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Causal Relationship Between Education, Carbon Dioxide (co2) Emission and Economic Growth in Bangladesh

Mirza Md. Moyen Uddin
Assistant Professor (Economics)
Bangladesh Civil Service General Education Cadre, Under National University, Directorate of Secondary and Higher Education, Ministry of Education, Bangladesh, Dhaka

Abstract/ Executive Summary:

The purpose of this study is to test the relationship among education, environmental pollution and economic growth in Bangladesh. Perhaps this is the first research done using time series data for 37 years from 1974-2010. Vector Error Correction Mechanism (VECM) techniques is applied to establish the long run and short run relationships among the variables in the model. Using Johansen cointegration method, the empirical findings indicate that there exists long run cointegration among the variables. Unit root test were performed to test the statistical properties of the data used in variables. Results show strong positive relationship among environmental pollution, education expenditure and economic growth. The results of this study will help the environmental authorities to understand the effects of economic growth for degrading the environmental quality and manage the environmental pollution by creating awareness through education.

Keywords: Environment, Education, VECM, GDP, Bangladesh

Location: All over Bangladesh

Start Date:

End Date:

Program Area: Socio-economic and Policy

Non-commodity: Carbon sequestration, Education

Objectives:

To test the relationship among education, environmental pollution and economic growth in Bangladesh.

Methodology:

The study applied multivariate model analysis techniques to examine the relationships among environmental pollution i.e. CO₂ emission, education expenditure and GDP growth in Bangladesh. The study based on the assumption that in GDP production is driven by high energy consumption that is likely to produce CO₂ emissions that causes environmental pollution and education expenditure in GDP is driven to create awareness through education among people about environmental pollution. The basic form of the relationship among the variables can be expressed as: Ep_t = α + α₁Ed_t + α₁GDP_t+ ?_t. Where, Ep = Environmental pollution i.e. CO₂ emission, Ed = Education Expenditure, GDP= Gross Domestic Production, ? = Error terms. The empirical study consists of unit root test, the cointegration test and error correction mechanism. Testing for the unit root problem the Augmented Dickey-Fuller test and Phillips- Perron test were used here. Phillips-Perron (1988) test deals with serial correlation and heteroscedasticity. An important assumption of the DF test is that the error term ?_t is independently and identically distributed. The ADF test adjusts the DF test to take care of possible serial correlation in the error terms by adding the lagged difference terms of the regressand. Phillips and Perron use non parametric statistical methods to take care of serial correlation in the terms with adding lagged difference terms. Phillips-Perron test detects the presence of a unit root in a series. Suppose, y_t is estimating as Δ y_t = α + β_t+ P*Y_t-1+?_t.Where, the P.P test is the t value associated with the estimated co-efficient of ρ*. The series is stationary if ρ* is negative and significant. The test is performed for all the variables where both the original series and the difference of the series are tested for stationary. The concept of cointigration was introduced by Granger (1983) and the statistical analysis of cointigrated process was organized by Engle and Granger (1987). Cointegration means that despite being individually non-stationary, a linear combination of two or more time series can be stationary (Gujarati, 2011). When a linear combination of non stationary variables is stationary, the variables are said to be cointegrated and the vector that is quite possible for a linear combination of integrated variables to be stationary. In this case the variables are said to be cointegrated. The key point of cointegration is: (i) cointegration refers to a linear combination of non stationary variables. (ii) all the variables must be integrated of the same order. Suppose, considering the following cointegrated regression equation as: y_t = α + βx_{t +}?_t.In this series y_t and x_t are I (1) and the error term u_t is I (0). Then the coefficient measures the equilibrium relationship between the series y and x. The term u_t, indicates the deviation from the long run equilibrium path of y_t and x_t. A time series data ( y_t ) is said to be integrated of order one and that can be denoted as I (1). If the original non stationary series has to be differenced‘d’ times for stationary process, the original series is integrated of order‘d’ that can be denoted by I (d). Consistency in ECM requires all of terms to be integrated of order zero, I (0). This is possible only if y and x are cointegrated in a linear form, that is X_t = αy_t + u_t. which is stationary. There are several methods for testing cointegration. Here Granger Causality test, Trace Statistics and Maximum Eigen value test were used to find cointegration among the variables.

Source of Information: IOSR Journal Of Humanities And Social Science (IOSR-JHSS); Volume 19, Issue 4, Ver. VIII (Apr. 2014), PP 60-67

Article Link (Online Journal): e-ISSN: 2279-0837, p-ISSN: 2279-0845. www.iosrjournals.org

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

In this study we have used carbon emission data as the environmental pollution indicator, GDP as the economic growth indicator and education expenditure in GDP as the education indicator. Time series data for 37 years from 1974 to 2010 was used to analyze causal relationship between environmental pollution, education and economic growth in Bangladesh using VECM based test techniques to establish the short run and long run relationship among the variables in the model. Result shows that there have the long run linear deterministic relationships among the variables. From VECM results it is clear that carbon dioxide (CO₂) emission (EM), and education expenditure (ED) are statistically significant and cointegrated and that is why they effects on each other. It can be said that more education share in GDP intensify the literacy rate and increase in literacy rate create awareness among the people that reduces emission, i.e., environmental pollution. The educational attainments lead to reduce environmental pollution and it also leads to GDP growth, i.e., sustainable development. There has the unidirectional causality between education expenditure and environmental pollution i.e. education and sustainable economic growth. These results will help the environmental authorities to understand the effect of economic growth to the environmental pollution as well as the necessity of environmental awareness through education in Bangladesh. This results postulates that Bangladesh can obtain higher economic growth with better environmental pollution management by creating awareness through education.

Knowledge Management: Journal