We propose solutions and recommendations to mitigate carbon emissions on a global scale, promoting strategies that decouple economic growth from environmental degradation... Testing the
Reason for choosing topic
Climate change, driven by global warming, has become a pressing environmental concern Carbon dioxide emissions are considered major contributors to both climate instability and global warming Understanding the relationship between economic growth and carbon dioxide emissions is crucial in energy economics, as it helps policymakers address this challenge.
Growth in the global economy is a significant element in both production and consumption and is strongly correlated with carbon emissions Moreover, the majority of carbon emissions originate from electricity and heat production, which is a necessary component of transportation, industry, agriculture, and buildings that are directly tied to economic expansion and development
The research team selected "Determinants of Carbon Emissions in G20 Countries from 2017 to 2022" as their research topic This decision was made to investigate factors influencing carbon emissions within G20 nations, which collectively account for approximately 85% of global GDP, 75% of international trade, and roughly two-thirds of the world's population.
Research objectives
Finding the key influencing elements that have a significant impact on carbon emissions is the aim of this research Our research team uses that as the basis for our analysis and proof of the relative importance and influence level of each element for the amount of CO2 emission in G20 countries, and from that recommend some solutions to this situation.
Research subject and Scope
• Research subject: Determinants of carbon emissions in G20 countries from 2009 to
• Scope o in terms of scope: This report uses data regarding one dependent variable (CO2 emissions) and 5 independent variables (population, GDP, FDI, HDI, and forest area) o in terms of time: This report collects yearly data in the period of 12 years from
2009 to 2021 (with a total of 260 observations)
Topic layout
To accomplish the aforementioned objective, our research team will apply knowledge of Econometrics as well as knowledge from allied fields relevant to the economic field to study the relationship between variables in the following chapters:
THEORETICAL BASIS AND LITERATURE REVIEW
Theories related to factors affecting carbon emissions
The Environmental Kuznets Curve typically illustrates the dynamic connection between economic progress and environmental health, showing an inverted U-shaped relationship between average income per capita and pollution levels Initially, as income rises, so does carbon emissions (representing pollution level), reaching a peak before declining while income continues to grow This implies that until the pollution level hits its maximum, economic expansion tends to degrade the environment, but beyond that point, both economic growth and environmental quality can improve In other words, in the former stage of industrialization, pollution escalates due to heightened resource consumption and emissions, causing significant environmental harm Yet, as economies advance in the latter stage, environmental awareness, policies, enforcement, and technology improve, which lead to better environmental conditions
However, the Kuznets curve does not imply that economic growth itself directly reduces environmental harm Rather, it suggests that when combined with effective policies and technological advancements, economic development can eventually enhance environmental quality Nonetheless, the theory faces criticism due to its limited universal applicability, as factors like economic structure, governance, technology, and resource availability can influence the relationship between income and environmental degradation Additionally, it overlooks irreversible ecological damage from certain pollutants and the unequal distribution of environmental impacts across income brackets
In conclusion, while the Environmental Kuznets Curve offers valuable insights into income-environment relations, it should be approached with caution Real-world complexities necessitate comprehensive, context-specific environmental policies to address environmental challenges effectively
1.2 Relationship between current emissions and ambient pollution
Ambient pollution refers to the concentration of various pollutants suspended in the surrounding air These pollutants originate from diverse sources, with current emissions playing a primary role This report explores the mechanisms by which current emissions directly influence ambient air quality
When discussing pollutants, a common classification involves distinguishing between accumulative and non accumulative pollutants, which is based on their persistence and - potential for accumulation in the environment
Accumulative pollutants refer to substances that can persist in the environment for extended periods, accumulating in living organisms or various environmental compartments These pollutants are often characterized by their resistance to degradation or slow natural breakdown processes Examples include Persistent Organic Pollutants, Heavy Metals, and Persistent Inorganic Pollutants
On the contrary, non-accumulative pollutants are substances that do not persist or accumulate in the environment or living organisms They are generally more easily biodegradable or undergo natural degradation processes within a relatively short timeframe Examples include Biodegradable Organic Compounds and Non-toxic or Low-toxicity substances
In Figure 2, Panel (a) illustrates ambient pollution for non-accumulative pollutants, where damages are proportional to current emissions In Panel (b), damages depend on the total stock of pollutant released over time for accumulative pollutants
Carbon dioxide (CO2) is classified as an accumulative pollutant, primarily due to its role in climate change While not persisting in the environment for as long as some other accumulative pollutants, such as persistent organic pollutants or heavy metals, CO2 has the capacity to accumulate in the atmosphere, contributing to long-term changes in global temperature.
RESEARCH METHODOLOGY
Methodology
Based on the previous theoretical basis and studies, our group used the semi-log linear regression model to carry out research proposal and present the relationship of: the dependent variable is Carbon emissions (CO2) and 5 independent variables are Population (POP), forest area (FOR), FDI, GDP, and Human Development Index (HDI)
Method our group uses to derive the model: theoretical basis, mathematical model, statistical model
Method our group use to collect and analyze the data:
• Collected data from 260 observations from 19 countries and 1 regional body (EU) in G20 from 2009 to 2021
• Estimated values using statistical methods for quantitative results with the same number of outputs and inputs
• Compared our regression model results with previous research and studies to figure out the best result
Our research team relied on theoretical foundations and previous experimental studies From there, we build a model to study determinants affecting CO carbon emissions of 2 the G20 countries Besides, our group uses analysis software STATA, Microsoft Excel and Microsoft Word to build standard model and verify model to bring complete and accurate conclusions
The data in the study was collected and aggregated from published data on reputable statistical system websites of global (World Bank, UNDP, …) After reviewing the data, testing and determining its credibility, our research team came to the conclusions on determinants of carbon emissions in G20 countries from 2009 to 2021.
Model specification
Based on the theoretical basis as well as previous research, our team has built the model below to study the determinants of carbon emissions in G20 countries from 2009 to
2021 Realizing that some data in its original form is of substantial value and the difference among variables is quite large, our team has decided to get the natural base logarithm of the variables to represent the field of the data Therefore, we have came up with the following model:
CO2 = bbbbb 𝟎 + bbbbb 𝟏 POP + bbbbb 𝟐 FOR + bbbbb 𝟑 GDP+ !bbbbb 𝟒 HDI + bbbbb 𝟓 FDI +!𝒂 𝒊 + 𝒖 𝒊
10 b bb b b 𝟎 Intercept of the model b b b b b 𝟏 ,bbbbb 𝟐 ,bbbbb 𝟑 ,bbbbb 𝟒 ,bbbbb 𝟓 The regression coefficient of the equivalent variables
𝒂𝒊 Unobserved and unchanged overtime factors
𝒖𝒊 random error, representing the factors affecting the dependent variable that are not included in the model
TESTING AND RESULTS
Data description
Variable Meaning Unit Expected sign
CO2 the amount of carbon emissions of each country metric tons World Bank
POP population of each country people (+) World Bank
GDP Gross Domestic Product of each country
FDI Foreign Direct Investment of each country
HDI The measure of average achievement in key dimensions of human development in each country
FOR the percentage of forest area in each country
% of land area (-) World Bank
The data set in use is panel data To analyze the determinants of global carbon emissions of G20 countries during the period from 2009 to 2021, we utilized data collected from World Bank and UNDP
This study employs a dataset comprising 260 observations categorized into 13 distinct groups The inclusion of these diverse variables, namely population, foreign direct investment (FDI), GDP, Human Development Index (HDI), and forest area, introduces significant heterogeneity into the dataset This variability enables the analysis to capture the varying effects of independent variables on the dependent variable, providing insights into the relationships between these factors.
Analyzing collected data requires an initial understanding of the model and parameters involved Using the sum command in STATA provides insights into the Observations, Mean, Standard Deviation, Minimum, and Maximum values of the variables under examination.
sum C O2 POP FOR GDP HDI FD I
Variable Obs Mean Std dev Min Max
• CO2 : The mean value of carbon emissions among 260 observations from the dataset is 1.52e+09, the standard deviation is 2.31e+09, min value is 1.67e+08 and max value is 1.13e+10 (metric tons)
• POP : The mean value of among 260 observations from the dataset is 2.44e+08, the standard deviation is 3.84e+08, min value is 2.17e+07 and max value is 1.41e+09 (people)
• FOR : The mean value of the forest area among 260 observations from the dataset is 2.55e+13, the standard deviation is 1.02e+14, min value is 10.40076 and max value is 4.54e+14
• GDP : The mean value of GDP among 260 observations from the dataset is
3.80e+12, the standard deviation is 5.02e+12, min value is 3.24e+11 and max value 2.33e+13
• HDI : The mean value of HDI among 260 observations from the dataset is
0.8352009, the standard deviation is 0.09535, min value is 0.562 and max value is 0.987
• FDI : The mean value of FDI among 260 observations from the dataset is
8.23e+10, the standard deviation is 1.40e+11, min value is 3.49e+07 and max value is 8.78e+11
To identify the correlation among the seven variables of the model, we used command corr O the results are illustrated as following:
corr O2 POP FOR GDP HDI FD C I
CO2 POP FOR GDP HDI FDI
• POP has an extremely high correlation coefficient of (+0.7561), and the plus sign indicates a positive impact it has on CO2 ị POPshould be included in the model
• GDP has an extremely high correlation coefficient of (+0.7447), and the plus sign indicates a positive impact it has on CO2 ị GDP should be included in the model
• FOR has a marginal correlation coefficient of (+0.0202), and the plus sign indicates a positive impact it has on CO2 ị FOR should be included in the model
• HDI has a moderately low correlation coefficient of (-0 4881 ), and the minus sign indicates a negative impact it has on CO2 ịHDI should be included in the model
• FDI has a moderately high correlation coefficient of (0 0895 ), and the plus sign indicates a positive impact it has on CO2 ị FDI should be included in the model
Among the independent variables, POPappears to have the greatest impact on CO2 with a correlation coefficient of (+0.7561) Additionally, GDP also has a great impact on CO2 with a second highest correlation coefficient of (+0.7447) Whereas, the correlation coefficient that HDI regarding is the smallest, which suggests an insignificant effect that HDIhave on CO2.
DISCUSSION AND RECOMMENDATIONS
Result discussion
From the corrected RE model, the research team has come to the conclusion that population, HDI and GDP all contribute positively to the increase in the amount of CO2 emissions Among these factors, population seems to have the strongest effect on carbon emissions Forest area and FDI inflows, however, are not statistically significant according to the result
The biggest reason why population, GDP and HDI has a positive effect on CO2 concentration appears to be the large amount of fossil fuels consumption Population growth leads to an increase in consumer demand for food, energy and shelter, which translates into increased activity in sectors like agriculture and industry, which often rely on fossil fuels for production As these industries expand to meet the needs of a larger population, they burn more fossil fuels, releasing more carbon dioxide into the atmosphere
Regarding GDP and HDI, the same rationale is applied As economies grow, which is reflected in high GDP, overall activity increases More goods and services are produced, requiring more energy, mostly from fossil fuels, hence more CO2 released
In addition, high HDI often indicates better living standards and increased consumption People tend to consume more energy, buy more cars, and travel more – all activities that rely heavily on fossil fuels in today's world
However, please note that this result is not applicable to all situations because the analyzed data set does not include data from all countries in the world The Environmental Kuznets Curves (EKC) suggests a more nuanced relationship between GDP, FDI and carbon emissions: a future decline in emissions for developed countries might happen This decrease is due to factors such as shifting industries, technological advancements and environmental awareness As countries get wealthier, their
22 economies might move away from industries (major CO2 emitters) towards service industries, which seem to release less carbon dioxide Wealthier nations can also invest more in cleaner technologies due to their technological advancements, thus reducing their reliance on fossil fuels Additionally, with higher HDI often comes greater environmental consciousness, leading to policies and practices that curb emissions
Nonetheless, EKC is still a hypothesis, and the point at which emissions decline has not yet become universal Some argue that the decline might never happen, or that developed nations simply just outsource their pollution to developing countries In that case, the global carbon emissions could not be considered as declined.
Recommendations and solutions
2.1 Solutions for factors affecting carbon emissions
As the world's population grows, demands for essential resources like food, clothing, and transportation intensify, leading to the depletion of natural resources and increased pollution, particularly CO2 emissions resulting from production and human activities By promoting the concept of limiting families to 1-2 children, we can effectively manage population expansion, reduce carbon emissions, and promote sustainable societal development.
The increase in GDP leads to an increase in CO2 emissions due to increased production and the rapid expansion of factories and industrial sites, leading to air pollution that exacerbates the greenhouse effect and contributes to global warming However, merely reducing GDP isn't a viable solution to lowering CO2 emissions, especially as many countries, particularly developing ones, prioritize economic advancement Consequently, nations must pursue alternative strategies like technological advancements, implementing emission controls, or diversifying energy sources in production to achieve more favorable outcomes for both the economy and the environment.
An increase in Foreign Direct Investment (FDI) often results in higher carbon emissions, as it fuels industrial expansion and infrastructure development, leading to increased production and resource consumption This growth in economic activity leads to heightened air pollution, exacerbating the greenhouse effect and contributing to global warming However, simply curtailing FDI isn't a feasible solution for reducing carbon emissions, particularly as many countries, especially those in the developing world, prioritize attracting foreign investment to drive economic growth Therefore,
23 nations need to adopt alternative measures, such as leveraging FDI to promote sustainable technologies, implementing stringent emission regulations for foreign- owned industries, or encouraging investment in renewable energy sources By pursuing these alternative strategies, countries can achieve more favorable outcomes for both economic development and environmental sustainability
The Human Development Index (HDI) also plays a significant role in influencing carbon emissions As countries strive for higher levels of human development, which include improvements in education, healthcare, and living standards, there is often a corresponding increase in carbon emissions This is because enhanced human development typically leads to greater industrialization, transportation demands, and energy consumption, all of which contribute to carbon emissions However, solely focusing on reducing HDI isn't a feasible solution to mitigate carbon emissions, particularly as many nations prioritize improving human development as a primary goal Instead, countries should pursue alternative approaches, such as investing in clean technology, implementing sustainable transportation solutions, and promoting renewable energy sources, to achieve a balance between human development and environmental sustainability These strategies can lead to more favorable outcomes for both human well being and environmental health.-
To combat escalating deforestation globally, stringent punitive measures and policies are crucial to deter damaging practices Additionally, enhancing afforestation efforts through tree-planting projects is essential Raising public awareness about forest preservation, the perils of littering and pollution, and the myriad benefits of forests is paramount to safeguarding our precious forest ecosystems.
2.2 Recommendations to reduce carbon emissions
Numerous countries, corporations, and entities worldwide have committed to attaining net zero emissions by a designated target year, often set for 2050 or earlier Net zero entails achieving equilibrium between the volume of greenhouse gases released into the atmosphere and those removed from it, representing a pivotal objective in combating climate change When pursuing net zero emissions, whether as a nation, business, or individual, it signifies striving for a balance wherein any emitted greenhouse gases are counterbalanced by an equivalent amount removed or absorbed from the atmosphere
To reach net zero emissions, entities implement diverse measures to minimize their own emissions, including adopting renewable energy sources, enhancing energy efficiency,
24 and embracing sustainable practices Any remaining emissions can be compensated for through actions such as carbon capture and storage, afforestation initiatives (such as tree planting for CO2 absorption), or investment in carbon offset projects
The aspiration for net zero emissions aims to curb global warming and alleviate the adverse impacts of climate change This endeavor involves transitioning away from fossil fuels, embracing cleaner technologies, advocating for sustainable practices, and conserving natural ecosystems that absorb carbon dioxide The concept of net zero represents an essential component of global endeavors to address climate change and forge a sustainable future
Firstly, to ensure the sustainability and continuity of forest management policies and practices, it is essential to conduct research, assess current forestry policies, and propose national adjustments and improvements This will facilitate the gradual enhancement of investment policies and the creation of incentives for both organizations and individuals to engage in forest protection and development
Incentivizing businesses to invest in sustainable technologies is paramount to reducing CO2 emissions Governments can stimulate this investment by providing financial assistance, such as research funding and low-interest loans By encouraging the adoption of environmentally friendly techniques throughout the production process, from raw material selection to consumption, businesses can significantly contribute to mitigating climate change.
Thirdly, educational campaigns should be implemented to raise public awareness about the ramifications of CO2 emissions It's imperative for people to grasp the interrelation between CO2 emissions, population dynamics, forest conservation, energy usage, and economic development This comprehension will facilitate the implementation of appropriate measures based on various influencing factors
It's important for citizens to engage and collaborate with the government to disseminate information and raise awareness about CO2 emissions among the population
Individuals can play a significant role in reducing CO2 emissions by undertaking simple actions such as:
• Walking or cycling instead of using motorized transportation for short distances, including commuting to school or work, not only saving money but also benefiting the environment
• Choosing reusable products over single-use ones to minimize waste Recycling just half of household waste can lead to a reduction of approximately 1.2 tons of CO2 emissions annually
• Employing energy-saving practices such as maximizing natural light, using energy-efficient light bulbs, and powering off electrical devices when not in use to diminish CO2 emissions stemming from fossil fuel combustion
Population, GDP, and HDI significantly impact carbon emissions, while forest area and FDI have minimal effects This study suggests mitigation measures such as alternative energy, population control, deforestation prevention, reduced vehicle use, and technological advancements to address these factors and promote sustainability.
Our research team would like to express our deep and sincere gratitude to PhD Dinh Thi Thanh Binh Econometrics lecturer, who provided us with theoretical knowledge - and valuable support our group in formulating and investigating the research subject
We would also like to thank her for her empathy, patience, and knowledge that she imparts onto us It was a great honor and priviledge to work under her guidance