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Figure : CO2 emissions per sector from CSO Mauritius (2010)

Methodology and estimation techniques:

According to the EKC hypothesis, there is a nonlinear quadratic relation that exists between income of a country and carbon emissions. However, since other variables than income can also exist as the determinants of CO2 emissions, we added other variables which could have an influence on CO2 as well as the income of the country.

Econometrics methods were applied as well as time series processes to understand the data behaviour and stationarity. Unit root testing would be used to test the proposition that in autoregressive time series the parameter is always one. Also unit root analysis would be used to ensure that the data for the different estimates that will be used in the model follow the same mean and variance, if not, differencing and redifferencing techniques would be applied to make the data stationary. The step for differencing and redifferencing includes the following general formulae which are used again to test the stationarity of the processes.

\Delta y_t = \alpha + \beta t + \gamma y_{t-1} + \delta_1 \Delta y_{t-1} + \cdots + \delta_p \Delta y_{t-p} + \varepsilon_t,

The Augmented Dicker Fuller Test (ADF) would be applied because the set of data is a larger one and a complicated set of time series model. The ADF test will be used to test the null hypothesis of stationarity and to investigate the possibility that a time series is seasonal and fractionally integrated.

The DF-GLS test will also be performed before running the regression analysis for both univariate modeling and multivariate modelling.

Likewise for the specifications of the ADF test, we will be using the Bayesian Information criteria (BIC), the Akaike Information Criteria (AIC) and the Ng-Perron Modified AIC (MAIC), were used to test the results for different lag lengths respectively.

Multivariate regression techniques were applied so as to be able to build the regression curve as well as the regression formulae. The multivariate model regression is a single parameter estimation model that attempts to estimate the regression outcome with more than 2 variables.

Similarly, quadratic equation models were developed so as to smooth the Kuznets curve as well to be able to develop forecasting procedures and this was developed through the use of matrices. The quadratic equation will be used to find the turning point of the Kuznets curve and predict how the GDP and Carbon Dioxide level may vary the future.

After running a series of multivariate regression analysis we will then be able to devise a formula that would be help in predicting and forecast本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。