Author
Listed:
- Chang Wang
(School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
These authors contributed equally to this work.)
- Siyuan Wang
(School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Beijing Laboratory of Urban and Rural Ecology and Environment, Beijing Forestry University, Beijing 100083, China
National Forestry and Grassland Administration Key Laboratory of Urban and Rural Landscape Construction, Beijing Forestry University, Beijing 100083, China
These authors contributed equally to this work.)
- Bing Qi
(School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)
- Chuling Jiang
(School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)
- Weiyang Sun
(School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)
- Yilun Cao
(School of Architecture, Southeast University, Nanjing 210096, China)
- Yunyuan Li
(School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)
Abstract
Urban–rural fringe areas are critical transition zones where ecological functions and human activities interact intensely, often leading to complex spatial patterns and trade-offs among ecosystem services (ESs). Understanding these patterns and their socio-ecological drivers across multiple spatial scales is essential for sustainable land-use planning and ecosystem management. This study, using the urban–rural fringe (URF) of Beijing as an example, quantified eight representative ecosystem services at the 1 km grid, 3 km grid, and township scales. It employed hotspot analysis, Moran’s Index, and the Spearman correlation to analyze trade-offs and synergies (TOSs) among ESs. The study also applied a self-organizing map and the NbClust function to identify and determine the optimal number of ecosystem service bundles (ESBs) for ecological functional zoning. Redundancy analysis was used to explore the impacts of six socio-ecological drivers on the spatial distribution of ESs. The results revealed the following: (1) The spatial distribution of ESs in Beijing’s URF exhibits clustering and cross-scale variations, with spatial clustering intensifying as the scale expands. (2) TOSs among ESs vary in strength and direction across the three spatial scales. (3) The primary drivers of TOSs at all three scales are the normalized vegetation index and annual precipitation. (4) Based on the supply intensity of various ESs, the study area was classified into four types of ESBs across the three scales: ecologically restricted areas, food production areas, ecologically balanced areas, and high-quality ecological areas. The township scale is more conducive to planning and management, while the 1 km and 3 km grid scales are more helpful for understanding the relationship between land use and ESs.
Suggested Citation
Chang Wang & Siyuan Wang & Bing Qi & Chuling Jiang & Weiyang Sun & Yilun Cao & Yunyuan Li, 2025.
"Trade-Offs, Synergies, and Driving Factors of Ecosystem Services in the Urban–Rural Fringe of Beijing at Multiple Scales,"
Land, MDPI, vol. 14(5), pages 1-26, May.
Handle:
RePEc:gam:jlands:v:14:y:2025:i:5:p:1009-:d:1650470
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