Spatial Effects of Technological Progress and Financial Support on China’s Provincial Carbon Emissions
Abstract
:1. Introduction
2. Literature Review
2.1. Factors Affecting Carbon Emissions: Openness of Trade, Population Size, Environment, Human Capital, Output Per Capita, Industrial Structure, Gross Output, Energy Structure, and Energy iIntensity
2.2. Energy Consumption
2.3. The Impact of Transportation, Highway Construction, Construction, and Tourism Development on Carbon Emissions
2.4. The Impacts of Financial Support and Technological Progress on Carbon Emissions
2.5. Conclusions from the Literature Review
3. Model Setting and Data Description
3.1. Model Setting
3.2. Variable Selection and Data Description
4. Empirical Analysis
4.1. Provincial Spatial Autocorrelation Test of Carbon Emissions
4.2. Analysis of the Spatial Durbin Model Estimation Results
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Variables | Definition and Measurement |
---|---|
Carbon intensity Y | Y = CO2/GDP, in tons/ten thousand yuan. |
Scale variables that reflect financial support FGM | FGM = Total financial assets/GDP = (Total financial resources used by financial institutions + Stock market value + Bond balance + Premium balance)/GDP |
Structural variables that reflect financial support FJG | FJG = Financial institution loan balance at the end of the year/Total financial assets = Financial institution loan balance at the end of the year/(Total financial resources used by financial institutions + Stock market value + Bond balance + Premium balance) |
Efficiency variable that reflects financial support FXL | FXL = Total loans of financial institutions/Total financial institutions deposits |
R&D expenditures internal expenditures RD | Measured by the internal expenditures of R&D expenditures in various regions; the unit is 100 million yuan. |
Per capita patent applications PAT | PAT = Patent authorization number/Year-end population; the unit for which is 10,000 people. |
Adjacency weight matrix | , that is, if two regions are geographically adjacent, let , otherwise 0. |
Geographic weight matrix | , Dij is the distance between the two capital cities calculated by latitude and longitude. |
Economic weight matrix | , GDP as per region GDP per capita during the sample period |
Variable | Spatial Fixed Effect | Timed Fixed Effect | Two-Way Fixed Effect |
---|---|---|---|
lnFJG | 0.584253 *** (4.106919) | 0.240464 *** (4.033609) | −0.015657 *** (9.649219) |
lnFGM | 0.639430 *** (6.340458) | −0.030902 (0.928114) | −0.057254 *** (5.085647) |
lnFXL | 0.029533 (0.238726) | −0.213918 *** (−4.530700) | 0.250308 *** (−4.509987) |
lnRD | 0.160020 *** (−5.350509) | −0.058099 *** (−3.242875) | −0.145259 ** (−2.382171) |
lnPAT | −0.056001 ** (−2.321551) | −0.272632 *** (−2.716159) | −0.212585 *** (−2.716159) |
R2 | 0.1914 | 0.3983 | 0.8030 |
Log-likelihood | 304.6192 | −248.4110 | 201.5277 |
LMlag | 3.7554 * | 95.1616 *** | 18.3844 *** |
Robust-LMlag | 1.8364 | 22.6243 *** | 5.9763 ** |
LMerror | 2.6604 | 72.5769 *** | 14.3132 * |
Robust-LMerror | 0.7414 | 0.0396 | 1.9051 |
Variables | Two-Way Fixed Effect | Random Effect | ||||
---|---|---|---|---|---|---|
Adjacency Matrix | Economic Matrix | Geographic Matrix | Adjacency Matrix | Economic Matrix | Geographic Matrix | |
lnFJG | 0.425188 *** | 0.566384 *** | 0.603039 *** | 0.245802 *** | 0.682680 *** | 0.444862 *** |
lnFGM | 0.419447 *** | 0.623021 *** | 0.613533 *** | 0.201089 ** | 0.730343 *** | 0.481504 *** |
lnFXL | −0.013464 * | 0.024588 | −0.047724 ** | 0.057773 * | −0.050647 * | 0.194269 * |
lnRD | −0.14500 *** | −0.172148 *** | −0.169546 *** | 0.057233 *** | 0.140653 *** | 0.128634 *** |
lnPAT | −0.017683 ** | −0.044200 * | −0.035167 *** | −0.090255 ** | −0.121580 *** | −0.051648 * |
W*lnFJG | 0.039356 * | 0.303015 * | 1.080740 *** | −0.016419 * | −0.551633 *** | 0.295951 |
W*lnFGM | 0.148659 | −0.344371 * | 0.282024 | −0.055532 * | −0.617813 *** | −0.316510 ** |
W*lnFXL | 0.134168 | −0.196592 ** | −1.245640 *** | −0.193713 ** | 0.097805 | −0.625544 *** |
W*lnRD | −0.115651 ** | −0.081394 * | −0.187231 * | 0.020980 * | −0.045078 *** | 0.024865 |
W*lnPAT | −0.042882 ** | −0.141555 ** | −0.130469 * | −0.134885 *** | −0.166242 *** | −0.217064 *** |
W*lnY | 0.304158 *** | 0.586095 *** | 0.276900 *** | 0.602980 *** | 0.505000 *** | 0.566995 *** |
teta | 0.072218 | 0.054061 | 0.057969 | |||
Wald_spatial_lag | 21.5664 *** | 19.0941 *** | 16.9135 *** | 12.9903 *** | 13.8764 *** | 20.0609 *** |
LR_spatial_lag | 23.1102 *** | 22.0631 *** | 24.3116 *** | |||
Wald_spatial_error | 28.4327 *** | 21.8123 *** | 22.6681 *** | 16.2104 *** | 12.0887 *** | 16.6862 *** |
LR_spatial_error | 26.8217 *** | 18.0258 *** | 20.766 |
Variables | Effect | Adjacency Weight Matrix | Economic Weight Matrix | Geographic Weight Matrix |
---|---|---|---|---|
lnFJG | Direct Effect | −0.270553 ** (2.572870) | −0.567431 *** (4.776008) | −0.667972 *** (5.532768) |
Indirect Effect | 0.314399 (0.934393) | −0.229892 (−0.792254) | 1.720065 *** (3.258294) | |
Total Effect | 0.043846 * (1.775649) | −0.797323 (1.079492) | 1.052093 *** (4.241222) | |
lnFGM | Direct Effect | −0.212548 ** (2.146843) | −0.622376 *** (5.445147) | −0.638185 *** (5.449312) |
Indirect Effect | 0.252683 (0.588840) | −0.269054 (0.588840) | 0.924663 (1.453559) | |
Total Effect | 0.040135 * (1.859323) | −0.89143 (1.198610) | 0.286478 *** (2.774347) | |
lnFXL | Direct Effect | −0.024885 * (1.906190) | −0.023065 * (−1.870949) | 0.111247 (−0.777147) |
Indirect Effect | −0.369290 *** (−2.736349) | −0.241266 ** (2.359365) | −1.739281 *** (−2.961039) | |
Total Effect | −0.394175 * (−1.961541) | −0.264331 (0.638459) | −1.628034 *** (−2.797616) | |
lnRD | Direct Effect | 0.069459 ** (2.290024) | −0.168532 *** (4.304425) | 0.163037 *** (4.028740) |
Indirect Effect | −0.130082 ** (2.304254) | −0.057522 (−0.452338) | −0.186815 (−1.286994) | |
Total Effect | −0.060623 *** (3.396772) | −0.226054 (0.838887) | −0.023778 (0.838887) | |
lnPAT | Direct Effect | −0.440598 *** (−8.745832) | −0.157121 ** (2.012049) | −0.191621 * (−1.928890) |
Indirect Effect | −0.128854 *** (−5.546450) | −0.040166 * (−1.935555) | −0.042469 * (−1.99295) | |
Total Effect | −0.569452 *** (−10.708107) | −0.197287 (1.255859) | −0.234090 ** (−2.322743) |
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Zhou, Y.; Xu, Y.; Liu, C.; Fang, Z.; Guo, J. Spatial Effects of Technological Progress and Financial Support on China’s Provincial Carbon Emissions. Int. J. Environ. Res. Public Health 2019, 16, 1743. https://doi.org/10.3390/ijerph16101743
Zhou Y, Xu Y, Liu C, Fang Z, Guo J. Spatial Effects of Technological Progress and Financial Support on China’s Provincial Carbon Emissions. International Journal of Environmental Research and Public Health. 2019; 16(10):1743. https://doi.org/10.3390/ijerph16101743
Chicago/Turabian StyleZhou, Yingying, Yaru Xu, Chuanzhe Liu, Zhuoqing Fang, and Jiayi Guo. 2019. "Spatial Effects of Technological Progress and Financial Support on China’s Provincial Carbon Emissions" International Journal of Environmental Research and Public Health 16, no. 10: 1743. https://doi.org/10.3390/ijerph16101743