Journal of Geographical Sciences >
The impact of energy-intensive industries on air quality in China’s industrial agglomerations
Zhang Xiaoping (1972–), PhD and Associate Professor, specialized in economic geography and regional sustainable development. E-mail: zhangxp@ucas.ac.cn |
Received date: 2020-09-02
Accepted date: 2020-12-07
Online published: 2021-06-25
Supported by
National Natural Science Foundation of China, No(41771133)
National Natural Science Foundation of China, No(41771181)
The Strategic Priority Research Program of the Chinese Academy of Sciences, No(XDA19040403)
Copyright
Understanding the driving forces of regional air pollution and its mechanism has gained much attention in academic research, which can provide scientific policy-making basis for economy-environment sustainability in China. Being an important energy and industrial base, the North China Plain region has been experiencing severe air pollution. Therefore, understanding the relationship between industrialization and air quality in this region is of great importance for air quality improvement. In this study, the average annual concentrations of SO2, NO2 and PM10 in 47 sample cities at and above the prefecture level in the North China Plain region from 2007 to 2016 were used to illustrate the spatiotemporal characteristics of air pollution within this region. Furthermore, panel data model, panel vector autoregression model, and impulse response function were used to explore the correlation and driving mechanism between energy-intensive industries and regional air quality. The results show that:first, overall air quality improved in the study area between 2007 and 2016, with a significant greater fall in concentration of SO2 than that of NO2 and PM10; second, provincial border areas suffered from severe air pollution and showed an apparent spatial agglomeration trend of pollution; and third, the test results from different models all proved that energy-intensive industries such as the chemical, non-metallic mineral production, electric and thermal power production and supply industries, had a significant positive correlation with concentrations of air pollutants, and indicated an obvious short-term impulse response effect. It concludes that upgradation of industrial structure, especially that of energy-intensive industries, plays a very important role in the improvement of regional air quality, which is recommended to be put in top priority for authorities. Therefore, policies as increasing investments in technological innovation in energy-intensive industries, deepening cooperation in environmental governance between different provinces and cities, and strengthening supervision and entry restrictions are suggested.
ZHANG Xiaoping , LIN Meihan , WANG Zhenbo , JIN Fengjun . The impact of energy-intensive industries on air quality in China’s industrial agglomerations[J]. Journal of Geographical Sciences, 2021 , 31(4) : 584 -602 . DOI: 10.1007/s11442-021-1860-x
Figure 1 The research area and distribution of main energy and mineral resources in the North China Plain |
Table 1 Concentration limits of air pollutants for second-class air quality in GB3095-2012 |
Individual air pollutant | Average period | Second-class concentration limit | Unit |
---|---|---|---|
SO2 | Annual average | 60 | μg/m3 |
NO2 | Annual average | 40 | μg/m3 |
PM10 | Annual average | 70 | μg/m3 |
Table 2 Spearman’s coefficient of ambient pollutants in the North China Plain |
SO2 | NO2 | PM10 | SO2 | NO2 | PM10 | SO2 | NO2 | PM10 | |||
---|---|---|---|---|---|---|---|---|---|---|---|
North China | -0.576 | 0.685 | 0.491 | Jinan | -0.127 | 0.867 | 0.491 | Zhengzhou | -0.867 | 0.842 | 0.709 |
Hebei | -0.503 | 0.539 | 0.455 | Qingdao | -0.806 | 0.152 | -0.261 | Kaifeng | -0.867 | -0.345 | 0.697 |
Shandong | -0.358 | 0.758 | 0.539 | Zibo | 0.176 | 0.758 | 0.564 | Luoyang | -0.394 | 0.721 | 0.648 |
Henan | -0.515 | 0.503 | 0.758 | Zaozhuang | -0.297 | 0.055 | 0.552 | Pingdingshan | -0.539 | -0.503 | 0.588 |
Beijing | -0.988 | -0.358 | -0.915 | Dongying | -0.285 | 0.442 | 0.467 | Anyang | -0.127 | 0.721 | 0.624 |
Tianjin | -0.806 | 0.455 | 0.721 | Yantai | -0.964 | -0.503 | 0.115 | Hebi | -0.273 | 0.370 | 0.794 |
Shijiazhuang | 0.236 | 0.830 | 0.503 | Weifang | -0.564 | 0.164 | 0.648 | Xinxiang | -0.345 | 0.418 | 0.867 |
Tangshan | -0.491 | 0.600 | 0.636 | Jining | -0.042 | 0.370 | 0.758 | Jiaozuo | -0.867 | 0.479 | 0.733 |
Qinhuangdao | -0.382 | 0.855 | 0.455 | Tai’an | -0.067 | 0.576 | 0.806 | Puyang | -0.467 | -0.370 | 0.709 |
Handan | -0.236 | 0.515 | 0.503 | Weihai | -0.830 | -0.952 | 0.539 | Xuchang | -0.806 | 0.745 | 0.903 |
Xingtai | 0.152 | 0.467 | 0.455 | Rizhao | -0.055 | -0.042 | 0.794 | Sanmenxia | -0.515 | 0.758 | 0.624 |
Baoding | -0.152 | 0.745 | 0.564 | Laiwu | -0.030 | 0.552 | 0.867 | Nanyang | -0.212 | 0.042 | 0.552 |
Zhangjiakou | -0.745 | -0.164 | 0.733 | Linyi | -0.818 | 0.261 | 0.455 | Shangqiu | -0.067 | 0.321 | 0.891 |
Chengde | -0.952 | 0.103 | 0.430 | Dezhou | -0.806 | -0.224 | 0.539 | Xinyang | -0.055 | 0.455 | 0.491 |
Cangzhou | 0.503 | 0.588 | 0.370 | Liaocheng | -0.406 | 0.745 | 0.782 | Zhoukou | -0.758 | -0.503 | 0.709 |
Langfang | -0.855 | 0.479 | 0.455 | Binzhou | 0.067 | 0.564 | 0.770 | Zhumadian | -0.285 | 0.636 | 0.758 |
Hengshui | -0.406 | 0.588 | 0.491 | Heze | -0.794 | 0.018 | 0.358 | Luohe | -0.624 | 0.297 | 0.552 |
Notes: When n is 10 and the level of significance is 0.01, the absolute value of Wρ is 0.746. Bold numbers in the table indicate those with statistical significance. |
Figure 2 Variation coefficient of annual average concentration of three air pollutants in the North China Plain |
Figure 3 Spatial characteristics of air pollution indexes for three pollutants in the North China Plain |
Figure 4 Flow chart of air pollutant influencing factors analysis |
Table 3 Explanatory variables of urban air quality and their Pearson correlation test |
Factor type | Influencing factor | Variable name | Correlation test | ||
---|---|---|---|---|---|
SO2 | NO2 | PM10 | |||
Nature | Annual mean temperature | temp | -0.045 | 0.019 | 0.108** |
Socio-economy | Gross domestic product | GDP | -0.151*** | 0.358*** | 0.125*** |
Actual use of foreign investment | UFI | -0.133*** | 0.240*** | 0.038 | |
Gross industrial output value | Indus | 0.047 | 0.283*** | 0.241*** | |
Population density | Popden | -0.036 | 0.255*** | 0.225*** | |
Population size | Popu | -0.165*** | 0.107** | 0.194*** | |
Environment | Industrial sulfur dioxide emission | SOP | 0.471*** | 0.157*** | 0.056 |
Smoke and dust emissions | AshP | 0.115** | 0.184*** | 0.123*** | |
Wastewater discharge | Water | 0.372*** | 0.248*** | 0.146*** | |
Green area | Garea | -0.129*** | 0.329*** | 0.065 | |
Industrial structure | Oil processing and coking and nuclear fuel output value | Oil | 0.142*** | 0.257*** | 0.188*** |
Chemical industrial output value | Chemical | 0.153*** | 0.256*** | 0.270*** | |
Non-metallic mineral production output value | N-metal | 0.064 | 0.233*** | 0.254*** | |
Ferrous metal smelting and pressing output value | B-metal | 0.145*** | 0.199*** | 0.178*** | |
Non-ferrous metals smelting and pressing output value | C-metal | -0.037 | 0.155*** | 0.099** | |
Electric and thermal power production and supply output value | E-power | -0.062 | 0.295*** | 0.181*** |
Note: *, **, *** represent significance levels of 0.1, 0.05 and 0.01, respectively. |
Table 4 Results of panel data models for the North China Plain |
SO2 | NO2 | PM10 | |||||||
---|---|---|---|---|---|---|---|---|---|
OLS | FE | RE | OLS | FE | RE | OLS | FE | RE | |
lgtemp | -3.441 | -0.66 | -1.277 | 3.092 | 1.908 | 2.302 | 25.476*** | 12.626 | 22.294** |
lgGDP | -5.512** | -4.906 | -6.181** | -1.325 | 1.374 | -0.349 | 0.116 | 3.623 | 0.107 |
lgUFI | -0.893 | 5.257*** | 1.825* | 0.030 | 3.022*** | 1.372** | -1.852 | 13.673*** | 1.049 |
lgIndus | -2.678 | -3.576 | -2.961 | 2.517** | 7.076*** | 3.473** | 6.920 | 16.172** | 10.453** |
lgPopden | 4.064** | -2.498 | 1.293 | 3.849*** | 0.600 | 2.955** | 8.325** | -9.838 | 6.800 |
lgPopu | -6.268*** | -46.969** | -5.444** | -3.803*** | -0.415 | -3.012** | 0.428 | -8.234 | 1.478 |
lgSOP | 9.633*** | 6.615*** | 8.572*** | -2.650*** | -2.108* | -2.424*** | -13.422*** | -4.576 | -13.271*** |
lgAshP | 1.663 | 0.649 | 0.692 | 3.402*** | 3.308*** | 3.440*** | 16.061*** | 10.679*** | 16.009*** |
lgWatew | 5.768*** | 3.266 | 5.533*** | 1.880** | -2.068 | 0.454 | -1.924 | -7.598 | -3.296 |
lgGarea | 2.421 | -6.117 | -0.177 | 2.777*** | -3.638 | 0.869 | -5.168 | 3.745 | -7.524* |
lgOil | 0.222 | -1.343* | 0.230 | 0.126 | -1.024** | 0.054 | 0.297 | -4.075** | 0.002 |
lgChemical | 2.659** | 1.910 | 2.329* | 0.099 | 0.177 | 0.096 | 5.249** | 9.108** | 4.891* |
lgN-metal | -0.494 | -0.043 | -0.648 | -0.923 | -1.163 | -0.940 | -1.418 | -8.091* | -1.475 |
lgB-metal | -0.795** | 0.955 | -0.281 | -0.261 | -1.958** | -0.250 | -0.712 | -3.380 | -0.553 |
lgC-metal | 0.070 | -1.334 | -0.030 | 0.222 | 0.146 | 0.200 | 1.113** | 4.643 | 1.057 |
lgE-power | 0.443 | 2.615 | 1.011 | 1.495 | -1.201 | 0.356 | 2.857 | -2.053 | 0.776 |
Constant | -56.650** | 304.721** | -29.997 | -60.205*** | -25.437 | -51.989*** | -172.894*** | -231.650 | -184.514*** |
R2 | 0.393 | 0.210 | 0.312 | 0.238 | 0.287 | 0.350 | |||
rho | 0.862 | 0.180 | 0.643 | 0.168 | 0.663 | 0.068 | |||
F-test | 4.33 | 4.33 | 4.33 | ||||||
Wald | 148.60 | 134.28 | 163.12 | ||||||
LM | 35.43 | 46.81 | 19.83 | ||||||
Hausman | 55.79 | 54.73 | 92.61 | ||||||
Model | Fixed effect model | Fixed effect model | Fixed effect model |
Note: *, **, *** represent significance levels of 0.1, 0.05 and 0.01, respectively. |
Table 5 Results of influencing factors on air pollution in two-way fixed effect model |
SO2 | NO2 | PM10 | |
---|---|---|---|
lgtemp | 0.812 | 1.039 | 7.752 |
lgGDP | 1.718 | -0.751 | -0.368 |
lgUFI | 2.947** | 1.119 | 4.199** |
lgIndus | 0.808 | 4.834** | 14.240** |
lgPopden | -0.870 | 0.877 | -7.154 |
lgPopu | 5.538 | 8.273 | 72.651* |
lgSOP | 0.266 | -1.610 | -6.177* |
lgAshP | 1.272 | 2.310*** | 6.100*** |
lgWatew | 0.861 | -1.221 | -4.947 |
lgGarea | -3.995 | -7.141*** | -10.531 |
lgOil | -1.402** | -0.749* | -2.035 |
lgChemical | 1.952 | -0.550 | 5.209** |
lgN-metal | 0.071 | -0.336 | -4.588 |
lgB-metal | -0.007 | -1.544** | -1.884 |
lgC-metal | -0.858 | -0.141 | 3.165 |
lgE-power | -0.498 | -1.536 | -4.294 |
year2 | -3.018 | -1.037 | -4.633 |
year3 | -5.145* | 1.167 | -4.235 |
year4 | -8.427*** | 1.969 | -7.662 |
year5 | -11.537*** | 1.366 | -18.482*** |
year6 | -12.184*** | 0.007 | -19.098** |
year7 | 2.786 | 12.176*** | 43.293*** |
year8 | -6.843 | 10.043*** | 27.893*** |
year9 | -18.946*** | 5.552 | 16.435* |
year10 | -25.908*** | 7.177** | 1.584 |
Constant | -17.177 | 15.912 | -414.978* |
N | 460 | 460 | 460 |
R2 | 0.416 | 0.380 | 0.660 |
F-test | 5.64 | 4.35 | 4.27 |
rho | 0.687 | 0.786 | 0.824 |
Note: *, **, *** represent significance levels of 0.1, 0.05 and 0.01, respectively. |
Table 6 Estimated results of the PVAR model |
h_SO2 | h_NO2 | h_PM10 | ||||
---|---|---|---|---|---|---|
Coef. | P>|z| | Coef. | P>|z| | Coef. | P>|z| | |
h_ Oil L1. | -1.363 | 0.733 | 0.171 | 0.212 | 0.418 | 0.327 |
h_ Chemical L1. | 1.136 | 0.615 | 0.071*** | 0 | 0.141*** | 0 |
h_ N-metal L1. | 0.817 | 0.531 | 0.062*** | 0 | 0.116*** | 0 |
h_ B-metal L1. | 0.088 | 0.174 | 0.050 | 0.151 | 0.085** | 0.014 |
h_ C-metal L1. | -0.379 | 0.745 | 0.082 | 0.135 | 0.775 | 0.666 |
h_ E-power L1. | 0.668 | 0.322 | 0.100*** | 0.001 | 0.192*** | 0 |
Note: *, **, *** represent significance levels of 0.1, 0.05 and 0.01, respectively. |
1 |
|
2 |
|
3 |
|
4 |
|
5 |
|
6 |
|
7 |
|
8 |
|
9 |
|
10 |
|
11 |
|
12 |
|
13 |
|
14 |
|
15 |
|
16 |
|
17 |
|
18 |
|
19 |
|
20 |
|
21 |
|
22 |
|
23 |
|
24 |
|
25 |
|
26 |
|
27 |
|
28 |
|
29 |
|
30 |
|
31 |
|
32 |
|
33 |
|
34 |
|
35 |
|
36 |
|
/
〈 |
|
〉 |