Abstract 4 I. Introduction 5 II. Literature review 6 II.1. Theories related to factors affecting carbon emissions 6 II.1.1. Environmental Kuznets Curve (EKC) 6 II.1.2. Relationship between current emissions and ambient pollution 7 II.2. Related published researches 8 II.3. Research Limitations 9 III. Methodology 10 III.1. Methodology 10 III.2. Model specification 10 III.3. Explaining the variables 12 III.4. Theoretical relationship and supporting researches 12 IV. Results and testing 13 IV.1. Data description 13 IV.1.1. Sum 13 IV.1.2. Correlation matrix between the variables 14 IV.1.3. Selecting proper model 15 IV.2. Testing the model 17 IV.2.1. Test for Multicollinearity 17 IV.2.2. Test for Heteroskedasticity 18 IV.2.3. Test for Autocorrelation 18 IV.2.4. Fixing the problems in the model 20 IV.2.5. Test for the significance of the coefficients 21 V. Recommendations and solutions 22 V.1. Solutions for factor affecting carbon emissions 22 V.2. Recommendations to reduce carbon emissions 24 VI. Conclusion 26 References 27 Do File 28 Data table 30 Abstract Carbon dioxide is an important greenhouse gas that helps to trap heat in our atmosphere. Without it, our planet would be inhospitably cold. However, an increase in CO2 concentrations in our atmosphere is causing average global temperatures to rise, disrupting other aspects of Earths climate. Human activities had brought tremendous impacts on Co2 emissions and transformed it into hazardous issues threatening our planet. This paper aims to statistically estimate the factors resulting in the rise of carbon emission during the second decade of the twentyfirst century (20102019). Utilizing the semilog linear regression model along with data acquired from various trustworthy sources, we have examine the statistics of 104 nations from different regions in the world to determine the relationship of carbon dioxide (CO2) emission with five factors influencing it which are population, forest area, renewable energy, GDP, and vehicle on use. By incorporating evidence from various related articles
https://tailieuluatkinhte.com/ FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS *** ECONOMETRICS REPORT DETERMINANTS OF GLOBAL CARBON EMISSIONS FROM 2010 TO 2019 Name: Group Class: KTEE318 Instructor: MS Đinh Thanh Binh Hanoi, June 2023 ECONOMETRICS II REPORT KTEE318 GROUP Abstract I Introduction II Literature review II.1 Theories related to factors affecting carbon emissions II.1.1 Environmental Kuznets Curve (EKC) II.1.2 Relationship between current emissions and ambient pollution II.2 Related published researches II.3 Research Limitations III Methodology III.1 Methodology III.2 Model specification III.3 Explaining the variables III.4 Theoretical relationship and supporting researches IV Results and testing IV.1 Data description IV.1.1 Sum IV.1.2 Correlation matrix between the variables IV.1.3 Selecting proper model IV.2 Testing the model IV.2.1 Test for Multicollinearity IV.2.2 Test for Heteroskedasticity IV.2.3 Test for Autocorrelation IV.2.4 Fixing the problems in the model IV.2.5 Test for the significance of the coefficients V Recommendations and solutions V.1 Solutions for factor affecting carbon emissions V.2 Recommendations to reduce carbon emissions VI Conclusion References Do File Data table ECONOMETRICS II REPORT KTEE318 GROUP Abstract Carbon dioxide is an important greenhouse gas that helps to trap heat in our atmosphere Without it, our planet would be inhospitably cold However, an increase in CO concentrations in our atmosphere is causing average global temperatures to rise, disrupting other aspects of Earth's climate Human activities had brought tremendous impacts on Co2 emissions and transformed it into hazardous issues threatening our planet This paper aims to statistically estimate the factors resulting in the rise of carbon emission during the second decade of the twenty-first century (2010-2019) Utilizing the semi-log linear regression model along with data acquired from various trustworthy sources, we have examine the statistics of 104 nations from different regions in the world to determine the relationship of carbon dioxide (CO2) emission with five factors influencing it which are population, forest area, renewable energy, GDP, and vehicle on use By incorporating evidence from various related articles along with our own research method, we come to the conclusion that GDP, number of vehicles in use and population has shown a medium-to-strong correlation to the amount of carbon emission Along with that, our group has included recommendations that should be taken in order to combat the effects deprived from the rise in carbon emission. ECONOMETRICS II REPORT I KTEE318 GROUP Introduction Ever since the first Industrial Revolution, humanity has come a long way in advancing our technology and improving the lifestyle of our species like never before However, along with our achievements in technology and society, another problem has risen: the preservation of the natural environment The excessive use of fossil fuel to power our daily lifestyle resulted in the rise in carbon emission leading to a phenomenon called “global warming” In this study, we will describe the factors affecting carbon emission and prove the relationship and influence level of each of these factors to the amount of CO2 emission Our research scope includes parts: content scope, time scope and spatial scope The content scope is researching factors affecting carbon emission The time scope of the data in this research is from 2010 to 2019 The spatial scope includes 104 countries from all around the world This research consists of main sections In section 1, we present the definition of all research objects mentioned in the research, the economic theories related to the research, other related researches and the research hypothesis In section 2, we mention parts: the methodology to derive the model and analyze the data, the theoretical model specification and describing the data In the last section, we strive to fix the model errors and estimate the model result, from there we recommend some detailed solutions toward each factor ECONOMETRICS II REPORT II KTEE318 GROUP Literature review II.1 Theories related to factors affecting carbon emissions II.1.1 Environmental Kuznets Curve (EKC) The relationship between environmental quality and economic growth over time is normally represented in Kuznets environment curve, the logic of which is average income per capita and pollution level taking an inverted U shape Carbon emissions (representing pollution level) will increase in tandem with the increase of income to a certain extent, reaching its maximum value before declining while income continues on the rise This means as long as its value is still below the maximum, economic growth will entail degradation of environmental quality, but beyond that maximal point, both will ascend In other words, in the initial stage of the industrialization process, pollution rapidly augments due to the increased use of natural resources and increased emission of pollutants, causing serious environmental degradation In the later stages of the industrialization process, when income elevates, people are better aware of environmental protection, environmental policies and legislation as well as enforcement agencies are stricter, more advanced technologies are applied, etc., then environmental quality will improve. ECONOMETRICS II REPORT KTEE318 GROUP However, the environmental Kuznets curve does not imply that economic growth itself directly reduces environmental degradation Instead, it suggests that economic development, combined with appropriate policies and technological advancements, can eventually lead to an improvement in environmental quality It's important to note that the EKC theory has been subject to various criticisms and has mixed empirical support Critics argue that the relationship between income and environmental degradation is not universally applicable and can be influenced by a range of factors, such as the structure of the economy, governance, technological advancements, and resource availability Moreover, the EKC does not consider the potential irreversible ecological damage caused by certain pollutants or the unequal distribution of environmental impacts across different income groups In conclusion, the EKC serves as a useful framework for understanding the relationship between income and environmental degradation, but it should be interpreted with caution, considering the complexities of real-world dynamics and the need for comprehensive and context-specific environmental policies II.1.2 Relationship between current emissions and ambient pollution When discussing pollutants, a common classification is to distinguish between accumulative pollutants and non-accumulative pollutants These classifications are based on the persistence and potential for accumulation of pollutants in the environment Accumulate pollutants are substances that have the ability to persist in the environment for long periods and tend to accumulate in living organisms or various environmental ECONOMETRICS II REPORT KTEE318 GROUP compartments These pollutants are often characterized by their resistance to degradation or slow natural breakdown processes Examples of accumulative pollutants include: Persistent organic pollutants, Heavy metal, Persistent inorganic pollutants On the other hand, non-accumulative pollutants are substances that not tend to persist in the environment or accumulate in living organisms They are generally more easily biodegradable or undergo natural degradation processes within a relatively short timeframe Examples of non-accumulative pollutants include: Biodegradable organic compounds and Non-toxic or Low-toxicity substances Panel (a) in Figure denotes ambient pollution in case of non-accumulative pollutant in which damages are proportional to current emissions Whereas in panel (b), damages are dependent on the total stock of pollutant that has been released over time in the case of accumulative pollutants Carbon dioxide (CO2) is considered an accumulative pollutant, primarily due to its role in climate change While it is not persistent in the environment for long periods like some other accumulative pollutants, such as persistent organic pollutants or heavy metals, CO2 has the capacity to accumulate in the atmosphere and contribute to long-term changes in global temperature II.2 Related published researches The discussion of how to promote “green growth” is rising to the top of the policy agenda for every national leader as a result of the burning issue in recognition of the major challenges presented by global warming and climate change derived from carbon dioxide emissions Therefore, this topic has raised attention for many researchers for years and some featured researches can be listed out such as: Determinants of CO2 Emissions: A Global Evidence (Jeremiás Máté Balogh and Attila Jambor, 2017) established a link between carbon dioxide emissions and its various reasons by employing a complex model comprising economic growth, industrial structure, FDI, energy use, trade and agriculture globally The research employed GMM models on a panel dataset and tested the result by the standard environmental Kuznets curve hypothesis As a result, the research concluded that financial development reduced air pollution Theoretical and Empirical Analyses on the factors affecting Carbon Emissions: Case of Zhejiang Province, China (Shaolong Zeng & Minglin Wang, 2022) made a theoretical analysis and established the motion trajectory of carbon emissions in order to contribute in logical understanding of the factors affecting carbon emissions Based on time series data ECONOMETRICS II REPORT KTEE318 GROUP and some methods such as linear regression, ARDL model analysis, vector autoregression (VAR) analysis, the empirical results show that there is a long-run relationship between AGDP, TECH, TRADE, KL, and AC, while in the short-run, the effect of TRADE is elastic and positively significant.The research concluded that the upgrading of the industrial structure is essential to break the solidified energy consumption mode. Factors Affecting CO2 Emissions in the BRICS Countries: A Panel Data Analysis (Zakarya, Mohammed Abbes and Seghir, 2015) analyzed the interactions that existed between total energy consumption, FDI, economic growth and the emissions of CO2 in the BRICS countries, using the co-integration tests and panel Granger causality in panel The result indicated the existence of a unidirectional causality from CO2 to the independent variables, and helped decision makers in these countries to understand and grasp the complexity of this phenomenon II.3 Research Limitations Studies on domestic and foreign CO2 emission are various However, some research papers are based on qualitative and personal opinions, some only take data from a few countries that limit the result precision Therefore, these studies can only have an exiguous perspective, it has not been shown that countries can encourage and support each other in CO2 emissions reduction ECONOMETRICS II REPORT III KTEE318 GROUP Methodology III.1 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 independent variables are population (pop), forest area (forest), renewable energy (ener), GDP, and vehicle on use (veh) 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 1040 observations from 104 countries from 2010 to 2019 ● 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 Knowledge and subject that has been applied: econometrics, macroeconomics and quantitative methods, environmental economics Tools for calculation and synthesis: STATA software, Microsoft Excel, Microsoft Word, Google Docs Data sources are collected and aggregated from published data on reputable statistical system websites of global (World Bank, Worldometer, OECD, OICA, ) III.2 Model specification In order to build an econometric model, it is pivotal to identify the factors related to the interaction and description of economic variables The models are often suggested by economics To compute and analyze the output, we choose the statistical method in these fields, which is the estimation and verification of the hypothesis Realizing that some data in its original form is of substantial value, our group decided to get the natural base logarithm of some data fields to represent the field of that data Therefore, our group proposes the following research model: Population regression model: ECONOMETRICS II REPORT KTEE318 GROUP 𝐥𝐧(Co2) = 𝜷𝟎 + 𝜷1∗ 𝐥𝐧(ener) + 𝜷𝟐 ∗ 𝐥𝐧(GDP) + 𝜷𝟑 ∗ 𝐥𝐧(forest) + 𝜷𝟒 ∗ 𝐥𝐧(pop) + 𝜷𝟓 ∗ veh + ci + 𝒖i Where: ● 𝜷0: intercept of the model ● 𝜷j (j = 1,2,3,4,5): the regression coefficient of the equivalent independent variables ● ci: unobserved factors ● ui: random error, representing the factors affecting the dependent variable that are not included in the model