The Quality Inequity of the Ecosystem Services delivered by Green Spaces: Evidence from 226 Urban Parks in Hangzhou, China DOI

Jiajun Huang,

Qinghai Guo,

G. Lian

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: May 5, 2025

Abstract Green spaces are vital for the urban ecosystem, providing residents with diverse ecosystem services. However, heterogeneity in spatial locations and landscape configurations, caused variousness of services quality green spaces. Previous studies mainly examined equity based on quantitative factors like distance scale. service space remains unclear. We studied 226 park spaces(UPGS) Hangzhou constructed Ecosystem Service Quality Index (ESQI) to evaluate regional analyze driving by Gini coefficient Lorenz curve. Results showed that (1) 77.43% parks had moderately low ESQIs (0.100–0.230), spatially heterogeneous distribution. (2) There was a high degree unfairness regarding area, number points interest facilities, proportion water bodies, effective grid patches, total edge length. (3) Heterogeneity distribution internal patterns UPGSs significant quality. (4) The main influencing consisted natural factors(green cover impervious cove) location factors(Park Cooling Index). characteristics cannot reflect level Urban ecological construction should not simply focus increasing quantity, rather it is more crucial improve UPGS.

Language: Английский

Assessing and predicting green gentrification susceptibility using an integrated machine learning approach DOI
Rayan H. Assaad, Yasser Jezzini

Local Environment, Journal Year: 2024, Volume and Issue: 29(8), P. 1099 - 1127

Published: May 14, 2024

Greenery initiatives, such as green infrastructures (GIs), create sustainable and climate-resilient environments. However, they can also have unintended consequences, displacement gentrification in low-income areas. This paper proposes an integrated machine learning (ML) approach that combines both unsupervised supervised ML algorithms. First, 35 indicators contribute to were identified categorised into 7 categories: social, economic, demographic, housing, household, amenities, GIs. Second, data was collected for all census tracts New York City. Third, the susceptibility modelled 6 levels using k-means clustering analysis, which is model. Fourth, Technique Order of Preference by Similarity Ideal Solution (TOPSIS) used map their level. Finally, different algorithms trained tested predict susceptibility. The results showed artificial neural network (ANN) model most accurate classifying predicting with overall accuracy 96%. Moreover, outcomes Normal Difference Vegetation Index (NDVI), proximity GIs, GIs frequency, total area important Ultimately, proposed allows practitioners researchers perform micro-level (i.e. on census-tracts level) predictions inferences about more focused targeted mitigation actions be designed implemented affected communities, thus promoting environmental justice.

Language: Английский

Citations

3
The relationship between urban land expansion and spatiotemporal dynamics of SDG 11.7: evidence from Xi’an, China DOI
Kan Wang, Xing Dang, Jianjun Bai

et al.

Environment Development and Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 18, 2025

Language: Английский

Citations

0
Does urban sprawl lessen green space exposure? Evidence from Chinese cities DOI
Yang Chen, Daniele La Rosa, Wenze Yue

et al.

Landscape and Urban Planning, Journal Year: 2025, Volume and Issue: 257, P. 105319 - 105319

Published: Feb. 6, 2025

Citations

0
Impact of Urban Shrinkage on Pollution Reduction and Carbon Mitigation Synergy: Spatial Heterogeneity and Interaction Effects in Chinese Cities DOI Creative Commons
Jianwen Zhang,

Meichen Fu,

Li Wang

et al.

Land, Journal Year: 2025, Volume and Issue: 14(3), P. 537 - 537

Published: March 4, 2025

Increasing air pollution, rising carbon emissions, and urban shrinkage pose significant challenges for sustainable development in China. Exploring the relationship between synergy effect of pollution reduction mitigation (SPRCR) can contribute to systematically addressing green development. However, few studies have analyzed all three factors within a unified analytical framework. Therefore, our study takes 288 cities at prefecture level above China as research objects endeavors apply Coupling Coordination Degree (CCD), Multi-scale Geographically Weighted Regression (MGWR), Geodetector (v2.1.0) analyze influence on SPRCR. From analysis, it was demonstrated that (1) general, inhibit an improvement synergistic degree SPRCR, but inhibition is weak. (2) The this shows spatial heterogeneity, with negative impact SPRCR mainly concentrated northeast region. (3) interaction construction land expansion more than other factors, enhancement most obvious. Given regional differences development, provides valuable insights promoting

Language: Английский

Citations

0
The Quality Inequity of the Ecosystem Services delivered by Green Spaces: Evidence from 226 Urban Parks in Hangzhou, China DOI

Jiajun Huang,

Qinghai Guo,

G. Lian

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: May 5, 2025

Abstract Green spaces are vital for the urban ecosystem, providing residents with diverse ecosystem services. However, heterogeneity in spatial locations and landscape configurations, caused variousness of services quality green spaces. Previous studies mainly examined equity based on quantitative factors like distance scale. service space remains unclear. We studied 226 park spaces(UPGS) Hangzhou constructed Ecosystem Service Quality Index (ESQI) to evaluate regional analyze driving by Gini coefficient Lorenz curve. Results showed that (1) 77.43% parks had moderately low ESQIs (0.100–0.230), spatially heterogeneous distribution. (2) There was a high degree unfairness regarding area, number points interest facilities, proportion water bodies, effective grid patches, total edge length. (3) Heterogeneity distribution internal patterns UPGSs significant quality. (4) The main influencing consisted natural factors(green cover impervious cove) location factors(Park Cooling Index). characteristics cannot reflect level Urban ecological construction should not simply focus increasing quantity, rather it is more crucial improve UPGS.

Language: Английский

Citations

0