Indian Journal of Sustainable Development

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

The Determinants of Carbon Emissions : Panel Quantile Regression Estimation of the Differential Effects of Foreign Direct Investment, Energy Consumption and Economic Growth


Affiliations
1 Department of Econometrics, University of Madras, Chennai, Tamil Nadu, India
     

   Subscribe/Renew Journal


This paper estimates the effect of FDI, economic growth and energy consumption on carbon emissions in five ASEAN countries for the period 1981 to 2014. The panel quantile regression estimates show that while the effect of FDI is insignificant, economic growth and energy consumption significantly increase carbon emissions in high-emission ASEAN-5 countries. At higher levels of energy consumption, adaption of green renewable energy and emission control technology mitigate the increase in carbon emissions. The quantile estimates of this paper do not lend support to the U-shaped Environmental Kuznets Curve (EKC) hypothesis. At the same time, the insignificant effect of FDI on carbon emissions does not lend sufficient support to the pollution haven hypothesis in lower-emission ASEAN-5 countries. The negative influence of FDI on carbon emissions at the middle quantiles supports the halo effect hypothesis. The estimated quantile results suggest that uniform carbon emissions control policies are unlikely to succeed equally across low-emissions and high-emissions economies.

Keywords

FDI, Energy, Growth, Emissions, Environment, Quantile Regression.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Alexander, M., Harding, M., & Lamarche, C. (2011). Quantile regression for time-series-cross section data. International Journal of Statistics and Management Systems, 6(1), 47-72.

  • Arouri, M. E. H., Youssef, A. B., & Rault, C. (2012). Energy consumption, economic growth and Co2 emissions in middle east and North African countries. Energy Policy, 45, 342-349.

  • Barbieri, L. (2008). Panel cointegration tests: A survey. Review of International Social Science, 116(1), 3-36.

  • Barbieri, L. (2009). Panel unit root tests under cross-sectional dependence: An overview. Journal of Statistics: Advances in Theory and Applications, 1(2), 117-158.

  • Breitung J., & Pesaran, M. H. (2008). Unit roots and cointegration in panels. In L. Matyas & P. Sevestre (eds.), The Econometrics of Panel Data, Advanced Studies in Theoretical and Applied Econometrics, (vol. 46, pp. 279-322). Berlin, Heidelberg: Springer-Verlag.

  • Canay, I. (2011). A simple approach to quantile regression for panel data. Econometrics Journal, 14(3), 368-386.

  • Breitung, J. (2000). The local power of some unit root tests for panel data. In B. H. Baltagi, T. B. Fombay & R. Carter (eds.), Nonstationary Panels, Panel Cointegration, and Dynamic Panels, Advances in Econometrics, (vol. 15, pp. 161-178). London: Emerald Publishing.

  • Breitung, J., & Meyer, W. (1994). Testing for unit roots in panel data: Are wages on different bargaining levels cointegrated? Applied Economics, 26(4), 353-361.

  • Choi, I. (2001). Unit root tests for panel data. Journal of International Money and Finance, 20(2), 249-272.

  • Hadri, K. (2000). Testing for unit roots in heterogeneous panel data. Econometrics Journal, 3(2), 148-161.

  • Hossain, M. S. (2011). Panel estimation for C2 emissions, energy consumption, economic growth, trade openness and urbanization of newly industrialized countries. Energy Policy, 39(11), 6991-6999.

  • Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12, 231-254.

  • Koenker, R. (1017). Quantile regression: 40 years on. Annual Review of Economics, 9, 155-176.

  • Koenkar, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74-89.

  • Koenker, R. (2005) Quantile regression. Cambridge: Cambridge University Press. Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.

  • Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives, 15(4), 143-156.

  • Lamarche, C. (2010). Robust penalized quantile regression estimation for panel data. Journal of Econometrics, 157, 396-408.

  • Lamarche, C. (2011). Measuring the incentives to learn in Colombia using new quantile regression approaches. Journal of Development Economics, 96(2), 278-288.

  • Lancaster, T. (2000). The incidental parameter problem since 1948. Journal of Econometrics, 95(2), 391-413.

  • Levin, A., Lin, C. H., & Chu, C.-S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1-24.

  • Maddala, G. S., & Wu, Y. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61(S1), 631-652.

  • Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53-74.

  • Omri, A. (2013). CO2 emissions, energy consumption and economic growth Nexus in MENA countries: Evidence from simultaneous equations models. Energy Economics, 40, 657-664.

  • Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61(S1), 653-670.

  • Pedroni, P. (2001). Fully-modified OLS for heterogeneous Cointegrated panels. In B. H. Baltagi, T. B. Fombay & R. Carter (eds.), Nonstationary Panels, Panel Cointegration, and Dynamic Panels, Advances in Econometrics (vol. 15, pp. 93-130). London: Emerald Publishing.

  • Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory, 20(3), 597-625.

  • Perman, R., & Stern, D. I. (2003). Evidence from panel unit root and cointegration tests that the environmental Kuznets curve does not exist. Australian Journal of Agricultural and Resource Economics, 47(3), 325-347.

  • Phillips, P., & Perron, P. (1988). Testing for a unit root in time series regressions. Biometrika, 75(2), 335-346.

  • Quah, D. (1994). Exploiting cross-section variation for unit root inference in dynamic data. Economics Letters, 44(1), 9-19.

  • Rafindadi, A. A., Yusof, Z., Zaman, K., Kyophilavong, P., & Akhmat, G. (2014). The relationship between air pollution, fossil fuel energy consumption, and water resources in the panel of selected Asia-Pacific countries. Environmental Science and Pollution Research, 21(19), 11395-11400.

  • Wang, S., Fang, C., Guan, X., Pang, B., & Ma, H. (2014). Urbanization, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s province. Applied Energy, 136, 738-749.

  • Zhang, C., & Lin, Y. (2012). Panel estimation for urbanization, energy consumption and CO2 emissions: A regional analysis in China. Energy Policy, 49(C), 488-498.

  • Zhang, J., Wang, C. M., Liu, L., Guo, H., Liu, G. D., Li., Y. W., & Deng, S. H. (2014). Investigation of carbon dioxide emission in China by primary component analysis. Science of the Total Environment, 472, 239-247.


Abstract Views: 209

PDF Views: 0




  • The Determinants of Carbon Emissions : Panel Quantile Regression Estimation of the Differential Effects of Foreign Direct Investment, Energy Consumption and Economic Growth

Abstract Views: 209  |  PDF Views: 0

Authors

T. Lakshmanasamy
Department of Econometrics, University of Madras, Chennai, Tamil Nadu, India

Abstract


This paper estimates the effect of FDI, economic growth and energy consumption on carbon emissions in five ASEAN countries for the period 1981 to 2014. The panel quantile regression estimates show that while the effect of FDI is insignificant, economic growth and energy consumption significantly increase carbon emissions in high-emission ASEAN-5 countries. At higher levels of energy consumption, adaption of green renewable energy and emission control technology mitigate the increase in carbon emissions. The quantile estimates of this paper do not lend support to the U-shaped Environmental Kuznets Curve (EKC) hypothesis. At the same time, the insignificant effect of FDI on carbon emissions does not lend sufficient support to the pollution haven hypothesis in lower-emission ASEAN-5 countries. The negative influence of FDI on carbon emissions at the middle quantiles supports the halo effect hypothesis. The estimated quantile results suggest that uniform carbon emissions control policies are unlikely to succeed equally across low-emissions and high-emissions economies.

Keywords


FDI, Energy, Growth, Emissions, Environment, Quantile Regression.

References