Is Improvement of Innovation Efficiency Conducive to Haze Governance? Empirical Evidence from 283 Chinese Cities
Abstract
:1. Introduction
2. Literature Review and Theoretical Hypotheses
3. Measurement Model and Index Description
3.1. Benchmark Model
3.2. Variable Selection and Data Source
3.3. Spatial Weight Matrix
3.4. Endogenous Issues
4. Benchmark Regression Analysis
4.1. Benchmark Regression
4.2. Robustness Test
5. Influence Difference and Transmission Mechanism
5.1. Regional Differences on the Impact of Innovation Efficiency on Haze pollution
5.2. The Stage Difference in the Impact of Innovation Efficiency on Haze Pollution
5.2.1. Threshold Model Setting
5.2.2. Threshold Regression Analysis
5.3. The Influence Mechanism of Innovation Efficiency Improvement on Haze Pollution
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
lnPM | lnE | (lnE)2 | lnEco | (lnEco)2 | lnpop | lntec | lnsec | lnes | lnpe | lntri | lnfdi | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
lnPM | 1 | |||||||||||
lnE | 0.237 *** | 1 | ||||||||||
(lnE)2 | 0.237 *** | 0.995 *** | 1 | |||||||||
lnEco | 0.396 *** | 0.446 *** | 0.453 *** | 1 | ||||||||
(lnEco)2 | −0.389 *** | −0.116 *** | −0.108 *** | −0.513 *** | 1 | |||||||
lnpop | 0.334 *** | 0.109 *** | 0.110 *** | 0.141 *** | −0.101 *** | 1 | ||||||
lntec | 0.298 *** | 0.539 *** | 0.528 *** | 0.451 *** | −0.222 *** | 0.0727 *** | 1 | |||||
lnsec | 0.140 *** | −0.0694 *** | −0.0750 *** | 0.171 *** | −0.277 *** | −0.00620 | 0.146 *** | 1 | ||||
lnes | 0.178 *** | 0.240 *** | 0.248 *** | 0.318 *** | −0.0351 | 0.168 *** | 0.212 *** | −0.110 *** | 1 | |||
lnpe | 0.309 *** | 0.304 *** | 0.317 *** | 0.446 *** | −0.0314 | 0.395 *** | 0.169 *** | −0.138 *** | 0.374 *** | 1 | ||
lntri | 0.363 *** | 0.206 *** | 0.210 *** | 0.412 *** | −0.226 *** | 0.340 *** | 0.160 *** | 0.134 *** | 0.236 *** | 0.652 *** | 1 | |
lnfdi | 0.467 *** | 0.305 *** | 0.315 *** | 0.553 *** | −0.235 *** | 0.266 *** | 0.280 *** | 0.0857 *** | 0.392 *** | 0.620 *** | 0.501 *** | 1 |
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Variable Type | Variable Name | Indicator | Data Sources |
---|---|---|---|
Explained variable | Haze pollution | PM2.5 annual average concentration | Columbia University International Earth Science Information Network (https://beta.sedac.ciesin.columbia.edu/) |
Core explanatory variable | Innovation efficiency | Input-output ratio of innovative behaviour | Calculated by DEA method |
Threshold variable | The level of economic development | Average night light brightness | NOAA WEBSITE (https://ngdc.noaa.gov/eog/index.html) |
Intermediary variable | Industrial structure | The output value of secondary industry accounts for the proportion of GDP | China City Statistical Yearbook (2013–2017) |
Technical progress | The number of patent authorisations per hundred scientific research practitioners | ||
Energy saving | Annual LPG gas supply | EPS DATABASE | |
Population agglomeration | Population per unit area | China City Statistical Yearbook (2013–2017) | |
Control variable | Fiscal expenditure | Local government general budget expenditure | EPS DATABASE |
Transportation | Total passenger transport of public motor vehicles | China City Statistical Yearbook (2013–2017) | |
Trade openness | The amount of foreign capital actually utilised |
Variable | (1) | (2) | (3) | (4) |
---|---|---|---|---|
FE | RE | FE | RE | |
W1 * lnPM | 1.098 *** (0.361) | 1.044 *** (0.075) | 1.082 *** (0.348) | 0.925 *** (0.075) |
lnE | 0.733 *** (0.093) | 1.322 *** (0.050) | 0.371 *** (0.109) | 0.501 *** (0.101) |
(lnE)2 | −0.084 *** (0.013) | −0.166 *** (0.008) | −0.034 ** (0.015) | −0.054 *** (0.014) |
lnEco | 0.062 *** (0.010) | 0.066 *** (0.009) | 0.046 *** (0.010) | 0.039 *** (0.010) |
(lnEco)2 | −0.025 *** (0.004) | −0.025 *** (0.004) | −0.025 *** (0.004) | −0.027 *** (0.004) |
lnpop | 0.030 *** (0.008) | 0.047 *** (0.008) | 0.023 *** (0.008) | 0.033 *** (0.008) |
lntec | −0.071 *** (0.011) | −0.059 *** (0.010) | −0.073 *** (0.011) | −0.057 *** (0.010) |
lnsec | 0.155 *** (0.036) | 0.170 *** (0.033) | ||
lnes | −0.009 ** (0.004) | −0.008 ** (0.004) | ||
lnpe | 0.027 * (0.016) | 0.045 *** (0.015) | ||
lntri | 0.041 *** (0.014) | 0.047 *** (0.013) | ||
lnFDI | −0.004 (0.006) | 0.001 (0.006) | ||
Adjust R2 | 0.980 | 0.980 | 0.981 | 0.981 |
Wald test (p) | 293.861 (0.000) | 5271.420 (0.000) | 358.234 (0.000) | 5775.189 (0.000) |
Hausman test (p) | 94.820 (0.000) | 117.609 (0.000) |
Variable | Replace Explained Variables | Replace Spatial Weight Matrix | Replace Tool Variables |
---|---|---|---|
W * lnPM | 0.768 *** (0.188) | 0.146 *** (0.036) | 0.923 *** (0.076) |
lnE | 0.135 * (0.227) | 0.614 *** (0.103) | 0.504 *** (0.101) |
(lnE)2 | −0.005 * (0.031) | −0.068 *** (0.015) | −0.054 *** (0.014) |
lnEco | −0.017 (0.012) | 0.034 *** (0.010) | 0.039 *** (0.010) |
(lnEco)2 | −0.016 *** (0.006) | −0.029 *** (0.004) | −0.027 *** (0.004) |
lnpop | 0.024 ** (0.012) | 0.040 *** (0.008) | 0.033 *** (0.008) |
lntec | −0.041 *** (0.015) | −0.045 *** (0.010) | −0.057 *** (0.010) |
lnsec | 0.272 *** (0.054) | 0.201 *** (0.034) | 0.170 *** (0.033) |
lnes | −0.009 ** (0.005) | −0.007 * (0.004) | −0.008 ** (0.004) |
lnpe | −0.027 (0.027) | 0.052 *** (0.015) | 0.045 *** (0.014) |
lntri | 0.035 * (0.019) | 0.043 *** (0.014) | 0.047 *** (0.013) |
lnFDI | 0.003 (0.008) | 0.012 * (0.006) | 0.001 (0.006) |
Adjust R2 | 0.650 | 0.980 | 0.981 |
Wald test (p) | 89.025 (0.000) | 4863.616 (0.000) | 5767.449 (0.000) |
Group | lnPM | lnE | (lnE)2 |
---|---|---|---|
Non-policy pilot cities (mean) | 3.420 | 3.482 | 12.389 |
Innovative pilot cities (mean) | 3.614 | 3.896 | 15.506 |
Mean test (t value) | −6.449 *** | −12.894 *** | −13.461 *** |
Variable | Innovative Pilot City | Non-Pilot City |
---|---|---|
W1 * lnPM | 0.780 * (0.408) | 1.066 *** (0.246) |
lnE | 0.334 (0.299) | −0.601 *** (0.214) |
(lnE)2 | −0.025 (0.040) | 0.079 *** (0.030) |
lnEco | 0.007 (0.024) | 0.002 (0.015) |
(lnEco)2 | −0.025 ** (0.013) | −0.055 *** (0.006) |
lnpop | 0.064 *** (0.019) | 0.045 ** (0.018) |
lntec | 0.041 (0.026) | 0.028 * (0.015) |
lnsec | 0.496 *** (0.103) | 0.064 (0.056) |
lnes | −0.032 *** (0.009) | −0.002 (0.005) |
lnpe | 0.175 *** (0.054) | 0.050 (0.032) |
lntri | −0.106 *** (0.037) | 0.054 * (0.020) |
lnFDI | 0.058 *** (0.020) | 0.003 (0.001) |
Adjust R2 | 0.538 | 0.648 |
Wald test (p) | 186.156 (0.000) | 289.628 (0.000) |
Group | All the Cities | Eastern Cities | Central Cities | Western Cities |
---|---|---|---|---|
Single threshold | 37.920 *** | 29.510 *** | 13.850 * | 17.510 ** |
Double threshold | 14.310 * | 16.590 | 7.480 | 16.020 * |
Three thresholds | 11.690 | 7.820 |
Group | All the Cities | Eastern Cities | Central Cities | Western Cities |
---|---|---|---|---|
Threshold estimate 1 | −0.984 | −0.945 | −0.424 | −1.271 |
95% confidence interval | [−1.008, −0.974] | [−1.016, −0.847] | [−0.438, −0.423] | [−1.295, −1.144] |
Night light brightness 1 | 0.374 | 0.389 | 0.654 | 0.281 |
Threshold estimate 2 | −0.507 | −1.037 | ||
95% confidence interval | [−0.524, −0.505] | [−1.048, −1.034] | ||
Night light brightness 2 | 0.602 | 0.355 |
Variable | All the Cities | Eastern Cities | Central Cities | Western Cities |
---|---|---|---|---|
lnE (lnEco ≤ γ1) | −0.249 *** (0.060) | −0.229 ** (0.090) | 0.420 *** (0.117) | −2.646 *** (0.722) |
lnE (γ1 < lnEco ≤ γ2) | −0.269 *** (0.059) | −0.262 *** (0.090) | 0.431 *** (0.118) | −0.139 (0.258) |
lnE (lnEco > γ2) | −0.276 *** (0.060) | −0.160 (0.259) | ||
W1 * lnPM | 2.241 *** (0.054) | 2.105 *** (0.071) | 2.096 *** (0.090) | 2.552 *** (0.192) |
lnEco | −0.195 *** (0.035) | −0.149 *** (0.054) | −0.272 *** (0.060) | 0.049 (0.137) |
lnpop | −0.014 (0.032) | −0.061 (0.144) | −0.511 *** (0.128) | −0.050 (0.066) |
lntec | 0.365 *** (0.045) | 0.496 *** (0.079) | 0.175 ** (0.087) | 0.503 *** (0.125) |
lnsec | −1.319 *** (0.145) | −0.583 ** (0.226) | −0.851 *** (0.285) | −1.699 *** (0.476) |
lnes | 0.196 *** (0.026) | 0.174 *** (0.048) | 0.207 *** (0.044) | 0.011 (0.057) |
lnpe | −0.534 *** (0.079) | −0.550 *** (0.126) | −0.226 (0.199) | −1.265 *** (0.256) |
lntri | 0.235 *** (0.071) | 0.172 * (0.087) | 0.179 (0.135) | −0.109 (0.288) |
lnFDI | 0.090 *** (0.023) | 0.094 *** (0.029) | 0.060 (0.047) | −0.061 (0.082) |
Variable | D = lnsec | D = lnes | ||||
(6) | (7) | (8) | (6) | (7) | (8) | |
lnE | 0.554 *** (0.102) | 1.185 *** (0.077) | 0.370 *** (0.109) | 0.373 *** (0.110) | −0.186 (0.800) | 0.370 *** (0.109) |
(lnE)2 | −0.061 *** (0.014) | −0.175 *** (0.011) | −0.033 ** (0.015) | −0.034 ** (0.015) | 0.066 (0.113) | −0.033 ** (0.015) |
D | 0.155 *** (0.035) | −0.009 ** (0.004) | ||||
Variable | D = lntec | D = lnpop | ||||
(6) | (7) | (8) | (6) | (7) | (8) | |
lnE | 0.282 ** (0.110) | 1.167 *** (0.283) | 0.370 *** (0.109) | 0.397 *** (0.109) | 1.082 *** (0.365) | 0.370 *** (0.109) |
(lnE)2 | −0.028 * (0.016) | −0.062 (0.040) | −0.033 ** (0.015) | −0.037 ** (0.015) | −0.146 *** (0.051) | −0.033 ** (0.015) |
D | −0.073 *** (0.010) | 0.023 *** (0.008) |
Variable | D = lnsec | D = lnes | ||||
(6) | (7) | (8) | (6) | (7) | (8) | |
lnE | −0.622 *** (0.213) | −0.264 ** (0.126) | −0.601 *** (0.214) | −0.602 *** (0.214) | −0.765 (1.400) | −0.601 *** (0.214) |
(lnE)2 | 0.081 *** (0.030) | 0.024 (0.018) | 0.079 *** (0.030) | 0.079 *** (0.030) | 0.166 (0.194) | 0.079 *** (0.030) |
D | 0.064 (0.056) | −0.002 (0.005) | ||||
Variable | D = lntec | D = lnpop | ||||
(6) | (7) | (8) | (6) | (7) | (8) | |
lnE | −0.551 ** (0.212) | 1.754 *** (0.452) | −0.601 *** (0.214) | −0.602 *** (0.214) | −0.045 (0.389) | −0.601 *** (0.214) |
(lnE)2 | 0.076 ** (0.030) | −0.119 * (0.063) | 0.079 *** (0.030) | 0.080 *** (0.030) | 0.026 (0.054) | 0.079 *** (0.030) |
D | 0.028 * (0.015) | 0.045 ** (0.018) |
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Fan, F.; Cao, D.; Ma, N. Is Improvement of Innovation Efficiency Conducive to Haze Governance? Empirical Evidence from 283 Chinese Cities. Int. J. Environ. Res. Public Health 2020, 17, 6095. https://doi.org/10.3390/ijerph17176095
Fan F, Cao D, Ma N. Is Improvement of Innovation Efficiency Conducive to Haze Governance? Empirical Evidence from 283 Chinese Cities. International Journal of Environmental Research and Public Health. 2020; 17(17):6095. https://doi.org/10.3390/ijerph17176095
Chicago/Turabian StyleFan, Fei, Dailin Cao, and Ning Ma. 2020. "Is Improvement of Innovation Efficiency Conducive to Haze Governance? Empirical Evidence from 283 Chinese Cities" International Journal of Environmental Research and Public Health 17, no. 17: 6095. https://doi.org/10.3390/ijerph17176095