How does population affect the amount of CO2 emissions per country

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Part I:

1) What is your research question?

2) Why is it an important question (i.e., why should we care)?

3) Which data set will you be working with?

4) If you are collecting extra data, please describe what it is. If you are creating new variables within you data set please describe those

5) Based on your research question, what is your hypothesis about the relationship between the main independent variable and the dependent variable?

6) Give your reasons why you have this hypothesis; that is, please try to give your thoughts on the underlying economic theory provides a reason for your hypothesis.

1. Which data set will you be working with?

CO2 Emissions by Country

2. What is your dependent variable and what is your main or key independent variable?

Dependent variable： CO2 emissions by country

Independent variable：population

3. What is your research question?

How does population affect the amount of CO2 emissions per country between the years 1960 and 2014?

4. Why is it an important question (i.e., why should we care)?

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.

5. Do you think there will be a need to collect additional data? If so, what variable do you have in mind? You do not have to have a full answer to this question yet, but do your best.

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.

6. Based on your research question, what is your hypothesis about the relationship between the main independent variable and the dependent variable?

Hypothesis: The rate of CO2 emissions by country may not be directly proportional to the respective country’s size of the population.

7. Give your reasons why you have this hypothesis; that is, please try to give your thoughts on the underlying economic theory provides a reason for your hypothesis.

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.

Part II:

Cut and past two regressions results from Stata. The first one is a simple regression of your dependent variable on your key independent variable (use logs if you think it necessary). The second is a multiple variable regression, where you regress your dependent variable on your key independent variable and at least two more control variables (In this case, set manufacturing and GDP as two more control variables). Answer the following questions:

1) What does the simple regression suggest about the relationship between your x and y variables? Is the slope coefficient statistically significant? What is the size? How do you interpret it?

reg lnco2 lnPop

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

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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

2) Describe the additional control variables that you included and why you included them in your regression. Now explain the results: What does the table suggest about how they affect your dependent variable (or not) and how do you interpret the coefficients and the t-statistics?

reg lnco2 lnPop lnManu lnGDP

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

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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

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3) What happens to the size and significance of your key independent variable across the two regressions? Does it change in an appreciable manner when you include additional controls? If so, why do you think this is? If not, why might this be?

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