Urban Ecological Economic Resilience in China: Spatio-Temporal Evolution, Influencing Factors, and Trend Prediction

This article adopted exploratory spatio-temporal data analysis (ESTDA), geographic detector, and spatial Markov chain model to analyze the spatio-temporal evolution characteristics, main influencing factors, and future trend predictions of urban ecological economic resilience (EER). The results show...

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Published inSystems (Basel) Vol. 12; no. 12; p. 525
Main Authors Wang, Kexin, Zhang, Bowen, Jiang, Shuyue, Ding, Rui
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2024
Subjects
Agglomeration
Analysis
Carbon
China
Cities
Data analysis
ecological economic resilience
Ecological restoration
Ecology
Economic aspects
Economic development
Economic growth
Energy consumption
Environmental economics
Environmental impact
Environmental protection
ESTDA
influencing factors
Information management
Literature reviews
Markov chains
Markov processes
Natural resources
Regions
Resilience
Social change
Spatial distribution
spatial Markov chains
spatio-temporal evolution
Spatiotemporal data
Sustainable development
trend prediction
Trends
United Kingdom
United Kingdom
China
United Kingdom > UK
Yangtze River
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Abstract This article adopted exploratory spatio-temporal data analysis (ESTDA), geographic detector, and spatial Markov chain model to analyze the spatio-temporal evolution characteristics, main influencing factors, and future trend predictions of urban ecological economic resilience (EER). The results show that EER has been significantly enhanced, and high-level cities have a “rhombus” spatial distribution pattern. EER has a noticeable spatial agglomeration effect and the range of high–high agglomeration areas has gradually expanded. The LISA time path reflects that the spatial structure of EER is relatively stable, and urban units and neighboring cities show a more apparent synergistic growth trend. Social development, economic support, ecological restoration, and innovation and transformation strongly influence the development of EER, and the interaction between factors is more significant. In the future, EER will still tend to maintain the existing stable and unchanged state, and cross-grade leapfrogging development will not be achieved.
AbstractList This article adopted exploratory spatio-temporal data analysis (ESTDA), geographic detector, and spatial Markov chain model to analyze the spatio-temporal evolution characteristics, main influencing factors, and future trend predictions of urban ecological economic resilience (EER). The results show that EER has been significantly enhanced, and high-level cities have a “rhombus” spatial distribution pattern. EER has a noticeable spatial agglomeration effect and the range of high–high agglomeration areas has gradually expanded. The LISA time path reflects that the spatial structure of EER is relatively stable, and urban units and neighboring cities show a more apparent synergistic growth trend. Social development, economic support, ecological restoration, and innovation and transformation strongly influence the development of EER, and the interaction between factors is more significant. In the future, EER will still tend to maintain the existing stable and unchanged state, and cross-grade leapfrogging development will not be achieved.
Audience Academic
Author Jiang, Shuyue
Ding, Rui
Wang, Kexin
Zhang, Bowen
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SubjectTerms Agglomeration
Analysis
Carbon
China
Cities
Data analysis
ecological economic resilience
Ecological restoration
Ecology
Economic aspects
Economic development
Economic growth
Energy consumption
Environmental economics
Environmental impact
Environmental protection
ESTDA
influencing factors
Information management
Literature reviews
Markov chains
Markov processes
Natural resources
Regions
Resilience
Social change
Spatial distribution
spatial Markov chains
spatio-temporal evolution
Spatiotemporal data
Sustainable development
trend prediction
Trends
United Kingdom
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Title Urban Ecological Economic Resilience in China: Spatio-Temporal Evolution, Influencing Factors, and Trend Prediction
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Volume 12
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