Evidence map›Paper›PMID 35966337›Full record

ArticleEnvironment, development and sustainability2022

Spatial-temporal heterogeneity and influencing factors of the coupling between industrial agglomeration and regional economic resilience in China.

Ziyan Zheng, Yingming Zhu, Yu Pei, Litao Wang

Abstract read
In one paragraph

Article in Environment, development and sustainability, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Ziyan ZhengSchool of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094 China.ORCID 0000-0002-8822-3430
Yingming ZhuSchool of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094 China.
Yu PeiIndustrial Economy Research Division, The Research Institute of Informatization and Industrialization Integration, China Academy of Information and Communications Technology, 100083 Beijing, China.ORCID 0000-0003-1679-0425
Litao WangResearch Division, Urban & Rural Development Research Center of Jiangsu Province, 210003 Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coordinated development of industrial agglomeration and economic resilience can drive regional economic advantages; this type of development has become a catalyst for sustainable growth and high-quality development of the economy in China. This study applied models, including the coupling coordination degree, spatial autocorrelation, and Tobit, to explore the heterogeneous characteristics of the coupling of China's industrial agglomeration and regional economic resilience from 2005 to 2019. Additionally, by applying the perspectives of economic and geographic location, indicators were selected to analyze the associated influencing factors, including industrial externalities, new economic geographies, economic policy factors, and other aspects. We found that the overall coupling between industrial agglomeration and economic resilience increased over the study period, but with only a moderate level of coordination. Provinces with high, moderate, and low levels of coordination eventually emerged along a strip-like alternating pattern in space. The dependence increased with an increase in space, but was not significant, and there was a lack of benign interaction between the regions. With respect to interactivity between locations, the interaction of the east and the coast was the most active. There were lower levels of interaction between the west and inland regions. This further confirmed the significant temporal and spatial heterogeneity of the coupling. Environmental pollution, market consumption, the quality of space, and technological support significantly promoted the coupling; opening to the outside world did not. Specifically, with respect to economic location, market consumption and spatial quality had a significant positive effect on the eastern coupling. The center and west regions were significantly affected by economic density and market consumption, and the northeast region was affected by spatial quality and capital intensity. Concerning geographical location, market and technological forces strongly promoted interactions in both the coast and inland regions. The study found that both the government and the market need better guidance to effectively engage with and shape industrial agglomeration and economic resilience in a scientific, reasonable, localized, and distinctive manner.

Indexed as

ChinaCouplingIndustrial agglomerationInfluencing factorsRegional economic resilience

Identifiers

PMID35966337
PMCPMC9362362

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.