paper
mt19941i24
paper.docxIntroduction
How does population affect the amount of CO2 emissions per country between the years 1960 and 2014? This paper will focus on the relationship between population and CO2 emissions. The historical data released by the World Bank indicate that population may have played an important role in addressing CO2 emission.
Analyzing the relationship population between the amount of CO2 emissions by country helps in determining the manner its effects are distributed. The aim is to devise means at which this emission can be addressed. I think there is a need to collect additional data of GDP in every country. Part of the research was to understand the pattern of distribution of CO2. However, the main aim was to explore ways in which its adverse effects could be countered. Therefore, knowing the GDP of every country would also aid in addressing any negative effects that come of it.
Based on the data, I have the hypothesis that the rate of CO2 emissions by country may not be directly proportional to the respective country’s size of the population.
The amount of CO2 produced by a country is dependent on factors such as weather changes and other aspects such as plantations. However, a huge population size may not imply any variance to the level of CO2. A country with a small size of population may have a higher level of this element than those with more population.
Hypothesis
To measure the impact of population size on carbon dioxide emissions, we established model (1):
Source | SS df MS Number of obs = 12,248
-------------+---------------------------------- F(1, 12246) = 37067.11
Model | 103764.011 1 103764.011 Prob > F = 0.0000
Residual | 34280.9068 12,246 2.79935544 R-squared = 0.7517
-------------+---------------------------------- Adj R-squared = 0.7516
Total | 138044.917 12,247 11.2717333 Root MSE = 1.6731
------------------------------------------------------------------------------
lnco2 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lnPop | .9880223 .0051318 192.53 0.000 .9779631 .9980815
_cons | -6.35125 .0833884 -76.16 0.000 -6.514704 -6.187796
The dependent variable is the logarithm of carbon dioxide emissions (lnco2), and the independent variable is the logarithm of the population, or the the error term.
From the specific regression results, we can get:
(1) The adjusted R2 value is 0.7516, which indicates that the independent variable lnpopulation can explain the information of dependent variable lnco 275.16%.
(2) The value of F statistic is 37067.11, and its corresponding P value is less than 0.05 (in this case, the significance level is controlled at 5%).
(3) From the specific results, the independent variable lnpopulation has a significant positive effect on the dependent variable lnco2. At this time, its t statistic value is 192.53, its corresponding P value is less than 0.05, showing that the regression coefficient value of the variable lnpopulation is not equal to 0, that is, it is meaningful in statistical sense, and its corresponding regression coefficient number. The value is 0.988, which means that when the variable lnpopulation changes 1 unit, the variable lnco2 changes 0.988 units.
For this hypothesis, I added two more control variables to the regression.
Source | SS df MS Number of obs = 7,365
-------------+---------------------------------- F(3, 7361) = 30919.34
Model | 62263.3061 3 20754.4354 Prob > F = 0.0000
Residual | 4941.02965 7,361 .671244349 R-squared = 0.9265
-------------+---------------------------------- Adj R-squared = 0.9264
Total | 67204.3358 7,364 9.12606407 Root MSE = .8193
------------------------------------------------------------------------------
lnco2 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lnPop | .1871355 .0066558 28.12 0.000 .1740882 .2001827
lnManu | .418834 .016292 25.71 0.000 .386897 .450771
lnGDP | .3929029 .0179113 21.94 0.000 .3577917 .4280142
_cons | -11.79659 .107627 -109.61 0.000 -12.00757 -11.58561
------------------------------------------------------------------------------
In the model (1), we only considered the influence of lnpopulation on the dependent variable lnco2. There are many factors affecting carbon emissions, such as the level of economic development, the level of manufacturing expansion and so on. Therefore, considering these factors may have a significant impact on carbon emissions, this paper studies the effect of lnpopulation on carbon emissions. Model (1) based on the establishment of the model (2):
From the regression results, we can see that:
