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A new perspective on the whole process of ecological vulnerability analysis based on the EFP framework.

Authors :
Ma, Lixia
Hou, Kang
Tang, Haojie
Liu, Jiawei
Wu, Siqi
Li, Xuxiang
Sun, Pengcheng
Source :
Journal of Cleaner Production. Nov2023, Vol. 426, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

Ecological vulnerability (EV) assessment is an effective method for evaluating ecological environments. However, there are few studies on the internal transformation process of EV and its influencing factors. As an important water source of China's South-to-North Water Diversion Project, the southern Shaanxi has been gradually affected by different degrees of human influence in recent years. Therefore, the framework of "Evolution-Factor analysis-Prediction (EFP)" was established to analyze the evolution process from point to surface in this area, which was a new perspective that can be used to analyze the whole process of tracking ecological vulnerability evolution. The results showed that the ecological vulnerability had an obvious zonal distribution found significant ecological improvement, with an increase of 24.61% in ecological vulnerability level I and level II, and a decrease of 6.82% in level IV and level V. The causes obtained using the factor analysis model were mainly human factors, with the most significant factor being the industrial/agricultural output and its value exceeding 0.7. The center of gravity of ecological vulnerability shifted from the south to the north-east and then to the west by 17,390 and 20,206 m, respectively. This framework can better analyze the change process of regional ecological vulnerability, and is a feasible way to track the evolution of global environmental change. [Display omitted] • An "ecological vulnerability evolution-factor analysis-prediction (EFP)" framework was established for the first time. • A new perspective for the whole process analysis of ecological vulnerability. • The study proposed an evolutionary process of ecological vulnerability from point to point. • Quantitative determination of influencing factors based on structural equation modeling. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09596526
Volume :
426
Database :
Academic Search Index
Journal :
Journal of Cleaner Production
Publication Type :
Academic Journal
Accession number :
172977690
Full Text :
https://doi.org/10.1016/j.jclepro.2023.139160