Download The Research on Influence of Industrial Structure on Low-carbon

EASTERN ACADEMIC FORUM
The Research on Influence of Industrial Structure on Low-carbon
Economy in China- Based on Empirical Analysis of Regional
Differences
ZHOU Rui, LI Shuang
School of Economics, Shandong University of Technology, P.R.China, 255049
[email protected]
Abstract: The idea of low-carbon economy focus on the future of mankind. China, as high energy
consumption in developing countries, it is necessary to address climate change while ensure sustainable
development of economic society, so the development of low-carbon economy is more significant.
Based on the calculation of the carbon emission caused by energy consumption, this article select the
correlation data of 30 regions to empirical analyze the influence of regional industrial structure on
carbon dioxide emissions from 1998 to 2008. The results show that the adjustment of industrial
structures has a great influence on low-carbon economy. Therefore, in order to develop low-carbon
economy, regions must select leading industry, and speed up industrial restructuring.
Keywords: low-carbon economy, industrial restructuring, regional differences, carbon emissions
1 Literature Review
With the deepening of industrialization, emissions of large amounts of greenhouse gas have contributed
to global warming, change of climate, ecological deterioration, and these make sustainable development
of economy and society face major challenges. Thus, the British Government published "Our future
energy: creating a low carbon economy", and first proposed the concept of "low-carbon economy" in
2003. Low-carbon economy has become increasingly popular as a concept, and has become an
important policy of Governments demand points. In the economic times, each country will try to
combine economic recovery and economic transition, want to obtain one from this attractive cake, and
seize the commanding height of the new round of economic growth.
The development of low-carbon economy is so rapid, and the research on low-carbon economy is
gradually increased at home and abroad. External studies on low-carbon economy are earlier. British
pioneered the concept of low-carbon economy and start practicing at home. On October 30, 2006, the
United Kingdom issued the "Stern Report" which was leaded to complete by the former World Bank
chief economist Nicholas • Stern, it make significant assessment of the potential economic impact on
global warming. Panayotou (2003) agreed with the statement by Grossman et al who believed inverted
"U" shaped relationship between the part of the environmental pollutants (such as particulate matter,
sulfur dioxide) emissions and long-term economic growth, and he explained the reasons from view of
people's propensity of consumption to environmental services: With the increase of national income,
industrial structure has changed, and people's consumption structure also has changed. People have
started to pay attention to issues of environmental protection, environmental services have become
normal goods, and the phenomenon of environmental degradation gradually slow down and even
disappear. Ankarhem (2005) investigated the case of Sweden, noted that emissions of carbon dioxide,
sulfur dioxide and volatile organic compounds also showed by distribution of the Environmental
Kuznets Curve from 1918 to 1994. Grubb (2004) thought that in the initial stage of industrialization,
with the increase in per capita income, per capita emissions of CO2 became higher, but beyond this
phase, the per capita emissions of CO2 will arrive at saturation on different levels. Thistle (2006)
transferred the scientific debate on climate change to the level of economic laws. He noted that the size
of the world economy in 2050 will be 3-4 times than today, but emissions will lower 1/4 level than today.
And he also pointed out that the policy requires to climate change should need three key elements: First,
pricing mechanism of carbon should be established; Second, we need technical policy; Third, we should
79
EASTERN ACADEMIC FORUM
establish a global system, a worldwide carbon market, will together strengthen clean development
mechanism to cover more industries and markets, an promote the development of these mechanisms in
developing countries.
Domestic scholars little research in this field, few literature mainly related to the experience of overseas
development of low-carbon economy, the economic impact and technology of low-carbon economy,
analysis on the significance, opportunities and challenges of China's development of low-carbon
economy. Because the purpose of developing low-carbon economy is to reduce carbon emissions,
promote virtuous cycle of economy. Therefore, few scholars study the relationship between the carbon
emissions and some factors, search for ways to reduce carbon emissions and promote development of
low carbon economic. XU Guoquan, LIU Zeyuan, JIANG Zhaohua (2006) believed that the role of
energy efficiency to curb Chinese carbon emissions was weakening, coal-dominated energy structure
was not fundamental change, the inhibitory effect of energy efficiency and energy structure was difficult
to pull off growth of carbon emissions by the economic development in China. WEI Wei, XIAN
Yangfang (2010) propounded that CO2 emissions in China were positively related to economic scale, the
level of industrialization and free trade, and were negatively related to indigenous R D and technology
import. However, our findings also indicated that lower absorptive capacity of indigenous R D
cumbered the productivity growth and had no significant effects on the improvement of environment
quality. Moreover, the relationships among R D, technology import and CO2 emissions also took on
different patterns in different regions. LIN Boqiang, YAOXin (2010) thought that the Government's
renewable energy plan have important positive effects on carbon emissions, but the change of energy
structure by control of carbon emissions would increase energy costs, which had a negative impact to
macroeconomic. Because dependence of many important industry for coal and thermal power was still
high, so this stage, there is not much room for CO2 reduction by changing the energy structure, and we
should pay attention to other aspects of energy saving.
If we move toward low carbon economy, we must change the existing economic structure, especially the
adjustment of industrial structure, which is the premise and effective way of low-carbon economy. Three
major industries are likely to increase carbon dioxide emissions. For agriculture, fertilizer, agricultural
waste, burning of crop stalk and so on could increase carbon emissions; For the industry, the
development of heavy industry, demand of energy, transport and so on also along with increased carbon
emissions; For the tertiary industry, with the continuous development of electronic information industry,
electricity consumption will increase, the increase in power will put on carbon emissions. In China,
every region has difference industrial structures, so the analysis of regional industrial structure on the
development of low-carbon economy is significant.
＆
＆
＆
2 Establish and Analysis of Model
2.1 The set of variables and data sources
2.1.1 Set of variables
In order to illustrate the influence of industrial structure on the low-carbon economy, we refer to four
independent variables PI, SI, TI and GDP and a dependent variable CQ. We use the symbols PI, SI, TI
and GDP to respectively denote the production value of first industrial, secondary industry GDP,
tertiary industry GDP, and GDP in all regions. CQ said carbon dioxide emissions in all regions. In order
to fully explain the relationship between industrial structure and carbon emissions, we will select per
unit of output on carbon emissions. We use ratio of carbon dioxide emissions to GDP as indicator to
measure development of low-carbon economic in every region, the smaller its value, the less per unit of
output on carbon dioxide emissions, and its economic model tends to low-carbon economy. The changes
in industrial structure we use ratio of three industrial outputs to gross output value, which uses PI / GDP,
SI / GDP, and TI / GDP to represent.
2.1.2 Variables handling
The independent variables are handed. Carbon emissions (CQ).Because the present statistics have not
80
EASTERN ACADEMIC FORUM
the index of carbon emissions, this index is made a new estimate. According to Xu Guoquan said that
carbon emissions and energy consumption is proportional, we believe that the data of carbon emissions
is based on the data of energy consumption which used formula (1) to estimate.
CQt =∑Etj * ηj
(1)
Where CQt is carbon emissions of the t year, Etj is a class j energy consumption of the t year, and ηj is
Carbon emission coefficient of the class j energy. Carbon emission coefficient of coal is 0.7476t(c)/t.
Carbon emission coefficient of oil is 0.5825 t(c)/t. Carbon emission coefficient of Natural Gas is
0.4435t(c)/t.
The data on gross output value of primary industry, secondary industry, tertiary industry, and GDP come
from the data of gross regional product by three strata of industry in “China Statistical Yearbook" from
1999 to 2009. The fixed sample data (T.S.C.S.) combines advantages between cross-section (C.S.) and
time series (T.S.) data, so this article uses a fixed sample data (also known as panel data), study duration
from 1998 to 2008, the scope of cross-section is 30 regions in china (due to lack of data, Tibet not
included in the analysis). Natural logarithm transformation of data does not change the original
cointegration relationship of variables, can make it trend to linearization, and eliminate the existence of
heteroscedasticity in time series, therefore, all the data have been treatment before input it.
2.1.3 Variable of unit root test
In modern statistical theory, once the traditional regression analysis among the non-stationary data, there
will appear the pseudo-regression phenomenon. Despite it have the very good fitting degree and t values,
the regression results are not credible. So we need to test the stationary of the data before the regression.
We use method of panel unit root test (Im, Pesaran and Shin W-stat) to examine the stability of the
column data. Upon examination, the level value of variables are not stable, but their first-order
differential value is stable, that is, variables have the first-order unit root, so we should make the first
difference treatment to the data before regression; In addition, the differential can also eliminate the
impact on different economic structure of regions.
2.2 Set the basic model and the regression results
As different levels of economic development in every region, industrial structures are different. We only
consider influence of industrial structures on the degree of carbon dioxide emissions while ignoring
other factors such as technological progress, so we establish model of Cross-section specific and
variable coefficients. With the above analysis, we finally set models such as equation (2) below:
(CQ/GDP)it = αi + β1i ( PIP /GDP)it +β2i ( SIP /GDP)it + β3i ( TIP /GDP)it + µi
(2)
i i=1 2 … 30 said the 30 regions; t (t = 1,2, ..., 11) said year; αi said constant coefficient; β1i, β2i and
β3i that the coefficient of each independent variables; µi that time dummies. In order to analyze the
dynamic effects in model, we added lagged dependent variable to the right side of equation. After the
differential treatment, the variable data is eliminated the regional differences in economic structure, so
here only have the time dummy variables µi which use to represent the impact of time.
As indicators of industrial restructuring in the first, second and tertiary industries associated with each
other, the output value changes, so inevitably there will be heteroscedastic. Cross section weights that
the weights of the first to do the same right to return the original estimate, and then use the estimated
weights for the weighted least squares method, to reduce the cross-sectional data due to the impact of
heteroscedasticity. This article chooses this method. And select the weighted least squares regression
method, as in Table 1 can be seen from the table, all results are the mathematical test.
（ ，， ， ）
81
EASTERN ACADEMIC FORUM
Table 1 The regression results of the mode
Weighted Statistics
R-squared
0.987008
Mean dependent var
Adjusted R-squared
0.982190
S.D. dependent var
S.E. of regression
0.353893
Sum squared resid
Durbin-Watson stat
1.757056
Unweighted Statistics
R-squared
0.940759
Mean dependent var
Sum squared resid
30.05770
Durbin-Watson stat
4.950575
2.985942
30.05770
1.508538
1.266494
From Table 1, due to estimation method is selected Cross section weights and GLS estimates, the results
are given two evaluations of statistics in weighted and unweighted. We can see that R-squared of GLS
estimate with weighted make better, and D. W. statistic has improved markedly. This result in adjusted
R2 is 0.982190, showed a better fitting degree of model. DW value is 1.757056, and passed test.
Table 2 Regression coefficient of the some regions
Variable
Coefficient Std. Error t-Statistic Prob.
Variable
PIP /GDP
8.9676
Coefficient Std. Error t-Statistic Prob.
Jiangsu
0.0188
0.003569 5.267082
0
0
Shandong
0.020974
0.006168 3.400703 0.0008
Beijing
0.382106
0.04261
Tianjin
0.210464
0.018815 11.18627
0
Hubei
0.020599
0.007034 2.928435 0.0037
Hebei
0.127108
0.008151 15.59485
0
Guizhou
0.091181
0.035785 2.548021 0.0115
Liaoning
0.146097
0.041864 3.48976 0.0006
Xinjiang
-0.0252
0.008627 -2.92121 0.0038
Heilongjiang
0.111619
0.033736 3.308659 0.0011
Jiangsu
0.057971
0.0039
Shandong
0.043357
Hubei
14.86351
TIP /GDP
0
Beijing
0.005557
0.01738 2.494674 0.0133
Tianjin
0.018975
0.037409
0.01705 2.194054 0.0292
Hebei
0.030683
0.012128 2.529968 0.012
Guizhou
0.10149
0.017769 5.711751
0
Liaoning
0.041213
0.01563 2.636727 0.0089
Xinjiang
0.070359
0.015453 4.55322
0
Heilongjiang -0.05374
0.023364 -2.30007 0.0223
SIP /GDP
Jiangsu
0.002253 2.466743 0.0143
0.0041
4.627766
0
-0.02228
0.005898 -3.77753 0.0002
Beijing
0.01998
0.007267 2.74902 0.0064 Shandong
-0.01848
0.015127 -1.22191 0.0229
Tianjin
-0.00958
0.002233 -4.29052
Hubei
-0.01318
0.006471 -2.03746 0.0427
Hebei
-0.02585
0.006715 -3.84999 0.0002
Guizhou
-0.07032
0.033476 -2.10052 0.0367
Liaoning
-0.0306
0.009255 -3.30605 0.0011
Xinjiang
0.038846
0.016179 2.40108 0.0171
Heilongjiang
0.03232
0.01177 2.745866 0.0065
0
Note: The table only lists the area which three independent variables all through t test.
Through the quantitative analysis of between emissions of carbon dioxide and industrial structure on 30
regions in China, we draw the following conclusions: Data of table 2 show that industrial structure
makes a great impact on low-carbon economy. In general, in the industrial structure, the first, second
and third industries will both increase emissions of carbon dioxide, but increase of unit output will
82
EASTERN ACADEMIC FORUM
successive reduce. As economic level and industrial structure of 30 regions is far different, so the
influential coefficient in each region is also inconsistent. However, the data can be seen that the impact
of primary industry is positively correlated with carbon emissions. For the second industry, as long as
proportion of secondary industry above a certain extent, then with increase in the proportion of second
industry, carbon emissions will reduce. This result due to the growth of output in secondary industry is
faster than the growth of carbon emissions. For the tertiary industry, if the proportion of secondary
industry in the general level, the growth of tertiary industry will reduce emissions of carbon dioxide.
This result due to technology is not advanced, and then makes high impact of primary industry and
secondary industry on carbon emissions. For the third industry-dominated and developed areas, is in line
with the general situation.
3 Policy Suggestions
The influence of industrial structure on regional carbon emissions shows that there are vary greatly
difference among regions. The low-carbon economy must be based on their location and economic
conditions in every region, according as their comparative advantage, to promote the development of
key industries, and then optimize the industrial structure. Making a general observation impact of
industrial structure on development of low-carbon economy in all regions, we make the following
suggestions:
(1) We should develop the tertiary industry, and accelerate the adjustment of intensity and pace.
Technological advances in the tertiary industry is relying on human capital, norms of institutions and
other "soft power", and has more than regional and reproducibility compare with technological progress
in the second industry. So regions should learn from the advanced experience of foreign countries, and
emphasize ways of self-innovative to promote technological progress in tertiary industry.
(2) The development of secondary industry can not stop, but the mode of development should be
adjusted. The development of secondary industry should take style of introduction and innovation to
make technological progress, in which it is necessary to pay attention to digestion, absorption and
innovation of technology, and the form of technological introduction.
(3) Adjusting the energy structure. We should rely on technological progress, improve energy efficiency,
actively develop and promote new energy, expand use of renewable fuels, and improve fuel efficiency.
According to the actual conditions, various regions should use different new energy (wind, solar, etc.) to
promote low-carbon economy.
(4) Adjusting the industrial energy structure. We should accelerate the adjective pace of industrial
structure (industry structure, product structure) to the direction of the low-energy, reduce the proportion
of high energy-consuming industries and products, and promote industries with energy conservation and
high energy efficiency. Adjustment of industrial structure related to sustainable growth of economy and
national strategies for security. Adjustment of energy industrial structure needs leading of the national
and local governments. When national government regulate overall situation of macro-economic, they
should fully pay attention to the planning, guidance and supervision of energy industry, improve policies
of industrial development, put forward new and higher objectives and requirements of industrial energy
structure restructuring. They should take full advantage of policies which have formulated to indent
development of new and low carbon energy, and play its role in the guidance and encouragement of
industrial development. According to local conditions, local government also formulates supporting
policies to promote low-carbon economy.
References
[1]. Xu guoquan. Analysis on Decomposition Model of China's carbon emissions: 1995-2004. China
Population. Resources and Environment; 2006; 6: 16. (in Chinese)
[2]. Lin S J, Lu J, Lewis C. Grey relation performance correlations among economics, energy use and
83
EASTERN ACADEMIC FORUM
carbon dioxide emission in Taiwan. Energy Policy.2007.35 (3): 1948-1955.
National Bureau of Statistical of China. China Statistical Yearbook, 1999-2009. (in Chinese)
China energy Statistical Yearbook. China Statistical Press, 1999-2009. (in Chinese)
Gujarati D.N. Basic Econometrics; Bei jing: China Renmin University Press, 2000. (in Chinese)
Sturluson JT. Economic Instrument for decoupling Environmental Pressure from Economic
Growth [J]. Project Description, 2002, August13.
[7]. Panayotou T. Economic Growth and the Environmental. Economic Survey of Europe, 2003,
(2):45-72.
[8]. Ankarhem M. A Dual Assessment of the Environmental Kuznets Curve: The Case of Sweden.
Umea Economic studies, Umea University, Sweden. 2005,
[9]. Grubb M, Muller B and Butter L. The relationship between carbon dioxide emissions and
economic growth. Oxbridge study on CO2-GDP relationships, 2004.
[10]. WEI Wei, XIAN YangFang. Impact of Technology Advance on Carbon Dioxide Emission in China.
Statistical Research, 2010 (7):36-44. (in Chinese)
[11]. LIN Boqiang, YAO Xin. The Strategic Adjustment of Energy Structure under Constraints of
Energy and Carbon Emission in China. China Social Science, 2010(1):58-71. (in Chinese)
[3].
[4].
[5].
[6].
84