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 the

FOREIGN TRADE UNIVERSITY

The faculty of International Economics

-š &š ›&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

Hanoi, October, 2019

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

In the last three decades, the threat of global warming and climate change hasbeen the major on-going concern for all societies from developing countries todeveloped countries The principle reason is originated from greenhouse gas effect, ofwhich Carbon dioxide (CO2) is regarded to be the main source Global climate changehas altered water supplies and weather patterns, changed the growing season for foodcrops and threatened coastal communities with increasing sea levels According to ourresearch, in recent years, developing countries is responsible for more than 60% ofCO2 emissions due to industrialization and urbanization

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

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

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

Chapter 2: Methodology

Chapter 3: Statistical inferences and test assumptions

Chapter 4: Result analysis and policy implication

Finally, we would like to express our sincere thanks to the dedicated guidancefrom 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 researchmore 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 about60% higher than that of dry air Carbon dioxide consists of a carbon atom covalentlydouble bonded to two oxygen atoms It occurs naturally in Earth's atmosphere as atrace gas

CO2 one of the most important gases on the earth because plants use it toproduce carbohydrates in a process called photosynthesis Since humans and animalsdepend 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 ithas a warming effect that could change the earth’s climate Indoors, CO2 levels easilyrise 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 themanufacture of cement They include carbon dioxide produced during consumption ofsolid, 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 growthand GDP per capita

1.1.2.3 GDP per capita

GDP per capita is a measure of a country's economic output that accounts for itsnumber of people It divides the country's gross domestic product by its totalpopulation That makes it a good measurement of a country's standard of living It tellsyou how prosperous a country feels to each of its citizens

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 socialdevelopment with environmental protection: the so-called ‘Three Pillars’ model In thelong term, Planet Earth will impose its own constraints on the use of its physicalresources and on the absorption of contaminants, whilst the ‘laws’ of the naturalsciences (such as those arising from thermodynamics) and human creativity will limitthe potential for new technological developments SD is a process or journey towardthe destination of ‘sustainability’ It is a key concept when examining energy use andassociated emissions, and has foundations in engineering, economics, ecology andsocial 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 environmentalimpact to the production of population size, affluence, and environmental impact per unit

of economic activity known as “IPAT” IPAT is useful framework for assessing theanthropogenic 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 thatrich economies destroyed the environment at a faster pace than poorer countries.However, with the Environmental Kuznets Curve, the relationship between theenvironment’s health and the economy has been reanalyzed

The idea is that as economic development growth occurs, the environment willworsen 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 environmentand 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 warmingand climate change to happen, particularly the consumption of fossil energy Theenvironmental adverse effects of such energy used are not only coming from the energyconsumption but also from the exploitation process Meanwhile, the renewable energyconsumption 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 indeveloping countries, and renewable energy consumption has a positive effect oneconomic growth In this case, the consumption of fossil energy can cause pollution andenvironmental damage because the remaining burning of fossil energy is harmful to theenvironment; while, the renewable energy residue is considered more environmentally

CO2 emissions found that nonrenewable energy consumption has a positive relationship

to CO2 emissions, which means that an increase in non-renewable energy consumptionwill increase CO2 emissions In contrast, renewable energy consumption has a negativerelationship 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 tothe 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 potentialgrowth 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 theopposite view, which argues that high population densities were a prerequisite fortechnological innovation in 4 agriculture The technological innovation made possiblethe increased yields and more efficient distribution of food It could then enable thenatural 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 ofpeople, the greater the demands on energy Birdsall (1992) specified two mechanismsthrough 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, populationgrowth could contribute to greenhouse gas emissions through its effect ondeforestation The destruction of the forests, changes in land use, and combustion offuel wood could significantly contribute to greenhouse gas emissions

Thus, two questions remain to be addressed fully and empirically: (1) doespopulation pressure have a net impact on carbon dioxide emissions holding constantthe affluence and technology? and (2) has population pressure exhibited a greaterimpact 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 environmentaldegradation and GDP The theories discussed are the Environmental Kuznets curve(EKC), the Brundtland curve and the Daly curve All three hypotheses recognize thatthe level of GDP affect the environmental degradation, but in different ways TheEKC hypothesis argues that an increasing level of GDP would initially increasepollution 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 thecase of the EKC graphically shown as an inverted U-shape The Brundtland curvetheory provides another picture, where the graphical form is the opposite, U-shaped,which implies the poorest and wealthiest countries to have the highest levels ofpollution The Daly curve theory suggests increasing levels of pollution with anincreasing GDP that keeps on going, without any turning point Bratt points out thatthe three different environmental/GDP curves deals with different aspects ofenvironmental degradation The EKC hypothesis could be used when measuringemissions or concentration The Brundtland curve could be used when measuringproduction 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 apositive, 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 relationshipbetween CO2 and just one other factor such as GDP per capita, population growth orenergy use There are not many existing studies that specifically examine the effect ofGDP per capita, energy use, population growth, all together on CO2 emissions,especially in developing countries In this research we will use the existed data andlinear regression to analyze the relationship between CO2 emissions and three otherfactors: 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 ofthe fundamental factors concerning the amount of CO2 emissions (metric tons percapita): GDP per capita, energy use, population growth The secondary data weregathered 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 correlationmatrix among variables

2.1.3 Methodology in researching

Using Gretl to bring out regression models by using Ordinary Least Squaresmethod (OLS) to estimate the parameter of multi-variables regression models As aresult, 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 otherfactors, the regression function can be constructed as follows:

(PRF): Y=ββ 1 +β 2 EU+β 3 popgrowth+β 4 GDPpc+µ i (SRF): #=β & +&EU+&pop-growth+ &GDPpc+е i

Researchers found that energy consumption is the long-run causes for CO2emissions 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 gasemissions, 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 intothe atmosphere Human activity, which often leads to increased GDP such asgoods production and services, frequently produces carbon dioxide emissions.For example, most goods and services involve some use of energy, often in theform of coal or petroleum Therefore, as the amount of produced goodsincreases, the amount of fossil fuels spent also increases

Dependent

variable

Independent

variables

Exhibition 2.1 Variables explanation

The amount of CO2

Product per capita

dioxide per capita into the

atmosphere

The amount of

capita)

growth

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

CO2 4.88 2.60 6.59 0.100 34.0

EU 1.97e+03 1.02e+03 2.53e+03 66.3 1.44e+04

GDPpc 8.00e+03 5.61e+03 8.82e+03 428 4.41e+04

From the data, we can infer that:

- 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 andthe maximum one is 1.44×10ˆ4 kg of oil equivalent per capita

Popgrowth: 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

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:

relationship

relationship

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)

but it does not pass all because the p-valueassumptions so we do in heteroskedasticitynot use model 1 is too small

Exhibition 3 Table of estimated result

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): , =β -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 paribus.

β&0= 0.007: When the population growth (%) increases by one, the expected amount of CO2 emissions (metric tons per capita) increases by 0.7%, ceteris paribus.

paribus.

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.

12