Spatial-Temporal Evolution and Obstacle Factors of the Disaster Resilience in the Central Plains Urban Agglomeration, China DOI Open Access
Yongling Zhang, Zhiyan Cai, Xiaobing Zhou

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 17(1), P. 205 - 205

Published: Dec. 30, 2024

With the accelerating process of global urbanization, disaster risks faced by urban agglomerations are becoming more and complex diversified, strengthening research resilience is crucial to achieving sustainable economic social development agglomerations. Taking 30 cities in Central Plains Urban Agglomeration (CPUA) between 2012 2022 as objects, this paper innovatively fixed a common assessment index system; then, was calculated significance detected paired t-test. Finally, spatial evolution obstacle factors CPUA were explored Moran’s I SDM Model. The results show that 2012–2022 shows significant growth (p < 0.01), rate early period greater than late period. In terms distribution, showed pattern high northwest low southeast, which obviously evolved over time, presenting obvious regional asynchrony incoherence. heterogeneity strong, with agglomeration account for only 30%, mainly belonging L-L type. spillover effects resistance, recovery, adaptability among resistance dimension main factor. This study contributes existing literature two ways. It explores temporal well effect evolution, seldom seen. can provide reference construction governance both other cities.

Language: Английский

Factors influencing urban socioeconomic resilience after the withdrawal of nonpharmaceutical interventions: Evidence from intra-city travel intensity in China DOI
Qingyun Tang, Tao Wang, Bingsheng Liu

et al.

Journal of Transport Geography, Journal Year: 2025, Volume and Issue: 124, P. 104172 - 104172

Published: March 5, 2025

Language: Английский

Citations

0
A multi-index comprehensive evaluation method for assessing the water use balance between economic society and ecology considering efficiency-development-health-harmony DOI Creative Commons

Z S Quan,

Qiting Zuo,

Chao Zang

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 29, 2024

Quantitative assessment of the water use balance between economic society and ecology (EEWB) is basis for coordinating competitive relationship human activity ecological requirements and. It great significance optimizing resources carrying capacity achieving a healthy regional balance. Based on concept harmonious balance, this paper puts forward definition connotation EEWB regarding competition in ecology. And, novel framework assessing proposed. has four aspects relating to resources, society, ecology, human-water relationship. Linked these Data Envelopment Analysis (DEA) technique, Water Ecological Footprint (WEF) model, InVEST model indicators system are used establish efficiency index (I

Language: Английский

Citations

3
Spatiotemporal Evolution and Obstacle Factor Analysis of Coupling Coordination Between Economic Resilience and Green, Low-Carbon Development in China DOI Open Access
Shujuan Ding, Zhenyu Fan

Sustainability, Journal Year: 2024, Volume and Issue: 16(24), P. 11006 - 11006

Published: Dec. 15, 2024

To achieve economic resilience and green, low-carbon development are two goals of China’s high-quality development. This paper uses the entropy weight method coupling coordination degree model to estimate level Kernel density estimation, Moran index, Dagum Gini coefficient, Markov chain, obstacle used explore spatiotemporal evolution characteristics factors. The results as follows. (1) between has increased overall. However, eastern region highest, central fastest growth. (2) shows positive spatial autocorrelation, with most provinces exhibiting high–high or low–low aggregation characteristics. (3) contribution imbalance mainly comes from inter-regional differences, but intra-regional differences is increasing. (4) spatio-temporal pattern generally better, probability maintaining initial state largest. neighborhood’s affects transition does not affect that high-level provinces. (5) Innovation capacity main improving resilience, per capita water resources green Finally, this puts forward suggestions for creating a good innovation environment, increasing R&D investment, promoting technology progress, optimizing regional cooperation resource allocation, industrial transformation.

Language: Английский

Citations

1
Analysis of the Spatiotemporal Heterogeneity and Influencing Factors of Regional Economic Resilience in China DOI Creative Commons
Qiuyue Zhang, Yili Lin,

Yu Cao

et al.

Entropy, Journal Year: 2024, Volume and Issue: 27(1), P. 23 - 23

Published: Dec. 31, 2024

This study estimates regional economic resilience in China from 2000 to 2022, focusing on resistance resilience, recovery and reorientation resilience. The entropy method, kernel density estimation, spatial Durbin model are applied examine the spatiotemporal evolution influencing factors. results show significant clustering, with stronger east weaker west. While has generally improved, disparities persist. Key factors such as human capital, urban hospitals, financial development, market consumption, environmental quality have a positive effect spillover effects. However, capital hospitals also negative indirect impact surrounding regions. influence of these varies across regions periods, indicating strong heterogeneity.

Language: Английский

Citations

0
Spatial-Temporal Evolution and Obstacle Factors of the Disaster Resilience in the Central Plains Urban Agglomeration, China DOI Open Access
Yongling Zhang, Zhiyan Cai, Xiaobing Zhou

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 17(1), P. 205 - 205

Published: Dec. 30, 2024

With the accelerating process of global urbanization, disaster risks faced by urban agglomerations are becoming more and complex diversified, strengthening research resilience is crucial to achieving sustainable economic social development agglomerations. Taking 30 cities in Central Plains Urban Agglomeration (CPUA) between 2012 2022 as objects, this paper innovatively fixed a common assessment index system; then, was calculated significance detected paired t-test. Finally, spatial evolution obstacle factors CPUA were explored Moran’s I SDM Model. The results show that 2012–2022 shows significant growth (p < 0.01), rate early period greater than late period. In terms distribution, showed pattern high northwest low southeast, which obviously evolved over time, presenting obvious regional asynchrony incoherence. heterogeneity strong, with agglomeration account for only 30%, mainly belonging L-L type. spillover effects resistance, recovery, adaptability among resistance dimension main factor. This study contributes existing literature two ways. It explores temporal well effect evolution, seldom seen. can provide reference construction governance both other cities.

Language: Английский

Citations

0