Nexus among Energy Consumption, Economic Growth, Urbanization and Carbon Emissions: Heterogeneous Panel Evidence Considering China’s Regional Differences
Abstract
:1. Introduction
- (1)
- According to the World Bank’s standard line for per capita GDP of developed countries, this paper divided China’s 30 provinces into developed areas, medium developed areas and underdeveloped regions. Furthermore, the relationship between GDP, CO2 emissions, urbanization rate and the energy consumption structure of provinces in different areas is examined to compensate for the gaps in previous research, which can help formulate appropriate carbon reduction policies based on regional development characteristics.
- (2)
- Taking into account the heterogeneity among Chinese provincial panels, this paper adopted heterogeneous panel analysis techniques, including heterogeneous panel estimation based on dynamic ordinary least squares (DOLS) and fully modified least squares (FMOLS) analysis, and the heterogeneous panel Granger test based on Dumitrescu and Hurlin, which overcomes the deficiencies of conventional panel analysis techniques, ensuring the validity of parameter estimation results and the reliability of the Granger causality test.
2. Model, Data and Descriptive Statistics
2.1. Model and Data
2.2. Descriptive Statistics of Variables
3. Econometric Methodologies
3.1. Cross-Sectional Dependence Test
3.2. Panel Unit Root Test
3.3. Panel Co-Integration Test
3.4. Panel Data Model Estimation
3.5. Heterogeneous Panel Causality Test
4. Empirical Findings and Interpretations
4.1. Cross-Sectional Dependence Test
4.2. Panel Unit Root Test
4.3. Panel Co-Integration Test
4.4. Panel Data Model Estimation
4.5. Heterogeneous Panel Causality Test
- (1)
- At national stage, there exists unidirectional causal relationships from CE to UR, from GDP to UR, from GDP to ECS and from UR to ECS, and a bidirectional causal nexus between GDP and CE and between CE and ECS. The results show that at the national level, CE will affect the evolution of GDP and UR, and economic development will also accelerate the process of urbanization and promote energy restructuring. In addition, with the further development of UR, ECS will be adjusted. Meanwhile, for CE and GDP, ECS has a feedback relationship, and that is the reason why CE will affect GDP and ECS, and GDP and ECS also have obvious consequent on CE. This shows that the processes of national GDP and UR still mainly rely on using large amounts of fossil fuels, and urbanization and adjustment of energy consumption structure are driven by continuous development of the economy. Therefore, at a national stage, under the premise of taking economic development as the primary task, we must speed up the energy consumption structural adjustment, and change the economic development mode of energy-dependence to an innovation-driven mode, which is low carbon and high quality. Besides, green urbanization is also important for the realization of a low-carbon and high-quality economy.
- (2)
- The results of developed regions show that there are unidirectional causal relationships from CE to ECS, from GDP to UR, from GDP to ECS and from UR to ECS, and a bidirectional causal relation between GDP and CE. This shows that both CE and UR in developed regions have an impact on the ECS, and GDP will have effect on the UR and ECS. Besides, in developed regions, economic development has matured and the rate of urbanization has not changed much. Therefore, under the premise of maintaining stable economic growth, we must attach importance to optimizing ECS and reducing CE. Hence, in developed regions, the experience of foreign developed cities can be learned from in order to speed up the mainstreaming of ECS and industrial upgrading, so as to achieve low-carbon development and a green economy in parallel.
- (3)
- In the medium developed regions, the unidirectional causal relationships from GDP to CE, from UR to CE, from GDP to UR and from GDP to ECS are supported and the bidirectional causal relationships between ECS and CE, and between ECS and UR are proven, revealing that the GDP of medium developed regions have impacts on CE, UR and ECS, and UR will affect CE. Furthermore, ECS and CE, ECS and UR have feedback effects, similar to that of the national level, showing that the medium developed regions are at a high-speed stage of GDP and UR, and GDP is the center of all things, driving the transformation of UR and ECS. The results also show that the medium-developed regions are still in an energy-dependent development mode and require a large amount of fossil fuels to support their development. Therefore, against the background of taking economic development as the core to promote urbanization, it is essential to speed up the optimization of ECS, change the development mode from energy-dependent to innovation-driven, and advocate the low-carbon and green road in the process of urbanization, which can be helpful for achieving the high-quality development of economy and urbanization.
- (4)
- In underdeveloped regions, there are unidirectional causalities from GDP to CE, from CE to ECS, from GDP to UR and from UR to ECS, and a bidirectional causal nexus between ECS and GDP. The results express the fact that the stage of economic development in underdeveloped areas will affect the level of local urbanization, while CE will promote the adjustment of ECS. Furthermore, the feedback effect exists between ECS and GDP in the underdeveloped regions, that is, ECS will affect GDP, and GDP will also advance adjustment of ECS. Moreover, GDP will also promote the adjustment of ECS, showing that current economic development in underdeveloped areas is still an extensive economic development mode heavily depending on energy, which is unsustainable. Therefore, in order to promote carbon reduction in underdeveloped regions, it is necessary to change the economic development mode of energy-dependent to innovation-driven, according to the experience of developed regions and medium developed regions. At the same time, it is important to optimize ECS, which can reduce CE.
5. Conclusions and Policy Implications
- (1)
- For developed regions, the empirical results showed that both CE and UR had impacts on ECS, while GDP had an impact on UR and ECS, and feedback exists on CE and GDP. Therefore, the economic development of developed regions tends to be mature. Under the premise of maintaining stable economic growth, optimizing ECS and reducing CE are more essential in these areas. Specifically, we can learn from the experiences of developed cities in global countries to quicken the mainstreaming of ECS and industrial upgrading, which can realize low-carbon development and the green economy in parallel.
- (2)
- For medium developed regions, the empirical results showed that GDP had impacts on CE, UR and ECS, and UR would affect CE. There were feedback effects between ECS and CE, and between ECS and UR, showing that the medium developed regions were still in the development mode of energy-dependence. Therefore, taking economic development as the core role to promote urbanization, we should speed up the optimization of ECS and realize the innovation-driven GDP mode. Meanwhile, in the process of urbanization, we should promote low-carbon green ways to achieve high-quality economic and urban development.
- (3)
- For underdeveloped regions, GDP would affect UR, while CE would advance adjustment of ECS, revealing that the current economic development in underdeveloped regions is still an energy-dependent mode, which is extensive and unsustainable. Therefore, in order to promote carbon reduction in underdeveloped regions, we must change the mode of economic development via drawing on the experience of developed areas and medium developed areas, and finally achieve innovation-driven development. Moreover, it is important to optimize ECS, and reduce the level of CE.
Author Contributions
Funding
Conflicts of Interest
References
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Panel | Provinces |
---|---|
Developed areas (DA) | TJ, BJ, SH, ZJ, FJ, IM, GD, SD, JS |
Medium developed areas (MDA) | CQ, HuB, S’X, JL, LN, NX, HuN, HaiN, QH, HeB, HeN, XJ, HLJ, JX |
Underdeveloped areas (UDA) | SC, AH, YN, GX, GZ, GS, SX |
Objects | Variables | Mean | Median | Max. | Min. | Std. Dev. | Skewness | Kurtosis |
---|---|---|---|---|---|---|---|---|
Nation | LCE | 9.6709 | 9.7599 | 11.709 | 6.3969 | 0.9208 | −0.6400 | 0.9171 |
UR | 0.4513 | 0.4380 | 0.8961 | 0.1352 | 0.1682 | 0.5393 | 0.0432 | |
ECS | 0.6609 | 0.6842 | 0.9671 | 0.1215 | 0.1756 | −0.3236 | 0.6389 | |
LGDP | 8.5446 | 8.5377 | 11.195 | 5.1227 | 1.1782 | −0.2399 | −0.1363 | |
DA | LCE | 9.9795 | 9.9043 | 11.709 | 8.4118 | 0.7533 | 0.2434 | −0.6009 |
UR | 0.5859 | 0.5828 | 0.8961 | 0.1827 | 0.1899 | −0.2780 | −0.6840 | |
ECS | 0.5867 | 0.5908 | 0.9671 | 0.1215 | 0.1761 | −0.0326 | −0.4116 | |
LGDP | 9.1691 | 9.1840 | 11.195 | 6.7535 | 1.0577 | −0.1567 | −0.6143 | |
MDA | LCE | 9.4672 | 9.6541 | 11.432 | 6.3969 | 1.0634 | −0.6840 | 0.1826 |
UR | 0.4229 | 0.4350 | 0.6894 | 0.1683 | 0.1134 | −0.2760 | −0.4710 | |
ECS | 0.6717 | 0.6928 | 0.9282 | 0.2579 | 0.1684 | −0.5998 | −0.3879 | |
LGDP | 8.2654 | 8.2638 | 10.518 | 5.1227 | 1.2085 | −0.3922 | −0.1641 | |
UDA | LCE | 9.6817 | 9.7333 | 11.162 | 8.5034 | 0.6679 | 0.1228 | −0.4433 |
UR | 0.3350 | 0.3356 | 0.5622 | 0.1352 | 0.1038 | −0.0800 | −0.7620 | |
ECS | 0.7218 | 0.6953 | 0.9241 | 0.3336 | 0.1625 | −0.2125 | −1.3756 | |
LGDP | 8.2999 | 8.2719 | 10.311 | 6.3239 | 0.9419 | 0.0888 | −0.8095 |
LCE | LGDP | UR | ECS | |
---|---|---|---|---|
LCE | 1.0000 | |||
LGDP | 0.833248 (0.0000) *** | 1.0000 | ||
UR | 0.231509 (0.0000) *** | 0.466737 (0.0000) *** | 1.0000 | |
ECS | 0.335314 (0.0000) *** | −0.351735 (0.0000) *** | −0.466566 (0.0000) *** | 1.0000 |
Region | Variable | LCE | LGDP | UR | ECS | Overall |
---|---|---|---|---|---|---|
Nation | Pesaran CD test | 80.573 | 80.507 | 79.978 | 15.362 | 22.134 |
Prob. | 0.0000 *** | 0.0000 *** | 0.0000 *** | 0.0000 *** | 0.0000 *** | |
DA | Pesaran CD test | 23.104 | 23.934 | 22.914 | 8.310 | 4.872 |
Prob. | 0.0000 *** | 0.0000 *** | 0.0000 *** | 0.0000 *** | 0.0000 *** | |
MDA | Pesaran CD test | 37.022 | 35.442 | 36.735 | 10.061 | 10.803 |
Prob. | 0.0000 *** | 0.0000 *** | 0.0000 *** | 0.0000 *** | 0.0000 *** | |
UDA | Pesaran CD test | 17.634 | 18.255 | 17.677 | 5.391 | 82.73 |
Prob. | 0.0000 *** | 0.0000 *** | 0.0000 *** | 0.0000 *** | 0.0000 *** |
Region | CIPS Test | Variable | Criterion | |||||
---|---|---|---|---|---|---|---|---|
LCE | LGPD | UR | ECS | 10% Level | 5% Level | 1% Level | ||
Nation | Level | −2.007 | −2.028 | −2.076 | −1.697 | −2.11 | −2.2 | −2.38 |
1st diff. | −3.376 *** | −2.612 *** | −2.977 *** | −3.286 *** | −2.14 | −2.25 | −2.45 | |
DA | Level | −1.680 | −1.867 | −1.419 | −1.394 | −2.18 | −2.33 | −2.64 |
1st diff. | −4.542 *** | −3.116 *** | −2.596 ** | −2.627 ** | −2.22 | −2.4 | −2.76 | |
MDA | Level | −2.063 | −1.831 | −2.006 | −1.880 | −2.11 | −2.22 | −2.45 |
1st diff. | −3.280 *** | −3.522 *** | −3.492 *** | −3.631 *** | −2.16 | −2.28 | −2.52 | |
UDA | Level | −2.099 | −1.847 | −1.614 | −1.166 | −2.18 | −2.33 | −2.64 |
1st diff. | −2.483 ** | −2.252 * | −4.069 *** | −3.019 *** | −2.22 | −2.4 | −2.76 |
Pedroni Residual Cointegration Test | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Test Statistics | Nation | DA | MDA | UDA | ||||||||
Panel v-Statistic | −1.641530 * | −2.153581 ** | −3.457571 *** | −1.110789 | ||||||||
Panel rho-Statistic | 1.955698 | 1.943861 | 3.886977 ** | 2.509975 * | ||||||||
Panel PP-Statistic | 5.265526 ** | −1.057682 | 2.208866 * | 2.076040 * | ||||||||
Panel ADF-Statistic | −4.228109 *** | −1.791574 ** | −0.444975 | −2.241201 ** | ||||||||
Group rho-Statistic | −0.092068 | 3.028955 | 4.445418 | 3.641918 | ||||||||
Group PP-Statistic | 6.455908 *** | −2.219356 ** | 2.208195 ** | 2.611854 ** | ||||||||
Group ADF-Statistic | −2.414394 ** | −2.526311 *** | −0.303020 | −1.707920 ** | ||||||||
Kao Cointegration Test | ||||||||||||
Test Statistics | Nation | DA | MDA | UDA | ||||||||
ADF | −3.673287 *** | −4.634771 *** | −4.130794 *** | −2.320584 ** | ||||||||
Fisher-Type Johansen Cointegration Test | ||||||||||||
Hypothesized No. of CE(s) | Trace test | Max-Eigen Test | ||||||||||
Nation | DA | MDA | UDA | Nation | DA | MDA | UDA | |||||
None | 852.6 *** | 259.1 *** | 370.3 *** | 223.5 *** | 626.4 *** | 194.8 *** | 276.4 *** | 156.1 *** | ||||
At most 1 | 370.4 *** | 102.6 *** | 157.5 *** | 109.9 *** | 234.5 *** | 61.24 *** | 99.99 *** | 72.97 *** | ||||
At most 2 | 209.5 *** | 60.34 *** | 91.93 *** | 57.15 *** | 143.9 *** | 44.18 *** | 61.24 *** | 38.37 *** | ||||
At most 3 | 179.1 *** | 47.74 *** | 84.67 *** | 46.72 *** | 179.1 *** | 47.74 *** | 84.67 *** | 46.72 *** |
Region | Variable | DOLS | FMOLS | ||
---|---|---|---|---|---|
Coefficient | t-Statistic | Coefficient | t-Statistic | ||
Nation | LGDP | 0.326027 | 7.678835 *** | 0.359891 | 7.156279 *** |
UR | 3.520260 | 7.146156 *** | 3.088734 | 5.385906 *** | |
ECS | 1.359559 | 6.331141 *** | 1.556592 | 7.077251 *** | |
R-squared | 0.980714 | 0.965530 | |||
DA | LGDP | 0.626879 | 12.18511 *** | 0.659694 | 9.420428 *** |
UR | 0.919203 | 1.622341 | 0.488946 | 0.5354 | |
ECS | 2.039462 | 8.543109 *** | 2.366439 | 8.299984 *** | |
R-squared | 0.988573 | 0.975810 | |||
MDA | LGDP | 0.232068 | 3.699689 *** | 0.245420 | 3.183068 *** |
UR | 4.907433 | 6.727691 *** | 4.528544 | 5.248588 *** | |
ECS | 1.380554 | 4.071370 *** | 1.529320 | 4.200622 *** | |
R-squared | 0.980030 | 0.963835 | |||
UDA | LGDP | 0.506100 | 5.491478 *** | 0.521565 | 6.112268 *** |
UR | 0.486777 | 0.467970 | 0.303345 | 0.316176 | |
ECS | 1.645008 | 3.805031 *** | 1.648253 | 5.197572 *** | |
R-squared | 0.982220 | 0.969150 |
Null Hypothesis | Nation | DA | MDA | UDA |
---|---|---|---|---|
LGDP does not homogeneously cause LCE | 3.10338 (0.0020) *** | 2.12600 (0.0339) ** | 2.54247 (0.0112) ** | 3.13033 (0.0019) *** |
LCE does not homogeneously cause LGDP | 2.50263 (0.0123) ** | 1.93272 (0.0533) * | 1.27842 (0.2011) | 1.18148 (0.2374) |
UR does not homogeneously cause LCE | 3.59742 (0.0003) *** | −0.25600 (0.7980) | 4.15303 (0.0001) *** | 0.47575 (0.6343) |
LCE does not homogeneously cause UR | 0.31890 (0.7498) | 0.66250 (0.5077) | 0.34312 (0.7315) | 0.83167 (0.4056) |
ECS does not homogeneously cause LCE | 2.73181 (0.0063) *** | 0.29076 (0.7712) | 3.85200 (0.0001) *** | −0.12187 (0.9030) |
LCE does not homogeneously cause ECS | 5.04663 (0.0000) *** | 1.69990 (0.0891) * | 4.45963 (0.0000) *** | 2.21315 (0.0269) ** |
UR does not homogeneously cause LGDP | 1.22444 (0.2208) | −0.09315 (0.9258) | 1.23966 (0.2151) | 0.89106 (0.3729) |
LGDP does not homogeneously cause UR | 16.4150 (0.0000) *** | 5.00276 (0.0000) *** | 16.2335 (0.0000) *** | 5.37282 (0.0000) *** |
ECS does not homogeneously cause LGDP | 1.40445 (0.1602) | −0.50073 (0.6166) | 0.45791 (0.6470) | 2.82768 (0.0047) *** |
LGDP does not homogeneously cause ECS | 7.66748 (0.0000) *** | 3.57335 (0.0004) *** | 4.59099 (0.0000) *** | 5.32876 (0.0000) *** |
ECS does not homogeneously cause UR | 1.29042 (0.1969) | −0.46360 (0.6429) | 2.64362 (0.0082) *** | −0.51684 (0.6053) |
UR does not homogeneously cause ECS | 5.41728 (0.0000) *** | 2.08701 (0.0369) ** | 4.37912 (0.0000) *** | 2.64840 (0.0081) *** |
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Sun, J.; Shi, J.; Shen, B.; Li, S.; Wang, Y. Nexus among Energy Consumption, Economic Growth, Urbanization and Carbon Emissions: Heterogeneous Panel Evidence Considering China’s Regional Differences. Sustainability 2018, 10, 2383. https://doi.org/10.3390/su10072383
Sun J, Shi J, Shen B, Li S, Wang Y. Nexus among Energy Consumption, Economic Growth, Urbanization and Carbon Emissions: Heterogeneous Panel Evidence Considering China’s Regional Differences. Sustainability. 2018; 10(7):2383. https://doi.org/10.3390/su10072383
Chicago/Turabian StyleSun, Jingqi, Jing Shi, Boyang Shen, Shuqing Li, and Yuwei Wang. 2018. "Nexus among Energy Consumption, Economic Growth, Urbanization and Carbon Emissions: Heterogeneous Panel Evidence Considering China’s Regional Differences" Sustainability 10, no. 7: 2383. https://doi.org/10.3390/su10072383