Journal of Resources and Ecology >
Promote or Inhibit? The Green Effect of Environmental Regulation in China—Based on the Perspective of FDI
Received date: 2023-02-11
Accepted date: 2023-04-06
Online published: 2023-08-02
Supported by
The Scientific Research Key Project of Anhui(SK2021A0402)
The Excellent Talents Support Project(2022KYQD008)
Promoting the green development effect characterized by green total factor productivity (GTFP) is the key to achieving high-quality development in the new era. Using the 2001‒2021 inter-provincial panel data, the energy and environmental factors were simultaneously included in the analysis framework for assessing the green effect of environmental regulations in China. The Malmquist-Luenberger index based on the SBM directional distance function was used to measure the GTFP and its decomposition terms, the dynamic panel model was further constructed, and the GMM method was used to empirically test the direct and indirect effects of three types of environmental regulation and foreign direct investment (FDI) on GTFP. The results show that China’s GTFP is growing at an average annual rate of 2.13%, green technology progress is the source of GTFP growth, and the GTFP regional gap is expanding. There is not a non-linear effect in command-controlled environmental regulation, while the economic incentive type and the voluntary agreement type of environmental regulation respectively show a “U” shaped relationship and an inverted “U” shaped relationship. The control type regulation does not have an indirect effect on GTFP through FDI, but the incentive type and protocol type regulations can drive the promotion of GTFP indirectly through FDI. The GTFP lifting effects of the different types of environmental regulation and FDI show regional heterogeneity. Exploring the green development effect and characteristics of environmental regulation has important theoretical significance and practical value for selecting rational environmental regulation types, adopting differentiated environmental regulation intensities, implementing two-wheel drive to boost GTFP growth, realizing the benign interactions between environmental regulation and FDI, and ultimately promoting high-quality economic development.
Key words: environmental regulation; FDI; GTFP
CHENG Yongsheng , ZHANG Deyuan , WANG Xia . Promote or Inhibit? The Green Effect of Environmental Regulation in China—Based on the Perspective of FDI[J]. Journal of Resources and Ecology, 2023 , 14(5) : 951 -964 . DOI: 10.5814/j.issn.1674-764x.2023.05.006
Table 1 Average annual growth rates of the GTFP and decomposition terms |
Province | GTFP index | Green technology efficiency | Green technology progress | Province | GTFP index | Green technology efficiency | Green technology progress |
---|---|---|---|---|---|---|---|
Beijing | 1.08 | 1.01 | 1.07 | Hunan | 1.06 | 1.00 | 1.06 |
Tianjin | 1.08 | 1.02 | 1.06 | Guangdong | 0.96 | 0.96 | 1.00 |
Hebei | 1.02 | 0.98 | 1.03 | Guangxi | 1.05 | 1.01 | 1.04 |
Shanxi | 0.95 | 0.99 | 0.97 | Hainan | 0.99 | 0.98 | 1.02 |
Inner Mongolia | 1.00 | 0.99 | 1.01 | Sichuan | 1.06 | 1.01 | 1.05 |
Liaoning | 1.00 | 0.99 | 1.01 | Chongqing | 0.98 | 1.00 | 0.97 |
Jilin | 1.01 | 0.99 | 1.01 | Guizhou | 1.00 | 0.98 | 1.02 |
Heilongjiang | 1.00 | 0.99 | 1.01 | Yunnan | 0.99 | 0.99 | 1.00 |
Shanghai | 1.11 | 1.03 | 1.07 | Shaanxi | 0.98 | 0.98 | 1.00 |
Jiangsu | 1.07 | 1.02 | 1.05 | Gansu | 1.01 | 0.99 | 1.02 |
Zhejiang | 1.08 | 1.02 | 1.06 | Qinghai | 0.97 | 0.97 | 1.00 |
Anhui | 1.02 | 1.00 | 1.02 | Ningxia | 1.00 | 0.98 | 1.02 |
Fujian | 1.50 | 1.41 | 1.06 | Xinjiang | 1.02 | 0.99 | 1.03 |
Jiangxi | 1.15 | 1.13 | 1.02 | Nationwide | 1.04 | 1.01 | 1.03 |
Shandong | 1.04 | 1.00 | 1.04 | Eastern | 1.08 | 1.04 | 1.04 |
Henan | 1.01 | 0.99 | 1.02 | Midwestern | 1.01 | 1.00 | 1.02 |
Hubei | 1.03 | 1.00 | 1.03 | - | - | - | - |
Note: The total of 12 provinces in the eastern region includes Beijing, Tianjin, Hebei, Liaoning, Shandong, Jiangsu, Zhejiang, Shanghai, Fujian, Guangdong, Hainan and Guangxi, and the remaining provinces are the midwestern region. |
Table 2 Econometric regression results for the national sample |
Variables | Command control environmental regulation | Economic incentive-based environmental regulation | Voluntary agreement-based environmental regulation | |||
---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
GTFPt-1 | 0.291*** (7.99) | 0.322*** (6.58) | 0.613*** (19.25) | 0.580*** (16.10) | 0.593*** (20.26) | 0.570*** (18.91) |
ERC | -0.0211 (-1.26) | 0.031 (1.32) | ||||
ERC2 | 0.214 (1.10) | |||||
FDI | 1.905 (1.36) | -1.238* (-1.94) | -0.749** (-2.24) | |||
ERC×FDI | 1.052 (1.24) | |||||
ERP | -0.502*** (-4.18) | 0.201*** (5.09) | ||||
ERP2 | 2.032** (2.22) | |||||
ERP×FDI | 1.093** (2.35) | |||||
ERS | 0.182*** (3.03) | 0.095* (1.81) | ||||
ERS2 | -3.322*** (-2.99) | |||||
ERS×FDI | 1.297*** (3.27) | |||||
Control variables | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
Sargan test | 20.037 [1.000] | 19.887 [1.000] | 25.441 [1.000] | 21.932 [1.000] | 19.312 [1.000] | 21.967 [1.000] |
AR(1) test | -3.891 [0.000] | -3.340 [0.000] | -4.023 [0.000] | -4.351 [0.000] | -3.600 [0.000] | -3.744 [0.000] |
AR(2) test | 1.732 [0.191] | 1.652 [0.211] | 1.383 [0.123] | 1.491 [0.176] | 1.842 [0.156] | 1.731 [0.161] |
Regional effects | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
Time effect | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
Number of samples | 660 | 660 | 660 | 660 | 660 | 660 |
Note: ***, ** and * denote significance at the 1%, 5% and 10% significance levels, respectively. The data in ( ) are the Z statistic and the data in [ ] are the test probability of the corresponding test. |
Fig. 1 “U” shaped curve of the relationship between incentive-based regulation and GTFP |
Fig. 2 Inverted “U” shaped curve of the relationship between protocol-based regulation and GTFP |
Table 3 Econometric regression results for the regional samples |
Variables | Command control environmental regulation | Economic incentive-based environmental regulation | Voluntary agreement-based environmental regulation | |||
---|---|---|---|---|---|---|
Eastern | Midwestern | Eastern | Midwestern | Eastern | Midwestern | |
GTFPt-1 | 0.304*** (3.94) | 0.276** (2.10) | 0.763*** (7.14) | 0.352*** (3.55) | 0.502*** (4.03) | 0.431*** (3.86) |
ERC | -0.202 (-1.18) | -0.323** (-2.18) | ||||
ERC2 | 1.233 (0.85) | 0.982 (0.70) | ||||
FDI | -1.443** (-2.25) | -1.121 (-1.09) | -2.298*** (-3.18) | -1.903 (-1.34) | -2.092** (-2.33) | -1.364 (-0.47) |
ERC×FDI | 2.368 (0.99) | -3.086* (-1.97) | ||||
ERP | -0.465** (-2.40) | -0.288 (-1.06) | ||||
ERP2 | 4.233** (2.19) | 2.496 (1.18) | ||||
ERP×FDI | 3.591*** (4.43) | -1.791 (-1.44) | ||||
ERS | -1.359* (-1.94) | -0.544 (-1.12) | ||||
ERS2 | 6.608* (1.92) | 2.956 (1.01) | ||||
ERS×FDI | 2.438*** (7.10) | -1.432 (-0.78) | ||||
Control variables | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
Sargan test | 12.312 [1.000] | 10.073 [1.000] | 11.094 [1.000] | 9.851 [1.000] | 8.733 [1.000] | 8.644 [1.000] |
AR(1) test | -2.132 [0.039] | -1.903 [0.020] | -2.166 [0.034] | -2.732 [0.010] | -2.086 [0.058] | -1.804 [0.002] |
AR(2) test | 1.186 [0.254] | -0.679 [0.487] | 1.211 [0.172] | -0.764 [0.307] | 1.300 [0.187] | 0.471 [0.680] |
Regional effects | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
Time effects | Controlled | Controlled | Controlled | Controlled | Controlled | Controlled |
Number of samples | 264 | 396 | 264 | 396 | 264 | 396 |
Note: ***, ** and * denote significance at the 1%, 5% and 10% significance levels, respectively. The data in ( ) are the Z statistic and the data in [ ] are the test probability of the corresponding test. The direct effect results are combined with the interaction term results in one table due to space limitations. |
Table 4 Robustness test results |
Variables | Command control environmental regulation | Economic incentive-based environmental regulation | Voluntary agreement-based environmental regulation | ||||||
---|---|---|---|---|---|---|---|---|---|
Model 7 | Model 8 | Model 9 | Model 7 | Model 8 | Model 9 | Model 7 | Model 8 | Model 9 | |
ERC | -0.110 (-1.02) | -0.072 (-0.78) | 0.019* (1.98) | ||||||
ERC2 | 0.409 (1.37) | 0.511 (0.86) | 0.281 (1.71) | ||||||
FDI | 1.048 (0.98) | 1.883 (1.55) | 0.900 (0.83) | -1.114** (-2.40) | -0.932*** (-2.99) | -1.307* (-1.96) | -0.873** (-2.31) | -1.463 (-1.51) | -0.894** (-2.24) |
ERC×FDI | 2.210 (1.44) | 1.400* (1.96) | 1.519** (2.21) | ||||||
ERP | -0.202*** (-3.84) | -0.330** (-2.40) | -0.232*** (-5.23) | ||||||
ERP2 | 1.305** (2.26) | 0.856*** (5.82) | 1.644* (1.99) | ||||||
ERP×FDI | 1.843* (1.98) | 1.277** (2.35) | 0.853 (1.49) | ||||||
ERS | 0.302*** (3.03) | 0.244 (0.88) | 0.094 (1.12) | ||||||
ERS2 | -2.029*** (-2.98) | -1.631*** (-4.76) | -0.846** (-2.39) | ||||||
ERS×FDI | 0.658** (2.30) | 1.974* (1.95) | 1.290*** (3.70) |
Note: ***, ** and * denote significance at the 1%, 5% and 10% significance levels, respectively, the Z statistic is given in parentheses, and the direct effect is combined with the interaction term results in a single table due to space limitations. |
[1] |
|
[2] |
|
[3] |
|
[4] |
|
[5] |
|
[6] |
|
[7] |
|
[8] |
|
[9] |
|
[10] |
|
[11] |
|
[12] |
|
[13] |
|
[14] |
|
[15] |
|
[16] |
|
[17] |
|
[18] |
|
[19] |
|
[20] |
Ministry of Commerce. PRC. 2022. China Foreign Investment Development Report (2022): China’s actual use of foreign direct investment grows steadily amid structural adjustment. http://chinawto.mofcom.gov.cn/article/ap/p/202212/20221203374648.shtml. Viewed on 2022-12-20. (in Chinese)
|
[21] |
|
[22] |
|
[23] |
|
[24] |
|
[25] |
|
[26] |
|
[27] |
|
[28] |
|
[29] |
|
[30] |
|
[31] |
|
[32] |
|
[33] |
|
[34] |
|
[35] |
|
[36] |
|
[37] |
|
[38] |
|
[39] |
|
[40] |
|
[41] |
|
/
〈 |
|
〉 |