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     Research Journal of Environmental and Earth Sciences


Environmental Kuznets Curve and SO2 Emission in Pakistan

1Muhammad Ismail, 2Momina Saleem and 2Khurram Shahzad
1Department of Statistics, COMSATS Institute of Information Technology
2Minhaj University, Lahore, Pakistan
Research Journal of Environmental and Earth Sciences  2014  4:195-200
http://dx.doi.org/10.19026/rjees.6.5760  |  © The Author(s) 2014
Received: November 26, 2013  |  Accepted: December 05, 2013  |  Published: April 20, 2014

Abstract

In this study, the Environmental Kuznets Curve (EKC) is hypothesized to examine the connection among SO2 emission, economic growth, trade liberalization, energy consumption and population density in Pakistan with annual data from 1970-2008. The co integration analysis using Auto Regressive Distributed Lag (ARDL) bounds testing approach is used. The outcomes support the hypothesis both in short-run and long-run and inverted U shaped relationship is found between SO2 emission and growth. Interestingly we found trade support the environment positively and population contributes to environmental degradation in Pakistan. The energy consumption and growth are the major explanatory variables which contribute to environmental pollution in Pakistan. Moreover, the time series data analysis is utilized and the stability of variables in estimated model is also evaluated.

Keywords:

Auto Regressive, Distributed Lag (ARDL), Environmental Kuznets Curve (ECK), environmental degradation, SO2,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2041-0492
ISSN (Print):   2041-0484
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