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

FOREIGN TRADE UNIVERSITY The faculty of International Economics -šš&šš - ECONOMETRICS ASSIGNMENT FACTORS AFFECT THE AMOUNT OF CO2 EMISSIONS IN DEVELOPING COUNTRIES IN 2014 STUDENT NAME – ID: Nguyễn Thu Ngân – 1815520207 Nguyễn Cao Hoài Anh – 1815520151 Nguyễn Minh Anh – 1810520152 CLASS: E6 + E7 – K57 JIB SUPERVISOR: Dr Tu Thuy Anh Hanoi, October, 2019 INDEX PREFACE Chapter 1: LITERATURE REVIEW 1.1 Theories 1.2 Empirical researches Chapter 2: METHODOLOGY 2.1 Methodology used 2.2 Constructing econometrics model 2.3 Data overview 2.4 Data description Chap 3: TEST ASSUMPTIONS & STATISTICAL INFERENCES 11 3.1 Model 12 3.2 Model 15 3.3 Model ……………………………………………………………………………16 Chapter 4: RESULT ANALYSIS AND POLICY IMPLICATIONS 17 4.1 Result analysis 17 4.2 Policy implication 18 CONCLUSION 19 APPENDIX 20 REFERENCES 26 PREFACE In the last three decades, the threat of global warming and climate change has been the major on-going concern for all societies from developing countries to developed countries The principle reason is originated from greenhouse gas effect, of which Carbon dioxide (CO2) is regarded to be the main source Global climate change has altered water supplies and weather patterns, changed the growing season for food crops and threatened coastal communities with increasing sea levels According to our research, in recent years, developing countries is responsible for more than 60% of CO2 emissions due to industrialization and urbanization Facing the challenge to find out solutions to balance between sustainable economic development and harmfulness to the environment, our group decide to examine “Factors affect the amount of CO2 emissions in developing countries in 2014” In the report, we will apply what we learn in Econometrics course to investigate into this matter The report is divided into main parts: Chapter 1: Literature review about the relationship between CO2 emissions and GDP per capita, population growth, energy use Chapter 2: Methodology Chapter 3: Statistical inferences and test assumptions Chapter 4: Result analysis and policy implication Finally, we would like to express our sincere thanks to the dedicated guidance from Mrs Tu Thuy Anh and Mrs Chu Thi Mai Phuong Due to our limited knowledge, there are certainly some deficiencies in our report We look forward to receive comments and suggestions from you to make our research more completely Chapter 1: LITERATURE REVIEW 1.1 Theories 1.1.1 CO2 emissions (metric tons per capita) 1.1.1.1 Definition and roles of CO2 (Carbon dioxide) Carbon dioxide (chemical formula CO2) is a colorless gas with a density about 60% higher than that of dry air Carbon dioxide consists of a carbon atom covalently double bonded to two oxygen atoms It occurs naturally in Earth's atmosphere as a trace gas CO2 one of the most important gases on the earth because plants use it to produce carbohydrates in a process called photosynthesis Since humans and animals depend on plants for food, photosynthesis is necessary for the survival of life on earth However, CO2 can also have negative effects As CO2 builds up in our atmosphere it has a warming effect that could change the earth’s climate Indoors, CO2 levels easily rise above the recommended amount which has adverse effects 1.1.1.2 What is CO2 emission? CO2 emission are those stemming from the burning of fossil fuels and the manufacture of cement They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring 1.1.2 Factors affect CO2 emissions There are a lot of factors that affect CO2 emissions However, in this research, we focus mainly on three significant ones, which are energy use, population growth and GDP per capita 1.1.2.1 Energy use Energy use is the amount of energy or power used (kg of oil equivalent per capita) 1.1.2.2 Population growth Population growth is an increase in the number of people that reside in a country, state, county, or city To determine whether there has been population growth, the following formula is used: (birth rate + immigration) – (death rate + emigration) Businesses and governmental bodies use this information to make determinations about investing in certain communities or regions 1.1.2.3 GDP per capita GDP per capita is a measure of a country's economic output that accounts for its number of people It divides the country's gross domestic product by its total population That makes it a good measurement of a country's standard of living It tells you how prosperous a country feels to each of its citizens 1.1.3 Theories about the relationships between energy use, population growth, GDP per capita and CO2 emissions 1.1.3.1 Energy use and CO2 emissions Sustainable development (SD) implies the balancing of economic and social development with environmental protection: the so-called ‘Three Pillars’ model In the long term, Planet Earth will impose its own constraints on the use of its physical resources and on the absorption of contaminants, whilst the ‘laws’ of the natural sciences (such as those arising from thermodynamics) and human creativity will limit the potential for new technological developments SD is a process or journey toward the destination of ‘sustainability’ It is a key concept when examining energy use and associated emissions, and has foundations in engineering, economics, ecology and social science 1.1.3.2 Population growth and CO2 emissions The impact of population change on environmental stress was posited by Ehrlich (1968) and Holder and Ehrlich (1974) in the form of an equation relating environmental impact to the production of population size, affluence, and environmental impact per unit of economic activity known as “IPAT” IPAT is useful framework for assessing the anthropogenic environmental change, particularly the impacts of population, affluence, and technology on the environmental change (CO2 emissions) 1.1.3.3 GDP per capita and CO2 emissions The Environmental Kuznets Curve hypothesis, it was generally assumed that rich economies destroyed the environment at a faster pace than poorer countries However, with the Environmental Kuznets Curve, the relationship between the environment’s health and the economy has been reanalyzed The idea is that as economic development growth occurs, the environment will worsen until a certain point where the country reaches a specific average income Then money is invested back into the environment, and the ecosystem is restored Critics argue that economic growth doesn’t always lead to a better environment and sometimes the opposite may actually be true 1.2 Empirical researches 1.2.1 Empirical research on effects of energy use on CO2 emissions Thao and Chon [1] stated that energy use has a positive impact on economy, but not to the environment Energy use is widely known as the main reason for global warming and climate change to happen, particularly the consumption of fossil energy The environmental adverse effects of such energy used are not only coming from the energy consumption but also from the exploitation process Meanwhile, the renewable energy consumption has a negative relationship to CO2 emissions, which means that an increase in the consumption of renewable energy will reduce CO2 emissions Additionally, Ito [2] found that fossil energy consumption has a negative impact on economic growth in developing countries, and renewable energy consumption has a positive effect on economic growth In this case, the consumption of fossil energy can cause pollution and environmental damage because the remaining burning of fossil energy is harmful to the environment; while, the renewable energy residue is considered more environmentally friendly Moreover, Shafei and Ruhul [3] who conducted a study on OECD countries on the Kuznets Curve Hypothesis (EKC) between urbanization and CO2 emissions found that nonrenewable energy consumption has a positive relationship to CO2 emissions, which means that an increase in non-renewable energy consumption will increase CO2 emissions In contrast, renewable energy consumption has a negative relationship to CO2 emissions, once again, it consolidates the conclusion that an increase in the consumption of renewable energy will reduce CO2 emissions 1.2.2 Empirical research on effects of population growth on CO2 emissions The role of population pressure on environmental quality can be traced back to the early debate on the relationship between population and natural resources Malthus (1798 [1970]) was concerned with increasing population growth, which put pressure on limited source of land Because of a lower marginal product of labor, the potential growth in food supply could not keep up with that of the population He predicted that if mankind did not exercise preventive checks, population growth would be curtailed by welfare checks (poverty, disease, famine and war) Boserup (1981) held the opposite view, which argues that high population densities were a prerequisite for technological innovation in agriculture The technological innovation made possible the increased yields and more efficient distribution of food It could then enable the natural environment to support a large population at the same level of welfare The impact of population growth on environment quality is obvious Each person in a population makes some demand on the energy for the essentials of life—food, water, clothing, shelter, and so on If all else is equal, the greater the number of people, the greater the demands on energy Birdsall (1992) specified two mechanisms through which population growth could contribute to greenhouse gas emissions First, a larger population could result in increased demand for energy for power, industry, and transportation, hence the increasing fossil fuel emissions Second, population growth could contribute to greenhouse gas emissions through its effect on deforestation The destruction of the forests, changes in land use, and combustion of fuel wood could significantly contribute to greenhouse gas emissions Thus, two questions remain to be addressed fully and empirically: (1) does population pressure have a net impact on carbon dioxide emissions holding constant the affluence and technology? and (2) has population pressure exhibited a greater impact in developing countries than in developed countries? 1.2.3 Empirical research on effects of GDP per capita on CO2 emissions Three Totally Different Environmental/GDP Curves (2012) - In this paper Bratt compares three different theories explaining the connection between environmental degradation and GDP The theories discussed are the Environmental Kuznets curve (EKC), the Brundtland curve and the Daly curve All three hypotheses recognize that the level of GDP affect the environmental degradation, but in different ways The EKC hypothesis argues that an increasing level of GDP would initially increase pollution until a certain level of GDP, at which the level of pollution starts to decrease The relationship between environmental degradation and economic growth is in the case of the EKC graphically shown as an inverted U-shape The Brundtland curve theory provides another picture, where the graphical form is the opposite, U-shaped, which implies the poorest and wealthiest countries to have the highest levels of pollution The Daly curve theory suggests increasing levels of pollution with an increasing GDP that keeps on going, without any turning point Bratt points out that the three different environmental/GDP curves deals with different aspects of environmental degradation The EKC hypothesis could be used when measuring emissions or concentration The Brundtland curve could be used when measuring production and the Daly curve when measuring consumption Bratt’s final conclusion is that even though either curve could be true, the most possible scenario seems to be a positive, monotonic relationship between environmental degradation and GDP In summary, many research studies have been conducted in areas related to this study However, a major part of the researches conducted were on the relationship between CO2 and just one other factor such as GDP per capita, population growth or energy use There are not many existing studies that specifically examine the effect of GDP per capita, energy use, population growth, all together on CO emissions, especially in developing countries In this research we will use the existed data and linear regression to analyze the relationship between CO2 emissions and three other factors: GDP per capita, energy use and population growth Chapter 2: METHODOLOGY 2.1 Methodology used 2.1.1 Methodology in collecting data The collected data are secondary data, mixed data, which indicate information of the fundamental factors concerning the amount of CO2 emissions (metric tons per capita): GDP per capita, energy use, population growth The secondary data were gathered from prestigious and reliable source of information - World Bank 2.1.2 Methodology in processing data Using Gretl in order to process data cursorily then calculate the correlation matrix among variables 2.1.3 Methodology in researching Using Gretl to bring out regression models by using Ordinary Least Squares method (OLS) to estimate the parameter of multi-variables regression models As a result, we can: - Depend on variance inflation factor (VIF) to identify multicollinearity - Test Normality of residual - Use white test to test heteroscedasticity - Conduct Breusch-Godfrey to identify the correlations - Use F-test to evaluate the concordance model - Use T-test to evaluate the confidence interval 2.2 Constructing econometrics model To demonstrate the relationship between the amount of CO2 emissions and other factors, the regression function can be constructed as follows: (PRF): Y=β1+β2EU+β3popgrowth+β4GDPpc+µi (SRF): #= & & & & + EU+ pop-growth+ GDPpc+еi Where: ● Dependent variable: Y - The amount of CO2 emissions, measured in metric tons per capita ● Independent variables: - EU: Energy use, measured in kg of oil equivalent per capita - Researchers found that energy consumption is the long-run causes for CO2 emissions For example, the burning of fossil fuels such as gasoline, coal, oil, natural gas in combustion reactions results in the production of carbon dioxide Pop-growth: Population growth, measured in % - Theoretically, population growth is believed to increase greenhouse gas emissions, particularly CO2 emissions through the increase in human activities GDP pc: GDP per capita, measured in US$ As a country’s GDPpc increases, so does its production of carbon dioxide into the atmosphere Human activity, which often leads to increased GDP such as goods production and services, frequently produces carbon dioxide emissions For example, most goods and services involve some use of energy, often in the form of coal or petroleum Therefore, as the amount of produced goods increases, the amount of fossil fuels spent also increases Exhibition 2.1 Variables explanation Name Dependent variable Y Meaning The amount of CO2 emissions (metric tons per capita) Signal + As a country’s GDP per capita increases, so does its production of carbon dioxide per capita into the atmosphere + Energy consumption is the long-run causes for CO2 emissions + Population growth increases greenhouse gas emissions, particularly CO2 emissions through the increase in human activities Gross Domestic GDP pc Independent EU variables Popgrowth Product per capita The amount of energy or power used (kg of oil equivalent per capita) Population growth is the increase in the number of individuals in a population Explanation 3.1.2.2 Confidence intervals for coefficients Definition: A 95% two-sided confidence interval for the coefficient is the set of values of b that cannot be rejected by a 5% two-sided hypothesis test Using Gretl, we define the confidence interval for coefficients as follows (Ceteris paribus, with 95% confidence interval): - When popgrowth (%) increases by unit, CO2 emission increases by a range from 0.0021% to 0.0025% - When EU (kg of oil equivalent per cap) increases by unit, CO2 emission increases by a range from -0.174kg per capita to 0.188 kg per capita - When GDPpc ($US) increases by unit, CO2 emission increases by a range from 3.22703e-05$ to 0.0001$ 3.1.2.3 Concordance of regression model F-test = 918.8297, p-value = 4.18e-41 < α = 0.05 Therefore, we can conclude that the regression model is concordant 3.1.3 Test Multicollinearity Multicollinearity is the high degree of correlation amongst the explanatory variables, which may make it difficult to separate out the effects of the individual regressors, standard errors may be overestimated and t-value depressed The problem of Multicollinearity can be detected by examining the correlation matrix of regressors and carry out auxiliary regressions amongst them In Gretl, we can examine by using the command View – Correlation Matrix If the correlation between each independent variable is bigger than 80%, there will be high Multicollinearity amongst variables but it does not affect the statistical inference If the correlation amongst independent variable is bigger than 1, there will be a perfect multicollinearity, which affect the estimation result The result from Exhibition 2.2 Correlation Matrix shows that: - The correlation between population growth and energy use is 0.026 The correlation between population growth and GDPpc is 0.015 The correlation between GDPpc and energy use is 0.803 13 The correlation between population growth – energy use and population growth – GDPpc are all smaller than 0.8, while the correlation between GDPpc and energy use is bigger than 0.8, which means it has a high multicollinearity However, as it is not perfect multicollinearity that Multicollinearity is not a significant problem Another way to test the Multicollinearity is to use the Collinearity command in Gretl to know the VIF (variance inflation factor) If VIF is bigger than 10, there will be a perfect multicollinearity The result from Gretl shows: VIF EU = 2.822 < 10 VIF popgrowth = 1.001 < 10 VIF GDPpc = 2.821 < 10 As the VIF is lower than 10, indicating that Multicollinearity is not a worrisome problem for this set of data 3.1.4 Test Autocorrelation Model with cross-sectional data not need to check autocorrelation 3.1.5 Test normality assumption Using the command Normality of Residual in Gretl, the result of p-value = 0.000 As the result shows: p-value = 0.000 < 0.05 It can be concluded that Model does not follow the normal distribution The cause of this problem maybe come from the nature of data About the cure, we see that the number of observations in model is 91 observations As the Central Limit Theorem established, in some situations, when independent random variables are added, their properly normalized sum tends toward a normal distribution (informally a "bell curve") even if the original variables themselves are not normally distributed So that even though in model 1, it does not follow the normal distribution, the estimates will asymptotically be normal 3.1.6 Test Heteroskedasticity Heteroskedasticity indicates that the variance of the error term is not constant, which makes the least squares results no longer efficient and t tests and F tests results may be misleading The problem of Heteroskedasticity can be detected by plotting the residuals against each of the regressors, most popularly the White’s test It can be remedied by looking back to the model – look for other missing variables In Gretl the White’s test command is used As the result shows, the p-value = 0.0046 14 At the 5% significance level, there is enough evidence (p-value = 0.0046 < 0.05) to conclude that this set of data meets the problem of Heteroskedasticity To fix the problem, we decide to use the logarithm of dependent variable (CO2 emissions) and independent variables (EU and GDPpc), which will be specified in the Model To sum up, it can be said that Model is not perfect for estimation as it indicates the Heteroskedasticity, so that we decide to establish another model (log-log model) 3.2 Model Based on the data collected, the sample regression function is established: (SRF): = -8.426 + 0.99*l_EU - 0.052*popgrowth + 0.276*l_GDPpc 3.2.1 Test Multicollinearity Using the command Collinearity in Gretl, we have the result as follows: VIF = 1.039 < 10 VIF = 3.585 < 10 VIF = 3.632 < 10 As the VIF is lower than 10, indicating that Multicollinearity is not a worrisome energyuse popgrowth GDPpc problem for this set of data 3.2.2 Test Autocorrelation: model with cross-sectional data not need to check autocorrelation 3.2.3 Test normality assumption Using the command Normality of Residual in Gretl, the result shows that: p-value = 0.07445 > 0.05 => Abnormal distribution is fixed when we use model 3.2.4 Test Heteroskedasticity Heteroskedasticity indicates that the variance of the error term is not constant, which makes the least squares results no longer efficient; t tests and F tests results may be misleading The problem of Heteroskedasticity can be detected by plotting the residuals against each of the regressors, most popularly the White’s test In order to fix heteroskedasticity, we use log transformation method In this case, we will take log for CO2 emissions, GDP per capita and energy use, as their units are not % Using Gretl, we have the result that p-value = 0.022445 < α = 0.05 15 Therefore, the model indicates heteroskedasticity To fix this, we decide to use the OLS model with Robust standard errors as specified in Model 3.3 Model (using Robust standard errors) Check Heteroskedasticity: Using Gretl, we have the result that p-value = 0.00463 < α = 0.05, which shows that the model indicates heteroskedasticity However, as we use Robust standard errors, which are not related to residuals, that it does not affect the estimated result From the regression model using Robust standard errors, we draw a conclusion: In 2014, the amount of CO2 emissions (metric tons per capita) in developing countries around the world was affected by GDP per capita, population growth rate and the amount of energy use per capita, as described in the model 3: , = -0.378 + 0.002*EU + 0.007*popgrowth + (7.919e-05)*GDPpc The model is consistent with all the assumptions (Multicollinearity, Autocorrelation, Heteroskedasticity, Normality Residuals) and has statistical significance The research process helps to answer the questions raised in the preface: How GDP per capita, population growth and energy use affect CO2 emissions in developing countries in 2014 16 Chapter 4: RESULT ANALYSIS AND POLICY IMPLICATIONS 4.1 Result analysis From the estimator result, GDP per capita and energy use are variables that are significant to the amount of CO2 emissions The correlation between GDP per capita and CO2 emissions is positive, which meets the expectation that GDP per cap is a factor affects CO2 emissions, following the EKC hypothesis This can be explained that almost all developing countries had not reached the peak of Kuznets Curve in 2014, so that as GDP per capita increases, so does the amount of CO2 emissions per capita The coefficient of GDPpc is (7.919e-05), which stands for: If the GDP per capita increases by $1, the amount of CO2 emissions increases by 7.919.10^(-5) metric tons per capita It can be understood that human activities, such as producing goods production and services (which involve use of energy in form of oil, coal, petroleum), which often leads to increased GDP, frequently produce CO2 emissions In 2014, as the global economy stably developed, especially in developing countries that as the amount of produced goods increase, the amount of fossil fuels spent also increases The correlation between the amount of energy use and CO2 emissions is positive, which meets the expectation that energy use per capita is a factor affects the amount of CO2 emissions, following the “sustainable development” This correlation is consistent with the empirical researches about the relation between energy use and environment, such as: Thao and Chon (2016), Ito (2017), Shafei and Ruhul (2013) The coefficient of EU is 0.002, which stands for: If the amount of energy use increases by kg per capita, the amount of CO2 emissions increases by 0.002 metric tons per capita In 2014, the resources expoilation activities in developing countries are popular, which leads to a rise on CO2 emissions as well Besides, it can be concluded that the coefficient of independent variable population growth is not significant to the amount of CO2 emissions To explain for this, we believe that the Ordinary Least Square is not a suitable model to research about the relation between population growth rate and the amount of CO2 emissions, which results in an erroneous estimation result Additionally, the data we use are cross-sectional in nature, so that our study cannot address the issue of whether the impact of population growth on emissions could vary across countries with different levels of economic development, even though they are all developing countries 17 4.2 Policy implication From the estimated result, it can be said that in order to reduce the amount of CO2 emissions, we have to pay attention to the amount of factors which affect it Firstly, with GDP per capita, we know that not only in 2014 but also in recent days, economic development is one of the top priorities in every countries In developing countries, as the hypothesis Environmental Kuznets Curve showed, because these countries are on the way to develop but did not reach the peak of development yet, means that the keep-rising GDP per capita necessarily leads to an increase in CO2 emissions Therefor, the amount of CO2 will only decrease once they reach the peak of economic development To achieve that, it will be a long process for all developing countries This raises needs for economic development policies from government to swiftly increase GDP per cap, such as: take advantage of global open market; make policies in management to encourage business; solve political problems; practise family-planning; pay more attention to education… In addition, about the energy use, recently exploitation of resources and energy is a way that many developing countries use to make economic development The result from our estimator could be an evidence for countries’ government to examine their national energy consumption, the dependence of economy on energy and the trade of between energy – environment It is impossible to prevent these exploitation activities, however, there are ways to exploit it efficiently, which is known as “energy efficiency” Varying from countries, government should adopt the appropriated policies, such as: Energy star federal tax credits for consumer energy efficiency; use renewable energy sources; green economy development to ensure sustainaility… These are some of the implications that we can point out from the estimated result There are stills shortcomings in our research process, such as the lack of time and the limitation of our knowledge, that we could not find out a suitable model to estimate the impact of population growth on CO2 emissions 18 CONCLUSION The above research has given us a clear view on the effects of population growth, energy use and GDP per capita to the amount of CO2 emissions in 2014 From the model examination, we have comprehensive assessments about the influence of each variable, its meaning to the dependent variable This result is also the evidence for governments of developing countries to evaluate the amount of CO2 emissions emitted to the environment, which demonstrates the necessary role for developing countries in enabling transitions to the low-carbon economy needed to limit global temperature Developing countries can be motivated to engage in more ambitious emission reductions by eliminating fossil fuel subsidies, utilizing novel technology options like soil carbon capture… However, the focus on developing countries should not take interest away from what developed countries can and should to meet their targets International policies calling for transitions to low-carbon development are required to be cognizant of the inequalities that underlie the global economic order, where developing countries are the most disadvantaged due to limited capacity and technology To ensure that a balance between developed and developing county commitments and efforts is achieved, climate policies that encapsulate the different principles that have proven to be effective will have to be adopted 19 APPENDIX Appendix (Model 1) Ordinary Least Square regressor running Model 1: OLS, using observations 1-81 Dependent variable: CO2 Coefficient Std Error t-ratio p-value const −0.378199 0.212392 −1.781 0.0789 EU 0.00234346 8.22578e-05 28.49 10.0 may indicate a collinearity problem EU popgrowth GDPpc 2.822 1.001 2.821 VIF(j) = 1/(1 - R(j)^2), where R(j) is the multiple correlation coefficient between variable j and the other independent variables Belsley-Kuh-Welsch collinearity diagnostics: lambda cond - variance proportions const EU popgrowth 20 GDPpc 2.947 0.705 0.235 0.113 1.000 2.045 3.539 5.114 0.030 0.054 0.863 0.053 0.019 0.078 0.050 0.853 0.032 0.296 0.665 0.007 0.018 0.050 0.001 0.931 lambda = eigenvalues of X'X, largest to smallest cond = condition index note: variance proportions columns sum to 1.0 Appendix (Model 1) Command Test – Normality of Residual on Gretl Frequency distribution for uhat1, obs 1-81 number of bins = 9, mean = -2.9277e-15, sd = 1.10727 interval midpt frequency rel cum 1.23% -3.4701 < -3.4701 -3.9255 1.23% - -2.5592 -2.5592 - -1.6484 -1.6484 - -0.73764 -0.73764 - 0.17317 **************** 0.17317 - 1.0840 1.0840 - 1.9948 1.9948 - 2.9056 >= 2.9056 -3.0147 -2.1038 -1.1930 -0.28224 0.62857 37 22 1.23% 2.47% 9.88% 45.68% 27.16% 1.5394 2.4502 3.3610 7.41% 3.70% 1.23% Test for null hypothesis of normal distribution: Chi-square(2) = 25.937 with p-value 0.00000 21 2.47% 4.94% *** 14.81% 60.49% 87.65% ********* 95.06% ** 98.77% * 100.00% Appendix (Model 1) Command Test – Heteroskedasticity – White’s test on Gretl White's test for heteroskedasticity OLS, using observations 1-81 Dependent variable: uhat^2 coefficient std error t-ratio p-value const −1.89562 0.865094 −2.191 0.0317 EU 0.00126866 0.000644878 1.967 0.0531 popgrowth 1.12496 0.420134 2.678 0.0092 GDPpc 0.000135130 0.000132093 1.023 0.3098 sq_EU −8.75517e-08 5.57872e-08 −1.569 0.1210 X2_X3 0.000183496 0.000248911 0.7372 0.4634 X2_X4 5.68806e-11 5.12568e-08 0.001110 0.9991 sq_popgrowth −0.179590 0.100455 −1.788 0.0781 X3_X4 −5.85932e-05 5.30745e-05 −1.104 0.2733 sq_GDPpc −2.91815e-09 8.42358e-09 −0.3464 0.7300 ** * *** * Unadjusted R-squared = 0.293822 Test statistic: TR^2 = 23.799557, with p-value = P(Chi-square(9) > 23.799557) = 0.004630 Appendix (Model 2) OLS Regressor Running Model 2: OLS, using observations 1-81 Dependent variable: l_CO2 Coefficient Std Error t-ratio p-value const −8.42630 0.378054 −22.29 19.340984) = 0.022445 24 Appendix 10 (Model 3) OLS using Heteroskedasticity – Robust standard errors Model 3: OLS, using observations 1-81 Dependent variable: CO2 Heteroskedasticity-robust standard errors, variant HC1 const EU popgrowth GDPpc Coefficient −0.378199 0.00234346 0.00713192 7.91894e-05 Mean dependent var Sum squared resid R-squared F(3, 77) Log-likelihood Schwarz criterion Std Error 0.170401 0.000112025 0.0891795 2.96012e-05 4.882716 94.40630 0.972825 476.8120 −121.1369 259.8517 t-ratio −2.219 20.92 0.07997 2.675 p-value 0.0294