Analyzing Spatial–Temporal Patterns and Driving Mechanisms of Ecological Resilience Using the Driving Force–Pressure–State–Influence–Response and Environment–Economy–Society Model: A Case Study of 280 Cities in China

Unveiling the spatial and temporal distribution of urban ecological resilience and analyzing the configuration paths for enhancing its levels are crucial for promoting sustainable development in China. Our study integrates the DPSIR and EES models, considering the causal relationships between systems affecting urban ecological resilience while also examining their internal structures. Based on this, we construct an evaluation system for urban ecological resilience indicators. Utilizing the entropy-TOPSIS method, we assess the ecological resilience index (ERI) across 280 Chinese cities from 2011 to 2021, and the kernel density estimation and Markov chain are used to study the evolution process while the magnitude and source of spatial–regional differences are examined by the Dagum Gini coefficient decomposition method. Additionally, we empirically investigate the driving mechanisms toward high ERI with the focused stepwise quantitative case analysis (fsQCA) method based on the technology–organization–environment (TOE) framework. The results find that the ERI in China shows a tendency of moderate growth in variability, with an obvious gradient distribution: higher levels in the eastern and southern and lower levels in the western and northern regions. Also, ERI exhibits evolutionary features of increasing polarization and inter-regional differentiation. Spatial disparities gradually increase with fluctuations, driven primarily by transvariation density and intra-regional differences, contributing to a dual non-equilibrium state of east–west and north–south directions. Achieving a high ERI is influenced by various antecedent variables interacting with each other, and there are three predominant driving paths among these variables, with the level of informatization playing a central role in each pathway.

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Publication Details

Journal
Systems
Published
2024-08-20
DOI
https://doi.org/10.3390/systems12080311
Citations
5
Primary Topic
Sustainability and Ecological Systems Analysis
Type
article
Field-Weighted Citation Impact
1.03

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article

Analyzing Spatial–Temporal Patterns and Driving Mechanisms of Ecological Resilience Using the Driving Force–Pressure–State–Influence–Response and Environment–Economy–Society Model: A Case Study of 280 Cities in China

Tao Huang, Xiaoling Yuan, Rang Liu
5 citations
Systems
Sustainability and Ecological Systems Analysis
1.03
article

Analyzing Spatial–Temporal Patterns and Driving Mechanisms of Ecological Resilience Using the Driving Force–Pressure–State–Influence–Response and Environment–Economy–Society Model: A Case Study of 280 Cities in China

Tao Huang, Xiaoling Yuan, Rang Liu
article en
5 citations

Abstract

Unveiling the spatial and temporal distribution of urban ecological resilience and analyzing the configuration paths for enhancing its levels are crucial for promoting sustainable development in China. Our study integrates the DPSIR and EES models, considering the causal relationships between systems affecting urban ecological resilience while also examining their internal structures. Based on this, we construct an evaluation system for urban ecological resilience indicators. Utilizing the entropy-TOPSIS method, we assess the ecological resilience index (ERI) across 280 Chinese cities from 2011 to 2021, and the kernel density estimation and Markov chain are used to study the evolution process while the magnitude and source of spatial–regional differences are examined by the Dagum Gini coefficient decomposition method. Additionally, we empirically investigate the driving mechanisms toward high ERI with the focused stepwise quantitative case analysis (fsQCA) method based on the technology–organization–environment (TOE) framework. The results find that the ERI in China shows a tendency of moderate growth in variability, with an obvious gradient distribution: higher levels in the eastern and southern and lower levels in the western and northern regions. Also, ERI exhibits evolutionary features of increasing polarization and inter-regional differentiation. Spatial disparities gradually increase with fluctuations, driven primarily by transvariation density and intra-regional differences, contributing to a dual non-equilibrium state of east–west and north–south directions. Achieving a high ERI is influenced by various antecedent variables interacting with each other, and there are three predominant driving paths among these variables, with the level of informatization playing a central role in each pathway.

SystemsVol. 12(8)
Xi'an Jiaotong University (CN)
National Office for Philosophy and Social Sciences, National Social Science Fund Youth Project, Ministry of Education Key Projects of Philosophy and Social Sciences Research
Openalex Percentile: Top 27%
Sustainability and Ecological Systems Analysis
1.03
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