tiểu luận kinh tế lượng analysing certain factors that affect air pollution

FOREIGN TRADE UNIVERSITY ECONOMETRICS REPORT Analysing certain factors that affect air pollution Tutor: MSc Quynh Thuy Nguyen Class: KTEE218 (1-1920).1_LT Ha Noi, September 2019 Foreign Trade University ECONOMETRICS REPORT Tutor: Msc Quynh Thuy Nguyen Class: KTEE218(1-1920).1_LT Group members: Phạm Đức Thịnh – 1814450065 Nguyễn Gia Dương – 1814450024 Nguyễn Quang Huy – 1814450043 Nguyễn Anh Đức – 1814450020 Bạch Quốc Hoàng – 1814450039 Ha Noi, September 2019 1| INTRODUCTION CONTENTS I DATA DESCRIPTION REGRESSION MODEL FORMATION DATA DEPICTION II REGRESSION ANALYSIS REGRESSION FUNCTION CORRELATIONS ANALYSIS REGRESSION MODEL EXAMINATIONS 11 III EXAMINATION OF VIOLATED ESTIMATIONS IN THE MODEL 12 Normal distribution 12 Multicollinearity .15 CONCLUSION 16 Referrence: 17 2| INTRODUCTION Under the pressure of enviromental destruction, air pollution also one of the most significant contribution of humannity to our own habitat and natural world Air pollution occurs when harmful or excessive quantities of substances including gases, particles, and biological molecules are introduced into Earth's atmosphere It may cause diseases, allergies and even death to humans; it may also cause harm to other living organisms such as animals and food crops, and may damage the natural or built environment Both human activity and natural processes can generate air pollution Indoor air pollution and poor urban air quality are listed as two of the world's worst toxic pollution problems in the 2008 Blacksmith Institute World's Worst Polluted Places report Outdoor air pollution alone causes 2.1 to 4.21 million premature deaths annually According to the 2014 World Health Organization report, air pollution in 2012 caused the deaths of around million people worldwide, an estimate roughly echoed by the International Energy Agency Understanding the urgency of this phenomenon, our group decide to choose the topic: “Analysing certain factors that affect air pollution” for our econometrics assignment With the tools of econometrics, we can easily set a most suitable model to optimize analysis process because the econometric relationships depict the random behaviour of economic relationships which are generally not considered in economics and mathematical formulations Unanimously, we have decided to select these factors (or variables) to describe the scale of air pollution which is measured by AQI: HPI (Happy Planet Index), GDP per capita, Oil 3| consumtion per day and population density of 35 countries and their biggest cities over whole world in the 2016-2019 period We would give our farmost appreciation to our lecturer who also our tutor for helpful conduction Throuhout the progression of making this report, we have give our best effort but mistakes are unavoidable If they are significant, please let us know CONTENTS I DATA DESCRIPTION REGRESSION MODEL FORMATION ➢ Dependent variable: Out put: Level of air pollution measured by AQI (Air Quality Index) It is used by government agencies to communicate to the public how polluted the air currently is or how polluted it is forecast to become AQI is computed by these components: PM2.5 (small particle), PM10 (large particle), Ozon, CO and NO It’s the most precisive statistical figure to measure air pollution level ➢ o Y = AQI Independent variables: o X1 : HPI (Happy Planet Index) o X2 : Gper (GDP per capita) o X3 : Ocons (Oil consumption per day) 4| o X4 : Dens (People per sq kilometer) POPULATION REGRESSION MODEL (PRM) = + + + + + The model studies about depandence between the level of air pollution of representing cities amongst 35 nations and their Happy planet Index, GDP per capita (USD), Oil consumption per day (thousand barrels), population density (people per sq.kilometer) • X1: HPI_ Happy Planet Index measures what matters: sustainable wellbeing for all It tells us how well nations are doing at achieving long, happy, sustainable lives This variable was added in order to test whether people with high life expectancy, wellbeing, low inequality would show positive impact on their living enviroment or not • X2: Gper_ GDP per capita (USD) This variable is a most convenient statistical norm to assess the economic growth for international comparation or productivity per captia on the period of time We would like to find out the relation between people’s productivity and air pollution level • X3: Ocons_Oil Consumption (thousand barrels/day): This variable has certain effect on output AQI components are made of emissions from oil consuming activities of human: vehicles powering, manufactureing operation… • X4: Dens_ Population density The higher density the more exploitation of natural resources.A dense area obviously faces with 5| more social issues, which included air pollution However, we still wanna test the significant level of this variables DATA DEPICTION o Using STATA 12 for model description with des command, We have collected this result: des aqi hpi Gper Ocons Dens storage display value variable name type format label variable label -aqi hpi int %8.0g float %8.0g AQI HPI Gper long %8.0g GDP per capita Ocons float %8.0g Oil Consumtion Dens long %8.0g Population Density o We continue use “sum” command for data descripton “sum” has shown us number of observation (Obs), mean, standard deviation (std dev.), and also maximum value (Max), minimum value (Min) of variables sum aqi hpi Gper Ocons Dens 6| Variable | Obs Mean Std Dev Min Max aqi | 148 55.03378 35.07532 16 188 hpi | 148 6.302273 15.9 40.7 -+ 28.9 Gper | 148 29417.34 21348.31 1923 82773 Ocons | 148 2224.618 3732.938 141.1 20094 Dens | 148 5928.304 5100.263 864 46781 o It can be seen clearly from the table that the difference of level between the largest (82773) and the smallest (1923) of Gper variable, which is also the highest statistical figure from the rest o Along with Gper, Ocons and Dens also witnessed a noticable disparity between the minimum value and maximum value The reason is our target are random countries around whole world included developed and under-developed nations II REGRESSION ANALYSIS REGRESSION FUNCTION a Population ( | regression, function)=+(PRF):+ + + 14011223344 7| + = + + + + b Sample regression function (SRF): 1 2 3 4 CORRELATIONS ANALYSIS ➢ Using “corr” comand to test the correlation of variables Y = aqi X1 = hpi, X2 = Gper, X3 = Ocons, X4 = Dens corr aqi hpi Gper Ocons Dens (obs=148) | aqi hpi + - Gper Ocons Dens aqi |1.0000 hpi | -0.1581 1.0000 Gper | -0.5124 -0.0516 1.0000 Ocons | 0.2017 -0.2333 0.0702 1.0000 Dens | 0.1935 0.1305 -0.2629 0.1231 1.0000 ➢ Explaining variables relationship: - Correlative coefficient between aqi and hpi is -0.1581 - Correlative coefficient between aqi and Gper is -0.5124 - Correlative coefficient between aqi and Ocons is 0.2017 8| - Correlative coefficient between aqi and Dens is 0.1935 According to the figures from the table, there are no coefficient greater than 0.8 ➔ the multicollinearity didn’t occur in our model EXPOSING REGRESSION FUNCTION AND RESULTS a Run regression model diagnosis: Using “reg” command to run regression model dianosis in STATA Y = aqi X1 = hpi, X2 = Gper, X3 = Ocons, X4 = Dens reg aqi hpi Gper Ocons Dens Source | SS df MS Number of obs = 148 F( 4, 143) = 18.37 15347.4244 Prob > F -+ Model | 61389.6976 Residual | 119461.134 143 835.392542 R-squared -+ Total | 180850.831 = 0.0000 = 0.3394 Adj R-squared = 0.3210 147 1230.27776 Root MSE = 28.903 -aqi | Coef Std Err t P>|t| [95% Conf Interval] -+ hpi | -.8097223 3944118 -2.05 0.042 -1.589353 -.0300916 Gper | -.0008548 0001164 -7.34 0.000 -.001085 -.0006247 Ocons | 0018599 000669 2.78 0.006 0005375 Dens | 0003528 0004963 0.71 0.478 -.0006282 _cons | 97.35311 12.57025 7.74 0.000 72.50559 0031823 0013338 122.2006 9| (Table 1) b Linear Regression function: With result from STATA, we have LRF: aqi = 97.35311 - 0.8097223 hpi – 0.0008548 Gper + 0.0018599 Ocons + 0.0003528 Dens + ui c Explaining results: Regression Coefficient Regression coeficient Meanings Ceteris paribus, when hpi increase value 0 Estimator of Ceteris paribus, when Dens increase by unit then aqi increase by the amount of 0.0003528, which means the increase in population density lead to the rise in air polluton level 10| R2 = 0.3394 : Independent variables can explain 33.94% of dependent variable’s fluctuation REGRESSION MODEL EXAMINATIONS a Testing the coincidence of model: ➢ Method of using critical value : : 2=0( = Considering this hypothesis: - = = = ) : ≠0 { - Using the “test” command to test the hypothesis: test hpi Gper Ocons Dens ( 1) hpi = ( 2) Gper = ( 3) Ocons = ( 4) Dens = F( 4, 143) = 18.37 Prob > F = 0.0000 di Ftail(4, 143, 05) 99526432 F(4, H0 143) = 18.37 > (4,143) = 0.99526432 → Reject - Conclusion: Regression model coincide with sample ➢ Method of using P-value: 11| : 2=0( = Considering this hypothesis: { = = = ) - : ≠0 From Table 1, we have P–Value = 0.0000 < = 0.05 → Reject H0 Conclusion: Regression model coincide with sample b Testing the regression coefficients ➢ Level of significants: i = 0.05 Variables P-Value Statiscal Conclusion significance hpi 0.042 < Yes Happy planet index afftect the air quality Gper 0.000 < Yes GDP per capita affect the air quality Ocons 0.006 < Yes Oil consumption per day affect the air quality Dens 0.478 > No Population density has no effect on air pollution level III EXAMINATION OF VIOLATED ESTIMATIONS IN THE MODEL Normal distribution : Testing hypothesis: { : ′ 12| Method 1: Testing normal distribution by graphs 50 AQI100 01 005 Density 015 02 150 200 ➢ 50 100 AQI 150 200 10 Frequency 15 20 The graphs illustrated that AQI are seem like coutinuously normal distributed variable (Bell-shaped distribution) - Can not draw conclusion ➢ Method 2: Testing normal distributtion by considering Skewness & Kurtosis values - A fundamental task in many statistical analyses is to characterize the location and variability of a data set A further characterization of the data includes skewness and kurtosis Skewness is a measure of symmetry, or more precisely, the lack of symmetry A distribution, or data set, is symmetric if it looks the same to the left and right of the center point Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution That is, data sets with high kurtosis tend to have heavy tails, or outliers Data sets with low kurtosis tend to have light tails, or lack of outliers A uniform distribution would be the extreme case A variables has normal distribution whether the skewness and kurtosis value almost equal or • Variable description with STATA: AQI 13| Percentiles 1% 18 Smallest 16 5% 23 18 10% 25 19 Obs 148 25% 30.5 21 Sum of Wgt 148 50% 41.5 Mean Largest 55.03378 Std Dev 75% 67 162 90% 108 165 Variance 95% 126 165 Skewness 99% 165 188 Kurtosis 35.07532 1230.278 1.597139 5.162938 • Estimating values: sktest aqi Skewness/Kurtosis tests for Normality - joint -Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -+ -aqi | 148 0.0000 From estimated result of AQI variables_Air =quality0.05 index,→ p-value of skewness and kurtosis estimation are both smaller than ➔ 0.0008 37.18 0.0000 Must reject H0 Regression model doesn’t has normal distribution 14| However, with number of observations n = 148, we believe model could given acceptable outputs and could be used to infer statistics Multicollinearity a b CORRELATIVE ANALYSIS: presented in II.2 TESTING VARIANCE =1 INFLATION FACTOR (VIF) 1−R2 We have: Variable | VIF -+ -Dens | Ocons | hpi | Gper | 1.13 1/VIF 0.887027 1.10 0.911262 1.09 0.919770 1.09 0.920100 -+ Mean VIF | 1.10 Conclusion: All VIF values are smaller than 10 doesn’t has multicollinearity phenomenon ➔ The regression model 15| CONCLUSION Throughout running data diagnosis, model analysing, conducting estimation, surmount model violations, we have given these conclusions: • Previous Sample Regression Model: Output = 97.35311 - 0.8097223 hpi – 0.0008548 Gper + 0.0018599 Ocons + 0.0003528 Dens After all, these steps upward have helped us to answer the question in the introduction: Do HPI (Happy Planet Index), GDP per capita, Oil consumption and population affect the level of air pollution (AQI)? And in which scale? With the useful tool of STATA, our group have given specific figure, run mathematic model in order to form the most coincide regression model, estimated all the task of regression analysis • Some limitations in implementation and recommendations: - Limitations: + Data collecting is manual methods by variety sources from internet so that errors are unavoidable + In facts, there are uncountable factors beside HPI, Gper, Ocons, Dens that affect the air pollution level It’s might be not the most accordant variable to depict the output - Recommendations: If it’s possible, we should add more variables to this model such as: Enviromental tax, average rain quantity, constructions per sq mile, etc for more incisive overview of our research 16| Referrence: - Basic of econometrics 5th edition by D N Gujarati - STATA 12 software - Internet source: o https://air.plumelabs.com/en/ o http://happyplanetindex.org/ o http://statisticstimes.com/economy/projected-world-gdp-ranking.php o https://ceoworld.biz/2018/11/13/the-worlds-biggest-oil-consumingcountries/ o https://populationof2019.com/population-of-beijing-2019.html o http://www.thongke.info.vn/Desktop.aspx/Quan_ly_so_lieu/Phanbo-chuan-Normal-distribution-trongStata/Phan_bo_chuan_Normal_distribution_trong_Stata/ 17| ... Understanding the urgency of this phenomenon, our group decide to choose the topic: Analysing certain factors that affect air pollution for our econometrics assignment With the tools of econometrics,... the air quality Gper 0.000 < Yes GDP per capita affect the air quality Ocons 0.006 < Yes Oil consumption per day affect the air quality Dens 0.478 > No Population density has no effect on air pollution. .. activity and natural processes can generate air pollution Indoor air pollution and poor urban air quality are listed as two of the world's worst toxic pollution problems in the 2008 Blacksmith