4.3.1. Spatial Correlation Analysis of the Atmospheric Environment in the “2+26” Cities
Considering the “2+26” cities as a whole, the global spatial correlation of the atmospheric environmental efficiency of the “2+26” cities was explored based on Moran’s I.
Table 5 presents the results of Moran’s I used to measure the “2+26” cities from 2009 to 2018. Over the entire study period, the distribution of atmospheric environmental efficiency was initially irregular and gradually became more concentrated, demonstrating significant agglomeration features. From 2009 to 2014, except for 2013, Moran’s I did not pass the significance test, indicating that the atmospheric environmental efficiency did not exhibit spatial autocorrelation and had no obvious spatial distribution features. However, in 2015, Moran’s I passed the 5% significance level test, and from 2016 to 2018, Moran’s I passed the 1% significance level test, demonstrating a significant positive spatial correlation. That is, cities with a high atmospheric environmental efficiency value were adjacent to other cities with a high atmospheric environmental efficiency value, whereas cities with a low atmospheric environmental efficiency value were adjacent to other cities with a low atmospheric environmental efficiency value.
increased from 0.24 in 2015 to 0.399 in 2018, with an annual growth rate of 18.5%, indicating that the urban atmospheric environmental efficiency was rapidly agglomerating, and the spatial agglomeration degree was quickly becoming stronger.
To further depict the local spatial characteristics of the atmospheric environmental efficiency in the “2+26” cities, this paper employed Stata 16 software to draw Moran scatter plots of atmospheric quality efficiency in 2009, 2013 and 2018, which facilitated the exploration of the spatial distribution characteristics (
Figure 5). The reasons for selecting these three years for specific analysis were as follows: 2009 and 2018 were the start and end years, and 2015 was the middle year between 2009 and 2018. The
p-value accurately reflects the trend of Moran’s index of changes.
The four quadrants of the Moran scatter plot represent different types of spatial autocorrelation: The first quadrant (H–H) represents high-value areas around high-value areas, whereas the third quadrant (L–L) represents low-value areas around low-value areas, reflecting positive spatial autocorrelation in high- or low-value areas. The second quadrant (L–H) represents high-value areas around low-value areas, whereas the fourth quadrant (H–L) represents low-value areas around high-value areas, both reflecting negative spatial autocorrelation. Although the significance test was not passed in 2009, the majority of cities showed an H–L or L–H distribution, with more than half of the cities located in the second (13 cities) and fourth (7 cities) quadrants, only 5 cities in the first quadrant (H–H) and 3 cities in the third quadrant (L–L). This indicates that more cities had negative spatial correlation than positive spatial correlation in 2009. In 2013, the overall dispersion level of the “2+26” cities increased significantly, with the distribution pattern shifting from L–H and H–L distribution clustering to H–H and L–L distributions. In 2018, the clustering level of the “2+26” cities further increased, mainly reflected in the increase in H–H city clustering, with a total of 23 cities in the first (H–H) and third (L–L) quadrants. The local Moran scatter plots from 2009 to 2018 show that the “2+26” cities were significantly clustered, showing spatial positive correlation and demonstrating a distribution pattern of clustering toward H–H and L–L.
Table 6 shows the distribution of cities in each quadrant. Overall, in 2009, 2013 and 2018, the number of cities in the first and third quadrants increased, accounting for 29%, 68% and 82%, respectively. That is, most cities had similar efficiency values compared to the surrounding cities, with the overall spatial difference narrowing. In 2018, the cities located in the first quadrant included Beijing, Tianjin (both municipalities directly under the central government), Jinan and Zhengzhou (both provincial capital cities), with abundant funds and technology, radiating and driving the development of surrounding cities and demonstrating a concentration of atmospheric environmental efficiency (H–H). Shijiazhuang and Taiyuan were located in the third quadrant, although they are provincial capital cities, and their radiation-driven development effect was not strong, showing a concentration of atmospheric environmental efficiency (L–L). It was necessary to explore a path suitable for one’s own economic and environmental development to improve the atmospheric environmental efficiency.
4.3.2. Spatial Evolution in the “2+26” Cities
Based on previous research and incorporating the calculation results of this study, the research units were categorized as follows: 0–0.65, representing a relatively low-efficiency city in terms of MEA; 0.65–0.85, representing a relatively medium-efficiency city; 0.85–1, representing a relatively high-efficiency city; and 1, representing a high-efficiency city in terms of MEA. The mapped areas of the spatial evolution of the atmospheric environmental efficiency (city MEA efficiency) scores of the “2+26” cities are depicted in
Figure 6. This provides a more intuitive representation of the uneven spatial distribution and demonstrates the process of spatial evolution.
In terms of the city MEA efficiency (
Figure 6), the proportions of cities categorized as having high MEA efficiency, relatively high MEA efficiency, relatively medium MEA efficiency and relatively low MEA efficiency in 2009 were 5:3:11:9. However, in 2018, 12 cities, including Beijing, Tianjin and Jinan, ascended to become cities with high MEA efficiency, adding 7 cities compared to 2009, which represents significant improvement. The proportions of cities with high, relatively high, relatively medium and relatively low MEA efficiency were 12:1:8:7. The proportion of cities categorized as having high MEA efficiency increased by 25%, whereas the proportions of those with relatively high, relatively medium and relatively low MEA efficiency decreased by 7%, 11% and 7% respectively. From 2009 to 2015, the urban MEA efficiency generally declined, but from 2015 to 2018, there was a noticeable improvement in the urban MEA efficiency, primarily concentrated in the northern region of the “2+26” cities, including the Beijing–Tianjin area, as well as the Zhengzhou–Zibo line in the southern and southeastern parts. Throughout the study period, there were significant variations in the MEA efficiency values of the “2+26” cities, and in terms of spatial distribution, a cluster of cities with high MEA efficiency gradually formed, centered around the Beijing–Tianjin region, including cities like Beijing, Tianjin, Tangshan, Langfang, Cangzhou and the Zhengzhou–Zibo line, which included Liaocheng, Zhengzhou and Kaifeng. In the central region, there was a cluster of cities with medium efficiency, including Baoding, Hengshui, Dezhou and Binzhou. In the western region, there was a cluster of cities with low efficiency along the Shijiazhuang–Xinxiang line and in western areas, including Jincheng, Handan and Xinxiang, comprising a total of seven cities, primarily located in Hebei, Henan and Shanxi provinces.
In terms of city smoke (dust) MEA efficiency (
Figure 7), in 2009, the number of cities with high smoke (dust) MEA efficiency accounted for half of the “2+26” cities. However, by 2012, it had decreased to only six cities (Heze, Yangquan, Zibo, Dezhou, Liaocheng and Kaifeng). Among them, Baoding, Cangzhou, Langfang, Hengshui, Taiyuan, Handan and Jincheng transitioned from high smoke (dust) MEA efficiency to relatively low smoke (dust) MEA efficiency. Especially by 2015, only Tianjin and Tangshan remained cities with high smoke (dust) MEA efficiency. This indicates an overall decline in smoke (dust) MEA efficiency from 2009 to 2015, with a significant reduction in cities with high smoke (dust) MEA efficiency. It was not until 2015–2018 that there was some improvement in smoke (dust) MEA efficiency. In 2018, the number of cities with high smoke (dust) MEA efficiency significantly increased to 15. Smoke (dust) MEA-efficient cities were mainly distributed in the northern part of the “2+26” city region and along the Tangshan–Heze line, including the southernmost cities of Zhengzhou and Kaifeng. On the contrary, the area along the Shijiazhuang–Xinxiang line and its western region consisted of relatively low-efficiency cities. This highlights a significant exacerbation in regional disparities in smoke (dust) MEA efficiency. Notably, the cities of Shijiazhuang and Anyang consistently remained in a state of relatively low smoke (dust) MEA efficiency. The provincial capital cities of Jinan and Zhengzhou experienced a substantial improvement in smoke (dust) MEA efficiency, transitioning from a state of relatively low smoke (dust) MEA efficiency to high efficiency, indicating further optimization of the input and output.
In terms of SO
2 MEA efficiency (
Figure 8), the high-efficiency cities in 2009 included 12 cities, such as Baoding and Langfang, whereas the remaining cities, except for Beijing, which showed relatively high efficiency, were considered to have relatively low SO
2 MEA efficiency. By 2012, the number of cities with high SO
2 MEA efficiency had decreased by three-fourths compared to 2009, with only Kaifeng and Heze maintaining high efficiency in both smoke (dust) MEA and SO
2 MEA. Significant declines in efficiency were observed in Baoding, Langfang, Hengshui, Taiyuan, Jining, Liaocheng, Jincheng and Binzhou, highlighting noticeable disparities among the cities, with over half of the “2+26” cities having relatively low SO
2 MEA efficiency. In 2015, the spatial distribution pattern of SO
2 MEA efficiency was similar to that of 2012, with the exception of specific cities such as Beijing, Tangshan and Baoding. However, in 2018, there was a significant improvement in SO
2 MEA efficiency, indicating a shift from deteriorating efficiency to a positive trend. This resulted in the formation of high SO
2 MEA efficiency clusters centered around the Beijing–Tianjin region in the north and along the southeastern borderlines. The cities with relatively medium efficiency were primarily located in Henan and Shanxi provinces.
In 2009, there were nine cities that demonstrated high CO
2 MEA efficiency (
Figure 9), which was lower compared to the cities with high smoke (dust) MEA and SO
2 MEA efficiency. These cities were sparsely distributed in the eastern and western regions of the “2+26” cities, without any apparent distribution pattern. In 2012, Dezhou exhibited remarkable performance and ascended to the status of a city with high CO
2 MEA efficiency. However, the CO
2 MEA efficiency of this city displayed significant fluctuations. In 2015, it declined to a relatively medium-efficiency city, only to rise again in 2018 as a city with high CO
2 MEA efficiency. Yangquan, Changzhi, Liaocheng and Zibo consistently maintained high CO
2 MEA efficiency in both 2009 and 2012. In 2015, the number of high-efficiency cities further decreased, but the distribution pattern of relatively low-efficiency cities remained similar to that of 2012, with a few exceptions, such as Anyang and Zhengzhou. In 2018, the distribution of cities with high CO
2 MEA efficiency exhibited a trend of concentration from the surrounding areas toward the east and north, whereas the relatively low-efficiency cities were concentrated along the Baoding–Xinxiang line and its western side.
Based on the aforementioned analysis, it can be observed that, in 2018, the spatial agglomeration characteristics of the urban MEA efficiency and the undesirable output MEA efficiency (smoke (dust) MEA efficiency, SO2 MEA efficiency and CO2 MEA efficiency) were essentially consistent. These high-efficiency cities were primarily concentrated in the northern region centered around Beijing and Tianjin, as well as along the southeastern border. Over the course of the study period, these cities gradually witnessed improvements in their MEA efficiency values. The reasons for variations in the undesirable output MEA efficiency values in other cities can be attributed to the differing levels of technological prowess, which ultimately determined the potential direction of improvement based on the optimal direction vector determined by the individual indicators. This direction vector changed with the shifting technological frontier, resulting in variations in efficiency values among different cities and time periods, leading to distinct spatial distribution characteristics. In actual production, factors such as the enhancement of production technology, the application of new processes and equipment and the form of production organization affected the level of technology. Cities with relatively low urban MEA efficiency were located along the Shijiazhuang–Xinxiang line and its western region. Although Shijiazhuang is the provincial capital city, its urban MEA efficiency values remained relatively low, possibly due to a lack of robust economic growth stimuli. Despite active industrial restructuring, the tertiary industry’s growth was not sufficiently strong, and the input–output ratio was not reasonable, indicating a need for improvement in production technology levels. Cities with relatively low MEA efficiency were primarily distributed in Hebei, Henan and Shanxi provinces. This could be attributed to the underdeveloped nature of these cities’ economies, with a lower proportion of high-tech and emerging industries. These cities faced challenges in terms of economic development and technological progress, with industrial technological development failing to keep pace with economic demands, relying more on the consumption of resources and energy. These cities should prioritize improving their undesirable output MEA efficiency by focusing on technological innovation and management levels. This includes the introduction of industrial equipment for pollution recovery, the improvement of production process techniques, the enhancement of industrial technological levels, gradual reductions in the proportion of heavy industries, the active development of emerging technology industries and the gradual establishment of a virtuous cycle between economic development and environmental protection.