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Spatiotemporal Evolution Characteristics and Impact Factors of Urbanrural Integrated Development in China
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作者 LI Li YANG Lan +2 位作者 WANG Hao YANG Shuang YAN Zhihan 《Chinese Geographical Science》 2025年第4期802-818,共17页
Understanding the urban-rural development mechanism is critical for implementing rural revival and new-type urbanization.However,it remains a challenge to quantify the urban-rural integrated development level(URIDL)an... Understanding the urban-rural development mechanism is critical for implementing rural revival and new-type urbanization.However,it remains a challenge to quantify the urban-rural integrated development level(URIDL)and its impact factors.Hence,we constructed an assessment system for the URIDL from spatial,economic,social,life,and ecological integration.The spatial autocorrelation and Spearman rank correlation coefficients were used to assess the spatiotemporal variation of the URIDL and the trade-off synergistic relationship among the subsystems at the provincial scale in China using socio-economic statistical data from 2000 to 2020.A spatial panel quantile regression model was used to analyze the driving mechanism.The results showed that the URIDL of China increased by 0.19%from 2000 to 2020,and a high-high(H-H)spatial agglomeration pattern occurred in the Yangtze River Delta and the Beijing-Tianjin-Hebei regions.Spatial integration significantly contributed to the other subsystems,whereas economic integration had a significant negative impact on the other subsystems in the eastern coastal and southwestern regions.Per capita Gross Domestic Product(GDP)improved the URIDL,whereas other factors,such as fiscal revenue decentralization,had inhibiting effects.Notably,the impact of factors on URIDL varies across different quantiles.Finally,we proposed policy recommendations for differentiated improvement of URIDL based on its evolution and regional development level during the research period. 展开更多
关键词 urban-rural integrated development level(URIDL) spatial pattern spatial autocorrelation spatial panel quantile regression China
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