Comparing the Effects of Using Nuclear and Renewable Power on the CO 2. emissions, PM10 and Income

Transcription

1 Comparing the Effects of Using Nuclear and Renewable Power on the CO 2 emissions, PM10 and Income Yen-Lien Kuo, Chun-Li Tsai & Jhe-Ming Guo Department of Economics, National Cheng Kung University Both nuclear power and renewable energy are low carbon energy which has lower life-cycle greenhouse gas emissions than fossil fuel energy. In order to mitigate climate change, which one is better or is there an optimal composition of power generation from low carbon energy are studied in this paper. This paper uses the data of World Development Indicators and Taiwanese data, and difference GMM model proposed by Arellano and Bond to estimation the effects of using nuclear power and renewable energy on the CO2 emissions, PM10 and Income. The effect of using renewable energy on environment is firstly evaluated in this paper. The data is from 1990 to 2012 which covers the base year and the first commitment period of Kyoto Protocol, that can fully reflect GHG reduction effects from Annex B country. High and high-middle income economies defined by World Bank and OECD countries are assessed. The empirical results indicate that the ratio of electricity generated by nuclear power has negative impacts on income, CO2 emissions and PM10 concentrations while the ratio of renewable energy has positive impacts on income and negative impacts on CO2 emissions and PM10 concentrations. Concerning to the CO2 emissions and income, the optimal ratio of electricity generated by renewable energy is 11.6%. Keywords: nuclear power, renewable energy, PM10, CO2, economic growth JEL: O13, O44, Q43, Q54, Q56-1 -

2 Comparing the Effects of Using Nuclear and Renewable Power on the CO 2 emissions, PM10 and Income 1. INTRODUCTION The IPCC AR5 had stated that human influence on the climate system is clear, and recent anthropogenic emissions of greenhouse gases are the highest in history. Recent climate changes have had widespread impacts on human and natural systems. Substantial emissions reductions over the next few decades can reduce climate risks in the 21st century and beyond (IPCC, 2015). The first commitment period of Kyoto Protocol ( ) had passed and the next global climate change mitigation agreement is discussing though many countries are hesitate to commit. What is the impact of mitigation? Once the mitigation is committed, the adoption of zero or low-carbon energy is inevitable since the sector of electricity and heat production credit 25% of greenhouse gas emission. The adoption of renewable energy is increasing in recent decades though the fossil fuel still the major power source. Germany has 74.6% electricity from fossil fuel while it uses has the largest share of renewable energy in the developed countries in The other zero or low-carbon energy nuclear power- is cheaper than renewable energy. Phasing out nuclear power plants will increase total discounted mitigation costs between 2015 to 2100 relative to default technology assumptions by 7% to 13% depends on the target of 2100 concentrations (IPCC, 2015). Besides greenhouse gas emission, coal power plants are one of the major sources of particulate matter (PM) emissions in many areas. World Health Organization estimated that ambient (outdoor air pollution) in both cities and rural areas was estimated to cause 3.7 million premature deaths worldwide per year in 2012; this mortality is due to exposure to small particulate matter of 10 microns or less in diameter (PM10), which cause cardiovascular and respiratory disease, and cancers. Reducing annual average particulate matter (PM10) concentrations from levels of 70 μg/m3, common in many developing cities, to the WHO guideline level of 20 μg/m3, could reduce air pollution-related deaths by around 15% (WHO, 2005). The power generated by renewable and nuclear energy does not cause air pollution and increased carbon emissions. The nuclear power is cheaper but generates radiation waste and has nuclear accident risk. Should a higher income country mitigate and is there an optimal share of renewable energy that minimize the impact or maximize the benefit to the society are asked in this paper

3 2. LITERATURE REVIEW There is rich literature discussed the relationship between energy and economic growth, energy and carbon emission, and income and air pollution but no energy and air pollution. This section will review the literature of energy, GHG emission and income. Then the literature of income and environment will be reviewed The Relationship between energy, GHG and economic growth Table 1 reviewed the literature of the relationship of energy, GHG and economic growth. Apergis et al. (2010) founded that consuming nuclear power decreases economic growth while adopting renewable energy increases economic growth. The other two papers also found that using renewable energy can increases economic growth (Apergis and Payne 2012, 2014). Apergis et al. (2010) and Menyah and Wolde-Rufael (2010) pointed out that nuclear power could reduce carbon emissions. The impact of using renewable energy on GHG emission is inconsistent. Two Apergis s studies (Apergis and Payne 2014, Apergis et al. 2010) found that renewable energy would increase carbon emissions. However, Chiu and Chang (2009) indicated that using renewable energy would increase the carbon emissions when it was less than 8.39% of the total energy supply and decrease the carbon emissions when it was more than 8.39% of the total supply. Thus, the impact of using a kind of energy may have diminishing marginal returns and the quadratic terms can capture these effects. Apergis and Payne (2012) founded that the increased use of non-renewable and renewable energy would promote economic growth, and the economic growth would increase the use of non-renewable and renewable energy. In order to separate the effects of total energy consumption and the use a kind of energy, the total energy consumption and the proportion of an energy option should be used. Many researches found that the economic growth or the higher income would increase the consumption of renewable energy except Wolde-Rufael (2010). Many papers found that there is positive intercorrelation between GHG emission and income or economic growth except Apergis and Payne (2014) which indicated that economic growth can decreases GHG emission. The literature shows that the energy use has high correlation with GHG emission and economic growth. The endogeneity of energy, income and GHG should be treated in order to estimate the pure effects The Relationship of Income and Environment Table. 2 reviewed the literature of environmental Kuznets curve (EKC) on air pollution and CO 2 emission. The environmental Kuznets curve is a hypothesized relationship between environmental quality and economic development: various indicators of environmental degradation tend to get worse as economic growth until the income reaches a certain point over the course of development. The traditional empirical research of EKC only estimate the - 3 -

4 relationship between per capita income and an environmental quality indicator, such as Shafik (1994). If the coefficient of per capita income is negative but it is positive for the quadratic income, the reverse U shape (EKC) between income and an environmental quality indicator is verified. Many paper had found that EKC exists in the GHG emission and air pollution, particularly in high and middle income countries (Huang, Hwang, and Yang 2008, Heerink, Mulatu, and Bulte 2001, Cho, Chu, and Yang 2013, Shaw et al. 2010, Huang, Lee, and Wu 2008, Ibrahim and Law 2014) There are some non-traditional EKC studies, such as Fujii and Managi (2013) and López-Menéndez, Pérez, and Moreno (2014), using cubic income to explain CO 2 emission, and found that the coefficient of cubic income is positive. The EKCs are usually explained by that the higher income, the industry change to services which is less polluted and demand the higher environmental quality by adopting more environmental policies. For example, De Bruyn (1997) found that environmental policy, fostered by international agreements, gives a better explanation why sulphur emissions curbs downward at high income levels. The effect of industrial structure change can be excluded by simply add industrial structure into the traditional model of EKC. 3. Data and Econometric Specifications This section describes the model, empirical econometric specifications, data, and variable definitions. Also, describing the model that was used to analysis the influence of the energy categories to the income and environment Model Based on the growth model proposed by Solow (1956), output (Y(t)) is function of the capital (K(t)) and the labor (L(t)) as the following, Y(t) = f(k(t), A(t)L(t)) where A(t) denotes the efficiency of knowledge of labor at time t. This implies the economy produces output by using the capital, the labor and the knowledge to the output of goods or services. We assume the above production function as Cobb - Douglas function. That is, it is constant returns to scale on production. On the other hand, price levels and wages are assumed to be variable; the quantity of labor at full employment; labor and capital are substitutable for each other and there exists technical progress. We rewrite the production function as following, Y = AK α L 1 α, 0 < α < 1. Both sides in the above equation are divided by L, then we rewrite the equation, y = Ak α where y = Y, k = K. y and k denote the output per capita, and capital per capita,respectively. L L - 4 -

5 3.2. General methods and moments model (GMM model) In order to solve the problems of endogenous explanatory variables, and time-varying omitted variables, we use generalized method of moments (GMM) econometric model to estimate the effects of nuclear and renewable power on the CO2 emissions, PM10 and income. The GMM model is designed for our panel analysis in which the following 8 assumptions are fulfilled. (1) The process is dynamic, which means the current variable will be affected by lagged variables. (2) The fixed individual effect exists in the dynamic, which is different from the cross-sectional dataset assumption. (3) Some explanatory variables may be endogenous. (4) Disturbance apart from fixed effects may contain heteroskedasticity and serial correlation. (5) Disturbances across individuals are uncorrelated. (6) Some explanatory variables that are predetermined but not strictly exogenous may be influenced by lagged variables. (7) T The panel dataset has a short time dimension (small T) and a larger firm s dimension (large N) is permitted as an effective collection of dataset. (8) The lags of the instrumented variables are internal. According to the 8 assumptions above, the use of Arellano-Bond Dynamic GMM Estimators is applied to analyze our panel regression. In general, the basic model to generate data can be described as follows. y it = αy i,t 1 + x it β + ε it ε it = μ i + v it E[μ i ] = E[v it ] = E[μ i v it ] = 0 Here the disturbances in the above equation are composed of the fixed effects, μ i and the error term of white noise assumption, v it. By using Ordinary Least Squares (OLS) method, it is noticed that a bias will exist resulted from the correlation between lagged variables and fixed effects, which made the regression inconsistent. In order to eliminate the fixed effect, although we set a dummy variable for each fixed effect by using Least Squares Dummy Variables (LSDV), the fixed effects are eliminated while the model still exist bias. Therefore, to solve this problem, a first differencing GMM model is derived by Arellano and Bond (1991) and Arellano and Bover (1995) to eliminate fixed effects. y it = α y i,t 1 + x it β + v it Although we have eliminated fixed effects through the above Equation, however, in order to solve the problem of endogeneity within model, the use of instrument variable is applied. That is, we choose the lagged variable as instrument variable to solve the problem of endogeneity within model, and the 2 requirements below must be satisfied by the chosen instrument variables

6 E[y i,t s v it ] = 0 for s 2,t = 3,4,.., T E[x i,t s v it ] = 0 for s 2,t = 3,4,.., T However, the problem of weak instruments will exist when α approaches to 1, which means the relation between instrument variables and endogenous explanatory variables is not significant (Wooldridge 2007). On the other hand, when α equals to 1, it would be difficult to obtain a consistent estimator since there is no relation between instrument variables and endogenous explanatory variables. Continually, Arellano and Bond (1991) and Arellano and Bover (1995) introduced system GMM estimator, which is the mixture of first differencing GMM estimator and level GMM estimator. Despite to fulfilling the 2 requirements above, another 2 requirements below are needed to be satisfied as well, which means the changes of lagged variable and dependent variable are not related to both fixed effects and disturbances. E[ y i,t s (μ i + v it )] = 0 for s = 1 E[ x i,t s (μ i + v it )] = 0 for s = 1 In choosing instrument variables, we choose lagged variables of original variables as instrument ones. Therefore, a lagged variable of our main dependent variable is chosen as one of the instrument in our empirical study by using system GMM model Econometric Specifications Three econometric specifications are estimated in this paper. Those are income, CO 2 and PM10. The income (GDP per capita) can be regard as economic aspect, the CO 2 is global environmental aspect, and the PM10 can be regard as local environmental aspect. Econometric Specification 1: Income Firstly, we estimate the effects of Nuclear and Renewable Power on Income (GDP per capita), the system GMM specification is set as follows. ln (y i,t ) = β 1 k i,t + β 2 R&D i,t + β 3 E i,t + β 4 N i,t + β 5 N 2 i,t + β 6 R i,t + β 7 R 2 i,t + β 8 P i,t + β 9 K t + η i + υ i,t where y denotes GDP per capita, k denotes capital per capita, R & D denotes the ratio of research and development to GDP, and E denotes the energy consumption per capita. In order to remove the problem of high correlations among the total energy use per capita, the electric power consumption per capita, the electricity production from nuclear sources and renewable energy, the ratio of two kinds of energy use the measurement of percentage of total electric power consumption. N denotes the proportion of the nuclear power to total electric power consumption, and R is the proportion of renewable energy to total electric power consumption. We also use the quadratic term of nuclear power to capture the non-linear relationship with GDP per capita. P is the price of electricity. Then, the quantity, quality (source) and the price of energy and electricity - 6 -

7 are all considered. K denotes the first commitment period of Kyoto Protocol. K is 1 for the Annex I countries during 1998 to Econometric Specification 2: CO 2 lnco 2 i,t = β 1 lny i,t + β 2 lny 2 i,t + β 3 R&D i,t + β 4 N i,t + β 5 N 2 i,t + β 6 R i,t + β 7 R 2 i,t + β 8 Ag i,t + β 9 Se i,t + β 10 P i,t + β 11 K t + η i + υ i,t where CO 2 denotes emissions per capita. Our econometric specification includes the one term, lny, and the second term of GDP per capita, lny 2, which capture the impact on the carbon emissions. If the coefficient of lny is positive and the coefficient of lny 2 is negative, it means that the curve of GDP per capita and CO 2 is inverted-u relationship with the increase in GDP per capita will finally reduce CO 2 emissions per capita. R&D, research and development to GDP ratio, is a measure of the degree of investment in research and development of a country and represents the technical factor. N and R, and their quadratic terms are the same with those in the Econometric Specification 1: Income. Ag denotes the added value of agriculture sector to GDP ratio, the agricultural sector accords the international standard industrial classification (ISIC) categories sector 1-5. Se denotes the added value of services sector to GDP ratio, the services sector accords ISIC P and K are the same with those in the Econometric Specification 1: Income. Econometric Specification 2: PM10 ln PM10 i,t = β 1 lny i,t + β 2 lny 2 i,t + β 3 R&D i,t + β 4 N i,t + β 5 N 2 i,t + β 6 R i,t + β 7 R 2 i,t + β 8 Ag i,t + β 9 Se i,t + β 10 P i,t + η i + υ i,t where PM10 was PM10 s concentration. All the variables are the same with those in Econometric Specification 1: CO 2. Besides adding the proportion of two low carbon energies, the share of agriculture and services sectors, and the major international environmental protection policy - Kyoto Protocol are added into models to clarify the effect of energy source on income and environment Data Our data covers the time spans form1990 to The global data comes from the World Bank's World Development Indicators (WDI), and Taiwanese data comes from the Directorate-General of Budget, Accounting and Statistics and Bureau of energy. The econometric specifications are estimated for high income and upper middle income economies defined by WDI. The World Bank classified the world s economies based on estimates of gross national income (GNI) per capita. The classification of 2013 is adopted, that is the GNI per capita higher than $12,616 for high income economies and between $4,086 to $12,615 for upper middle income economies. Since the GNI per capita of Taiwan is higher than $12,615, Taiwan is added into high income - 7 -

8 economies. Lower middle income and low income economies are not considered in this paper because they do not use or use a few nuclear power and renewable energy. The descriptive statistics of variables of high income and upper middle income is reported in Table 3. Although hydroelectric power was currently one of the main use of renewable energy, but, according to the 2013 World Energy Outlook (International Energy Agency (IEA 2013)) stated that renewable energy was mainly hydro majority, however, hydropower had been developed completely in the past in OECD countries. Thus, renewable energy growth in the OECD countries can be expected to be contributed by the non-hydraulic, especially wind. The commercial use of nuclear power is all for electricity generation. The non-hydraulic renewable energy is mainly for electricity as well. The electricity production from renewable sources, excluding hydroelectric, is adopted in this paper. In order to estimate the effect of using nuclear power and non-hydraulic renewable energy, the price of electricity cannot be avoided. However, the variable of electricity price in three econometric specifications cannot be found in WDI. This paper adopts the electricity price stated in the Energy Prices and Taxes (IEA 2014). Since the industrial demand for electricity is greater than households, but also to avoid collinearity, so we use the industrial electricity prices. 4. Empirical The GMM is used to estimate three econometric specifications for two higher income level economies and OECD countries. The results are elaborated as following Income Both logged and non-log functional forms are estimated by GMM. Since the number of GDP per capita is large and the logged income model has more significance, the estimation results of logged income for high income and upper middle income economies are showed in table 4. The coefficient of lagged income and total energy use are both significantly positive for high income and upper middle income economies. The capital can significantly increase income in the high income economies. The coefficient of the share of nuclear power are both negative and the renewable energy are both positive in two income categories. The result indicate that income and nuclear have U-shaped relationship for high income economies but not for upper middle income economies. That was, although using nuclear would reduce income in the early stage, it would increase income finally in high income economies. Renewable energy would increase income, but it is not significant in the quadratic term in the high income economies. We founded the sign of R&D was negative in high income economies but it is negative in upper middle income economies. According to the study of the US National Aeronautics and Space Administration (NASA) by Griliches (1979), the expense of research and development raised the US economic growth until mid-1960s, but was slightly lower economic growth after mid-1970s. It - 8 -

9 means the expense of R&D had a marginal effect of diminishing. It may increase economic growth at lower income but decrease economic growth at higher income. The capital per capita can only significantly increase income in the high income economies. The high income economies have longer economic development history that may accumulate capital. The capital accumulation would increase the GDP per capita. The coefficients of Kyoto Protocol are not significant in high income economies. The sample was too few to incorporate this variable in upper middle income economies. In order to incorporate the electricity price into the models, the models for OECD countries are also estimated. The result of income model showed in Table 5. The signs of coefficients of lagged income, R&D ratio, energy use, and capital are the same with the income model for high income economies. However, the coefficient of industrial electricity price and the quadratic renewable energy are significantly negative. That means the higher electricity price reduces income. Using renewable energy would increase income in the early stage, but with the using of the ratio of increase, it would reduce income CO 2 Both logged and non-log functional forms are estimated. Because the number of CO 2 per capita is large and the logged CO 2 model has more significance, the estimation results of the logged CO 2 for high income and upper middle income economies are showed in table 6. The coefficient of logged income and its quadratic term are significant positive and negative, respectively in both high income and upper middle income economies. That means the carbon emissions and income had inverted U relationship, i.e. EKC. The Kyoto Protocol had effect to reduce carbon emissions in high economies. Since two major industrial sectors and the international environmental protection policy, i.e. Kyoto protocol, are controlled, the EKC could be caused by increasing environmental quality demand at higher income. Although income increasing would lead to increased carbon emissions in the early stage, but the effect of diminishing marginal returns would reduce carbon emissions after a certain level of income. Whether in terms of nuclear power or renewable energy, the first degree was negative and the quadratic term was positive except in the upper middle income economies. That represents using nuclear power and renewable energy can reduce the carbon emissions but subject to diminishing marginal returns. R&D had not significant effect to reduce carbon emissions in high income and upper middle income economies. The estimation results of CO 2 for OECD countries are showed in Table 7. The signs of coefficients of income, nuclear power and renewable energy and their quadratic terms, and Kyoto protocol are the same with high income economies. The lagged CO 2 emission is significantly positive that means carbon emission has defer effect. The ratio of agriculture sector can - 9 -

10 significantly reduce carbon emission. Because the forestry belongs to agriculture sector, that may be the sink of carbon emission. The industrial electricity price is insignificant to carbon emission PM10 The estimation results of PM10 for high income and upper middle income economies are showed in Table 8. Both the coefficients of lagged PM10 in the high income and upper middle income economies are significant positive. That means the concentration of PM10 has defer effect and that is similar with CO 2. In the high income economies, the income and its quadratic term are positive and negative, respectively. The EKC of PM10 exists in high income economies. The ratio of R&D is significant negative in the high income and upper middle income economies, that indicates the expense of research and development can improve air quality. The share of nuclear power is significant negative in the model of high income and upper middle income economies, and the share of renewable energy is significant negative in the upper middle income economies. That means nuclear power and renewable energy can reduce PM10. The nuclear power and PM10 had U relationship in upper middle income economies. Contrary to the effect in the model of CO 2, the share of agriculture sector has significant positive to PM10 in the upper middle income economies. Some agricultural activity, such as farming, generates PM10. The estimation results of CO 2 for OECD countries are showed in Table 7. The signs of coefficients of income and its quadratic term are the same with the model for upper middle income economies, which means EKC exists in OECD. Both nuclear power and renewable energy can reduce PM10. Similar with the model of CO 2 for OECD countries, the share of agriculture sector is significant negative to PM10. This inconsistent result of agriculture sector can only be verified by detailed sector information. The industrial electricity prices had not significant effect to reduce PM Turn Point Since the quadratic term of the proportions of energy sources are added into the econometric specifications and most of them are significant, the best proportion of energy can be calculated. The models in this paper are assumed to be linear. The coefficients of the share of nuclear power, renewable energy and their quadratic terms are significant and having reverse signs in the models of income and CO 2 for OECD countries. Their maximum or minimum value can be estimated. Using nuclear power would reduce the income, but to increase income when using excessed of 41.94%. While using renewable energy excluding hydroelectricity can increase income before 11.62%. The nuclear power and renewable energy would reduce CO 2 in OECD counties. Using nuclear power can reduce carbon emission until 81.03%, but using non-hydro renewable energy can reduce carbon emission before 13.56%. There is no way to have both benefits from income

11 and CO 2 emission by using nuclear power. Considering only income and carbon emission aspects in the high income (OECD level) countries, the best proportion of non-hydro renewable energy is under 11.62% which can increases income and decreases CO 2 emission. 5. CONCLUSION Nuclear power and renewable energy had positive impacts for environmental protection which could reduce carbon emissions and reduce PM10 s concentration. Using non-hydro renewable energy can increase income but using nuclear power cannot do that. Besides, using unclear power generates radioactive waste and nuclear power plants have nuclear accident risk. However, the empirical results in this paper show that the total energy use has positive effect on income and the electricity price has negative effect on income. The EKCs of CO 2 and PM10 exist in high income economies subject to the same industrial structure. That means the higher income may bring higher demand on environmental quality and use environmental and/or energy policies to get it. Nowadays, the cost of non-hydro renewable energy is higher than nuclear power and fossil fuel considering availability. Choosing the cost effective renewable energy in order to prevent raising too much on electricity price is a no regret policy

12 Table 1 The Relationship between energy and economic growth NR-> RE-> NU-> GHG-> NU-> GR-> GR-> GR-> Literature RE-> GHG Method Data GR GR GR GR GHG GHG RE NR Chiu and Chang + 8.4% Panel threshold regression 1996~ (2009) 8.4% model 30 OECD countries Apergis et al. Panel error correction 1984~ (2010) model 19 countries Menyah and 1960~2007 Wolde-Rufael Granger non-causality test 19 countries (2010) Fully modified ordinary Apergis and least squares (FMOLS) 1990~ Payne (2012) Panel error correction 80 countries model Omri and System-GMM 1990~ Nguyen (2014) Panel VAR model 64 countries FMOLS Apergis and 1980~ Panel error correction Payne (2014) 25 OECD countries model Note: NR: Non-renewable energy; RE: Renewable energy; NU: Nuclear power; GHG: Greenhouse Gas; GR: Economic growth. 12

13 Table 2 The relationship of income and environment Literature Y->CO 2 Y 2 ->CO 2 Y->AP Y 2 ->AP Model Source Heerink, Mulatu, Sys- and Diff-GMM 1960~ and Bulte (2001) Panel VAR model 149 countries. Huang, Hwang, and Yang (2008) Huang, Lee, and Wu (2008) Shaw et al. (2010) Cho, Chu, and Yang (2013) Ibrahim and Law (2014) + - System-GMM 1971~2002 -(high income countries) Panel VAR model 82 countries. +(middle income countries) - (Belgium, Canada, Greece, 1971~ Iceland, Japan, Netherlands OLS 41 countries and the US) and EU Panel OLS 1992~2004 China 1971~ FMOLS 22 OECD countries + - Sys- and Diff-GMM 2000~ countries Note: Y: Inome; AP: Air pollution (SOx, NOx, PM10, or PM2.5) 13

14 Table 3. Descriptive statistics High income economies Upper middle income economies Variable Obs Mean Std. Dev. Obs Mean Std. Dev. GDP per capita (current US$, y) CO 2 emissions per capita (metric tons) PM10, country level (micrograms per cubic meter) Research and development expenditure (% of GDP, R&D) Energy use per capita (kg of oil equivalent, E) Gross capital formation per capita (current US$, k) Electricity production from nuclear sources (% of total, N) Electricity production from renewable sources, excluding hydroelectric (% of total, R) Agriculture, value added (% of GDP, Ag) Services, etc., value added (% of GDP, Se) Kyoto Protocol (K)

15 Table 4 The Impact of nuclear and renewable energy to income High income economies Upper middle income economies Ln(y) Coef. Std. Err. P> t Coef. Std. Err. P> t Ln(y(t-1)) *** *** R&D * *** E *** *** k ** N ** ** N^ ** R *** *** R^ ** K Sargan test of overid. restrictions: chi2(28) =35.95 Prob > chi2 = (Not robust, but not weakened by many instruments.) Sargan test of overid. restrictions: chi2(37) =38.02 Prob > chi2 = (Not robust, but not weakened by many instruments.) P < 0.01, P < 0.05, P <

16 Table 5 The impact of nuclear and renewable energy to income OECD Ln(y) Coef. Std. Err. P> t Ln(y(t-1)) *** R&D *** E *** k ** N *** N *** R *** R *** P ** K Sargan test of overid. restrictions: chi2(26) = Prob > chi2 =0.096 (Not robust, but not weakened by many instruments.) P < 0.01, P < 0.05, P <

17 Table 6 The impact of nuclear and renewable energy to CO 2 High income economies Upper middle income economies Ln(CO 2 ) Coef. Std. Err. P> t Coef. Std. Err. P> t Ln(CO 2 (t-1)) *** Ln(y) ** *** (Ln(y)) * *** R&D N *** ** N *** * R *** * R *** Ag Se k ** Sargan test of overid. restrictions: chi2(48) =55.94,Prob > chi2= (Not robust, but not weakened by many instruments.) Sargan test of overid. restrictions: chi2(46) =61.99,Prob > chi2 = (Not robust, but not weakened by many instruments.) P < 0.01, P < 0.05, P <

18 Table 7 The Influence of Nuclear and Renewable Energy to CO 2. OECD Ln(CO 2 ) Coef. Std. Err. P> t Ln(CO 2 (t-1)) * Ln(y) *** (Ln(y)) ** R&D N *** N * R *** R *** P Ag *** Se K * Sargan test of overid. restrictions: chi2(46) = 62.37,Prob > chi2 =0.054 (Not robust, but not weakened by many instruments.) P < 0.01, P < 0.05, P <

19 Table 8 The influence of Nuclear and Renewable Energy to PM10. High income economies High-middle income economies PM10 Coef. Std. Err. P> t Coef. Std. Err. P> t PM10(t-1) *** *** Ln(y) * (Ln(y)) * R&D *** * N ** *** N *** R * R Ag * Se Sargan test of overid. restrictions: chi2(50)=62.69,prob> chi2=0.107 (Not robust, but not weakened by many instruments.) Sargan test of overid. restrictions: chi2(53)=70.59,prob>chi2= (Not robust, but not weakened by many instruments.) P < 0.01, P < 0.05, P <

20 Table 9 The influence of Nuclear and Renewable Energy to PM10 OECD PM10 Coef. Std. Err. P> t PM10(t-1) *** Ln(y) ** (Ln(y)) ** R&D N * N R ** R P Ag ** Se Sargan test of overid. restrictions: chi2(49)= 43.22,Prob > chi2 =0.705 (Not robust, but not weakened by many instruments.) P < 0.01, P < 0.05, P <

21 Reference Apergis, Nicholas, and James E. Payne "Renewable and Non-renewable Energy Consumption-growth Nexus: Evidence from a Panel Error Correction Model." Energy Economics 34 (3): doi: Apergis, Nicholas, and James E. Payne "The Causal Dynamics between Renewable Energy, Real GDP, Emissions and Oil Prices: Evidence from OECD Countries." Applied Economics 46 (36): doi: / Apergis, Nicholas, James E. Payne, Kojo Menyah, and Yemane Wolde-Rufael "On the Causal Dynamics between Emissions, Nuclear Energy, Renewable Energy, and Economic Growth." Ecological Economics 69 (11): doi: Arellano, Manuel, and Stephen Bond "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations." The Review of Economic Studies 58 (2): doi: / Arellano, Manuel, and Olympia Bover "Another look at the instrumental variable estimation of error-components models." Journal of Econometrics 68 (1): doi: Chiu, Chien-Liang, and Ting-Huan Chang "What Proportion of Renewable Energy Supplies is Needed to Initially Mitigate CO2 Emissions in OECD Member Countries?" Renewable and Sustainable Energy Reviews 13 (6 7): doi: Cho, C. H., Y. P. Chu, and H. Y. Yang "An Environment Kuznets Curve for GHG Emissions: A Panel Cointegration Analysis." Energy Sources, Part B: Economics, Planning, and Policy 9 (2): doi: / De Bruyn, Sander M "Explaining the Environmental Kuznets Curve: Structural Change and International Agreements in Reducing Sulphur Emissions." Environment and Development Economics 2 (04): doi: doi: /s x Fujii, Hidemichi, and Shunsuke Managi "Which Industry is Greener? An Empirical Study of Nine Industries in OECD Countries." Energy Policy 57 (0): doi: Griliches, Zvi "Issues in Assesssing the Contribution of Research and Development to Productivity Growth." Bell Journal of Economics 10 (1):

22 Heerink, Nico, Abay Mulatu, and Erwin Bulte "Income Inequality and the Environment: Aggregation Bias in Environmental Kuznets Curves." Ecological Economics 38 (3): doi: Huang, Bwo-Nung, M. J. Hwang, and C. W. Yang "Causal Relationship between Energy Consumption and GDP Growth Revisited: A Dynamic Panel Data Approach." Ecological Economics 67 (1): doi: Huang, Wei Ming, Grace W. M. Lee, and Chih Cheng Wu "GHG Emissions, GDP Growth and the Kyoto Protocol: A Revisit of Environmental Kuznets Curve Hypothesis." Energy Policy 36 (1): doi: Ibrahim, Mansor H., and Siong Hook Law "Social Capital and CO2 Emission Output Relations: A panel analysis." Renewable and Sustainable Energy Reviews 29 (0): doi: IEA World Energy Outlook Paris, France: OECD/IEA. IEA Energy Prices and Taxes. Paris, France: OECD/IEA. López-Menéndez, Ana Jesús, Rigoberto Pérez, and Blanca Moreno "Environmental costs and renewable energy: Re-visiting the Environmental Kuznets Curve." Journal of Environmental Management 145 (0): doi: Menyah, Kojo, and Yemane Wolde-Rufael "CO2 Emissions, Nuclear Energy, Renewable Energy and Economic Growth in the US." Energy Policy 38 (6): doi: Omri, Anis, and Duc Khuong Nguyen "On the determinants of renewable energy consumption: International evidence." Energy 72 (0): doi: Shafik, Nemat "Economic Development and Environment Quality: An Econometric Analysis." Oxford Economic Papers 46 (Special Issue on Environmental Economics): Shaw, Daigee, Arwin Pang, Chang-Ching Lin, and Ming-Feng Hung "Economic Growth and Air Quality in China." Environmental Economics and Policy Studies 12 (3): Solow, Robert M "A Contribution to the Theory of Economic Growth." The Quarterly Journal of Economics 70 (1): doi: /

23 Wooldridge, Jeffrey M "Inverse Probability Weighted Estimation for General Missing Data Problems." Journal of Econometrics 141 (2): doi: 23