Economic and Population Growth: Their Association with Total Carbon Emissions

The Costs of Many 

Economic and Population Growth: Their Association with Total Carbon Emissions

Author: Mary Ann F Daclan, Mindanao State University at Marawi, PH

Introduction

“The earth will not continue to offer its harvest, except with faithful stewardship. We cannot say we love the land and then take steps to destroy it for use by future generations.” 

― Pope John Paul II

Carbon emission has become a worldwide concern tagged along the phenomenon of climate change. The latter has turned into a byword as concepts El Niño, La Niña and the like came afloat when experiential devastations beset countless people in various parts of the world. For the Intergovernmental Panel on Climate Change (2007), climate change refers to any change in climate over time, whether due to natural variability or as a result of human activity.

The Population Reference Bureau (2007) reported that in the environment, “carbon dioxide emissions have grown dramatically in the past century because of human activity. These emissions are a key contributor to climate change that is expected to produce rising temperatures, lead to more extreme weather patterns, facilitate the spread of infectious diseases, and put more stress on the environment.”

Intergovernmental Panel on Climate Change (2014) highlighted that carbon emissions have increased since the pre-industrial era, driven largely by economic and population growth. This has led to atmospheric concentrations of carbon dioxide, methane and nitrous oxide that are unprecedented in at least the last 800,000 years (IPCC 2014). Their effects, together with those of other anthropogenic drivers, have been detected throughout the climate system and are extremely likely to have been the dominant cause of the observed warming since the mid-20th century (IPCC 2014). Total anthropogenic GHG emissions have continued to increase over 1970 to 2010 with larger absolute increases between 2000 and 2010, despite a growing number of climate change mitigation policies (IPCC 2014).

There are both natural and human sources of carbon dioxide emissions. Natural sources include decomposition, ocean release and respiration. Human sources come from activities like cement production, deforestation as well as the burning of fossil fuels like coal, oil and natural gas (WYI 2016). Continuous rise of carbon emission to the atmosphere threatens the world’s stability primarily affecting all life forms: human beings, plants and animals.

Objectives

This paper intends to find out whether the extent of carbon emissions is associated with population and economic growths. Specifically, it seeks to address the following:

  1. To describe the economic growth of different countries.
  2. To describe population growth of the different countries.
  3. To determine the level of carbon emissions in different countries.
  4. To assess possible significant association of economic and population growths with level of carbon emissions.
  5. To test the difference between three-grouped of economic and population growth on total carbon emissions.
  6. To know the possibility of attaining zero carbon emission.

 

Hypotheses

The following hypotheses tested were:

Ho1: There is no association between economic growth and level of carbon emissions.

Ho2: There is no association between population growth and level of carbon emissions.

Ho3: There is no difference between three-grouped of economic growth on total carbon emissions.

Ho4: There is no difference between three-grouped of population growth on total carbon emissions.

Significance of the Study

The world is beset with countless challenges that have to be confronted head on by particular countries most affected with or unanimously by alliances of countries concerned. These challenges are in a mixture of chronic societal ills in universal scope and magnitude since time immemorial and contemporary problems that just surfaced about a decade or two. Carbon emissions belong to the latter, for although these have been in existence a long time (as per World Bank records) they have not troubled the world as they recently do. They just surfaced along with the contemporary problem called climate change.

Countries of the world forged alliances, like the Paris Treaty, to particularly address climate change that includes carbon emissions.  This particular study comes timely as knowledge about carbon emissions as may be associated with population and economic growths may render better understanding about this social phenomenon.

Limitations of the Study

The study includes only 187 out of 210 countries for lack of data on gross national income for year 2013 of thirty-three countries, and on carbon emissions of twelve countries. Moreover, countries with less than one million metric ton total carbon emissions were not included. Hence, the results of the computations can only describe the 187 countries.

The paper focused only on year 2013 based on the latest available data on carbon emissions. The other two variables – population and economic growths – may have 2016 data, but for consistency purposes with carbon emissions, only the 2013 data are used.

Definition of Terms

The following terms, which pose technicalities, are hereby defined for easy reference in the foregoing discussions:

Carbon Dioxide Emissions (CO2). These are the total values from burning oil, coal and gas for energy use, burning wood and waste materials, and from industrial processes such as cement production. The carbon dioxide emissions of a country are only an indicator of one greenhouse gas that affects the Earth’s radiative balance. It is the reference gas against which other greenhouse gases are measured, thus having a Global Warming Potential of 1. CO2 are measured in metric tons per capita (PRB 2016; WB 2017). The data for this variable were culled out from the 2016 Population Data Sheet that featured the 2013 GNI (PRB 2016).

Gross National Income (GNI). This is used to measure the economic growth of a country. It refers to per capita based on purchasing power parity (PPP). PPP GNI is gross national income (GNI) converted to international dollars using purchasing power parity rates (WB 2017). An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current international dollars (WB 2017). The data for this variable were culled out from the 2014 Population Data Sheet that featured the 2013 GNI (PRB 2013).

Population Growth. This refers to the estimated population count of the countries included in the study in Population Data Sheet for year 2013 (PRB 2013). The data for this variable were culled out from the 2013 Population Data Sheet that featured the 2013 population count (PRB 2013). Data on population count are measured in millions.

Methodology

This section describes the research design, locale inclusion for the analysis, methods of data collection, and statistical tools used with the corresponding formulas and procedures used in the calculations of the economic and population growth with total carbon emissions.

Research Design

This paper used the descriptive-comparative design. It describes significant demographic attributes such as population and economic growths and carbon emissions among countries classified as least developed (low-income), less developed (lower and upper middle-income) and more developed (high-income).

Locale

For lack of data on carbon emissions at the local level in the Philippines, including the Mindanao regions, this paper levelled up to involve countries classified by World Bank as least developed, less developed, and more developed. These categories were specifically differentiated using gross national income: low-income, lower middle-income, upper middle-income, and high-income (Table 1).

carbon emissions fig1

There are 187 out of 211 countries with corresponding data on carbon emissions in 2013, gross national income for economic growth, and population count for population growth variables included in this exercise.

Categorization of Countries

The Population Reference Bureau (2016) Data Sheet lists all geopolitical entities with populations of 150,000 or more and all members of the UN. These include sovereign states, dependencies, overseas departments, and some territories whose status or boundaries may be undetermined or in dispute. More developed regions, following the UN classification, comprise all of Europe and North America, plus Australia, Japan, and New Zealand. All other regions and countries are classified as less developed. The least developed countries consist of 48 countries with especially low incomes, high economic vulnerability, and poor human development indicators; 34 of these countries are in sub-Saharan Africa, 13 in Asia, and one in the Caribbean (Appendix A).

Sub-Saharan Africa includes all countries of Africa except the northern African countries of Algeria, Egypt, Libya, Morocco, Sudan, Tunisia, and Western Sahara. World and regional totals pertain to regional population totals that are independently rounded and include small countries or areas not shown. Regional and world rates and percentages are weighted averages of countries for which data are available. Regional averages are shown when data or estimates are available for at least three-quarters of the region’s population.

Countries Included

The final list of countries included those with complete available data for the three variables, and which carbon emissions’ data does not fall below 1 million metric tons. The countries that lacked were removed from the rows for encoding. Hence, from 210 countries, only 187 are included.

carbon emissions fig2

 

Methods of Data Collection

This paper primarily used data mining to obtain data on carbon emission, gross national income and population growth from World Bank and Population Reference Bureau’s 2013, 2014 and 2016 Population Data Sheets. The latest available data (year 2013) for carbon emission is in 2016 Population Data Sheet. To have a uniform data for the same year 2013, the population count used is from 2013 Population Data Sheet. While the gross national income data come from 2014 Population Data Sheet. Hence, all three variables are for year 2013.

The data culled from the Population Reference Bureau were copied and encoded in the spreadsheets of Microsoft Excel version 2010. This allowed manageable editing and sorting while in SPSS software, each variable was specifically defined in the variable view mode. When the data were already sorted according to countries, they were copied and pasted on the data view of the SPSS version 17 software. Using the class notes, the steps were followed in the computations using various statistical tools.

Statistical Tools Used for Analysis

The data culled were encoded in Microsoft Excel to weed out those that have no entries. Final entries were copied and pasted on the data view of statistical software SPSS version 17. Class notes on the procedures were used as guide in the computations.

The unit of analysis for this paper is country for which three variables are particularly focused, namely: economic growth, population growth, and carbon emissions. Based on the continuous and categorical data, the statistical tools applied in computations include: frequency and percentages; measures of central tendencies, particularly mean and median and Pearson correlations. The strength of correlation value is based on this guide (Class Notes 2016). The categorical data come from the recodes in SPSS of the continuous variables, for the purposes of having more extensive data analysis of the variables involved.

One-Way Analysis of Variance (ANOVA). This was done on the three-grouped independent variables economic and population growth as factors against the dependent variable total carbon emissions. If a significant difference among the grouped income and population would result, a posthoc (a posteriori) test would be done using the Tukey test to determine which among the three groups has the most contribution on total carbon emissions. SPSS would put a asterisk ( * ) on the significant differences and would show which group was significantly different to the other two groups (Garth 2008).

The procedure included choosing the following options while in SPSS: Analyze, Compare means, One-way ANOVA, Enter dependent variables on Dependent List Box, Enter independent variable on Factor List, Options: set confidence interval at 95%, Continue, and Ok. And to proceed to pot-hoc test, choose post-hoc, Tukey, set Significance level AT 0.05, and ok.

Correlation. The primary purpose of correlation analysis here is to measure the strength of relationship between the independent variables, economic growth and population growth, with the dependent variable, carbon emissions. The coefficient of linear correlation, r, is the measure of the strength of linear relationship between pairings of these included variables. This strength of relationship is determined by the amount of effect any change in one variable has on the other (Babbie 2001).

The linear correlation coefficient, r, will always have a value somewhere between -1 and +1.  Positive (+1) is the measure of perfect positive correlation while negative (-1) is the measure of perfect negative correlation. Correlation will be considered high when it is close to +1 or close to -1 and low when it is close to zero.

carbon emissions fig3

 

Dataset Used. The dataset used for the statistical computations is:

[DataSet1] d:\ANNfiles\Ann_SI\PhD\FS2016 Soc461 Data Mgt & Processing in Soc Research ALOVERA\data for final exercise carbon emissions.sav

Frequency and Percentage Distribution. These were used to describe the categorical data from grouped values of population counts, gross national incomes, and total carbon emissions.

Median. This is a measure of central tendency, the middle number when pieces of data are ranked in order according to size.

carbon emissions fig4

Recoding. To transform continuous variables into categorical, recoding was done using SPSS. The gross national income was recoded into three groups: countries with less than $1,035 GNI per capita are classified as low-income countries, those with between $1,036 and $4,085 as lower middle income countries, those with between $4,086 and $12,615 as upper middle income countries, and those with incomes of more than $12,615 as high-income countries (UN 2014). GNI per capita in dollar terms is estimated using the World Bank Atlas method.

Standard Deviation.  It is a measure of the unpredictability of a random variable, expressed as the average deviation of a set of data from its mean and computed as the positive square root of the variance. It is considered the most useful and important measure of dispersion which has all the essential properties of the variance plus the advantage of being determined in the same units as those of the original data. In this study is centered on finding out the relationship between two variable, it is simply mean to include this statistical tool in the analysis of data.

The standard of deviation of a population is:

Ƿ = sqrt{ ƿ2} = sqrt {∑ ( Xi – µ)2 /N }

Where,

Ƿ = population standard deviation

Ƿ2 = population variance

µ = population mean

Xi = ith element from the population

N = number of the elements in the population.

 

Weighted Mean. This is a measure of central tendency.  This was used to describe the centrality of responses of the respondents on the continuous data for population counts, gross national incomes, and total carbon emissions. The expected value is denoted by using one of the following equations:

Formula:

Population mean = µ = ∑X / N   OR    Sample mean = x = ∑x / n

Where,

∑X = sum of all the population observations,

N = number of population observations,

Procedure in the Ranking of the weighted mean include: (1) To rank in terms of weighted mean, place 1st rank to the highest weighted mean, then 2nd rank to the next highest weighted mean and so on. (2) In case of a tie, get the average corresponds to their rank. For example, the two observations tie at 2nd and 3rd rank, so their average rank is: 2 + 3 / 2 = 2.5. Thus, the two observations have an equal rank of 2.5th.

Discussion

This section presents and discusses the findings of the paper in accordance to the objectives of this exercise: differential economic growth among countries; population growth; the level of total carbon emissions; associations of economic and population growths with level of carbon emissions; and, the possibility to attain zero carbon emission.

Differential Economic Growth

The United Nations (2014) Data Sheet lists all geopolitical entities with populations of 150,000 or more and all members of the UN. These include sovereign states, dependencies, overseas departments, and some territories whose status or boundaries may be undetermined or in dispute. More developed regions, with high income, following the UN classification (Appendix A), comprise all of Europe and North America, plus Australia, Japan, and New Zealand.

Other regions and countries classified as less developed in relation to more developed countries are comprised of upper and lower middle income countries. The least developed countries consist of 48 countries with especially low incomes, high economic vulnerability, and poor human development indicators; 34 of these countries are in sub-Saharan Africa, 13 in Asia, and one in the Caribbean. Sub-Saharan Africa includes all countries of Africa except the northern African countries of Algeria, Egypt, Libya, Morocco, Sudan, Tunisia, and Western Sahara (PRB 2016).

carbon emissions fig5

By United Nations’ income specification, the 187 countries included in this exercise are dominated by high-income (83) countries with over 12,615 gross national income. This is followed by the upper middle-income (56) countries, which the Philippines belong, having 7,820 gross national income in 2013 (Table 1). The Philippines need to almost double such gross national income to level up to the high-income category.

The income classification’s advantages over the older “three worlds” system revolve around the focus on economic development and the depiction of the relative economic development of various countries that does not group together all less developed nations into a single “Third World” (Macionis 2012). There is also specific differentiation of middle income countries into upper and lower income, giving due consideration to the wide range of the middle income from 1,036 to 12,615. This is an objective and forthright classification based on an easily measurable variable.

Countries where Industrial Revolution first took place more than two centuries ago have gone extremely farther ahead in economic arena compared to the fledgling ones. Formidable years have seen these countries with productivity increase more than a hundredfold. The power of industrial and computer technology makes a small centuries-old country as economically productive as a whole continent (Macionis 2012). The culture lag in countries still largely in agricultural setting renders them at the poor extreme of the income spectrum.

Table 2. The Countries’ Gross National Income Per Capita, 2013 (US Dollars)
Minimum Maximum Mean Median Std. Deviation
GNI 2013 600 123,860 17,261 10,850 19,537
N 125

Table 2 shows the wide economic gap between countries. The minimum gross national income of poorer countries (600) is more than a thousand-fold to that of richer countries (123,860). A huge amount (SD = 19,537) separates a country’s gross national income from the mean (x=17,261). The average gross national income tends to be higher owing to the few countries with extremely higher amounts. The median income (10,850) of these 187 countries fell farther from the mean, lower by over six thousand dollars. This means that half of the countries (93 to 94) have gross national income below 10,850.

The six countries at the bottom of the chronology of gross national income, below a thousand dollars, are from Africa: Central African Republic, Democratic Republic of Congo, Malawi, Liberia, Burundi, and Niger (Appendix A). While high-income countries are dominated by American and European countries, the topmost two countries with over a hundred dollars gross national income are from Asia: Macao and Qatar (Appendix A).

 

Differential Population Growth

Figure 3 depicts the distribution of population in different countries in 2013. While the graph for gross national income (Figure 2) has higher columns to the right side, indicating higher incomes, the graph below (Figure 3) has lower column to the right side. However, it is not what it seems, for the value of the few countries (18 of 187) is high (as high as billions) the cumulative total population is a whooping billions-fold bigger (Table 3). The larger group with (82 of 187) countries that have lesser number of people (less than 5 million) have a meager cumulative population of only 130 million.

Geographically small countries have the smallest population count. Tuvalu has only 10,000 population followed by Palau with 20 thousand and San Marino with 30 thousand. Tuvalu is located in the Pacific Ocean, north east of Australia (CIA World Factbook 2017). Palau is an archipelago of over 500 islands, part of the Micronesia region in the western Pacific Ocean. And, San Marino, also known as the Most Serene Republic of San Marino, claim to be the oldest surviving sovereign state in the world (CIA World Factbook 2017).

The ten most populous countries of the world are Japan, Russia, Bangladesh, Nigeria, Pakistan, Brazil, Indonesia, United States, India, and China (PRB 2013). Consistently, the topmost two countries with over one billion people are in Asia – India and China. The over 96 million population placed Philippines at rank 12th amongst all countries.

carbon emissions fig6

 

carbon emissions fig9g.jpg

 

There is a wide gap between populous and less populated countries with a standard deviation of over 140 million from the mean of over 37 million (Table 4). The pull of populous countries on their extremely sparsely populated counterparts separates the mean from the median by about 30 million. The minimum number of people is in the 10-thousand populated Tuvalu while the maximum number of people can be found in 1.3 billion populated China. Despite China’s one-child policy for over 40 years already, because of its huge land area, it remained to be highly populated.

carbon emissions fig9h.jpg

 

Differential Level of Total Carbon Emissions

     Amid the world’s hullaballoo about climate change and how mitigation should be done through reduction of total carbon emissions by countries under the Paris Treaty, are the very few countries (16 of 187) with over a hundred million metric tons of total carbon emissions (Figure 3). These are only 16 countries, but their contribution to total carbon emissions is tremendously a high 2 billion (Table 5). While the 115 countries with less than 10 million total carbon emissions have only given off a total carbon emissions of 230 million (Table 5). Not even three percent (2.4%) of what the 16 countries have emitted.

The same could be said of the 56 countries with considerably moderate contributions of 10 to 100 million metric tons of total carbon emissions (Figure 3). Their contributions to total carbon emissions are even less than a quarter (21.9%) of what the 16 countries have emitted. The Philippines is one of these moderately contributing countries with 26.8 million metric tons of total carbon emissions. The Philippines is side by side with Nigeria, Kuwait and Czech Republic having similar range of total carbon emissions at 26 million metric tons (Appendix B).

carbon emissions fig7.jpg

 

The high-emitting countries have contributed three-fourths (75.6%) of the total carbon emissions. The top two countries with very high carbon emissions are the United States with 1.4 billion and China with 3 billion total carbon emissions (Appendix B).

carbon emissions fig9c.jpg

Countries with barely 50 thousand total carbon emissions are Kiribati, Marshall Islands, Vanuatu, Sao Tome and Principe, Dominica, Federated States of Micronesia, and Comoros (Appendix B). The minimum total carbon emissions of 17 thousand only (Table 6) is from Kiribati, an independent republic located in the central Pacific Ocean, about 4,000 km (about 2,500 mi) southwest of Hawaii (Kurain 2007). The Marshall Islands, with only 28 thousand total carbon emissions, are a sprawling chain of volcanic islands and coral atolls in the central Pacific Ocean, between Hawaii and the Philippines (Kurain 2007).

 

carbon emissions fig9i.jpg

There is a wide gap among countries’ total level of carbon emissions. The high-emission countries pull up the values and render the average at 50.6 million metric tons, about 46 million away from the median, which is only 3.7 million. Hence, countries are far apart from the mean by 235 thousand (Table 6).

Association between Economic and Population Growth

with Level of Carbon Emissions

This section presents and analyses computed data using correlation to address the fourth objective.

Table 7 shows the results of correlations of the variables economic and population growth with total carbon emissions. Literature suggests (IPCC 2014) that total carbon emissions are brought about by population and economic growth. However, in this particular study, there appears to be no association between economic growth, through gross national income, and total carbon emissions (r = .070, n = 187 p = .343) for year 2013 data.

When examined thoroughly, the listing (Appendix B) shows inconsistency of countries with high gross national income and high total carbon emissions. Not one of the topmost nine countries with highest gross national income is included as highest in total carbon emissions.  It is possible that the high-income countries do not correspondingly contribute high total carbon emissions for their sophisticated measures at mitigation. For instance, being on top in total carbon emissions among the Southeast Asian countries in the 1980s to 1990s, Singapore’s carbon emissions have considerably decreased in 2000 onwards; possibly may be the effects of Singapore’s implementing mitigation measures in the country’s key sectors (NCCS 2016).

carbon emissions fig9b.jpg

It is totally a different story for population and total carbon emissions as there appears to be a high and positive association between the countries’ population and total carbon emissions (r = .799, n = 187 p = .000) for year 2013 data. This may entail that as population may increase, total carbon emissions may also rise. It could be that when there are more people, there is possibility for more extractions from the environment, like overconsumption of trees, may increase carbon emission. Increased population may entail expansion that requires more resources extracted from the environment.

Difference between Grouped Economic and Population Growth

on Level of Total Carbon Emissions

This section presents and analyses results of computed data of the grouped independent variables against the dependent variable total carbon emissions.

The results on Table 8 shows no significant difference among grouped countries’ gross national income to total carbon emissions (F (3,183) = .709, p = .548). This is consistent with the correlation results above. It may be that when there’s no association between the tested variables, there may also be no difference. It may be that whatever is the income state of countries, whether low-income or high-income, each has somehow contributed to the total carbon emissions. The high-income countries may also have crafted mitigation policies to reduce total carbon emissions amid highly technological processes that increase income, as in the case of Singapore (NCCS 2016).

carbon emissions fig9a.jpg

The results on Table 9 shows significant difference among grouped countries’ population to total carbon emissions (F (2,184) = 22.726, p = .000). Notably, this result somehow support the positive correlation of population and total carbon emissions in correlated variables.

carbon emissions fig9.jpg

There is a significant difference (0.000) among the three groups of countries’ populations. So it is appropriate to proceed to a posthoc (a posteriori) test, using the Tukey test to find out which of the three groups has the most contribution to total carbon emissions. The results show that the group of countries with over 75 million population 3 was significantly different to the other two groups of lower population counts.

carbon emissions fig8.jpg

Possibility to Attain Zero Carbon Emission

In 2008, four countries Iceland, New Zealand, Norway and Costa Rica were competing to be the first of the world’s 195 nations to go entirely carbon neutral (Lean and Ray 2008). The four countries formally signed up to go zero carbon, joining the Climate Neutral Network launched at the annual meeting of the Governing Council of the United Nations Environment Programme.

The Goal to Attain Carbon Neutral

All the main contenders get much of their energy from renewable sources. Iceland has gone the furthest, already achieving almost complete carbon neutrality in heating buildings and in electricity generation using geothermal energy that heats much of the rest of the country (Lean and Ray 2008).

New Zealand aimed to generate 90 per cent of its energy from renewable sources by 2025, and to halve its transport emissions per head by 2040. But the country has a particular problem with agriculture, which accounts for half its emissions of greenhouse gases.

Norway has set an even more ambitious target, aiming for carbon neutrality by 2030, despite being the world’s third largest oil exporter. It already gets 95 per cent of its electricity from hydroelectric power, and heavily taxes cars and fuel: a 4×4 costs four times as much as in the United States (Lean and Ray 2008).

Costa Rica plans to reach its goal by 2021. It has just released a plan of action, which relies heavily on planting trees to soak up emissions. Last year it planted five million of them, a world record, and the banana industry – the country’s largest exporter – has promised to go carbon neutral. However, its number of cars has increased more than five-fold in the past 20 years and its air traffic more than seven-fold in just six, making its task far harder (Lean and Ray 2008).

The Dark Horse in Attaining Zero Carbon  

The only country in the world to make such a switch and now as of 2016 is the world’s first country to become carbon negative is Bhutan (Protano-Goodwin 2016), a country often overlooked by the international community (Mellino 2016). This small nation lies deep within the Himalayas between China and India, two of the most populated countries in the world. But the country of about 750,000 people has set some impressive environmental benchmarks (Mellino 2016).

Bhutan’s massive tree cover, 72% of the country is still forested, which made it a carbon sink. Being a carbon sink means that Bhutan absorbs over 6 million tons of carbon annually while only producing 1.5 million tons.

How did Bhutan become carbon negative? It is noteworthy that Bhutan has long based their political decisions on a Gross National Happiness (GNH) index, abandoning economic growth as their compass (Mellino 2016). Environment as a central component in human happiness catapulted environmental protection as top priority in Bhutan’s political agenda. A promise made back in 2009 to remain carbon neutral in the days ahead picked up speed from there. Bhutan banned export logging, amended the constitution to include that forested areas would not drop below 60%, and utilized free hydroelectric power generated by many rivers over environmentally devastating fossil fuels.

carbon emissions fig9dOther creative environmental initiatives include a partnership with Nissan to provide the country with electrical cars (Protano-Goodwin 2016). The government has also started providing rural farmers with free electricity in order to lessen their dependence on wood stoves for cooking. More trees have been planted by volunteers who set a world record by planting 49,672 trees in just an hour’s time. To celebrate the birth of the first child of the royalty, all 82,000 households in Bhutan planted a tree, while volunteers planted another 26,000 in various districts around the country, for a total of 108,000 trees. Bhutan is aiming for zero net greenhouse gas emissions, zero-waste by 2030 and to grow 100 percent organic food by 2020.

 

Conclusions

From the data presented earlier, it was shown that there is differential economic growth among countries. There is a wide economic gap with the minimum gross national income of poorer countries at 600 dollars is more than a thousand-fold to that of richer countries at over 120 dollars. Many countries have gross national incomes extremely distant from the mean income due to extremely high income of few countries. The median income even differs from the mean by over six thousand dollars.

There is differential population growth with few countries in billions of cumulative population count compared to numerous countries with lesser than 5 million people in meager cumulative population of only 130 million. There is a wide gap between populous and less populated countries with a standard deviation of over 140 million from the mean of over 37 million.

There is differential level of total carbon emissions among countries. It takes only 16 high-emitting countries to have total carbon emissions of 2 billion metric tons while 115 low-emitting countries have only given off total carbon emissions of 230 million. Not even three percent of what the 16 countries have emitted. The 16 high-emitting countries have contributed three-fourths (75.6%) of the total carbon emissions.

Associations favor population growth and total carbon emissions, but not economic growth and level of total carbon emissions. There appears to be a high and positive association between the countries’ population and total carbon emissions for year 2013 data. This may entail that as population may increase, total carbon emissions may also rise. It could be that when there are more people, there is possibility for more extractions from the environment, like overconsumption of trees, may increase carbon emission. Hence, there is enough evidence of a failure to reject null hypothesis Ho1 there is indeed no association between economic growth and level of carbon emissions. However, there is enough evidence to reject null hypothesis Ho2 as there appears to be an association between population growth and level of carbon emissions.

While there is no shown difference between gross national income and total carbon emissions, one-way ANOVA results showed significant difference between three-grouped countries’ population and total carbon emissions. Hence, there is enough evidence of a failure to reject null hypothesis Ho3 as there is indeed no significant difference between economic growth and level of carbon emissions. However, there is enough evidence to reject null hypothesis Ho4 as there appears to be a significant difference between population growth and level of carbon emissions, with grouped countries of over 75 million population having more contribution to total carbon emissions than those with lesser population.

Finally, there is possibility to attain zero carbon emission with what Bhutan has already achieved. People of Bhutan have actively and seriously follow measures the country specified to hit their goal at zero carbon emissions. The possibility of attaining zero carbon emission is not just an impossible ambition for Bhutan, the first country in the world to be. Bhutan stopped destroying their environment and started protecting it, something every country and individual has the power to do. For a country that has already gained the world’s respect and attention. By 2030 Bhutan plans to reach zero net greenhouse gas admission and to produce zero waste by increasing its share on renewable energy sources such as wind and biogas, among others.

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Protano-Goodwin, Tyler. August 2016. Bhutan Becomes the World’s First Carbon Negative Country. Retrieved 10 January 2017 from Global Vision International Website: http://www.gvi.co.uk/blog/bhutan-carbon-negative-country-world/ UK: GVI.

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Student.com. 2008. What are Pearson’s r and scatterplots? Retrieved on 10 January 2017 from: http://statistics-help-for-students.com/How_do_I_report_Pearsons_r_and_scatterplots_in_APA_style.htm#.WCcJYvl97IU

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WYI. 2016. Main sources of carbon dioxide emissions. Retrieved 10 January 2017 from What’s Your Impact website: http://whatsyourimpact .org/greenhouse-gases/carbon-dioxide-emissions

APPENDIX A

carbon emissions fig9e.jpg

APPENDIX B

 

carbon emissions fig9f.jpg

 

+this was submitted as a required exercise in Data Management and Processing in Social Research, SS2016-17

Posted on April 19, 2017, in Sociological. Bookmark the permalink. Leave a comment.

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