Advanced Science and Technology Letters
Vol.121 (AST 2016), pp.95-99
http://dx.doi.org/10.14257/astl.2016.121.18
Empirical Analysis on the Factors Influencing China's
Sulfur Dioxide Emissions Based on IPAT
Wei Zhang1, Yanchun Zhu*2 and Wenjian Bo1
1
Central University of Finance and Economics, Beijing, 100081, China
2
Beijing Normal University, Beijing, 100875, China
[email protected], [email protected], [email protected]
Abstract. Thermal power restructuring plays an important role in achieving
nationwide energy saving target. Shutting down small thermal power generating
units is the main instrument to control total emissions of main pollutants. In this
study, correlation analysis is conducted in order to find out the factors which
have impacts on emission intensity of sulfur dioxide. Based on sulfur dioxide
emissions data from 1991 to 2009, the sulfur dioxide emission density and
proportion of small thermal power are analyzed by regression method. The
empirical results confirmed that shutting down small thermal power generating
units does have emission reduction effect.
Keywords: Sulfur dioxide; Emissions reduction; Regression analysis; thermal
power plants
1
Introduction
Electric power industry is always the main battlefield in China which can cut
emissions of sulfur dioxide. Shutting down small thermal power generating units is
the main task of electric power industry to implement energy conservation and
emission reduction. Until now, China’s share of thermal power generating units with
300,000-kilowatt and above in thermal power has grow from 43.37% to 67.11% since
the beginning of 11th five-year [1-2].
Small thermal power generation refers to pure condensing steam or other types of
thermal power generating units. The capacity of each is 50,000 kilowatts. High share
of small thermal power generating units accounts for China's electric power industry
with high coal consumption, pollution. Because of the the low efficiency of small
units, the coal consumption per kWh of it is about 450 g, more than 600,000 kilowatts
of supercritical thermal power units in excess of 150 grams, and it is also higher than
single 300,000 kilowatts of thermal power up to 110 grams. 121 million kilowatts of
small thermal power generating units will consume more than 100 million tons of
coal, more than 2 million tons of sulfur dioxide emissions, more than 200 million tons
of carbon dioxide every year compared to others [3].
Industry is the origin of energy pollution, the electric power industry is one of the
main pollution sources, and small thermal power is the star of high energy
consumption and high pollution[4]. Electric power industry is a focus area for energy
ISSN: 2287-1233 ASTL
Copyright 2016 SERSC
Advanced Science and Technology Letters
Vol.121 (AST 2016)
saving and pollutants emission reduction. Speeding up shutting down small thermal
power units is very crucial for the achievement of the 11th five-year energy
consumption and total emissions of major pollutants control objective, as well as for
building a resource-saving and environment-friendly society. Based on it, this paper
attempts to define the factors which affect China’s sulfur dioxide emissions, and
further regression analysis is adopted to detect the effect of sulfur dioxide emissions.
2
Influencing Factors of Emissions of Sulfur Dioxide
Currently, researches on sulfur dioxide emissions are scarce. Based on South Korea’s
study of emissions of SO2 , there are 16 indicators related to[5]: GDP, as the
average growth rate of GDP, the total energy consumption and total coal consumption,
average annual growth rate of the total energy consumption and total coal
consumption, energy composition, elasticity of energy consumption, sculpture content
of coal，control objectives of total SO2, control policy of SO2, governance strength
of SO2, charging standard of SO2, cleaner production levels and the scientific and
the
technological content, total population, per capita energy consumption,
proportion of secondary industries, the tertiary industry ratio. There are also studies
suggesting that per capita GDP, energy consumption per unit of GDP has some
relations with shutting down capacity [6].
Common indicators of China's power industry, which are in control of sulfur
dioxide emissions, includes installed capacity accounting for coal desulfurization (oil)
the proportion of the total installed capacity, sulfur dioxide emissions per kWh, the
size of the land area, energy installed capacity, thermal coal, sculpture content of fuel,
etc. [7].
Therefore, our selections of explanatory variables are: share of sculpture dioxide
emissions for coal-fired power plant in the national, installed capacity accounting for
coal desulfurization (oil) the proportion of the total installed capacity, sulfur dioxide
emissions per kWh, installed capacity of coal, the sulfur content of the fuel, shutting
down capacity, electrical fires accounted for a small proportion of thermal power,
thermal power average standard coal consumption, the average installed capacity of
thermal power, energy consumption per unit of GDP, per capita GDP, the total energy
consumption, and total coal consumption amount.
3
Sulfur Dioxide Emissions Target Selection
Under environmental constraints, the key goal of the development path of
ecological evolution of industrial structure depends on the greatest degree of
controlling over the economic growth of negative external effects on the ecological
environment [8]. The smaller negative environmental externalities lead to the greater
eco-efficiency of economic growth. Ecological Environmental Impact formula, also
called IPAT equation [9], is the study of environmental negative externalities as a
conceptual model, its expression is: Environmental Impact= Population×GDP per
capita (Environmental Impact/ Unit of GDP).
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Copyright © 2016 SERSC
Advanced Science and Technology Letters
Vol.121 (AST 2016)
In the IPAT equation, we can minimize negative effects of environmental impact
through controlling three variables’ possible change trends. Under normal
circumstances, the first one variable, population will certainly maintain a certain
growth in the future. The first two variables, GDP growth showed that the
improvement of people’s living standards, which is one of the basic goals of
economic growth, is the primary goal to achieve. Thus, the environmental impact of
per unit of GDP is a key factor in controlling the negative effects of the environment,
and is also the greatest hope to attain sustainable economic and social development,
so is the evolution of the industrial structure of the main tasks of eco-development
path. Therefore, the explanatory variables we analyzed are: sulfur dioxide emissions
per unit of GDP.
4
Correlation Test and Regression Analysis
Through the analysis of sculpture dioxide emissions from 1993 to 2009, regression
analysis of the sulfur dioxide emission density and proportion of small thermal power
are presented. The regression estimation process and test results are shown in Table 1.
Table 1. Final regression processes
Source
Model
Residual
Total
Lny
Lnx
Lnx2
_cons
SS
5.615
0.624
6.239
Coef.
35.844
3.534
95.372
df
2
14
16
Std.Err
.
10.969
MS
2.807
0.045
0.389
t
1.009
29.767
3.50
3.20
-3.27
Number obs=17F(2,14)=62.99
Prob>F=0.000 R-squared=0.900
Adj R-squared=0.886
Root MSE=2.111
p>|t|
[95% Conf.
Interval]
0.006
-59.37
-12.318
0.004
0.006
1.369
31.528
5.699
159.217
Table 2. Error correction model of measurement proces
Vector error-corrction model
Sample:3-17
Log likehood=40.33354
Det(Sigma_ml)=0.0000158
Equation
parms
D_lny
4
D_lnx
4
RMSE
0.073
0.097
R-sq
0.823
0.636
Coef.
Std.Err
.
z
p>|z|
-0.0463
0.131
-0.35
0.724
D_lny
_ce1
Copyright © 2016 SERSC
No. of obs=15
AIC=-4.178
HQIC=-4.182
SBIC=-3.753
Chi2
p>chi2
50.929
0.0000
19.186
0.0007
[95% Conf.
Interval]
-0.304
0.211
97
Advanced Science and Technology Letters
Vol.121 (AST 2016)
L1.
lny LD.
lnx LD.
_cons
D_lnx
_ce1
L1.
lny LD.
lnx LD.
_cons
0.117
0.154
-0.106
0.295
0.237
0.044
0.40
0.65
-2.41
0.690
0.515
0.016
-0.460
-0.310
-0.192
0.695
0.619
-0.020
0.651
0.175
3.72
0.000
0.308
0.993
0.280
0.795
-0.008
0.392
0.315
0.0587
0.71
2.52
-0.13
0.475
0.012
0.898
-0.489
0.177
-0.123
1.050
1.414
0.108
Therefore, regression results show the density of sulphur dioxide emissions and
share small thermal power relationship exists are as follows:
ln yi  95.3724  35.844ln xi  3.534  ln xi 
2
(1)
Among them, the y for sulfur dioxide emission intensity, x for a small percentage
of total installed capacity of thermal power installed capacity.
To enhance the accuracy of the estimate, econometric model established by further
using of time series analysis. Sample of less than 20, time series analysis methods are
not sufficient. This article focuses on the final equilibrium model. Table 2 shows the
final error correction modeling results, get the following VEM model.
ln yi  10.5373  2.8668ln xi
(2)
The regression equation (2) is similar with the initial OLS estimation results:
ln yi  8.7594  2.5563ln xi
(3)
Therefore, Long-term equilibrium which is performing based on error correction
model, the regression model equation (3) obtained in sulfur dioxide emissions in this
paper can well reflect the relationship between density and specific gravity between
small thermal power.
5
Conclusions
The studies in this paper are for analysis of the effect of emission reduction policies
during China's 10th five-year, 11th five-year. It also answered relevant questions—
whether desulphurization technologies and practices are major causes of reduced
sulfur dioxide emissions in China or not. Conclusions show that the design of
environmental policies in China are shifting towards a more scientific and effective
direction.
98
Copyright © 2016 SERSC
Advanced Science and Technology Letters
Vol.121 (AST 2016)
Acknowledgements. The research supported by Key Technologies Research and
Development Program of China (2013BAH16F02), Science Foundation of Ministry
of Education for Young Scholars of China (Grant No. 13YJC630250, 11YJC630288),
Beijing's Philosophical and Social Science Foundation (Grant No. 11JGB092), and
Program for Innovation Research in Central University of Finance and Economics (ECommerce and E-Government).
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