A Comprehensive Study of Water Resource–Environment Carrying Capacity via a Water-Socio-Ecological Framework and Differential Evolution-Based Projection Pursuit Modeling

Water resources are fundamental to sustaining life, fostering social development, and maintaining ecological balance. This study focuses on Anhui Province (AP) as the research area, employing 22 indicators from the Water-Socio-Ecological (WSE) framework for the water resource-water environment carrying capacity (WR-WECC) of AP. The WR-WECC of AP is assessed via differential evolution projection pursuit modeling (DE-PPM). Additionally, the degree of coupled coordination model (DCCM) is utilized to analyze the coordinated development among the municipalities of AP, whereas the obstacle degree model is employed to identify the primary obstacles affecting the enhancement of the WR-WECC and to forecast them via autoregressive composite moving averages. The findings of the study are as follows: (1) WR-WECC in AP showed a steady upward trend, and the water, socio-economic, and ecological subsystems showed a fluctuating upward trend, with ES increasing the fastest. The overall WR-WECC of each city shows a fluctuating upward trend, and the spatial gap narrows, with Southern Anhui (SA) > Central Anhui (CA) > Northern Anhui (NA). (2) The DCC of the WSE carrying capacity of AP also shows a fluctuating upward trend, gradually transitioning from barely coordinated to well coordinated. The DCC level of the WR-WECC in SA is better than that in CA and NA, and the growth rate is greater than that in CA and NA, whereas the DCC level in CA is better than that in NA, and the DCCs of the three major regions show an upward trend. (3) The degree of obstruction at the criterion level of AP’s WR-WECC basically maintains the following order: WS > SS > ES. In the indicator layer, the water supply modulus, water production modulus, and proportion of tertiary industry are the main obstacle factors restricting the enhancement of the WR-WECC of AP. (4) The prediction results for 2025–2040 indicated that the WR-WECC level and the three subsystem levels of AP showed a continuous increasing trend. Measuring WR-WECC plays a crucial role in regional sustainable development.

Authors

Institutions

Publication Details

Journal
Water
Published
2025-05-27
DOI
https://doi.org/10.3390/w17111624
Citations
2
Primary Topic
Water resources management and optimization
Type
article
Field-Weighted Citation Impact
2.29

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Comprehensive Study of Water Resource–Environment Carrying Capacity via a Water-Socio-Ecological Framework and Differential Evolution-Based Projection Pursuit Modeling

Shumiao Shu, Jie Geng, Yucheng Liu, Quan Fang et al.
2 citations
Water
Water resources management and optimization
2.29
article

A Comprehensive Study of Water Resource–Environment Carrying Capacity via a Water-Socio-Ecological Framework and Differential Evolution-Based Projection Pursuit Modeling

Shumiao Shu, Jie Geng, Yucheng Liu, Quan Fang, Yuelong Su
article en
2 citations

Abstract

Water resources are fundamental to sustaining life, fostering social development, and maintaining ecological balance. This study focuses on Anhui Province (AP) as the research area, employing 22 indicators from the Water-Socio-Ecological (WSE) framework for the water resource-water environment carrying capacity (WR-WECC) of AP. The WR-WECC of AP is assessed via differential evolution projection pursuit modeling (DE-PPM). Additionally, the degree of coupled coordination model (DCCM) is utilized to analyze the coordinated development among the municipalities of AP, whereas the obstacle degree model is employed to identify the primary obstacles affecting the enhancement of the WR-WECC and to forecast them via autoregressive composite moving averages. The findings of the study are as follows: (1) WR-WECC in AP showed a steady upward trend, and the water, socio-economic, and ecological subsystems showed a fluctuating upward trend, with ES increasing the fastest. The overall WR-WECC of each city shows a fluctuating upward trend, and the spatial gap narrows, with Southern Anhui (SA) > Central Anhui (CA) > Northern Anhui (NA). (2) The DCC of the WSE carrying capacity of AP also shows a fluctuating upward trend, gradually transitioning from barely coordinated to well coordinated. The DCC level of the WR-WECC in SA is better than that in CA and NA, and the growth rate is greater than that in CA and NA, whereas the DCC level in CA is better than that in NA, and the DCCs of the three major regions show an upward trend. (3) The degree of obstruction at the criterion level of AP’s WR-WECC basically maintains the following order: WS > SS > ES. In the indicator layer, the water supply modulus, water production modulus, and proportion of tertiary industry are the main obstacle factors restricting the enhancement of the WR-WECC of AP. (4) The prediction results for 2025–2040 indicated that the WR-WECC level and the three subsystem levels of AP showed a continuous increasing trend. Measuring WR-WECC plays a crucial role in regional sustainable development.

WaterVol. 17(11)
Nanjing University of Science and Technology (CN), Central China Normal University (CN), Neijiang Normal University (CN), Nanjing University of Science and Technology ZiJin College, Nanjing University (CN)
Government of Jiangsu Province, Jilin Office of Philosophy and Social Science
Openalex Percentile: Top 13%
Water resources management and optimization
2.29
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.