tiểu luận kinh tế lượng FACTORS AFFECT THE AMOUNT OF CO2 EMISSIONS IN DEVELOPING COUNTRIES IN 2014

1.1.3 Theories about the relationships between energy use, population growth, GDP per capita and CO2 emissions 1.1.3.1 Energy use and CO2 emissions Sustainable development SD implies t

FOREIGN TRADE UNIVERSITY

The faculty of International Economics

-š›&š› -

ECONOMETRICS ASSIGNMENT

FACTORS AFFECT THE AMOUNT OF CO2 EMISSIONS IN

DEVELOPING COUNTRIES IN 2014

STUDENT NAME – ID:

Nguyễn Thu Ngân – 1815520207 Nguyễn Cao Hoài Anh – 1815520151 Nguyễn Minh Anh – 1810520152

CLASS: E6 + E7 – K57 JIB SUPERVISOR: Dr Tu Thuy Anh

INDEX

PREFACE 1

Chapter 1: LITERATURE REVIEW 2

1.1 Theories 2

1.2 Empirical researches 4

Chapter 2: METHODOLOGY 7

2.1 Methodology used 7

2.2 Constructing econometrics model 7

2.3 Data overview 9

2.4 Data description 9

Chap 3: TEST ASSUMPTIONS & STATISTICAL INFERENCES 11

3.1 Model 1 12

3.2 Model 2 15

3.3 Model 3 ………16

Chapter 4: RESULT ANALYSIS AND POLICY IMPLICATIONS 17

4.1 Result analysis 17

4.2 Policy implication 18

CONCLUSION 19

APPENDIX 20

REFERENCES 26

PREFACE

In the last three decades, the threat of global warming and climate change has been the major on-going concern for all societies from developing countries to developed countries The principle reason is originated from greenhouse gas effect, of which Carbon dioxide (CO2) is regarded to be the main source Global climate change has altered water supplies and weather patterns, changed the growing season for food crops and threatened coastal communities with increasing sea levels According to our research, in recent years, developing countries is responsible for more than 60% of CO2 emissions due to industrialization and urbanization

Facing the challenge to find out solutions to balance between sustainable economic development and harmfulness to the environment, our group decide to examine “Factors affect the amount of CO2 emissions in developing countries in 2014”

In the report, we will apply what we learn in Econometrics course to investigate into this matter The report is divided into 4 main parts:

Chapter 1: Literature review about the relationship between CO2 emissions and GDP per capita, population growth, energy use

Chapter 2: MethodologyChapter 3: Statistical inferences and test assumptions Chapter 4: Result analysis and policy implicationFinally, we would like to express our sincere thanks to the dedicated guidance from Mrs Tu Thuy Anh and Mrs Chu Thi Mai Phuong

Due to our limited knowledge, there are certainly some deficiencies in our report

We look forward to receive comments and suggestions from you to make our research more completely

Chapter 1: LITERATURE REVIEW

1.1 Theories

1.1.1 CO2 emissions (metric tons per capita)

1.1.1.1 Definition and roles of CO2 (Carbon dioxide)

Carbon dioxide (chemical formula CO2) is a colorless gas with a density about 60% higher than that of dry air Carbon dioxide consists of a carbon atom covalently double bonded to two oxygen atoms It occurs naturally in Earth's atmosphere as a trace gas

CO2 one of the most important gases on the earth because plants use it to produce carbohydrates in a process called photosynthesis Since humans and animals depend on plants for food, photosynthesis is necessary for the survival of life on earth However, CO2 can also have negative effects As CO2 builds up in our atmosphere it has a warming effect that could change the earth’s climate Indoors, CO2 levels easily rise above the recommended amount which has adverse effects

1.1.1.2 What is CO2 emission?

CO2 emission are those stemming from the burning of fossil fuels and the manufacture of cement They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring

1.1.2 Factors affect CO2 emissions

There are a lot of factors that affect CO2 emissions However, in this research, we focus mainly on three significant ones, which are energy use, population growth and GDP per capita

Businesses and governmental bodies use this information to make determinations about investing in certain communities or regions

1.1.3 Theories about the relationships between energy use, population growth, GDP

per capita and CO2 emissions

1.1.3.1 Energy use and CO2 emissions

Sustainable development (SD) implies the balancing of economic and social development with environmental protection: the so-called ‘Three Pillars’ model In the long term, Planet Earth will impose its own constraints on the use of its physical resources and on the absorption of contaminants, whilst the ‘laws’ of the natural sciences (such as those arising from thermodynamics) and human creativity will limit the potential for new technological developments SD is a process or journey toward the destination of ‘sustainability’ It is a key concept when examining energy use and associated emissions, and has foundations in engineering, economics, ecology and social science

1.1.3.2 Population growth and CO2 emissions

The impact of population change on environmental stress was posited by Ehrlich (1968) and Holder and Ehrlich (1974) in the form of an equation relating environmental impact to the production of population size, affluence, and environmental impact per unit of economic activity known as “IPAT” IPAT is useful framework for assessing the anthropogenic environmental change, particularly the impacts of population, affluence, and technology on the environmental change (CO2 emissions)

1.1.3.3 GDP per capita and CO2 emissions

The Environmental Kuznets Curve hypothesis, it was generally assumed that rich economies destroyed the environment at a faster pace than poorer countries However, with the Environmental Kuznets Curve, the relationship between the environment’s health and the economy has been reanalyzed

The idea is that as economic development growth occurs, the environment will worsen until a certain point where the country reaches a specific average income Then money is invested back into the environment, and the ecosystem is restored

Critics argue that economic growth doesn’t always lead to a better environment and sometimes the opposite may actually be true

1.2 Empirical researches

1.2.1 Empirical research on effects of energy use on CO2 emissions

Thao and Chon [1] stated that energy use has a positive impact on economy, but not to the environment Energy use is widely known as the main reason for global warming and climate change to happen, particularly the consumption of fossil energy

The environmental adverse effects of such energy used are not only coming from the energy consumption but also from the exploitation process Meanwhile, the renewable energy consumption has a negative relationship to CO2 emissions, which means that an increase in the consumption of renewable energy will reduce CO2 emissions

Additionally, Ito [2] found that fossil energy consumption has a negative impact on economic growth in developing countries, and renewable energy consumption has a positive effect on economic growth In this case, the consumption of fossil energy can cause pollution and environmental damage because the remaining burning of fossil energy is harmful to the environment; while, the renewable energy residue is considered more environmentally friendly Moreover, Shafei and Ruhul [3] who conducted a study

on OECD countries on the Kuznets Curve Hypothesis (EKC) between urbanization and

CO2 emissions found that nonrenewable energy consumption has a positive relationship

to CO2 emissions, which means that an increase in non-renewable energy consumption will increase CO2 emissions In contrast, renewable energy consumption has a negative relationship to CO2 emissions, once again, it consolidates the conclusion that an increase in the consumption of renewable energy will reduce CO2 emissions

1.2.2 Empirical research on effects of population growth on CO2 emissions

The role of population pressure on environmental quality can be traced back to the early debate on the relationship between population and natural resources Malthus (1798 [1970]) was concerned with increasing population growth, which put pressure on limited source of land Because of a lower marginal product of labor, the potential growth in food supply could not keep up with that of the population He predicted that

if mankind did not exercise preventive checks, population growth would be curtailed

by welfare checks (poverty, disease, famine and war) Boserup (1981) held the opposite view, which argues that high population densities were a prerequisite for technological innovation in 4 agriculture The technological innovation made possible the increased yields and more efficient distribution of food It could then enable the natural environment to support a large population at the same level of welfare

The impact of population growth on environment quality is obvious Each person

in a population makes some demand on the energy for the essentials of life—food, water, clothing, shelter, and so on If all else is equal, the greater the number of people, the greater the demands on energy Birdsall (1992) specified two mechanisms through which population growth could contribute to greenhouse gas emissions First, a larger population could result in increased demand for energy for power, industry, and transportation, hence the increasing fossil fuel emissions Second, population growth could contribute to greenhouse gas emissions through its effect on deforestation The destruction of the forests, changes in land use, and combustion of fuel wood could significantly contribute to greenhouse gas emissions

Thus, two questions remain to be addressed fully and empirically: (1) does population pressure have a net impact on carbon dioxide emissions holding constant the affluence and technology? and (2) has population pressure exhibited a greater impact in developing countries than in developed countries?

1.2.3 Empirical research on effects of GDP per capita on CO2 emissions

Three Totally Different Environmental/GDP Curves (2012) - In this paper Bratt

compares three different theories explaining the connection between environmental degradation and GDP The theories discussed are the Environmental Kuznets curve (EKC), the Brundtland curve and the Daly curve All three hypotheses recognize that the level of GDP affect the environmental degradation, but in different ways The EKC hypothesis argues that an increasing level of GDP would initially increase pollution until a certain level of GDP, at which the level of pollution starts to decrease The relationship between environmental degradation and economic growth is in the case of the EKC graphically shown as an inverted U-shape The Brundtland curve theory provides another picture, where the graphical form is the opposite, U-shaped, which implies the poorest and wealthiest countries to have the highest levels of pollution The Daly curve theory suggests increasing levels of pollution with an increasing GDP that keeps on going, without any turning point Bratt points out that the three different environmental/GDP curves deals with different aspects of environmental degradation

The EKC hypothesis could be used when measuring emissions or concentration The Brundtland curve could be used when measuring production and the Daly curve when measuring consumption Bratt’s final conclusion is that even though either curve could

be true, the most possible scenario seems to be a positive, monotonic relationship between environmental degradation and GDP

In summary, many research studies have been conducted in areas related to this

study However, a major part of the researches conducted were on the relationship between CO2 and just one other factor such as GDP per capita, population growth or energy use There are not many existing studies that specifically examine the effect of GDP per capita, energy use, population growth, all together on CO2 emissions, especially in developing countries In this research we will use the existed data and linear regression to analyze the relationship between CO2 emissions and three other factors: GDP per capita, energy use and population growth

Chapter 2: METHODOLOGY

2.1 Methodology used

2.1.1 Methodology in collecting data

The collected data are secondary data, mixed data, which indicate information of the fundamental factors concerning the amount of CO2 emissions (metric tons per capita): GDP per capita, energy use, population growth The secondary data were gathered from prestigious and reliable source of information - World Bank

2.1.2 Methodology in processing data

Using Gretl in order to process data cursorily then calculate the correlation matrix among variables

2.1.3 Methodology in researching

Using Gretl to bring out regression models by using Ordinary Least Squares method (OLS) to estimate the parameter of multi-variables regression models As a result, we can:

- Depend on variance inflation factor (VIF) to identify multicollinearity

- Test Normality of residual

- Use white test to test heteroscedasticity

- Conduct Breusch-Godfrey to identify the correlations

- Use F-test to evaluate the concordance model

- Use T-test to evaluate the confidence interval

2.2 Constructing econometrics model

To demonstrate the relationship between the amount of CO2 emissions and other factors, the regression function can be constructed as follows:

(PRF): Y=β 1 +β 2 EU+β 3 popgrowth+β 4 GDPpc+µ i

(SRF): 𝐘#= 𝛃&𝟏+𝛃&EU+𝛃𝟐 &pop-growth+𝛃𝟑 &GDPpc+е𝟒 i

Researchers found that energy consumption is the long-run causes for CO2 emissions For example, the burning of fossil fuels such as gasoline, coal, oil, natural gas in combustion reactions results in the production of carbon dioxide

- Pop-growth: Population growth, measured in %

Theoretically, population growth is believed to increase greenhouse gas emissions, particularly CO2 emissions through the increase in human activities

- GDP pc: GDP per capita, measured in US$

As a country’s GDPpc increases, so does its production of carbon dioxide into the atmosphere Human activity, which often leads to increased GDP such as goods production and services, frequently produces carbon dioxide emissions

For example, most goods and services involve some use of energy, often in the form of coal or petroleum Therefore, as the amount of produced goods increases, the amount of fossil fuels spent also increases

Exhibition 2.1 Variables explanation

Dependent variable Y

The amount of CO2 emissions (metric tons per capita)

Independent variables

GDP pc Gross Domestic

Product per capita +

As a country’s GDP per capita increases, so does its production of carbon dioxide per capita into the

atmosphere

EU

The amount of energy or power used (kg of oil equivalent per capita)

Pop-Population growth is the increase in the number of individuals in a population

+

Population growth increases greenhouse gas emissions, particularly CO2 emissions through the increase in human activities

2.3 Data overview

- This set of data is a secondary one, as they are collected from a given source:

- Data source: https://data.worldbank.org/

- The structure of Economic data: cross-sectional data

2.4 Data description

2.4.1 Summary statistics

By using Summary statistics command on Gretl, we have:

Exhibition 2.2 Summary Statistics, using the observations 1 – 81

- Energy use per capita: The average energy use per capita is 1.97×10ˆ3 kg of oil

equivalent per capita, the minimum one is 66.3 kg of oil equivalent per capita and the maximum one is 1.44×10ˆ4 kg of oil equivalent per capita

- Pop-growth: The average population growth is 1.46%, the minimum one is -1.70%

and the maximum one is 6.70%

- GDPpc: The average GDP per capita is 8×10ˆ5 US$, the minimum one is 428 US$

and the maximum one is 4.41×10ˆ4 US$

2.4.2 Table of correlation matrix

Exhibition 2.3 Correlation coefficients, using the observations 1 - 81

5% critical value (two-tailed) = 0.2185 for n = 81

Evaluation:

Correlation between dependent variable and independent variables:

- r(Y, EU) = 0.9843: CO2 emissions and energy use have a very strong, uphill

relationship (energy use affects 98.43% of CO2 emissions)

- r(Y, popgrowth) = 0.0261: CO2 emissions and population growth have a very weak,

uphill relationship (population growth affects only 2.61% of CO2 emissions)

- r(Y, GDPpc) = 0.8284: CO2 emissions and GDP per capita have a strong, uphill relationship (GDP pc affects 82.84% of CO2 emissions)

In summary, all the correlations above are appropriate with theories

Correlation among independent variables:

- r(EU, GDPpc) = 0.8034: Energy use and GDPpc have strong weak, uphill relationship

- r(EU, popgrowth) = 0.0256: Energy use and pop-growth have a very weak, uphill relationship

- r(GDPpc, popgrowth) = 0.0154: GDP per capita and population growth have a very weak, uphill relationship

Chap 3: TEST ASSUMPTIONS & STATISTICAL INFERENCES

After running the regression, the result is summarized in the following table (the s.e is showed in parentheses)

X2 (energy use) 0.002 ***

X3 (Population growth)

0.007 (0.091)

-0.052 (0.033)

0.007 (0.089) X4 (GDP per cap) 7.919e-05 ***

(2.356e-05) 7.919e-05 *** (2.96e-05)

Indicate heteroskedasticity p-value = 0.022445

p-value = 0.004630 but not affect the estimated result

Not violated

1.039 3.585 3.632

Not violated

2.822 2.821 1.001

Normality assumption Not indicate a normal distribution, but not

affect estimated result p-value = 0.000

Indicate a normal distribution p-value = 0.074

Not indicate a normal distribution, but not affect estimated result p-value = 0.000 Conclusion The model is significant

but it does not pass all assumptions so we do not use model 1

Do not use model 2 because the p-value

3.1 Model 1

3.1.1 Overview of the regression model

Based on the data collected from the table, the sample regression function is established:

(SRF): 𝑪𝑶𝟐, = -0.378 + 0.002*EU + 0.007*popgrowth + (7.919e-05)*GDPpc

It can be inferred that:

Energy use and GDPpc both have statistically significant effects on the amount of

CO2 emissions (metric tons per capita) at the 1% significant level (as all p-values are

smaller than 0.01) while the variable population growth does not have In particular,

those effects can be specified by the regression coefficients following:

β&= -0.378: When all the independent variables are zero, the expected amount of CO2 .emissions is -0.378

β&= 0.002: When energy use (kg of oil equavilent per capita) increases by one, the /expected amount of CO2 emissions (metric tons per capita) increases by 0.2%, ceteris

The coefficient of determination R-squared = 0.973: all independent variables (EU,

popgrowth, GDPpc) jointly explain 97.3% of the variation in the dependent variable (CO2)

3.1.2 Statistical Inferences

3.1.2.1 Statistical significance of coefficients

Applying p-value method, we can see that:

- p-value of popgrowth = 0.938 > 0.1 => Population growth is not an important determinant of CO2 emissions

- p-value of energyuse < 0.0001 => EU is an important determinant of CO2 emissions

at a 1% significance level

- p-value of GDPpc = 0.001 < 0.01 => GDP per capita is an important determinant

of CO2 emissions at a 1% significance level

Therefor, all coefficients are statistically significant, except for popgrowth