Corruption, Hidden Economy and Environmental Pollution: A Spatial Econometric Analysis Based on China’s Provincial Panel Data
Abstract
:1. Introduction
2. Literature Review
2.1. Corruption and Environmental Pollution
2.2. Hidden Economy and Environmental Pollution
2.3. Limitations of Existing Research
3. Calculation of the Size of the Hidden Economy in China’s Provinces
3.1. Calculation Method of the Hidden Economy
3.2. Explanation of Causes and Indicators
3.2.1. Causes
3.2.2. Indicators
3.2.3. Source of Data
3.3. Estimation Results of the MIMIC Model
3.4. Calculation Results of the Hidden Economy and Analysis
4. Empirical Analysis of the Influence of Corruption and Hidden Economy on Environmental Pollution
4.1. Specifications of the Econometric Model
4.2. Explanation of Variables
4.3. Empirical Results and Analysis
4.4. Robustness Test
4.5. Empirical Results in Different Regions
4.6. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Province | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 |
---|---|---|---|---|---|---|---|---|---|---|
Beijing | 14.01 | 14.31 | 15.99 | 20.13 | 16.62 | 13.01 | 13.93 | 20.30 | 19.11 | 19.68 |
Tianjin | 6.00 | 11.36 | 12.50 | 13.89 | 14.53 | 13.88 | 13.84 | 13.46 | 13.20 | 13.44 |
Hebei | 7.34 | 8.13 | 8.99 | 11.39 | 13.02 | 14.10 | 14.38 | 14.13 | 13.71 | 13.92 |
Liaoning | 10.31 | 10.82 | 11.17 | 9.74 | 18.92 | 18.96 | 19.18 | 16.59 | 15.10 | 12.84 |
Shanghai | 9.71 | 11.48 | 11.48 | 1.75 | 14.72 | 14.75 | 14.08 | 1.09 | 13.07 | 13.12 |
Jiangsu | 9.18 | 9.96 | 11.53 | 13.01 | 14.84 | 14.55 | 13.43 | 12.11 | 11.58 | 11.01 |
Zhejiang | 13.18 | 14.51 | 14.76 | 16.26 | 17.82 | 17.48 | 17.11 | 15.62 | 14.58 | 13.73 |
Fujian | 9.70 | 10.50 | 12.47 | 17.83 | 19.23 | 18.51 | 18.23 | 18.11 | 18.00 | 18.04 |
Shandong | 10.70 | 10.69 | 11.28 | 11.77 | 12.53 | 12.24 | 11.47 | 11.22 | 11.12 | 10.93 |
Guangdong | 8.74 | 9.20 | 9.96 | 11.56 | 11.83 | 13.11 | 10.00 | 9.18 | 9.25 | 9.06 |
Hainan | 17.02 | 17.13 | 17.11 | 17.98 | 16.28 | 20.86 | 17.54 | 18.39 | 18.66 | 18.20 |
Eastern China Average | 10.53 | 11.65 | 12.48 | 13.21 | 15.48 | 15.59 | 14.84 | 13.66 | 14.31 | 14.00 |
Shanxi | 7.27 | 7.31 | 7.77 | 9.02 | 11.22 | 9.67 | 10.13 | 10.26 | 10.82 | 10.80 |
Jilin | 6.36 | 6.98 | 7.89 | 6.93 | 7.82 | 9.44 | 8.87 | 8.87 | 8.82 | 8.35 |
Heilongjiang | 4.19 | 4.58 | 6.15 | 8.70 | 9.10 | 7.78 | 8.42 | 8.22 | 8.16 | 8.01 |
Anhui | 10.95 | 11.24 | 11.54 | 12.80 | 13.60 | 13.94 | 14.28 | 14.92 | 14.47 | 14.21 |
Jiangxi | 11.95 | 12.80 | 14.30 | 16.28 | 16.44 | 17.30 | 17.13 | 16.42 | 16.86 | 16.18 |
Henan | 10.23 | 10.55 | 10.75 | 11.68 | 12.05 | 12.63 | 13.61 | 13.67 | 13.91 | 13.69 |
Hubei | 12.86 | 13.68 | 14.81 | 16.84 | 17.87 | 17.72 | 17.43 | 17.86 | 17.41 | 17.35 |
Hunan | 9.19 | 9.44 | 9.23 | 10.08 | 10.09 | 11.22 | 10.89 | 10.57 | 10.65 | 10.70 |
Central China Average | 9.12 | 9.57 | 10.31 | 11.54 | 12.27 | 12.47 | 12.59 | 12.60 | 12.64 | 12.41 |
Neimenggu | 13.17 | 13.45 | 14.08 | 15.46 | 17.09 | 19.07 | 19.88 | 18.62 | 17.85 | 17.41 |
Guangxi | 6.05 | 6.41 | 6.31 | 7.05 | 7.21 | 6.70 | 7.80 | 7.82 | 7.79 | 7.14 |
Sichuan and Chongqing | 7.78 | 7.83 | 8.09 | 8.91 | 9.24 | 8.58 | 8.93 | 9.29 | 9.08 | 8.60 |
Guizhou | 15.47 | 16.85 | 16.20 | 17.06 | 17.45 | 17.31 | 17.37 | 17.74 | 17.37 | 17.13 |
Yunnan | 5.05 | 6.02 | 6.28 | 7.57 | 8.83 | 7.80 | 9.20 | 8.91 | 9.08 | 9.06 |
Shaanxi | 16.99 | 14.86 | 15.26 | 18.02 | 17.86 | 20.97 | 21.08 | 22.88 | 22.34 | 22.28 |
Gansu | 16.56 | 14.69 | 14.38 | 15.00 | 16.78 | 22.13 | 17.38 | 16.58 | 18.28 | 16.90 |
Qinghai | 15.79 | 16.19 | 15.43 | 21.34 | 21.67 | 20.85 | 22.69 | 22.68 | 22.49 | 22.29 |
Ningxia | 15.95 | 15.61 | 16.01 | 15.65 | 15.15 | 10.53 | 15.31 | 15.34 | 14.55 | 14.25 |
Xinjiang | 9.12 | 8.92 | 9.05 | 8.85 | 8.86 | 6.93 | 8.20 | 9.07 | 9.00 | 9.19 |
Western China Average | 12.19 | 12.08 | 12.11 | 13.49 | 14.01 | 14.09 | 14.78 | 14.89 | 14.78 | 14.43 |
National Average | 10.72 | 11.22 | 11.75 | 12.85 | 14.09 | 14.21 | 14.20 | 13.79 | 14.01 | 13.71 |
Province | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |
---|---|---|---|---|---|---|---|---|---|---|
Beijing | 20.58 | 16.70 | 15.71 | 17.11 | 15.81 | 15.30 | 13.09 | 12.42 | 12.27 | 11.24 |
Tianjin | 13.52 | 13.63 | 13.69 | 13.89 | 13.82 | 14.06 | 13.80 | 13.70 | 13.28 | 13.25 |
Hebei | 14.50 | 14.05 | 14.47 | 14.02 | 13.97 | 14.15 | 13.78 | 13.60 | 13.49 | 13.77 |
Liaoning | 11.77 | 11.55 | 10.99 | 11.25 | 10.96 | 10.38 | 9.88 | 9.16 | 10.92 | 10.94 |
Shanghai | 12.96 | 12.90 | 13.08 | 10.86 | 9.39 | 11.80 | 12.19 | 12.69 | 11.62 | 11.53 |
Jiangsu | 10.76 | 10.59 | 10.13 | 10.28 | 10.00 | 9.21 | 9.05 | 8.79 | 7.86 | 7.80 |
Zhejiang | 14.64 | 13.73 | 13.35 | 13.20 | 12.71 | 12.28 | 11.10 | 9.85 | 10.27 | 9.14 |
Fujian | 17.69 | 17.46 | 16.88 | 16.61 | 16.26 | 16.25 | 15.52 | 15.67 | 15.54 | 15.75 |
Shandong | 12.52 | 11.46 | 11.33 | 11.29 | 11.26 | 10.96 | 10.93 | 10.76 | 10.68 | 10.52 |
Guangdong | 9.23 | 9.32 | 9.02 | 9.05 | 9.06 | 8.83 | 8.34 | 7.97 | 7.31 | 7.65 |
Hainan | 20.23 | 19.32 | 18.09 | 11.95 | 13.41 | 13.96 | 14.86 | 14.85 | 14.46 | 13.91 |
Eastern China Average | 14.40 | 13.70 | 13.34 | 12.68 | 12.42 | 12.47 | 12.05 | 11.77 | 11.61 | 11.41 |
Shanxi | 11.15 | 12.94 | 11.85 | 11.56 | 11.16 | 10.60 | 11.19 | 11.21 | 10.90 | 11.04 |
Jilin | 8.45 | 8.31 | 7.80 | 7.60 | 7.47 | 7.45 | 6.69 | 6.65 | 6.50 | 6.50 |
Heilongjiang | 7.88 | 7.94 | 7.86 | 7.58 | 7.60 | 8.13 | 8.35 | 8.56 | 7.87 | 7.98 |
Anhui | 13.92 | 14.01 | 13.25 | 13.56 | 13.42 | 12.61 | 11.82 | 11.44 | 11.19 | 10.22 |
Jiangxi | 16.25 | 16.42 | 15.77 | 14.12 | 14.58 | 15.67 | 15.99 | 16.14 | 15.97 | 15.23 |
Henan | 13.63 | 13.89 | 13.41 | 13.30 | 12.33 | 12.81 | 12.19 | 12.07 | 11.85 | 11.17 |
Hubei | 17.21 | 17.02 | 16.82 | 16.38 | 15.22 | 13.51 | 11.39 | 9.44 | 8.13 | 8.90 |
Hunan | 10.40 | 10.26 | 10.23 | 10.20 | 10.30 | 10.28 | 10.05 | 9.53 | 10.15 | 9.52 |
Central China Average | 12.36 | 12.60 | 12.12 | 11.79 | 11.51 | 11.38 | 10.96 | 10.63 | 10.32 | 10.07 |
Neimenggu | 17.59 | 17.10 | 16.92 | 16.47 | 15.91 | 15.14 | 14.57 | 15.01 | 15.13 | 15.04 |
Guangxi | 7.04 | 7.05 | 6.92 | 6.53 | 6.40 | 6.22 | 5.89 | 5.35 | 5.15 | 3.97 |
Sichuan and Chongqing | 9.24 | 8.80 | 8.65 | 8.66 | 8.34 | 8.50 | 8.36 | 7.91 | 7.64 | 7.10 |
Guizhou | 17.01 | 16.46 | 16.01 | 15.95 | 14.79 | 14.59 | 14.53 | 14.10 | 13.00 | 12.80 |
Yunnan | 9.14 | 9.25 | 9.29 | 8.94 | 8.91 | 8.75 | 8.48 | 8.35 | 7.34 | 6.56 |
Shaanxi | 21.86 | 22.16 | 21.88 | 20.48 | 17.75 | 17.95 | 18.17 | 17.99 | 17.48 | 17.64 |
Gansu | 16.43 | 16.42 | 16.17 | 15.66 | 13.79 | 12.04 | 11.42 | 11.16 | 10.88 | 13.34 |
Qinghai | 22.55 | 22.97 | 22.92 | 22.91 | 20.85 | 21.06 | 20.16 | 20.27 | 18.77 | 18.89 |
Ningxia | 14.58 | 14.66 | 14.50 | 14.74 | 14.14 | 14.02 | 13.47 | 12.95 | 11.90 | 12.08 |
Xinjiang | 8.90 | 9.24 | 8.03 | 8.42 | 8.72 | 8.81 | 8.24 | 7.33 | 6.54 | 6.65 |
Western China Average | 14.43 | 14.41 | 14.13 | 13.88 | 12.96 | 12.71 | 12.33 | 12.04 | 11.38 | 11.41 |
National Average | 13.85 | 13.64 | 13.28 | 12.85 | 12.36 | 12.25 | 11.84 | 11.55 | 11.18 | 11.04 |
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Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) |
---|---|---|---|---|---|---|---|
TT | 0.12 *** (0.06) | ||||||
IT | 2.16 *** (0.22) | 2.01 *** (0.11) | 1.72 *** (0.15) | 1.75 *** (0.18) | 1.71 *** (0.13) | 1.65 *** (0.17) | |
DT | −1.6 *** (0.27) | −1.2 *** (0.11) | −1.9 *** (0.14) | −1.8 *** (0.17) | −1.9 *** (0.13) | −2.1 *** (0.20) | |
INC | −0.06 * (0.03) | −0.1 *** (0.01) | −0.1 *** (0.01) | −0.1 *** (0.01) | −0.1 *** (0.01) | ||
UNE | 0.69 *** (0.14) | 0.33 *** (0.12) | 0.77 *** (0.13) | 0.69 *** (0.12) | 0.36 ** (0.18) | 1.11 *** (0.16) | |
GR | −2.2 *** (0.36) | −2.3 *** (0.16) | |||||
SFE | −0.09 *** (0.02) | −0.01 (0.02) | −0.01 (0.01) | −0.02 ** (0.01) | |||
RGDP | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
LAB | −1.08 ** (0.53) | −0.71 * (0.37) | −1.23 *** (0.28) | −1.41 *** (0.26) | −1.36 *** (0.28) | −1.08 *** (0.32) | −0.42 *** (0.39) |
16.74 p = 0.02 | 19.27 p = 0.01 | 13.40 p = 0.03 | 1.89 p = 0.58 | 0.96 p = 0.75 | 1.49 p = 0.62 | 0.23 p = 0.81 | |
df (degree of freedom) | 6 | 4 | 5 | 3 | 4 | 2 | 2 |
RMSEA | 0.11 | 0.12 | 0.12 | 0.15 | 0.07 | 0.02 | 0.01 |
CFI | 0.83 | 0.80 | 0.88 | 0.86 | 0.93 | 1.00 | 1.00 |
SRMR | 0.14 | 0.12 | 0.13 | 0.08 | 0.09 | 0.05 | 0.01 |
Variable | Meaning | Unit |
---|---|---|
EP1 | Per capita industrial waste gas emission | normal cubic meter/person |
EP2 | Per capita industrial waste water dischare | ton/person |
COR1 | Number of duty crime cases per public servant | case/ten thousand people |
COR2 | Number of duty crime cases per capita | case/million people |
SE | Share of hidden economy in GDP | % |
Y | Per capita gross domestic product | Yuan/person |
IS | Added value of second industry’s share of GDP | % |
PD | Population at the end of the year per land area | person/suare kilometer |
OPEN | Value of import and export’s share of GDP | % |
EE | Energy consumption per real GDP | tons of stadard coal/100,000 yuan |
URB | Urban population’s share of total pollution at the end of the year | % |
Explanatory Variables | EP1 | EP2 | ||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
COR1 | 0.16 ** | 0.16 ** | 0.14 ** | 0.26 ** | 0.30 *** | 0.29 *** |
(0.09) | (0.09) | (0.09) | (0.11) | (0.11) | (0.11) | |
SE | 0.04 ** | 0.04 ** | 0.04 ** | 0.05 ** | 0.05 ** | 0.05 ** |
(0.02) | (0.02) | (0.02) | (0.02) | (0.02) | (0.02) | |
COR1* SE | 0.01 *** | 0.01 *** | 0.01 *** | 0.01 *** | 0.01 ** | 0.01 ** |
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | |
Y | 1.62 *** | 1.61 *** | 1.65 *** | 0.51 * | 0.25 ** | 0.33 ** |
(0.24) | (0.22) | (0.23) | (0.16) | (0.16) | (0.16) | |
Y2 | −0.04 *** | −0.04 *** | −0.04 *** | −0.03 *** | −0.02 *** | −0.03 *** |
(0.01) | (0.01) | (0.01) | (0.04) | (0.04) | (0.04) | |
IS | 0.01 *** | 0.01 *** | 0.01 *** | 0.01 *** | 0.01 ** | 0.01 *** |
(0.01) | (0.01) | (0.01) | (0.02) | (0.01) | (0.01) | |
PD | −0.47 *** | −0.37 ** | −0.32 ** | −0.77 *** | −0.60 *** | −0.65 *** |
(0.15) | (0.14) | (0.14) | (0.18) | (0.18) | (0.18) | |
OPEN | 0.01 ** | 0.01 ** | 0.01 *** | 0.01 ** | 0.01 ** | 0.01 * |
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | |
EE | 0.73 *** | 0.73 *** | 0.73 *** | 0.87 *** | 0.77 *** | 0.79 *** |
(0.05) | (0.05) | (0.05) | (0.07) | (0.07) | (0.07) | |
URB | 0.02 *** | 0.02 *** | 0.02 *** | 0.01 ** | 0.01 * | 0.01 * |
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | |
0.28 *** | 0.22 *** | 0.18 *** | 0.59 ** | 0.16 ** | 0.04 ** | |
(0.06) | (0.04) | (0.04) | (0.13) | (0.07) | (0.06) | |
R2 | 0.86 | 0.84 | 0.79 | 0.88 | 0.81 | 0.79 |
LM Lag | 359 *** | 15 *** | 34.9 *** | 5.45 ** | 4.61 ** | 3.32 ** |
LM Lag (Robust) | 357 *** | 15 *** | 34.8 *** | 5.51 ** | 4.56 ** | 3.28 ** |
LM Error | 1.91 | 0.05 | 0.11 | 0.01 | 0.05 | 0.04 |
LM Error (Robust) | 0.17 | 0.01 | 0.01 | 0.07 | 0.01 | 0.01 |
Weight Type | WD | WE | WM | WD | WE | WM |
Explanatory Variables | EP1 | EP2 | ||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
COR2 | 0.09 *** | 0.08 *** | 0.07 *** | 0.32 ** | 0.38 *** | 0.37 *** |
(0.11) | (0.11) | (0.11) | (0.13) | (0.13) | (0.13) | |
SE | 0.02 ** | 0.02 ** | 0.02 ** | 0.07 *** | 0.08 *** | 0.08 *** |
(0.02) | (0.02) | (0.02) | (0.03) | (0.03) | (0.03) | |
COR2*SE | 0.01 *** | 0.01 ** | 0.01 ** | 0.02 *** | 0.02 *** | 0.02 *** |
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | |
Y | 1.58 *** | 1.58 *** | 1.62 *** | 0.56 ** | 0.31 ** | 0.38 ** |
(0.23) | (0.22) | (0.23) | (0.26) | (0.27) | (0.27) | |
Y2 | −0.04 *** | −0.04 ** | −0.04 ** | −0.04 *** | −0.02 ** | 0.03 ** |
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | |
IS | 0.01 * | 0.01 * | 0.01 * | 0.01 *** | 0.01 ** | 0.01 *** |
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | |
PD | −0.49 *** | −0.38 *** | −0.33 ** | −0.81 *** | −0.64 *** | −0.69 *** |
(0.14) | (0.13) | (0.13) | (0.17) | (0.17) | (0.17) | |
OPEN | 0.01 *** | 0.01 ** | 0.01 ** | 0.01 ** | 0.01 ** | 0.01 ** |
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | |
EE | 0.71 *** | 0.72 *** | 0.71 *** | 0.85 | 0.74 *** | 0.76 *** |
(0.05) | (0.05) | (0.05) | (0.01) | (0.07) | (0.07) | |
URB | 0.02 *** | 0.02 *** | 0.02 *** | 0.01 ** | 0.01 * | 0.01 * |
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | |
0.29 *** | 0.28 *** | 0.23 *** | 0.67 *** | 0.14 ** | 0.04 * | |
(0.06) | (0.04) | (0.04) | (0.13) | (0.07) | (0.06) | |
R2 | 0.84 | 0.88 | 0.85 | 0.87 | 0.87 | 0.83 |
Weight Type | WD | WE | WM | WD | WE | WM |
Explanatory Variables | Eastern | Central | Western | |||
---|---|---|---|---|---|---|
EP1 | EP2 | EP1 | EP2 | EP1 | EP2 | |
(1) | (2) | (3) | (4) | (5) | (6) | |
COR1 | 0.01 * | 0.59 *** | 0.02 * | 0.71 *** | 0.03 * | 0.68 *** |
(0.14) | (0.16) | (0.15) | (0.16) | (0.17) | (0.18) | |
SE | 0.01 ** | 0.14 *** | 0.05 ** | 0.21 *** | 0.04 ** | 0.13 *** |
(0.03) | (0.03) | (0.04) | (0.04) | (0.03) | (0.03) | |
COR1*SE | 0.01 ** | 0.04 *** | 0.02 ** | 0.06 *** | 0.02 ** | 0.05 *** |
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | |
Y | 1.08 ** | 0.36 *** | 0.32 ** | 2.44 *** | 1.81 *** | 0.44 ** |
(0.54) | (0.67) | (0.44) | (0.59) | (0.42) | (0.46) | |
Y2 | −0.01 ** | −0.02 ** | −0.04 * | −0.13 *** | −0.05 ** | −0.03 ** |
(0.02) | (0.03) | (0.02) | (0.03) | (0.02) | (0.02) | |
IS | 0.01 | 0.01 | 0.01 *** | 0.01 *** | 0.01 ** | 0.01 ** |
(0.01) | (0.01) | (0.01) | (0.00) | (0.00) | (0.00) | |
PD | −0.89 *** | −0.89 *** | −0.44 | −1.69 * | −0.61 * | −0.41 |
(0.24) | (0.28) | (0.41) | (0.51) | (0.32) | (0.39) | |
OPEN | 0.01 ** | 0.01 ** | 0.01 ** | 0.01 *** | 0.01 ** | 0.01 ** |
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | |
EE | 0.61 *** | 0.67 *** | 0.18 * | 0.36 *** | 0.71 *** | 0.88 *** |
(0.09) | (0.12) | (0.10) | (0.12) | (0.11) | (0.14) | |
URB | 0.02 *** | 0.01 | 0.02 *** | 0.01 | 0.01 | 0.01 |
(0.01) | (0.01) | (0.01) | (0.01) | (0.01) | (0.01) | |
0.01 ** | 0.36 *** | 0.28 *** | 0.09 ** | 0.34 *** | 0.56 *** | |
(0.08) | (0.12) | (0.08) | (0.12) | (0.08) | (0.15) | |
R2 | 0.83 | 0.80 | 0.83 | 0.92 | 0.89 | 0.84 |
Weight Type | WD | WD | WD | WD | WD | WD |
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Wang, S.; Yuan, Y.; Wang, H. Corruption, Hidden Economy and Environmental Pollution: A Spatial Econometric Analysis Based on China’s Provincial Panel Data. Int. J. Environ. Res. Public Health 2019, 16, 2871. https://doi.org/10.3390/ijerph16162871
Wang S, Yuan Y, Wang H. Corruption, Hidden Economy and Environmental Pollution: A Spatial Econometric Analysis Based on China’s Provincial Panel Data. International Journal of Environmental Research and Public Health. 2019; 16(16):2871. https://doi.org/10.3390/ijerph16162871
Chicago/Turabian StyleWang, Shi, Yizhou Yuan, and Hua Wang. 2019. "Corruption, Hidden Economy and Environmental Pollution: A Spatial Econometric Analysis Based on China’s Provincial Panel Data" International Journal of Environmental Research and Public Health 16, no. 16: 2871. https://doi.org/10.3390/ijerph16162871
APA StyleWang, S., Yuan, Y., & Wang, H. (2019). Corruption, Hidden Economy and Environmental Pollution: A Spatial Econometric Analysis Based on China’s Provincial Panel Data. International Journal of Environmental Research and Public Health, 16(16), 2871. https://doi.org/10.3390/ijerph16162871