Environmental pollution and income as a measure of economic growth in Malaysia / Halimahton Borhan and Elsadig Musa Ahmed

• Main Authors: Borhan, Halimahton (Author), Ahmed, Elsadig Musa (Author)
• Format: Book
• Published: Universiti Teknologi MARA, Shah Alam, 2010-12.
• Subjects: Article; PeerReviewed
• Online Access: Link Metadata
• Notes: https://ir.uitm.edu.my/id/eprint/43656/1/43656.pdf
• Keywords: Regression. Correlation; Economics as a science. Relation to other subjects

It is possible to distinguish three main channels whereby income growth affects the quality of the environment as first suggested by Grossman (1995). They are firstly, a scale effect, secondly a composition effect and thirdly, technological progress. A recent research criticism by Cole (2003 and 2004) of the environmental Kuznets curve hypothesis is based on the occurrence of foreign direct investment and international trade. In the previous EKC literature, EKC is always estimated in the form of a single equation. However, according to Shen (2006), since both income and environmental quality are endogenous variables in which they impact upon each other, therefore the estimation of single equation relationships where simultaneity exists will produce biased and inconsistent estimates. The general objective of this study is to measure the relationship between economic growth and different indicators of air pollution in Malaysia. Air pollution indicators were assessed on a number of measures: Carbon Monoxide (CO), Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), Ozone (O3) and Particulate Matter (PM10). The income level per capita GDP (Gross Domestic Product) were measured from the year 1996 to 2006 quarterly. This study contributes to the available literature by Hung et al (2004) and Shen (2006) by adopting the model and extending it to include variables such as the number of motor vehicles, local labour, and foreign labour, the number of university graduates, foreign direct investment and government spending. Being different from the study by Hung et al (2004) and Shen (2006), this study estimates population density as an endogenous variable. It formulates a four-equation simultaneous model for empirical research. It is testing for exogeneity with the Hausman test and estimating the simultaneity model using the two-stages least squares method. The EKC hypothesis is supported in the cases of SO2 and PM10 and there are several differences found between single polynomial equation estimators commonly used in EKC literatures and simultaneous equation estimators.