A Study on the Non-Linear Impact of Digital Technology Innovation on Carbon Emissions in the Transportation Industry
Abstract
:1. Introduction
2. Research Hypothesis
2.1. The Impact of Digital Technology Innovation on Carbon Emissions in the Transportation Industry and Its Non-Linear Characteristic
2.2. Spatial Spillover Effects of Carbon Emissions from the Transportation Sector
2.3. Specific Mechanisms of the Role of Digital Technology Innovation on Carbon Emissions in the Transportation Industry
3. Model Specification, Variables, and Data Description
3.1. Model Specification
3.1.1. Modeling of the Static Panel
3.1.2. Modeling of the Panel Threshold
3.1.3. Modeling of the Spatial Dependence
3.2. Variables and Data Description
3.2.1. Explained Variables
3.2.2. Core Explanatory Variables
3.2.3. Control Variables
4. Results and Discussion
4.1. Panel Model Regression Analysis
4.2. Spatial Effect Analysis
4.3. Endogenous Problems
4.4. Robustness Test
4.5. Mechanism Analysis
5. Conclusions and Policy Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Energy | Carbon Emission Factor (Tons of Carbon/Tons of Standard Coal) CEF | Discount Factor for Standard Coal (kg Standard Coal/kg) SCC |
---|---|---|
Coal | 0.7476 | 0.7143 |
Coke | 0.1128 | 0.9714 |
Crude Oil | 0.5854 | 1.4286 |
Gasoline | 0.5532 | 1.4714 |
Kerosene | 0.3416 | 1.4714 |
Diesel | 0.5913 | 1.4571 |
Fuel Oil | 0.6176 | 1.4286 |
Natural Gas | 0.4479 | 1.3300 |
Power | 2.2132 | 0.1229 |
Digital technology innovation level | Full-time volume of R&D staff R&D funding Number of R&D projects Number of digital economy-related patent applications |
Variable Category | Variable Name | Variable Symbol | Variable Definition | Variable Unit | Total Number of Variables | Average Value | Standard Deviation | Minimum Value | Maximum Value |
---|---|---|---|---|---|---|---|---|---|
Explained variables | Transportation energy carbon emissions | CO2 | Measured by the method provided by IPCC in 2006; the formula is (4) | million tons | 270 | 1518.202 | 447.704 | 83.053 | 5079.747 |
Core explanatory variables | Digital Innovation level | dig | Comprehensive evaluation system according to Table 2 | - | 270 | 0.480 | 0.220 | 0.120 | 1.430 |
Control variables | Economic growth level | pgdp | Expressed as gross economic value added per capita | 10,000 Yuan/person | 270 | 5.070 | 2.470 | 1.310 | 14.02 |
Urbanization level | urban | Total urban population/resident population | - | 270 | 0.570 | 0.130 | 0.340 | 0.900 | |
Open to the public | open | Total imports and exports/GDP | - | 270 | 0.250 | 0.320 | 0.000 | 1.550 | |
Regional consumption power | consume | Total retail sales of social consumer goods/GDP | - | 270 | 0.380 | 0.0700 | 0.230 | 0.600 | |
Industry Structure | transd | Value added of transportation industry/GDP | - | 270 | 0.050 | 0.010 | 0.020 | 0.100 |
Variable | (1) | (2) | (3) | (4) |
---|---|---|---|---|
dig | 1.9204 *** (0.2337) | 1.8875 *** (0.6715) | 1.8553 *** (0.5071) | 2.1443 *** (0.7324) |
Constant | 5.1941 *** (0.0713) | 5.4022 *** (0.1110) | 5.9019 *** (0.4946) | 5.2232 *** (0.4990) |
Controls | Uncontrolled | Control | Control | Control |
Time effect | No | No | No | Yes |
Spatial effects | No | No | Yes | Yes |
R-squared | 0.3532 | 0.4096 | 0.5237 | 0.5021 |
Obs. | 270 | 270 | 270 | 270 |
Variable | dig |
---|---|
Threshold value | 0.582 |
Dig × I (Th ≤ q1) | 2.434 *** (0.173) |
Dig × I (Th > q1) | −0.292 *** (0.0520) |
Controls | Control |
Constant | 6.495 *** (0.144) |
Obs. | 270 |
R-squared | 0.639 |
Year | Moran’s I | Z-Value |
---|---|---|
2010 | 0.258 *** | 2.905 |
2011 | 0.283 *** | 3.142 |
2012 | 0.271 *** | 3.029 |
2013 | 0.258 *** | 2.902 |
2014 | 0.276 *** | 3.075 |
2015 | 0.269 *** | 2.996 |
2016 | 0.301 *** | 3.333 |
2017 | 0.145 ** | 1.781 |
2018 | 0.115 * | 1.483 |
Year | Moran’s I | Z-Value |
---|---|---|
2010 | 0.178 | 2.191 |
2011 | 0.359 *** | 4.716 |
2012 | 0.349 *** | 4.565 |
2013 | 0.344 *** | 4.501 |
2014 | 0.359 *** | 4.686 |
2015 | 0.356 *** | 4.655 |
2016 | 0.337 *** | 4.398 |
2017 | 0.291 ** | 3.333 |
2018 | 0.362 *** | 4.759 |
Model Setting | SDM | |||
---|---|---|---|---|
Spatial Matrix Type | Economic Distance | Geographical Distance | Adjacency Matrix | |
Variable | (1) | (2) | (3) | |
rho | −0.231 ** (0.0903) | −1.020 *** (0.238) | −0.150 ** (0.0638) | |
dig | 0.274 * (0.161) | 0.337 ** (0.139) | 0.370 ** (0.154) | |
dig2 | −0.0339 (0.0266) | −0.0422 * (0.0230) | −0.0544 ** (0.0254) | |
W*dig | 1.214 *** (0.523) | −0.943 (0.860) | 0.597 * (0.326) | |
W*dig2 | −0.209 *** (0.0720) | 0.163 (0.151) | −0.0966 * (0.0551) | |
Controls | Yes | Yes | Yes | |
Direct effect | dig | 0.224 * (0.175) | 0.385 ** (0.150) | 0.358 ** (0.160) |
dig2 | −0.0276 * (0.0289) | −0.0502 ** (0.0249) | −0.0527 ** (0.0263) | |
Indirect effects | dig | 1.157 ** (0.477) | 0.660 * (0.467) | 0.498 * (0.303) |
dig2 | −0.157 ** (0.0674) | −0.105 * (0.0819) | −0.0819 (0.0509) | |
Total effect | dig | 1.381 *** (0.417) | 1.045 ** (0.443) | 0.855 *** (0.310) |
dig2 | −0.185 *** (0.0578) | −0.155 ** (0.0779) | −0.135 ** (0.0527) | |
LogL | 231.0028 | 258.2256 | 217.8049 | |
R-squared | 0.164 | 0.094 | 0.071 |
Variable | Instrumental Variables Method 2SLS | Generalized Moment Estimation Method | ||
---|---|---|---|---|
Phase I dig | Phase II lnc | DIF-GMM lnc | Twostep SYS-DMM lnc | |
dig | 2.159 ** (0.149) | 2.720 *** (0.135) | 2.319 *** (0.187) | 2.547 *** (0.177) |
iv | 0.238 ** (0.0769) | - | - | - |
L.lnc | - | - | 0.7488 *** (0.132) | 0.985 *** (0.0885) |
Controls | Yes | Yes | Yes | Yes |
Constant | 4.991 *** (0.206) | 3.838 *** (0.277) | - | 0.484 (0.442) |
Kleibergen–Paap rk LM | 21.075 [0.000] | - | - | |
Kleibergen–Paap rk Wald F | 9.537 {8.96} | - | - | |
Hansen | - | - | 1 | 0.552 |
AR(1) | - | - | 0.00450 | 0.00047 |
AR(2) | - | - | 0.216 | 0.220 |
Obs. | 240 | 240 | 210 | 270 |
Variables | (1) | (2) | (3) | ||
---|---|---|---|---|---|
Carbon Emission Intensity | lnc | East | Central | West | |
Inter | - | 0.862 *** (0.286) | - | - | - |
dig | 0.749 ** (0.4102) | - | 2.500 ** (0.247) | 2.809 ** (0.330) | 2.482 * (0.277) |
Controls | Yes | Yes | Yes | Yes | Yes |
Constant | 5.699 *** (0.0784) | 3.479 *** (0.531) | 4.386 *** (0.411) | 8.046 *** (0.825) | 3.359 *** (0.731) |
Obs. | 270 | 270 | 99 | 81 | 90 |
R-squared | 0.028 | 0.121 | 0.465 | 0.495 | 0.479 |
Number of id | 30 | 30 | 11 | 9 | 10 |
Model Setting | Sar | |||
---|---|---|---|---|
Spatial Matrix Type | Economic Distance | Geographical Distance | Adjacency Matrix | |
Variable | (1) | (2) | (3) | |
rho | −0.191 ** (0.0870) | −0.138 ** (0.0890) | −0.112 * (0.0607) | |
dig | 0.427 *** (0.153) | 0.392 ** (0.154) | 0.433 *** (0.154) | |
dig2 | −0.0600 ** (0.0252) | −0.0540 ** (0.0253) | −0.0599 ** (0.0253) | |
Controls | Yes | Yes | Yes | |
Direct effect | dig | 0.436 *** (0.159) | 0.398 ** (0.158) | 0.440 *** (0.159) |
dig2 | −0.0617 ** (0.0262) | −0.0554 ** (0.0261) | −0.0613 ** (0.0261) | |
Indirect effects | dig | 0.0719 * (0.0437) | 0.0386 (0.0743) | 0.0455 (0.0315) |
dig2 | −0.0102 (0.00667) | −0.0054 (0.0107) | −0.0064 (0.00466) | |
Total effect | dig | 0.508 *** (0.131) | 0.437 ** (0.156) | 0.486 *** (0.141) |
dig2 | −0.0719 ** (0.0216) | −0.0608 ** (0.0253) | −0.0677 ** (0.0232) | |
LogL | 211.4288 | 209.3413 | 210.7885 | |
R-squared | 0.071 | 0.083 | 0.088 |
Variables’ Moderator | Traffic Development Level | Transportation Energy Consumption Structure |
---|---|---|
dig2 * moderator | 0.0028 * (1.238) | −0.0057 ** (−2.490) |
dig | 0.6066 (0.539) | 0.7594 (1.583) |
dig2 | −0.0422 * (−0.023) | −0.0640 * (−0.031) |
W * dig2 * moderator | 0.0045 * (1.381) | −0.0092 *** (−2.751) |
W*dig | 3.9163 * (1.721) | 3.3268 * (1.192) |
Controls | Yes | Yes |
Time Effect | Yes | Yes |
Spatial effects | Yes | Yes |
Obs. | 270 | 270 |
LogL | 227 | 228 |
R-squared | 0.081 | 0.071 |
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Chen, X.; Mao, S.; Lv, S.; Fang, Z. A Study on the Non-Linear Impact of Digital Technology Innovation on Carbon Emissions in the Transportation Industry. Int. J. Environ. Res. Public Health 2022, 19, 12432. https://doi.org/10.3390/ijerph191912432
Chen X, Mao S, Lv S, Fang Z. A Study on the Non-Linear Impact of Digital Technology Innovation on Carbon Emissions in the Transportation Industry. International Journal of Environmental Research and Public Health. 2022; 19(19):12432. https://doi.org/10.3390/ijerph191912432
Chicago/Turabian StyleChen, Xiaoqin, Shenya Mao, Siqi Lv, and Zhong Fang. 2022. "A Study on the Non-Linear Impact of Digital Technology Innovation on Carbon Emissions in the Transportation Industry" International Journal of Environmental Research and Public Health 19, no. 19: 12432. https://doi.org/10.3390/ijerph191912432