Local Government Competition and Regional Green Development in China: The Mediating Role of Environmental Regulation
Abstract
:1. Introduction
2. Literature Review
2.1. The Conceptual Evolution of Green Development
2.2. Measurement of Green Development and Its Influencing Factors
2.3. Local Government Competition and Green Development
3. Measurement and Decomposition of Green Development
3.1. Methods
3.1.1. Total-Factor Non-Radial Directional Distance Function and Slack-Based Measure Data Envelopment Analysis (SBM-DEA) Model
3.1.2. Malmquist–Luenberger Index
3.2. Relevant Data Processing
3.3. Measurement Results and Analysis
3.3.1. Green Development Efficiency Analysis
3.3.2. Driving Factors of Green Development
4. Econometric Model Variable Setting and Data Processing
4.1. Mediating Effect Model
4.2. Variable Setting
4.2.1. Core Independent Variable and Intervening Variable
4.2.2. Control Variables
4.3. Data Description
5. Empirical Results and Discussion
5.1. Benchmark Regression Results
5.2. Discussion on Endogenous and Robustness
5.3. The Test of Mediating Effect Based on Environmental Regulation
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Region | Mean Value | Ranking | Region | Mean Value | Ranking | Region | Mean Value | Ranking |
---|---|---|---|---|---|---|---|---|
Beijing | 1.0000 | 1 | Fujian | 0.6431 | 11 | Xinjiang | 0.3872 | 21 |
Shanghai | 1.0000 | 2 | Heilongjiang | 0.6232 | 12 | Hubei | 0.3825 | 22 |
Guangdong | 1.0000 | 3 | Inner Mongolia | 0.5721 | 13 | Chongqing | 0.3592 | 23 |
Hainan | 1.0000 | 4 | Liaoning | 0.5318 | 14 | Hebei | 0.3450 | 24 |
Qinghai | 1.0000 | 5 | Jiangxi | 0.4932 | 15 | Shaanxi | 0.3409 | 25 |
Tianjin | 0.9321 | 6 | Hunan | 0.4840 | 16 | Sichuan | 0.3276 | 26 |
Jiangsu | 0.8203 | 7 | Jilin | 0.4676 | 17 | Shanxi | 0.3238 | 27 |
Shandong | 0.7896 | 8 | Henan | 0.4653 | 18 | Gansu | 0.3199 | 28 |
Zhejiang | 0.6841 | 9 | Guangxi | 0.4569 | 19 | Yunnan | 0.3122 | 29 |
Ningxia | 0.6531 | 10 | Anhui | 0.4369 | 20 | Guizhou | 0.2804 | 30 |
Province | TFP | Technical Progress | Pure Technical Efficiency | Scale Efficiency |
---|---|---|---|---|
Beijing | 1.1524 | 1.1524 | 1.0000 | 1.0000 |
Tianjin | 1.0914 | 1.1650 | 1.0109 | 0.9897 |
Hebei | 0.9961 | 1.0371 | 0.9724 | 0.9937 |
Liaoning | 1.0210 | 1.0720 | 0.9666 | 0.9958 |
Shanghai | 1.1097 | 1.1097 | 1.0000 | 1.0000 |
Jiangsu | 1.0670 | 1.0881 | 1.0383 | 0.9705 |
Zhejiang | 1.0493 | 1.0891 | 0.9906 | 0.9771 |
Fujian | 1.0087 | 1.0649 | 0.9587 | 0.9934 |
Shandong | 1.0143 | 1.0904 | 1.0437 | 1.0323 |
Guangdong | 1.0132 | 1.0536 | 1.0000 | 0.9651 |
Hainan | 1.0309 | 1.1191 | 1.0000 | 0.9464 |
Eastern Region | 1.0504 | 1.0947 | 0.9983 | 0.9876 |
Shanxi | 1.0133 | 1.0315 | 0.9926 | 0.9904 |
Jilin | 0.9924 | 1.0437 | 0.9564 | 1.0100 |
Helongjiang | 0.9549 | 1.0440 | 0.9505 | 1.0038 |
Anhui | 0.9553 | 1.0048 | 0.9689 | 1.0030 |
Jiangxi | 0.9798 | 1.0422 | 0.9490 | 1.0180 |
Henan | 1.0029 | 1.0283 | 0.9976 | 0.9967 |
Hubei | 1.0041 | 1.0336 | 0.9855 | 0.9919 |
Hunan | 0.9726 | 1.0299 | 0.9557 | 0.9918 |
Central Region | 0.9844 | 1.0323 | 0.9695 | 1.0007 |
Inner Mongolia | 1.0571 | 1.1140 | 1.0447 | 1.0949 |
Guangxi | 1.0099 | 1.0523 | 0.9803 | 1.0095 |
Chongqing | 1.0103 | 1.0359 | 0.9779 | 1.0026 |
Sichuan | 0.9937 | 1.0280 | 0.9783 | 0.9921 |
Guizhou | 0.9938 | 1.0199 | 0.9782 | 0.9978 |
Yunnan | 0.9877 | 1.0284 | 0.9710 | 0.9922 |
Shaanxi | 1.0134 | 1.0378 | 0.9791 | 1.0018 |
Gansu | 0.9959 | 1.0242 | 0.9989 | 0.9769 |
Qinghai | 1.0180 | 1.0547 | 1.0000 | 0.9655 |
Ningxia | 1.0250 | 1.0546 | 1.0384 | 1.0091 |
Xinjiang | 1.0135 | 1.0621 | 0.9726 | 0.9830 |
Western Region | 1.0107 | 1.0465 | 0.9927 | 1.0023 |
Whole Country | 1.0183 | 1.0604 | 0.9886 | 0.9965 |
Time Interval | TFP | Technical Progress | Pure Technical Efficiency | Scale Efficiency | Time Interval | TFP | Technical Progress | Pure Technical Efficiency | Scale Efficiency |
---|---|---|---|---|---|---|---|---|---|
1997–1998 | 0.9478 | 0.9450 | 0.9847 | 1.0276 | 2007–2008 | 1.0782 | 1.1838 | 0.9651 | 0.9790 |
1998–1999 | 1.0784 | 1.0724 | 1.0244 | 0.9867 | 2008–2009 | 0.9609 | 0.9975 | 0.9994 | 0.9698 |
1999–2000 | 1.0588 | 1.0779 | 0.9594 | 1.0475 | 2009–2010 | 1.0758 | 1.0897 | 0.9955 | 0.9968 |
2000–2001 | 1.0351 | 1.1754 | 1.0172 | 0.9290 | 2010–2011 | 0.7067 | 0.6522 | 1.1911 | 0.9593 |
2001–2002 | 0.9617 | 1.0725 | 0.8637 | 1.1112 | 2011–2012 | 1.0307 | 1.0628 | 0.9875 | 0.9831 |
2002–2003 | 0.9818 | 1.0012 | 0.9642 | 1.0296 | 2012–2013 | 1.0705 | 1.1174 | 0.9857 | 0.9740 |
2003–2004 | 1.0368 | 1.0549 | 0.9790 | 1.0048 | 2013–2014 | 1.0285 | 1.0748 | 0.9638 | 1.0229 |
2004–2005 | 0.9896 | 1.0588 | 0.9877 | 0.9685 | 2014–2015 | 1.0250 | 1.0670 | 0.9856 | 0.9929 |
2005–2006 | 1.0346 | 1.1041 | 0.9507 | 0.9911 | 2015–2016 | 1.1007 | 1.1234 | 1.0248 | 0.9668 |
2006–2007 | 1.0688 | 1.1260 | 0.9567 | 0.9925 | 2016–2017 | 1.0948 | 1.1507 | 0.9852 | 0.9971 |
Mean Value | 1.0183 | 1.0604 | 0.9886 | 0.9965 |
Variables | gde | gcgdp | gcfre | gcfdi | erl | mar | tec | str | open | hum | cap |
---|---|---|---|---|---|---|---|---|---|---|---|
Mean | 0.5811 | 0.1311 | 1.9795 | 5.9858 | 1.1692 | 5.1101 | 8.5105 | 0.8780 | 0.2996 | 8.4473 | 9.1901 |
Median | 0.4122 | 0.1211 | 2.0450 | 6.1356 | 1.0761 | 4.8873 | 8.4141 | 0.8798 | 0.1252 | 8.4607 | 9.3355 |
Maximum | 1.0000 | 0.3227 | 6.3057 | 9.0489 | 4.2314 | 10.0000 | 12.7149 | 0.9964 | 1.6985 | 12.5025 | 11.2807 |
Minimum | 0.2198 | −0.2240 | 0.5577 | 2.1527 | 0.0038 | 1.1030 | 4.0254 | 0.6532 | 0.0164 | 4.6926 | 6.7749 |
Std.Dev. | 0.3003 | 0.0620 | 0.8200 | 1.4297 | 0.6775 | 1.9388 | 1.7234 | 0.0640 | 0.3711 | 1.1011 | 1.0409 |
Skewness | 0.5224 | 0.0621 | 0.6249 | −0.2934 | 1.3384 | 0.4787 | 0.0895 | −0.4127 | 1.9906 | 0.3003 | −0.1796 |
Kurtosis | 1.4956 | 4.4086 | 3.7014 | 2.3227 | 5.8606 | 2.6844 | 2.6228 | 3.5958 | 6.1476 | 4.0310 | 1.8918 |
Jarque–Bera | 88.0617 | 52.4860 | 53.9108 | 21.0813 | 402.9012 | 26.6721 | 4.5767 | 27.2043 | 676.1262 | 37.3746 | 35.6279 |
Observations | 630 | 630 | 630 | 630 | 630 | 630 | 630 | 630 | 630 | 630 | 630 |
Cross Sections | 30 | 30 | 30 | 30 | 30 | 30 | 30 | 30 | 30 | 30 | 30 |
Explanatory Variable | gcgdp | gcfre | gcfdi | |||
---|---|---|---|---|---|---|
Model | (1) | (2) | (3) | (4) | (5) | (6) |
β1 | −0.6040 *** | −0.4190 *** | −0.1610 *** | −0.1258 *** | −0.0975 *** | −0.0517 *** |
(−5.31) | (−3.76) | (−8.56) | (−5.92) | (−12.89) | (−4.28) | |
mar | −0.0304 *** | −0.0448 *** | −0.0259 ** | |||
(−2.84) | (−4.12) | (−2.41) | ||||
tec | 0.0644 *** | 0.0936 *** | 0.0873 *** | |||
(3.59) | (5.62) | (5.22) | ||||
str | 0.2600 | 0.0975 | 0.3050 * | |||
(1.46) | (0.54) | (1.72) | ||||
open | −0.1070 * | −0.1560 *** | −0.1280 ** | |||
(−1.96) | (−2.99) | (−2.41) | ||||
hum | 0.0520 ** | 0.0564 *** | 0.0392 * | |||
(2.41) | (2.59) | (1.80) | ||||
cap | −0.1390 *** | −0.1390 *** | −0.1180 *** | |||
(−7.35) | (−7.42) | (−5.84) | ||||
Region/Time | Yes | Yes | Yes | Yes | Yes | Yes |
Adj R2 | 0.0449 | 0.2280 | 0.1091 | 0.2996 | 0.2171 | 0.2929 |
N | 630 | 630 | 630 | 630 | 630 | 630 |
Method | Endogenous Discussion | Robustness Test | ||||
---|---|---|---|---|---|---|
DIF-GMM | Tobit | |||||
Model | (1) | (2) | (3) | (4) | (5) | (6) |
Explanatory Variable | gcgdp | gcfre | gcfdi | gcgdp | gcfre | gcfdi |
β1 | 0.0105 | −0.0540 *** | −0.0070 ** | −0.4940 *** | −0.1410 *** | −0.0409 ** |
(0.62) | (−5.91) | (−2.00) | (−3.23) | (−5.26) | (−2.18) | |
L.gde | 0.5820 *** | 0.5650 *** | 0.5730 *** | |||
(35.39) | (32.30) | (43.99) | ||||
sigma_u | 0.5500 *** | 0.5860 *** | 0.6150 *** | |||
(5.63) | (5.93) | (5.91) | ||||
sigma_e | 0.1870 *** | 0.1830 *** | 0.1870 *** | |||
(27.28) | (27.30) | (27.26) | ||||
AR(2)_P | 1.8959 {0.0580} | 1.8855 {0.0594} | 1.8805 {0.0600} | |||
Sargan_P | 23.0303 {1.0000} | 24.3448 {1.0000} | 23.5503 {1.0000} | |||
Control Variables | Yes | Yes | Yes | Yes | Yes | Yes |
Region/Time | Yes | Yes | Yes | Yes | Yes | Yes |
N | 570 | 570 | 570 | 630 | 630 | 630 |
Model | (1) | (2) | (3) | (4) | (5) | (6) |
---|---|---|---|---|---|---|
Explained Variable | erl | gde | erl | gde | erl | gde |
gcgdp | 0.5580 | −0.4060 *** | ||||
(1.51) | (−3.65) | |||||
gcfre | −0.4110 *** | −0.1140 *** | ||||
(−6.52) | (−5.68) | |||||
gcfdi | 0.1050 *** | −0.0496 *** | ||||
(2.63) | (−4.09) | |||||
erl | −0.0194 | −0.0434 *** | −0.0221 * | |||
(−1.57) | (−3.45) | (−1.78) | ||||
Control Variables | Yes | Yes | Yes | Yes | Yes | Yes |
Region/Time | Yes | Yes | Yes | Yes | Yes | Yes |
Adj R2 | 0.1083 | 0.5239 | 0.1648 | 0.3134 | 0.1152 | 0.6462 |
N | 630 | 630 | 630 | 630 | 630 | 630 |
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Zhang, N.; Deng, J.; Ahmad, F.; Draz, M.U. Local Government Competition and Regional Green Development in China: The Mediating Role of Environmental Regulation. Int. J. Environ. Res. Public Health 2020, 17, 3485. https://doi.org/10.3390/ijerph17103485
Zhang N, Deng J, Ahmad F, Draz MU. Local Government Competition and Regional Green Development in China: The Mediating Role of Environmental Regulation. International Journal of Environmental Research and Public Health. 2020; 17(10):3485. https://doi.org/10.3390/ijerph17103485
Chicago/Turabian StyleZhang, Na, Jinqian Deng, Fayyaz Ahmad, and Muhammad Umar Draz. 2020. "Local Government Competition and Regional Green Development in China: The Mediating Role of Environmental Regulation" International Journal of Environmental Research and Public Health 17, no. 10: 3485. https://doi.org/10.3390/ijerph17103485