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ORIGINAL RESEARCH
Quantitative Analysis of Drivers of
Ecosystem Service Evolution
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
Publication date: 2026-01-29
Corresponding author
Pol. J. Environ. Stud. 2026;35(1):395-412
KEYWORDS
ecosystem servicesdriving factorsland use/cover changetime and space evolutionoptimal parameters-based geographical detector model
TOPICS
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.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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