Government R&D Subsidies, Environmental Regulations, and Their Effect on Green Innovation Efficiency of Manufacturing Industry: Evidence from the Yangtze River Economic Belt of China
Abstract
:1. Introduction
2. Theoretical Basis and Research
2.1. Mechanism of Government R&D Subsidies Affecting MGIE
2.2. The Mechanism of Influence of Environmental Regulations on MGIE
2.3. Mechanism of the Joint Effect of R&D Subsidies and Environmental Regulation on MGIE
3. Data and Methods
3.1. Model Construction
3.2. Selection of Variables, Source of Data, and Data Processing
3.2.1. Input Variables
3.2.2. Output Variables
4. Empirical Results and Analysis
4.1. Analysis of Estimation Results
4.2. Analysis of Endogenous Problems
4.3. Robustness Test
5. Conclusion and Policy Implications
6. Discussion
Author Contributions
Funding
Conflicts of Interest
References
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Name | Indicator | Unit | ||
---|---|---|---|---|
variables about efficiency calculation | input variables | human input | the full-time equivalent of R&D personnel in industrial enterprises above designated size | person-year |
financial input | the R&D expenditure in industrial enterprises above designated size | ten thousand yuan | ||
resources (energy) input | the energy consumption per unit of industrial production | ten thousand yuan/ton of standard coal | ||
output variables | expected output | the number of domestic invention patents | pcs | |
sales revenue of new product | ten thousand yuan | |||
unexpected output | industrial environmental pollution index | |||
variables about effect analysis | explanatory variables | government R&D subsidies | the logarithm of the government funds for science and technology activities of large- and medium-sized industrial enterprises | |
environmental regulation | the ratio of the actual amount of industrial pollution control completed investment to industrial added value | % | ||
control variables | enterprises scale | the logarithm of the average original value of fixed assets of large- and medium-sized enterprises | ten thousand yuan | |
industrial structure | the proportion of the output value of the secondary industry to gross domestic product (GDP) | 1 | ||
agglomeration | the logarithm of the number of large- and medium-sized industrial enterprises | |||
openness | the proportion of fixed assets investment funds of large- and medium-sized enterprises from foreign capital | % |
Variable | Obs | Mean | SD | Min | Max |
---|---|---|---|---|---|
human input | 110 | 92,871.16 | 107,281 | 6134.28 | 455,468 |
financial input | 110 | 3094,582 | 3537,871 | 77,081.4 | 1.80 × 107 |
resource(energy) input | 110 | 0.662 | 0.483 | 0.193 | 2.277 |
the number of domestic invention patents | 110 | 5.55 × 107 | 6.26 × 107 | 615,675 | 2.90 × 108 |
sales revenue of new product | 110 | 15,675.74 | 21,861.21 | 279 | 140,346 |
unexpected output | 110 | 0.034 | 0.003 | 0.028 | 0.042 |
MGIE | 110 | 0.731 | 0.301 | 0.093 | 1 |
government R&D subsidies | 110 | 11.348 | 0.846 | 9.060 | 12.825 |
environmental regulations | 110 | 1.200 | 0.468 | 0.520 | 2.660 |
enterprises scale | 110 | 9.464 | 0.732 | 7.873 | 11.271 |
industrial structure | 110 | 0.462 | 0.056 | 0.298 | 0.554 |
agglomeration | 110 | 9.385 | 0.852 | 7.753 | 11.090 |
openness | 110 | 0.011 | 0.013 | 0.000 | 0.084 |
Variable | Model 1 | Model 2 | Model 3 | Model 4 |
---|---|---|---|---|
SUB | 0.211 ** (2.34) | 0.147 (1.53) | 0.088 (0.85) | |
ER | −0.888 * (−1.71) | −0.768 (−1.27) | −1.399 ** (−1.98) | |
ER2 | 0.424 ** (2.04) | 0.376 (1.49) | ||
SUB∗ER | 0.372(1.57) | 0.055 (0.95) | ||
sca | −0.401 ** (−2.08) | −0.273 ** (−2.25) | −0.400 *** (−2.47) | −0.404 ** (−2.43) |
indus | −2.380 * (−1.95) | −4.396 *** (−4.11) | −3.001 ** (−2.51) | −4.175 *** (−4.36) |
agg | 0.495 ** *(2.82) | 0.639 *** (5.00) | 0.580 *** (5.18) | 0.575 *** (4.90) |
fdi | 0.543 (0.10) | 3.459 (0.55) | 3.737 (0.75) | 3.723 (0.72) |
χ2 | 112.71 *** (p = 0.0000) | 169.51 ***(p = 0.0000) | 271.96 *** (p = 0.0000) | 252.77 *** (p = 0.0000) |
Variable | Model 1 | Model 2 | Model 3 |
---|---|---|---|
SUB | 0.540 *** (4.43) | 0.633 ** (2.29) | |
ER | −4.396 (−1.22) | 0.052 (0.01) | |
ER2 | 1.521 (1.12) | −0.092 (−0.06) | |
Sca | −0.544 *** (−4.08) | 0.069 (0.47) | −0.615 * (−1.76) |
Indus | −0.791 (−1.19) | −1.543 (−0.62) | 0.462 (0.18) |
Agg | 0.237 *** (3.01) | 0.193 (1.52) | 0.196 ** (1.98) |
Fdi | 1.656 (0.601) | 4.057 (0.75) | 0.980 (0.20) |
con_s | −2.128 *** (3.63) | 1.684 (0.77) | −2.614 (−0.82) |
χ2 | 59.14 *** (p = 0.0001) | 21.35 *** (p = 0.0016) | 48.28 *** (p = 0.0000) |
Wald test | 33.55 *** (p = 0.0000) | 12.04 *** (p = 0.0024) | 52.48 *** (p = 0.0024) |
AR | 138.57 *** (p = 0.0000) | 83.05 *** (p = 0.0000) | 163.97 *** (p = 0.0024) |
Variable | Model 1 | Model 2 | Model 3 | Model 4 |
---|---|---|---|---|
SUB | 0.062 ** (2.12) | 0.042 (1.59) | 0.034 (1.07) | |
ER | −0.888 * (−1.71) | −0.729 * (−1.82) | −1.557 ** (−2.51) | |
ER2 | 0.424 ** (2.04) | 0.351 ** (2.17) | 0.346 ** (2.20) | |
SUB∗ER | 0.074 (1.13) | |||
sca | −0.177 (−1.46) | −0.273 ** (2.25) | −0.267 *** (−2.87) | −0.340 *** (−2.78) |
indus | −0.905 (−0.97) | −4.396 *** (−4.11) | −3.320 ** (−2.53) | −3.342 ** (−2.53) |
agg | 0.353 ** (1.96) | 0.639 *** (5.00) | 0.517 *** (3.79) | 0.501 *** (3.69) |
fdi | −1.136 (−0.26) | 3.459 (0.55) | 1.783 (0.39) | 2.111 (0.47) |
χ2 | 110.86 (p = 0.0000) | 169.51 (p = 0.0000) | 524.57 *** (p = 0.0000) | 628.35 *** (p = 0.0000) |
Variable | Model 1 | Model 2 | Model 3 | Model 4 |
---|---|---|---|---|
L.SUB | 0.208 ** (2.12) | 0.143 * (1.76) | 0.167 (1.21) | |
L.ER | 0.00004 (0.01) | 0.003 (0.78) | 0.003 (0.81) | |
L.ER2 | 0.0110 *** (2.93) | 0.096 *** (4.25) | 0.159 (0.55) | |
L.SUB∗L.ER | −0.016 (0.24) | |||
sca | −0.311 (−2.08) | −0.255 (−1.29) | −0.339 ** (−2.11) | −0.336 ** (−1.98) |
indus | −2.529 ** (−2.12) | −4.459 *** (−3.76) | −3.729 *** (−3.14) | −3.730 *** (−3.26) |
agg | 0.370 ** (2.44) | 0.560 *** (2.97) | 0.474 ** (2.39) | 0.469 ** (2.35) |
fdi | 11.385 ** (−2.19) | 13.086 (1.11) | 12.796 (1.54) | 13.309 (1.43) |
χ2 | 111.68 *** (p = 0.0000) | 134.75 *** (p = 0.0000) | 448.64 *** (p = 0.0000) | 640.12 *** (p = 0.0000) |
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Yi, M.; Wang, Y.; Yan, M.; Fu, L.; Zhang, Y. Government R&D Subsidies, Environmental Regulations, and Their Effect on Green Innovation Efficiency of Manufacturing Industry: Evidence from the Yangtze River Economic Belt of China. Int. J. Environ. Res. Public Health 2020, 17, 1330. https://doi.org/10.3390/ijerph17041330
Yi M, Wang Y, Yan M, Fu L, Zhang Y. Government R&D Subsidies, Environmental Regulations, and Their Effect on Green Innovation Efficiency of Manufacturing Industry: Evidence from the Yangtze River Economic Belt of China. International Journal of Environmental Research and Public Health. 2020; 17(4):1330. https://doi.org/10.3390/ijerph17041330
Chicago/Turabian StyleYi, Ming, Yiqian Wang, Modan Yan, Lina Fu, and Yao Zhang. 2020. "Government R&D Subsidies, Environmental Regulations, and Their Effect on Green Innovation Efficiency of Manufacturing Industry: Evidence from the Yangtze River Economic Belt of China" International Journal of Environmental Research and Public Health 17, no. 4: 1330. https://doi.org/10.3390/ijerph17041330
APA StyleYi, M., Wang, Y., Yan, M., Fu, L., & Zhang, Y. (2020). Government R&D Subsidies, Environmental Regulations, and Their Effect on Green Innovation Efficiency of Manufacturing Industry: Evidence from the Yangtze River Economic Belt of China. International Journal of Environmental Research and Public Health, 17(4), 1330. https://doi.org/10.3390/ijerph17041330