Non-Linear Relationship between Economic Growth and CO2 Emissions in China: An Empirical Study Based on Panel Smooth Transition Regression Models
Abstract
:1. Introduction
2. Literature Review
3. Model and Data
3.1. Model Set-Up
3.2. Data Specification
- (1)
- GDP per capita (GDPpc): the EKC curve reflects that the development of economic levels probably pollutes the environment while, with constant economic growth, the pollution levels increase at first and then decrease after the economic levels reach a turning point. The GDPpc is taken as the variable representing the economic growth levels. Based on 1995 constant prices, the practical GDP converted by GDP index per capita represents the income level in unit of yuan. Province-level GDP per capita can be found in China Statistical Yearbook.
- (2)
- Energy structure (Es): restricted by natural resource endowment during energy production, coal is always the primary constituent of China’s energy consumption and the burning of coal releases huge amounts of CO2. For this reason, the influence of coal consumption, as a proportion of overall energy use, on CO2 emissions cannot be ignored while investigating CO2 emissions in China. Es in the study refers to the proportion of coal consumption of overall energy consumption in China (unit: %). Coal consumption data can be found in China Energy Yearbook.
- (3)
- Urbanisation level (Ul): this exerts an effect on CO2 emissions mainly through changes in land use type and the improvement of Ul, which leads numerous people to change their lifestyles, thus directly improving residential CO2 emissions. In this study, the proportion of urban population in the total population is applied to represent Ul (unit: %). The urban population and the total population can be found in local statistical year book.
- (4)
- Trade openness (To): this exerts a significant influence on China’s carbon emissions because China enjoys a trade surplus all year round due to her abundant export volumes. However, a direct negative effect caused by this condition is that it causes domestic CO2 emissions and environmental pollution during production while acquiring abundant export exchanges: To is measured by using the ratio of the total export-import volume to GDP (unit: %). The total export-import volume and the province-level GDP can be found in local statistical year book.
- (5)
- Marketisation index (Mi): this refers to a concept used for measuring the capacity to absorb foreign capital inflow by calculating the ratio of foreign direct investment (FDI) to GDP. In this study, a model with Mi was applied to measure the influence of the degree of marketisation on CO2 emissions. The ratio of FDI to GDP was used to measure the marketisation index (unit: %). FDI and the province-level GDP can be found in local statistical year book.
- (6)
- Carbon emission per capita (Rce): this can be calculated by using the material balance algorithm as recommended by the Intergovernmental Panel on Climate Change (IPCC) (unit: ton/person):
4. Empirical Results
4.1. Relative Test
4.2. Determination of Models
4.3. Empirical Results and Discussion
4.3.1. PSTR Model Taking as the Transition Parameter
4.3.2. The PSTR Model Taking lnEsit as the Transition Parameter
4.3.3. PSTR Model Taking lnUlit as the Transition Parameter
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- González, A.; Teräsvirta, T.; Dijk, D.V. Panel Smooth Transition Regression Models; Research Paper Series: Rochester, NY, USA, 2005. [Google Scholar]
- Kuznets, S. Economic growth and income inequality. Am. Econ. Rev. 1955, 1, 1–28. [Google Scholar]
- Grossman, G.M.; Krueger, A.B. Environmental Impacts of a North American Free Trade Agreement; National Bureau of Economic Research: Cambridge, MA, USA, 1991. [Google Scholar]
- Grossman, G.M.; Krueger, A.B. Economic Growth and the Environment; National Bureau of Economic Research: Cambridge, MA, USA, 1994. [Google Scholar]
- Perman, R.; Stern, D.I. Evidence from panel unit root and cointegration tests that the environmental Kuznets curve does not exist. Aust. J. Agric. Resour. Econ. 2003, 47, 325–347. [Google Scholar] [CrossRef]
- Stern, D.I. The rise and fall of the environmental Kuznets curve. World Dev. 2004, 32, 1419–1439. [Google Scholar] [CrossRef]
- Galeotti, M.; Lanza, A.; Pauli, F. Reassessing the environmental Kuznets curve for CO2 emissions: A robustness exercise. Ecol. Econ. 2006, 57, 152–163. [Google Scholar] [CrossRef]
- Yavuz, N.Ç. CO2 emission, energy consumption, and economic growth for Turkey: Evidence from a cointegration test with a structural break. Energy Sources Part B Econ. Plan. Policy 2014, 9, 229–235. [Google Scholar] [CrossRef]
- Moomaw, W.R.; Unruh, G.C. Are environmental Kuznets curves misleading us? The case of CO2 emissions. Environ. Dev. Econ. 1997, 2, 451–463. [Google Scholar] [CrossRef]
- Martı́nez-Zarzoso, I.; Bengochea-Morancho, A. Pooled mean group estimation of an environmental Kuznets curve for CO2. Econ. Lett. 2004, 82, 121–126. [Google Scholar] [CrossRef]
- Ajmi, A.N.; Hammoudeh, S.; Nguyen, D.K.; Sato, J.R. On the relationships between CO2 emissions, energy consumption and income: The importance of time variation. Energy Econ. 2015, 49, 629–638. [Google Scholar] [CrossRef]
- Yang, G.; Sun, T.; Wang, J.; Li, X. Modeling the nexus between carbon dioxide emissions and economic growth. Energy Policy 2015, 86, 104–117. [Google Scholar] [CrossRef]
- Li, F.; Dong, S.; Li, X.; Liang, Q.; Yang, W. Energy consumption-economic growth relationship and carbon dioxide emission in China. Energy Policy 2011, 39, 568–574. [Google Scholar]
- Apergis, N. Environmental Kuznets curves: New evidence on both panel and country-level CO2 emissions. Energy Econ. 2016, 54, 263–271. [Google Scholar] [CrossRef]
- Fernández-Amador, O.; Francois, J.; Oberdabernig, D.A.; Tomberger, P. Carbon dioxide emissions and economic growth: An assessment based on production and consumption emission inventories. Ecol. Econ. 2017, 135, 269–279. [Google Scholar] [CrossRef]
- Ahmad, A.; Zhao, Y.; Shahbaz, M.; Bano, S.; Zhang, Z.; Wang, S.; Liu, Y. Carbon emissions, energy consumption and economic growth: An aggregate and disaggregate analysis of the Indian economy. Energy Policy 2016, 96, 131–143. [Google Scholar] [CrossRef]
- Ito, K. CO2 emissions, renewable and non-renewable energy consumption, and economic growth: Evidence from panel data for developing countries. Int. Econ. 2017, 151, 1–6. [Google Scholar] [CrossRef]
- Narayan, P.K.; Saboori, B.; Soleymani, A. Economic growth and carbon emissions. Econ. Model. 2016, 53, 388–397. [Google Scholar] [CrossRef]
- Zhu, H.; Duan, L.; Guo, Y.; Yu, K. The effects of FDI, economic growth and energy consumption on carbon emissions in ASEAN-5: Evidence from panel quantile regression. Econ. Model. 2016, 58, 237–248. [Google Scholar] [CrossRef]
- Hansen, B.E. Threshold effects in non-dynamic panels: Estimation, testing, and inference. J. Econ. 1999, 93, 345–368. [Google Scholar] [CrossRef]
- Heidari, H.; Katircioğlu, S.T.; Saeidpour, L. Economic growth, CO2 emissions, and energy consumption in the five ASEAN countries. Int. J. Electr. Power Energy Syst. 2015, 64, 785–791. [Google Scholar] [CrossRef]
- Chiu, Y.B. Carbon dioxide, income and energy: Evidence from a non-linear model. Energy Econ. 2017, 61, 279–288. [Google Scholar] [CrossRef]
- Granger, C.; Teräsvirta, T. Modelling Non-Linear Economic Relationships; Oxford University Press: Oxford, UK, 1993. [Google Scholar]
- Wu, Y.M.; Tian, B. The extension of regional Environmental Kuznets Curve and its determinants: An empirical research based on spatial econometrics model. Geogr. Res. 2012, 31, 627–640. [Google Scholar]
- Davies, R.B. Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 1977, 64, 247–254. [Google Scholar] [CrossRef]
- Colletaz, G.; Hurlin, C. Threshold Effects of the Public Capital Productivity: An International Panel Smooth Transition Approach; Université d’Orléans: Orléans, France, 2006. [Google Scholar]
Energy | Coal | Coke | Crude Oil | Gasoline | Kerosene | Diesel Oil | Fuel Oil | Nature Gas |
---|---|---|---|---|---|---|---|---|
carbon emissions factor | 1.9003 | 2.8604 | 3.0202 | 2.9251 | 3.0179 | 3.0959 | 3.1705 | 2.1622 |
Variables | Rce | GDPpc | Es | Ul | To | Mi |
---|---|---|---|---|---|---|
Observation | 600 | 600 | 600 | 600 | 600 | 600 |
Mean | 6.64 | 16,247.37 | 69.76 | 44.85 | 30.92 | 7.03 |
Standard deviation | 5.180 | 27,093 | 16.652 | 16.634 | 39.861 | 7.9899 |
Maximum | 31.20 | 369,120.78 | 97.92 | 89.6 | 205.12 | 49.51 |
Minimum | 0.737 | 1826 | 15.543 | 17.19 | 0.336 | 0.067 |
Model | 1 | 2 | 3 |
---|---|---|---|
threshold variables | |||
LM | 150.772 | 153.199 | 83.540 |
(0.000) | (0.000) | (0.000) | |
LMF | 18.795 | 38.745 | 18.278 |
(0.000) | (0.000) | (0.000) | |
LR | 173.639 | 176.889 | 89.959 |
(0.000) | (0.000) | (0.000) |
Model | 1 | 2 | 3 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Threshold variables | ||||||||||||
(r, m) | (1, 1) | (1, 2) | (2, 1) | (2, 2) | (1, 1) | (1, 2) | (2, 1) | (2, 2) | (1, 1) | (1, 2) | (2, 1) | (2, 2) |
AIC | −3.54 | −3.57 | −3.60 | −3.57 | −3.60 | −3.59 | −3.60 | −3.59 | −3.54 | −3.61 | −3.64 | −3.61 |
BIC | −3.45 | −3.48 | −3.46 | −3.48 | −3.51 | −3.50 | −3.51 | −3.50 | −3.45 | −3.51 | −3.50 | −3.51 |
Is the location parameter out the of region? | Yes | No | Yes | No | Yes | No | No | Yes | No | No | Yes | No |
Final model (r, m) | (1, 2) | (1, 1) | (1, 2) |
Model | 1 | 2 | 3 | |||
---|---|---|---|---|---|---|
Threshold Variables | ||||||
β | β (1) | β | β (1) | β | β (1) | |
0.6339 *** | −0.4035 *** | −0.6746 *** | 2.1772 *** | 0.7185 *** | −0.5622 *** | |
(6.8580) | (−3.4128) | (−5.7824) | (9.1263) | (7.8991) | (−4.3070) | |
−0.1592 *** | 0.1921 ** | −0.2218 | 0.3254 | −0.1660 *** | 0.1779 ** | |
(−3.0084) | (2.4590) | (−1.4833) | (1.1975) | (−3.1589) | (2.4991) | |
−0.6490 *** | 1.3625 *** | −3.5520 *** | 1.7178 *** | −0.6829 *** | 1.3278 *** | |
(−4.0785) | (10.8281) | (−3.0020) | (5.8940) | (−4.6742) | (9.5960) | |
1.4856 *** | −0.9914 ** | 2.9676 *** | −4.4498 *** | 1.4559 *** | −0.3981 | |
(5.4447) | (−2.7422) | (3.9627) | (−3.3184) | (4.9173) | (−1.0857) | |
−0.2777 *** | 0.3182 *** | −0.2874 | 0.5500 | −0.2496 *** | 0.2958 *** | |
(−4.2554) | (3.0523) | (−1.3856) | (1.4246) | (−4.4999) | (3.8394) | |
0.6468 | 1.1481 | 10.8779 | ||||
c | ||||||
RSS | 15.718 | 15.408 | 15.165 |
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Wang, Z.-X.; Hao, P.; Yao, P.-Y. Non-Linear Relationship between Economic Growth and CO2 Emissions in China: An Empirical Study Based on Panel Smooth Transition Regression Models. Int. J. Environ. Res. Public Health 2017, 14, 1568. https://doi.org/10.3390/ijerph14121568
Wang Z-X, Hao P, Yao P-Y. Non-Linear Relationship between Economic Growth and CO2 Emissions in China: An Empirical Study Based on Panel Smooth Transition Regression Models. International Journal of Environmental Research and Public Health. 2017; 14(12):1568. https://doi.org/10.3390/ijerph14121568
Chicago/Turabian StyleWang, Zheng-Xin, Peng Hao, and Pei-Yi Yao. 2017. "Non-Linear Relationship between Economic Growth and CO2 Emissions in China: An Empirical Study Based on Panel Smooth Transition Regression Models" International Journal of Environmental Research and Public Health 14, no. 12: 1568. https://doi.org/10.3390/ijerph14121568