Journal of Resources and Ecology >
The Relationship between Urban Spatial Expansion and Haze Pollution: An Empirical Study in China
GU Fangfang, E-mail: gff_0817@163.com |
Received date: 2022-06-08
Accepted date: 2023-01-30
Online published: 2023-10-23
Supported by
The Philosophy and Society Project of Universities in Jiangsu Province(2020SJA0492)
China’s economic development has brought about high-speed urbanization and haze pollution problems. Large populations are concentrated in cities, which also brings air pollution, environmental problems and infectious diseases. Based on the haze pollution data of 29 capital cities in China in 2017, the geographically weighted regression method was used to investigate the relationship between urban spatial expansion (USE) and haze. The results of this study reveal some interesting phenomena. The USE in most cities has a significant positive correlation with haze pollution. The USE of cities in the Southwest Region (SW), Southern coast (SC), and Middle Reaches of the Yangtze River (MYTR) have significant positive impacts on the haze in those cities. Among them, the coefficient of spatial expansion of the SC cities is the largest at 0.438, followed by the SW at 0.4104, and finally, the MYTR at 0.296. In addition, the urban expansion of two cities in the Northern coast (NC) and the Middle reaches of the Yellow River (MYR) passed the significance test while only one city in each of the Eastern coast (EC), the Northwest region (NW), and the Northeast region (NE) passed the significance test, indicating that the impacts of the spatial expansion of these three regions on the haze pollution are minimal. The economic development of the MYR has a significant negative impact on the haze. The effect of the urban greening level on haze is significantly negative in the SC and the SW. The impacts of urban consumption expenditures on haze in the NE, SW, and MYR are also negative. These results indicate that to reduce haze pollution, different countermeasures should be taken in the different regions in China.
GU Fangfang , LIU Xiaohong . The Relationship between Urban Spatial Expansion and Haze Pollution: An Empirical Study in China[J]. Journal of Resources and Ecology, 2023 , 14(6) : 1164 -1175 . DOI: 10.5814/j.issn.1674-764x.2023.06.006
Fig. 1 The framework of city development and haze pollutionNote: “+” means positive impact, while the meaning of “-” is negative impact on haze pollution. |
Table 1 Distribution of the 29 provincial captical cities in the eight areas of China |
Region | Cities | Region | Cities |
---|---|---|---|
Eastern coast | Hangzhou (HZ), Shanghai (SH), Nanjing (NJ) | Middle Reaches of the Yangtze River | Nanchang (NC), Hefei (HF), Changsha (CS), Wuhan (WH) |
Northern coast | Beijing (BJ), Shijiazhuang (SJZ), Jinan (JN), Tianjin (TJ) | Middle Reaches of the Yellow River | Taiyuan (TY), Hohhot (HHHT), Xi’an (XA), Zhengzhou (ZZ) |
Southern coast | Fuzhou (FZ), Haikou (HK), Guangzhou (GZ) | Southwest region | Nanning (NN), Guiyang (GY), Kunming (KM), Chongqing (CQ), Chengdu (CD) |
Northeast region | Harbin (HRB), Changchun (CC), Shenyang (SY) | Northwest region | Yinchuan (YC), Xining (XN), Lanzhou (LZ) |
Table 2 Definitions of the explanatory and dependent variables |
Variable | Definition | Units |
---|---|---|
PM2.5 | Fine particulate matter, Annual average concentration | μg m-3 |
CLA | Area of urban construction land | km2 |
PCGDP | Per capita GDP | yuan |
GSP | Public recreational green space per capita | m2 |
DPI | Per capita disposable income of urban residents | yuan |
CON | Per capita consumption expenditure of urban residents | yuan |
Table 3 Statistics of the variables |
Variable | PM2.5 | CLA | PCGDP | GSP | DPI | CON |
---|---|---|---|---|---|---|
Mean | 48.482 | 564.765 | 101205.3 | 13.538 | 40781.40 | 27458.02 |
Median | 48.000 | 446.000 | 94477.0 | 12.920 | 38536.00 | 25852.00 |
Maximum | 86.000 | 1910.740 | 152441.0 | 22.670 | 62596.00 | 42304.00 |
Minimum | 20.000 | 89.670 | 61589.00 | 7.580 | 30043.00 | 17279.00 |
Std. Dev. | 14.857 | 410.486 | 26917.71 | 3.347 | 9383.099 | 6507.272 |
Sum | 1406.000 | 16378.20 | 2934955 | 392.610 | 1182661 | 796282.7 |
Table 4 The correlation coefficient matrix |
Variables | VIF | lnPM2.5 | lnCLA | lnPCGDP | lnGSP | lnDPI | lnCON |
---|---|---|---|---|---|---|---|
lnPM2.5 | 1 | ||||||
lnCLA | 1.433 | 0.346* | 1 | ||||
lnPCGDP | 2.984 | 0.104 | 0.468** | 1 | |||
lnGSP | 1.071 | ‒0.085 | ‒0.058 | ‒0.089 | 1 | ||
lnDPI | 7.153 | 0.024 | 0.596*** | 0.801*** | 0.048 | 1 | |
lnCON | 4.182 | ‒0.042 | 0.542*** | 0.676*** | ‒0.006 | 0.884*** | 1 |
Notes: * means P < 0.10, ** means P < 0.05, *** means P < 0.01. |
Fig. 2 Moran scatter plot of PM2.5 in the 29 provincial capital cities in ChinaNote: Negative values in the coordinate axis indicate the direction of change. |
Table 5 The overall fitting results of the GWR model |
Weight decay type | Exponential | Gaussian | Tricube |
---|---|---|---|
Explained variable | lnPM2.5 | lnPM2.5 | lnPM2.5 |
R2 | 0.9899 | 0.9540 | 0.6090 |
Adjusted R2 | 0.9878 | 0.9444 | 0.5276 |
Bandwidth | 4.4721 | 0.8169 | ‒ |
Observation | 29 | 29 | 29 |
Table 6 Estimation results of the GWR model based on exponential functions |
Region | City | Constant | lnCLA | lnPCGDP | lnGSP | lnDPI | lnCON |
---|---|---|---|---|---|---|---|
SC (Southern coast) | FZ | 7.386* | 0.433*** | 1.042*** | ‒0.272* | ‒2.938*** | 1.329*** |
HK | ‒7.449*** | 0.466*** | ‒0.891*** | ‒0.960*** | 2.533*** | ‒0.575*** | |
GZ | 7.559** | 0.416*** | 0.738** | ‒0.413*** | ‒1.620*** | 0.325 | |
NC (Northern coast) | BJ | 14.160*** | 0.204** | ‒0.652** | 0.035 | 0.108 | ‒0.491 |
SJZ | 17.562*** | 0.248*** | ‒1.215*** | ‒0.174 | ‒0.079 | 0.031 | |
JN | 12.767*** | ‒0.022 | ‒0.632*** | 0.165 | ‒0.698* | 0.568 | |
TJ | 11.632*** | 0.096 | ‒0.395 | 0.166 | ‒0.014 | ‒0.372 | |
EC (Eastern coast) | HZ | 8.092** | 0.391*** | 0.990*** | 0.371 | ‒3.812*** | 2.104*** |
SH | 12.681*** | ‒0.080 | ‒0.287 | 0.123 | ‒1.354** | 0.909* | |
NJ | 12.101*** | 0.162 | 0.105 | 0.208 | ‒2.392*** | 1.426** | |
NE (Northeast) | HRB | 40.476*** | 0.959*** | ‒0.137 | 0.409*** | 5.649*** | ‒9.924*** |
CC | 21.059* | 0.330 | 0.127 | 0.271* | 4.072*** | ‒6.276*** | |
SY | 7.670 | ‒0.035 | 0.309 | 0.293** | 1.985*** | ‒2.800** | |
MYTR (Middle Reaches of the Yangtze River) | NC | 10.307*** | 0.395*** | 0.709** | ‒0.138 | ‒2.764*** | 1.234*** |
HF | 12.231*** | 0.237** | 0.158 | 0.120 | ‒2.452*** | 1.390** | |
CS | 10.360*** | 0.294*** | 0.705** | ‒0.280** | ‒2.087*** | 0.620* | |
WH | 12.082*** | 0.258** | 0.167 | ‒0.132 | ‒2.040*** | 1.013** | |
MYR (Middle Reaches of the Yellow River) | TY | 17.356*** | 0.300*** | ‒1.271*** | ‒0.183 | 0.240 | ‒0.250 |
HHHT | 18.442*** | 0.404*** | ‒1.334*** | 0.001 | 0.386 | ‒0.549* | |
XA | 5.551 | 0.001 | ‒0.888** | ‒0.615** | 2.270*** | ‒1.349** | |
ZZ | 14.079*** | 0.062 | ‒0.793*** | ‒0.124 | ‒0.341 | 0.269 | |
NW (Northwest) | YC | 9.938*** | 0.309*** | ‒0.970* | 0.098 | 1.795** | ‒1.565*** |
XN | ‒18.923*** | 0.008 | ‒0.921* | ‒0.120 | 1.873** | 1.387** | |
LZ | ‒12.603** | 0.067 | ‒0.781 | ‒0.196 | 1.859** | 0.603 | |
SW (Southwest region) | NN | ‒7.845*** | 0.473*** | ‒0.942*** | ‒0.975*** | 2.635*** | ‒0.583*** |
GY | ‒8.724*** | 0.486*** | ‒0.845*** | ‒0.959*** | 2.641*** | ‒0.622*** | |
KM | ‒14.612*** | 0.562*** | ‒0.862* | ‒1.186*** | 3.310*** | ‒0.701*** | |
CQ | ‒1.480 | 0.324*** | ‒0.293 | ‒0.796** | 1.780** | ‒0.993* | |
CD | 5.556*** | 0.207*** | ‒0.428 | ‒1.039** | 2.496*** | ‒2.136** |
Notes: * means P < 0.10, ** means P < 0.05, *** means P < 0.01. |
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[5] |
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[6] |
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[7] |
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[8] |
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[9] |
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[10] |
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[11] |
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[12] |
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[13] |
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[14] |
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[15] |
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[16] |
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[17] |
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[18] |
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[20] |
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[21] |
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[22] |
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[23] |
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[24] |
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[25] |
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[26] |
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[27] |
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[30] |
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[31] |
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[33] |
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