The Impact of Public Transportation on Carbon Emissions—From the Perspective of Energy Consumption
Abstract
:1. Introduction
2. Literature Review and Research Hypotheses
3. Materials and Methods
3.1. Model Specification
3.2. Model Specification
- (1)
- Explained variable. The explained variable CO2 (ln CO2) was measured in the empirical analysis. CO2 emissions of 30 provinces issued by CEADs throughout 2010–2019 were selected in this paper.
- (2)
- Explanatory variables. Public transport development level (lnptdl) functions as the core explanatory variable in this paper. In the selection of public transport development level index, the public transport passenger volume was added to reflect the public transport load level and consumer behavior choice and measure the public transport development level in two dimensions: government financial input and consumer behavior choice. Specific method: the public transport development level = the number of public buses (electric vehicles) and rail transit vehicles × passenger volume of public buses (electric vehicles) and rail transit. Some scholars have measured the public transport development level using the number of public transport vehicles and the public transport passenger volume [22,26]. Therefore, this paper will test the robustness of the empirical results through using the number of public transport vehicles and the public transport passenger volume as the core explanatory variables.
- (3)
- Control variables. CO2 emissions are affected by multiple factors. To minimize the statistical bias due to ignoring the missing variables, the influencing factors related to carbon emissions are also considered as control variables. Per capita GDP (lnpgdp): There is a Kuznets “Inverted U-shaped” curve between economic growth and environmental pollution. China is still on the left side of the environmental Kuznets curve [34]. This paper uses per capita GDP as a substitute variable that reflects the level of economic development to control the impact of economic growth on carbon emissions. Population density (lnpd): This is measured by the ratio of the number of permanent residents in each province of China to the land area. Private car ownership (lncar): This is measured by the number of private cars in various provinces of China. The automobile exhaust generated by private cars will increase carbon emissions, and the expected symbol is positive. Foreign direct investment (lnfdi): There are always two opposite views, “pollution heaven” and “pollution halo”, about foreign direct investment. The former emphasizes that foreign direct investment will transfer high-intensity polluting industries to the host country; instead, the latter holds that increased foreign direct investment will let advanced technology flow into the host country, increasing energy efficiency and reducing carbon emission intensity [35]. Therefore, in order to control the impact of foreign direct investment on carbon emissions, this paper uses the practice of Wang K.L. et al. [36], who selected foreign direct investment in Chinese provinces and converted it into RMB at the exchange rate of US dollar against RMB in the current year. Industrial added value (lniav): The large amount of primary energy consumed in the industrial sector will increase carbon emissions. Trade openness (lnto): The proportion of total trade to GDP is selected to measure the degree of openness. Trade openness affects energy intensity through technology spillover, and technology spillover has a great impact on carbon emissions [37].
3.3. Data Description
4. Results
4.1. Baseline Regression Analysis
4.2. Robustness Analysis
4.2.1. Endogeneity Test
4.2.2. Substitution Test
4.2.3. Time Heterogeneity Test
4.3. Mechanism Analysis
4.4. Regional Heterogeneity Analysis
5. Discussion
6. Conclusions
7. Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variables | Variable Meaning (Unit) | Sample Number | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|---|
lnCO2 | CO2 emissions (million tons) | 300 | 5.569 | 0.731 | 3.365 | 6.843 |
lnptdl | Public transport development level | 300 | 21.666 | 1.554 | 17.984 | 25.114 |
ecs | Energy consumption structure | 300 | 0.807 | 0.104 | 0.485 | 0.991 |
lnpgdp | GDP per capita (people/yuan) | 300 | 10.763 | 0.459 | 9.482 | 12.009 |
lnpd | Population density (person/km2) | 300 | 5.458 | 1.277 | 2.053 | 8.250 |
lncar | Private cars | 300 | 14.835 | 0.946 | 11.876 | 16.775 |
lnfdi | Foreign direct investment (ten thousand Yuan) | 300 | 17.587 | 1.388 | 14.263 | 21.022 |
lniva | Industrial added value (ten thousand yuan) | 300 | 17.923 | 0.977 | 15.164 | 19.792 |
lnto | Trade openness | 300 | −1.808 | 0.967 | −4.477 | 0.383 |
Variables | lnCO2 | |||
---|---|---|---|---|
lnptdl | −0.173 *** | −0.187 *** | ||
(−4.40) | (−5.31) | |||
lnptdl2 | −0.087 *** | −0.093 *** | ||
(−4.40) | (−5.31) | |||
lnpgdp | −0.332 *** | −0.332 *** | ||
(−3.44) | (−3.44) | |||
lnpd | 2.234 *** | 2.234 *** | ||
(7.20) | (7.20) | |||
lncar | 0.388 *** | 0.388 *** | ||
(10.05) | (10.05) | |||
lnfdi | 0.025 | 0.025 | ||
(1.09) | (1.09) | |||
lniva | 0.107 * | 0.107 * | ||
(1.88) | (1.88) | |||
lnto | −0.060 ** | −0.060 ** | ||
(−2.42) | (−2.42) | |||
Cons | 9.098 *** | −7.144 *** | 9.098 *** | −7.144 *** |
(10.85) | (−4.07) | (10.85) | (−4.07) | |
R2 | 0.400 | 0.620 | 0.400 | 0.620 |
F statistics | 17.320 | 25.955 | 17.320 | 25.955 |
Sample number | 300 | 300 | 300 | 300 |
GMM | Two-Way Fixed Effect | 2010–2013 | 2014–2019 | ||
---|---|---|---|---|---|
lnCO2 | |||||
lnptdl | −0.197 *** | −0.084 | −0.154 *** | ||
(−4.56) | (−1.20) | (−3.57) | |||
lnnpto | −0.233 *** | ||||
(−3.60) | |||||
lnptpv | −0.222 *** | ||||
(−4.47) | |||||
lnpgdp | −0.389 *** | −0.359 *** | −0.386 *** | 0.516 | −0.313 *** |
(−5.01) | (−3.61) | (−4.00) | (1.48) | (−3.54) | |
lnpd | 2.178 *** | 2.280 *** | 1.989 *** | 1.440 ** | 1.935 *** |
(6.77) | (6.98) | (6.40) | (2.08) | (3.90) | |
lncar | 0.240 *** | 0.404 *** | 0.380 *** | 0.517 *** | 0.180 *** |
(6.73) | (10.16) | (9.65) | (3.64) | (2.98) | |
lnfdi | 0.017 | 0.031 | 0.036 | −0.072 | 0.031 |
(0.92) | (1.32) | (1.58) | (−1.02) | (1.46) | |
lniva | 0.181 *** | 0.103 * | 0.114 ** | −0.140 | 0.147 *** |
(4.14) | (1.76) | (1.97) | (−0.74) | (3.08) | |
lnto | −0.021 | −0.068 ** | −0.082 ** | −0.092 ** | 0.001 |
(−0.85) | −2.62 | (−3.38) | (−2.02) | (0.03) | |
Cons | −10.660 *** | −9.214 *** | −6.775 *** | −9.638 ** | −4.112 |
(−4.00) | (−5.24) | (−3.72) | (−2.05) | (−1.56) | |
Sargan | 0.002 | ||||
(0.964) | |||||
R2 | 0.996 | 0.599 | 0.609 | 0.690 | 0.503 |
F statistics | 1028.550 | 23.688 | 24.728 | 17.781 | 11.650 |
Sample number | 240 | 300 | 300 | 300 | 300 |
Variables | lnCO2 | ecs | lnCO2 |
---|---|---|---|
lnptdl | −0.237 *** | −0.021 ** | −0.227 *** |
(−7.17) | (−2.02) | (−6.89) | |
ecs | 0.483 ** | ||
(2.43) | |||
lnpd | 2.369 *** | −0.032 | 2.383 *** |
(8.17) | (−0.34) | (8.29) | |
lncar | 0.332 *** | 0.078 *** | 0.298 *** |
(9.33) | (6.85) | (7.75) | |
Cons | −7.045 *** | 0.373 | −7.209 *** |
(−3.98) | (0.66) | (−4.10) | |
R2 | 0.596 | 0.524 | 0.604 |
F statistics | 31.711 | 23.678 | 30.144 |
Sample number | 300 | 300 | 300 |
Variables | East | Central of China | West | Northeast |
---|---|---|---|---|
lnCO2 | ||||
lnptdl | 0.035 | −0.289 ** | −0.275 *** | −0.125 |
(0.82) | (−2.52) | (−5.56) | (−0.51) | |
ecs | −0.037 | 7.086 *** | 0.057 | 1.815 |
(−0.20) | (4.88) | (0.14) | (0.98) | |
lnpd | 0.333 | −0.296 | 4.411 | 9.101 |
(1.05) | (−0.13) | (1.03) | (2.09) | |
lncar | 0.310 *** | 1.009 *** | 0.109 *** | −1.855 ** |
(5.88) | (4.96) | (6.16) | (−3.09) | |
Cons | −1.886 | −6.801 | −10.008 *** | −13.167 |
(−0.85) | (−0.58) | (−2.72) | (−0.63) | |
R2 | 0.695 | 0.714 | 0.770 | 0.751 |
F statistics | 13.50 | 7.85 | 22.13 | 3.25 |
Sample number | 100 | 60 | 110 | 30 |
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Jing, Q.-L.; Liu, H.-Z.; Yu, W.-Q.; He, X. The Impact of Public Transportation on Carbon Emissions—From the Perspective of Energy Consumption. Sustainability 2022, 14, 6248. https://doi.org/10.3390/su14106248
Jing Q-L, Liu H-Z, Yu W-Q, He X. The Impact of Public Transportation on Carbon Emissions—From the Perspective of Energy Consumption. Sustainability. 2022; 14(10):6248. https://doi.org/10.3390/su14106248
Chicago/Turabian StyleJing, Qin-Lei, Han-Zhen Liu, Wei-Qing Yu, and Xu He. 2022. "The Impact of Public Transportation on Carbon Emissions—From the Perspective of Energy Consumption" Sustainability 14, no. 10: 6248. https://doi.org/10.3390/su14106248
APA StyleJing, Q. -L., Liu, H. -Z., Yu, W. -Q., & He, X. (2022). The Impact of Public Transportation on Carbon Emissions—From the Perspective of Energy Consumption. Sustainability, 14(10), 6248. https://doi.org/10.3390/su14106248