4.3.1 Global autocorrelation analysis
Table 3 shows that the global Moran's I values are all positive and pass the significance test (Z (I) > 2.58, P (I) < 0.01) from 2003 to 2017, implying that the coupling coordination degree of the YEB presents a significant spatial positive autocorrelation. The coupling coordination degree between adjacent provinces in the region is interrelated and interdependent, showing a spatial aggregation pattern. The provinces with a high degree of coupling coordination show a spatial trend of adjacent aggregation, and vice versa. The global Moran’s I present a fluctuating downward trend, showing that the spatial agglomeration of the coupling coordination degree in the YEB is weakening and the gap between provinces is gradually narrowing.
Table 3 Global Moran's I value in 2003–2017.
Year
|
Moran's I
|
S(I)
|
Z(I)
|
2003
|
0.5318
|
0.1885
|
3.3622
|
2004
|
0.5454
|
0.1922
|
3.3669
|
2005
|
0.5602
|
0.1934
|
3.4281
|
2006
|
0.5509
|
0.1943
|
3.3664
|
2007
|
0.5498
|
0.1945
|
3.3615
|
2008
|
0.5573
|
0.1949
|
3.3728
|
2009
|
0.5577
|
0.1944
|
3.4029
|
2010
|
0.5745
|
0.1949
|
3.4774
|
2011
|
0.5227
|
0.1905
|
3.2888
|
2012
|
0.5584
|
0.1968
|
3.3563
|
2013
|
0.4662
|
0.1926
|
2.9433
|
2014
|
0.3815
|
0.1870
|
2.5837
|
2015
|
0.4793
|
0.1975
|
2.9634
|
2016
|
0.5049
|
0.1972
|
3.0708
|
2017
|
0.4465
|
0.1947
|
2.8100
|
4.3.2 Local autocorrelation analysis
(1) The Moran scatter plots of the coupling coordination degree between urbanization and atmospheric environment in the representative years (2003, 2006, 2009, 2012, 2014, and 2017) are obtained by GeoDa software. The four quadrants of the Moran scatter plot correspond to the following four zones: High-High (H-H), Low-High (L-H), Low-Low (L-L), and High-Low (H-L). As shown in Fig. 7 and Table 4, in 2003 3 provinces (i.e., Zhejiang, Jiangsu, and Shanghai) are located in the H-H zone. The coordination degrees of these provinces are high, and the values of surrounding areas are similar, indicating an obvious spatial autocorrelation. In addition, 2 provinces (i.e., Jiangxi and Anhui) are located in the L-H zone, which are heterogeneous with the neighborhoods. Furthermore, 5 provinces (i.e., Hunan, Chongqing, Sichuan, Hunan, and Guizhou) are located in the L-L zone. Moreover, Hubei is located in the H-L zone, whose coupling coordination level is higher than that of the adjacent areas. The spatial distribution of the Moran scatter plots in 2006, 2009 and 2012 is the same as that in 2003. In 2014, the provinces included in the H-H and L-H zones remain constant. Chongqing and Sichuan provinces that are included in the L-L zone transfer into the H-L zone. In 2017, the provinces in the H-H and L-H zones are still unchanged. Sichuan Province returns to the L-L zone from the H-L zone.
During the investigated period, most provinces are included in the H-H and L-L zones, indicating that the spatially positive autocorrelation of the coupling coordination degree in the YEB is remarkable. Comparing the results over representative years, most provinces do not make a quadrant transition, presenting a certain spatial stability.
(2) The LISA diagram represents the spatial connection mode of the coupling coordination degree between adjacent regions in geographical space and can further test the significance level of spatial agglomeration. As shown in Fig. 8, the agglomeration mode of the provinces of the YEB in the representative years has the following characteristics: In 2003, the “High-High” cluster mode includes 3 provinces (i.e., Jiangsu, Shanghai, and Zhejiang), and Yunnan Province is in the “Low-Low” cluster mode. The rest of the provinces in the YEB do not have significant spatial agglomeration characteristics. In 2006, the provinces in the “High-High” and “Low-Low” cluster modes remain unchanged, and Anhui Province transforms into the “Low-High” outlier mode. The LISA diagram of the spatial agglomeration mode in 2009 is the same as that in 2006. In 2012, the “High-High” cluster and “Low-High” outlier modes remain constant. The provinces of the “Low-Low” cluster mode are increased to 2, with Guizhou Province being added. In 2014, Jiangsu and Shanghai provinces are included in the “High-High” cluster, Anhui Province is included in the “Low-High” outlier mode, and Sichuan Province transforms from the “Low-Low” cluster mode to the “High-Low outlier mode. In 2017, there are only two agglomeration modes on the diagram: “High-High” cluster mode with Jiangsu and Shanghai provinces and “Low-High” outlier mode with Anhui Province.
Based on the above analysis, the “High-High” cluster appears in the provinces of the lower regions, showing the characteristics of a high coupling coordination degree and a significant spatial aggregation effect. These provinces have a good social and economic foundation in promoting urbanization and atmospheric environment governance. “Low-Low” cluster provinces are mainly in the middle and upper regions of the YEB. Lagging urbanization development and inadequate atmospheric environmental protection affected by geographical location and economic structure results in a state of low coupling coordination. The existence of “High-High” and “Low-Low” cluster provinces reflects the spatial dependence of the coupling coordination degree between urbanization and atmospheric environment. In various years, some provinces showing “Low-High” and “High-Low” outlier modes reflect the spatial heterogeneity of the coordination between urbanization and atmospheric environment. With the evolution of time, fewer provinces show significant agglomeration, and the “Low-Low” cluster areas and “High-Low” outlier areas gradually disappear. This phenomenon shows that the overall coupling coordination degree of the YEB is on the rise, and the differences and imbalance between regions are reducing.
Table 4 Provincial distribution of Moran scatter plot.
Year
|
The first quadrant (H-H)
|
The second quadrant (L-H)
|
The third quadrant (L-L)
|
The fourth quadrant (H-L)
|
2003
|
Zhejiang、Jiangsu、Shanghai
|
Jiangxi、Anhui
|
Hunan、Chongqing、Sichuan、Yunnan、Guizhou
|
Hubei
|
2006
|
Zhejiang、Jiangsu、Shanghai
|
Jiangxi、Anhui
|
Hunan、Chongqing、Sichuan、Yunnan、Guizhou
|
Hubei
|
2009
|
Zhejiang、Jiangsu、Shanghai
|
Jiangxi、Anhui
|
Hunan、Chongqing、Sichuan、Yunnan、Guizhou
|
Hubei
|
2012
|
Zhejiang、Jiangsu、Shanghai
|
Jiangxi、Anhui
|
Hunan、Chongqing、Sichuan、Yunnan、Guizhou
|
Hubei
|
2014
|
Zhejiang、Jiangsu、Shanghai
|
Jiangxi、Anhui
|
Hunan、Yunnan、
Guizhou
|
Chongqing、Hubei、Sichuan
|
2017
|
Zhejiang、Jiangsu、Shanghai
|
Jiangxi、Anhui
|
Hunan、Sichuan、Guizhou、Yunnan
|
Chongqing、Hubei
|