Effects of Regional Innovation Capability on the Green Technology Efficiency of China’s Manufacturing Industry: Evidence from Listed Companies
Abstract
:1. Introduction
2. Literature Review and Theoretical Framework
2.1. Literature Review
2.2. Theoretical Framework
2.2.1. Direct Effect
2.2.2. Spatial Heterogeneity
2.2.3. Indirect Effect
3. Indicator Construction and Data Sources
3.1. Construction of a Green Technology Efficiency Index System for China’s Manufacturing Industry
3.2. Construction of the Regional Innovation Capability Index System
3.3. Control Variable Setting
3.4. Selection of Intermediary Variables
3.5. Data Sources
4. Research Methods
4.1. Entropy Method
4.2. Stochastic Frontier Analysis
4.3. Spatial Autocorrelation Method
4.3.1. Local Indicators of Spatial Association (LISA)
4.3.2. Bivariate Moran′s I
4.3.3. Bivariate Local Moran′s I
4.4. Empirical Methods
5. Research Results
5.1. Spatial Association Pattern
5.1.1. Spatial Correlation Distribution Characteristics of Green Technology Efficiency and Innovation Capability in the Manufacturing Industry
5.1.2. Bivariate Spatial Correlation Distribution Characteristics of Green Technology Manufacturing Efficiency and Innovation Capability
5.2. Empirical Analysis and Testing
5.2.1. Benchmark Regression Results
5.2.2. Mediation Effect Regression Results
5.2.3. Robustness and Endogenous Test
6. Conclusions and Discussion
Author Contributions
Funding
Conflicts of Interest
References
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Target Layer | Feature Layer | Indicator Layer | Unit |
---|---|---|---|
Regional innovation capability | Knowledge innovation capability | Number of ordinary colleges and universities | Institute instead |
Number of full-time teachers in ordinary colleges and universities | Person | ||
Public library book collection | Ten-thousand volumes | ||
Proportion of education expenditure that made up local public financial expenditure | % | ||
Technological innovation capability | Number of employees in scientific research, technical services, and geological exploration | Person | |
The proportion of foreign capital actually used in that year as a percentage of GDP | % | ||
Total patent grants at the end of the year | Grant | ||
Science and technology expenditure accounts for the local public finance expenditure | % | ||
Innovative infrastructure | Greening rate of the built-up area | % | |
Telecommunications revenue | Ten-thousand yuan | ||
Number of Internet broadband users | Ten-thousand households | ||
Urban road area at the end of the year | Ten-thousand square meters |
Indicator | (1) | (2) | (3) | (4) | (5) | (6) | (7) |
---|---|---|---|---|---|---|---|
Tobit | Tobit | OLS | East | Central | West | Northeast | |
INN | 0.297 *** | 0.211 *** | 0.182 *** | 0.285 *** | 0.041 | 0.188 | −0.117 |
(0.057) | (0.048) | (0.049) | (0.060) | (0.134) | (0.117) | (0.162) | |
GDP | 0.590 *** | 0.239 ** | 0.651 *** | 0.703 *** | 0.045 | 1.074 *** | |
(0.113) | (0.110) | (0.192) | (0.198) | (0.262) | (0.255) | ||
FC | 0.078 *** | 0.040 * | 0.052 *** | 0.152 *** | 0.079 ** | 0.159 *** | |
(0.012) | (0.022) | (0.015) | (0.029) | (0.033) | (0.031) | ||
GI | −0.050 * | −0.049 * | 0.037 | −0.057 | −0.063 | −0.078 * | |
(0.025) | (0.026) | (0.054) | (0.052) | (0.043) | (0.045) | ||
MS | −1.149 *** | −1.493 *** | −1.260 *** | −1.094 *** | −1.184 *** | −1.081 *** | |
(0.099) | (0.094) | (0.157) | (0.226) | (0.187) | (0.248) | ||
Cons | 0.159 *** | 0.677 *** | 1.312 *** | 0.606 ** | 0.642 ** | 1.170 *** | 0.469 |
(0.047) | (0.143) | (0.134) | (0.237) | (0.302) | (0.293) | (0.411) | |
Sigma_u:_cons | 0.238 *** | 0.280 *** | 0.278 *** | 0.279 *** | 0.301 *** | 0.247 *** | |
(0.014) | (0.017) | (0.027) | (0.032) | (0.037) | (0.048) | ||
Sigma_e:_cons | 0.055 *** | 0.045 *** | 0.043 *** | 0.043 *** | 0.048 *** | 0.029 *** | |
(0.001) | (0.001) | (0.002) | (0.002) | (0.003) | (0.003) | ||
N | 887 | 887 | 887 | 371 | 241 | 212 | 63 |
Indicator | (1) | (2) | (3) | (4) | (5) | (6) | (7) |
---|---|---|---|---|---|---|---|
Tobit | Human Capital | Government Revenue | Waste Treatment Rate | ||||
INN | 1.320 *** | 0.901 *** | 0.684 *** | 0.156 *** | 1.051 *** | 0.102 | 1.316 *** |
(0.103) | (0.036) | (0.145) | (0.014) | (0.129) | (0.067) | (0.103) | |
HC | 0.676 *** | ||||||
(0.109) | |||||||
GR | 1.727 *** | ||||||
(0.312) | |||||||
WTR | 0.034 | ||||||
(0.042) | |||||||
Cons | −0.588 *** | 0.134 *** | −0.640 *** | −0.031 *** | −0.537 *** | 0.847 *** | −0.617 *** |
(0.077) | (0.027) | (0.082) | (0.011) | (0.090) | (0.050) | (0.085) | |
Sigma:_cons | 0.236 *** | 0.077 *** | 0.225 *** | 0.029 *** | 0.240 *** | 0.154 *** | 0.236*** |
(0.006) | (0.002) | (0.006) | (0.001) | (0.006) | (0.004) | (0.006) | |
Obs. | 887 | 717 | 717 | 717 | 717 | 887 | 887 |
Sobel Z | 0.0000 | 4.722 | 0.6596 | ||||
Standard error | 0.1002 | 0.0530 | 0.0810 | ||||
- P-value | 0.0000 | 0.0000 | 0.2316 | ||||
Proportion | 0.4740 | 0.1929 | 0.0025 |
Indicator | (1) | (2) | (3) | (4) | (6) | (7) |
---|---|---|---|---|---|---|
Tobit | 2SLS | IV–Tobit | ||||
INN | 0.219 *** | 0.529 *** | 0.214 *** | 0.244 *** | 2.327 *** | 2.237 *** |
(0.055) | (0.071) | (0.033) | (0.055) | (0.807) | (0.794) | |
GDP | 0.742 *** | 1.026 *** | −4.567 *** | 0.548 *** | −2.322 *** | −2.231 *** |
(0.122) | (0.145) | (0.144) | (0.114) | (0.846) | (0.815) | |
FC | 0.096 *** | 0.029 | 0.797 *** | 0.082 *** | 0.072 | 0.078 |
(0.015) | (0.037) | (0.063) | (0.012) | (0.071) | (0.066) | |
GI | −0.045 | −0.094 ** | −0.001 | −0.047 * | −0.197 ** | −0.194 ** |
(0.029) | (0.038) | (0.014) | (0.025) | (0.091) | (0.093) | |
MS | −1.178 *** | −0.966 *** | −0.268 *** | −1.116 *** | 3.357 *** | 3.391 *** |
(0.121) | (0.143) | (0.067) | (0.098) | (0.187) | (0.191) | |
Cons | 0.507 *** | −0.035 | 4.131 *** | 0.658 *** | −2.035 *** | −2.080 *** |
(0.164) | (0.200) | (0.122) | (0.144) | (0.219) | (0.222) | |
Sigma_u:_cons | 0.258 *** | 0.262 *** | 0.318 *** | 0.278 *** | ||
(0.017) | (0.016) | (0.019) | (0.017) | |||
Sigma_e:_cons | 0.046 *** | 0.052 *** | 0.024 *** | 0.044 *** | ||
(0.001) | (0.002) | (0.001) | (0.001) | |||
N | 668 | 505 | 759 | 863 | 887 | 887 |
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Fu, Y.; Supriyadi, A.; Wang, T.; Wang, L.; Cirella, G.T. Effects of Regional Innovation Capability on the Green Technology Efficiency of China’s Manufacturing Industry: Evidence from Listed Companies. Energies 2020, 13, 5467. https://doi.org/10.3390/en13205467
Fu Y, Supriyadi A, Wang T, Wang L, Cirella GT. Effects of Regional Innovation Capability on the Green Technology Efficiency of China’s Manufacturing Industry: Evidence from Listed Companies. Energies. 2020; 13(20):5467. https://doi.org/10.3390/en13205467
Chicago/Turabian StyleFu, Yu, Agus Supriyadi, Tao Wang, Luwei Wang, and Giuseppe T. Cirella. 2020. "Effects of Regional Innovation Capability on the Green Technology Efficiency of China’s Manufacturing Industry: Evidence from Listed Companies" Energies 13, no. 20: 5467. https://doi.org/10.3390/en13205467