Can Innovation-Driven Policy Reduce China’s Carbon Emission Intensity?—A Quasi-Natural Experiment Based on the National Innovative City Pilot Policy
Abstract
:1. Introduction
2. Policy Background and Mechanism Analysis
2.1. Policy Background
2.2. Mechanism Analysis
3. Research Design
3.1. Model Design: Time-Varying DID Benchmark Regression Model
3.2. Variable Selection
3.3. Data Sources
4. Empirical Results and Analysis
4.1. Impact of Innovation-Driven Policy on Carbon Emission Intensity
4.2. Parallel Trend Test and Dynamic Effects Analysis
4.3. Sensitivity Analysis
4.4. Propensity Score Matching–Difference-in-Difference (PSM-DID)
4.5. Placebo Test
5. Further Analysis: Mechanism Test and Heterogeneity Discussion
5.1. Mechanism Test
5.2. Heterogeneity
5.2.1. Heterogeneity Analysis While Considering the Characteristic of Urban Innovation Capability
5.2.2. Heterogeneity Analysis While Considering the Characteristic of City Size
5.2.3. Heterogeneity Analysis While Considering the Characteristic of Industrial Structure
6. Conclusions and Policy Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Variable Meaning | Obs | Mean | SD | Min | Max |
---|---|---|---|---|---|---|
Logarithm of carbon emission intensity | 4256 | −13.12854 | 0.594217 | −15.79712 | −10.90308 | |
National innovative city pilot policy | 4256 | 0.0850564 | 0.2789984 | 0 | 1 | |
LnInd | Logarithm of industry structure | 4155 | −4.506574 | 1.317381 | −13.25711 | −0.978727 |
LnGreen | Logarithm of greening coverage of built-up areas | 4256 | 3.564658 | 0.3885407 | −0.52763 | 5.957494 |
LnPo | Logarithm of population density | 4256 | −2.760653 | 1.668129 | −14.5289 | 3.558215 |
LnER | Logarithm of environmental regulation intensity | 4256 | −1.378301 | 1.155832 | −5.771594 | 1.795783 |
LnFdi | Logarithm of foreign direct investment intensity | 4256 | −10.15975 | 0.41433 | −14.78662 | −7.762087 |
(1) | (2) | (3) | (4) | (5) | |
---|---|---|---|---|---|
Policyit | −0.0591 *** | −0.0650 *** | −0.0613 *** | −0.0620 *** | −0.083 *** |
(−5.37) | (−6.31) | (−5.96) | (−6.05) | (−5.67) | |
LnFdi | −0.0108 *** | −0.0103 *** | −0.0123 *** | −0.0119 *** | |
(−4.40) | (−4.24) | (−5.01) | (−4.86) | ||
LnGreen | −0.0358 *** | −0.0351 *** | −0.0386 *** | −0.0379 *** | |
(−4.47) | (−4.39) | (−4.82) | (−4.76) | ||
LnER | 0.0306 *** | 0.0294 *** | 0.0320 *** | 0.0307 *** | |
(13.79) | (13.20) | (14.43) | (13.83) | ||
LnPo | −0.2301 *** | −0.2260 *** | −0.2309 *** | −0.2265 *** | |
(−8.86) | (−8.74) | (−8.94) | (−8.81) | ||
LnInd | −0.0354 *** | −0.0375 *** | −0.0314 *** | −0.0333 *** | |
(−3.29) | (−3.49) | (−2.93) | (−3.11) | ||
Smart City | −0.0410 *** | −0.0432 *** | |||
(−4.92) | (−5.22) | ||||
Low Carbon Cities | −0.0506 *** | −0.0518 *** | |||
(−6.56) | (−6.75) | ||||
_Cons | −12.62227 *** | −13.16513 *** | −13.18269 *** | −13.1186 *** | −13.13436 *** |
(−1527.14) | (−107.95) | (−108.40) | (−107.96) | (−108.43) | |
Fixed effects | Control | Control | Control | Control | Control |
Observations | 4256 | 4155 | 4154 | 4155 | 4154 |
R2 | 0.8674 | 0.8910 | 0.8919 | 0.8922 | 0.8931 |
Sensitivity Analysis | PSM-DID | |||
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
LnSO2 | LnSO2 | |||
Policyit | −0.1854 *** | −0.1907 *** | −0.0497 *** | −0.0629 *** |
(−3.98) | (−4.76) | (−4.80) | (−6.19) | |
Control variables | No | Yes | No | Yes |
Fixed effect | Control | Control | Control | Control |
Observations | 4256 | 4155 | 3833 | 3833 |
R2 | 0.4925 | 0.6444 | 0.8848 | 0.8949 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
GTI | ||||
0.4860 *** | −0.0481 *** | −0.0714 *** | −0.0581 *** | |
(9.46) | (−4.60) | (−3.10) | (−5.77) | |
GTI | −0.0121 *** | |||
(−3.33) | ||||
EE | 0.0959 *** | |||
(13.68) | ||||
Control variables | Control | Control | Control | Control |
Fixed effect | Yes | Yes | Yes | Yes |
Proportion of mediator effect | 10.89% | 10.54% | ||
Observations | 3353 | 3353 | 4155 | 4155 |
R2 | 0.7526 | 0.8884 | 0.3128 | 0.8960 |
Innovation Ability | City Size | Industry Structure | ||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Strong | Weak | Large | Small | High Level | Low Level | |
Policy | −0.0611 *** | −0.0482 | −0.1196 *** | 0.0420 * | −0.0293 ** | −0.1963 *** |
(−5.93) | (−0.79) | (−12.04) | (1.92) | (−2.43) | (−8.48) | |
Control variables | Control | Control | Control | Control | Control | Control |
Fixed effect | Yes | Yes | Yes | Yes | Yes | Yes |
Observations | 2124 | 2031 | 2126 | 2029 | 2084 | 2071 |
R2 | 0.9076 | 0.8780 | 0.9281 | 0.8726 | 0.8916 | 0.9004 |
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Wang, Z.; Zhou, X. Can Innovation-Driven Policy Reduce China’s Carbon Emission Intensity?—A Quasi-Natural Experiment Based on the National Innovative City Pilot Policy. Sustainability 2023, 15, 4383. https://doi.org/10.3390/su15054383
Wang Z, Zhou X. Can Innovation-Driven Policy Reduce China’s Carbon Emission Intensity?—A Quasi-Natural Experiment Based on the National Innovative City Pilot Policy. Sustainability. 2023; 15(5):4383. https://doi.org/10.3390/su15054383
Chicago/Turabian StyleWang, Zicheng, and Xiaoliang Zhou. 2023. "Can Innovation-Driven Policy Reduce China’s Carbon Emission Intensity?—A Quasi-Natural Experiment Based on the National Innovative City Pilot Policy" Sustainability 15, no. 5: 4383. https://doi.org/10.3390/su15054383