Effects of Land Urbanization on Smog Pollution in China: Estimation of Spatial Autoregressive Panel Data Models
Abstract
:1. Introduction
2. Materials and Methods
2.1. Spatial Weights Matrix
2.2. Moran’s I Statistic
2.3. SAR Panel Data Model
2.4. Variable Definitions and Data Description
- (1)
- Dependent variable: Smog pollution is the dependent variable in the proposed model. We selected the PM2.5 concentration, which is the main source of smog pollution and that which most concerns the Chinese public, as the measurement index. The original data come from the American Atmospheric Composition Analysis Group. The data type is gridded datasets (resolution is 0.01° × 0.01°) [45]. We used ArcGIS to analyze the original data packet and match them with the maps of China’s provinces to obtain the average PM2.5 concentration. Finally, we obtained the panel datasets of 31 province-level administrative regions in China from 2000 to 2017. The advantage of this analysis is the comprehensive integration of satellite monitoring data and ground monitoring data, which can accurately and objectively reflect the true situation of China’s smog pollution.
- (2)
- Independent variable: Land urbanization is the independent variable that this article focuses on. With reference to Liu and Wang’s approach [46,47], we used land urbanization rate in the measurement, and the specific calculation formula is as follows:
- (3)
- Control variable: To improve the accuracy of the model estimation results, we selected some relevant factors that may affect smog pollution as control variables and put them in the model. First, considering the traditional stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, we controlled the impact of population, wealth, and technology on smog pollution [48,49]. Second, we measured the demographic factor by the population per km2, the wealth factor by per capita GDP, and the technical factor by the number of patent application authorizations. Third, informed by other studies, we considered the impact of industrial structure, education level, and degree of openness on smog pollution [50,51,52]. We model industrial structure by the proportion of the added value of tertiary industry to GDP, the education level by the number of college students per 10,000 people, and the degree of openness by the proportion of total import and export to GDP.
3. Results
3.1. Spatiotemporal Distribution of Land Urbanization and Smog Pollution
3.2. Spatial Autocorrelation of Smog Pollution
3.3. Regression Results
- (1)
- The estimation results in Columns 1–4 show that under different constraint conditions, the estimated coefficient of W * PM2.5 is significantly positive, thereby indicating that China’s smog pollution has a significant positive spatial correlation. This result is consistent with Moran’s I test result. The estimated coefficient value shows that an increase of 1 μg/m3 in the PM2.5 concentration in the neighborhood increases the local PM2.5 concentration by more than 0.7 μg/m3.
- (2)
- The estimation results in Columns 2–4 show that without the addition of control variables, the estimated coefficient of the first-order term of landurban is significantly positive. By contrast, the estimated coefficient of the quadratic term is significantly negative. After gradually adding the control variables, the estimated coefficient of the first-order term of landurban is still significantly positive. The estimated coefficient of the quadratic term is still significantly negative. These results indicate that land urbanization has a nonlinear impact on smog pollution. Specifically, land urbanization and smog pollution have an inverted U-shaped relationship, that is, with the increase in land urbanization rate, the level of smog pollution shows a trend of first rising and then falling.
- (3)
- The estimated results in Columns 3–4 also show the impact of each control variable on smog pollution. The estimated coefficients of lnpop, lnpgdp, third, and lnedu are all significantly negative, thereby indicating that the improvement of regional economic level, the agglomeration effect brought about by the increase in population size, the improvements of industrial structure and education level significantly reduce the concentration of PM2.5 in the region. The estimated coefficients of lnrd and open fail to pass the significance test. As far as the data used in this study are concerned, the evidence to prove that the level of technology and openness of the region have a statistically significant impact on smog pollution is insufficient.
3.4. Robustness Test
3.4.1. Change the Regression Method
3.4.2. Change the Spatial Weight Matrix
4. Discussion
4.1. The Inverted U-Shaped Relationship between Land Urbanization and Smog Pollution
4.2. The Current Stage of China
4.3. The Positive Spatial Correlation of Smog Pollution in China
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
OLS | PSEM | PSAC | PSDM | |
---|---|---|---|---|
landurban | 5.4874 *** | 8.3873 *** | 9.0453 *** | 1.7832 *** |
(0.8481) | (1.0444) | (1.0890) | (0.6627) | |
landurban2 | −0.1955 *** | −0.3972 *** | −0.4159 *** | −0.0497 * |
(0.0374) | (0.0510) | (0.0519) | (0.0289) | |
lnpop | −25.3098 *** | 1.8561 ** | 0.7898 | −18.1424 *** |
(5.2241) | (0.8062) | (0.8947) | (4.7883) | |
lnpgdp | −8.9677 *** | −4.0329 ** | −5.9698 *** | −3.8970 *** |
(1.9668) | (2.0239) | (2.1397) | (1.3824) | |
lnrd | −0.6272 | 0.1825 | 0.3155 | 0.8311 |
(0.6693) | (0.4837) | (0.4833) | (0.5145) | |
third | −0.1878 *** | −0.9008 *** | −1.0591 *** | −0.1824 *** |
(0.0678) | (0.0723) | (0.0760) | (0.0486) | |
lnedu | −4.6847 *** | −0.8077 | −0.1635 | −3.6917 *** |
(1.6107) | (1.6918) | (1.6795) | (1.2361) | |
open | 0.0071 | −0.0234 | −0.0167 | −0.0102 |
(0.0186) | (0.0228) | (0.0229) | (0.0132) | |
Ind fixed | Yes | Yes | Yes | Yes |
Time fixed | Yes | Yes | Yes | Yes |
N | 558 | 558 | 558 | 558 |
R2 | 0.5772 | 0.2605 | 0.1466 | 0.1498 |
Distance | Economic | |
---|---|---|
landurban | 3.4904 *** | 5.9174 *** |
(0.6671) | (0.8228) | |
landurban2 | −0.1226 *** | −0.2131 *** |
(0.0292) | (0.0362) | |
lnpop | −18.8221 *** | −25.4375 *** |
(4.0371) | (4.9237) | |
lnpgdp | −5.7109 *** | −9.1912 *** |
(1.5281) | (1.8563) | |
lnrd | −0.1388 | −0.7187 |
(0.5149) | (0.6321) | |
third | −0.1589 *** | −0.1926 *** |
(0.0520) | (0.0639) | |
lnedu | −3.1253 ** | −5.0727 *** |
(1.2413) | (1.5281) | |
open | −0.0023 | 0.0100 |
(0.0143) | (0.0175) | |
Ind fixed | Yes | Yes |
Time fixed | Yes | Yes |
N | 558 | 558 |
R2 | 0.1771 | 0.1821 |
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Types | Variables | Symbol |
---|---|---|
Dependent variable | PM2.5 concentration (μg/m3) | PM2.5 |
Independent variable | Land urbanization rate (%) | landurban |
Square of land urbanization rate | landurban2 | |
Control variable | Population per km2 (logarithm) | lnpop |
Per capita gross domestic product (logarithm) | lnpgdp | |
Number of patent application authorization (logarithm) | lnrd | |
Proportion of the added value of tertiary industry to GDP (%) | third | |
Number of college students per 10,000 people (logarithm) | lnedu | |
Proportion of total imports and exports to GDP (%) | open |
Variable | Obs | Mean | SD | Min | Max | VIF |
---|---|---|---|---|---|---|
PM2.5 | 558 | 38.06 | 16.30 | 4.730 | 84.50 | — |
landurban | 558 | 1.547 | 2.770 | 0.00562 | 15.75 | 4.16 |
lnpop | 558 | 5.266 | 1.477 | 0.742 | 8.249 | 4.23 |
lnpgdp | 558 | 10.01 | 0.841 | 7.887 | 11.77 | 7.79 |
lnrd | 558 | 8.589 | 1.837 | 1.946 | 12.72 | 5.07 |
third | 558 | 41.66 | 8.546 | 28.60 | 80.56 | 2.05 |
lnedu | 558 | 4.835 | 0.589 | 3.055 | 5.876 | 4.10 |
open | 558 | 30.47 | 38.15 | 1.688 | 172.2 | 2.55 |
Year | Global Moran’s I | Z-Statistic | p-Value | Sig |
---|---|---|---|---|
2000 | 0.405 | 3.653 | 0.000 | *** |
2001 | 0.434 | 3.877 | 0.000 | *** |
2002 | 0.432 | 3.867 | 0.000 | *** |
2003 | 0.486 | 4.333 | 0.000 | *** |
2004 | 0.409 | 3.693 | 0.000 | *** |
2005 | 0.451 | 4.046 | 0.000 | *** |
2006 | 0.498 | 4.455 | 0.000 | *** |
2007 | 0.500 | 4.464 | 0.000 | *** |
2008 | 0.453 | 4.067 | 0.000 | *** |
2009 | 0.448 | 4.045 | 0.000 | *** |
2010 | 0.425 | 3.838 | 0.000 | *** |
2011 | 0.496 | 4.423 | 0.000 | *** |
2012 | 0.444 | 3.990 | 0.000 | *** |
2013 | 0.493 | 4.419 | 0.000 | *** |
2014 | 0.417 | 3.762 | 0.000 | *** |
2015 | 0.486 | 4.341 | 0.000 | *** |
2016 | 0.505 | 4.511 | 0.000 | *** |
2017 | 0.451 | 4.041 | 0.000 | *** |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
W * PM2.5 | 0.7459 *** | 0.7392 *** | 0.7282 *** | 0.7407 *** |
(0.0325) | (0.0332) | (0.0340) | (0.0332) | |
landurban | 0.6292 *** | 1.4049 *** | 2.1935 *** | 2.2452 *** |
(0.2086) | (0.5274) | (0.5938) | (0.6060) | |
landurban2 | −0.0391 * | −0.0611 ** | −0.0677 ** | |
(0.0244) | (0.0259) | (0.0266) | ||
lnpop | −13.1397 *** | −19.4974 *** | ||
(3.4545) | (3.6331) | |||
lnpgdp | −3.6263 *** | −4.6639 *** | ||
(1.2787) | (1.3778) | |||
lnrd | 0.0166 | 0.0978 | ||
(0.4676) | (0.4654) | |||
third | −0.2035 *** | |||
(0.0470) | ||||
lnedu | −3.8029 *** | |||
(1.1180) | ||||
open | −0.0194 | |||
(0.0129) | ||||
Ind fixed | Yes | Yes | Yes | Yes |
Time fixed | Yes | Yes | Yes | Yes |
N | 558 | 558 | 558 | 558 |
R2 | 0.2176 | 0.2301 | 0.1462 | 0.1937 |
Inflection point | 17.96 | 17.95 | 16.58 | |
Cross the inflection point | None | None | None |
OLS | PSEM | PSAC | PSDM | |
---|---|---|---|---|
landurban | 5.4874 *** | 8.3873 *** | 9.0453 *** | 1.7832 *** |
(0.8481) | (1.0444) | (1.0890) | (0.6627) | |
landurban2 | −0.1955 *** | −0.3972 *** | −0.4159 *** | −0.0497 * |
(0.0374) | (0.0510) | (0.0519) | (0.0289) | |
Control variable | Yes | Yes | Yes | Yes |
Ind fixed | Yes | Yes | Yes | Yes |
Time fixed | Yes | Yes | Yes | Yes |
N | 558 | 558 | 558 | 558 |
R2 | 0.5772 | 0.2605 | 0.1466 | 0.1640 |
Inflection point | 14.03 | 10.56 | 10.01 | 17.94 |
Cross the inflection point | Shanghai | Shanghai | Shanghai | None |
Distance | Economic | |
---|---|---|
landurban | 3.4904 *** | 5.9174 *** |
(0.6671) | (0.8228) | |
landurban2 | −0.1226 *** | −0.2131 *** |
(0.0292) | (0.0362) | |
Control variable | Yes | Yes |
Ind fixed | Yes | Yes |
Time fixed | Yes | Yes |
N | 558 | 558 |
R2 | 0.1771 | 0.1821 |
Inflection point | 14.23 | 13.88 |
Cross the inflection point | Shanghai | Shanghai |
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Yu, X.; Shen, M.; Shen, W.; Zhang, X. Effects of Land Urbanization on Smog Pollution in China: Estimation of Spatial Autoregressive Panel Data Models. Land 2020, 9, 337. https://doi.org/10.3390/land9090337
Yu X, Shen M, Shen W, Zhang X. Effects of Land Urbanization on Smog Pollution in China: Estimation of Spatial Autoregressive Panel Data Models. Land. 2020; 9(9):337. https://doi.org/10.3390/land9090337
Chicago/Turabian StyleYu, Xuan, Manhong Shen, Weiteng Shen, and Xiao Zhang. 2020. "Effects of Land Urbanization on Smog Pollution in China: Estimation of Spatial Autoregressive Panel Data Models" Land 9, no. 9: 337. https://doi.org/10.3390/land9090337