The influence of economic growth, urbanization, renewable energy consumption and industrialization on carbon emissions in multiple nations around the world in 2021

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS ENVIRONMENTAL ECONOMICS ESSAY TOPIC: THE INFLUENCE OF ECONOMIC GROWTH, URBANIZATION, RENEWABLE ENERGY CONSUMPTION AND INDUST

FOREIGN TRADE UNIVERSITY

FACULTY OF INTERNATIONAL ECONOMICS

ENVIRONMENTAL ECONOMICS ESSAY

TOPIC: THE INFLUENCE OF ECONOMIC GROWTH, URBANIZATION, RENEWABLE ENERGY CONSUMPTION AND INDUSTRIALIZATION

ON CARBON EMISSIONS IN MULTIPLE NATIONS AROUND THE

WORLD IN 2021

Student group : No 09

Lecturer : Nguyen Thi Thanh Huyén, Ph.D

Hanoi, March 2025

Therefore, during the period of completing our assignment, we have been working hard to complete this research paper with the best of our abilities - as a token of our sincere appreciation to you

To be honest, we do acknowledge that our paper still has many shortcomings and limitations because of our limited knowledge and experiences; hence, we really hope to receive your feedback and comments to improve further Once again, we sincerely thank you and wish you the best health, success, and happiness

With heartfelt appreciation,

impact on CO2 emissions in low-growth regimes but a positive impact in high-growth regimes The effect in the high growth regime is however stronger Thus, the validity of the Environmental Kuznets Curve (inverted-U) hypothesis could not be established for these panels of countries for the period under study However, this study is held back by its utilization of CO2 emissions per capita as the sole proxy for environmental degradation, neglecting other important environmental indicators Despite its limitations, this study can still inform policymakers in developing countries about the environmental consequences

of economic growth and the need for targeted policies to mitigate CO2 emissions In addition, the study results could be used in the construction of global greenhouse gas emission models

Arouri et al (2012) suggested the presence of an EKC at the regional level after conducting the study “Energy consumption, economic growth and CO2 emissions in Middle East and North African countries” that investigated the relationship between carbon dioxide (CO2) emissions, energy consumption, and real GDP in 12 Middle East and North African (MENA) countries from 1981 to 2005 This study employed bootstrap panel unit root tests and cointegration techniques to analyze the long-run dynamics between these variables before achieving results that revealed evidence of a quadratic relationship between real GDP and CO2 emissions However, country-specific analysis shows that while the estimated long-run coefficients of income and its square often satisfy the EKC hypothesis, the turning points (income levels at which emissions start to decline) vary considerably and are often outside the range of observed GDP levels for many countries, casting doubt on the applicability of the EKC hypothesis at the individual country level Meanwhile, concerning the energy consumption variable, it is found that at the regional level, this variable has a positive and significant impact on CO2 emissions in the MENA region These findings help MENA countries’ policymakers realize that they can pursue energy conservation policies to reduce CO2 emissions without necessarily sacrificing economic growth Howbeit, despite being significantly valuable and insightful, this study should be interpreted with discretion since the data only covers the period of 1981-2005 This may not reflect current dynamics, as economic structures and environmental policies have evolved since then

In the study named “The relationship between CO2 emissions, economic growth, available energy, and employment in SEE countries” Mitic et al (2022) investigated eight

South-Eastern European (SEE) countries (Albania, Bulgaria, Croatia, Greece, North

Macedonia, Romania, Serbia, and Slovenia) from 1995 to 2019 by utilizing a wide range

of research methodology, for instance, Panel Cointegration Tests, Panel Vector Error

CHAPTER 1 : LITERATURE REVIEW AND THEORETICAL FRAMEWORK 1.1 Literature Review:

1.1.1 Background:

The year 2021 represents an imperative period for investigating CO2 emissions and its determinants The International Energy Agency (IEA) reported that global COz emissions rose by 6% in 2021, reaching 36.3 billion tons, the highest level in history at the time This unprecedented surge in emissions significantly worsened global warming, leading to more extreme weather events, rising sea levels, and widespread environmental destruction Additionally, the emissions spike pushed the world further off track from climate targets, increasing the risk of irreversible climate tipping points

2021 is also a year marked by rapid economic recovery, increased urbanization, and industrial growth Following the severe economic stagnation caused by the pandemic, 2021 businesses quickly sought to recover from the prolonged downturn This resurgence led to a substantial increase in productivity, as industries and enterprises endeavored to restore pre- pandemic levels of economic activity As reported by The World Bank, global GDP grew by approximately 6% in 2021, representing one of the strongest annual growth rates in decades A similar trend can be witnessed in urban population and industrial production, both accelerating greatly to compensate for the lengthy period of stagnation Furthermore, the consumption of renewable energy also underwent substantial advancements According to the International Energy Agency (IEA), global renewable energy capacity increased by approximately 290 GW, the highest annual addition on record, driven primarily by solar and wind power installations This development is also marked by the COP26 Climate Summit, emphasizing the global transition to renewable energy, with over 190 countries agreeing to phase down coal and increase investments

in clean energy, signalling a more ubiquitous utilization of renewable energy

A thorough understanding of the underlying factors contributing to the crisis of elevated COz emissions is essential, as it enables policymakers to formulate well-informed and effective policies aimed at mitigating the risk of severe climate consequences that endanger global ecosystems, economic stability, and human well-being Interestingly, all of the aforementioned aspects are key variables that can have an impact on CO:z emissions Therefore, it 1s crucial to conduct

comprehensive studies that investigate the relationship between these variables and COz emissions

to identify the primary drivers behind the unprecedented increase in carbon dioxide levels in 2021 From that, our previously mentioned aim of designing effective policies can be realized

1.1.2 Research into the impact of Economic Growth, Urbanization, Renewable Energy Consumption, Industrialization on Carbon Emissions:

a Researches in other countries:

The study titled “Effect of economic growth on CO2 emissions in developing countries: Evidence from a dynamic panel threshold model” by C Aye and Edoja (2017), after using a dynamic panel threshold model that utilized panel data from 31 developing

Conclusion, References and Appendix In which the content includes:

Chapter 1: Literature Review and Theoretical Framework

Chapter 2: Research Methodology and Model Specification

Chapter 3: Result and Discussion

INTRODUCTION 6 CONTENT 8 CHAPTER 1 : LITERATURE REVIEW AND THEORETICAL FRAMEWORK

8 1.1 Literature Review: 8 1.1.1 Background: 8 1.1.2 Research into the impact of Economie Growth, Urbanization, Renewable Energy Consumption, Industrialization on Carbon EHHSSIOHS? e«ee 8 1.2 Theoretical Framework: 21 1.2.1 Carbon Emissions: 21 1.2.2 Economic Growth: 21 1.2.3 Urbanization: 22 1.2.4 Renewable Energy C ption: 23 1.2.5 Industrialization: 23 1.3 Research Hypotheses: 24 CHAPTER 2 : RESEARCH METHODOLOGY AND MODEL SPECIFICATION 2.1 Research Methodology: 25 2.1.1 Data Collection Methodology: 25 2.1.2 Data Analysis Methodology: 25 2.2 Theoretical model specification: 25 2.2.1 Research Model: 25 2.2.2 Variable explanafions, measurernenís and dqÍd SOHFC@S e«ee- 26 2.2.3 Expected effects of Independent Variables on Dependent Variable 26 2.3 Statistical Description of the Data: 27 2.3.1 Statistical Description of the Data: 27 2.3.2 Correlation Matrix of Variables: 28 CHAPTER 3 : RESULT AND DISCUSSION 30 3.1 Estimation of econometrics model: 30 3.1.1 Estimated results: 30 3.1.2 Sample regression function obtained: 30 3.1.3 Diagnosing and correcting the problems of the tmodlel- « 30

4

long-run Granger causality relationships exist between CO2 emissions, GDP, and employment Howbeit, variance decomposition revealed that CO2 emissions are primarily explained by their own past shocks, with limited contributions from GDP, available energy, and employment This study still possesses some clear limitations, for instance, the absence

of other imperative indicators of economic growth such as urbanization and industrialization

In the study titled “Impact of Urbanization and Economic Growth on CO2 emissions:

A Case of Far East Asian Countries”, Anwar et al (2020), the researchers adopted a panel data-fixed effect model that accounts for time-invariant country-specific characteristics, and concluded that urbanization was significantly correlated with CO2 emission in the panel of Far East Asian countries from 1980 to 2017 Their findings also espoused aforementioned studies that came to the conclusion that economic growth (GDP) and trade openness are positively associated with CO2 emissions Adding to this, Shahbaz et al (2015) investigates the impact of urbanization on CO2 emissions in Malaysia using the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, covering the period of 1970 to 2011 The results indicated that besides the significant impact of economic growth on CO2 emissions due to the intense consumption

of energy and trade openness in the period of accelerating economic development, the relationship between urbanization and CO2 emissions is also a notable finding The results revealed the existence of an U-shaped relationship between urbanization and CO2 emissions, implying that initially, urbanization reduces emissions, but after a certain threshold, it increases emissions This provides insights for policymakers in designing comprehensive environmental urbanization, energy and trade openness policies to mitigate CO2 emissions However, the study still possessed clear limitations: the study only relies

on aggregate national-level data, which might mask regional variations and specific sectoral impacts Moreover, despite controlling for several key factors, the possibility of omitted variable bias is still present within these studies There might be other variables, such as, renewable energy adoption rates and industrialization, that influence CO2 emissions but are not included in the model due to data limitations

Interestingly, the previously mentioned studies are opposed by Wang et al (2021) who organized the study “Impacts of urbanization on carbon emissions: An empirical analysis from OECD countries” This research paper investigated the complex relationship between urbanization and carbon emissions, focusing on OECD (Organisation for Economic Co-operation and Development) high-income countries In this study, a dynamic panel Autoregressive Distributed Lag (ARDL) model to analyze long-run equilibrium, short-run dynamics, impact mechanisms, and lag effects between urbanization and various

Conclusion, References and Appendix In which the content includes:

Chapter 1: Literature Review and Theoretical Framework

Chapter 2: Research Methodology and Model Specification

Chapter 3: Result and Discussion

CHAPTER 1 : LITERATURE REVIEW AND THEORETICAL FRAMEWORK 1.1 Literature Review:

1.1.1 Background:

The year 2021 represents an imperative period for investigating CO2 emissions and its determinants The International Energy Agency (IEA) reported that global COz emissions rose by 6% in 2021, reaching 36.3 billion tons, the highest level in history at the time This unprecedented surge in emissions significantly worsened global warming, leading to more extreme weather events, rising sea levels, and widespread environmental destruction Additionally, the emissions spike pushed the world further off track from climate targets, increasing the risk of irreversible climate tipping points

2021 is also a year marked by rapid economic recovery, increased urbanization, and industrial growth Following the severe economic stagnation caused by the pandemic, 2021 businesses quickly sought to recover from the prolonged downturn This resurgence led to a substantial increase in productivity, as industries and enterprises endeavored to restore pre- pandemic levels of economic activity As reported by The World Bank, global GDP grew by approximately 6% in 2021, representing one of the strongest annual growth rates in decades A similar trend can be witnessed in urban population and industrial production, both accelerating greatly to compensate for the lengthy period of stagnation Furthermore, the consumption of renewable energy also underwent substantial advancements According to the International Energy Agency (IEA), global renewable energy capacity increased by approximately 290 GW, the highest annual addition on record, driven primarily by solar and wind power installations This development is also marked by the COP26 Climate Summit, emphasizing the global transition to renewable energy, with over 190 countries agreeing to phase down coal and increase investments

in clean energy, signalling a more ubiquitous utilization of renewable energy

A thorough understanding of the underlying factors contributing to the crisis of elevated COz emissions is essential, as it enables policymakers to formulate well-informed and effective policies aimed at mitigating the risk of severe climate consequences that endanger global ecosystems, economic stability, and human well-being Interestingly, all of the aforementioned aspects are key variables that can have an impact on CO:z emissions Therefore, it 1s crucial to conduct

comprehensive studies that investigate the relationship between these variables and COz emissions

to identify the primary drivers behind the unprecedented increase in carbon dioxide levels in 2021 From that, our previously mentioned aim of designing effective policies can be realized

1.1.2 Research into the impact of Economic Growth, Urbanization, Renewable Energy Consumption, Industrialization on Carbon Emissions:

a Researches in other countries:

The study titled “Effect of economic growth on CO2 emissions in developing countries: Evidence from a dynamic panel threshold model” by C Aye and Edoja (2017), after using a dynamic panel threshold model that utilized panel data from 31 developing

CHAPTER 1 : LITERATURE REVIEW AND THEORETICAL FRAMEWORK 1.1 Literature Review:

1.1.1 Background:

The year 2021 represents an imperative period for investigating CO2 emissions and its determinants The International Energy Agency (IEA) reported that global COz emissions rose by 6% in 2021, reaching 36.3 billion tons, the highest level in history at the time This unprecedented surge in emissions significantly worsened global warming, leading to more extreme weather events, rising sea levels, and widespread environmental destruction Additionally, the emissions spike pushed the world further off track from climate targets, increasing the risk of irreversible climate tipping points

2021 is also a year marked by rapid economic recovery, increased urbanization, and industrial growth Following the severe economic stagnation caused by the pandemic, 2021 businesses quickly sought to recover from the prolonged downturn This resurgence led to a substantial increase in productivity, as industries and enterprises endeavored to restore pre- pandemic levels of economic activity As reported by The World Bank, global GDP grew by approximately 6% in 2021, representing one of the strongest annual growth rates in decades A similar trend can be witnessed in urban population and industrial production, both accelerating greatly to compensate for the lengthy period of stagnation Furthermore, the consumption of renewable energy also underwent substantial advancements According to the International Energy Agency (IEA), global renewable energy capacity increased by approximately 290 GW, the highest annual addition on record, driven primarily by solar and wind power installations This development is also marked by the COP26 Climate Summit, emphasizing the global transition to renewable energy, with over 190 countries agreeing to phase down coal and increase investments

in clean energy, signalling a more ubiquitous utilization of renewable energy

A thorough understanding of the underlying factors contributing to the crisis of elevated COz emissions is essential, as it enables policymakers to formulate well-informed and effective policies aimed at mitigating the risk of severe climate consequences that endanger global ecosystems, economic stability, and human well-being Interestingly, all of the aforementioned aspects are key variables that can have an impact on CO:z emissions Therefore, it 1s crucial to conduct

comprehensive studies that investigate the relationship between these variables and COz emissions

to identify the primary drivers behind the unprecedented increase in carbon dioxide levels in 2021 From that, our previously mentioned aim of designing effective policies can be realized

1.1.2 Research into the impact of Economic Growth, Urbanization, Renewable Energy Consumption, Industrialization on Carbon Emissions:

a Researches in other countries:

The study titled “Effect of economic growth on CO2 emissions in developing countries: Evidence from a dynamic panel threshold model” by C Aye and Edoja (2017), after using a dynamic panel threshold model that utilized panel data from 31 developing

INTRODUCTION 6 CONTENT 8 CHAPTER 1 : LITERATURE REVIEW AND THEORETICAL FRAMEWORK

8 1.1 Literature Review: 8 1.1.1 Background: 8 1.1.2 Research into the impact of Economie Growth, Urbanization, Renewable Energy Consumption, Industrialization on Carbon EHHSSIOHS? e«ee 8 1.2 Theoretical Framework: 21 1.2.1 Carbon Emissions: 21 1.2.2 Economic Growth: 21 1.2.3 Urbanization: 22 1.2.4 Renewable Energy C ption: 23 1.2.5 Industrialization: 23 1.3 Research Hypotheses: 24 CHAPTER 2 : RESEARCH METHODOLOGY AND MODEL SPECIFICATION 2.1 Research Methodology: 25 2.1.1 Data Collection Methodology: 25 2.1.2 Data Analysis Methodology: 25 2.2 Theoretical model specification: 25 2.2.1 Research Model: 25 2.2.2 Variable explanafions, measurernenís and dqÍd SOHFC@S e«ee- 26 2.2.3 Expected effects of Independent Variables on Dependent Variable 26 2.3 Statistical Description of the Data: 27 2.3.1 Statistical Description of the Data: 27 2.3.2 Correlation Matrix of Variables: 28 CHAPTER 3 : RESULT AND DISCUSSION 30 3.1 Estimation of econometrics model: 30 3.1.1 Estimated results: 30 3.1.2 Sample regression function obtained: 30 3.1.3 Diagnosing and correcting the problems of the tmodlel- « 30

4

long-run Granger causality relationships exist between CO2 emissions, GDP, and employment Howbeit, variance decomposition revealed that CO2 emissions are primarily explained by their own past shocks, with limited contributions from GDP, available energy, and employment This study still possesses some clear limitations, for instance, the absence

of other imperative indicators of economic growth such as urbanization and industrialization

In the study titled “Impact of Urbanization and Economic Growth on CO2 emissions:

A Case of Far East Asian Countries”, Anwar et al (2020), the researchers adopted a panel data-fixed effect model that accounts for time-invariant country-specific characteristics, and concluded that urbanization was significantly correlated with CO2 emission in the panel of Far East Asian countries from 1980 to 2017 Their findings also espoused aforementioned studies that came to the conclusion that economic growth (GDP) and trade openness are positively associated with CO2 emissions Adding to this, Shahbaz et al (2015) investigates the impact of urbanization on CO2 emissions in Malaysia using the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, covering the period of 1970 to 2011 The results indicated that besides the significant impact of economic growth on CO2 emissions due to the intense consumption

of energy and trade openness in the period of accelerating economic development, the relationship between urbanization and CO2 emissions is also a notable finding The results revealed the existence of an U-shaped relationship between urbanization and CO2 emissions, implying that initially, urbanization reduces emissions, but after a certain threshold, it increases emissions This provides insights for policymakers in designing comprehensive environmental urbanization, energy and trade openness policies to mitigate CO2 emissions However, the study still possessed clear limitations: the study only relies

on aggregate national-level data, which might mask regional variations and specific sectoral impacts Moreover, despite controlling for several key factors, the possibility of omitted variable bias is still present within these studies There might be other variables, such as, renewable energy adoption rates and industrialization, that influence CO2 emissions but are not included in the model due to data limitations

Interestingly, the previously mentioned studies are opposed by Wang et al (2021) who organized the study “Impacts of urbanization on carbon emissions: An empirical analysis from OECD countries” This research paper investigated the complex relationship between urbanization and carbon emissions, focusing on OECD (Organisation for Economic Co-operation and Development) high-income countries In this study, a dynamic panel Autoregressive Distributed Lag (ARDL) model to analyze long-run equilibrium, short-run dynamics, impact mechanisms, and lag effects between urbanization and various

3.2.1 Coefficient of determination (R?): 33 3.2.2 Hypothesis test: 33 3.2.3 Explanation of the Results: 35 CHAPTER 4 : RECOMMENDATIONS AND SOLUTIONS FOR CARBON

4.1 Solutions for Economic Growth: 38 4.2 Solutions for Urban Population: 38 4.3 Solutions for Renewable Energy Consumption: 39 4.4 Solutions for Industrial sectors: 39 CONCLUSION 41 REFRENCES 42 APPENDIX 44

INTRODUCTION 6 CONTENT 8 CHAPTER 1 : LITERATURE REVIEW AND THEORETICAL FRAMEWORK

8 1.1 Literature Review: 8 1.1.1 Background: 8 1.1.2 Research into the impact of Economie Growth, Urbanization, Renewable Energy Consumption, Industrialization on Carbon EHHSSIOHS? e«ee 8 1.2 Theoretical Framework: 21 1.2.1 Carbon Emissions: 21 1.2.2 Economic Growth: 21 1.2.3 Urbanization: 22 1.2.4 Renewable Energy C ption: 23 1.2.5 Industrialization: 23 1.3 Research Hypotheses: 24 CHAPTER 2 : RESEARCH METHODOLOGY AND MODEL SPECIFICATION 2.1 Research Methodology: 25 2.1.1 Data Collection Methodology: 25 2.1.2 Data Analysis Methodology: 25 2.2 Theoretical model specification: 25 2.2.1 Research Model: 25 2.2.2 Variable explanafions, measurernenís and dqÍd SOHFC@S e«ee- 26 2.2.3 Expected effects of Independent Variables on Dependent Variable 26 2.3 Statistical Description of the Data: 27 2.3.1 Statistical Description of the Data: 27 2.3.2 Correlation Matrix of Variables: 28 CHAPTER 3 : RESULT AND DISCUSSION 30 3.1 Estimation of econometrics model: 30 3.1.1 Estimated results: 30 3.1.2 Sample regression function obtained: 30 3.1.3 Diagnosing and correcting the problems of the tmodlel- « 30

4

Carbon emissions are one of the most pressing environmental challenges of the modern world As economies grow and industrial activities expand, the demand for energy increases, leading to higher emissions of greenhouse gases, particularly carbon dioxide (CO2) These emissions are a major contributor to global climate change, affecting ecosystems, weather patterns, and overall human well-being Addressing carbon emission

is not just an environmental concern but also an economic and social imperative, as it influences sustainable development, energy security, and public health

The importance of managing carbon emissions cannot be understated Rapid industrialization and urbanization have led to rising emissions, causing environmental degradation and global warming Countries worldwide are striving to balance economic growth with environmental sustainability by adopting cleaner technologies, renewable energy sources, and carbon reduction policies Reducing emissions is not only a responsibility toward future generations but also a strategic necessity to ensure long-term economic stability, resource conservation, and resilience against climate change impacts

To address and assess carbon emissions effectively, various economic and environmental factors must be taken into consideration Carbon emissions are closely

linked to key variables such as GDP growth, urbanization, industrial activities, and

renewable energy adoption Understanding these relationships helps policymakers and researchers develop strategies to mitigate emissions while supporting economic progress

By examining the factors influencing carbon emissions, nations can implement targeted policies that promote sustainability without hindering development

Recognizing the significance of carbon emissions in shaping environmental and economic policies, our group has chosen the topic “The influence of economic growth, urbanization, renewable energy consumption and industrialization on carbon emissions

in multiple nations around the world in 2021”

Research objective: Analyze and assess the impact of various economic and environmental factors on carbon emissions worldwide, thereby providing insights into effective mitigation strategies

Research subjects: The influence of economic growth, urbanization, industrialization

and renewable energy on carbon emissions of countries worldwide in 2021

Research scope: The spatial scope includes 183 countries globally, and the temporal scope is from January 2021 to December 2021 Our data collection methods include gathering data from World Bank and the estimation method uses OLS, with the model run

INTRODUCTION 6 CONTENT 8 CHAPTER 1 : LITERATURE REVIEW AND THEORETICAL FRAMEWORK

8 1.1 Literature Review: 8 1.1.1 Background: 8 1.1.2 Research into the impact of Economie Growth, Urbanization, Renewable Energy Consumption, Industrialization on Carbon EHHSSIOHS? e«ee 8 1.2 Theoretical Framework: 21 1.2.1 Carbon Emissions: 21 1.2.2 Economic Growth: 21 1.2.3 Urbanization: 22 1.2.4 Renewable Energy C ption: 23 1.2.5 Industrialization: 23 1.3 Research Hypotheses: 24 CHAPTER 2 : RESEARCH METHODOLOGY AND MODEL SPECIFICATION 2.1 Research Methodology: 25 2.1.1 Data Collection Methodology: 25 2.1.2 Data Analysis Methodology: 25 2.2 Theoretical model specification: 25 2.2.1 Research Model: 25 2.2.2 Variable explanafions, measurernenís and dqÍd SOHFC@S e«ee- 26 2.2.3 Expected effects of Independent Variables on Dependent Variable 26 2.3 Statistical Description of the Data: 27 2.3.1 Statistical Description of the Data: 27 2.3.2 Correlation Matrix of Variables: 28 CHAPTER 3 : RESULT AND DISCUSSION 30 3.1 Estimation of econometrics model: 30 3.1.1 Estimated results: 30 3.1.2 Sample regression function obtained: 30 3.1.3 Diagnosing and correcting the problems of the tmodlel- « 30

4

Conclusion, References and Appendix In which the content includes:

Chapter 1: Literature Review and Theoretical Framework

Chapter 2: Research Methodology and Model Specification

Chapter 3: Result and Discussion

3.2.1 Coefficient of determination (R?): 33 3.2.2 Hypothesis test: 33 3.2.3 Explanation of the Results: 35 CHAPTER 4 : RECOMMENDATIONS AND SOLUTIONS FOR CARBON

4.1 Solutions for Economic Growth: 38 4.2 Solutions for Urban Population: 38 4.3 Solutions for Renewable Energy Consumption: 39 4.4 Solutions for Industrial sectors: 39 CONCLUSION 41 REFRENCES 42 APPENDIX 44

INTRODUCTION 6 CONTENT 8 CHAPTER 1 : LITERATURE REVIEW AND THEORETICAL FRAMEWORK

8 1.1 Literature Review: 8 1.1.1 Background: 8 1.1.2 Research into the impact of Economie Growth, Urbanization, Renewable Energy Consumption, Industrialization on Carbon EHHSSIOHS? e«ee 8 1.2 Theoretical Framework: 21 1.2.1 Carbon Emissions: 21 1.2.2 Economic Growth: 21 1.2.3 Urbanization: 22 1.2.4 Renewable Energy C ption: 23 1.2.5 Industrialization: 23 1.3 Research Hypotheses: 24 CHAPTER 2 : RESEARCH METHODOLOGY AND MODEL SPECIFICATION 2.1 Research Methodology: 25 2.1.1 Data Collection Methodology: 25 2.1.2 Data Analysis Methodology: 25 2.2 Theoretical model specification: 25 2.2.1 Research Model: 25 2.2.2 Variable explanafions, measurernenís and dqÍd SOHFC@S e«ee- 26 2.2.3 Expected effects of Independent Variables on Dependent Variable 26 2.3 Statistical Description of the Data: 27 2.3.1 Statistical Description of the Data: 27 2.3.2 Correlation Matrix of Variables: 28 CHAPTER 3 : RESULT AND DISCUSSION 30 3.1 Estimation of econometrics model: 30 3.1.1 Estimated results: 30 3.1.2 Sample regression function obtained: 30 3.1.3 Diagnosing and correcting the problems of the tmodlel- « 30

4

Carbon emissions are one of the most pressing environmental challenges of the modern world As economies grow and industrial activities expand, the demand for energy increases, leading to higher emissions of greenhouse gases, particularly carbon dioxide (CO2) These emissions are a major contributor to global climate change, affecting ecosystems, weather patterns, and overall human well-being Addressing carbon emission

is not just an environmental concern but also an economic and social imperative, as it influences sustainable development, energy security, and public health

The importance of managing carbon emissions cannot be understated Rapid industrialization and urbanization have led to rising emissions, causing environmental degradation and global warming Countries worldwide are striving to balance economic growth with environmental sustainability by adopting cleaner technologies, renewable energy sources, and carbon reduction policies Reducing emissions is not only a responsibility toward future generations but also a strategic necessity to ensure long-term economic stability, resource conservation, and resilience against climate change impacts

To address and assess carbon emissions effectively, various economic and environmental factors must be taken into consideration Carbon emissions are closely

linked to key variables such as GDP growth, urbanization, industrial activities, and

renewable energy adoption Understanding these relationships helps policymakers and researchers develop strategies to mitigate emissions while supporting economic progress

By examining the factors influencing carbon emissions, nations can implement targeted policies that promote sustainability without hindering development

Recognizing the significance of carbon emissions in shaping environmental and economic policies, our group has chosen the topic “The influence of economic growth, urbanization, renewable energy consumption and industrialization on carbon emissions

in multiple nations around the world in 2021”

Research objective: Analyze and assess the impact of various economic and environmental factors on carbon emissions worldwide, thereby providing insights into effective mitigation strategies

Research subjects: The influence of economic growth, urbanization, industrialization

and renewable energy on carbon emissions of countries worldwide in 2021

Research scope: The spatial scope includes 183 countries globally, and the temporal scope is from January 2021 to December 2021 Our data collection methods include gathering data from World Bank and the estimation method uses OLS, with the model run

long-run Granger causality relationships exist between CO2 emissions, GDP, and employment Howbeit, variance decomposition revealed that CO2 emissions are primarily explained by their own past shocks, with limited contributions from GDP, available energy, and employment This study still possesses some clear limitations, for instance, the absence

of other imperative indicators of economic growth such as urbanization and industrialization

In the study titled “Impact of Urbanization and Economic Growth on CO2 emissions:

A Case of Far East Asian Countries”, Anwar et al (2020), the researchers adopted a panel data-fixed effect model that accounts for time-invariant country-specific characteristics, and concluded that urbanization was significantly correlated with CO2 emission in the panel of Far East Asian countries from 1980 to 2017 Their findings also espoused aforementioned studies that came to the conclusion that economic growth (GDP) and trade openness are positively associated with CO2 emissions Adding to this, Shahbaz et al (2015) investigates the impact of urbanization on CO2 emissions in Malaysia using the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, covering the period of 1970 to 2011 The results indicated that besides the significant impact of economic growth on CO2 emissions due to the intense consumption

of energy and trade openness in the period of accelerating economic development, the relationship between urbanization and CO2 emissions is also a notable finding The results revealed the existence of an U-shaped relationship between urbanization and CO2 emissions, implying that initially, urbanization reduces emissions, but after a certain threshold, it increases emissions This provides insights for policymakers in designing comprehensive environmental urbanization, energy and trade openness policies to mitigate CO2 emissions However, the study still possessed clear limitations: the study only relies

on aggregate national-level data, which might mask regional variations and specific sectoral impacts Moreover, despite controlling for several key factors, the possibility of omitted variable bias is still present within these studies There might be other variables, such as, renewable energy adoption rates and industrialization, that influence CO2 emissions but are not included in the model due to data limitations

Interestingly, the previously mentioned studies are opposed by Wang et al (2021) who organized the study “Impacts of urbanization on carbon emissions: An empirical analysis from OECD countries” This research paper investigated the complex relationship between urbanization and carbon emissions, focusing on OECD (Organisation for Economic Co-operation and Development) high-income countries In this study, a dynamic panel Autoregressive Distributed Lag (ARDL) model to analyze long-run equilibrium, short-run dynamics, impact mechanisms, and lag effects between urbanization and various

CHAPTER 1 : LITERATURE REVIEW AND THEORETICAL FRAMEWORK 1.1 Literature Review:

1.1.1 Background:

The year 2021 represents an imperative period for investigating CO2 emissions and its determinants The International Energy Agency (IEA) reported that global COz emissions rose by 6% in 2021, reaching 36.3 billion tons, the highest level in history at the time This unprecedented surge in emissions significantly worsened global warming, leading to more extreme weather events, rising sea levels, and widespread environmental destruction Additionally, the emissions spike pushed the world further off track from climate targets, increasing the risk of irreversible climate tipping points

2021 is also a year marked by rapid economic recovery, increased urbanization, and industrial growth Following the severe economic stagnation caused by the pandemic, 2021 businesses quickly sought to recover from the prolonged downturn This resurgence led to a substantial increase in productivity, as industries and enterprises endeavored to restore pre- pandemic levels of economic activity As reported by The World Bank, global GDP grew by approximately 6% in 2021, representing one of the strongest annual growth rates in decades A similar trend can be witnessed in urban population and industrial production, both accelerating greatly to compensate for the lengthy period of stagnation Furthermore, the consumption of renewable energy also underwent substantial advancements According to the International Energy Agency (IEA), global renewable energy capacity increased by approximately 290 GW, the highest annual addition on record, driven primarily by solar and wind power installations This development is also marked by the COP26 Climate Summit, emphasizing the global transition to renewable energy, with over 190 countries agreeing to phase down coal and increase investments

in clean energy, signalling a more ubiquitous utilization of renewable energy

A thorough understanding of the underlying factors contributing to the crisis of elevated COz emissions is essential, as it enables policymakers to formulate well-informed and effective policies aimed at mitigating the risk of severe climate consequences that endanger global ecosystems, economic stability, and human well-being Interestingly, all of the aforementioned aspects are key variables that can have an impact on CO:z emissions Therefore, it 1s crucial to conduct

comprehensive studies that investigate the relationship between these variables and COz emissions

to identify the primary drivers behind the unprecedented increase in carbon dioxide levels in 2021 From that, our previously mentioned aim of designing effective policies can be realized

1.1.2 Research into the impact of Economic Growth, Urbanization, Renewable Energy Consumption, Industrialization on Carbon Emissions:

a Researches in other countries:

The study titled “Effect of economic growth on CO2 emissions in developing countries: Evidence from a dynamic panel threshold model” by C Aye and Edoja (2017), after using a dynamic panel threshold model that utilized panel data from 31 developing

CHAPTER 1 : LITERATURE REVIEW AND THEORETICAL FRAMEWORK 1.1 Literature Review:

1.1.1 Background:

The year 2021 represents an imperative period for investigating CO2 emissions and its determinants The International Energy Agency (IEA) reported that global COz emissions rose by 6% in 2021, reaching 36.3 billion tons, the highest level in history at the time This unprecedented surge in emissions significantly worsened global warming, leading to more extreme weather events, rising sea levels, and widespread environmental destruction Additionally, the emissions spike pushed the world further off track from climate targets, increasing the risk of irreversible climate tipping points

2021 is also a year marked by rapid economic recovery, increased urbanization, and industrial growth Following the severe economic stagnation caused by the pandemic, 2021 businesses quickly sought to recover from the prolonged downturn This resurgence led to a substantial increase in productivity, as industries and enterprises endeavored to restore pre- pandemic levels of economic activity As reported by The World Bank, global GDP grew by approximately 6% in 2021, representing one of the strongest annual growth rates in decades A similar trend can be witnessed in urban population and industrial production, both accelerating greatly to compensate for the lengthy period of stagnation Furthermore, the consumption of renewable energy also underwent substantial advancements According to the International Energy Agency (IEA), global renewable energy capacity increased by approximately 290 GW, the highest annual addition on record, driven primarily by solar and wind power installations This development is also marked by the COP26 Climate Summit, emphasizing the global transition to renewable energy, with over 190 countries agreeing to phase down coal and increase investments

in clean energy, signalling a more ubiquitous utilization of renewable energy

A thorough understanding of the underlying factors contributing to the crisis of elevated COz emissions is essential, as it enables policymakers to formulate well-informed and effective policies aimed at mitigating the risk of severe climate consequences that endanger global ecosystems, economic stability, and human well-being Interestingly, all of the aforementioned aspects are key variables that can have an impact on CO:z emissions Therefore, it 1s crucial to conduct

comprehensive studies that investigate the relationship between these variables and COz emissions

to identify the primary drivers behind the unprecedented increase in carbon dioxide levels in 2021 From that, our previously mentioned aim of designing effective policies can be realized

1.1.2 Research into the impact of Economic Growth, Urbanization, Renewable Energy Consumption, Industrialization on Carbon Emissions:

a Researches in other countries:

The study titled “Effect of economic growth on CO2 emissions in developing countries: Evidence from a dynamic panel threshold model” by C Aye and Edoja (2017), after using a dynamic panel threshold model that utilized panel data from 31 developing

INTRODUCTION 6 CONTENT 8 CHAPTER 1 : LITERATURE REVIEW AND THEORETICAL FRAMEWORK

8 1.1 Literature Review: 8 1.1.1 Background: 8 1.1.2 Research into the impact of Economie Growth, Urbanization, Renewable Energy Consumption, Industrialization on Carbon EHHSSIOHS? e«ee 8 1.2 Theoretical Framework: 21 1.2.1 Carbon Emissions: 21 1.2.2 Economic Growth: 21 1.2.3 Urbanization: 22 1.2.4 Renewable Energy C ption: 23 1.2.5 Industrialization: 23 1.3 Research Hypotheses: 24 CHAPTER 2 : RESEARCH METHODOLOGY AND MODEL SPECIFICATION 2.1 Research Methodology: 25 2.1.1 Data Collection Methodology: 25 2.1.2 Data Analysis Methodology: 25 2.2 Theoretical model specification: 25 2.2.1 Research Model: 25 2.2.2 Variable explanafions, measurernenís and dqÍd SOHFC@S e«ee- 26 2.2.3 Expected effects of Independent Variables on Dependent Variable 26 2.3 Statistical Description of the Data: 27 2.3.1 Statistical Description of the Data: 27 2.3.2 Correlation Matrix of Variables: 28 CHAPTER 3 : RESULT AND DISCUSSION 30 3.1 Estimation of econometrics model: 30 3.1.1 Estimated results: 30 3.1.2 Sample regression function obtained: 30 3.1.3 Diagnosing and correcting the problems of the tmodlel- « 30

4

INTRODUCTION 6 CONTENT 8 CHAPTER 1 : LITERATURE REVIEW AND THEORETICAL FRAMEWORK

8 1.1 Literature Review: 8 1.1.1 Background: 8 1.1.2 Research into the impact of Economie Growth, Urbanization, Renewable Energy Consumption, Industrialization on Carbon EHHSSIOHS? e«ee 8 1.2 Theoretical Framework: 21 1.2.1 Carbon Emissions: 21 1.2.2 Economic Growth: 21 1.2.3 Urbanization: 22 1.2.4 Renewable Energy C ption: 23 1.2.5 Industrialization: 23 1.3 Research Hypotheses: 24 CHAPTER 2 : RESEARCH METHODOLOGY AND MODEL SPECIFICATION 2.1 Research Methodology: 25 2.1.1 Data Collection Methodology: 25 2.1.2 Data Analysis Methodology: 25 2.2 Theoretical model specification: 25 2.2.1 Research Model: 25 2.2.2 Variable explanafions, measurernenís and dqÍd SOHFC@S e«ee- 26 2.2.3 Expected effects of Independent Variables on Dependent Variable 26 2.3 Statistical Description of the Data: 27 2.3.1 Statistical Description of the Data: 27 2.3.2 Correlation Matrix of Variables: 28 CHAPTER 3 : RESULT AND DISCUSSION 30 3.1 Estimation of econometrics model: 30 3.1.1 Estimated results: 30 3.1.2 Sample regression function obtained: 30 3.1.3 Diagnosing and correcting the problems of the tmodlel- « 30

4

3.2.1 Coefficient of determination (R?): 33 3.2.2 Hypothesis test: 33 3.2.3 Explanation of the Results: 35 CHAPTER 4 : RECOMMENDATIONS AND SOLUTIONS FOR CARBON

4.1 Solutions for Economic Growth: 38 4.2 Solutions for Urban Population: 38 4.3 Solutions for Renewable Energy Consumption: 39 4.4 Solutions for Industrial sectors: 39 CONCLUSION 41 REFRENCES 42 APPENDIX 44

long-run Granger causality relationships exist between CO2 emissions, GDP, and employment Howbeit, variance decomposition revealed that CO2 emissions are primarily explained by their own past shocks, with limited contributions from GDP, available energy, and employment This study still possesses some clear limitations, for instance, the absence

of other imperative indicators of economic growth such as urbanization and industrialization

In the study titled “Impact of Urbanization and Economic Growth on CO2 emissions:

A Case of Far East Asian Countries”, Anwar et al (2020), the researchers adopted a panel data-fixed effect model that accounts for time-invariant country-specific characteristics, and concluded that urbanization was significantly correlated with CO2 emission in the panel of Far East Asian countries from 1980 to 2017 Their findings also espoused aforementioned studies that came to the conclusion that economic growth (GDP) and trade openness are positively associated with CO2 emissions Adding to this, Shahbaz et al (2015) investigates the impact of urbanization on CO2 emissions in Malaysia using the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, covering the period of 1970 to 2011 The results indicated that besides the significant impact of economic growth on CO2 emissions due to the intense consumption

of energy and trade openness in the period of accelerating economic development, the relationship between urbanization and CO2 emissions is also a notable finding The results revealed the existence of an U-shaped relationship between urbanization and CO2 emissions, implying that initially, urbanization reduces emissions, but after a certain threshold, it increases emissions This provides insights for policymakers in designing comprehensive environmental urbanization, energy and trade openness policies to mitigate CO2 emissions However, the study still possessed clear limitations: the study only relies

on aggregate national-level data, which might mask regional variations and specific sectoral impacts Moreover, despite controlling for several key factors, the possibility of omitted variable bias is still present within these studies There might be other variables, such as, renewable energy adoption rates and industrialization, that influence CO2 emissions but are not included in the model due to data limitations

Interestingly, the previously mentioned studies are opposed by Wang et al (2021) who organized the study “Impacts of urbanization on carbon emissions: An empirical analysis from OECD countries” This research paper investigated the complex relationship between urbanization and carbon emissions, focusing on OECD (Organisation for Economic Co-operation and Development) high-income countries In this study, a dynamic panel Autoregressive Distributed Lag (ARDL) model to analyze long-run equilibrium, short-run dynamics, impact mechanisms, and lag effects between urbanization and various

long-run Granger causality relationships exist between CO2 emissions, GDP, and employment Howbeit, variance decomposition revealed that CO2 emissions are primarily explained by their own past shocks, with limited contributions from GDP, available energy, and employment This study still possesses some clear limitations, for instance, the absence

of other imperative indicators of economic growth such as urbanization and industrialization

In the study titled “Impact of Urbanization and Economic Growth on CO2 emissions:

A Case of Far East Asian Countries”, Anwar et al (2020), the researchers adopted a panel data-fixed effect model that accounts for time-invariant country-specific characteristics, and concluded that urbanization was significantly correlated with CO2 emission in the panel of Far East Asian countries from 1980 to 2017 Their findings also espoused aforementioned studies that came to the conclusion that economic growth (GDP) and trade openness are positively associated with CO2 emissions Adding to this, Shahbaz et al (2015) investigates the impact of urbanization on CO2 emissions in Malaysia using the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, covering the period of 1970 to 2011 The results indicated that besides the significant impact of economic growth on CO2 emissions due to the intense consumption

of energy and trade openness in the period of accelerating economic development, the relationship between urbanization and CO2 emissions is also a notable finding The results revealed the existence of an U-shaped relationship between urbanization and CO2 emissions, implying that initially, urbanization reduces emissions, but after a certain threshold, it increases emissions This provides insights for policymakers in designing comprehensive environmental urbanization, energy and trade openness policies to mitigate CO2 emissions However, the study still possessed clear limitations: the study only relies

on aggregate national-level data, which might mask regional variations and specific sectoral impacts Moreover, despite controlling for several key factors, the possibility of omitted variable bias is still present within these studies There might be other variables, such as, renewable energy adoption rates and industrialization, that influence CO2 emissions but are not included in the model due to data limitations

Interestingly, the previously mentioned studies are opposed by Wang et al (2021) who organized the study “Impacts of urbanization on carbon emissions: An empirical analysis from OECD countries” This research paper investigated the complex relationship between urbanization and carbon emissions, focusing on OECD (Organisation for Economic Co-operation and Development) high-income countries In this study, a dynamic panel Autoregressive Distributed Lag (ARDL) model to analyze long-run equilibrium, short-run dynamics, impact mechanisms, and lag effects between urbanization and various

impact on CO2 emissions in low-growth regimes but a positive impact in high-growth regimes The effect in the high growth regime is however stronger Thus, the validity of the Environmental Kuznets Curve (inverted-U) hypothesis could not be established for these panels of countries for the period under study However, this study is held back by its utilization of CO2 emissions per capita as the sole proxy for environmental degradation, neglecting other important environmental indicators Despite its limitations, this study can still inform policymakers in developing countries about the environmental consequences

of economic growth and the need for targeted policies to mitigate CO2 emissions In addition, the study results could be used in the construction of global greenhouse gas emission models

Arouri et al (2012) suggested the presence of an EKC at the regional level after conducting the study “Energy consumption, economic growth and CO2 emissions in Middle East and North African countries” that investigated the relationship between carbon dioxide (CO2) emissions, energy consumption, and real GDP in 12 Middle East and North African (MENA) countries from 1981 to 2005 This study employed bootstrap panel unit root tests and cointegration techniques to analyze the long-run dynamics between these variables before achieving results that revealed evidence of a quadratic relationship between real GDP and CO2 emissions However, country-specific analysis shows that while the estimated long-run coefficients of income and its square often satisfy the EKC hypothesis, the turning points (income levels at which emissions start to decline) vary considerably and are often outside the range of observed GDP levels for many countries, casting doubt on the applicability of the EKC hypothesis at the individual country level Meanwhile, concerning the energy consumption variable, it is found that at the regional level, this variable has a positive and significant impact on CO2 emissions in the MENA region These findings help MENA countries’ policymakers realize that they can pursue energy conservation policies to reduce CO2 emissions without necessarily sacrificing economic growth Howbeit, despite being significantly valuable and insightful, this study should be interpreted with discretion since the data only covers the period of 1981-2005 This may not reflect current dynamics, as economic structures and environmental policies have evolved since then

In the study named “The relationship between CO2 emissions, economic growth, available energy, and employment in SEE countries” Mitic et al (2022) investigated eight

South-Eastern European (SEE) countries (Albania, Bulgaria, Croatia, Greece, North

Macedonia, Romania, Serbia, and Slovenia) from 1995 to 2019 by utilizing a wide range

of research methodology, for instance, Panel Cointegration Tests, Panel Vector Error

impact on CO2 emissions in low-growth regimes but a positive impact in high-growth regimes The effect in the high growth regime is however stronger Thus, the validity of the Environmental Kuznets Curve (inverted-U) hypothesis could not be established for these panels of countries for the period under study However, this study is held back by its utilization of CO2 emissions per capita as the sole proxy for environmental degradation, neglecting other important environmental indicators Despite its limitations, this study can still inform policymakers in developing countries about the environmental consequences

of economic growth and the need for targeted policies to mitigate CO2 emissions In addition, the study results could be used in the construction of global greenhouse gas emission models

Arouri et al (2012) suggested the presence of an EKC at the regional level after conducting the study “Energy consumption, economic growth and CO2 emissions in Middle East and North African countries” that investigated the relationship between carbon dioxide (CO2) emissions, energy consumption, and real GDP in 12 Middle East and North African (MENA) countries from 1981 to 2005 This study employed bootstrap panel unit root tests and cointegration techniques to analyze the long-run dynamics between these variables before achieving results that revealed evidence of a quadratic relationship between real GDP and CO2 emissions However, country-specific analysis shows that while the estimated long-run coefficients of income and its square often satisfy the EKC hypothesis, the turning points (income levels at which emissions start to decline) vary considerably and are often outside the range of observed GDP levels for many countries, casting doubt on the applicability of the EKC hypothesis at the individual country level Meanwhile, concerning the energy consumption variable, it is found that at the regional level, this variable has a positive and significant impact on CO2 emissions in the MENA region These findings help MENA countries’ policymakers realize that they can pursue energy conservation policies to reduce CO2 emissions without necessarily sacrificing economic growth Howbeit, despite being significantly valuable and insightful, this study should be interpreted with discretion since the data only covers the period of 1981-2005 This may not reflect current dynamics, as economic structures and environmental policies have evolved since then

In the study named “The relationship between CO2 emissions, economic growth, available energy, and employment in SEE countries” Mitic et al (2022) investigated eight

South-Eastern European (SEE) countries (Albania, Bulgaria, Croatia, Greece, North

Macedonia, Romania, Serbia, and Slovenia) from 1995 to 2019 by utilizing a wide range

of research methodology, for instance, Panel Cointegration Tests, Panel Vector Error

3.2.1 Coefficient of determination (R?): 33 3.2.2 Hypothesis test: 33 3.2.3 Explanation of the Results: 35 CHAPTER 4 : RECOMMENDATIONS AND SOLUTIONS FOR CARBON

4.1 Solutions for Economic Growth: 38 4.2 Solutions for Urban Population: 38 4.3 Solutions for Renewable Energy Consumption: 39 4.4 Solutions for Industrial sectors: 39 CONCLUSION 41 REFRENCES 42 APPENDIX 44

Conclusion, References and Appendix In which the content includes:

Chapter 1: Literature Review and Theoretical Framework

Chapter 2: Research Methodology and Model Specification

Chapter 3: Result and Discussion

Conclusion, References and Appendix In which the content includes:

Chapter 1: Literature Review and Theoretical Framework

Chapter 2: Research Methodology and Model Specification

Chapter 3: Result and Discussion

3.2.1 Coefficient of determination (R?): 33 3.2.2 Hypothesis test: 33 3.2.3 Explanation of the Results: 35 CHAPTER 4 : RECOMMENDATIONS AND SOLUTIONS FOR CARBON

4.1 Solutions for Economic Growth: 38 4.2 Solutions for Urban Population: 38 4.3 Solutions for Renewable Energy Consumption: 39 4.4 Solutions for Industrial sectors: 39 CONCLUSION 41 REFRENCES 42 APPENDIX 44

long-run Granger causality relationships exist between CO2 emissions, GDP, and employment Howbeit, variance decomposition revealed that CO2 emissions are primarily explained by their own past shocks, with limited contributions from GDP, available energy, and employment This study still possesses some clear limitations, for instance, the absence

of other imperative indicators of economic growth such as urbanization and industrialization

In the study titled “Impact of Urbanization and Economic Growth on CO2 emissions:

A Case of Far East Asian Countries”, Anwar et al (2020), the researchers adopted a panel data-fixed effect model that accounts for time-invariant country-specific characteristics, and concluded that urbanization was significantly correlated with CO2 emission in the panel of Far East Asian countries from 1980 to 2017 Their findings also espoused aforementioned studies that came to the conclusion that economic growth (GDP) and trade openness are positively associated with CO2 emissions Adding to this, Shahbaz et al (2015) investigates the impact of urbanization on CO2 emissions in Malaysia using the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, covering the period of 1970 to 2011 The results indicated that besides the significant impact of economic growth on CO2 emissions due to the intense consumption

of energy and trade openness in the period of accelerating economic development, the relationship between urbanization and CO2 emissions is also a notable finding The results revealed the existence of an U-shaped relationship between urbanization and CO2 emissions, implying that initially, urbanization reduces emissions, but after a certain threshold, it increases emissions This provides insights for policymakers in designing comprehensive environmental urbanization, energy and trade openness policies to mitigate CO2 emissions However, the study still possessed clear limitations: the study only relies

on aggregate national-level data, which might mask regional variations and specific sectoral impacts Moreover, despite controlling for several key factors, the possibility of omitted variable bias is still present within these studies There might be other variables, such as, renewable energy adoption rates and industrialization, that influence CO2 emissions but are not included in the model due to data limitations

Interestingly, the previously mentioned studies are opposed by Wang et al (2021) who organized the study “Impacts of urbanization on carbon emissions: An empirical analysis from OECD countries” This research paper investigated the complex relationship between urbanization and carbon emissions, focusing on OECD (Organisation for Economic Co-operation and Development) high-income countries In this study, a dynamic panel Autoregressive Distributed Lag (ARDL) model to analyze long-run equilibrium, short-run dynamics, impact mechanisms, and lag effects between urbanization and various

CHAPTER 1 : LITERATURE REVIEW AND THEORETICAL FRAMEWORK 1.1 Literature Review:

1.1.1 Background:

The year 2021 represents an imperative period for investigating CO2 emissions and its determinants The International Energy Agency (IEA) reported that global COz emissions rose by 6% in 2021, reaching 36.3 billion tons, the highest level in history at the time This unprecedented surge in emissions significantly worsened global warming, leading to more extreme weather events, rising sea levels, and widespread environmental destruction Additionally, the emissions spike pushed the world further off track from climate targets, increasing the risk of irreversible climate tipping points

2021 is also a year marked by rapid economic recovery, increased urbanization, and industrial growth Following the severe economic stagnation caused by the pandemic, 2021 businesses quickly sought to recover from the prolonged downturn This resurgence led to a substantial increase in productivity, as industries and enterprises endeavored to restore pre- pandemic levels of economic activity As reported by The World Bank, global GDP grew by approximately 6% in 2021, representing one of the strongest annual growth rates in decades A similar trend can be witnessed in urban population and industrial production, both accelerating greatly to compensate for the lengthy period of stagnation Furthermore, the consumption of renewable energy also underwent substantial advancements According to the International Energy Agency (IEA), global renewable energy capacity increased by approximately 290 GW, the highest annual addition on record, driven primarily by solar and wind power installations This development is also marked by the COP26 Climate Summit, emphasizing the global transition to renewable energy, with over 190 countries agreeing to phase down coal and increase investments

in clean energy, signalling a more ubiquitous utilization of renewable energy

A thorough understanding of the underlying factors contributing to the crisis of elevated COz emissions is essential, as it enables policymakers to formulate well-informed and effective policies aimed at mitigating the risk of severe climate consequences that endanger global ecosystems, economic stability, and human well-being Interestingly, all of the aforementioned aspects are key variables that can have an impact on CO:z emissions Therefore, it 1s crucial to conduct

comprehensive studies that investigate the relationship between these variables and COz emissions

to identify the primary drivers behind the unprecedented increase in carbon dioxide levels in 2021 From that, our previously mentioned aim of designing effective policies can be realized

1.1.2 Research into the impact of Economic Growth, Urbanization, Renewable Energy Consumption, Industrialization on Carbon Emissions:

a Researches in other countries:

The study titled “Effect of economic growth on CO2 emissions in developing countries: Evidence from a dynamic panel threshold model” by C Aye and Edoja (2017), after using a dynamic panel threshold model that utilized panel data from 31 developing