Effect of Carbon Dioxide Levels on Life Expectancy on Countries with Different Income Levels

13963 words (56 pages) Essay in Environmental Studies

18/05/20 Environmental Studies Reference this

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Research Question:

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 

refers 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

Below average CO2 emission

H = 0

UM = 3

LM = 13

L = 19

H = 0

UM = 1

LM = 0

L = 0

H =1

UM = 1

LM = 0

L = 0

Close to average CO2 emission   4.18 + 2

H = 0

UM = 4

LM = 15

L = 4

H = 2

UM = 7

LM = 2

L = 0

H = 2

UM = 5

LM = 1

L  = 0

Above average CO2 emission 

H =1

UM = 3

LM = 2

L = 0

H = 4

UM = 14

LM = 1

L = 0

H = 31

UM = 2

LM = 0

L = 0

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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