Download Analysis on the Effect of Technological Innovation Diffusion Based on

EASTERN ACADEMIC FORUM
Analysis on the Effect of Technological Innovation Diffusion Based on
the Carbon Emission Reduction
ZHOU Feixue1,2, LI Lianshui2
1. School of Economics and Management, Southeast University, Nanjing, China, 210018
2. China Institute of Manufacturing Development, Nanjing University of Information Science &
Technology, Nanjing, China, 210044
[email protected]
Abstract: Energy saving and emission reduction is an important content of the development of low
carbon economy. Some scholars believe that carbon emission reduction mainly should be executed from
the economic structure and the carbon emission intensity, but the view is difficult to be implemented in
the autonomous behaviors of industrial enterprises. The authors redefined the effect of technological
innovation diffusion of carbon emission and constructed technical innovation diffusion model based on
the theories of the low carbon economy and technological innovation diffusion, and then made the
empirical analysis. The results showed that: (1) The key to realizing the goal of reducing carbon
emission is to strengthen the carbon emission effect level of technology innovation diffusion-carbon
productivity (the reciprocal of the strength of carbon emission per unit of GDP); (2) The carbon
emission reduction effect of technology innovation and diffusion mainly depends on innovation rather
than imitation. These conclusions have positive theoretical and practical significance for industrial
enterprises to further standardize the economic behaviors, such as how to develop the measures of
energy-saving and emission reduction and how to achieve the goal of carbon emission reduction.
Keywords: Carbon emission, Carbon productivity, Technology Innovation Diffusion, Low carbon
economy
1 Introduction
China now is in the rapid development of industrialization and urbanization, so carbon emission
reduction became a great challenge in the development of China industrial economy. To achieve the
desired level of carbon emission, the general idea is to optimize the economic structure or to reduce the
intensity of carbon emission, or to improve energy standards, or to make and promote the measures
related to energy-saving emission reduction. This general idea already showed a path to energy-saving
emission reduction management for the government or the agency, but no consensus has been reached in
implementing it in enterprises and markets. In view of the diffusion of technological innovation and the
low carbon economic theory and method, the goal and the path of reducing carbon emission was
redefined and the relations was reanalyzed between technological innovation diffusion and carbon
reduction in the paper, which was expected to provide a consistent perception in promoting the path of
energy-saving and emission reduction for the government, enterprises and markets. The research has a
positive theoretical and practical significance to strengthen the level of technical innovation diffusion
related to energy-saving and emission reduction, or to strengthen the effect of the emission reduction in
the industrial sector technological innovation diffusion behavior.
2 Literature on the Effect of Carbon Emission Reduction
The literature on the carbon emission reduction mainly researched the influence factors of the carbon
emission reduction and the influences of carbon emission reduction on the energy and the economy,
which are related to elements like energy efficiency, economic level and technology innovation etc.
Wang (2005) believed that the energy intensity is the most important factor to reducing carbon
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emissions, and economic growth brings about more carbon emission; Xu (2006) indicated that the
contribution rate to China's per capita carbon emission grows exponentially with the meas of stimulating
economic development, while the contribution rate were tested inverted "U" by the meas of the energy
efficiency and energy structure to carbon emissions per capita; Sun (2011) pointed out the fact that
technological advances have led to the reduction in energy intensity is the most important factor causing
carbon emissions reduction in all manufacturing industries. Qi (2008) and Zhang (2009) pointed out that
an average annual rate of implicit CO2 emissions of Chinese exports accounted for about 1/3 of the total
emissions; Cole e.t al (2005) researched and pointed out that to improve productivity and increase
investment is conducive to reducing carbon emissions; Karen e.t al (2006) and Matthew e.t al (2008)
also expressed the same view and proposed that technological progress is one of the important ways to
decrease energy consumption and reduce carbon emission intensity; Li (2011) also pointed out that the
technology effect of FDI has a significant positive impact on carbon emission reduction.
Through the literature analysis, the general conclusions showed that economic growth causes the carbon
emissions to continue to increase and technological progress has a positive effect on the inhibition of
carbon emission, and economy and technology are all the influence factors of carbon emission reduction,
but in fact the relationship between technical and economic factors and carbon emissions is interactive
and integrative. So we will research this content to balance the relationship among economy, technology
and carbon emissions.
3 Factor Analysis of Technology Innovation Diffusion Effect of Carbon
Abatement
An industrial carbon emission is considered to be a negative output in the industrial production process.
The carbon emissions intensity per unit of GDP indicates the input-output relationship of the carbon
emissions. The basic expression of the carbon emissions intensity per unit of GDP is:
i Yt  yit  cit
Ct
(1)
ct 

  y it  cit
Yt
Yt
i
The change in carbon emissions intensity per unit of GDP also can be decomposed into two factors, the
change in the industrial structure and the change in carbon emissions intensity of the industrial sector
(technological progress) (Xu Guoquan, 2006).
c  ct  c0   yit  cit   yi 0  ci 0  [ yit  ci 0   yi 0  ci 0 ]  [ yi 0  cit   yi 0  ci 0 ]  r
i
In which,
ct
i
i
i
i
(2)
i
represents carbon emission intensity per unit of GDP in the period t;
Yt
is the GDP in
C t represents carbon emissions; yit represents the proportion of the GDP of the sector i
c
in the total GDP; it represents the carbon emission intensity per unit of GDP of the sector i; r is the
the period t;
surplus of decomposition.
From the above formula, the reduction of carbon emission intensity is relied on the optimization of
departmental structure and carbon emission intensity reduction of the next level departments. In fact, the
structure optimization of micro departments finally should regard the reduction of carbon emission
intensity as the optimization index, such as increasing the proportion of the departments with low carbon
emission intensity and reducing the proportion of the departments with high carbon emission intensity.
Therefore, the key to achieving the structure optimization and the carbon reduction of each department
is to reduce carbon emission intensity per unit of GDP.
In order to further analyze, in this paper the reciprocal industrial carbon emission intensity is called
carbon productivity and it is decomposed into the long-time trend and the fluctuating trend reflecting the
carbon productivity and its waveform based on the method of HP filter decomposition, as shown below
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in Fiigure1. (CP represents the actual carbon productivity; Trend represents long-term trend of carbon
productivity; Cycle represents the fluctuation of carbon productivity)
Through the Figure 1, trend curve of carbon productivity shows a S shape, which meets the cumulative
curve shape of technical innovation diffusion and demonstrates a regular pattern of technical innovation
diffusion, therefore, we can delimit the carbon productivity (the reciprocal of carbon emission intensity)
as technology innovation diffusion accumulation effect of carbon emission, which is the comprehensive
effect of technology, economy and carbon emissions etc.
.5
CP
Trend
Cycle
.4
.3
.03
.2
.02
.01
.1
.00
-.01
-.02
-.03
1980
Hodrick-Prescott Filter (lambda=100)
1985
1990
1995
2000
2005
2010
Figure 1 HP filter analysis of carbon productivity
4 Construction of Technology Innovation Diffusion Effect Model
4.1 The basic model
The basic BASS model is used to make a further analysis on the carbon productivity and to find out the
characteristic of the S -shaped curve; On the basis of the above views, carbon productivity is long-term
cumulative result of technology innovation diffusion. Therefore, by solving the differential equation,
carbon productivity can be obtained as follows:
- p  q)t
1  e（
(3)
Mt  M *
q
1  e ( p  q )t
p
Where M (t) is a cumulative value, M* is the ultimate goal of future value of carbon productivity to
achieve, p is the innovation coefficient of technological innovation diffusion of carbon productivity, and
q is the imitation coefficient of technological innovation diffusion of carbon productivity. The basic
assumption is that dynamic carbon productivity mainly comes from the development of technology,
given the basic assumption that enhancing carbon productivity mainly comes from the force of
technological developmental law.
4.2 The extended model
According to the fluctuation of carbon productivity and the extended BASS model, the external factors
are added into the new model proposed here.
M t  [M *
- p  q)t
1  e（
]e Wt
q ( p  q )t
1 e
p
(4)
Where λ is external influence coefficient; W is the external factors.
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5 Empirical Analysis
5.1 Data collection and processing
Carbon emissions data can be gathered from various types of industrial energy consumption, such as the
consumption of coal, coke, crude oil, gasoline, kerosene, diesel, fuel oil, natural gas and electricity. The
data of industrial energy consumption and GDP (1980-1994) are from the book "Energy Statistical Data
Compilation of China Industrial Transportation of 50 Years: 1949-1999", the data of the other years
(1995-2010) are from the database of National Research Network and China statistical yearbook. The
industrial GDP data are transformed according to constant prices of 2005 thus the influence of price is
eliminated. In addition, the calculation of carbon emission from this different energy consumption uses
carbon emission coefficients of the various energies which are from "Provincial Greenhouse Gas
Inventory Guidelines". (Climate No. [2011] 1041 of Development and Reform Office)
5.2 Parameters estimation of the model
Here the nonlinear least square method was used for parameter estimation, and the Levenberg
Marquardt method and universal global optimization method were used for optimization. The industrial
carbon productivity was studied firstly according to the data of 1980-2010, the calculation results
showed SSE(0.034), R(0.964), R^2(0.930), the coefficient of determination DC(0.911) and F-Statistic
(383.937); Thereafter technology innovation diffusion effect of carbon emission reduction of 1990-2010
was analyzed, and the calculation results showed SSE(0.076), R(0.975), R^2(0.950), DC(0.455) and
F-Statistic (359.626), and reached convergence criterion.
Table 1 Parameter estimates
Year
Parameters
Best estimate (1980-2010)
Best estimate (1990-2010)
M*
0.636 52
0.636 52
p
0.036 80
0.071 31
q
1.109 74E-16
6.531 34E-21
Table 2 Measured values and simulated values of data (10,000 Yuan/ton)
Measured
Simulated
Simulated
Measured
Simulated
values
values
values
Year
values
values
1980-2010
1980-2010
1990-2010
1980-2010
1980-2010
Simulated
values
1990-2010
1980
0.114 81
0.023 00
-
1981
0.120 44
0.045 17
-
1996
0.286 94
0.296 02
0.250 13
1982
0.122 22
0.066 53
-
1997
0.316 46
0.308 32
0.276 73
1983
0.125 87
0.087 13
-
1998
0.345 57
0.320 18
0.301 49
1984
0.134 79
0.106 98
-
1999
0.357 43
0.331 61
0.324 55
1985
0.149 33
0.126 11
-
2000
0.372 61
0.342 63
0.346 02
1986
0.151 99
0.144 55
-
2001
0.389 27
0.353 25
0.366 02
1987
0.158 51
0.162 33
-
2002
0.399 52
0.363 48
0.384 63
1988
0.170 55
0.179 46
-
2003
0.384 95
0.373 35
0.401 97
1989
0.169 48
0.195 97
-
2004
0.371 55
0.382 86
0.418 11
1990
0.169 50
0.211 89
0.043 81
2005
0.366 90
0.392 02
0.433 15
1991
0.182 46
0.227 23
0.084 60
2006
0.372 93
0.400 86
0.447 14
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1992
0.205 77
0.242 02
0.122 59
2007
0.395 28
0.409 37
0.460 18
1993
0.228 18
0.256 28
0.157 96
2008
0.417 83
0.417 58
0.472 31
1994
0.254 89
0.270 01
0.190 90
2009
0.430 31
0.425 49
0.483 62
1995
0.265 83
0.283 26
0.221 57
2010
0.442 44
0.433 11
0.494 14
Some inclusions were drawn from the above data.
(1) Accorded to technology development law, carbon emission reduction effect of technology innovation
diffusion of China’s industry (industrial carbon productivity) can achieve the maximum potential (M*),
which is maintained at about 0.637.
(2) Industrial carbon productivity innovation coefficient P value is far greater than the imitation
coefficient q value, which indicated that the industrial carbon productivity is mainly from innovation,
not imitation.
(3) It was discovered that innovation coefficient of the industrial carbon productivity of 1990-2010 years
is almost 2 times larger than that of 1980-2010 years, and the simulated values of carbon productivity of
1990-2010 years were much larger than the corresponding value of the 1980-2010 years, which suggests
that innovation in the carbon emission reduction effect of technology innovation diffusion would be
considered more and more significant.
6 Conclusion
Through the above theoretical analysis and empirical analysis, our basic views are below:
(1) The key to implementing carbon reduction targets is to strengthen the technological innovation
diffusion effect of carbon emissions, therefore, when formulating and implementing of the
energy-saving emission reduction policies and measures in the process of structure adjustment and the
transformation, we need consider whether these policies and measures can help improve the level of
carbon emission reduction effect of technology innovation diffusion;
(2) Considering the existing structure, scale and technology level, we also believe that the industrial
carbon productivity mainly comes from the innovation rather than imitation, which means that taking
direct and simple measures on carbon emission reductions straightforwardly in industrial enterprises are
not conducive to the implementation of the carbon reduction target. The industrial enterprises should
actively adopt the innovation and use fitting carbon reduction technology to enhance carbon
productivity in the process of production and operation, thus reducing carbon emission levels gradually
and sustainably.
In addition, the influence of industry differences and other factors had not been considered in the paper;
the authors will make a detailed analysis in the follow-up study.
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