The Impact of Economic Growth and Air Pollution on Public Health in 31 Chinese Cities
Abstract
:1. Introduction
2. Materials and Methods
2.1. EBM DEA, Network SBM DEA and Modified Undesirable EBM Two Stage DEA Framework
2.2. Non-Oriented Network SBM DEA
2.3. The Empirical Model for this Study: Modified Undesirable EBM Two Stage DEA Model
2.4. Fixed Assets, Labor, Energy Consumption, GDP, Health Expenditure, Birth Rate, Respiratory Diseases, and Mortality Rate Efficiency
2.5. Data Sources and Description
2.5.1. Input Variables
2.5.2. Output Variable
- Desirable output (GDP): GDP in each city; unit: 100 million CNY;
- Production Stage and health stage link variables:
- Carbon dioxide: CO2; a common greenhouse gas.
- The second stage: health stage
- Input variables: Health Expenditure
- Output variables: Birth rate, Respiratory Diseases, Mortality Rate
3. Results and Discussion
3.1. Input-Output Index Statistical Analyses
3.2. Total City Efficiency Scores for Each Year
3.3. Comparison of the Radial and Non-Radial Non-Efficiency Scores in Each City
3.4. 2013 to 2016 Efficiency Scores and Rankings for Labor, Fixed Assets, and Energy Consumption
3.5. 2013 to 2016 Health Expenditure, GDP, and Birth Rate Efficiency Scores and Rankings
3.6. 2013 to 2016 Respiratory Diseases and Mortality Rate Efficiency Scores and Rankings
3.7. 2013 to 2016 CO2 and AQI Efficiency Scores and Rankings
3.8. Comparison of Undesirable Output Efficiency and Main Policy
4. Conclusions and Policy Recommendation
- (1)
- Of the 31 cities, only Lhasa, Guangzhou, and Shanghai had overall efficiency scores of 1 for all four years. Nanning’s total efficiency score was 1 from 2013 to 2015 but fluctuated down in 2016. The overall efficiency in Beijing was 1 in 2013 and 2015, 0.94 in 2014, and 0.84 in 2016, indicating that the need for improvements in Beijing was expanding, which was also true for most other cities.
- (2)
- Compared with the number of cities with increasing overall efficiency scores, most cities had decreasing overall efficiency scores; therefore, the improvements in overall efficiencies were not optimistic.
- (3)
- Health expenditure efficiency in most cities was rising except for the input efficiencies in Beijing, Wuhan, Xining, and Nanning, all of which dropped significantly.
- (4)
- Except for the cities that had GDP efficiencies of 1, there were less cities with rising GDP efficiencies (6) than cities with decreasing GDP efficiencies (11); therefore, the need for GDP efficiency improvements was expanding.
- (5)
- The birth rate efficiency scores in Fuzhou, Guangzhou, Haikou, Lhasa, Nanning, and Shanghai were all 1, 7 cities had downward trends while the other 28 cities had upward trends; therefore, in general, this indicator was improving in most cities.
- (6)
- The respiratory diseases treatment efficiencies decreased in 10 cities; however, in all other cities it was rising, and the need for improvements was significantly reducing.
- (7)
- Five cities had mortality rate efficiencies of 1 and the efficiency increased significantly in 15 other cities; however, the mortality rate efficiencies declined in 11 cities.
- (8)
- The carbon emissions efficiencies rose in most cities.
- (1)
- Carbon dioxide emissions and air pollutants have brought great challenges to the environmental governance of central and local governments. The impact of air pollutants on human health is large and long-lasting and should be prioritized in governance work. Even the air pollutant efficiency of some cities is better than the carbon dioxide emissions, the reduction of air pollutants cannot be overlooked. Chengdu, Hangzhou, Hefei, Huhehot, Jinan, Lanzhou, Nanchang, Nanjing, Shenyang, Shijiazhuang, Urumqi, Yinchuan should be more effective in controlling carbon dioxide emissions and air pollution emissions, using industrial transformation, and energy transformation to achieve emission reduction. Beijing, Changchun, Wuhan, Xian, Xining, Zhengzhou should control air pollutants first and then consider carbon dioxide emissions reduction. Changsha, Chongqing, Guiyang, Harbin, Kunming, Nanning, Taiyuan, Tianjin, should control carbon dioxide emissions first and then consider the treatment and control of air pollutants. Fuzhou, Guangzhou, Haikou, Lhasa, Shanghai should maintain better environmental efficiency and continue to improve air pollutants and carbon dioxide emissions.
- (2)
- Because China has a vast territory, there are very large differences in economic and social development. The economy in most regions in the east is significantly better than in the central and western regions. Therefore, the efficiency scores in most eastern cities (Beijing, Shanghai, Guangzhou) were at a higher level and there was less need for improvement. With rapid economic development being at the cost of increased energy consumption and environmental pollution, it is necessary to use comprehensive treatments and increase the sustainable development of the cities. As Beijing is the capital, it attracts significant government investment. However, the treatment efficiency was not high and was on a downward trend. Urban development consumes a great deal of energy that generates pollution, which had led to a significant decline in environmental efficiency; therefore, the treatments associated with respiratory diseases, air pollution, and carbon emissions should be strengthened.
- (3)
- For inland cities, such as Chengdu and Chongqing, the efficiency scores in 2016 were significantly higher than in 2014 and 2015, indicating that the treatment effects were improving the overall environment. Compared with the coastal cities, many mid-western cities had lower efficiency scores, with many experiencing significant declines in treatment efficiencies in 2016. Therefore, while investment in many cities in 2016 increased, the effects have not yet been realized and it is still necessary to reduce excessive investment and resource waste.
- (4)
- Environmental pollution in cities is associated with respiratory diseases, which was measured in terms of the respiratory disease treatment and mortality rate efficiencies. Environmental pollution problems should be solved from the source and treatment efficiency increased. Also, Beijing, Changchun, Guiyang, Harbin, Kunming, Shenyang Shijiazhuang, Tianjin should pay attention to respiratory diseases and mortality, fund improvements to the quality of medical equipment and reduce disease.
Author Contributions
Funding
Conflicts of Interest
References
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Input Variables | Output Variables | Link | |
---|---|---|---|
Stage 1 | Labor (Lab) | GDP | AQI |
Fixed assets (asset) | CO2 | ||
Energy consumption (con) | |||
Stage 2 | Health Expenditure | Birth rate | |
Respiratory Diseases; | |||
Mortality Rate |
NO | DMU | 2013 | 2014 | 2015 | 2016 |
---|---|---|---|---|---|
1 | Beijing | 1 | 0.989601 | 1 | 0.849723 |
2 | Changchun | 0.815132 | 0.827072 | 0.827642 | 0.608184 |
3 | Changsha | 0.89342 | 0.878934 | 0.863749 | 0.902829 |
4 | Chengdu | 0.642234 | 0.608838 | 0.66257 | 0.560853 |
5 | Chongqing | 0.581473 | 0.594271 | 0.589402 | 0.707737 |
6 | Fuzhou | 0.964131 | 0.621109 | 0.934035 | 0.965527 |
7 | Guangzhou | 1 | 1 | 1 | 1 |
8 | Guiyang | 0.457578 | 0.485777 | 0.488281 | 0.599234 |
9 | Harbin | 0.808949 | 0.79447 | 0.796574 | 0.502568 |
10 | Haikou | 0.948872 | 0.669205 | 0.928245 | 0.948917 |
11 | Hangzhou | 0.843423 | 0.833258 | 0.833978 | 0.843787 |
12 | Hefei | 0.77078 | 0.777052 | 0.741251 | 0.97223 |
13 | Huhhot | 0.873322 | 0.852322 | 0.790979 | 0.754991 |
14 | Jinan | 0.760292 | 0.725372 | 0.653882 | 1 |
15 | Kunming | 0.453256 | 0.458886 | 0.50625 | 0.591795 |
16 | Lanzhou | 0.525941 | 0.457756 | 0.453991 | 0.682741 |
17 | Lhasa | 1 | 1 | 1 | 1 |
18 | Nanchang | 0.868581 | 0.84167 | 0.801384 | 0.694567 |
19 | Nanjing | 0.8851 | 0.846445 | 0.888145 | 0.802205 |
20 | Nanning | 1 | 1 | 1 | 0.865003 |
21 | Shanghai | 1 | 1 | 1 | 1 |
22 | Shenyang | 0.725947 | 0.686892 | 0.661513 | 0.860712 |
23 | Shijiazhuang | 0.456604 | 0.425277 | 0.384795 | 0.459909 |
24 | Taiyuan | 0.58708 | 0.49415 | 0.493603 | 0.63538 |
25 | Tianjin | 0.823033 | 0.823165 | 0.788025 | 0.664835 |
26 | Wuhan | 0.950552 | 0.789078 | 0.771077 | 0.766357 |
27 | Urumqi | 0.916579 | 0.893509 | 0.594525 | 0.984552 |
28 | Xian | 0.654069 | 0.65707 | 0.615445 | 0.598831 |
29 | Xining | 0.508144 | 0.435087 | 0.428298 | 0.590434 |
30 | Yinchuan | 0.646088 | 0.589195 | 0.572028 | 0.767119 |
31 | Zhengzhou | 0.904107 | 0.914056 | 0.928053 | 0.706123 |
2013 | 2014 | 2015 | 2016 | |
---|---|---|---|---|
Epsilon for EBMX | 0.057 | 0.051 | 0.095 | 0.1119 |
Epsilon for y | 0.3594 | 0.2465 | 0.242 | 0.2667 |
No. | DMU | 2013 Em | 2014 Em | 2015 Em | 2016 Em | 2013 Asset | 2014 Asset | 2015 Asset | 2016 Asset | 2013 Com | 2014 Com | 2015 Com | 2016 Com |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Beijing | 1 | 0.880763374 | 1 | 0.8524 | 1 | 0.9592901 | 1 | 0.9210143 | 1 | 0.99542 | 1 | 0.99856 |
2 | Changchun | 0.8991 | 0.90611047 | 0.90711 | 0.8057 | 0.7215939 | 0.7411042 | 0.744306 | 0.6096182 | 0.8991 | 0.90611 | 0.907108 | 0.80574 |
3 | Changsha | 0.94709 | 0.938663441 | 0.93327 | 0.952 | 0.5721516 | 0.5235194 | 0.460713 | 0.4841279 | 0.65715 | 0.66672 | 0.664614 | 0.65718 |
4 | Chengdu | 0.78317 | 0.757419183 | 0.7979 | 0.7446 | 0.6087696 | 0.6515081 | r | 0.6197391 | 0.78317 | 0.75742 | 0.797897 | 0.74458 |
5 | Chongqing | 0.63104 | 0.500024235 | 0.52621 | 0.5274 | 0.5582939 | 0.4286548 | 0.421291 | 0.3855462 | 0.7371 | 0.74854 | 0.747195 | 0.75362 |
6 | Fuzhou | 1 | 0.733257885 | 1 | 1 | 0.8617823 | 0.5204487 | 0.641657 | 0.6194099 | 1 | 0.76774 | 1 | 1 |
7 | Guangzhou | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
8 | Guiyang | 0.62894 | 0.654998838 | 0.65875 | 0.6882 | 0.6146963 | 0.5623476 | 0.485601 | 0.4681071 | 0.47867 | 0.55585 | 0.591906 | 0.61108 |
9 | Harbin | 0.89606 | 0.885502631 | 0.88703 | 0.7242 | 0.5813495 | 0.8774743 | 0.857175 | 0.6756626 | 0.89606 | 0.8855 | 0.887034 | 0.73003 |
10 | Haikou | 0.55735 | 0.586010339 | 0.96348 | 0.9998 | 0.9758649 | 0.8029476 | 1 | 0.8938472 | 0.97586 | 0.80295 | 1 | 1.000 |
11 | Hangzhou | 0.91704 | 0.910943901 | 0.91314 | 0.9319 | 0.7152775 | 0.6503048 | 0.608121 | 0.6141956 | 0.73959 | 0.76052 | 0.798248 | 0.80345 |
12 | Hefei | 0.87244 | 0.876119794 | 0.85484 | 1 | 0.5259938 | 0.5418133 | 0.477602 | 0.5705747 | 0.87244 | 0.87612 | 0.85484 | 1 |
13 | Huhehot | 0.93601 | 0.921804653 | 0.88594 | 0.9181 | 0.7780742 | 0.756976 | 0.753144 | 0.8292995 | 0.38497 | 0.75263 | 0.724236 | 0.69614 |
14 | Jinan | 0.86547 | 0.84230184 | 0.79403 | 1 | 0.8654708 | 0.8423018 | 0.695281 | 1 | 0.56354 | 0.54639 | 0.554859 | 1 |
15 | Kunming | 0.62511 | 0.630134621 | 0.67327 | 0.6673 | 0.5159696 | 0.544736 | 0.571462 | 0.5270656 | 0.53329 | 0.53631 | 0.673272 | 0.66728 |
16 | Lanzhou | 0.69119 | 0.62970179 | 0.62858 | 0.6486 | 0.6911945 | 0.6297018 | 0.607536 | 0.5982818 | 0.39525 | 0.34542 | 0.284083 | 0.31471 |
17 | Lhasa | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
18 | Nanchang | 0.93126 | 0.915454664 | 0.89282 | 0.8131 | 0.6223186 | 0.6027086 | 0.543299 | 0.4671772 | 0.93126 | 0.91545 | 0.892823 | 0.81313 |
19 | Nanjing | 0.94253 | 0.920028875 | 0.94426 | 0.9628 | 0.5596182 | 0.5317071 | 0.588637 | 0.6139003 | 0.64366 | 0.61318 | 0.884983 | 0.86169 |
20 | Nanning | 1 | 1 | 1 | 0.6126 | 1 | 1 | 1 | 0.5876837 | 1 | 1 | 1 | 0.87547 |
21 | Shanghai | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
22 | Shenyang | 0.84477 | 0.817273553 | 0.80052 | 1 | 0.4111169 | 0.4090846 | 0.52199 | 1 | 0.64117 | 0.64932 | 0.63962 | 1 |
23 | Shijiazhuang | 0.62809 | 0.597933513 | 0.55923 | 0.576 | 0.6155488 | 0.5774181 | 0.440956 | 0.4641232 | 0.4664 | 0.42375 | 0.378611 | 0.38869 |
24 | Taiyuan | 0.74324 | 0.664259912 | 0.66666 | 0.6827 | 0.7432409 | 0.6642599 | 0.642631 | 0.6826506 | 0.17809 | 0.17449 | 0.168238 | 0.20609 |
25 | Tianjin | 0.90639 | 0.906020087 | 0.8873 | 0.9113 | 0.4624992 | 0.4587871 | 0.420641 | 0.4033572 | 0.69375 | 0.70324 | 0.700194 | 0.67872 |
26 | Wuhan | 0.976 | 0.884533161 | 0.87519 | 0.8883 | 0.7022254 | 0.5266488 | 0.497459 | 0.559047 | 0.976 | 0.7285 | 0.760198 | 0.74618 |
27 | Urumqi | 0.95647 | 0.904900248 | 0.74691 | 0.9753 | 0.956474 | 0.943938 | 0.701972 | 1 | 0.95647 | 0.94394 | 0.672827 | 1 |
28 | Xian | 0.79259 | 0.794542591 | 0.76402 | 0.733 | 0.4964975 | 0.4999415 | 0.553458 | 0.585522 | 0.79259 | 0.79454 | 0.764021 | 0.73304 |
29 | Xining | 0.67649 | 0.608688283 | 0.60402 | 0.6183 | 0.6764852 | 0.5877194 | 0.588659 | 0.5693241 | 0.2649 | 0.23926 | 0.244148 | 0.56395 |
30 | Yinchuan | 0.78832 | 0.744749892 | 0.7347 | 0.75 | 0.6361415 | 0.547706 | 0.522813 | 0.50217 | 0.37095 | 0.33598 | 0.272031 | 0.2703 |
31 | Zhengzhou | 0.95091 | 0.956111727 | 0.96513 | 0.8493 | 0.7003059 | 0.7250648 | 0.667886 | 0.5763073 | 0.95091 | 0.95611 | 0.965132 | 0.84935 |
No. | DMU | 2013 Gov | 2014 Gov | 2015 Gov | 2016 Gov | 2013 GDP | 2014 GDP | 2015 GDP | 2016 GDP | 2013 Birth | 2014 Birth | 2015 Birth | 2016 Birth |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Beijing | 1 | 0.96094 | 0.05671 | 0.5166 | 1 | 0.995465 | 1 | 0.99857 | 1 | 0.963767 | 0.789418 | 0.88558 |
2 | Changchun | 0.25316 | 0.46671 | 0.47856 | 0.3657 | 0.91604 | 0.920954 | 0.92166 | 0.86009 | 0.852579 | 0.82427 | 0.789224 | 0.82091 |
3 | Changsha | 0.59978 | 0.94384 | 0.93633 | 0.9537 | 0.95215 | 0.945366 | 0.94113 | 0.9562 | 0.77771 | 0.949508 | 0.943526 | 0.95765 |
4 | Chengdu | 0.24204 | 0.78878 | 0.77694 | 0.6993 | 0.84876 | 0.836664 | 0.85607 | 0.83094 | 0.789178 | 0.851511 | 0.845755 | 0.81222 |
5 | Chongqing | 0.26016 | 0.47816 | 0.22884 | 0.9113 | 0.8277 | 0.832685 | 0.83209 | 0.83495 | 0.856535 | 0.852643 | 0.827296 | 0.92465 |
6 | Fuzhou | 1 | 1 | 1 | 1 | 0.82228 | 0.841409 | 0.70848 | 0.71394 | 1 | 1 | 1 | 1 |
7 | Guangzhou | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
8 | Guiyang | 0.48762 | 0.61125 | 0.77832 | 0.814 | 0.78701 | 0.795858 | 0.79718 | 0.80797 | 0.873615 | 0.871849 | 0.846413 | 0.86444 |
9 | Harbin | 0.25863 | 0.3673 | 0.34956 | 0.3512 | 0.91395 | 0.906837 | 0.90785 | 0.82469 | 0.7861 | 0.7448 | 0.750149 | 0.78346 |
10 | Haikou | 1 | 1 | 1 | 1 | 0.97698 | 0.858653 | 0.6879 | 0.6565 | 1 | 1 | 1 | 1 |
11 | Hangzhou | 0.27926 | 0.87943 | 0.74457 | 0.9093 | 0.92885 | 0.924408 | 0.92599 | 0.94004 | 0.828756 | 0.902858 | 0.830937 | 0.92326 |
12 | Hefei | 0.43444 | 0.43593 | 0.84694 | 1 | 0.89837 | 0.900718 | 0.8875 | 0.74737 | 0.798231 | 0.775016 | 0.904677 | 1 |
13 | Huhehot | 0.67207 | 0.78388 | 0.66767 | 0.8061 | 0.94327 | 0.93238 | 0.90713 | 0.9296 | 0.854945 | 0.849102 | 0.805043 | 0.86027 |
14 | Jinan | 0.31353 | 0.52193 | 0.74403 | 1 | 0.89399 | 0.880114 | 0.85412 | 1 | 0.7962 | 0.802036 | 0.830702 | 1 |
15 | Kunming | 0.68631 | 0.84896 | 0.77153 | 0.7134 | 0.78575 | 0.787401 | 0.8024 | 0.80022 | 0.975447 | 0.884002 | 0.843185 | 0.87089 |
16 | Lanzhou | 0.62664 | 0.84152 | 0.78017 | 0.666 | 0.8091 | 0.787258 | 0.78689 | 0.79364 | 1 | 1 | 0.847304 | 0.98908 |
17 | Lhasa | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
18 | Nanchang | 0.41275 | 0.50408 | 0.59055 | 0.5141 | 0.93957 | 0.927683 | 0.91174 | 0.86397 | 0.869366 | 0.875546 | 0.846009 | 0.879 |
19 | Nanjing | 0.29595 | 0.88139 | 0.72304 | 0.8249 | 0.94845 | 0.931056 | 0.94985 | 0.96537 | 0.807256 | 0.904131 | 0.821769 | 0.87031 |
20 | Nanning | 1 | 1 | 1 | 0.5744 | 1 | 1 | 1 | 0.86983 | 1 | 1 | 1 | 1 |
21 | Shanghai | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
22 | Shenyang | 0.56643 | 0.71526 | 0.49546 | 0.8638 | 0.88155 | 0.866179 | 0.85741 | 1 | 0.767789 | 0.818579 | 0.748871 | 0.89298 |
23 | Shijiazhuang | 0.35585 | 0.37972 | 0.7867 | 0.6887 | 0.78673 | 0.777141 | 0.76574 | 0.77057 | 0.813034 | 0.821527 | 0.850482 | 0.80813 |
24 | Taiyuan | 0.73754 | 0.7174 | 0.79461 | 0.877 | 0.83036 | 0.799136 | 0.8 | 0.80587 | 0.860941 | 0.885525 | 0.854415 | 0.9013 |
25 | Tianjin | 0.0753 | 0.55627 | 0.45309 | 0.6951 | 0.92116 | 0.92089 | 0.90803 | 0.92469 | 0.814586 | 0.793887 | 0.794337 | 0.81061 |
26 | Wuhan | 1 | 0.72251 | 0.58542 | 0.8523 | 0.9771 | 0.906196 | 0.90012 | 0.9087 | 1 | 0.87223 | 0.880181 | 0.88597 |
27 | Urumqi | 1 | 1 | 0.95092 | 1 | 0.95996 | 0.94959 | 0.83197 | 0.80415 | 1 | 1 | 0.95531 | 1 |
28 | Xian | 0.25664 | 0.37726 | 0.68999 | 0.626 | 0.8534 | 0.85438 | 0.83968 | 0.82596 | 0.804754 | 0.88843 | 0.828994 | 0.84331 |
29 | Xining | 0.87413 | 0.93019 | 0.83717 | 0.6702 | 0.80358 | 0.780485 | 0.77902 | 0.78355 | 0.899443 | 0.945976 | 0.87717 | 0.89988 |
30 | Yinchuan | 0.97799 | 0.94424 | 0.89527 | 0.9838 | 0.85128 | 0.831016 | 0.82667 | 0.83332 | 0.987265 | 0.988725 | 0.913409 | 0.99467 |
31 | Zhengzhou | 0.20557 | 0.27189 | 0.4091 | 0.3529 | 0.9553 | 0.959653 | 0.9674 | 0.88423 | 0.867986 | 0.863565 | 0.891459 | 0.87214 |
No. | DMU | 2013 Respiratory Diseases | 2014 Respiratory Diseases | 2015 Respiratory Diseases | 2016 Respiratory Diseases | 2013 Mort | 2014 Mort | 2015 Mort | 2015 Mort |
---|---|---|---|---|---|---|---|---|---|
1 | Beijing | 1 | 0.9351834 | 0.7547981 | 0.8173057 | 1 | 0.960936 | 0.780715 | 0.851629 |
2 | Changchun | 0.7845439 | 0.7290381 | 0.6356186 | 0.7209566 | 0.790939 | 0.729038 | 0.635619 | 0.720957 |
3 | Changsha | 0.5985419 | 0.943837 | 0.9363347 | 0.93773943 | 0.599781 | 0.943837 | 0.936335 | 0.953731 |
4 | Chengdu | 0.353543 | 0.5248746 | 0.5263787 | 0.40725183 | 0.635481 | 0.788785 | 0.776945 | 0.699285 |
5 | Chongqing | 0.7850896 | 0.7910674 | 0.7361652 | 0.87849315 | 0.798806 | 0.791067 | 0.736165 | 0.91128 |
6 | Fuzhou | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
7 | Guangzhou | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
8 | Guiyang | 0.8285099 | 0.8276835 | 0.7783176 | 0.81401447 | 0.830862 | 0.827684 | 0.778318 | 0.814014 |
9 | Harbin | 0.6223008 | 0.4787582 | 0.5005972 | 0.61802974 | 0.62618 | 0.478758 | 0.496996 | 0.61803 |
10 | Haikou | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
11 | Hangzhou | 0.5968402 | 0.8078024 | 0.6509197 | 0.63179293 | 0.739557 | 0.879434 | 0.744569 | 0.909349 |
12 | Hefei | 0.6587714 | 0.590962 | 0.8822236 | 1 | 0.661724 | 0.590962 | 0.882224 | 1 |
13 | Huhehot | 0.7929696 | 0.7838774 | 0.6804446 | 0.80607653 | 0.795665 | 0.783877 | 0.680445 | 0.806076 |
14 | Jinan | 0.651048 | 0.6722845 | 0.7440329 | 1 | 0.655975 | 0.672285 | 0.744033 | 1 |
15 | Kunming | 0.8505386 | 0.8489619 | 0.7715309 | 0.78514154 | 0.974179 | 0.848962 | 0.771531 | 0.825943 |
16 | Lanzhou | 0.999649 | 1 | 0.7801701 | 0.98883699 | 1 | 1 | 0.78017 | 0.988837 |
17 | Lhasa | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
18 | Nanchang | 0.8216687 | 0.8343026 | 0.7774755 | 0.84036978 | 0.823165 | 0.834303 | 0.777475 | 0.84037 |
19 | Nanjing | 0.6809365 | 0.881388 | 0.7230441 | 0.70638892 | 0.686346 | 0.881388 | 0.723044 | 0.824899 |
20 | Nanning | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.987957 |
21 | Shanghai | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
22 | Shenyang | 0.5664307 | 0.7152643 | 0.4954621 | 0.56899178 | 0.524542 | 0.629656 | 0.436969 | 0.557693 |
23 | Shijiazhuang | 0.6946655 | 0.7224598 | 0.7866965 | 0.64157819 | 0.701364 | 0.72246 | 0.786697 | 0.688659 |
24 | Taiyuan | 0.8035407 | 0.8515336 | 0.7946128 | 0.87701927 | 0.807366 | 0.851534 | 0.794611 | 0.877019 |
25 | Tianjin | 0.6923424 | 0.6493323 | 0.6506331 | 0.6523241 | 0.705305 | 0.649332 | 0.650633 | 0.695142 |
26 | Wuhan | 1 | 0.8283719 | 0.8424187 | 0.84428669 | 1 | 0.803032 | 0.842419 | 0.852283 |
27 | Urumqi | 1 | 1 | 0.8202437 | 1 | 1 | 1 | 0.950923 | 1 |
28 | Xian | 0.6739938 | 0.8563826 | 0.7401074 | 0.76480002 | 0.679666 | 0.577883 | 0.740107 | 0.771797 |
29 | Xining | 0.8740992 | 0.939432 | 0.8371692 | 0.87480972 | 0.874129 | 0.939432 | 0.83714 | 0.86864 |
30 | Yinchuan | 0.9857324 | 0.9884652 | 0.8952715 | 0.99461062 | 0.986932 | 0.988465 | 0.895263 | 0.992445 |
31 | Zhengzhou | 0.8206256 | 0.8123653 | 0.8613643 | 0.61338284 | 0.769943 | 0.730502 | 0.861364 | 0.828208 |
No. | DMU | 2013 CO2 | 2014 CO2 | 2015 CO2 | 2016 CO2 | 2013 AQI | 2014 AQI | 2015 AQI | 2016 AQI |
---|---|---|---|---|---|---|---|---|---|
1 | Beijing | 1 | 0.987029 | 1 | 0.99856 | 1 | 1 | 1 | 0.911188 |
2 | Changchun | 0.93627 | 0.872525 | 0.90711 | 0.80574 | 1 | 1 | 0.51444 | 0.787896 |
3 | Changsha | 0.66249 | 0.662893 | 0.66461 | 0.65718 | 1 | 1 | 0.66639 | 1 |
4 | Chengdu | 0.7123 | 0.830107 | 0.7979 | 0.74458 | 1 | 1 | 0.72416 | 0.85539 |
5 | Chongqing | 0.70269 | 0.772805 | 0.74719 | 0.75362 | 1 | 1 | 1 | 1 |
6 | Fuzhou | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
7 | Guangzhou | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
8 | Guiyang | 0.53085 | 0.504128 | 0.73552 | 0.61108 | 1 | 1 | 0.65398 | 1 |
9 | Harbin | 0.90319 | 0.875912 | 0.88703 | 0.73003 | 0.67555 | 1 | 0.65821 | 1 |
10 | Haikou | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
11 | Hangzhou | 0.75835 | 0.744039 | 0.79825 | 0.80345 | 1 | 1 | 0.57907 | 0.722116 |
12 | Hefei | 0.87818 | 0.872797 | 0.85484 | 1 | 0.75795 | 1 | 0.4995 | 1 |
13 | Huhehot | 0.75483 | 0.384172 | 0.72424 | 0.69614 | 1 | 0.87452 | 0.41833 | 1 |
14 | Jinan | 0.55995 | 0.546146 | 0.55486 | 1 | 1 | 1 | 0.40835 | 1 |
15 | Kunming | 0.53348 | 0.535843 | 0.67327 | 0.66728 | 1 | 1 | 0.92484 | 1 |
16 | Lanzhou | 0.38517 | 0.347773 | 0.28408 | 0.31471 | 0.78338 | 0.55009 | 0.43372 | 0.471312 |
17 | Lhasa | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
18 | Nanchang | 0.9182 | 0.93236 | 0.89282 | 0.81313 | 1 | 1 | 0.68149 | 0.829057 |
19 | Nanjing | 0.61562 | 0.645276 | 0.88498 | 0.86169 | 0.9357 | 1 | 0.62126 | 0.778348 |
20 | Nanning | 1 | 1 | 1 | 0.87547 | 1 | 1 | 1 | 1 |
21 | Shanghai | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
22 | Shenyang | 0.66545 | 0.627728 | 0.63962 | 1 | 1 | 1 | 0.48321 | 1 |
23 | Shijiazhuang | 0.50009 | 0.3922 | 0.59981 | 0.38869 | 0.82022 | 1 | 0.39356 | 0.487675 |
24 | Taiyuan | 0.19052 | 0.163025 | 0.16824 | 0.20609 | 0.96204 | 0.41809 | 0.40877 | 0.41523 |
25 | Tianjin | 0.69272 | 0.703578 | 0.70019 | 0.67872 | 0.93238 | 1 | 0.5781 | 0.655972 |
26 | Wuhan | 1 | 0.749974 | 0.7602 | 0.74618 | 1 | 1 | 0.62439 | 0.497028 |
27 | Urumqi | 1 | 1 | 0.67283 | 1 | 1 | 1 | 0.33052 | 1 |
28 | Xian | 0.77568 | 0.812061 | 0.76402 | 0.73304 | 0.74055 | 1 | 0.43468 | 0.654921 |
29 | Xining | 0.26656 | 0.233276 | 0.24415 | 0.56395 | 0.74043 | 0.49459 | 0.50091 | 0.480425 |
30 | Yinchuan | 0.33957 | 0.369273 | 0.27203 | 0.2703 | 0.74327 | 0.57298 | 0.41242 | 0.438677 |
31 | Zhengzhou | 0.95579 | 0.950753 | 0.96513 | 0.84935 | 0.87642 | 1 | 0.49866 | 0.749967 |
DMU | Respiratory Disease and Mortality Efficiency Score | CO and AQI Efficiency Score | Main Policy |
---|---|---|---|
Beijing | Respiratory disease efficiency and mortality efficiency continue to decline | The efficiency score of CO2 is slightly up and down, and the minimum is about 0.99. The room for improvement is very small. AQI is 1 in the first three years, but it declines slightly in the last year. AQI efficiency is slightly lower than CO2 in the last year. | There is small room for improvement in efficiency of carbon emissions and air pollution. Air pollution is more concerned in Beijing. |
Changchun | Respiratory disease efficiency and mortality efficiency decline slightly | CO2 efficiency score decreases, and drops to around 0.81 in the last year. The AQI efficiency score drops from 1 in the previous two years to 0.51 in 2015. Then rises to 0.79, the room for improvement in the last two years is greater than CO2. | Air pollution treatment should be more important than carbon emissions. |
Changsha | Respiratory disease efficiency and mortality efficiency have risen dramatically. | Carbon dioxide efficiency has not changed much, staying around 0.66. AQI efficiency score is better than CO2, only the efficiency score in 2015 is about 0.67, and other years are 1. | Carbon emission treatment should be prior to air pollutants. |
Chengdu | The increase in respiratory disease efficiency and mortality efficiency is small. | The efficiency score of carbon dioxide increases slightly, around 0.75. The AQI efficiency score drops from 1 in the previous two years to 0.72 in 2015 and 0.86 in 2016. AQI efficiency score is slightly better than CO2. | Should pay attention to carbon dioxide emissions and air pollutant emissions |
Chongqing | Respiratory disease efficiency and mortality efficiency have risen, room for improvement is shrinking, and room for improvement in mortality efficiency is shrinking more. | The efficiency score of CO2 increases slightly, around 0.75. AQI efficiency score is 1 and the room for improvement is 0. | Priority carbon dioxide emission control |
Fuzhou | The room for improvement is 0, indicating that these two indicators are more efficient than other cities. | The room for improvement of both indicators is 0. | |
Guangzhou | The room for improvement is 0, indicating that these two indicators are more efficient than other cities. | The room for improvement of both indicators is 0. | |
Guiyang | Respiratory disease efficiency and mortality efficiency do not change much, indicating that the improvement is not obvious. | The efficiency score of CO2 is rising but the room for improvement is large. In 2016, the efficiency score is only about 0.61. AQI efficiency score is better than CO2, only the efficiency score in 2015 is 0.65, and other years are 1 | Emissions management of carbon dioxide should be prior to air pollutants. |
Harbin | Fluctuation in the efficiency of respiratory disease efficiency and mortality efficiency, declines in 2014 and 2015, but returns to 2013 levels in 2016 | CO2 efficiency score decreases, and it is 0.73 in 2016. The AQI efficiency score fluctuate greatly, but it has reached 1. in 2016. The situation is better than CO2. | Emissions management of carbon dioxide should be prior to air pollutants. |
Haikou | The room for improvement is 0, indicating that these two indicators are more efficient than other cities. | The room for improvement of both indicators is 0. | |
Hangzhou | Mortality efficiency improves significantly, and the room for improvement is reduced; respiratory disease improvement is not obvious, only a small increase. | The efficiency score of carbon dioxide fluctuates, but the efficiency score is 0.8 in 2016; AQI is 1 in the previous two years, but the decline in the last two years is larger, and the efficiency score in 2016 is 0.72. | Focus on carbon emissions and air pollution control. The efficiency of carbon emission improvement is not obvious, the room for improvement of air pollution is declining, and treatment should be strengthened |
Hefei | Both efficiency scores fluctuate and rise to 1. | CO2 efficiency rises to 1 in 2016; AQI efficiency score increases, reaching 1 in 2016 | Should pay attention to the management of carbon emissions and air pollution |
Huhehot | Respiratory disease efficiency and mortality efficiency increase, but not much. | The CO2 efficiency is maintained at around 0.7, and there is room for improvement; AQI has some fluctuations, only 0.4 in 2015, but reached 1 in 2016. | Focus on carbon emissions and air pollution control. The efficiency improvement of carbon emissions is not obvious, and the efficiency of air pollution is not stable and should be paid attention to |
Jinan | Both indicators have risen to 1 in 2016 and the room for improvement is 0. | The efficiency score of CO2 reaches 1 in the last year, but it is only about 0.56 in the first three years; AQI is only about 0.48 in 2015, and it has reached 1 in 2016. | Strengthen the treatment of CO2 emissions and air pollutant emissions |
Kunming | The efficiency of respiratory diseases continues to decline, the efficiency score of mortality has fluctuated, and the mortality rate reached the highest in 2013. | The AQI efficiency score is significantly better than the efficiency score of carbon dioxide emissions. | Prioritize the control of CO2 emissions, but do not overlook the monitoring of air pollutant emissions. |
Lanzhou | Respiratory disease and mortality efficiency have declined from the previous two years, but rose to above 0.9 in 2016. | The carbon dioxide emission efficiency score is lower than the AQI efficiency score for four years. There is room for improvement; AQI efficiency has dropped even more. | Focus on CO2 emissions and AQI |
Lhasa | The room for improvement is 0, indicating that these two indicators are more efficient than other cities. | The room for improvement of both indicators is 0. | |
Nanchang | The efficiency scores of the two indicators rises slightly. | The efficiency score of CO2 decreases slightly, and the AQI efficiency score decreases from 1 in the previous two years to around 0.62 in 2015 and rises to around 0.83 in 2016. | Focus on CO2 emissions and AQI |
Nanjing | Respiratory disease efficiency increases slightly, mortality efficiency is greatly increased and the room for improvement is shrinking. | The CO2 efficiency score increases, and it is around 0.86 in 2016. The AQI efficiency score rises first and then falls, and it is only about 0.78 in 2016. | Focus on CO2 emissions and AQI |
Nanning | The room for improvement in the efficiency of respiratory diseases is zero, and the room for improvement in mortality has been zero for the first three years, with a slight decline in the last year. | AQI efficiency improvement room is 0, but CO2 efficiency is 1 in three years, but falls to 0.88 in the last year. | Carbon dioxide emission control is prior and maintain control over air pollution emissions. |
Shanghai | The room for improvement is 0, indicating that these two indicators are more efficient than other cities. | The room for improvement of both indicators is 0. | |
Shenyang | Both indicators are slightly reduced, and the room for improvement is slightly expanded. | The emission efficiency of carbon dioxide has improved significantly, reaching 1 in 2016; AQI is only low in 2015, only 0.4, and the other three years are 1. | Focus on the control of carbon dioxide emissions and air pollution emissions |
Shijiazhuang | Both indicators have declined, and the room for improvement has expanded slightly. | The CO2 efficiency score continues to decline, only about 0.39 in 2016. There is still room for improvement; AQI has also experiences a significant and sustained decline. By 2016, it is only about 0.42, and there is also room for improvement. | Strengthen the management of CO2 and air pollutant emissions, take comprehensive measures to reduce carbon emissions, and control pollutant emissions; |
Taiyuan | Two efficiency scores rise slightly. | The carbon dioxide efficiency score has been below 0.2, and the efficiency of AQI has decreased. In 2016, it is only about 0.41. | Focus on the treatment of carbon dioxide emissions, but the emissions of air pollutants are also serious |
Tianjin | Both efficiency drops. | The efficiency score of CO2 has not changed much, and it stays around 0.7, and it is only 0.67 in 2016; AQI declines, and it is only 0.66 in 2016. | Strengthen the management of carbon dioxide emissions and emissions of air pollution |
Wuhan | Both indicators have fallen from 1 in 2013 and then increases slightly, but there is still room for improvement. | The efficiency score of CO2 decreases, and it is only about 0.74 in 2016, AQI efficiency score drops, and it is only around 0.5 in 2016. | Air pollution emission is prior to control CO2 emissions. |
Urumqi | Respiratory efficiency and mortality efficiency have only room for improvement in 2015, and the room for improvement of other years is 0. | Both the CO2 efficiency score and the AQI efficiency score declines in 2015, 1 in other years, but AQI is only 0.33 in 2015. | Focus on carbon dioxide emissions and emissions of air pollutants |
Xian | Both indicators have risen slightly | The change in carbon dioxide emissions is not obvious, and it remains at around 0.75; AQI is fluctuating, only about 0.65 in 2016. | Air pollutant emissions treatment first but should be integrated with carbon dioxide emissions. |
Xining | Both indicators have risen, but the score for the last year of respiratory disease efficiency is comparable to 2013. The mortality efficiency has increased greatly. | The carbon dioxide efficiency score increases and reaches the highest level in 2016, but only about 0.56, AQI fluctuates and falls to around 0.48 in 2016. | Air pollutant emissions treatment first but should be integrated with carbon dioxide emissions. |
Yinchuan | The efficiency of respiratory diseases increases slightly, while the mortality rate decreases slightly, and the mortality efficiency score is close to 1. | Carbon dioxide is falling, and the highest efficiency score is only about 0.37. By 2016, only 0.27. AQI efficiency score is slightly higher than CO2, but declines. By 2016, it is only about 0.44, and there is still room for improvement. | Air pollutant emissions treatment first but should be integrated with carbon dioxide emissions. |
Zhengzhou | Respiratory efficiency decreases to 0.61 in 2016 and mortality efficiency score increases. | CO2 efficiency scores are better than AQI, but decline; AQI has fluctuated, and only 0.75 in 2016 | Air pollutant emissions treatment first but should be integrated with carbon dioxide emissions. |
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Li, Y.; Chiu, Y.-h.; Lin, T.-Y. The Impact of Economic Growth and Air Pollution on Public Health in 31 Chinese Cities. Int. J. Environ. Res. Public Health 2019, 16, 393. https://doi.org/10.3390/ijerph16030393
Li Y, Chiu Y-h, Lin T-Y. The Impact of Economic Growth and Air Pollution on Public Health in 31 Chinese Cities. International Journal of Environmental Research and Public Health. 2019; 16(3):393. https://doi.org/10.3390/ijerph16030393
Chicago/Turabian StyleLi, Ying, Yung-ho Chiu, and Tai-Yu Lin. 2019. "The Impact of Economic Growth and Air Pollution on Public Health in 31 Chinese Cities" International Journal of Environmental Research and Public Health 16, no. 3: 393. https://doi.org/10.3390/ijerph16030393