Comparing the Effects of Using Nuclear and Renewable Power on the CO2
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
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Comparing the Effects of Using Nuclear and Renewable Power on the CO2
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 (2008-2012) 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 2013. 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.
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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.
2.1. 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.
2.2. The Relationship of Income and Environment
Table. 2 reviewed the literature of environmental Kuznets curve (EKC) on air pollution and
CO2 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
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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 CO2 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.
3.1. 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 𝐿1−𝛼 , 0 < 𝛼 < 1.
Both sides in the above equation are divided by L, then we rewrite the equation,
𝑦 = 𝐴𝑘 𝛼
𝑌
𝐾
where 𝑦 = 𝐿 , 𝑘 = 𝐿 . y and k denote the output per capita, and capital per capita ,respectively.
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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.
yit = αyi,t−1 + xit′ β + εit
εit = μi + vit
E[μi ] = E[vit ] = E[μi vit ] = 0
Here the disturbances in the above equation are composed of the fixed effects, μi and the
error term of white noise assumption, vit . 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.
∆yit = α∆yi,t−1 + ∆xit′ β + ∆vit
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.
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E[yi,t−s ∙ ∆vit ] = 0 for s ≥ 2，t = 3,4, . . , T
′
E[xi,t−s
∙ ∆vit ] = 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[∆yi,t−s ∙ (μi + vit )] = 0 for s = 1
E[∆xi,t−s ∙ (μi + vit )] = 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.
3.3. Econometric Specifications
Three econometric specifications are estimated in this paper. Those are income, CO2 and
PM10. The income (GDP per capita) can be regard as economic aspect, the CO2 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.
2
ln(y𝑖,𝑡 ) = β1 k 𝑖,𝑡 + β2 R&D𝑖,𝑡 + β3 E𝑖,𝑡 + β4 N𝑖,𝑡 + β5 Ni,t
+ β6 R 𝑖,𝑡 + β7 R2i,t + β8 Pi,t + β9 K t + η𝑖
+ υ𝑖,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
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are all considered. K denotes the first commitment period of Kyoto Protocol. K is “1” for the Annex
I countries during 1998 to 2012.
Econometric Specification 2: 𝐂𝐎𝟐
2
2
lnCO2 𝑖,𝑡 = β1 lny𝑖,𝑡 + β2 lnyi,t
+ β3 R&D𝑖,𝑡 + β4 N𝑖,𝑡 + β5 Ni,t
+ β6 R 𝑖,𝑡 + β7 R2i,t + β8 Ag i,t
+ β9 Sei,t + β10 Pi,t + β11 K t + η𝑖 + υ𝑖,𝑡
where 𝐶𝑂2 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 𝐶𝑂2 is inverted-U relationship with the increase in GDP per
capita will finally reduce 𝐶𝑂2 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 50-99. P and K are the same with those in the Econometric Specification 1:
Income.
Econometric Specification 2: 𝐏𝐌𝟏𝟎
2
2
ln PM10i,t = β1 lnyi,t + β2 lnyi,t
+ β3 R&Di,t + β4 Ni,t + β5 Ni,t
+ β6 R i,t + β7 R2i,t + β8 Ag i,t
+ β9 Sei,t + β10 Pi,t + ηi + υi,t
where PM10 was PM10’s concentration. All the variables are the same with those in Econometric
Specification 1: CO2 . 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.
3.4. Data
Our data covers the time spans form1990 to 2012. 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
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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.
4.1. 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
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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.
4.2. CO2
Both logged and non-log functional forms are estimated. Because the number of CO2 per
capita is large and the logged CO2 model has more significance, the estimation results of the
logged CO2 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 CO2 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 CO2 emission is significantly
positive that means carbon emission has defer effect. The ratio of agriculture sector can
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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.
4.3. 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 CO2. 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 CO2, 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 CO2 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 CO2 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 PM10.
4.4. 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 CO2 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 CO2 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
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and CO2 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 CO2 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 CO2 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.
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Table 1 The Relationship between energy and economic growth
Literature
NR->
GR
RE->
GR
NU->
GR
GHG-> NU->
GR
GHG
GR->
GHG
GR->
RE
GR->
NR
+≤ 8.4%
+
−≥ 8.4%
Chiu and Chang
(2009)
Apergis et al.
+
(2010)
Menyah and
Wolde-Rufael
(2010)
Apergis and
Payne (2012)
RE-> GHG
+
-
+
-
+
-
+
Data
Panel threshold regression 1996~2005
model
30 OECD countries
+
+
+
-
+
Method
+
+
Panel error correction
1984~2007
model
19 countries
Granger non-causality test
1960~2007
19 countries
Fully modified ordinary
least squares (FMOLS)
Panel error correction
1990~2007
80 countries
model
Omri and
Nguyen (2014)
Apergis and
Payne (2014)
+
+
+
-
+
System-GMM
Panel VAR model
1990~2011
64 countries
+
FMOLS
Panel error correction
model
1980~2011
25 OECD countries
Note: NR: Non-renewable energy; RE: Renewable energy; NU: Nuclear power; GHG: Greenhouse Gas; GR: Economic growth.
12
Table 2 The relationship of income and environment
Literature
Y->𝐶𝑂2
Heerink, Mulatu,
+
and Bulte (2001)
Y2->𝐶𝑂2
Y->AP Y2->AP Model
-
+
-
+
Huang, Hwang,
-(high income countries)
and Yang (2008)
+(middle income countries)
Huang, Lee, and
+
Wu (2008)
- (Belgium, Canada, Greece,
Iceland, Japan, Netherlands
and the US)
Shaw et al.
(2010)
-
+
+
Sys- and Diff-GMM
Panel VAR model
1960~1990
149 countries.
System-GMM
Panel VAR model
1971~2002
82 countries.
OLS
1971~2003
41 countries
and EU
Panel OLS
1992~2004
China
1971~2000
Cho, Chu, and
+
Yang (2013)
Ibrahim and Law
+
(2014)
-
Source
-
-
FMOLS
Sys- and Diff-GMM
Note: Y: Inome; AP: Air pollution (SOx, NOx, PM10, or PM2.5)
13
22 OECD
countries
2000~2009
72 countries
Table 3. Descriptive statistics
High income economies
Variable
Obs
Mean
GDP per capita (current US$, y)
917
𝐶𝑂2 emissions per capita (metric tons)
Upper middle income economies
Std. Dev.
Obs
Mean
Std. Dev.
24162.9500 17928.9500 481
4196.2790
2696.6300
825
9.3080
5.0595
441
3.2824
1.7229
PM10, country level (micrograms per cubic meter)
874
35.4045
14.4269
462
59.1844
29.1375
Research and development expenditure (% of GDP, R&D)
571
1.7764
0.9824
238
0.5408
0.3567
Energy use per capita (kg of oil equivalent, E)
912
8407.2880
25523.7000 465
1412.4040
782.5882
Gross capital formation per capita (current US$, k)
908
5119.0260
4034.4120
471
987.2183
653.4758
Electricity production from nuclear sources (% of total, N)
430
34.3825
19.5674
148
15.8341
16.4545
Electricity production from renewable sources, excluding
hydroelectric (% of total, R)
887
3.6061
5.5794
465
1.6529
3.2563
Agriculture, value added (% of GDP, Ag)
785
4.5213
6.2203
473
9.6465
4.6584
Services, etc., value added (% of GDP, Se)
785
64.7815
9.9588
473
56.3323
10.2841
Kyoto Protocol (K)
30
3
14
Table 4 The Impact of nuclear and renewable energy to income
Upper middle income economies
High income economies
Ln(y)
Coef.
Std. Err.
P>|t|
Coef.
Std. Err.
P>|t|
Ln(y(t-1))
0.6081
0.0732
0.0000***
0.3271
0.1015
0.0020***
R&D
-0.2433
0.1309
0.0640*
0.0004
0.0001
0.0000***
E
0.0004
0.0001
0.0000***
0.8703
0.3937
0.0300***
k
0.0000
0.0000
0.0130**
-0.0002
0.0002
0.1800
N
-0.0608
0.0271
0.0260**
-0.0417
0.0209
0.0490**
N^2
0.0006
0.0003
0.0240**
0.0004
0.0003
0.2560
R
0.0578
0.0211
0.0070***
0.1159
0.0428
0.0080***
R^2
-0.0016
0.0010
0.1150
-0.0133
0.0053
0.0140**
K
0.0159
0.0330
0.6300
Sargan test of overid. restrictions: chi2(37) =38.02
Prob > chi2 = 0.423
(Not robust, but not weakened by many instruments.)
Sargan test of overid. restrictions: chi2(28) =35.95
Prob > chi2 = 0.144
(Not robust, but not weakened by many instruments.)
∗∗∗ P < 0.01， ∗∗ P < 0.05， ∗ P < 0.1
15
Table 5 The impact of nuclear and renewable energy to income
OECD
Ln(y)
Coef.
Std. Err.
P>|t|
Ln(y(t-1))
0.7223
0.1162
0.0000***
R&D
0.0002
0.0001
0.0020***
E
0.0000
0.0000
0.0010***
k
-0.2764
0.1361
0.0440**
N
-0.0671
0.0243
0.0060***
N2
0.0008
0.0003
0.0020***
R
0.1371
0.0379
0.0000***
R2
-0.0059
0.0019
0.0020***
P
-0.0028
0.0011
0.0130**
K
0.0426
0.0511
0.4050
Sargan test of overid. restrictions: chi2(26) =
35.79
Prob > chi2 =0.096
(Not robust, but not weakened by many
instruments.)
∗∗∗ P < 0.01， ∗∗ P < 0.05， ∗ P < 0.1
16
Table 6 The impact of nuclear and renewable energy to CO2
Upper middle income economies
High income economies
𝐿𝑛(𝐶𝑂2 )
Coef.
Std. Err.
P>|t|
Coef.
Std. Err.
P>|t|
𝐿𝑛(𝐶𝑂2(t-1))
-0.0895
0.1081
0.4090
0.5106
0.1023
0.0000***
Ln(y)
5.3344
2.3309
0.0230**
5.5722
1.8270
0.0030***
(Ln(y))2
-0.2403
0.1255
0.0570*
-0.3057
0.1081
0.0060***
R&D
-0.5127
0.5732
0.3720
0.2890
0.5126
0.5740
N
-0.4688
0.0783
0.0000***
-0.1100
0.0330
0.0010**
0.0061
0.0012
0.0000***
0.0010
0.0005
0.0500*
R
-0.4241
0.1101
0.0000***
-0.1977
0.1024
0.0570*
R2
0.0175
0.0057
0.0020***
0.0141
0.0117
0.2320
Ag
-0.0538
0.1540
0.7270
0.0074
0.0353
0.8330
Se
-0.0128
0.0371
0.7300
-0.0347
0.0261
0.1870
k
-0.3175
0.1460
0.0310**
N
2
Sargan test of overid. restrictions:
Sargan test of overid. restrictions:
chi2(48) =55.94,Prob > chi2= 0.201
chi2(46) =61.99,Prob > chi2 = 0.058
(Not robust, but not weakened by many
(Not robust, but not weakened by
instruments.)
many instruments.)
∗∗∗ P < 0.01， ∗∗ P < 0.05， ∗ P < 0.1
17
Table 7 The Influence of Nuclear and Renewable Energy to 𝐶𝑂2.
OECD
𝐿𝑛(𝐶𝑂2 )
Coef.
Std. Err.
P>|t|
𝐿𝑛(𝐶𝑂2(t-1))
0.1682
0.0889
0.0600*
Ln(y)
10.1861
3.7819
0.0080***
(Ln(y))2
-0.4974
0.1992
0.0130**
R&D
-0.3931
0.3741
0.2950
N
-0.3241
0.0797
0.0000***
0.0020
0.0012
0.0920*
R
-0.4121
0.1155
0.0000***
R2
0.0152
0.0051
0.0030***
P
-0.2638
0.2136
0.2190
Ag
-0.1186
0.0444
0.0080***
Se
-0.0017
0.0031
0.5870
K
-0.3163
0.1868
0.0920*
N
2
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 < 0.1
18
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)
0.6844
0.0844
0.0000***
0.6035
0.1057
0.0000***
Ln(y)
13.0435
6.8268
0.0570*
17.8885
11.3609
0.1190
(Ln(y))2
-0.6973
0.3690
0.0600*
-0.9420
0.6657
0.1610
R&D
-4.6235
1.5615
0.0030***
-7.4870
3.8085
0.0520*
N
-0.3384
0.1585
0.0340**
-0.9057
0.2967
0.0030***
N2
0.0031
0.0025
0.2120
0.0208
0.0047
0.0000***
R
-0.0470
0.2209
0.8320
-1.0540
0.6225
0.0940*
R2
-0.0032
0.0125
0.8000
-0.0233
0.0816
0.7760
Ag
0.3474
0.3488
0.3210
0.4336
0.2460
0.0810*
Se
-0.0322
0.0946
0.7340
0.1466
0.1574
0.3540
Sargan test of overid. restrictions:
Sargan test of overid. restrictions:
chi2(50)=62.69,Prob> chi2=0.107
chi2(53)=70.59,Prob>chi2= 0.053
(Not robust, but not weakened by many
(Not robust, but not weakened by many
instruments.)
instruments.)
∗∗∗ P < 0.01， ∗∗ P < 0.05， ∗ P < 0.1
19
Table 9 The influence of Nuclear and Renewable Energy to PM10
OECD
PM10
Coef.
Std. Err.
P>|t|
PM10(t-1)
0.6729
0.0966
0.0000***
Ln(y)
30.9268
14.6030
0.0360**
(Ln(y))2
-1.5727
0.7629
0.0410**
R&D
1.6318
1.1298
0.1510
N
-0.2700
0.1566
0.0860*
N2
0.0033
0.0024
0.1760
R
-0.5819
0.2887
0.0450**
R2
0.0162
0.0113
0.1540
P
0.6857
0.5865
0.2440
Ag
-0.2835
0.1252
0.0250**
Se
-0.0002
0.0103
0.9870
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 < 0.1
20
Reference
Apergis, Nicholas, and James E. Payne. 2012. "Renewable and Non-renewable Energy
Consumption-growth Nexus: Evidence from a Panel Error Correction Model."
Energy Economics 34 (3):733-738. doi:
http://dx.doi.org/10.1016/j.eneco.2011.04.007.
Apergis, Nicholas, and James E. Payne. 2014. "The Causal Dynamics between Renewable
Energy, Real GDP, Emissions and Oil Prices: Evidence from OECD Countries."
Applied Economics 46 (36):4519-4525. doi: 10.1080/00036846.2014.964834.
Apergis, Nicholas, James E. Payne, Kojo Menyah, and Yemane Wolde-Rufael. 2010. "On
the Causal Dynamics between Emissions, Nuclear Energy, Renewable Energy, and
Economic Growth." Ecological Economics 69 (11):2255-2260. doi:
http://dx.doi.org/10.1016/j.ecolecon.2010.06.014.
Arellano, Manuel, and Stephen Bond. 1991. "Some Tests of Specification for Panel Data:
Monte Carlo Evidence and an Application to Employment Equations." The
Review of Economic Studies 58 (2):277-297. doi: 10.2307/2297968.
Arellano, Manuel, and Olympia Bover. 1995. "Another look at the instrumental variable
estimation of error-components models." Journal of Econometrics 68 (1):29-51.
doi: http://dx.doi.org/10.1016/0304-4076(94)01642-D.
Chiu, Chien-Liang, and Ting-Huan Chang. 2009. "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):1669-1674.
doi: http://dx.doi.org/10.1016/j.rser.2008.09.026.
Cho, C. H., Y. P. Chu, and H. Y. Yang. 2013. "An Environment Kuznets Curve for GHG
Emissions: A Panel Cointegration Analysis." Energy Sources, Part B: Economics,
Planning, and Policy 9 (2):120-129. doi: 10.1080/15567241003773192.
De Bruyn, Sander M. 1997. "Explaining the Environmental Kuznets Curve: Structural
Change and International Agreements in Reducing Sulphur Emissions."
Environment and Development Economics 2 (04):485-503. doi:
doi:10.1017/S1355770X97000260.
Fujii, Hidemichi, and Shunsuke Managi. 2013. "Which Industry is Greener? An Empirical
Study of Nine Industries in OECD Countries." Energy Policy 57 (0):381-388. doi:
http://dx.doi.org/10.1016/j.enpol.2013.02.011.
Griliches, Zvi. 1979. "Issues in Assesssing the Contribution of Research and Development
to Productivity Growth." Bell Journal of Economics 10 (1):92-116.
21
Heerink, Nico, Abay Mulatu, and Erwin Bulte. 2001. "Income Inequality and the
Environment: Aggregation Bias in Environmental Kuznets Curves." Ecological
Economics 38 (3):359-367. doi:
http://dx.doi.org/10.1016/S0921-8009(01)00171-9.
Huang, Bwo-Nung, M. J. Hwang, and C. W. Yang. 2008. "Causal Relationship between
Energy Consumption and GDP Growth Revisited: A Dynamic Panel Data
Approach." Ecological Economics 67 (1):41-54. doi:
http://dx.doi.org/10.1016/j.ecolecon.2007.11.006.
Huang, Wei Ming, Grace W. M. Lee, and Chih Cheng Wu. 2008. "GHG Emissions, GDP
Growth and the Kyoto Protocol: A Revisit of Environmental Kuznets Curve
Hypothesis." Energy Policy 36 (1):239-247. doi:
http://dx.doi.org/10.1016/j.enpol.2007.08.035.
Ibrahim, Mansor H., and Siong Hook Law. 2014. "Social Capital and CO2
Emission—Output Relations: A panel analysis." Renewable and Sustainable
Energy Reviews 29 (0):528-534. doi:
http://dx.doi.org/10.1016/j.rser.2013.08.076.
IEA. 2013. World Energy Outlook 2013. Paris, France: OECD/IEA.
IEA. 2014. Energy Prices and Taxes. Paris, France: OECD/IEA.
López-Menéndez, Ana Jesús, Rigoberto Pérez, and Blanca Moreno. 2014.
"Environmental costs and renewable energy: Re-visiting the Environmental
Kuznets Curve." Journal of Environmental Management 145 (0):368-373. doi:
http://dx.doi.org/10.1016/j.jenvman.2014.07.017.
Menyah, Kojo, and Yemane Wolde-Rufael. 2010. "CO2 Emissions, Nuclear Energy,
Renewable Energy and Economic Growth in the US." Energy Policy 38
(6):2911-2915. doi: http://dx.doi.org/10.1016/j.enpol.2010.01.024.
Omri, Anis, and Duc Khuong Nguyen. 2014. "On the determinants of renewable energy
consumption: International evidence." Energy 72 (0):554-560. doi:
http://dx.doi.org/10.1016/j.energy.2014.05.081.
Shafik, Nemat. 1994. "Economic Development and Environment Quality: An
Econometric Analysis." Oxford Economic Papers 46 (Special Issue on
Environmental Economics):757-73.
Shaw, Daigee, Arwin Pang, Chang-Ching Lin, and Ming-Feng Hung. 2010. "Economic
Growth and Air Quality in China." Environmental Economics and Policy Studies
12 (3):79-96.
Solow, Robert M. 1956. "A Contribution to the Theory of Economic Growth." The
Quarterly Journal of Economics 70 (1):65-94. doi: 10.2307/1884513.
22
Wooldridge, Jeffrey M. 2007. "Inverse Probability Weighted Estimation for General
Missing Data Problems." Journal of Econometrics 141 (2):1281-1301. doi:
http://dx.doi.org/10.1016/j.jeconom.2007.02.002.
23