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Comparative analysis of greenery inequalities in New York and London: Social-economic and spatial dimensions

Yequan HU, Mingze CHEN*, Yuxuan CAI

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Abstract

With the rapid development of urbanization, the reduction of urban green spaces (UGS) has negatively impacted residents' quality of life and environmental quality. Recognizing that factors influencing environmental justice vary across different national and city contexts, this study aims to explore these differential impacts. However, most existing studies focus on single cities or specific regions, with limited comparative research between different countries. To explore the factors that differentially affect UGS in various cities, this study compares the distribution of UGS in New York City, U.S., and London, UK, investigating socio-economic variables (percentage of population in poverty, housing-cost burden, education level), demographic factors (proportion of minorities, elderly, individuals with disabilities), and built-environment indicators (residential density, road density, land-use types). These variables are measured using official census data and spatial datasets from each city, ensuring robust coverage of community vulnerability and development intensity. The research employs Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) methods to analyze the relationship between UGS. The OLS results indicate that in both New York and London, minority and elderly populations have a positive correlation with UGS usage, while low-income groups face greater inequalities. GWR and MGWR reveal that UGS inequalities are mainly concentrated in urban peripheries or economically weaker areas. Notably, New York is more affected by economic factors, showing significant spatial heterogeneity in economically underdeveloped areas. These findings are significant for developing more equitable and effective UGS policies. Such methods enable systematic evaluations across different countries based on case studies from major cities, breaking down regional isolation and fostering more equitable and effective UGS policies globally. Understanding these differences can lead to more targeted interventions, improve the quality of life for vulnerable groups, and promote sustainable urban development worldwide. © 2025 The Authors.
Original languageEnglish
Article number128939
Number of pages16
JournalUrban Forestry & Urban Greening
Volume112
Online published26 Jun 2025
DOIs
Publication statusPublished - Oct 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 1 - No Poverty
    SDG 1 No Poverty
  2. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities
  3. SDG 17 - Partnerships for the Goals
    SDG 17 Partnerships for the Goals

Research Keywords

  • Big data
  • Urban green space (UGS)
  • Green equity
  • Spatial heterogeneity
  • Multiscale Geographically Weighted
  • Regression (MGWR)

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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