Is There Any Difference in the Effect of Different R and D Sources on Carbon Intensity in China?
Abstract
:1. Introduction
2. Materials and Methods
2.1. Panel-Regression Model
2.2. Threshold-Regression Model
2.3. Variables and Data
2.3.1. Explained Variable
2.3.2. Core Explanatory Variables
2.3.3. Threshold Variable
2.3.4. Control Variables
2.4. Data Sources
2.5. Multicollinearity Test
3. Results
3.1. Linear Panel Regression Analysis
3.2. Threshold-Regression Model
4. Conclusions and Implications
4.1. Conclusions
4.2. Policy Implications
Author Contributions
Funding
Conflicts of Interest
References
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Coal | Gasoline | Diesel | Natural Gas | Kerosene | Fuel Oil | Crude Oil | Coke | |
---|---|---|---|---|---|---|---|---|
NCV | 20,908 | 43,070 | 42,652 | 38,931 | 43,070 | 41,816 | 41,816 | 28,435 |
CEF | 95,533 | 70,000 | 74,100 | 56,100 | 71,500 | 77,400 | 73,300 | 107,000 |
Variable | Definition | Unit | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|---|
CI | CO2 emissions divided by GDP | Ton/104 Yuan RMB | 3.2416 | 2.0662 | 0.5562 | 10.28856 |
GOVI | Government R and D investment | 108 Yuan RMB | 70.9664 | 107.8413 | 2.7933 | 649.8845 |
ENTI | Enterprise R and D investment | 108 Yuan RMB | 253.0658 | 310.0617 | 4.8884 | 1454.074 |
POP | Population at the end of each year | 104 | 4520.939 | 2702.857 | 568 | 10999 |
FDI | Actual use of foreign capita | 108 Yuan RMB | 455.3318 | 414.3027 | 0.8040679 | 1901.171 |
INDUS | Proportion of the secondary to the GDP | % | 0.4617 | .08303 | 0.1926 | 0.5905 |
URB | Proportion of urban population to the total | % | 0.5628 | 0.1269 | 0.3641 | 0.8960 |
Variables | Original | Lag-1 | Lag-2 |
---|---|---|---|
lnGOVI | −0.05914 (0.239) | ||
lnENTI | −0.21862 (0.000) | ||
lnGOVI1 | −0.19221 (0.000) | ||
lnENTI1 | −0.04336 (0.250) | ||
lnGOVI2 | −0.18298 (0.000) | ||
lnENTI2 | −0.04266 (0.172) | ||
Constant | 20.13513 (0.000) | 20.2273 (0.000) | 20.40902 (0.000) |
lnFDI | −0.0583623 (0.025) | −0.0572226 (0.023) | −0.0635421 (0.015) |
lnPOP | 0.111707 (0.288) | 0.0017492 (0.985) | −0.0302818 (0.750) |
INDUS | 1.583567 (0.000) | 1.633302 (0.000) | 1.699714 (0.000) |
Wald chi2 | 95.11 | 87.63 | 91.94 |
Prob > chi2 | 0.0000 | 0.0000 | 0.0000 |
Threshold Model | F-Value | P-Value | 1% | 5% | 10% |
---|---|---|---|---|---|
Single threshold | 4.8596 | 0.0750 | 10.7560 | 5.7565 | 4.2124 |
Double threshold | 6.1522 | 0.0290 | 10.2658 | 4.1972 | 2.3816 |
Triple threshold | 5.9762 | 0.0720 | 13.8880 | 6.9905 | 4.9525 |
Variable | Coefficient | t-Statistic | P-Value |
---|---|---|---|
lnFDI | −0.0105 | −0.3899 | 0.6972 |
lnPOP | −0.4720 | −0.4650 | 0.6426 |
INDUS | 0.3445 | 0.8798 | 0.3804 |
lnGOVI-1 (URB < 0.5334) | −0.0233 | −0.2236 | 0.8234 |
lnENTI-1 (URB < 0.5334) | −0.3099 | −4.1198 | 0.0001 |
lnGOVI-2 (0.5334 ≤ URB ≤ 0.5592) | −0.0828 | −0.8011 | 0.4244 |
lnENTI-2 (0.5334 ≤ URB ≤ 0.5592) | −0.2457 | −3.2507 | 0.0014 |
lnGOVI-3 (URB > 0.5592) | −0.1538 | −1.8189 | 0.0710 |
lnENTI-3 (URB > 0.5592) | −0.1911 | −2.8675 | 0.0048 |
F | 6.1522 | ||
P | 0.0290 |
Urbanization Stage | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 |
---|---|---|---|---|---|---|
1 | 0.24 | 0.20 | 0.10 | 0.15 | 0.19 | 0.20 |
2 | 0.13 | 0.23 | 0.45 | 0.30 | 0.08 | 0.20 |
3 | 0.63 | 0.57 | 0.45 | 0.55 | 0.72 | 0.59 |
Urbanization Stage | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 |
---|---|---|---|---|---|---|
1 | 0.24 | 0.19 | 0.17 | 0.23 | 0.19 | 0.13 |
2 | 0.16 | 0.27 | 0.29 | 0.18 | 0.22 | 0.54 |
3 | 0.60 | 0.54 | 0.54 | 0.59 | 0.59 | 0.32 |
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Feng, F.; Peng, L. Is There Any Difference in the Effect of Different R and D Sources on Carbon Intensity in China? Sustainability 2019, 11, 1701. https://doi.org/10.3390/su11061701
Feng F, Peng L. Is There Any Difference in the Effect of Different R and D Sources on Carbon Intensity in China? Sustainability. 2019; 11(6):1701. https://doi.org/10.3390/su11061701
Chicago/Turabian StyleFeng, Feng, and Linlin Peng. 2019. "Is There Any Difference in the Effect of Different R and D Sources on Carbon Intensity in China?" Sustainability 11, no. 6: 1701. https://doi.org/10.3390/su11061701