A reliable ecological vulnerability approach based on the construction of optimal evaluation systems and evolutionary tracking models

He, Dan; Hou, Kun; Li, Xu Xiang; Wu, Sunyong; Xia, Li

doi:10.1016/j.jclepro.2023.138246

Journal of Cleaner Production

2023

DOI: 10.1016/j.jclepro.2023.138246

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A reliable ecological vulnerability approach based on the construction of optimal evaluation systems and evolutionary tracking models

Dan He

Kun Hou

Xu Xiang Li

et al.

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2024

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Cited by 9 publications

References 59 publications

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Analysis of Spatiotemporal Evolution and Influencing Factors of Water Poverty and Ecological Resilience Coupling Coordination in Chinese

Haiqi

2024

Ecohydrology

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This study advances the understanding of urban sustainability by examining the interplay between water poverty and ecological resilience in China's megacities—a critical yet underexplored domain. Spanning a decade from 2013 to 2022, we leverage unique data sets from 10 megacities and introduce a pioneering coupling coordination model. This model is the cornerstone of our methodological innovation, allowing for an integrated analysis of the coupling relationship and spatiotemporal evolution of water poverty and ecological resilience. Our first innovation lies in applying this novel model, which provides a nuanced perspective on the dynamic linkages between water scarcity and ecosystem robustness. We reveal that from 2013 to 2022, there has been a significant upward trend in the comprehensive evaluation indices, with water poverty and ecological resilience witnessing a 166.13% and 204.29% increase, respectively. This underscores the substantial improvements achieved in these critical areas. Secondly, our research innovates by offering a detailed spatiotemporal analysis, highlighting the strengthening coupling and coordination degree between water poverty and ecological resilience over the observed period. The case of Wuhan, with a remarkable coupling coordination index of 9.305 in 2022, exemplifies a city that has reached a highly coordinated status, marking a significant advancement in urban environmental synergy. Lastly, our use of the Tobit model to explore the main influencing factors offers new insights into the drivers of the coupling and coordination level. We find that economic development, industrial structure and technological innovation positively influence this level, whereas population density and urbanization exert negative pressures.

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Analysis of Spatiotemporal Evolution and Influencing Factors of Water Poverty and Ecological Resilience Coupling Coordination in Chinese

Haiqi

2024

Ecohydrology

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Analysis of Ecological Vulnerability Control Strategies for Typical River Valley Cities in Northwest China: A Perspective of Spatial Heterogeneity

Zhu,

Hou,

Chang

et al. 2024

Land Degrad Dev

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The spatial heterogeneity of ecological vulnerability (EV) is a key indicator of regional ecosystem differentiation. However, identifying the factors that contribute to this heterogeneity remains a challenge in current research. This study proposed the “Ecological vulnerability‐spatial heterogeneity‐zone” (ESHZ) framework as a solution to overcome the difficulties of monitoring spatial heterogeneity. Then, based on the typical river valley city Baoji in Northwest China as the research object, this framework was used to analyze and predict the evolution law of heterogeneity of EV from 2000 to 2030. The findings indicate the following: (1) Synthesized ecological vulnerability index (SEVI) in 2000 and 2020 was 2.47 and 2.49, respectively, with less than 5% of the total area transitioning to areas of higher EV, indicating a relatively stable ecological environment. (2) The clustering characteristics of EV remained stable, primarily showing non‐significant, high–high, and low–low clustering, with varying degrees of heterogeneity across different regions. (3) Factors analysis revealed that DEM and LUCC had been the dominant factors of EV, and that interactions between factors were stronger than interactions within them, suggesting that its spatial heterogeneity was the result of a combination of factors. (4) The CA‐Markov model predicted a gradual improvement in the ecological environment by 2030, with a Kappa coefficient test value of 0.7733. The framework constructed in this study proposes a perspective for improved analyses of spatial heterogeneity of ecosystems, providing a viable approach to the management of regional ecological vulnerability.

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Modeling the spatial relationship between landscape services and vulnerability assessment

Darabi,

Bazhdar,

Ehsani

2024

Environ Monit Assess

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A reliable ecological vulnerability approach based on the construction of optimal evaluation systems and evolutionary tracking models

Cited by 9 publications

References 59 publications

Analysis of Spatiotemporal Evolution and Influencing Factors of Water Poverty and Ecological Resilience Coupling Coordination in Chinese

Analysis of Spatiotemporal Evolution and Influencing Factors of Water Poverty and Ecological Resilience Coupling Coordination in Chinese

Analysis of Ecological Vulnerability Control Strategies for Typical River Valley Cities in Northwest China: A Perspective of Spatial Heterogeneity

Modeling the spatial relationship between landscape services and vulnerability assessment

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