Spatial Correlation of Industrial NOx Emission in China’s 2 + 26 Policy Region: Based on Social Network Analysis
Abstract
:1. Introduction
2. Methods and Data
2.1. Modified Gravity Model
2.2. Social Network Analysis
2.2.1. Overall Indicators
2.2.2. Centrality Analysis
2.2.3. Quadratic Assignment Procedure
2.3. Data Resources
3. Result and Discussion
3.1. Spatial Correlation Network of Industrial NOx
3.2. Characteristics of the Network Structure
3.2.1. Overall Characteristics
3.2.2. Individual Characteristics
4. Influencing Factors of the Correlation Network of Industrial NOx Emission
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Association Network | 2011 | 2012 | 2013 | 2014 | 2015 |
---|---|---|---|---|---|
Industrial NOx | 0.570 | 0.567 | 0.579 | 0.556 | 0.552 |
City | 2011 | 2012 | 2013 | 2014 | 2015 | |||||
---|---|---|---|---|---|---|---|---|---|---|
Outdegree | Indegree | Outdegree | Indegree | Outdegree | Indegree | Outdegree | Indegree | Outdegree | Indegree | |
Beijing | 18.519 | 29.630 | 18.519 | 33.333 | 18.519 | 37.037 | 18.519 | 33.333 | 18.519 | 37.037 |
Tianjin | 18.519 | 25.926 | 18.519 | 25.926 | 18.519 | 33.333 | 18.519 | 33.333 | 18.519 | 33.333 |
Shijiazhuang | 37.037 | 25.926 | 37.037 | 25.926 | 37.037 | 25.926 | 37.037 | 25.926 | 37.037 | 25.926 |
Tangshan | 18.519 | 7.407 | 18.519 | 7.407 | 18.519 | 7.407 | 18.519 | 7.407 | 18.519 | 7.407 |
Langfang | 14.815 | 18.519 | 14.815 | 18.519 | 14.815 | 18.519 | 14.815 | 18.519 | 14.815 | 18.519 |
Baoding | 25.926 | 37.037 | 25.926 | 37.037 | 25.926 | 37.037 | 25.926 | 37.037 | 25.926 | 37.037 |
Cangzhou | 29.630 | 40.741 | 29.630 | 40.741 | 29.630 | 40.741 | 29.630 | 40.741 | 29.63 | 40.741 |
Hengshui | 25.926 | 18.519 | 29.630 | 18.519 | 29.630 | 25.926 | 29.630 | 25.926 | 33.333 | 25.926 |
Xingtai | 14.815 | 29.630 | 14.815 | 29.630 | 18.519 | 29.630 | 18.519 | 29.630 | 18.519 | 29.630 |
Handan | 22.222 | 40.741 | 22.222 | 40.741 | 18.519 | 40.741 | 22.222 | 40.741 | 22.222 | 48.148 |
Taiyuan | 29.630 | 7.407 | 29.630 | 7.407 | 29.630 | 7.407 | 25.926 | 7.407 | 29.630 | 7.407 |
Yangquan | 18.519 | 7.407 | 18.519 | 7.407 | 18.519 | 7.407 | 18.519 | 7.407 | 18.519 | 7.407 |
Changzhi | 29.630 | 11.111 | 29.630 | 11.111 | 29.630 | 11.111 | 29.630 | 11.111 | 29.630 | 11.111 |
Jincheng | 18.519 | 14.815 | 18.519 | 14.815 | 14.815 | 14.815 | 14.815 | 14.815 | 18.519 | 14.815 |
Jinan | 22.222 | 25.926 | 22.222 | 25.926 | 25.926 | 25.926 | 25.926 | 29.630 | 25.926 | 29.630 |
Zibo | 22.222 | 11.111 | 22.222 | 11.111 | 25.926 | 11.111 | 25.926 | 11.111 | 25.926 | 11.111 |
Jining | 29.630 | 14.815 | 29.630 | 14.815 | 25.926 | 18.519 | 25.926 | 18.519 | 25.926 | 18.519 |
Dezhou | 22.222 | 29.630 | 22.222 | 29.630 | 29.630 | 29.630 | 29.630 | 29.630 | 29.630 | 33.333 |
Liaocheng | 33.333 | 33.333 | 33.333 | 33.333 | 33.333 | 33.333 | 33.333 | 33.333 | 33.333 | 33.333 |
Bingzhou | 22.222 | 11.111 | 22.222 | 11.111 | 22.222 | 11.111 | 18.519 | 11.111 | 22.222 | 11.111 |
Heze | 29.630 | 22.222 | 29.630 | 22.222 | 29.630 | 22.222 | 33.333 | 22.222 | 33.333 | 22.222 |
Zhengzhou | 22.222 | 25.926 | 22.222 | 25.926 | 18.519 | 29.630 | 18.519 | 29.630 | 22.222 | 25.926 |
Kaifeng | 22.222 | 18.519 | 22.222 | 18.519 | 18.519 | 18.519 | 18.519 | 18.519 | 22.222 | 18.519 |
Anyang | 18.519 | 51.852 | 18.519 | 51.852 | 18.519 | 33.333 | 18.519 | 29.630 | 18.519 | 44.444 |
Hebi | 14.815 | 18.519 | 14.815 | 18.519 | 14.815 | 18.519 | 14.815 | 18.519 | 14.815 | 18.519 |
Xinxiang | 29.630 | 33.333 | 29.630 | 33.333 | 29.630 | 33.333 | 29.630 | 33.333 | 33.333 | 33.333 |
Jiaozuo | 22.222 | 18.519 | 22.222 | 18.519 | 18.519 | 18.519 | 18.519 | 18.519 | 18.519 | 18.519 |
Puyang | 25.926 | 29.630 | 25.926 | 29.630 | 33.333 | 25.926 | 33.333 | 29.630 | 33.333 | 29.630 |
Variables | Column 1 | ||||
2011 | 2012 | 2013 | 2014 | 2015 | |
Intercept | 0.14889 | 0.14913 | 0.14303 | 0.19766 | 0.19886 |
Environmental Regulation | −0.01196 | −0.01002 | −0.00323 | −0.08192 | −0.08325 |
(0.03164) | (0.03158) | (0.03201) | (0.0317 ***) | (0.03159 ***) | |
Geographical Proximity | 0.64877 | 0.64743 | 0.64978 | 0.65075 | 0.66776 |
(0.0521 ***) | (0.0529 ***) | (0.05198 ***) | (0.05287 ***) | (0.05242 ***) | |
0–100 km | - | - | - | - | - |
100–200 km | - | - | - | - | - |
200–300 km | - | - | - | - | - |
300–400 km | - | - | - | - | - |
Adj–R2 | 0.29 | 0.288 | 0.29 | 0.296 | 0.312 |
Variables | Column 2 | ||||
2011 | 2012 | 2013 | 2014 | 2015 | |
Intercept | 0.01913 | 0.01868 | 0.01045 | 0.03396 | 0.04005 |
Environmental Regulation | −0.01998 | −0.01965 | −0.00693 | −0.04760 | −0.05625 |
(0.02392) | (0.02405) | (0.02473) | (0.02655 **) | (0.02585 **) | |
Geographical Proximity | - | - | - | - | - |
0–100 km | 0.9753 | 0.97518 | 0.97552 | 0.97308 | 0.96851 |
(0.07125 ***) | (0.07197 ***) | (0.07251 ***) | (0.07154 ***) | (0.07009 ***) | |
100–200 km | 0.60546 | 0.60593 | 0.60623 | 0.62841 | 0.62986 |
(0.05238 ***) | (0.05325 ***) | (0.05207 ***) | (0.05320 ***) | (0.05288 ***) | |
200–300 km | 0.06728 | 0.07289 | 0.06794 | 0.07455 | 0.08643 |
(0.04132 **) | (0.04166 **) | (0.04105 **) | (0.04157 **) | (0.04020 **) | |
300–400 km | 0.00121 | 0.00142 | 0.00036 | 0.00496 | −0.00134 |
(0.03882) | (0.03915) | (0.03874) | (0.03844) | (0.03811) | |
Adj–R2 | 0.57 | 0.565 | 0.57 | 0.582 | 0.579 |
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Jiang, S.; Tan, X.; Wang, Y.; Shi, L.; Cheng, R.; Ma, Z.; Lu, G. Spatial Correlation of Industrial NOx Emission in China’s 2 + 26 Policy Region: Based on Social Network Analysis. Sustainability 2020, 12, 2289. https://doi.org/10.3390/su12062289
Jiang S, Tan X, Wang Y, Shi L, Cheng R, Ma Z, Lu G. Spatial Correlation of Industrial NOx Emission in China’s 2 + 26 Policy Region: Based on Social Network Analysis. Sustainability. 2020; 12(6):2289. https://doi.org/10.3390/su12062289
Chicago/Turabian StyleJiang, Shurui, Xue Tan, Yue Wang, Lei Shi, Rong Cheng, Zhong Ma, and Genfa Lu. 2020. "Spatial Correlation of Industrial NOx Emission in China’s 2 + 26 Policy Region: Based on Social Network Analysis" Sustainability 12, no. 6: 2289. https://doi.org/10.3390/su12062289