4.4. Efficiency Analysis of the Production and Health Treatment Stages
Table 2 shows the efficiencies for the cities in the production and health treatment/media impact stages from 2013 to 2016.
In the production stage, there were annual efficiencies of 1 only in Guangzhou, Lhasa, and Shanghai in all four years, and in the treatment stage, there were annual efficiencies of 1 in Fuzhou, Guangzhou, Haikou, Lhasa, Nanning, and Shanghai.
Beijing, Changchun, Changsha, Chengdu, Harbin, Hangzhou, Hefei, Hohhot, Jinan, Nanchang, Nanjing, Shenyang, Tianjin, Wuhan, and Zhengzhou had higher efficiency scores in the production stage than the treatment stage, and Chongqing, Guiyang, Haikou, Kunming, Lanzhou, Nanning, Shijiazhuang, Taiyuan, Urumqi, Xi’an, Xining, and Yinchuan had higher efficiency scores in the treatment stage than the production stage.
Chongqing, Guiyang, Kunming, Lanzhou, Shijiazhuang, Taiyuan, Xining, and Yinchuan had four-year production stage efficiencies below 0.6, with the poorest being in Shijiazhuang, with four-year efficiencies of around 0.4. All other cities had efficiencies between 0.4 and 0.6.
The worst performing cities in the treatment stage were Chengdu and Tianjin, with Chengdu having an efficiency of less than 0.4 in three years; therefore, there was a significant need for improvement.
The declines in the production stage were much smaller than in the treatment stage, with the largest being in Nanchang, which fell from 0.85 in 2013 to 0.66 in 2016. Efficiency changes in the treatment stage were more volatile, with Beijing and Wuhan having the largest declines, from 1 in 2013 to 0.6 in 2016.
The efficiency increases in both the production and treatment stages were much the same. Changsha, Hefei, Jinan had the largest increases, with Jinan rising from 0.47 in 2013 to 1 in 2016 and Hefei rising from 0.48 in 2013 to 1 in 2016.
In
Table 3, the Wilcoxon Test shows that the total efficiency of high-income and upper middle–income countries from 2013 to 2016 is weak significant. The total efficiency of 2014 is not significant, but the total efficiency of 2014 to 2016 is weakly significant, which is consistent with the H3 hypothesis.
In production stage, according to the Wilcoxon Test, the efficiency of the high-income and upper middle–income countries from 2013 and 2015 is strong significant. The efficiency of the 2014 and 2016 production stages is not significant, but the efficiency of the 2013 and 2015 production stages is strongly significant. The efficiency values of high-income countries are higher than those of upper middle–income countries, consistent with the H3 hypothesis in 2013 and 2015.
In treatment stage, Wilcoxon Test shows that the efficiency of high-income and upper middle–income countries from 2013 and 2015 is strongly significant. The efficiency of the treatment stage in 2014 and 2016 is not significant, but the efficiency of the treatment stage in 2013 and 2015 is strongly significant. The efficiency values of high-income countries are higher than those of upper middle–income countries, consistant with the H3 hypothesis in 2013 and 2015.
4.5. Efficiency Analysis of the Indicators in the 31 Cities from 2013 to 2016
Table 4 shows the labor, fixed assets, and energy consumption efficiencies, from which it can be seen that the worst performances were in fixed assets, followed by energy consumption, and labor efficiency, which was relatively good.
Only Guangzhou, Lhasa, and Shanghai had fixed assets efficiencies of 1 in all four years; however, Beijing, Haikou, Nanning, and Urumqi all had annual efficiencies higher than 0.8. The other 24 cities had a significant need for improvement. For example, Changsha, Chongqing, Guiyang, Hefei, Kunming, Nanchang, Nanning, Shijiazhuang, Tianjin, Xi’an, and Yinchuan all had efficiencies under or around 0.6, with Tianjin requiring the most improvements at only 0.45 in 2013.
Guangzhou, Lhasa, Nanning, and Shanghai had energy consumption efficiencies of 1 in all four years, and Beijing, Changchun, Fuzhou, Harbin, Haikou, Hefei, Nanchang, Urumqi, and Zhengzhou all had efficiencies higher than 0.8. However, Guiyang, Lanzhou, Shijiazhuang, Taiyuan, Xining, and Yinchuan had efficiencies lower that 0.6, with the worst performance being in Taiyuan, at below 0.2, followed by Shijiazhuang, Lanzhou, and Yinchuan at around 0.4.
Only Guangzhou, Lhasa, and Shanghai had labor efficiencies of 1 for all four years, and the worst performing cities were Chongqing, Guiyang, Kunming, Lanzhou, Shijiazhuang, Taiyuan, and Xining at below 0.7 in most years. All other cities had labor efficiencies between 0.8 and 0.9.
Table 5 shows the GDP, carbon dioxide emissions, and AOI efficiencies in each city, from which it can be seen that there were large differences.
Fuzhou, Guangzhou, Haikou, Lhasa, Nanning, and Shanghai had carbon dioxide emissions efficiencies of 1 in all four years. However, Lanzhou, Taiyuan, and Yinchuan all scored less than 0.4, with all of Taiyuan’s results below 0.2. Changchun, Harbin, Hefei, Nanchang, Urumqi, and Zhengzhou had carbon dioxide emissions efficiencies higher than 0.8, and the other cities had carbon dioxide emissions efficiencies between 0.6 and 0.8.
There were also large differences in the AQI efficiencies. Beijing. Chongqing, Fuzhou, Guangzhou, Haikou, Kunming, Lhasa, Nanjing, Nanning, Shanghai, and Urumqi had AQI efficiencies of 1 in all four years, and many cities had two- or three-year efficiencies of 1, with the other years being above 0.9. However, the AQI efficiencies in Lanzhou, Taiyuan, Xining, and Yinchuan began to decline from 2013 and, by 2016, had fallen to around 0.4. The largest declines were in Lanzhou, Nanchang, Shijiazhuang, Taiyuan, Wuhan, Xi’an, Xining, Yinchuan, and Zhengzhou, but there were AQI efficiency increases in Changchun, Chengdu, Harbin, Hefei, Jinan, Urumqi, and Shenyang.
The GDP efficiencies were better than the CO2 emissions and AQI efficiencies in most cities. Guangzhou, Lhasa, and Shanghai had GDP efficiencies of 1, and the GDP efficiencies in Beijing and Nanning in the first three years were all 1, but both declined slightly in 2016. Guiyang, Kunming, Lanzhou, Shijiazhuang, Taiyuan, and Xining had comparatively poor efficiencies at lower than 0.8, and all other cities had GDP efficiencies between 0.8 and 1.
Table 6 shows the health expenditure, media report, respiratory diseases, and birth rate efficiencies in the treatment stage. Fuzhou, Guangzhou, Haikou, Lhasa, Nanning, and Shanghai had media report efficiencies of 1 in all four years. Changchun, Guiyang, Hohhot, Kunming, Wuhan, Urumqi, and Xi’an had media report efficiencies higher than 0.8 in three years. Changsha, Chengdu, Harbin, Hangzhou, Hefei, Nanchang, Nanjing, Shenyang, and Tianjin had media report efficiencies between 0.5 and 0.7. The worst performances were in Lanzhou and Xining, with media report efficiencies of only 0.4 per year, and Shijiazhuang, Yinchuan, and Zhengzhou had media report efficiencies only slightly higher than 0.4 in one or two years. The efficiencies in 10 cities had volatile declines, and the media report efficiencies in the other 13 cities fluctuated up.
Fuzhou, Guangzhou, Haikou, Lhasa, Nanning, Shanghai. Beijing, Changsha, Urumqi, Xining, and Yinchuan had health expenditure efficiencies of 1 or at least two years above 0.9. However, Zhengzhou’s health expenditure efficiency in all four years was below 0.5, and Tianjin had a health expenditure efficiency less than 0.2 for three years. There were noticeable health expenditure efficiency volatilities. However, 13 cities had reduced efficiencies and needed improvements.
Fuzhou, Guangzhou, Haikou, Lhasa, Nanjing, and Shanghai had birth rate and respiratory diseases efficiencies of 1, but Chengdu, Harbin, Shijiazhuang, and Tianjin had four-year efficiencies just above 0.7.
Compared with the birth rate efficiency, the respiratory diseases efficiencies required significant improvements. Fuzhou, Guangzhou, Haikou, Lhasa, Nanning, and Shanghai had respiratory disease efficiencies of 1, Chengdu and Tianjin had respiratory disease efficiencies of around 0.6, and Harbin and Shenyang had three-year efficiencies between 0.7 and 0.8. Nine cities had reduced efficiencies, and 17 cities had rising efficiencies.
According to
Table 7, in 2013, the correlation coefficient between media efficiency and CO
2 and AQI efficiency exceeded 0.4 (at significant level p-value less than 0.05), and there is a high correlation, which is consistent with the H1 hypothesis. The correlation coefficient between Media efficiency and Respiratory Diseases efficiency is 0.5932 (at significant level
p-value less than 0.05), which is correlated and conforms to the H2 hypothesis.
In 2014, the correlation coefficient between media efficiency and CO2 and AQI efficiency are 0.4275 and 0.3387 (at significant level p-value less than 0.1), and there is a high correlation, which is consistent with the H1 hypothesis. The correlation coefficient between Media efficiency and respiratory diseases efficiency is 0.4252 (at significant level p-value of less than 0.05), which is correlated to and conforms to the H2 hypothesis.
In 2015, the correlation coefficient between media efficiency and CO2 and AQI efficiency exceeded 0.4 (at significant level p-value of less than 0.05), and there is a high correlation, which is consistent with the H1 hypothesis. The correlation coefficient between media efficiency and respiratory diseases efficiency is 0.5751 (at significant level p-value less than 0.05), which is correlated to and conforms to the H2 hypothesis.
In 2016, the correlation coefficient between media efficiency and CO2 and AQI efficiency exceeded 0.4 (at significant level p-value of less than 0.05), and there is a high correlation, which is consistent with the H1 hypothesis. The correlation coefficient between media efficiency and respiratory diseases efficiency is 0.3697 (at significant level p-value of less than 0.05), which is correlated and conforms to the H2 hypothesis.
4.6. Technology Gap Ratio and the Two-Stage Technology Gap Ratio in Each City
Table 8 and
Figure 6 show the technological frontier in the production and treatment stages from 2013 to 2016. The technology frontier was 1 in Guangzhou, Lhasa, and Shanghai, and there were large differences in the other cities.
Chengdu, Chongqing, Nanchang, Shijiazhuang, and Taiyuan had technology frontiers of 0.7, which fell in Nanchang, Shijiazhuang, and Taiyuan. However, the technology frontier in the other cities was mostly between 0.8 and 0.9.
Changsha, Chengdu, Chongqing, Fuzhou, Hangzhou, Hefei, Jinan, Kunming, Nanjing, Shenyang, Wuhan, Urumqi, and Zhengzhou had rising technology frontiers, but they fell in the other 15 cities, indicating that the technology gap between the cities was expanding, which is in line with the economic development and technical level characteristics in mainland China.
Technology Gap between the Production Stage and the Health Management Stage in Each City
Table 9 shows the production and treatment stage technology gaps in each city from 2013 to 2016, from which it can be seen that Guangzhou, Lhasa, and Shanghai had technology frontiers of 1 in the production stage, and Beijing, Fuzhou, Hangzhou, Nanjing, Nanning, Tianjin, Wuhan, and Zhengzhou were close to 1. However, there were large technology gap differences in Chengdu, Chongqing, Guiyang, Kunming, Lanzhou, Shijiazhuang, Taiyuan, and Yinchuan. While these cities were leading in their own regions, their technology frontier required significant improvements to catch up with other cities.
Fuzhou, Hefei, Jinan, Kunming, Shenyang, Wuhan, Urumqi, and Zhengzhou had rising technology frontiers during the production stage, but those of the other cities fell, further indicating that the technology gap between the regions was expanding. For example, while Harbin, Hohhot, Jinan, Nanchang, Shenyang, and Zhengzhou had higher technology gaps compared to cities in their regions, there was still a large gap compared with other cities in the country. Only Beijing and Shijiazhuang had declining technology gaps; however, the scores in the other 23 cities all rose, indicating that the technology gap between the regional cities in the treatment stage in most cities shrank.
According to the Wilcoxon Test in
Table 10, high-income and upper middle–income countries Total technology gap is strong significant from 2013 to 2016. In 2014 Total technology gap is week significant. The technology gap of high-income countries is higher than that of upper middle–income countries, consistent with the H4 hypothesis.
In production stage, Wilcoxon Test shows that the technology gap of strong-income and upper middle–income countries from 2013 to 2016 is strongly significant. In other word, the technology gap of high-income countries is higher than that of upper middle–income countries, consists with the H4 hypothesis.
In the treatment stage, the Wilcoxon Test shows that the technology gap of high-income and upper middle–income countries is strongly significant from 2013 to 2016, where the technology gap of the 2014 treatment stage is weakly significant. The treatment stages in 2013, 2015, and 2016, technology gap is strongly significant. The technology gap of high-income countries is higher than that of upper middle–income countries, consistent with the H4 hypothesis.