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摘要：本文是对毛里求斯的环境库兹涅茨曲线分析的留学生国民核算论文，本文试图通过考察碳排放的情况及其对国内生产总值(GDP)影响来估计毛里求斯的环境库兹涅茨曲线(EKC)，同时分析通货膨胀率、失业率和人口规模这些变量与GDP和碳排放之间的直接或间接联系。文章考察了毛里求斯在1990 - 2010年期间的经济增长与碳排放之间的关系。

ph shows the actual relationship of CO2 and GDP matched over a period of twenty years ranging from 1990 to 2010.

The above figure depicts the movement of GDP in regards to CO2 level of Mauritius over a period of 20 years. The figures in the above graph concerning GDP are in Millions of rupees while that of CO2 is in metric tons.

From Figure 13, it can be deduced that the more GDP rises, the higher is the level of CO2 emissions. However it can also be deduced that in some specific moments, when GDP has fallen, the level of CO2 even fell. Hence, it can be observed that there is a positive relationship between the level of GDP and carbon emissions. Likewise, it should be noted that this graph is a non smoothed one, and the smoothed one has been done through a quadratic equation so as to be able to derive the Kuznet’s curve and the turning point of Mauritius, whereby though GDP is rising but the level of carbon emissions will fall. This is discussed in further sections.

Multivariate Model Equation;

Under the Univariate model only GDP and CO2 were taken as variables, but there are various variables that affect GDP and CO2 amongst which are Inflation and Unemployment. Inflation is an important variable to take into account as it erodes the value of GDP and makes it appear to be a larger value. That is why we have taken inflation in the multivariate modelling so as to be able to consider the effects of inflation on the real value of GDP.

Likewise, Unemployment as a variable was also taken into consideration because unemployment has a direct impact on the level of GDP. The more the level unemployment is high the lower is the amount of GDP and the lower the rate of Unemployment, the higher is the level of GDP.

The figures and data trends for both inflation and unemployment were tested for stationarity and it was found that after ADF test the data were stationary and as per the AIC and BIC no time lags were necessary.

The following figures show the ADF test for inflation trends in Mauritius:

Figure : ADF test for inflation trends in Mauritius

As per figure 14, it can be observed that no time lags were assigned as following the AIC and BIC approach, it was not necessary to include a time lag and hence with zero time lags the data was stationary.

The following figure shows the ADF test for Unemployment trends in Mauritius:

Figure : ADF test for Unemployment trends in Mauritius

As per figure 15, it can be deduced that again no time lags were taken into consideration as per the AIC and BIC criteria and there was no need for data smoothing procedures.

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