Does economic growth affect environmental quality in some east asian countries

UNIVERSITY OF ECONOMICS STUDIES HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DOES INCOME PER CAPITA AFFECT CARBON DIOXIDE EMISSIONS IN SOME EAST ASIAN COUNTRIES? BY PHAM THI THU HUYEN MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, MAY 2012 i UNIVERSITY OF ECONOMICS STUDIES HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DOES INCOME PER CAPITA AFFECT CARBON DIOXIDE EMISSIONS IN SOME EAST ASIAN COUNTRIES? A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By PHAM THI THU HUYEN Academic Supervisor: PHAM KHANH NAM HO CHI MINH CITY, MAY 2012 ii iii ACKNOWLEDGEMENT This thesis could not complete without considerable and kindly mental supports from my supervisor, Dr Pham Khanh Nam, who spent his valuable time to help me to find materials, books and super my schedule tightly He also response quickly as soon as I asked for help and give comments, which partly contribute to the success of this thesis From bottom of my heart, I sincerely thank him for all I grateful acknowledge to Prof., Dr Nguyen Trong Hoai, Dr Nguyen Van Ngai and Dr Phan Dinh Nguyen – Public Defense Committee for their important comments and explanations that support me to fulfill the thesis This study also benefits greatly from the enthusiastic assistance of Dr Le Van Chon, whom I got knowledge on econometric and suggested the suitable models for the study I would like to express my thanks for all students of MDE 16 for their unceasing assistance and encouragement during the course I also show deep gratitude to the lectures, VNP staffs, library staffs for their helps in accumulating knowledge, accessing dataset and materials as well Last but not least, it gives my deepest grateful to my family members, husband, managers and colleagues for their dear encouragement, give favorable times and opportunities to finish the M.A course as well as the thesis I pledge to bear full responsibilities for errors, omissions and shortcomings of the study iv TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1 Problem Statement 1.2 Objectives of study 1.3 Research questions 1.4 Scope of the research 1.5 Structure of the thesis CHAPTER 2: EMPIRICAL AND THEORETICAL BACKGROUND 2.1 Theoretical background 2.2 Empirical review CHAPTER 3: RESEARCH METHODOLOGY 14 3.1 The conceptual framework 14 3.2 Variables 15 3.2.1 Dependent environment variables 15 3.2.2 Explanatory variables 17 GDP per capita 17 Foreign Direct Investment (FDI) 17 Trade openness 19 Population density 20 3.3 Data……………………………………… ……… 22 3.4 The Econometric model 22 CHAPTER 4: DATA ANALYSIS 28 4.1 Carbon Dioxide Emissions and GDP for East Asian countries 28 4.2 Descriptive analysis 30 4.3 The Environment Kuznets curve (EKC) 32 4.4 Determinants of pollution 40 CHAPTER 5: CONCLUSION AND POLICY IMPLICATIONS 46 5.1 Main findings and policy implications 46 5.2 Limitation of the research and recommendations for further study 50 REFERENCES 52 v APPENDICES A: Summary of empirical studies on different the EKC hypothesis 57 B: Description of variables and data sources 71 C: Description of CO2 –GDP of six East Asian countries individually 74 D: Estimated results 78 E: Tests 82 LIST OF FIGURES Figure 2.1 The Kuznets Curve (1955) and the Environmental Kuznets Curve Figure 2.2 The Environmental Kuznets Curve in N-shape Figure 3.1 Conceptual framework of the study 14 Figure 4.1 CO2 emissions for the period of 1990-2007 28 Figure 4.2 CO2 emissions and GDP for the period of 1990-2007 29 Figure 4.3 The EKC shape for six East Asian countries 37 Figure 5.1 An overview of determinants on environmental quality 48 vi LIST OF TABLES Table 3.1 Summary of variables description 21 Table 4.1 Summary Statistic on the sample observations 30 Table 4.2 Correlation on the sample observations 31 Table 4.3 Estimated results for the EKC with different models 33 (without time trend) Table 4.4 A comparison of estimated results for the EKC with FEM 34 models Table 4.5 Estimated results for the EKC (N-shape) with FEM 36 Table 4.6 Estimated results for EKC with FEM time effects 38 Table 4.7 Estimated results for FEM with extended model (time 41 effects) vii ABBREVIATIONS EKC : Environmental Kuznets curve FDI : Foreign Direct Investment NGOs : Non-government organizations CO2 : Carbon dioxide emissions per capita GDP : Gross Domestic Products GHG : Greenhouse gas OLS : Ordinary least square FEM : Fixed Effects Model REM : Random Effects Model X : Import M : Export NAFTA : The North American Free Trade Agreement viii CHAPTER 1: INTRODUCTION 1.1 Problem Statement For many centuries, the environmental degradation has become the issue of life with economic development Since the industrial revolution began in 18th century, it was as a real problem Industrial revolution with the technological progress brought great achievements, especially the worldwide usage of fossil fuels and coal in various industries As a result, global economy has growth fast along with human who significantly consumed natural resources leading environmental quality is worsening seriously (Irina, 2008) This implies that economic growth may affect environmental quality Every nation wishes to pursue targets of economic growth without damaging environment This is not feasible when history shows that any country becomes an industrial power, in the process of development, causing environmental damages, which cannot solve for a short time In order to serve quick economic growth targets, the policies of incomprehensive industrialization and urbanization with massive exploitation of natural resources have caused environmental problems in these countries for recent years Consequently, the air pollutions and global warming have continuously increased by human’s uncontrolled activities in the development process For China case, environmental degradation is becoming serious problems, which not only affects strongly to domestic but also causes international consequences Some specialists judged that environmental pollution is creating challenges to government It is also a long-term burden on the Chinese people and “China’s problem has become the world’s problem” (Kahn and Yardley, 2007, p.2) In the current context, environmental problem is not for an individual country to cope with It becomes the common hot issues and needs co-operation of worldwide countries because of its global matter Kim (1996) revealed that due to the quick economic growth, environmental pollution has been in red alarm in China and it crosses the boundaries to South Korea and Japan Especially, the global pollutants, in which carbon dioxide emissions are particular pollutant, causing global warming Carbon dioxide emissions have dramatically increased for recent century due to human economic development Measuring environmental-economic relationship is necessary for any country to orient adequate economic- environmental policy The Environment Kuznets curve (EKC) is reckoned as the best ruler in estimating this relationship since Grossman and Krueger made empirical study on the impact of the economic growth on environment in NAFTA countries in 1991 Under the EKC hypothesis, environmental quality changes with income levels It displays in a bell shape curve Environment degrades with income levels in the first stage of development and then improves as soon as it passes a definite high-income level Researchers use the EKC hypothesis commonly to connect economic growth with environmental reduction in both single countries and country groups with different models Local pollutants and global pollutants are treated as dependent variables Beside income, other factors are applied as explanatory variables Although the results are not the same for all studies, the economic growth links the environmental deterioration and vice versa However, this theory has still been debated for years because various empirical studies found ambiguous results on the EKC but it has meaning for policy makers to find the best solutions for development sustainability (Dinda, 2004) In this study, the research concerns if fast economic growth affects the environmental quality in some East Asian countries, specifically six countries consist of three developing countries in Southeast Asia: Malaysia, Thailand, Vietnam and one developing country: China and two developed country in Northeast Asia: Korea and Japan, using cross- country balanced panel data set for the period of 1990-2007 Government expenditure per capita Notes:  WIR: World Resources Institute  GEMS: Global Environment Monitoring System  IEA: International Energy Agency  EPA: US Environmental Protection Agency  OECD: Organization for Economic Cooperation and Development  UNEP: United Nations Environment Programme Ozone Secretariat  WDI: World Bank’s World Development Indicators  BESD: World Bank’s Bank Economic and Social Database  CCIW: Canada Center for Inland Waters  MARC: Monitoring and Assessment Research Center  ORNL: Oak Ridge National Laboratory  NBS: National Bureau of Statistics  FAO: Food and Agriculture Organization  DOE: Malaysian Department of Environment 70 Appendix B: Description of variables and data sources Variables Description Unit Definition CO2 Carbon Metric dioxide per capita Source ton Those stemming from the burning of fossil fuels Carbon Dioxide and the manufacture of cement They include Information emissions per carbon dioxide produced during consumption of Center, capita solid, liquid, and gas fuels and gas flaring Analysis Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, the United States of America GDP Gross In thousand The total output of a country’s economy divided by World Domestic US Product capita Bank’s World Dollars midyear population in constant 2000 US Dollars, Development Indicator per (constant 2000 prices) which allow for the comparison of GDP across years without interference from the effects of inflation on prices GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products It is 71 calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources FDI Foreign Direct In Investment current The net inflows of investment to acquire a lasting World US Dollars Bank, Global interest in or management control over an Development Finance enterprise operating in an economy other than that of the investor It is the sum of equity capital, reinvested earnings, other long-term capital, and short-term capital, as shown in the balance of payments TRADE Trade In US A share of the sum of exports (X) and imports (M) World Bank national intensity Dollars of goods and services in GDP It is calculated by accounts data and OECD (constant formula: (X+M)/GDP) National Accounts data 2000 prices) POP Population density Persons/km2 Population density is calculated by mid-year Food and Agriculture population divided by land area in square Organization and World kilometers Population bases on the de facto Bank 72 definition of population, which counts all residents regardless of legal status or citizenship, except for refugees not permanently settled in the country of asylum and who are generally considered part of the population of their country of origin Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones In most cases, the definition of inland water bodies includes major rivers and lakes Source: Data and definition are from World Bank’s http://data.worldbank.org/indicator 73 World Development Indicator Available at Appendix C: Description of CO2 –GDP of six East Asian countries individually 10.2 Japan's CO2-GDP 10 1995 2004 1994 9.8 2003 2007 1998 9.6 CO2 emissions per capita 1996 1997 2005 2002 2000 2006 2001 1992 9.4 1999 1990 1991 1993 34000 36000 38000 GDP per capita 40000 42000 South Korea's CO2-GDP 2007 2002 2003 2001 2000 2005 2006 1997 1996 1999 1995 1998 1994 1993 1992 1991 CO2 emissions per capita 10 2004 1990 6000 8000 10000 12000 GDP per capita 74 14000 16000 China's CO2-GDP 2007 2006 CO2 emissions per capita 2005 2004 2003 2002 1996 1997 1995 2001 1998 19992000 1994 1993 1992 1991 1990 500 1000 GDP per capita 1500 2000 Malaysia's CO2-GDP 2007 2005 2006 2004 1995 1996 1997 2001 2002 2000 1998 1993 1999 1994 1992 1991 1990 CO2 emissions per capita 2003 2500 3000 3500 4000 GDP per capita 75 4500 5000 Thailand's CO2-GDP 2006 2004 2005 2007 2003 3.5 2002 2001 1997 1996 1999 2000 1998 1995 2.5 1994 1993 1992 1991 1.5 1990 1500 2000 GDP per capita 2500 1.4 Vietnam's CO2-GDP 2007 1.2 2004 2005 2006 2003 2002 2001 2000 1998 1999 1997 1996 1995 1994 1993 1992 1990 1991 200 300 400 GDP per capita 76 500 600 South Korea China Malaysia Thailand Vietnam 10 CO2 10 Japan 1990 1995 2000 2005 1990 1995 2000 year Graphs by state 77 2005 1990 1995 2000 2005 Appendix D: Estimated results  Estimated results for the EKC with FEM (with time effects) xtreg CO2 GDP_k GDP_ksq i.t, fe Fixed-effects (within) regression Group variable: state Number of obs Number of groups = = 108 R-sq: Obs per group: = avg = max = 18 18.0 18 within = 0.8892 between = 0.9200 overall = 0.9176 corr(u_i, Xb) F(19,83) Prob > F = 0.1294 Std Err t P>|t| = = 35.04 0.0000 CO2 Coef [95% Conf Interval] GDP_k GDP_ksq 6639978 -.0119696 0579473 0010011 11.46 -11.96 0.000 0.000 5487429 -.0139608 7792527 -.0099784 1948524 2970882 4900125 6524635 9734099 1.134076 1.179643 7735103 7194385 9654495 1.042567 1.103546 1.358431 1.581947 1.570596 1.601695 1.772832 1964655 1969328 1975792 1988715 201011 2038099 205675 2019376 2039644 2079456 2089539 2118853 2146859 2197253 224376 2304645 23783 0.99 1.51 2.48 3.28 4.84 5.56 5.74 3.83 3.53 4.64 4.99 5.21 6.33 7.20 7.00 6.95 7.45 0.324 0.135 0.015 0.002 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -.1959095 -.0946032 0970354 2569161 5736071 728706 7705638 3718645 3137616 5518541 6269665 6821147 9314298 1.144923 1.124321 1.143311 1.299798 5856144 6887796 8829897 1.048011 1.373213 1.539446 1.588722 1.175156 1.125115 1.379045 1.458168 1.524977 1.785433 2.018972 2.016871 2.06008 2.245867 _cons 1.08189 342909 3.16 0.002 3998581 1.763923 sigma_u sigma_e rho 99329817 33965371 89531395 t 10 11 12 13 14 15 16 17 18 F test that all u_i=0: (fraction of variance due to u_i) F(5, 83) = 78 90.79 Prob > F = 0.0000  Estimated results for Fixed Effects Model for the EKC (without time trend) Fixed-effects (within) regression Group variable: state Number of obs Number of groups = = 108 R-sq: Obs per group: = avg = max = 18 18.0 18 within = 0.7408 between = 0.8248 overall = 0.8026 corr(u_i, Xb) F(2,100) Prob > F = -0.9275 = = CO2 Coef GDP_k GDP_ksq _cons 1.015438 -.0139572 -.6543284 0633616 0013608 3550032 sigma_u sigma_e rho 4.0852984 4732019 98676093 (fraction of variance due to u_i) F test that all u_i=0: Std Err t 16.03 -10.26 -1.84 F(5, 100) = P>|t| 0.000 0.000 0.068 57.78 142.90 0.0000 [95% Conf Interval] 8897304 -.0166569 -1.358645 1.141146 -.0112574 0499879 Prob > F = 0.0000  Estimated results for Fixed Effects Model for the EKC (with time trend) Fixed-effects (within) regression Group variable: state Number of obs Number of groups = = 108 R-sq: Obs per group: = avg = max = 18 18.0 18 within = 0.8470 between = 0.8999 overall = 0.8953 corr(u_i, Xb) F(4,98) Prob > F = -0.3254 = = CO2 Coef GDP_k GDP_ksq t tsq _cons 7062673 -.0121102 1080351 -.0013725 8341525 0620134 0010808 029436 0014857 35558 sigma_u sigma_e rho 1.1688066 3672266 91015417 (fraction of variance due to u_i) F test that all u_i=0: Std Err t 11.39 -11.21 3.67 -0.92 2.35 F(5, 98) = 79 77.97 P>|t| 0.000 0.000 0.000 0.358 0.021 135.65 0.0000 [95% Conf Interval] 5832036 -.014255 0496203 -.0043208 1285156 8293309 -.0099654 1664499 0015758 1.539789 Prob > F = 0.0000  Estimated results for Fixed Effects Model for the EKC (N- shape) Fixed-effects (within) regression Group variable: state Number of obs Number of groups = = 108 R-sq: Obs per group: = avg = max = 18 18.0 18 within = 0.9132 between = 0.8868 overall = 0.8789 corr(u_i, Xb) F(3,99) Prob > F = -0.8597 = = CO2 Coef GDP_k GDP_ksq GDP_kcb _cons 2.354925 -.1036598 0012989 -1.643681 1023851 0064458 0000926 2181913 sigma_u sigma_e rho 2.3976862 27521337 98699621 (fraction of variance due to u_i) F test that all u_i=0: Std Err F(5, 99) = 80 t 23.00 -16.08 14.02 -7.53 130.83 P>|t| 0.000 0.000 0.000 0.000 347.19 0.0000 [95% Conf Interval] 2.151771 -.1164496 0011151 -2.07662 2.55808 -.09087 0014827 -1.210743 Prob > F = 0.0000  Estimated results for extended model (with time effects) xtreg CO2 GDP_k GDP_ksq FDI TRADE POP i.t, fe Fixed-effects (within) regression Group variable: state Number of obs Number of groups = = 108 R-sq: Obs per group: = avg = max = 18 18.0 18 within = 0.9085 between = 0.2285 overall = 0.2581 corr(u_i, Xb) F(22,80) Prob > F = -0.5643 = = 36.11 0.0000 CO2 Coef GDP_k GDP_ksq FDI TRADE POP 8598346 -.0136298 1.51e-12 1.074421 -.0271261 0728483 001112 3.05e-12 4148518 0069955 11.80 -12.26 0.49 2.59 -3.88 0.000 0.000 0.623 0.011 0.000 7148618 -.0158428 -4.57e-12 2488395 -.0410475 1.004807 -.0114168 7.58e-12 1.900002 -.0132046 1778832 2949456 4864069 6021229 8833677 1.063925 1.133464 8085796 7073336 8832588 1.083741 1.115415 1.337087 1.434999 1.367659 1.418466 1.513844 1827481 1843335 1874875 195296 2069237 2122754 2174119 2134111 2247864 2494364 2434639 2554914 269502 306714 3299868 3324467 3742203 0.97 1.60 2.59 3.08 4.27 5.01 5.21 3.79 3.15 3.54 4.45 4.37 4.96 4.68 4.14 4.27 4.05 0.333 0.114 0.011 0.003 0.000 0.000 0.000 0.000 0.002 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -.1857972 -.0718898 1132948 2134714 4715764 6414839 7008006 383878 2599943 3868645 5992325 6069706 8007609 8246186 7109646 756876 7691215 5415635 661781 8595189 9907744 1.295159 1.486367 1.566128 1.233281 1.154673 1.379653 1.56825 1.623859 1.873413 2.045379 2.024354 2.080056 2.258566 _cons 5.022612 1.097228 4.58 0.000 2.839058 7.206166 sigma_u sigma_e rho 3.7548662 31433471 99304074 Std Err t P>|t| [95% Conf Interval] t 10 11 12 13 14 15 16 17 18 F test that all u_i=0: (fraction of variance due to u_i) F(5, 80) = 38.25 81 Prob > F = 0.0000 Appendix E: Tests  F-test for Overall Significant regress CO2 GDP_k Source SS df MS Model Residual 1089.42529 87.0865507 105 544.712647 829395721 Total 1176.51185 107 10.9954378 CO2 Coef GDP_k GDP_ksq _cons 8834554 -.0179444 1.532143 test GDP_k ( 1) ( 2) regress Std Err .0343336 0008909 1372764 F( 2, 105) = Prob > F = 0.000 0.000 0.000 CO2 GDP_k GDP_ksq FDI SS TRADE df 8153783 -.0197109 1.259949 POP MS 102 227.511866 381887417 Total 1176.51185 107 10.9954378 CO2 Coef GDP_k GDP_ksq FDI TRADE POP _cons 1.024592 -.0206309 2.54e-11 5564778 -.0031355 8620072 test GDP_k GDP_ksq FDI Std Err .0360888 0008994 3.35e-12 1484046 0008098 2590935 TRADE t 28.39 -22.94 7.58 3.75 -3.87 3.33 POP GDP_k = GDP_ksq = FDI = TRADE = POP = Constraint dropped 4, 102) = Prob > F = 733.34 0.0000 82 Number of obs F( 5, 102) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.000 0.000 0.000 0.000 0.000 0.001 = = = = = = 108 595.76 0.0000 0.9669 0.9653 61797 [95% Conf Interval] 9530095 -.0224149 1.87e-11 2621181 -.0047418 3480965 = = = = = = 108 656.76 0.0000 0.9260 0.9246 91071 [95% Conf Interval] 656.76 0.0000 1137.55933 38.9525165 F( 25.73 -20.14 11.16 P>|t| GDP_ksq Model Residual 1) 2) 3) 4) 5) t Number of obs F( 2, 105) Prob > F R-squared Adj R-squared Root MSE GDP_k = GDP_ksq = Source ( ( ( ( ( GDP_ksq 1.096174 -.0188469 3.20e-11 8508376 -.0015292 1.375918 9515325 -.016178 1.804337  Hausman test Coefficients (b) (B) fixed GDP_k GDP_ksq FDI TRADE POP _It_2 _It_3 _It_4 _It_5 _It_6 _It_7 _It_8 _It_9 _It_10 _It_11 _It_12 _It_13 _It_14 _It_15 _It_16 _It_17 _It_18 8598346 -.0136298 1.51e-12 1.074421 -.0271261 1778832 2949456 4864069 6021229 8833677 1.063925 1.133464 8085796 7073336 8832588 1.083741 1.115415 1.337087 1.434999 1.367659 1.418466 1.513844 (b-B) Difference 1.106344 -.023028 1.00e-11 -.3070483 -.0062667 2217277 3001093 4489065 5904926 916116 1.078778 1.12682 7453541 6519058 9689344 1.046935 1.07703 1.358721 1.61967 1.613268 1.674027 1.724959 -.2465091 0093982 -8.49e-12 1.381469 -.0208594 -.0438445 -.0051637 0375003 0116303 -.0327483 -.0148531 0066442 0632255 0554278 -.0856756 0368059 0383851 -.0216337 -.1846714 -.2456082 -.2555609 -.2111153 sqrt(diag(V_b-V_B)) S.E .124949 001751 3.36e-12 7100419 012461 0363856 0532033 0753208 1161564 162219 1801806 1947367 1773579 209837 2761494 2621323 290391 3233541 4002936 4449011 4541187 5157746 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 59.93 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) 83 84 ... close link to investment that investing physical capital directly into manufacture, especially in polluted intensive equipments means energy consumption is increasing and involving some kinds of... meaning that as the income rises, environmental pressure increases (ii) If β1