmap.org (accessed on 21 August 2025) |
POI/AOI data | https://www.amap.com (accessed on 21 August 2025) |
Type | Area (hm2) | Scope of Services (m) |
---|---|---|
Comprehensive Park | >50 | 3000 |
20–50 | 2000 | |
10–20 | 1000 | |
Community Park | 5–10 | 800 |
1–5 | 500 | |
Playground | <1 | 300 |
Specialized Park | / | Determine the scope of services based on the size of the area |
Category | SDG11 Indicator | Proxy Indicators | Explanations |
---|---|---|---|
Housing conditions | SDG 11.1 | Urban Housing Price-to-Income Ratio | [48] |
Urban construction | SDG11.2 | Road Network Density | [47] |
Urban construction | SDG11.3 | Constructed Area | The area within the urban administrative area that has actually been developed and constructed, and the municipal public facilities and public facilities are basically equipped [49]. |
Cultural protection | SDG11.4 | Location Entropy of Cultural Venues | where Q is locational entropy, Lij Li denotes the sum of the j factors in region i, Lkj denotes the sum of the j factors in region y and Lk denotes the sum of the factors in region k [50]. |
Vulnerable groups | SDG11.5 | Proportion of the vulnerable population | Minors and the elderly are two groups that are relatively disadvantaged socially, economically, and in terms of health and require appropriate policies and services. In this study, minors under the age of 18 and older persons over the age of 65 are collectively referred to as the “vulnerable population” [13,19]. |
Air pollution | SDG11.6 | Annual average PM2.5 concentration | This indicator reflects the annual average concentration of fine suspended particles with a diameter of less than 2.5 μm. Good air quality is the basis for a healthy life for urban residents and sustainable urban development [51]. |
Urban-rural coordination | SDG11.a | Urbanization rate | The urbanization rate is a direct reflection of the distribution of the population between urban and rural areas [52]. |
Classification | Index Name | Describe | Unit |
---|---|---|---|
Density index | PD | patch density | /100 hm2 |
Area indicator | LPI | The proportion of the largest patch to the landscape area | % |
MESH | The ratio of the sum of squares of patch areas in the landscape to the total landscape area | hm2 | |
AREA_AM | Weighted size of plaque area | hm2 | |
Shape index | SHAPE_MN | Average shape index of plaques | / |
LSI | Landscape Shape Index | / | |
Aggregation and dispersion index | AI | Connectivity between patches of each landscape type | % |
Other indicators | SPLIT | Separation degree of distribution of different patch numbers in a certain landscape | / |
DIVISION | The degree of fragmentation of landscape segmentation | / |
Type | Name | Area/hm2 | Park Boundaries and Gates |
---|---|---|---|
Comprehensive Park | Daming Lake Park | 103.97 | |
Community Park | Tangye Park | 7.63 | |
Specialized Park | Jinan Zoo | 56.29 | |
Playground | Quan Cheng Impression Garden | 0.28 |
Indicators | SDG11.1 | SDG11.2 | SDG11.3 | SDG11.4 | SDG11.5 | SDG11.6 | SDG11.a |
---|---|---|---|---|---|---|---|
Weight | 0.09 | 0.15 | 0.28 | 0.16 | 0.09 | 0.07 | 0.16 |
Indicators | Weight |
---|---|
Count | 0.17 |
Area | 0.35 |
Per capita PGS location entropy | 0.33 |
Per capita PGS service location entropy | 0.16 |
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Sui, M.; Sun, Y.; Meng, W.; Song, Y. Equity Evaluation of Park Green Space Based on SDG11: A Case Study of Jinan City, Shandong Province, China. Appl. Sci. 2025, 15, 9239. https://doi.org/10.3390/app15179239
Sui M, Sun Y, Meng W, Song Y. Equity Evaluation of Park Green Space Based on SDG11: A Case Study of Jinan City, Shandong Province, China. Applied Sciences. 2025; 15(17):9239. https://doi.org/10.3390/app15179239
Chicago/Turabian StyleSui, Mingxin, Yingjun Sun, Wenxue Meng, and Yanshuang Song. 2025. "Equity Evaluation of Park Green Space Based on SDG11: A Case Study of Jinan City, Shandong Province, China" Applied Sciences 15, no. 17: 9239. https://doi.org/10.3390/app15179239
APA StyleSui, M., Sun, Y., Meng, W., & Song, Y. (2025). Equity Evaluation of Park Green Space Based on SDG11: A Case Study of Jinan City, Shandong Province, China. Applied Sciences, 15(17), 9239. https://doi.org/10.3390/app15179239