Influence mechanism of NU-RR spatial divergence.

Integrating urban and rural areas is the only way to achieve sustainable regional development. To comprehensively build an evaluation index system for new urbanization and rural revitalization (NU-RR), taking the Yellow River Basin (YRB) as research object, and the coupled coordination degree (CCD) model, relative development degree (RDD) model and gravity model are used to quantitatively measure the spatial and temporal patterns, synchronous development status and spatial linkages of the coupled coordination of NU-RR from 2005 to 2020. The factors influencing the CCD are identified with the help of the geographic detector model. The findings indicate that: (1) From 2005 to 2020, the combined indexes of NU-RR in the YRB show an increasing trend, while rural revitalization is growing slower than new urbanization. (2) The CCD of NU-RR in the YRB shows spatial structure characterized by “high in the east and low in the west” and undergoes an evolutionary process of “low coupling-medium coupling-high coupling”. (3) The spatial disparities in the state of coupled and coordinated development of different cities are significant, mainly showing the spatial distribution characteristics dominated by the lagging new urbanization. (4) The spatial connection of CCD is networked and polarized, and the interprovincial barrier effect is weakened. (5) Total retail sales of consumer goods per capita and local general public budget expenditure as a share of GDP are the primary influencing elements affecting the CCD of NU-RR in the YRB. The interaction is manifested as bivariate enhance and nonlinear enhancement. The study’s findings can guide decisions to promote high-quality urban-rural integration development in the YRB.</div

Influence factors detection results.

Integrating urban and rural areas is the only way to achieve sustainable regional development. To comprehensively build an evaluation index system for new urbanization and rural revitalization (NU-RR), taking the Yellow River Basin (YRB) as research object, and the coupled coordination degree (CCD) model, relative development degree (RDD) model and gravity model are used to quantitatively measure the spatial and temporal patterns, synchronous development status and spatial linkages of the coupled coordination of NU-RR from 2005 to 2020. The factors influencing the CCD are identified with the help of the geographic detector model. The findings indicate that: (1) From 2005 to 2020, the combined indexes of NU-RR in the YRB show an increasing trend, while rural revitalization is growing slower than new urbanization. (2) The CCD of NU-RR in the YRB shows spatial structure characterized by “high in the east and low in the west” and undergoes an evolutionary process of “low coupling-medium coupling-high coupling”. (3) The spatial disparities in the state of coupled and coordinated development of different cities are significant, mainly showing the spatial distribution characteristics dominated by the lagging new urbanization. (4) The spatial connection of CCD is networked and polarized, and the interprovincial barrier effect is weakened. (5) Total retail sales of consumer goods per capita and local general public budget expenditure as a share of GDP are the primary influencing elements affecting the CCD of NU-RR in the YRB. The interaction is manifested as bivariate enhance and nonlinear enhancement. The study’s findings can guide decisions to promote high-quality urban-rural integration development in the YRB.</div

Spatial distribution of the RDD of NU-RR in the YRB.

Spatial distribution of the RDD of NU-RR in the YRB.</p

CCD from 2005 to 2020.

Integrating urban and rural areas is the only way to achieve sustainable regional development. To comprehensively build an evaluation index system for new urbanization and rural revitalization (NU-RR), taking the Yellow River Basin (YRB) as research object, and the coupled coordination degree (CCD) model, relative development degree (RDD) model and gravity model are used to quantitatively measure the spatial and temporal patterns, synchronous development status and spatial linkages of the coupled coordination of NU-RR from 2005 to 2020. The factors influencing the CCD are identified with the help of the geographic detector model. The findings indicate that: (1) From 2005 to 2020, the combined indexes of NU-RR in the YRB show an increasing trend, while rural revitalization is growing slower than new urbanization. (2) The CCD of NU-RR in the YRB shows spatial structure characterized by “high in the east and low in the west” and undergoes an evolutionary process of “low coupling-medium coupling-high coupling”. (3) The spatial disparities in the state of coupled and coordinated development of different cities are significant, mainly showing the spatial distribution characteristics dominated by the lagging new urbanization. (4) The spatial connection of CCD is networked and polarized, and the interprovincial barrier effect is weakened. (5) Total retail sales of consumer goods per capita and local general public budget expenditure as a share of GDP are the primary influencing elements affecting the CCD of NU-RR in the YRB. The interaction is manifested as bivariate enhance and nonlinear enhancement. The study’s findings can guide decisions to promote high-quality urban-rural integration development in the YRB.</div

Index system of influencing factors.

Integrating urban and rural areas is the only way to achieve sustainable regional development. To comprehensively build an evaluation index system for new urbanization and rural revitalization (NU-RR), taking the Yellow River Basin (YRB) as research object, and the coupled coordination degree (CCD) model, relative development degree (RDD) model and gravity model are used to quantitatively measure the spatial and temporal patterns, synchronous development status and spatial linkages of the coupled coordination of NU-RR from 2005 to 2020. The factors influencing the CCD are identified with the help of the geographic detector model. The findings indicate that: (1) From 2005 to 2020, the combined indexes of NU-RR in the YRB show an increasing trend, while rural revitalization is growing slower than new urbanization. (2) The CCD of NU-RR in the YRB shows spatial structure characterized by “high in the east and low in the west” and undergoes an evolutionary process of “low coupling-medium coupling-high coupling”. (3) The spatial disparities in the state of coupled and coordinated development of different cities are significant, mainly showing the spatial distribution characteristics dominated by the lagging new urbanization. (4) The spatial connection of CCD is networked and polarized, and the interprovincial barrier effect is weakened. (5) Total retail sales of consumer goods per capita and local general public budget expenditure as a share of GDP are the primary influencing elements affecting the CCD of NU-RR in the YRB. The interaction is manifested as bivariate enhance and nonlinear enhancement. The study’s findings can guide decisions to promote high-quality urban-rural integration development in the YRB.</div

The study area (YRB).

Shanxi Province (Taiyuan, Datong, Yangquan, Changzhi, Jincheng, Shuozhou, Jinzhong, Yuncheng, Xinzhou, Linfen, Luliang); Inner Mongolia (Hohhot, Baotou, Wuhai, Chifeng, Tongliao, Erdos, HulunBeier, Bayannur, Ulanqab); Shandong Province (Jinan, Qingdao, Zibo, Zaozhuang, Dongying, Yantai, Weifang, Jining, Tai’an, Weihai, Rizhao, Linyi, Dezhou, Liaocheng, Binzhou, Heze); Henan Province (Zhengzhou, Kaifeng, Luoyang, Pingdingshan, Anyang, Hebi, Xinxiang, Jiaozuo, Puyang, Xuchang, Luohe, Sanmenxia, Nanyang, Shangqiu, Xinyang, Zhoukou, Zhumadian); Sichuan Province (Chengdu, Zigong, Panzhihua, Luzhou, Deyang, Mianyang, Guangyuan, Suining, Neijiang, Leshan, Nanchong, Meishan, Yibin, Guang’an, Dazhou, Ya’an, Bazhong, Ziyang); Shaanxi Province (Xi’an, Tongchuan, Baoji, Xianyang, Weinan, Yan’an, Hanzhong, Yulin, Ankang, Shangluo); Gansu Province (Lanzhou, Jiayuguan, Jinchang, Baiyin, Tianshui, Wuwei, Zhangye, Pingliang, Jiuquan, Qingyang, Dingxi, Longnan); Qinghai Province (Xining); Ningxi (YingChuan, Shizuishan, Wuzhong, Guyuan, Zhongwei).</p

Duplicability-connectivity correlations.

<p>Correlations between gene duplicability and connectivity in six species: <i>H. sapien</i> (Hsap), <i>M. musculus</i> (Mmus), <i>D. melanogaster</i> (Dmel), <i>C. elegans</i> (Cele), <i>S. cerevisiae</i> (Scer), and <i>E. coli</i> (Ecol). The ‘Number of gene families’ row contains, for each species, the number of gene families that had at least one member for that species. The ‘Number of genes’ row contains, for each species, the number of genes covered by the gene families. The value is Spearman’s rank correlation coefficient between duplicability and connectivity, and the -value is computed for the correlation.</p

The average level and composite index of new urbanization (a) and rural revitalization (b).

The average level and composite index of new urbanization (a) and rural revitalization (b).</p

Relative development classification standards.

Integrating urban and rural areas is the only way to achieve sustainable regional development. To comprehensively build an evaluation index system for new urbanization and rural revitalization (NU-RR), taking the Yellow River Basin (YRB) as research object, and the coupled coordination degree (CCD) model, relative development degree (RDD) model and gravity model are used to quantitatively measure the spatial and temporal patterns, synchronous development status and spatial linkages of the coupled coordination of NU-RR from 2005 to 2020. The factors influencing the CCD are identified with the help of the geographic detector model. The findings indicate that: (1) From 2005 to 2020, the combined indexes of NU-RR in the YRB show an increasing trend, while rural revitalization is growing slower than new urbanization. (2) The CCD of NU-RR in the YRB shows spatial structure characterized by “high in the east and low in the west” and undergoes an evolutionary process of “low coupling-medium coupling-high coupling”. (3) The spatial disparities in the state of coupled and coordinated development of different cities are significant, mainly showing the spatial distribution characteristics dominated by the lagging new urbanization. (4) The spatial connection of CCD is networked and polarized, and the interprovincial barrier effect is weakened. (5) Total retail sales of consumer goods per capita and local general public budget expenditure as a share of GDP are the primary influencing elements affecting the CCD of NU-RR in the YRB. The interaction is manifested as bivariate enhance and nonlinear enhancement. The study’s findings can guide decisions to promote high-quality urban-rural integration development in the YRB.</div