Evaluation of the Policy Effect of China’s Environmental Interview System for Effective Air Quality Governance
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data Sources
2.2. Methodology
2.2.1. Dynamic Panel Model
2.2.2. Regression Discontinuity Model
3. Empirical Results and Analysis
3.1. Results of the Least Squares Dummy Variable Analysis
- (1)
- For the two dummy variables of rainfall and snowfall, the LSDV regression results show that rainfall significantly improved air quality more than snowfall. In terms of the effect of rainfall, the concentrations of pollutants such as AQI, PM2.5, PM10, SO2, NO2, and O3 were observed to be significantly negative, whereas CO was not. In terms of the effect of snowfall on the concentrations of pollutants, only SO2 was observed to be significantly negative while the remaining pollutants were negative too but not significantly so because the solubility of rainwater is better than that of snow; PM2.5, PM10, SO2, NO2, and CO dissolve in rainwater easily compared to snow and, therefore, the increase in precipitation frequency and rainfall results in increased concentrations of pollutants being dissolved, thereby reducing the concentration of pollutants in the air. Interestingly, SO2 was significantly lower during snowy weather because people will avoid using private cars and taxis to ensure travel safety resulting in lower vehicular exhaust emissions than usual.
- (2)
- Maximum temperature was positively and significantly correlated with values of AQI, PM2.5, PM10, NO2, CO, and O3 values, and it was positive but not significant for SO2. This means that the higher the maximum temperature, the greater the concentration of pollutants (AQI, PM2.5, PM10, SO2, NO2, CO, and O3), and the higher the degree of pollution. Elevation can improve the efficiency of pollutant decomposition and conversion, thereby reducing the concentrations of pollutants. However, after an in-depth analysis, we found that the highest temperature of the day was often accompanied by sunlight, temperature, and time; generally, the highest temperature occurs in the middle of the day at about 14:00 h. Under these circumstances, the lighting conditions were better, and SO2 and NO2 encounter light and heat to undergo chemical reactions, which result in the formation of gaseous multi-oxides. Correspondingly, the concentration of pollutants in the air will not be reduced. In addition, as daytime is the most active time for humans, this is the time when industrial, residential, and automobile, emissions are at their peak. At the highest temperature, sufficient light accelerates the generation of pollutants and their derivatives, whereas from morning to noon, accumulation is at its peak. The higher the maximum temperature, the more the amount of SO2 and NO2 pollutants released into the air, increasing air pollution.
- (3)
- Maximum wind speed has a significantly negative correlation with all the pollutants, which means that the higher the wind speed, the lower the concentration of pollutants in the air. This is because, for a city (district, county), under certain conditions of pollutant concentration, the atmospheric diffusion capacity is related to the concentration of pollutants in the air—i.e., the greater the wind speed—the more the diffusion and transportation of pollutants.
3.2. Determination of Discontinuity Location
3.3. Analysis of Short-Term Effects
3.4. Analysis of Long-Term Effects
4. Robustness Tests
4.1. Control Group Analysis
4.2. Sensitivity Analysis of Bandwidth
4.3. Validation of Air Quality
5. Discussion
- (1)
- The environmental interview system is too complex to quantify every aspect and throw into a regression equation. Other possible explanations for the observed changes should be considered, explored, and addressed in the future.
- (2)
- The sample can be subdivided to accommodate multiple perspectives to carefully consider the factors influencing policy transmission; more angles can be used for comparative analysis.
- (3)
- The choice of cities in the control group is crucial. It needs to be comprehensively considered from the aspects of population statistics, political system, political factors, and dependent variables. More control variables can be added, and the processing of variables can be more quantitative and refined to obtain assessment results with higher accuracy [47].
6. Conclusions
- (1)
- The regression discontinuity (RD) method, for both the whole sample and subsample, shows that the implementation of the environmental protection interview system improved air quality. Concentrations PM2.5 and PM10 were significantly reduced and were consistent in terms of environmental protection performance.
- (2)
- RD analysis of the long-term sustained effect of the interview system conducted using weekly average data showed air quality improvement and reduction in the concentrations of PM2.5 and PM10. However, the effect was not significant.
- (3)
- By removing likely ranges in which data falsification can occur the control group was analyzed using bandwidth sensitivity test, and the validation RD analysis proved that the interview system can improve air quality by ruling out data falsification.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Experimental Group | Control Group | (a) | (b) | |
---|---|---|---|---|
1 | Shenyang | Fushun | Yes | Yes |
2 | Kunming | Yuxi | No | No |
3 | Changchun | Jilin | Yes | Yes |
4 | Zhumadian | Luohe | No | No |
5 | Baoding | Shijiazhuang | Yes | No |
6 | Wuxi | Changzhou | No | No |
7 | Ziyang | Neijiang | No | No |
8 | Zhangye | Jinchang | Yes | No |
9 | Siping | Changchun | Yes | No |
10 | Jingdezhen | Nanchang | No | No |
11 | Zibo | Ji’nan | Yes | No |
12 | Anyang | Hebi | Yes | Yes |
13 | Harbin | Daqing | Yes | Yes |
14 | Cangzhou | Tianjin | Yes | Yes |
15 | Linyi | Rizhao | Yes | Yes |
16 | Chengde | Langfang | Yes | Yes |
17 | Lvliang | Taiyuan | Yes | No |
18 | Ma’anshan | Wuhu | No | No |
19 | Xingtai | Shijiazhuang | Yes | No |
20 | Zhengzhou | Shijiazhuang | Yes | No |
21 | Nanyang | Pingdingshan | No | No |
22 | Baise | Hechi | No | No |
23 | Haixi | Hainan | Yes | Yes |
24 | Dezhou | Liaocheng | Yes | Yes |
25 | Jining | Tai’an | Yes | No |
26 | Shangqiu | Zhoukou | Yes | No |
27 | Anqing | Chizhou | No | No |
28 | Changzhou | Jincheng | Yes | No |
29 | Xianyang | Xi’an | Yes | No |
30 | Yangquan | Taiyuan | Yes | Yes |
31 | Weinan | Xi’an | Yes | Yes |
32 | Linfen | Yuncheng | Yes | Yes |
33 | Beijing | Langfang | Yes | No |
34 | Tianjin | Langfang | Yes | No |
35 | Shijiazhuang | Xingtai | Yes | No |
36 | Tangshan | Tianjin | Yes | No |
37 | Handan | Xingtai | Yes | No |
38 | Hengshui | Cangzhou | Yes | No |
39 | Yuncheng | Jincheng | Yes | No |
40 | Tianjin② | Langfang | Yes | No |
41 | Handan ② | Langfang | Yes | No |
42 | Baoding ② | Shijiazhuang | Yes | No |
43 | Xinxiang | Jiaozuo | Yes | No |
44 | Harbin ② | Suihua | Yes | Yes |
45 | Jiamusi | Qitaihe | Yes | Yes |
46 | Shuangyashan | Jixi | Yes | Yes |
47 | Hegang | Yichun | Yes | Yes |
48 | Linfen ② | Yuncheng | Yes | No |
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Variable Symbol | Unit | Meaning | |
---|---|---|---|
Dependent variables | AQI | Index | It is the air quality index while considering GB3095-2012 * ambient air quality standard (current), as the reference standard, and the concentration of pollutants (SO2, NO2, PM10, PM2.5, O3, and CO); their corresponding indices are published once every hour. |
PM2.5 | μg/m3 | It is the fine particulate matter, aerodynamically less than or equal to 2.5 microns in diameter; it is divided into natural sources (such as dust) and anthropogenic sources (such as primary and secondary particulate matter). | |
PM10 | μg/m3 | It is a respirable particulate matter, with an aerodynamic diameter of less than or equal to 10 microns. | |
SO2 | μg/m3 | The concentration of sulfur dioxide. | |
NO2 | μg/m3 | The concentration of nitrogen dioxide. | |
CO | mg/m3 | The concentration of carbon monoxide. | |
O3 | μg/m3 | The concentration of ozone. | |
Independent variables | T | Dummy variable | It is also known as the driver variable, which is 0 before the interview and 1 after the interview; it indicates the policy treatment effect in RD. |
d | Days or weeks | It indicates the number of days or weeks from the date of the interview; negative values imply pre-interview; other values imply post-interview. | |
f(d) | It is a polynomial function with “d” as the independent variable, i.e., air quality improvement resulting from the gradual advancement of pollution prevention efforts. | ||
Control variables | I | Dummy variable | It is 1 if there is public heating; otherwise, it is 0. |
J | Dummy variable | It is 1 if the sample is in the heating period; otherwise, it is 0. | |
K | Dummy variable | It is 1 if the day is a working day; otherwise, it is 0. | |
YU | Dummy variable | It is 1 if it has rained on that day but not yet snowed; 0 if it has not yet rained. | |
XUE | Dummy variable | It is 1 if it has snowed on that day; otherwise, it is 0. | |
MAXT | °C | The highest temperature of the day. | |
MINT | °C | The lowest temperature of the day. | |
MAXW | Level | Maximum wind strength for the day. |
The Dependent Variable | AQI | PM2.5 | PM10 | SO2 | NO2 | CO | O3 |
---|---|---|---|---|---|---|---|
T | −3.49 ** | −3.38 ** | −4.308 ** | −2.038 ** | −0.948 ** | −0.06 *** | 1.73 *** |
YU | −6.93 *** | −3.03 * | −9.85 *** | −3.10 *** | −1.75 *** | −0.01 | −8.95 *** |
XUE | −3.44 | −5.38 | −5.47 | −8.72 *** | −1.51 | −0.04 | −1.25 |
MAXT | 1.31 *** | 0.66 ** | 2.16 *** | 0.31 | 0.43 *** | 0.01 ** | 0.84 *** |
MINT | −1.00 *** | −0.51 * | −1.70 *** | −0.59 *** | −0.58 *** | −0.01 *** | −0.01 |
MAXW | −7.41 *** | −9.86 *** | −6.64 *** | −6.86 *** | −4.96 *** | −0.13 *** | 1.93 *** |
Lag of first order | 0.58 *** | 0.61 *** | 0.54 *** | 0.70 *** | 0.56 *** | 0.57 *** | 0.61 *** |
Constant term | 55.08 *** | 54.24 *** | 53.20 *** | 36.24 *** | 31.35 *** | 0.98 *** | 4.26 * |
(1) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
(2) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
(3) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Sample size | 5712 | 5712 | 5712 | 5712 | 5712 | 5712 | 5712 |
Goodness of fit | 0.4866 | 0.5053 | 0.4581 | 0.7877 | 0.622 | 0.6122 | 0.7223 |
Region | T | Significant or Not | Region | T | Significant or Not | Region | T | Significant or Not |
---|---|---|---|---|---|---|---|---|
Shenyang | 0 | Yes | Lvliang | 1 | Yes | Beijing | 6 | No |
Kunming | 3 | Yes | Ma’anshan | 7 | Yes | Tianjin | 3 | Yes |
Changchun | 2 | Yes | Xingtai | 1 | No | Shijiazhuang | 8 | Yes |
Zhumadian | 5 | No | Zhengzhou | 0 | Yes | Tangshan | 6 | No |
Baoding | 1 | No # | Nanyang | 7 | Yes | Handan | 8 | Yes |
Wuxi | 6 | Yes | Baise | 0 | Yes | Hengshui | 8 | Yes |
Ziyang | 1 | Yes | Haixi | 3 | Yes | Yuncheng | 0 | Yes |
Zhangye | 7 | Yes | Dezhou | 1 | No | Tianjin ② | 1 | No |
Siping | 5 | Yes | Jining | 10 | Yes | Handan ② | 5 | Yes |
Jingdezhen | 8 | Yes | Shangqiu | 2 | No | Baoding ② | 1 | Yes |
Zibo | 2 | Yes | Anqing | 3 | Yes | Xinxiang | 5 | Yes |
Anyang | 6 | Yes | Changzhou | 2 | Yes | Harbin ② | 7 | No |
Harbin | 0 | No | Xianyang | 3 | No | Jimujia | 0 | Yes |
Cangzhou | 0 | No # | Yangquan | 0 | Yes | Shuangyashan | 0 | Yes |
Linyi | 2 | No # | Weinan | 3 | No | Hegang | 10 | No |
Chengde | 0 | Yes | Linfen | 0 | Yes | Linfen ② | 0 | Yes |
Dependent Variable | AQI | PM2.5 | PM10 | SO2 | NO2 | CO | O3 |
---|---|---|---|---|---|---|---|
First order | −46.46 *** | −42.51 *** | −52.12 *** | −7.40 | −8.33 ** | −0.35 | −0.82 |
8.8291 | 8.3258 | 10.5334 | 5.9949 | 2.8363 | 0.2237 | 3.9625 | |
Second order | −48.53 *** | −44.06 ** | −54.91 ** | −5.23 | −9.82 * | −0.20 | −0.06 |
14.9662 | 14.2437 | 17.7622 | 9.2229 | 4.7775 | 0.5901 | 6.1667 | |
(1) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
(2) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
(3) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Sample size (N) | 2688 | 2688 | 2688 | 2688 | 2688 | 2688 | 2688 |
The Dependent Variable | AQI | PM2.5 | PM10 | SO2 | NO2 | CO | O3 |
---|---|---|---|---|---|---|---|
Excluding airborne causes | −45.45 *** | −37.08 *** | −54.91 *** | −18.08 ** | −10.54 ** | −0.54 | −9.43 |
N = 616 | 11.7606 | 10.6945 | 13.1813 | 6.4863 | 3.5994 | 0.3463 | 5.3983 |
Includes airborne causes | −43.58 *** | −41.06 *** | −47.55 *** | −5.20 | −7.17 * | −0.22 | 1.04 |
N = 2072 | 10.0222 | 9.2230 | 12.5126 | 7.0135 | 3.2058 | 0.2567 | 4.0487 |
(1) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
(2) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
(3) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Time trend items | 1st order | 1st order | 1st order | 1st order | 1st order | 1st order | 1st order |
The Dependent Variable | AQI | PM2.5 | PM10 | SO2 | NO2 | CO | O3 |
---|---|---|---|---|---|---|---|
Heating cities | −48.98 *** | −44.61 *** | −55.02 *** | −9.72 | −9.31 ** | −0.42 | 1.82 |
N = 2184 | 10.1897 | 9.3674 | 12.6496 | 7.0766 | 3.1568 | 0.2665 | 4.0645 |
Non-heated cities | −36.12 *** | −30.78 *** | −41.47 *** | −2.28 | −6.77 * | −0.03 | −14.71 * |
N = 504 | 6.7566 | 6.1264 | 10.6002 | 2.9051 | 3.5847 | 0.1861 | 6.1101 |
(2) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
(3) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Time trend items | First order | First order | First order | First order | First order | First order | First order |
The Dependent Variable | AQI | PM2.5 | PM10 | SO2 | NO2 | CO | O3 |
---|---|---|---|---|---|---|---|
Heating | −79.76 *** | −78.47 *** | −90.95 *** | −21.39 | −18.05 ** | −1.02 * | 6.12 |
N = 896 | 21.3759 | 21.3534 | 25.2649 | 17.6944 | 5.8145 | 0.4556 | 4.4694 |
Non-heated | −28.99 *** | −25.13 *** | −31.56 *** | 1.77 | −4.00 | −0.04 | −3.89 |
N = 1792 | 5.1775 | 4.8046 | 7.0471 | 2.3667 | 2.4217 | 0.1602 | 4.1979 |
(1) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
(3) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Time trend items | 1st order | 1st order | 1st order | 1st order | 1st order | 1st order | 1st order |
Dependent Variable | AQI | PM2.5 | PM10 | SO2 | NO2 | CO | O3 |
---|---|---|---|---|---|---|---|
First order | 9.9954 | 8.4567 | 7.6231 | −1.4474 | 1.9527 | 0.0732 | 2.6421 |
5.2740 | 5.1546 | 6.3249 | 5.9426 | 2.1686 | 0.1578 | 3.4661 | |
Second order | 9.9568 | 8.7332 | 9.6521 | −1.8060 | 2.1293 | 0.1427 | 1.4919 |
7.6928 | 7.9641 | 9.1196 | 9.1845 | 3.3072 | 0.4084 | 5.2213 | |
(1) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
(2) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
(3) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Sample size (N) | 17,472 | 17,472 | 17,472 | 17,472 | 17,472 | 17,472 | 17,472 |
Dependent Variable | AQI | PM2.5 | PM10 | SO2 | NO2 | CO | O3 |
---|---|---|---|---|---|---|---|
First order | −3.4414 | 0.8399 | −1.3651 | 1.5155 | 3.9088 | 0.1210 | −1.7716 |
7.0512 | 6.2666 | 8.3059 | 4.1755 | 2.9094 | 0.1933 | 4.7489 | |
Second order | −13.4233 | −5.1951 | −7.3566 | 2.6986 | 3.0253 | 0.4285 | −4.9183 |
10.9033 | 9.4684 | 12.7148 | 6.3938 | 4.2867 | 0.3885 | 7.2495 | |
(1) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
(2) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
(3) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Sample size (N) | 2688 | 2688 | 2688 | 2688 | 2688 | 2688 | 2688 |
Dependent Variable | 2 Weeks before and after the Interview | 4 Weeks before and after the Interview | 6 Weeks before and after the Interview | 8 Weeks before and after the Interview |
---|---|---|---|---|
AQI | −48.58505 *** | −47.10203 *** | −48.6484 *** | −48.46691 *** |
PM2.5 | −44.81683 *** | −43.13503 *** | −45.12565 *** | −44.98694 *** |
PM10 | −53.16457 *** | −52.46213 *** | −53.90612 *** | −53.68375 *** |
SO2 | −53.16457 *** | −7.226675 | −6.67132 | −6.681053 |
NO2 | −8.765467 ** | −8.307712 ** | −8.775167 ** | −8.73947 ** |
CO | −0.35734 | −0.362782 | −0.355508 | −0.353217 |
O3 | −1.226568 | −1.387607 | −1.043695 | −1.079751 |
Sample size (N) | 1344 | 2688 | 4032 | 5376 |
Dependent Variable | Excluding (95–100) | Excluding (90–100) | Excluding (80–100) | |
---|---|---|---|---|
AQI | First order | −50.14 *** | −52.21 *** | −61.39 *** |
9.5744 | 10.0546 | 12.1070 | ||
Second order | −53.29 *** | −54.48 *** | −67.26 ** | |
16.2056 | 16.9812 | 21.1922 | ||
PM2.5 | First order | −45.98 *** | −47.45 *** | −54.59 *** |
9.0523 | 9.4447 | 11.0715 | ||
Second order | −49.47 *** | −50.37 ** | −61.05 ** | |
15.4643 | 16.0838 | 19.5383 | ||
PM10 | First order | −56.32 *** | −58.82 *** | −70.26 *** |
11.5673 | 12.1509 | 14.4111 | ||
Second order | −61.23 ** | −62.25 ** | −79.07 ** | |
19.5845 | 20.5753 | 25.1163 | ||
Sample size (N) | 2566 | 2422 | 2158 |
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Jin, X.; Sumaila, U.R.; Yin, K.; Qi, Z. Evaluation of the Policy Effect of China’s Environmental Interview System for Effective Air Quality Governance. Int. J. Environ. Res. Public Health 2021, 18, 9006. https://doi.org/10.3390/ijerph18179006
Jin X, Sumaila UR, Yin K, Qi Z. Evaluation of the Policy Effect of China’s Environmental Interview System for Effective Air Quality Governance. International Journal of Environmental Research and Public Health. 2021; 18(17):9006. https://doi.org/10.3390/ijerph18179006
Chicago/Turabian StyleJin, Xue, Ussif Rashid Sumaila, Kedong Yin, and Zhichao Qi. 2021. "Evaluation of the Policy Effect of China’s Environmental Interview System for Effective Air Quality Governance" International Journal of Environmental Research and Public Health 18, no. 17: 9006. https://doi.org/10.3390/ijerph18179006