ORIGINAL RESEARCH
Quantitative Analysis of Drivers of
Ecosystem Service Evolution
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1
School of Architecture and Planning, Anhui Jianzhu University, Hefei, 230601, China
2
Collaborative Innovation Center for Urbanization Construction of Anhui Province, Anhui Jianzhu University,
Hefei 230601, China.
3
School of Landscape Architecture, Nanjing Forestry University, Nanjing, 210037, China
4
School of Civil Engineering, Tianjin University, Tianjin, 300072, China
5
Institute of Water Resources and Hydropower Research, Beijing, 100044, China
Submission date: 2024-08-05
Final revision date: 2024-09-15
Acceptance date: 2024-11-10
Online publication date: 2025-02-28
Corresponding author
Haoran Yu
School of Landscape Architecture, Nanjing Forestry University, Nanjing, 210037, China
Xinchen Gu
School of Civil Engineering, Tianjin University, Tianjin, 300072, China
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ABSTRACT
Intensive human activities and climate change have led to the degradation of regional ecosystem
functions. Accurately assessing the dynamics of ecosystem services and their driving factors is
essential for developing differentiated ecological management strategies and supporting regional
sustainable development. However, understanding the responses of ESs’ drivers across different spatial
scales in various geographic contexts remains limited. This study focuses on the Hefei Metropolitan
Area, utilizing the InVEST model to evaluate changes in four ESs: water yield, soil retention, carbon
storage, and habitat quality from 2000 to 2022. The Optimal Parameter Geodetector (OPGD) model
was employed to quantify the driving factors at different spatial scales. The results reveal a general
decline in water yield, soil retention, and carbon storage, while habitat quality has improved. The spatial
distribution of ESs exhibits a pattern of "high in the west, low in the east; high in the south, low in the
north." Natural factors predominantly influence the changes in water yield and soil retention, while
human activities significantly impact the spatial variation of carbon storage and habitat quality. The
optimal spatial scale for detecting driving factors is 7~8 km. The findings provide a theoretical basis for
optimizing ecological space in rapidly urbanizing areas.