Effect of Carbon Dioxide Levels on Life Expectancy on Countries with Different Income Levels
| ✅ Paper Type: Free Essay | ✅ Subject: Environmental Studies |
| ✅ Wordcount: 25703 words | ✅ Published: 18th May 2020 |
To what extent do carbon dioxide emission levels affect the life expectancy in countries with different income levels?
Introduction
“In fact, the whole climate crisis, as they call it, is not only fake news, it's fake science. There is no climate crisis. There is weather and climate all around the world. And, in fact, carbon dioxide is the main building block of all life.” – Patrick Moore
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This controversial statement made by Patrick Albert Moore, a Canadian industry consultant, former activist, and past president of Greenpeace Canada was recently re tweeted by Donald Trump. This retweet by The President of the United States was understandably met with outrage and backlash from the twitter community. Personally, when I came across the statement I was taken aback by Moore’s candidness considering, he was a past president of a non-governmental environment organisation. As a student learning about global warming and its effect on the environment and society I disagree with Moore’s views and feel that this is one of the most ubiquitous and alarming issues of the 21st century. This sparked my interest in further analysing the validity behind the claim.
I started my process by searching for articles or research studies done on carbon dioxide emission levels and the climate, considering Moore directly addressed carbon dioxide as a “building block of life”. I was aiming to find a foundation on which I could start off and do a more in depth study on. I came across a research paper titled, ‘Pathways of human development and carbon emissions embodied in trade’ written by J Timmons Roberts, Julia K. Steinberger, Glen P. Peters and Giovanni Baiocchi. This provided me great insight on the issue and helped me in selecting and operationalizing my variables.
Luckily, I have opted for Economics HL as a part of my IB Curriculum which made this process much easier for me and equipped me with the necessary tools of analysis and evaluative skills required to study the data.
I decided to examine the relationship between human development and a country’s economic activity to see whether they are dependent on one another, mutually exclusive or independent of one another. I have chosen life expectancy as an indicator for human development and CO2 emission levels as an indicator for economic activity. For the sake of simplicity, I have chosen only two variables and therefore will not be addressing the other variables and indicators that play a role.
The independent variable here is carbon dioxide emission levels and the dependent variable is life expectancy of the sexes combined. The reason I chose CO2 levels as my independent variable was because I wanted to if the emission levels, which is interlinked with the economic growth of a country, has any effect on the human development of countries in varying income brackets.
My null hypothesis is that in low income countries, especially where there are not many technological advancements and there is a high reliability on outdated energy sources, the CO2 emissions will be higher than the average global average level, and the life expectancy will also be lower than the global average due to high pollution and CO2 levels present in the air. For the middle-income countries, I hypothesized that the CO2 levels will be less than those of the low-income countries since these counties mostly consist of developing nations where technological advancements are taking place and the governments of these countries are moving to more green energy sources. In high income countries I hypothesize that the life expectancy levels will be high while the emission levels will be relatively lower than the global average due other factors such as better development and healthcare however, the emission levels will still be high due to policies aimed at economic growth.
This investigation involves finding the carbon emission levels and life expectancy levels of 138 countries in total. These countries have been divided into four sub categories based on the Gross National Income (GNI) levels – high, upper middle, lower middle- and low-income countries. I have used the Pearson’s coefficient correlation to find both the strength and the direction of the association, the chi squared test to test if the variables are independent or dependent on one another and linear regression to form an equation in order to graph it.
My aim of this investigation is – ‘To find a co-relation between carbon dioxide levels and life expectancy levels of countries in varying income brackets.’
Pearson’s coefficient correlation
Understanding the concept
The Pearson product-moment correlation coefficient is a measure of the strength of the linear relationship between two variables.
It is the test statistics that measures the statistical relationship between two continuous variables. Since it is based on the method of covariance, it is a good method to test the association between the variables. It provides information on both the correlation as well as direction of the association.
Application
Interpretation of values
The Pearson correlation coefficient, r, can take a range of values from +1 to -1. A value of 0 indicates that there is no relationship between the two variables. A positive association is indicated by a value greater than 0; this shows that the value of both variables move in the same direction. A negative association is indicated by a value less than 0; this shows that the values of the two variables as the value of one variable move in different directions.
The closer the value of r is to either +1 or -1 the stronger the association between the two variables are.
The table below shows the interpretation of the values of r
Table (a) : Interpretation for the values of r
We have yet not covered this in the IB syllabus, so I had to study and do background research in order to calculate the value of r.
Understanding the formula
x is the first set of data points – here it refers to the CO2 emission levels
x̅refers to mean of the first set of data points
y is the second set of data points – here it refers to the life expectancy values.
ȳ refers to the mean of the second set of data points.
Calculation
In order to calculate the co relation between the two variables I found out the CO2 emission levels as well as the life expectancies of 138 countries which were chosen by a random country generator. The table below lists out all the calculations required to be plugged into the formula in order to calculate the value of r.
The countries have been categorized into 4 groups.
|
High Income - more than $12,615 |
|
|
Upper Middle Income - $4,086 and $12,615 |
|
|
Lower middle income - between $1,036 and $4,085 |
|
|
Low Income - less than $1,035 GNI per capita |
GNI per capita in dollar terms is estimated using the World Bank Atlas method.
Referring to Table (b): Calculations and values showing how the value of r was calculated in the appendix
r = 3445.42 11111111111111
√ (3062.285 x 10994.039)
r = 0.594 [ using GDC ]
As seen from table (a) the value 0.593 is moderately strong and shows a moderate association between the two variables. Next an equation will be modelled using the above data sets
Referring to Table (c): Calculations and values showing how equation for linear regression was calculated in the appendix.
Linear regression
Understanding the concept
It is a form of statistical analysis that models the relationship between two continuous variables, in this case it will be CO2 emission levels will be the independent variable and life expectancy the dependent.
It finds a statistical relationship which refers to the fact that one variable cannot be expressed by the other. Linear regression aims to find the line of best fir- which is the line for which total prediction error is as small as possible. Error is the distance between a data point and the regression line.
Formula
Y = mx +b
m refers to the slope
b refers to the y- intercept
In order to calculate linear regression, we require the value of both the slope and the y intercept
Application and Calculation
Slope
Slope (m) =
= 138 (47782.594970) - (635.97) (9620.76)
138 (5993.1427) - (635.97)2
= (6593998.106) - (6118514.737)
(827053.6926) - (404457.8409)
= 475483.369 6
422595.8517
Slope = 1.125 [ using GDC ]
Understanding the formula
n is the total number of data points in this investigation which is -
sig xy refers to the sum of the multiplication of the two sets of data points, which in this case is life expectancy values with CO2 emission levels
sig x refers to the first set of data points
sig x2 refers to the sum of the squared values of the first set of data points
sig y refers to the second set of data points
sig y2refers to the sum of the squared values of the second set of data points
The formula below has been derived using the above symbols, this in turn allows us to calculate the y-intercept.
Y- Intercept
Interception (b) =
= (9620.76)( 5993.1427) - (635.97) (47782.59493)
138 (5993.1427) - (635.97)2
= (57658587.56) - (30388296.88)
(827053.6926) - (404457.8409)
= 27270290.68
422595.8517
= 64.530 (Rounded off value) [ using GDC ]
We can obtain the linear regression equation by plugging the obtained values into the formula.
y = mx + b
= 1.125x + 64.530
Calculation on GDC Calculator
Graph
Using the linear regression equation and after modelling a formula we can input the data to form a scatter plot graph. Using the equation, we can input the values of one variable to calculate the value of another.
y = mx + b
= 1.125x + 64.530
Hence by inputting an x value we can find out the value of y.
However, the data calculated is not completely accurate due to the presence of outliers and all the data is not centered along the regression line.
Image from GDC Calculator
Chi squared test for independence of two variables
Understanding the concept
It is a statistical method assessing the goodness of fit between a set of observed values and those expected theoretically and It is used to check if two variables are independent of one another or not.
Formula
Degree of freedom = 
Understanding the formula
Oi refers to the observed count
r is number of rows
c is the number of columns
Ei is the expected count
Application
Null hypothesis : There is no association between the two variables being analyzed.
|
OBSERVED |
Below average life expectancy |
Close to average LE 64.29± 5 |
Above average LE |
Total |
|
Below average CO2 emission |
35 |
23 |
6 |
64 |
|
Close to average CO2 emission 4.18 + 2 |
1 |
11 |
19 |
31 |
|
Above average CO2 emission |
2 |
8 |
33 |
43 |
|
Total |
38 |
42 |
58 |
138 |
|
EXPECTED |
Below average life expectancy |
Close to average LE 64.29± 5 |
Above average LE |
|
Below average CO2 emission |
17.623 |
19.478 |
26.898 |
|
Close to average CO2 emission 4.18 + 2 |
8.5362 |
9.4347 |
13.028 |
|
Above average CO2 emission |
11.84 |
13.086 |
18.072 |
Calculation
X2 = (35 – 17.623)2 + (23 – 19.478)2 + …….. + (33 – 18.072)2
17.623 19.478 18.072
X2 = 66.1426 [ using GDC ]
Degree of freedom = (R – 1) (C – 1 )
= (3 - 1) (3 - 1) = 4
X2 critical value ( DF = 4 ) = 18. 467 [ for 0.001 accuracy ]
Since,
66.1426 > 18. 467
X2 calculated > X2 critical
The null hypothesis has been disproved.
Evaluation
One of the advantages of using a correlational study is that the researcher is able to assimilate and collect a large sample space of data and this in turn increases the reliability of the results calculated. One of the biggest advantages for me and the main reason I chose this technique is that I will be able to easily and effectively interpret and analyse the data using a scatter plot graph. This study can also act as a foundation for further research on other factors, apart from CO2 levels and life expectancy, which affect economic growth and human development.
However knowing the strengths we must also keep in mind the drawback of this analysis technique
Correlational studies do not imply causality between the variables. Even in cases where there is a high co efficient value (+/- 0.8 and above) it does not allow for a cause and effect relationship to be established between the two variables being analysed. Outliers in the graph also skew the data calculated.
Pearson’s coefficient of correlation also assumes the existence of a linear relationship between the variables, this may not always be the case. Even if a linear relationship is established, a high degree of correlation does not necessarily always mean a high degree of a linear relationship.
By looking at not just the global averages and correlations allows us to analyse the effects and causes of outlier nations, and the qualitative differences in the growth and development programmes adopted by nations in different income brackets.
Referring to the table below, the outliers in the graph are the high-income countries like which have developed socioeconomically and maintained human development standards having an above average life expectancy with either a below average or close to average carbon emission level. The upper middle-income countries also have close to or above average life expectancy with a below average carbon emission level. This may due to the fact that the government of these countries have progressed economically by using greener energy sources. There are a few low-income countries which have below or close to average life expectancy with an above average carbon emission level. This may be due to the fact that in order to develop and progress economically the governments of these countries focus more on their economic goals as compared to healthcare, environment control and sanitation leading to a low life expectancy.
On further analysis, the table shows that most of the low-income countries had a below average life expectancy as well as below average carbon emission level. The lower middle-income countries had a below average life expectancy with either a below average or close to average carbon emission level. The upper middle-income countries had a close to average life expectancy with either a close to or above average carbon emission level.
The high-income countries had an above average life expectancy as well as an above average carbon emission level.
|
OBSERVED |
Below average life expectancy |
Close to average LE 64.29± 5 |
Above average LE |
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|
Below average CO2 emission |
|
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|
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Close to average CO2 emission 4.18 + 2 |
|
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|
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Above average CO2 emission |
|
|
|
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Conclusion
My aim of this investigation was to find a co-relation between carbon dioxide levels and life expectancy levels of countries in varying income brackets. A moderately strong positive correlation was established between the two variables.
One of the major challenges I faced during my investigation was operationalizing and defining human development and economic growth in terms of an independent and dependent variable. It took me multiple tries to finally get a moderately strong coefficient value, I had to choose between various indicator options before I found two appropriate variables. However, it was quite an informative and interesting process and it equipped me with the skills of easily and quickly interpreting data on an excel sheet, which will be quite useful for me in any future projects. This investigation taught me to self-reflect at each and every stage to make sure my methodology was logical and efficient and to see if I had not deviated from the aim of my study.
After I had decided upon my variables my next hurdle was accurately curating data; I had to ensure that it was from the same year to make sure time was not a confounding variable in this investigation. This process was definitely time consuming but I wanted to ensure that all my data was reliable so that I could conduct a precise analysis
This investigation is extremely relevant, given that we are living in a world where climate change is a major crisis which is neither being accurately addressed or dealt with. Studies such as this should be done to establish empirical evidence and to make sure a change will take place.
At the end of my investigation my claim and opinion of the existence of a climate crisis has not changed in actuality it makes me disagree with the statements made by Moore to a stronger degree.
Citations
- “Chegg.com.” Definition of Chi-Square Test | Chegg.com, www.chegg.com/homework-help/definitions/chi-square-test-14.
- “PEARSON Function in Excel - Find PEARSON CORRELATION in Excel.” DataScience Made Simple, www.datasciencemadesimple.com/pearson-function-in-excel/.
- “Region and Country Classification.” Asian-Pacific Economic Literature, vol. 25, no. 2, 2011, pp. 195–195., doi:10.1111/j.1467-8411.2011.01315.x.
- “Star Trek.” Chi-Square Test for Independence: Definition, stattrek.com/statistics/dictionary.aspx?definition=chi-square test for independence.
|
CO2 Emissions per capita (tonnes) |
Life expectancy at birth for both sexes 2005 - 2010 |
||||||
|
Country |
X |
Y |
X - x̅ |
Y-ȳ |
(X - x̅)2 |
(Y-ȳ )2 |
(X - x̅))(Y-ȳ ) |
|
Albania |
1.35 |
75.64 |
-3.258 |
5.924 |
10.618 |
35.098 |
-19.304 |
|
Algeria |
4.14 |
73.89 |
-0.468 |
4.174 |
0.219 |
17.425 |
-1.956 |
|
Angola |
1.41 |
55.59 |
-3.198 |
-14.126 |
10.23 |
199.534 |
45.181 |
|
Argentina |
4.65 |
75.18 |
0.042 |
5.464 |
0.002 |
29.859 |
0.227 |
|
Armenia |
1.65 |
72.74 |
-2.958 |
3.024 |
8.753 |
9.147 |
-8.947 |
|
Australia |
19 |
81.48 |
14.392 |
11.764 |
207.116 |
138.4 |
169.307 |
|
Austria |
8.93 |
80.13 |
4.322 |
10.414 |
18.676 |
108.459 |
45.006 |
|
Azerbaijan |
3.68 |
70.10 |
-0.928 |
0.384 |
0.862 |
0.148 |
-0.357 |
|
Bangladesh |
0.28 |
69.05 |
-4.328 |
-0.666 |
18.736 |
0.443 |
2.881 |
|
Belarus |
5.82 |
69.26 |
1.212 |
-0.456 |
1.468 |
0.208 |
-0.552 |
|
Belgium |
10.88 |
79.57 |
6.272 |
9.854 |
39.332 |
97.108 |
61.802 |
|
Benin |
0.46 |
58.56 |
-4.148 |
-11.156 |
17.21 |
124.449 |
46.279 |
|
Bolivia |
1.38 |
64.95 |
-3.228 |
-4.766 |
10.423 |
22.711 |
15.386 |
|
Bosnia and Herzegovina |
7.68 |
75.53 |
3.072 |
5.814 |
9.434 |
33.807 |
17.859 |
|
Botswana |
2.64 |
56.53 |
-1.968 |
-13.186 |
3.875 |
173.861 |
25.956 |
|
Brazil |
1.94 |
72.91 |
-2.668 |
3.194 |
7.121 |
10.204 |
-8.524 |
|
Brunei Darussalam |
19.8 |
76.70 |
15.192 |
6.984 |
230.782 |
48.781 |
106.103 |
|
Bulgaria |
7.71 |
73.14 |
3.102 |
3.424 |
9.619 |
11.726 |
10.621 |
|
Burkina Faso |
0.12 |
55.27 |
-4.488 |
-14.446 |
20.146 |
208.677 |
64.839 |
|
Cameroon |
0.33 |
54.39 |
-4.278 |
-15.326 |
18.305 |
234.876 |
65.57 |
|
Canada |
17.91 |
80.76 |
13.302 |
11.044 |
176.93 |
121.978 |
146.907 |
|
Chile |
4.31 |
78.13 |
-0.298 |
8.414 |
0.089 |
70.801 |
-2.511 |
|
China |
4.92 |
74.68 |
0.312 |
4.964 |
0.097 |
24.645 |
1.547 |
|
Colombia |
1.43 |
72.86 |
-3.178 |
3.144 |
10.103 |
9.887 |
-9.994 |
|
Comoros |
0.19 |
60.89 |
-4.418 |
-8.826 |
19.523 |
77.892 |
38.996 |
|
Congo |
0.45 |
57.95 |
-4.158 |
-11.766 |
17.293 |
138.431 |
48.927 |
|
Costa Rica |
1.82 |
78.41 |
-2.788 |
8.694 |
7.776 |
75.592 |
-24.244 |
|
Cote d'Ivoire |
0.32 |
49.19 |
-4.288 |
-20.526 |
18.391 |
421.302 |
88.024 |
|
Croatia |
5.61 |
76.09 |
1.002 |
6.374 |
1.003 |
40.632 |
6.384 |
|
Cuba |
2.41 |
78.66 |
-2.198 |
8.944 |
4.833 |
80.001 |
-19.664 |
|
Cyprus |
9.6 |
78.96 |
4.992 |
9.244 |
24.915 |
85.458 |
46.143 |
|
Czech Republic |
12.66 |
76.98 |
8.052 |
7.264 |
64.827 |
52.771 |
58.489 |
|
Denmark |
9.83 |
78.58 |
5.222 |
8.864 |
27.264 |
78.577 |
46.285 |
|
Djibouti |
0.58 |
59.05 |
-4.028 |
-10.666 |
16.229 |
113.756 |
42.966 |
|
Dominican Republic |
2.12 |
72.19 |
-2.488 |
2.474 |
6.193 |
6.122 |
-6.157 |
|
Ecuador |
2.25 |
74.57 |
-2.358 |
4.854 |
5.562 |
23.565 |
-11.449 |
|
Egypt |
2.31 |
69.88 |
-2.298 |
0.164 |
5.283 |
0.027 |
-0.378 |
|
El Salvador |
1.1 |
71.13 |
-3.508 |
1.414 |
12.309 |
2 |
-4.962 |
|
Equatorial Guinea |
7.47 |
54.94 |
2.862 |
-14.776 |
8.188 |
218.32 |
-42.281 |
|
Eritrea |
0.12 |
60.70 |
-4.488 |
-9.016 |
20.146 |
81.282 |
40.467 |
|
Estonia |
14.22 |
73.78 |
9.612 |
4.064 |
92.381 |
16.519 |
39.065 |
|
Ethiopia |
0.08 |
59.08 |
-4.528 |
-10.636 |
20.507 |
113.117 |
48.163 |
|
Finland |
12.51 |
79.54 |
7.902 |
9.824 |
62.434 |
96.518 |
77.627 |
|
France |
6.5 |
80.82 |
1.892 |
11.104 |
3.578 |
123.307 |
21.004 |
|
Gabon |
1.43 |
61.33 |
-3.178 |
-8.386 |
10.103 |
70.319 |
26.654 |
|
Gambia |
0.25 |
58.83 |
-4.358 |
-10.886 |
18.996 |
118.497 |
47.445 |
|
Georgia |
1.38 |
72.65 |
-3.228 |
2.934 |
10.423 |
8.61 |
-9.473 |
|
Germany |
10.22 |
79.73 |
5.612 |
10.014 |
31.489 |
100.287 |
56.196 |
|
Ghana |
0.43 |
60.02 |
-4.178 |
-9.696 |
17.46 |
94.006 |
40.513 |
|
Greece |
10.22 |
80.01 |
5.612 |
10.294 |
31.489 |
105.974 |
57.767 |
|
Guatemala |
0.97 |
70.50 |
-3.638 |
0.784 |
13.239 |
0.615 |
-2.854 |
|
Guinea |
0.14 |
55.45 |
-4.468 |
-14.266 |
19.967 |
203.509 |
63.746 |
|
Guinea-Bissau |
0.19 |
54.18 |
-4.418 |
-15.536 |
19.523 |
241.356 |
68.644 |
|
Haiti |
0.25 |
60.23 |
-4.358 |
-9.486 |
18.996 |
89.978 |
41.343 |
|
Honduras |
1.23 |
72.01 |
-3.378 |
2.294 |
11.414 |
5.264 |
-7.751 |
|
Hungary |
5.76 |
73.74 |
1.152 |
4.024 |
1.326 |
16.195 |
4.634 |
|
Iceland |
10.67 |
81.39 |
6.062 |
11.674 |
36.742 |
136.29 |
70.764 |
|
India |
1.38 |
65.57 |
-3.228 |
-4.146 |
10.423 |
17.186 |
13.384 |
|
Indonesia |
1.77 |
67.68 |
-2.838 |
-2.036 |
8.057 |
4.144 |
5.778 |
|
Iran (Islamic Republic of) |
6.85 |
72.73 |
2.242 |
3.014 |
5.024 |
9.086 |
6.757 |
|
Iraq |
3.4 |
68.01 |
-1.208 |
-1.706 |
1.46 |
2.909 |
2.061 |
|
Ireland |
10.91 |
79.68 |
6.302 |
9.964 |
39.709 |
99.288 |
62.791 |
|
Israel |
9.63 |
80.94 |
5.022 |
11.224 |
25.216 |
125.986 |
56.363 |
|
Italy |
8.01 |
81.50 |
3.402 |
11.784 |
11.57 |
138.871 |
40.085 |
|
Jamaica |
5.18 |
74.20 |
0.572 |
4.484 |
0.327 |
20.109 |
2.563 |
|
Japan |
10.23 |
82.65 |
5.622 |
12.934 |
31.602 |
167.297 |
72.711 |
|
Jordan |
3.61 |
73.00 |
-0.998 |
3.284 |
0.997 |
10.787 |
-3.279 |
|
Kazakhstan |
14.76 |
66.08 |
10.152 |
-3.636 |
103.053 |
13.218 |
-36.907 |
|
Kenya |
0.3 |
59.72 |
-4.308 |
-9.996 |
18.563 |
99.913 |
43.066 |
|
Kyrgyzstan |
1.14 |
67.47 |
-3.468 |
-2.246 |
12.03 |
5.043 |
7.789 |
|
Latvia |
3.79 |
71.55 |
-0.818 |
1.834 |
0.67 |
3.365 |
-1.501 |
|
Lebanon |
3.21 |
77.74 |
-1.398 |
8.024 |
1.956 |
64.39 |
-11.222 |
|
Liberia |
0.19 |
58.11 |
-4.418 |
-11.606 |
19.523 |
134.691 |
51.279 |
|
Libyan Arab Jamahiriya |
9.29 |
71.79 |
4.682 |
2.074 |
21.917 |
4.303 |
9.711 |
|
Lithuania |
4.74 |
71.87 |
0.132 |
2.154 |
0.017 |
4.641 |
0.283 |
|
Madagascar |
0.12 |
62.23 |
-4.488 |
-7.486 |
20.146 |
56.035 |
33.599 |
|
Malaysia |
7.32 |
73.72 |
2.712 |
4.004 |
7.352 |
16.035 |
10.858 |
|
Malta |
6.71 |
79.40 |
2.102 |
9.684 |
4.416 |
93.787 |
20.352 |
|
Mauritania |
0.62 |
61.32 |
-3.988 |
-8.396 |
15.908 |
70.487 |
33.486 |
|
Mauritius |
3.06 |
72.76 |
-1.548 |
3.044 |
2.398 |
9.268 |
-4.714 |
|
Mexico |
4.39 |
75.72 |
-0.218 |
6.004 |
0.048 |
36.052 |
-1.312 |
|
Morocco |
1.49 |
72.89 |
-3.118 |
3.174 |
9.725 |
10.076 |
-9.899 |
|
Mozambique |
0.12 |
53.24 |
-4.488 |
-16.476 |
20.146 |
271.447 |
73.951 |
|
Myanmar |
0.27 |
64.26 |
-4.338 |
-5.456 |
18.822 |
29.764 |
23.669 |
|
Namibia |
1.45 |
54.98 |
-3.158 |
-14.736 |
9.976 |
217.139 |
46.542 |
|
Nepal |
0.12 |
66.79 |
-4.488 |
-2.926 |
20.146 |
8.559 |
13.132 |
|
Netherlands |
10.49 |
80.18 |
5.882 |
10.464 |
34.592 |
109.503 |
61.546 |
|
New Zealand |
8.4 |
80.32 |
3.792 |
10.604 |
14.376 |
112.452 |
40.207 |
|
Nicaragua |
0.82 |
72.83 |
-3.788 |
3.114 |
14.353 |
9.699 |
-11.799 |
|
Nigeria |
0.64 |
49.74 |
-3.968 |
-19.976 |
15.749 |
399.027 |
79.273 |
|
Norway |
9.53 |
80.60 |
4.922 |
10.884 |
24.221 |
118.469 |
53.568 |
|
Oman |
13.69 |
75.06 |
9.082 |
5.344 |
82.474 |
28.562 |
48.535 |
|
Pakistan |
0.9 |
64.37 |
-3.708 |
-5.346 |
13.753 |
28.576 |
19.824 |
|
Panama |
2.17 |
76.36 |
-2.438 |
6.644 |
5.946 |
44.147 |
-16.202 |
|
Papua New Guinea |
0.52 |
64.18 |
-4.088 |
-5.536 |
16.716 |
30.643 |
22.632 |
|
Paraguay |
0.67 |
71.75 |
-3.938 |
2.034 |
15.512 |
4.139 |
-8.012 |
|
Peru |
1.51 |
73.15 |
-3.098 |
3.434 |
9.601 |
11.795 |
-10.641 |
|
Philippines |
0.8 |
68.05 |
-3.808 |
-1.666 |
14.505 |
2.774 |
6.344 |
|
Poland |
8.61 |
75.56 |
4.002 |
5.844 |
16.012 |
34.156 |
23.386 |
|
Portugal |
5.9 |
79.28 |
1.292 |
9.564 |
1.668 |
91.477 |
12.353 |
|
Republic of Moldova |
1.28 |
68.27 |
-3.328 |
-1.446 |
11.079 |
2.09 |
4.812 |
|
Romania |
5.17 |
73.08 |
0.562 |
3.364 |
0.315 |
11.319 |
1.889 |
|
Russian Federation |
11.13 |
67.14 |
6.522 |
-2.576 |
42.53 |
6.634 |
-16.797 |
|
Rwanda |
0.08 |
60.05 |
-4.528 |
-9.666 |
20.507 |
93.425 |
43.771 |
|
Sao Tome and Principe |
0.81 |
65.47 |
-3.798 |
-4.246 |
14.428 |
18.026 |
16.127 |
|
Saudi Arabia |
16.31 |
73.22 |
11.702 |
3.504 |
136.926 |
12.28 |
41.006 |
|
Senegal |
0.46 |
62.41 |
-4.148 |
-7.306 |
17.21 |
53.373 |
30.307 |
|
Serbia and Montenegro |
5.13 |
73.33 |
0.522 |
3.614 |
0.272 |
13.064 |
1.885 |
|
Sierra Leone |
0.24 |
45.88 |
-4.368 |
-23.836 |
19.084 |
568.138 |
104.126 |
|
Singapore |
12.08 |
81.21 |
7.472 |
11.494 |
55.824 |
132.12 |
85.88 |
|
Slovakia |
7.07 |
74.77 |
2.462 |
5.054 |
6.059 |
25.546 |
12.441 |
|
Slovenia |
8.45 |
78.55 |
3.842 |
8.834 |
14.757 |
78.046 |
33.937 |
|
South Africa |
8.82 |
53.07 |
4.212 |
-16.646 |
17.737 |
277.078 |
-70.104 |
|
Spain |
8.32 |
81.21 |
3.712 |
11.494 |
13.775 |
132.12 |
42.662 |
|
Sri Lanka |
0.62 |
74.07 |
-3.988 |
4.354 |
15.908 |
18.96 |
-17.367 |
|
Sudan |
0.28 |
61.50 |
-4.328 |
-8.216 |
18.736 |
67.497 |
35.561 |
|
Sweden |
5.64 |
81.06 |
1.032 |
11.344 |
1.064 |
128.694 |
11.702 |
|
Switzerland |
5.81 |
81.78 |
1.202 |
12.064 |
1.444 |
145.548 |
14.496 |
|
Syrian Arab Republic |
3.41 |
74.45 |
-1.198 |
4.734 |
1.436 |
22.414 |
-5.674 |
|
Tajikistan |
1.07 |
68.71 |
-3.538 |
-1.006 |
12.521 |
1.011 |
3.558 |
|
Thailand |
4.14 |
74.17 |
-0.468 |
4.454 |
0.219 |
19.841 |
-2.087 |
|
The Former Yugoslav Rep. of Macedonia |
5.53 |
73.15 |
0.922 |
3.434 |
0.849 |
11.795 |
3.165 |
|
Togo |
0.21 |
55.80 |
-4.398 |
-13.916 |
19.347 |
193.645 |
61.208 |
|
Tunisia |
2.37 |
74.56 |
-2.238 |
4.844 |
5.011 |
23.468 |
-10.844 |
|
Turkey |
4.17 |
73.37 |
-0.438 |
3.654 |
0.192 |
13.354 |
-1.602 |
|
Turkmenistan |
9.2 |
65.87 |
4.592 |
-3.846 |
21.082 |
14.789 |
-17.657 |
|
Uganda |
0.1 |
55.15 |
-4.508 |
-14.566 |
20.326 |
212.158 |
65.669 |
|
Ukraine |
7.35 |
67.89 |
2.742 |
-1.826 |
7.516 |
3.333 |
-5.005 |
|
United Kingdom |
8.97 |
79.69 |
4.362 |
9.974 |
19.023 |
99.488 |
43.503 |
|
United Rep. of Tanzania |
0.15 |
58.82 |
-4.458 |
-10.896 |
19.878 |
118.715 |
48.578 |
|
United States |
19.74 |
78.16 |
15.132 |
8.444 |
228.963 |
71.307 |
127.776 |
|
Uruguay |
1.86 |
76.20 |
-2.748 |
6.484 |
7.554 |
42.047 |
-17.822 |
|
Uzbekistan |
4.32 |
69.10 |
-0.288 |
-0.616 |
0.083 |
0.379 |
0.178 |
|
Venezuela (Bolivarian Republic of) |
5.99 |
73.35 |
1.382 |
3.634 |
1.909 |
13.208 |
5.021 |
|
Viet Nam |
1.29 |
74.69 |
-3.318 |
4.974 |
11.012 |
24.744 |
-16.507 |
|
Yemen |
0.99 |
62.75 |
-3.618 |
-6.966 |
13.093 |
48.52 |
25.205 |
|
Zambia |
0.22 |
52.93 |
-4.388 |
-16.786 |
19.259 |
281.758 |
73.663 |
|
Zimbabwe |
0.77 |
48.35 |
-3.838 |
-21.366 |
14.734 |
456.491 |
82.012 |
|
Total |
635.97 |
9620.76 |
Mx: 4.608 |
My: 69.716 |
3062.285 |
10994.039 |
3445.421 |
|
Average |
4.184013158 |
63.29448684 |
72.32920395 |
22.66724342 |
|||
Table b : Calculations and values showing how the value of r was calculated.
|
NAME |
CO2 Emissions per capita (tonnes) |
Life expectancy at birth for both sexes 2005 - 2010 |
|||
|
Country |
X |
Y |
x2 |
y2 |
XY |
|
Albania |
1.35 |
75.64 |
1.8225 |
5721.561 |
102.11535 |
|
Algeria |
4.14 |
73.89 |
17.1396 |
5458.993 |
305.8839 |
|
Angola |
1.41 |
55.59 |
1.9881 |
3089.803 |
78.37626 |
|
Argentina |
4.65 |
75.18 |
21.6225 |
5651.431 |
349.5684 |
|
Armenia |
1.65 |
72.74 |
2.7225 |
5290.817 |
120.0177 |
|
Australia |
19 |
81.48 |
361 |
6638.176 |
1548.025 |
|
Austria |
8.93 |
80.13 |
79.7449 |
6420.817 |
715.5609 |
|
Azerbaijan |
3.68 |
70.10 |
13.5424 |
4913.309 |
257.9496 |
|
Bangladesh |
0.28 |
69.05 |
0.0784 |
4767.488 |
19.33316 |
|
Belarus |
5.82 |
69.26 |
33.8724 |
4796.809 |
403.08738 |
|
Belgium |
10.88 |
79.57 |
118.3744 |
6331.385 |
865.7216 |
|
Benin |
0.46 |
58.56 |
0.2116 |
3429.274 |
26.9376 |
|
Bolivia |
1.38 |
64.95 |
1.9044 |
4218.113 |
89.62686 |
|
Bosnia and Herzegovina |
7.68 |
75.53 |
58.9824 |
5704.781 |
580.0704 |
|
Botswana |
2.64 |
56.53 |
6.9696 |
3195.189 |
149.22864 |
|
Brazil |
1.94 |
72.91 |
3.7636 |
5315.868 |
141.4454 |
|
Brunei Darussalam |
19.8 |
76.70 |
392.04 |
5882.737 |
1518.6402 |
|
Bulgaria |
7.71 |
73.14 |
59.4441 |
5348.728 |
563.87085 |
|
Burkina Faso |
0.12 |
55.27 |
0.0144 |
3054.994 |
6.63264 |
|
Cameroon |
0.33 |
54.39 |
0.1089 |
2958.381 |
17.94903 |
|
Canada |
17.91 |
80.76 |
320.7681 |
6521.855 |
1446.37578 |
|
Chile |
4.31 |
78.13 |
18.5761 |
6103.516 |
336.71875 |
|
China |
4.92 |
74.68 |
24.2064 |
5577.401 |
367.43544 |
|
Colombia |
1.43 |
72.86 |
2.0449 |
5308.725 |
104.19123 |
|
Comoros |
0.19 |
60.89 |
0.0361 |
3707.105 |
11.56834 |
|
Congo |
0.45 |
57.95 |
0.2025 |
3358.203 |
26.0775 |
|
Costa Rica |
1.82 |
78.41 |
3.3124 |
6148.599 |
142.71166 |
|
Cote d'Ivoire |
0.32 |
49.19 |
0.1024 |
2419.459 |
15.74016 |
|
Croatia |
5.61 |
76.09 |
31.4721 |
5790.297 |
426.88734 |
|
Cuba |
2.41 |
78.66 |
5.8081 |
6188.025 |
189.58024 |
|
Cyprus |
9.6 |
78.96 |
92.16 |
6235.313 |
758.0544 |
|
Czech Republic |
12.66 |
76.98 |
160.2756 |
5926.536 |
974.61744 |
|
Denmark |
9.83 |
78.58 |
96.6289 |
6175.131 |
772.46106 |
|
Djibouti |
0.58 |
59.05 |
0.3364 |
3486.903 |
34.249 |
|
Dominican Republic |
2.12 |
72.19 |
4.4944 |
5211.252 |
153.04068 |
|
Ecuador |
2.25 |
74.57 |
5.0625 |
5560.834 |
167.78475 |
|
Egypt |
2.31 |
69.88 |
5.3361 |
4882.655 |
161.41356 |
|
El Salvador |
1.1 |
71.13 |
1.21 |
5059.192 |
78.2408 |
|
Equatorial Guinea |
7.47 |
54.94 |
55.8009 |
3018.623 |
410.41674 |
|
Eritrea |
0.12 |
60.70 |
0.0144 |
3683.883 |
7.2834 |
|
Estonia |
14.22 |
73.78 |
202.2084 |
5443.193 |
1049.12316 |
|
Ethiopia |
0.08 |
59.08 |
0.0064 |
3490.446 |
4.7264 |
|
Finland |
12.51 |
79.54 |
156.5001 |
6326.771 |
995.05791 |
|
France |
6.5 |
80.82 |
42.25 |
6531.226 |
525.304 |
|
Gabon |
1.43 |
61.33 |
2.0449 |
3761.86 |
87.70762 |
|
Gambia |
0.25 |
58.83 |
0.0625 |
3460.851 |
14.70725 |
|
Georgia |
1.38 |
72.65 |
1.9044 |
5278.604 |
100.26252 |
|
Germany |
10.22 |
79.73 |
104.4484 |
6357.351 |
814.87126 |
|
Ghana |
0.43 |
60.02 |
0.1849 |
3602.881 |
25.81032 |
|
Greece |
10.22 |
80.01 |
104.4484 |
6401.28 |
817.68176 |
|
Guatemala |
0.97 |
70.50 |
0.9409 |
4970.814 |
68.38888 |
|
Guinea |
0.14 |
55.45 |
0.0196 |
3075.035 |
7.76342 |
|
Guinea-Bissau |
0.19 |
54.18 |
0.0361 |
2935.364 |
10.29401 |
|
Haiti |
0.25 |
60.23 |
0.0625 |
3627.171 |
15.0565 |
|
Honduras |
1.23 |
72.01 |
1.5129 |
5185.152 |
88.56984 |
|
Hungary |
5.76 |
73.74 |
33.1776 |
5437.44 |
424.73664 |
|
Iceland |
10.67 |
81.39 |
113.8489 |
6624.82 |
868.46331 |
|
India |
1.38 |
65.57 |
1.9044 |
4298.9 |
90.48108 |
|
Indonesia |
1.77 |
67.68 |
3.1329 |
4580.853 |
119.79714 |
|
Iran (Islamic Republic of) |
6.85 |
72.73 |
46.9225 |
5289.071 |
498.1731 |
|
Iraq |
3.4 |
68.01 |
11.56 |
4625.904 |
231.2476 |
|
Ireland |
10.91 |
79.68 |
119.0281 |
6348.743 |
869.29789 |
|
Israel |
9.63 |
80.94 |
92.7369 |
6550.798 |
779.42331 |
|
Italy |
8.01 |
81.50 |
64.1601 |
6642.25 |
652.815 |
|
Jamaica |
5.18 |
74.20 |
26.8324 |
5506.234 |
384.37672 |
|
Japan |
10.23 |
82.65 |
104.6529 |
6831.518 |
845.54019 |
|
Jordan |
3.61 |
73.00 |
13.0321 |
5329 |
263.53 |
|
Kazakhstan |
14.76 |
66.08 |
217.8576 |
4365.906 |
975.267 |
|
Kenya |
0.3 |
59.72 |
0.09 |
3566.956 |
17.9172 |
|
Kyrgyzstan |
1.14 |
67.47 |
1.2996 |
4552.741 |
76.92036 |
|
Latvia |
3.79 |
71.55 |
14.3641 |
5119.689 |
271.18208 |
|
Lebanon |
3.21 |
77.74 |
10.3041 |
6043.819 |
249.55182 |
|
Liberia |
0.19 |
58.11 |
0.0361 |
3377.237 |
11.04166 |
|
Libyan Arab Jamahiriya |
9.29 |
71.79 |
86.3041 |
5154.235 |
666.95697 |
|
Lithuania |
4.74 |
71.87 |
22.4676 |
5164.578 |
340.6401 |
|
Madagascar |
0.12 |
62.23 |
0.0144 |
3872.324 |
7.46736 |
|
Malaysia |
7.32 |
73.72 |
53.5824 |
5435.081 |
539.65236 |
|
Malta |
6.71 |
79.40 |
45.0241 |
6304.519 |
532.78071 |
|
Mauritania |
0.62 |
61.32 |
0.3844 |
3760.02 |
38.01778 |
|
Mauritius |
3.06 |
72.76 |
9.3636 |
5294.163 |
222.64866 |
|
Mexico |
4.39 |
75.72 |
19.2721 |
5733.821 |
332.41958 |
|
Morocco |
1.49 |
72.89 |
2.2201 |
5312.369 |
108.60014 |
|
Mozambique |
0.12 |
53.24 |
0.0144 |
2834.285 |
6.38856 |
|
Myanmar |
0.27 |
64.26 |
0.0729 |
4129.476 |
17.35047 |
|
Namibia |
1.45 |
54.98 |
2.1025 |
3022.251 |
79.71375 |
|
Nepal |
0.12 |
66.79 |
0.0144 |
4460.904 |
8.0148 |
|
Netherlands |
10.49 |
80.18 |
110.0401 |
6428.512 |
841.06722 |
|
New Zealand |
8.4 |
80.32 |
70.56 |
6451.302 |
674.688 |
|
Nicaragua |
0.82 |
72.83 |
0.6724 |
5304.5 |
59.72224 |
|
Nigeria |
0.64 |
49.74 |
0.4096 |
2474.267 |
31.83488 |
|
Norway |
9.53 |
80.60 |
90.8209 |
6496.682 |
768.13706 |
|
Oman |
13.69 |
75.06 |
187.4161 |
5634.004 |
1027.5714 |
|
Pakistan |
0.9 |
64.37 |
0.81 |
4143.497 |
57.933 |
|
Panama |
2.17 |
76.36 |
4.7089 |
5831.002 |
165.70337 |
|
Papua New Guinea |
0.52 |
64.18 |
0.2704 |
4118.559 |
33.37152 |
|
Paraguay |
0.67 |
71.75 |
0.4489 |
5148.063 |
48.0725 |
|
Peru |
1.51 |
73.15 |
2.2801 |
5350.923 |
110.4565 |
|
Philippines |
0.8 |
68.05 |
0.64 |
4631.347 |
54.4432 |
|
Poland |
8.61 |
75.56 |
74.1321 |
5709.162 |
650.56299 |
|
Portugal |
5.9 |
79.28 |
34.81 |
6285.953 |
467.7756 |
|
Republic of Moldova |
1.28 |
68.27 |
1.6384 |
4661.203 |
87.38944 |
|
Romania |
5.17 |
73.08 |
26.7289 |
5340.102 |
377.80292 |
|
Russian Federation |
11.13 |
67.14 |
123.8769 |
4508.048 |
747.29046 |
|
Rwanda |
0.08 |
60.05 |
0.0064 |
3606.003 |
4.804 |
|
Sao Tome and Principe |
0.81 |
65.47 |
0.6561 |
4286.19 |
53.02989 |
|
Saudi Arabia |
16.31 |
73.22 |
266.0161 |
5361.608 |
1194.26713 |
|
Senegal |
0.46 |
62.41 |
0.2116 |
3894.509 |
28.70676 |
|
Serbia and Montenegro |
5.13 |
73.33 |
26.3169 |
5377.876 |
376.20342 |
|
Sierra Leone |
0.24 |
45.88 |
0.0576 |
2105.341 |
11.01216 |
|
Singapore |
12.08 |
81.21 |
145.9264 |
6594.902 |
981.00472 |
|
Slovakia |
7.07 |
74.77 |
49.9849 |
5591.002 |
528.64511 |
|
Slovenia |
8.45 |
78.55 |
71.4025 |
6170.731 |
663.7813 |
|
South Africa |
8.82 |
53.07 |
77.7924 |
2816.213 |
468.05976 |
|
Spain |
8.32 |
81.21 |
69.2224 |
6595.714 |
675.70048 |
|
Sri Lanka |
0.62 |
74.07 |
0.3844 |
5486.365 |
45.9234 |
|
Sudan |
0.28 |
61.50 |
0.0784 |
3782.496 |
17.22056 |
|
Sweden |
5.64 |
81.06 |
31.8096 |
6571.372 |
457.20096 |
|
Switzerland |
5.81 |
81.78 |
33.7561 |
6688.132 |
475.14761 |
|
Syrian Arab Republic |
3.41 |
74.45 |
11.6281 |
5543.1 |
253.88132 |
|
Tajikistan |
1.07 |
68.71 |
1.1449 |
4720.789 |
73.51756 |
|
Thailand |
4.14 |
74.17 |
17.1396 |
5501.041 |
307.05966 |
|
The Former Yugoslav Rep. of Macedonia |
5.53 |
73.15 |
30.5809 |
5351.215 |
404.53056 |
|
Togo |
0.21 |
55.80 |
0.0441 |
3113.305 |
11.71737 |
|
Tunisia |
2.37 |
74.56 |
5.6169 |
5559.79 |
176.71668 |
|
Turkey |
4.17 |
73.37 |
17.3889 |
5383.157 |
305.9529 |
|
Turkmenistan |
9.2 |
65.87 |
84.64 |
4338.593 |
605.9856 |
|
Uganda |
0.1 |
55.15 |
0.01 |
3041.523 |
5.515 |
|
Ukraine |
7.35 |
67.89 |
54.0225 |
4608.509 |
498.9621 |
|
United Kingdom |
8.97 |
79.69 |
80.4609 |
6350.815 |
714.83724 |
|
United Rep. of Tanzania |
0.15 |
58.82 |
0.0225 |
3459.204 |
8.82225 |
|
United States |
19.74 |
78.16 |
389.6676 |
6109.611 |
1542.95736 |
|
Uruguay |
1.86 |
76.20 |
3.4596 |
5806.135 |
141.72828 |
|
Uzbekistan |
4.32 |
69.10 |
18.6624 |
4775.086 |
298.52064 |
|
Venezuela (Bolivarian Republic of) |
5.99 |
73.35 |
35.8801 |
5380.809 |
439.39046 |
|
Viet Nam |
1.29 |
74.69 |
1.6641 |
5578.447 |
96.34881 |
|
Yemen |
0.99 |
62.75 |
0.9801 |
3937.186 |
62.11953 |
|
Zambia |
0.22 |
52.93 |
0.0484 |
2801.161 |
11.64372 |
|
Zimbabwe |
0.77 |
48.35 |
0.5929 |
2337.916 |
37.23104 |
|
Total |
635.97 |
9620.76 |
5993.1427 |
681713.032 |
47782.595 |
|
Average |
4.184013158 |
69.7156521 |
|||
Table c : Calculations and values showing how equation for linear regression was calculated.
GDC Calculations
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