Multi-Dimensional Coupling Perspective on the Compatibility of Ecosystem Service Supply and Demand in Megacities and Future Scenario Simulation: The Case of Shanghai

Amid global climate change and rapid urbanization, megacities such as Shanghai confront prominent ecological challenges. A critical issue is the growing mismatch between the supply of and demand for urban green space (UGS) ecosystem services. This study aims to explore the supply–demand compatibility of Shanghai’s UGS ecosystem services and simulate future scenarios. Guided by the SSP1-2.6 scenario, it integrates the PLUS model, InVEST model, and nSFCA method to conduct dynamic analysis, quantifying supply–demand alignment and identifying imbalance areas. Results show a significant spatial mismatch: high demand but low supply in Shanghai’s inner ring and low demand but high supply in the outer ring. UGS attractiveness presents a core-concentrated and peripheral-diffused pattern by level. By 2030, a coordinated supply framework of “city-level dominance, community-level support, and neighborhood-level supplementation” will form, improving supply–demand alignment, though accessibility gaps persist. The study reveals that urbanization, planning policies, and population–spatial expansion asynchrony drive these patterns, providing scientific decision-making support for optimizing Shanghai’s green space planning and building an ecologically livable city.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2026-02-25
DOI
https://doi.org/10.3390/su18052195
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-Dimensional Coupling Perspective on the Compatibility of Ecosystem Service Supply and Demand in Megacities and Future Scenario Simulation: The Case of Shanghai

Chenxi Su, Shaofeng Chen, Zheng Ding, Han Chen et al.
Sustainability
Land Use and Ecosystem Services
article

Multi-Dimensional Coupling Perspective on the Compatibility of Ecosystem Service Supply and Demand in Megacities and Future Scenario Simulation: The Case of Shanghai

Chenxi Su, Shaofeng Chen, Zheng Ding, Han Chen, Jiafang Huang, Miaomiao Yan
article en

Abstract

Amid global climate change and rapid urbanization, megacities such as Shanghai confront prominent ecological challenges. A critical issue is the growing mismatch between the supply of and demand for urban green space (UGS) ecosystem services. This study aims to explore the supply–demand compatibility of Shanghai’s UGS ecosystem services and simulate future scenarios. Guided by the SSP1-2.6 scenario, it integrates the PLUS model, InVEST model, and nSFCA method to conduct dynamic analysis, quantifying supply–demand alignment and identifying imbalance areas. Results show a significant spatial mismatch: high demand but low supply in Shanghai’s inner ring and low demand but high supply in the outer ring. UGS attractiveness presents a core-concentrated and peripheral-diffused pattern by level. By 2030, a coordinated supply framework of “city-level dominance, community-level support, and neighborhood-level supplementation” will form, improving supply–demand alignment, though accessibility gaps persist. The study reveals that urbanization, planning policies, and population–spatial expansion asynchrony drive these patterns, providing scientific decision-making support for optimizing Shanghai’s green space planning and building an ecologically livable city.

SustainabilityVol. 18(5)
Fujian Agriculture and Forestry University (CN), Lancaster University (GB)
Sustainable cities and communities
Openalex Percentile: Top 51%
Land Use and Ecosystem Services
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.