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Research on the influence mechanism of carbon trading on new energy—A case study of ESER system for China
Journal Pre-proof Research on the influence mechanism of carbon trading on new energy—A case study of ESER system for China Guochang Fang, Longxi Lu, Linxin Tian, Yu he, Huibo Yin
PII: DOI: Reference:
S0378-4371(19)31989-2 https://doi.org/10.1016/j.physa.2019.123572 PHYSA 123572
To appear in:
Physica A
Received date : 21 August 2019 Revised date : 29 October 2019 Please cite this article as: G. Fang, L. Lu, L. Tian et al., Research on the influence mechanism of carbon trading on new energy—A case study of ESER system for China, Physica A (2019), doi: https://doi.org/10.1016/j.physa.2019.123572. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2019 Published by Elsevier B.V.
Highlights (for review)
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Highlights ► The influence mechanism of carbon trading on new energy is explored ► The existence of chaotic motion is approved by using digital simulation
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► Only the mature carbon trading market can boost new energy
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► Carbon trading and new energy are very sensitive to government control
*Manuscript Click here to view linked References
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Research on the influence mechanism of carbon trading on new energy ——A case study of ESER system for China
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Guochang Fanga,*, Longxi Lu a, Linxin Tian b, Yu he a, Huibo Yin a
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a
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China; b School of Mathematical Sciences, Nanjing Normal University, Nanjing, Jiangsu 210023,
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China
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Abstract: This paper attempts to explore the influence mechanism of carbon trading on new
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energy based on a novel nonlinear energy-saving and emission-reduction system. The dynamics
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behavior of the novel system is discussed, and
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School of Economics, Nanjing University of Finance and Economics, Nanjing, Jiangsu, 210023,
the existence
of chaotic motion
is approved by using digital simulation. With the aid of genetic algorithm-back propagation neural
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network, the quantitative coefficients of the actual system are identified according to
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Chinese statistics. The result of scenario analysis shows that carbon trading can drive the
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development of new energy under given conditions, the driving effect is closely related to a
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threshold. The mature carbon trading can control carbon emissions and energy intensity effectively.
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The more perfect carbon trading market can better promote the development of new energy when
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meets such constraints. Carbon trading and new energy are very sensitive to government control.
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*
Corresponding author. Tel.: +86 (025) 84028202; fax: +86 (025) 84028202.E-mail addresses: [email protected] (G. Fang).
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Grasping the reasonable boundary of government and market is the key to realize sustainable
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development of carbon trading and new energy.
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Keywords: Carbon trading; new energy; carbon emissions; energy intensity
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1. Introduction
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Carbon trading, as a kind of market mechanism, whose main commodity is emission rights of
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greenhouse gas [1, 2]. The trading opportunities will exist if the allocation of carbon emissions
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quotas is not optimal. When all trading opportunities are fully utilized, carbon trading achieves
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optimal efficiency [3, 4]. From this perspective, carbon trading can reduce the total cost of
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emission reduction and achieve the unification of environmental and economic benefits [5-7].
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Objectively speaking, to achieve the maximum efficiency of emission reduction, carbon trading
27
needs to be coordinated with other environmental policies [8]. Meanwhile, the implementation of
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carbon trading will promote the development of new energy [9].
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As a substitute for traditional energy, new energy has an impact on all aspects of business
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production and household life [10, 11]. Developing new energy can effectively reduce greenhouse
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gas emissions and environmental pollution [12]. For developing countries, new energy also brings
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opportunities to eliminate energy poverty, stimulate production and improve environmental
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benefits [13-15]. The use of new energy means a cleaner environment, especially for today's
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large-scale frequent outbreaks of haze [16]. A survey showed that 80% of residents were willing to
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pay an extra fee to support the use of new energy buses to get cleaner air [17]. Therefore, it is an
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irreversible trend for new energy to replace traditional fossil energy [18].
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To achieve energy conservation and emission reduction, both "open-source" and "cut-costs" are
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needed. We can cut costs by reasonably distributing existing energy resources and improving
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energy efficiency [19]. We can also open source by increasing the types of energy available and
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developing new sources of energy. Reducing the reliance on fossil energy is even more important
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under the current resource situation [20, 21]. The use of new energy contributes to reducing the
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share of fossil energy consumption [22, 23]. The implementation of carbon trading can cut the
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cost of emission reduction and improve energy efficiency [24, 25]. Therefore, both carbon trading
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and new energy are effective measures to open source and cut cost. They are of great significance
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for energy saving and emission reduction [26].
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There are many research achievements in the field of carbon trading and new energy. But there
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is little research on the interaction between them. Fisher and Preonas [27] believed that the
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compatibility of various environmental policies in emission reduction targets, mechanism and
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effectiveness needs to be systematically studied. Wang and Guo [28] studied the asymmetric
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spillover effect between carbon trading market and energy market on the yield rate and volatility.
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Then they analyzed the information and risk transmission mechanism between the two markets.
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Müsgens [29] took the European electricity market as an example to analyze the relationship
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between European emission trading schemes and renewable energy subsidies. Mo et al. [30] used
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the real option model to evaluate the impact of China's carbon trading system on wind power
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investment and analyzed the coordination of the two environmental policies. They found that
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unstable carbon price would dampen investment in new energy. But the stable carbon price could
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reduce the risk of investment in new energy.
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Based on the current situation of carbon trading and new energy in China, this paper constructs
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a carbon trading system with multi-variable constraints and discusses the driving mechanism of
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carbon trading to new energy. There are few discussions on the interaction between carbon trading
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and new energy in previous studies. The variables in these studies were relatively single, failing to
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reflect the nonlinear properties and make comprehensive analysis. This paper establishes a
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nonlinear coupled system and strengthens the theoretical foundation of the interaction between
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carbon trading and new energy. This paper visualizes the evolution by dynamics method and gives
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suitable suggestions in line with national conditions. Compared with previous studies, this paper is
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more vivid and the conclusions are more applicable.
The rest of this paper is organized as follows. Section 2 provides a brief description of the
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model developed for this study. Section 3 is about parameter identification of the actual system
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based on the statistical data in China. Section 4 is about a scenario study of the actual system.
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Implications of the research for government policy are presented in Section 5. Conclusions and
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further perspectives are discussed in Section 6.
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2. ESER Model
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Both new energy and carbon trading policies are important components of environmental
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policy tools. The combination of them will expand their respective impact on the energy-saving
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and emission-reduction (ESER) system. The implementation of carbon trading promotes the
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development of new energy, and then promotes the process of energy conservation and emission
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reduction. At the same time, carbon emissions, economic growth and other variables in ESER
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system also affect carbon trading and new energy.
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Based on the evolutionary relationship among new energy, carbon emission and economic
growth, a nonlinear three-dimensional ESER system can be constructed as following:
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(1)
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Where
83
new energy;
84
period. Explanation of other coefficients please refer to the nomenclature.
is abbreviated as , the same as
, of economic growth.
Nomenclature development coefficient of
influence coefficient of
to
influence coefficient of
to
influence coefficient of
to
development coefficient of to
influence coefficient of
to
influence coefficient of
to
inflection of
to
to
to
inflection of
to
inflection of
to
inflection of
to
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Abbreviations
BP
to
back propagation
GDP
gross domestic product
influence coefficient of
ESER
energy-saving and emission-reduction
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influence coefficient of investment to
to
development coefficient of
influence coefficient of
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is a given economic
threshold value of
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influence coefficient of
inflection of
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influence coefficient of
influence coefficient of
,
p ro
, of carbon emissions;
and ) is the time-dependent variable of
of
(
In the first equation,
Subscript
to
component labels
indicates that the potential of new energy will affect the
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( x in short, the same as y and
z ). When
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development speed of new energy
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the total amount of carbon emission is less than a certain threshold and the energy consumption is
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small. External pressure for the development of new energy is small, so the development speed of
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new energy is slow. When
90
urgent to develop new energy to alleviate the energy gap and control carbon emission. The
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economic input will promote the development of new energy ( a2 z ).
of
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, with high energy consumption and large carbon emission, it is
In the second equation, the development of new energy can inhibit carbon emissions
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means when
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,
b1 x
, the threshold of carbon emissions has not
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arrived, the growth rate of
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gradually slows down after reaching the threshold (the system (1) is a highly coupling and
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nonlinear system. At this point,
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, the growth rate of
is affected by other variables in the system. So
slows down after reaching the threshold).
means when
, before reaching the inflection point, industry is the main driver of economic growth. The large
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is fast. When
consumption of fossil energy causes a lot of carbon emissions by industry, the growth rate of
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is fast. When
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upgraded. The demand for fossil energy decreases and the growth rate of
, after reaching the inflection point, the industrial structure is
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slows down.
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In the third equation,
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, the large
early investment to new energy plays a negative role in economic development. When
104
, with the maturity of new energy industry, investment in new energy promotes
of
105
indicates that when
economic development. c2 y represents the effect of carbon emissions on economic growth.
is the influence coefficient of new energy economic input to
p ro
102
, and the value of
will
107
change over time.
108
of new energy economic input to
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represents the restraining function on
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energy development, the huge economic input to new energy will stifle economic growth.
,
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, which can be divided into two parts
.
, promoting one. In the initial stage of new
;
. When the new energy (system (1)) is developed to a certain level, the input
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gives a comprehensive reflection of promoting function and restraining one
can generate higher economic profits, and create new sources of economic growth. The effect
113
becomes positive.
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,
. The same as
.
The implementation of carbon trading will bring a series of effects to ESER system. On the
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basis of the complex evolutionary relationships between new energy, carbon emission, economic
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growth and carbon trading, the nonlinear four-dimensional ESER system is constructed under the
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comprehensive consideration.
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(2)
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Where
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coefficients please refer to the nomenclature. The comparative analysis between system (1) and
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system (2) can highlight the impacts of carbon trading on ESER system. Compared with system
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(1), the effects of carbon trading on the corresponding variables are shown in the following
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chapters. The evolution analysis of the two systems can be found in section 4.
of
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Pr e-
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is the time-dependent variable of carbon trading volume. Explanation of the
In the first equation,
indicates that when
, the scope
125
of carbon trading is small, or the market mechanism is immature. Its driving effect on new energy
126
is not significant. When
127
which has a good driving effect on new energy. In the second equation, b4u indicates that the
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implementation of carbon trading can reduce carbon emissions. In the third equation, c4u
129
represents the comprehensive effect of carbon trading on economic growth. c4 can be divided
130
into two parts
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function.
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performance of c4u is depressing the economy. The comprehensive effect will be positive
, the development of carbon trading is mature,
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(
outstrips
.
represents the restraining function on
in the initial stage of carbon trading
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;
, promoting
, the
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(
) when the carbon trading market is mature.
In the fourth equation,
reflects the impact of changes in supply and demand
135
on carbon trading. When carbon emission exceeds the threshold value (
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emission rights is larger than the supply. In order to meet the government's emission target, carbon
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trading is active and the volume will naturally increase (
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carbon emission is low (
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demand, the carbon trading volume will decrease (
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trading can all promote the process of ESER. When new energy develops to some extent, it can
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substitute the role of carbon trading in promoting ESER ( d 2 x ). Economic inputs can speed up
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carbon trading development ( d3 z ).
p ro
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), the demand for
). On the contrary, when
). New energy and carbon
al
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), the supply of emission rights in the market is greater than the
System (2) will show different dynamic behaviors with various coefficients. After a lot of
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debugging and numerical simulation, it is found that when the parameters of system (2) are taken
145
as Table 1 and given the initial value as [0.015 0.758 1.83 0.01] (it has no practical meaning,
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which might just mean that the system will have chaos phenomenon in this case), the system will
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show very interesting dynamic behavior. A chaos attractor can be obtained. Fig. 1 shows the
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three-dimensional graph of the attractor.
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149 150
Table 1
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System parameter.
a2
a3
b1
b2
b3
b4
c1
c2
c3
0.1
0.005
0.012
0.412
0.088
0.8
0.072
0.035
0.008
0.1
c4
d1
d2
d3
M
C
E
0.025
0.016
0.002
0.0012
1.0
1.6
3.46
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a1
F
P
2.58
0.35
0.9222
p ro
N
A deterministic system is constrained by deterministic conditions. A certain solution or process
153
must be obtained if the initial value is given. Chaos is a seemingly random irregular motion in a
154
deterministic system, which is characterized by uncertainty, unpredictability and sensitivity to the
155
initial value. A minimal change of initial value will lead to the great change of solution. The most
156
intuitionistic method to judge the existence of chaos in a deterministic system is Lyapunov
157
exponent. Lyapunov exponent can measure the sensitivity of system motion to initial values.
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When the maximum Lyapunov exponent is greater than zero, the existence of chaos in the system
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can be proved.
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160 161
Let
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Fig. 1. ESER attractor with carbon trading constraints (4D ESER system).
be arbitrary and the other parameters are fixed as Table 1. Two diagrams can be
obtained. Fig. 2 is maximum Lyapunov exponent spectrum. Fig. 3 is sensitivity diagram to the
164
initial value. The maximum Lyapunov index is greater than zero in Fig. 2. Besides, Fig. 3 shows
165
that a very small change in the initial value can make a big difference. It means system (2) is very
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sensitive to initial values. Both Fig. 2 and Fig. 3 can prove the existence of chaos in system (2)
167
from the view of numerical simulation.
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Fig. 2. Lyapunov exponent spectrum of the 4D ESER system (b2).
170 171
172
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Fig. 3. The error value with changing initial conditions.
Take the parameters of system (1) as Table 1, let the initial value be [0.015 0.758 1.83]. Fig. 4
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shows the maximum Lyapunov exponent spectrum (from the perspective of simulation, if the
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maximum Lyapunov exponent is greater than zero, it is concluded that the system has chaos
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phenomenon). From the above analysis, it can be concluded that the three-dimensional ESER
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system (1) is also a chaotic system.
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Fig. 4. Lyapunov exponent spectrum of the 3D ESER system (b2).
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3. Parameter identification
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3.1. Statistical data
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The new energy evolution system is established on the complex relationship of mutual
182
promotion and mutual restriction among new energy, carbon emission, economic growth and
183
carbon trading. The identification of system parameters is of great realistic importance. To
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calculate the parameters of actual system, this paper collected the monthly statistical data of new
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energy, carbon emission, economic development and carbon trading in China. The range of data is
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from Jan. 2014 to Dec. 2017. The data in Dec. 2013 is taken as the base period. The processed
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data is shown in Table 2.
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Table 2
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Data of new energy, carbon emissions, economic growth and carbon price.
p ro
z
u
time
x
y
z
u
201401 0.8826
1.1144
0.8900
0.1693
201601
1.3439
0.9853
1.0689
2.0896
201402 0.7651
1.0789
0.8616
0.3241
201602
1.3940
1.0330
1.1207
4.4415
201403 0.9663
1.0433
0.8332
0.6704
201603
1.4441
1.0806
1.1724
12.7726
201404 1.0527
1.0315
0.9341
0.7004
201604
1.4655
0.9535
1.2354
2.0147
201405 1.2822
1.0433
0.9449
3.5256
201605
1.7437
0.9535
1.2354
7.3397
201406 1.4222
1.0907
0.9878
15.6288
201606
1.8943
0.9853
1.2766
20.5928
201407 1.7667
1.0670
1.1275
6.9629
201607
2.1117
0.9535
1.2810
5.0603
201408 1.8256
0.8180
0.8644
1.2239
201608
1.9315
1.0012
1.3451
2.3358
201409 1.7811
0.9485
201410 1.5757
0.9129
al 1.0022
1.1128
201609
1.6978
0.9694
1.3024
1.4192
1.1200
0.9314
201610
1.7239
0.9694
1.4308
1.1768
1.3057
0.8536
1.0472
1.3562
201611
1.6149
0.9853
1.4543
1.9722
201412 1.1599
0.9366
1.1491
3.4795
201612
1.4269
0.9535
1.4074
4.0383
201501 1.0285
1.1550
1.0326
2.0007
201701
1.4770
0.9873
1.1080
4.2503
201502 0.8970
1.0308
0.9216
1.3504
201702
1.5270
1.0679
1.1984
1.0177
201503 1.2437
0.9067
0.8107
3.8656
201703
1.5770
1.1485
1.2889
2.4172
201504 1.2834
0.9553
0.9625
3.9643
201704
1.6256
0.9823
1.2640
5.6588
201505 1.3852
0.9877
0.9952
4.6740
201705
1.8202
0.9823
1.2640
10.1373
201506 1.7315
1.1010
1.1094
8.5942
201706
1.9493
1.1485
1.4779
20.7179
201507 1.7929
0.9715
1.0784
15.9105
201707
2.2364
0.9672
1.4139
7.7271
201508 1.7217
0.9877
1.0964
1.3099
201708
2.1351
0.9067
1.3255
0.7615
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y
time
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0.9229
1.0245
4.4812
201709
2.0846
0.9974
1.4581
2.6918
201510 1.6787
0.9067
1.1207
2.5047
201710
2.0405
0.9370
1.5357
2.9146
201511
1.3941
1.0038
1.2408
4.8729
201711
1.8718
0.9219
1.5109
3.3352
201512 1.2937
0.9553
1.1807
4.2504
201712
1.6730
0.9370
1.5357
1.5656
The main utilizing form of new energy is electricity generation. Therefore, this paper chose the
191
sum of the four new energy — hydro-energy, nuclear energy, wind energy and solar energy to
192
measure the development of new energy in China. Electricity generation of new energy is derived
193
from the sum of hydroelectricity, nuclear, wind and solar power. The data comes from the website
194
of China's national bureau of statistics. The development of carbon trading can be measured by the
195
turnover in China’s carbon trading market. The larger the turnover, the better the development of
196
carbon trading. Total trading volume of China is the sum of eight carbon trading markets—Beijing,
197
Fujian, Guangdong, Hubei, Shanghai, Shenzhen, Tianjin and Chongqing. The data comes from
198
China carbon trading network (http://k.tanjiaoyi.com).
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Monthly data of new energy and carbon trading can be collected directly. However, monthly
200
data of economic growth and carbon emission cannot be collected directly. The data of economic
201
growth and carbon emissions needs to be split. Gross domestic product (GDP) represents the level
202
of economic development. But the minimum statistical frequency of GDP is quarterly. We can
203
split GDP into monthly data weighted by year-on-year growth rate of industrial added value. Both
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of them come from the website of China's national bureau of statistics. The data of carbon
205
emission is downloaded from Statistical Review of World Energy 2018 published by British
206
Petroleum. The frequency of this data is year，so we also split carbon emission into monthly data
207
weighted by year-on-year growth rate of industrial added value.
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There are two reasons to choose year-on-year growth rate of industrial added value to split
209
data. On the one hand, industry plays a leading role in national economy and is the most important
210
driving force for economic growth. On the other hand, the industrial sector is the largest consumer
211
of fossil energy. Energy consumption and carbon emission can be roughly estimated by the growth
212
of the industry. Using the same economic index to split GDP and carbon emission can maximize
213
the consistency and rationality. Splitting annual data into monthly data can enlarge the sample size,
214
which will help to improve the accuracy of identification parameters.
215
3.2. Parameter acquisition
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Neural network is a nonlinear system composed of large number of simple computing units. It
217
imitates the function of information storage and processing from human brain’s nervous system
218
[31]. Back propagation (BP) neural network is one of the most widely used neural networks. BP
219
neural network is simple in structure and calculation. It has significant advantages in dealing with
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220
nonlinear problems. It is widely used in function approximation, intelligent control, economic
221
prediction and other areas.
Using BP neural network to identify parameters, the first step is data processing. The former
223
n-1 group data is divided into input data; the latter n-1 group data is divided into output data. All
224
the variables are normalized. Then choose appropriate feedforward neural network and set all
225
adjustable parameters as random numbers. Finally, compare the result with the target and calculate
226
the error— . After several times of running and debugging, when error
227
value, the parameters of the actual system can be determined.
p ro
Pr e-
is less than a specific
x k 1 x k T a1 x k y k M a2 z k a3u k u k N 1 y k 1 y k T b1 x k b2 y k 1 y k C b3 z k 1 z k E b4u k z k +1 z k T c1 x k x k F 1 c2 y k c3 z k c4u k u k 1 u k T d1u k y k P 1 d 2 x k d 3 z k
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of
222
(3)
This paper uses BP neural network to calculate the actual parameters. First, discretize system
230
(2) and get the difference equation (3). Then, take the first 47 groups of data in Table 2 as input
231
data and the last 47 groups of data as output data. Finally, calculate the actual parameters shown in
232
Table 3,
233
acquisition, small error and stable system. Objectively speaking, we can get the smaller
234
However, system (2) will be unstable under this smaller error
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. The principles should be followed in the process of parameter
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.
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Table 3
236
Parameters of the actual system.
a3
b1
b2
b3
b4
c1
c2
c3
0.3927
0.2037
0.8952
0.0139
0.1229
0.7954
0.3133
0.3110
0.9192
0.0035
c4
d1
d2
d3
M
C
E
N
F
P
0.7012
0.4818
0.1125
0.1277
1.955
0.2576
0.7085
0.5871
0.1417
0.9222
of
a2
p ro
237
a1
4. Scenario analysis
To analyze the dynamic evolution relationship among new energy, carbon emission, economic
239
growth and carbon trading, this section will start from the changes in key parameters of the system.
240
In this section, we will analyze the dynamic evolution behavior and combine it with the actual
241
scenario. The data of Dec. 2013 are selected as initial conditions. The initial value can be
242
converted into [0.0027 0.0323 0.311 0.00000021] in units of
Pr e-
238
al
tons of standard coal.
represents the influence coefficient of carbon trading to new energy. Change the value of
244
and fix the remaining parameters as Table 3. Fig. 5 is the evolution diagram of new energy
urn
243
245
when
246
evolution tendency of new energy more concisely and clearly, an optimization method is adopted.
247
Take case 1 in Fig.5 as example, the timeline of case 1 is divided equally. The maximum value in
248
every small range is singled out, and these values are connected by a curve which is as smooth as
Jo
gradually increases. In fact, the evolution curves in Fig.5 are oscillatory. To reflect the
19
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249
possible. Repeat the previous steps until the evolution curve is monotonic (the same as
250
Fig.6-Fig.9). The red curve (case 1) in Fig. 5 represents the evolution trend of new energy when
=0.6952; the blue curve (case 2),
=0.7952; the green curve (case 3),
=0.8952. The brown
of
251
curve (case 4) is the new energy evolution diagram of the 3D ESER system (1) (the same as Fig. 6
253
and Fig. 9). The 2020 is the end of the "13th Five-Year Plan" (the same as Fig. 6-Fig. 9). For
254
system (1), when
255
economic inputs (bigger
) to guarantee ESER system reliability. So, case 4 in Fig. 5
256
corresponds to
=0.1235. The other parameters are the same as the ones in Table 3.
p ro
252
257 258
259
Jo
urn
al
=0.5041,
Pr e-
<0.5041, it will crash. Compared with system (2), new energy needs more
Fig. 5. The evolution diagram of new energy (
).
By observing Fig. 5, we find that the red curve is the highest, followed by the blue curve, and
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260
then the green curve. The three curves are higher than case 4, i.e. carbon trading brings new
261
energy better development. As for case 1-3, the increase in
262
indicates that the development of carbon trading is out of step with the development of new energy.
263
This is obviously contradictory to economic theory. Usually, the development of carbon trading
264
will raise the cost of fossil energy and increase the competitiveness of new energy. Carbon trading
265
encourages producers to continually improve new energy technologies and sell surplus carbon
266
quotas on the market. It can help producers reduce production costs and achieve additional
267
economic benefits. Therefore, in theory, carbon trading plays a role in promoting new energy.
270
of
p ro
Pr e-
a very important parameter.
is
represents the inflexion of carbon trading to new energy. When
al
269
What is the cause of the conflict in Fig. 5? Look back at the first equation in system (2),
, the development of carbon trading is not mature. Its driving effect on new energy is not
urn
268
leads to the lower curve. It
271
significant or even inhibiting. When
272
effect on new energy. Therefore, we take the change of
, carbon trading is mature and has a good driving
Jo
into consideration. Change the value of
273
and fix the remaining parameters as Fig. 5. Fig. 6 is the evolution diagram of new energy when
274
increases and
275
new energy when
decreases. The red curve (case 1) in Fig. 6 represents the evolution trend of
=0.6952 and
=0.5871; the blue curve (case 2),
21
=0.7952 and
=0.2871;
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the green curve (case 3),
=0.8952 and
=0.0871.
Pr e-
p ro
of
276
277
279
Fig. 6. The evolution diagram of new energy (
By observing Fig. 6, we find that when
,
).
becomes smaller, the green curve is obviously
al
278
higher than the one in Fig. 5. The value is 109% bigger than the green curve in Fig. 5 at the year
281
of 2020. The green curve is higher than the red and blue curve. It means carbon trading
282
significantly promotes the development of new energy. Only when
283
threshold, can the change in carbon trading keep pace with the development of new energy. In this
284
case, carbon trading can play a role in promoting new energy. Fig. 6 perfectly explains the
285
contradiction in Fig. 5. It is the threshold value
286
very important for the development of carbon trading and new energy to reach this threshold value
is less than a certain
Jo
urn
280
22
that causes the contradiction in Fig. 5. It is
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287
288
as soon as possible.
is the influence coefficient measuring the impact of carbon trading development on carbon
289
emissions, which reflects the emission reduction efficiency of carbon trading. Vary
290
other coefficients unchanged, Fig. 7 shows the evolution diagram of carbon emissions when
291
gradually increases. The red curve (case 1) in Fig. 7 represents the evolution trend of carbon
292
emissions when
of
p ro
=0.3133; the blue curve (case 2),
=0.5133. The curve is getting lower and lower as
=0.4133; the green curve (case 3),
increasing, the value of case 3 is 59.7%
Pr e-
293
and keep
lower than the one of case 1 at the year of 2020. Carbon trading can effectively restrain the
295
increase of carbon emissions. The more mature carbon trading system is, the more it can inhibit
296
the increase of carbon emissions.
Jo
urn
al
294
297
23
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298
299
Fig. 7. The evolution diagram of carbon emissions
).
Energy intensity is an important index to measure the utilization effect of energy resource,
300
which can reflect the effect of energy saving. Fixed the value of
301
diagram of energy intensity is shown in Fig. 8. Case 1-3 are the same as the ones in Fig. 7 (case 1,
of
=0.3133; case 2,
=0.4133; case 3,
p ro
302
as above, the evolution
=0.5133). When
increases gradually, the curve is
getting lower and lower. Energy intensity in case 3 is 55.7% lower than the one in case 1 by the
304
year of 2020. Result shows that carbon trading really can reduce energy intensity. Carbon trading
305
adds the cost of using fossil energy to some extent. These can help the development and
306
popularization of new energy, thereby lowering energy intensity.
Jo
urn
al
Pr e-
303
307 308
Fig. 8. The evolution diagram of energy intensity ( ).
24
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309
represents the development coefficient of carbon trading under the influence of supply and
310
demand. The larger
311
the remaining parameters as Table 3. Fig. 9 is the evolution diagram of new energy when
312
gradually increases. The red curve (case 1) in Fig. 9 represents the evolution trend of new energy
313
when
314
the increase of
315
40.2% bigger than the one of case 1, and 99.2% bigger than case 4 by the year of 2020. It means
316
that the faster the carbon trading develops, the greater the promotion to new energy.
and fix
=0.4818; the blue curve (case 2),
p ro
of
, the faster development of carbon trading. Change the value of
=0.5818; the green curve (case 3),
=0.6818. With
317 318
319
Jo
urn
al
Pr e-
, the evolution curve of new energy increases gradually. The value of case 3 is
Fig. 9. The evolution diagram of new energy (
On the contrary, Fig. 10 shows the evolution diagram of new energy when
25
gradually
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320
decreases. The green curve (case 1) represents the evolution trend of new energy when
321
the blue curve (case 2),
322
sharply. When
323
fluctuate below the zero line and once rise above the zero line. Eventually, the blue curve stops
324
running at
325
less than a certain threshold value, the system will crash (system (2) will stop running under this
326
situation, and only when the constraint conditions are changed, the system can run again). The
327
smaller the parameter is, the earlier the system will crash. Therefore, the slow development of
328
carbon trading will hinder the development of new energy.
=0.1100. Unlike Fig. 9, the green and blue curves in Fig. 10 fluctuate
, two curves
p ro
of
, two curves fluctuate around the zero line. When
. This indicates that if
Jo
urn
al
Pr e-
, the green curve stops running at
329 330
=0.1200;
Fig. 10. The evolution diagram of new energy (
26
gradually decreases).
is
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331
represents the influence coefficient of economic growth to carbon trading. The government
332
mainly controls carbon price and consummates carbon trading system through economic means,
333
such as financial support. So, to some extent,
334
carbon trading. Since system (2) is a nonlinear coupled system, any change in the coefficient will
335
cause the change in the entire system. The change of
336
affects the evolution trend of new energy. Fig. 11 shows the evolution diagram of new energy
337
when
338
energy when
p ro
of
can also measure the government's regulation to
not only affects carbon trading, but also
Pr e-
gradually increases. The green curve (case 1) represents the evolution trend of new
=0.9906.
339 340
341
Jo
urn
al
=0.9306; the blue curve (case 2),
Fig. 11. The evolution diagram of new energy (
gradually increases).
When the system only runs to around t=0.05, two curves begin to drop below zero and
27
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342
fluctuate greatly. Eventually, the blue curve stops running at
, the green curve stops
343
running at
, the system will crash.
344
With the increase of
345
market is still in its infancy. Carbon trading market is a policy market with low degree of
346
marketization. Therefore, the carbon trading market cannot be separated from the regulation and
347
support from government. However, through the above analysis, we can see that the government's
348
regulation and control should be moderate. The government should keep its control within a
349
reasonable range for sustainable development. Policy makers should not only give full play to the
350
function of macro-control, but also grasp the reasonable boundary between the government and
351
the market.
. Simulation results show that when
al
Pr e-
p ro
of
, the time of crash will be earlier. At present, China's carbon trading
New energy evolution system is based on the complex relationship between new energy,
353
carbon emissions, economic growth and carbon trading. The study of the complex relationship
354
between variables is helpful to further clarify the key problems in ESER system. It also helps
355
reveal the key to the evolution of these variables. Fig. 12 shows the relationship between carbon
356
trading, new energy, carbon emissions, economic growth and energy intensity. The two-way arrow
357
in the diagram shows the interaction between the five variables, forming an organic whole. Any
Jo
urn
352
28
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variable will affect the other variables through the conduction of the system. Therefore, Fig. 12 is
359
a visual representation of system (2). System (2) is the abstract and mathematical expression of
360
Fig. 12.
In the first equation, the relationship between carbon trading and new energy is discussed by
. The direct influence path corresponds to ① in Fig. 12.
p ro
361
of
358
362
analyzing the variation of
363
Carbon trading can also indirectly affect the new energy through other paths. When
364
a certain threshold, the change of
365
trading promotes new energy. When
366
appears. Carbon trading inhibits the development of new energy. In the fourth equation, we
367
analyze the influence of
368
carbon trading. The faster the carbon trading develops, the greater the contribution to new energy.
369
Conversely, the lagging development of carbon trading will hinder the development of new energy
370
and even lead to the collapse of the whole system.
371
energy. It corresponds to ③ in Fig. 12. When government intervention exceeds a certain
372
threshold, it will lead to the collapse of the system.
is less than
is consistent with the development of new energy. Carbon
Pr e-
and
on new energy.
reflects the development speed of
Jo
urn
al
and
is greater than this threshold, the contradiction to reality
29
measures government regulation on new
p ro
of
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373 374
Pr e-
375
Fig. 12. Interaction diagram.
In fact, carbon trading can also drive variables through other conduction pathways. As in the
376
second equation, changes in
377
trading, more and more enterprises participate in carbon trading. The development of carbon
378
trading has effectively curbed the growth of carbon emissions, corresponding to ② in Fig. 12. As
379
the system develops, carbon trading will affect energy intensity. The conduction mechanism of
380
carbon trading's impact on energy intensity is complex. This is the combined effect of ① ⑦
381
⑥ ⑤, ⑧ ③ ⑦ ④ and other paths. Similarly,
382
affecting carbon emissions. Carbon trading can also indirectly promote the development of new
383
energy through
Jo
urn
al
will affect carbon emissions. With the improvement of carbon
also affects new energy by
in the third equation. Due to space constraints, these driving effects will be
30
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384
further explored in future studies.
385
5. Policy enlightenment
China's total energy consumption has been rising steadily, ranking first in global energy
387
growth for many consecutive years. In terms of energy consumption and energy structure, China is
388
facing enormous pressure to address these thorny issues. In this sense, carbon trading is expected
389
to solve the present bottle-neck of resource and environment. Evolution results show that mature
390
carbon trading market can effectively control carbon emissions (Fig. 7). Carbon trading can urge
391
the related enterprises to improve energy utilization efficiency by technology, management and
392
other means. Carbon trading started late in China but is growing fast. As of December 31, 2018,
393
total carbon trading volume reached 163.35 million tons, total turnover reached 3.53963 billion
394
yuan. These numbers, however, are far from enough compared with large carbon emissions. To
395
cope with climate change and achieve emission reduction targets, China must accelerate the
396
construction of carbon trading market. Given the size of China's energy consumption and the
397
status of ESER, there is still huge room for carbon trading in the future.
Jo
urn
al
Pr e-
p ro
of
386
398
Developing new energy is the important way to promote the process of energy conservation
399
and emission reduction. At present, China's new energy resources have the conditions for
31
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large-scale development. China accounts for about 30 percent of the world's hydropower capacity
401
and a sixth of global geothermal resources. The control effect of new energy on energy intensity
402
can be predicted after a series of evolutions (Fig. 12), it can reduce energy intensity through
403
carbon trading (Fig. 8). It must be noted that, the lagging carbon trading market is not conducive
404
to the development of new energy. It is foreseeable that coal and oil will play an unshakable role
405
in China's energy production and consumption for a long time to come. However, new energy
406
such as natural gas, hydropower, solar energy, nuclear energy and wind energy also have a strong
407
momentum and broad space for development. By vigorously developing new energy, we will be
408
able to diversify the energy structure. In this way, energy conservation and emission reduction
409
targets can be achieved as soon as possible.
al
Pr e-
p ro
of
400
As important parts of environmental policy tools, carbon trading and new energy promotion
411
policies have been introduced simultaneously in many countries [32]. There are both synergies
412
and conflicts between carbon trading and new energy [33]. Through the above analysis, it is found
413
that carbon trading will inhibit the development of new energy when carbon trading system is
414
immature (Fig. 5). Only when carbon trading reaches a certain scale can carbon trading promote
415
the development of new energy. In the ideal economy without market failures, carbon trading
Jo
urn
410
32
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participants make the most of every trading opportunity to minimize the cost of emission
417
reduction. So, it would be superfluous to implement other emission reduction policies in the ideal
418
economy [34]. However, there are market failures and transaction costs in reality. Carbon trading
419
fails to achieve theoretical efficiency. So, carbon trading must be coordinated with other
420
environmental policies. New energy promotion policies can alleviate market failure. New energy
421
promotion policies can improve the design of carbon trading mechanism and reduce its uncertainty.
422
Policy makers should coordinate carbon trading and new energy to maximize their synergies in
423
energy saving and emission reduction.
Pr e-
p ro
of
416
Long-term since, the development of new energy is closely related to government control. As
425
an emerging industry, the initial investment to new energy industry is huge and the investment
426
cycle is long. New energy industry has high risks of technology and market. Therefore, support by
427
government and market is one of the most direct and effective ways to promote new energy
428
development [35]. On the contrary, when the government cancels the support policy, it may cause
429
adverse impact on new energy development. The experience of photovoltaic industry in Germany,
430
Italy and Switzerland had proven this point [36]. To realize the sustainable development of new
431
energy, the government should formulate new energy development strategy and implement
Jo
urn
al
424
33
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reasonable industrial policies. On the other hand, policy makers should also grasp the reasonable
433
boundary between government and market. Appropriate government regulation is beneficial. But
434
too much government control is counterproductive (Fig. 11). The government should support new
435
energy within a reasonable range.
436
6. Conclusions and further perspectives
p ro
of
432
Based on the theory of nonlinear dynamic system, this paper constructs a multi-variable
438
constrained four-dimensional (4D) carbon trading system with combining the current operation of
439
carbon trading and the development of new energy in China. After fully considering the complex
440
relationship among new energy, carbon trading, carbon emission and economic growth, this paper
441
discusses the nonlinear dynamical behavior of the 4D system and obtain an ESER attractor. Then,
442
the relevant statistics of China from 2014 to 2017 are collected, and BP neural network is used to
443
identify the actual system parameters. Finally, the key parameters are changed to analyze the
444
dynamic evolution behavior of the actual system, and the influence mechanism of carbon trading
445
on new energy under various constraints is explored.
Jo
urn
al
Pr e-
437
446
The study finds that carbon trading can drive the development of new energy within the
447
framework of 4D ESER system. But the driving effect is closely related to a threshold. Only when
34
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the carbon trading market is mature, can carbon trading promote the development of new energy.
449
The aggressive carbon trading in immature markets may stifle the development of new energy.
450
Carbon trading can control carbon emissions and energy intensity effectively. The development of
451
new energy is closely related to government regulation. The government can regulate the carbon
452
trading market through carbon price. However, when the regulation goes beyond a certain scope,
453
it will have a fatal impact on the 4D ESER system. Therefore, the government needs to regulate
454
new energy and carbon trading appropriately for sustainable development.
Pr e-
p ro
of
448
The analysis of this paper also has some shortcomings. In terms of model construction, only
456
four variables are introduced in system (2). But the reality is more complicated. Technological
457
progress, international trade and other factors also affect the evolution of carbon trading and new
458
energy. ESER system will further be improved in the future. In terms of data, economic growth
459
and carbon emission data are not available on monthly basis. Instead, we split them into monthly
460
data weighted by year-on-year growth rate of industrial added value. The ways of crunching data
461
will affect the data precision to some degree. It is believed that with the development of carbon
462
trading and new energy in China, the quality of data will improve in the future. This paper
463
analyzes the influence mechanism of carbon trading on new energy. But not all the relationships
Jo
urn
al
455
35
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among the five variables in Fig. 12 are analyzed. The evolution of carbon emission, economic
465
growth and energy intensity under various constraints will be given in the further study.
466
Acknowledgements
of
464
The research is supported by the National Natural Science Foundation of China (Nos.
468
71774077, 71690242, 71774087, 51976085), Jiangsu “Qing Lan” Project (No. JS20190401),
469
Jiangsu “Six Talent Peaks” High level Talent Project (No. JNHB-026), Jiangsu “333” Project
470
Research projects subsidy scheme (No. BRA2017447), Major Research plan of the National
471
Natural Science Foundation of China (No. 91546118), Jiangsu Social Science Foundation Project
472
(No. 18EYB020).
473
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*Declaration of Interest Statement
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To the best of our knowledge, the named authors have no conflict of interest, financial or otherwise. All authors declare that: (i) no support, financial or otherwise, has been received from any
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organization that may have an interest in the submitted work; and (ii) there are no other relationships
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or activities that could appear to have influenced the submitted work.
PII: DOI: Reference:
S0378-4371(19)31989-2 https://doi.org/10.1016/j.physa.2019.123572 PHYSA 123572
To appear in:
Physica A
Received date : 21 August 2019 Revised date : 29 October 2019 Please cite this article as: G. Fang, L. Lu, L. Tian et al., Research on the influence mechanism of carbon trading on new energy—A case study of ESER system for China, Physica A (2019), doi: https://doi.org/10.1016/j.physa.2019.123572. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Highlights (for review)
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Highlights ► The influence mechanism of carbon trading on new energy is explored ► The existence of chaotic motion is approved by using digital simulation
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► Only the mature carbon trading market can boost new energy
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► Carbon trading and new energy are very sensitive to government control
*Manuscript Click here to view linked References
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1
Research on the influence mechanism of carbon trading on new energy ——A case study of ESER system for China
2
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Guochang Fanga,*, Longxi Lu a, Linxin Tian b, Yu he a, Huibo Yin a
3
4
a
5
China; b School of Mathematical Sciences, Nanjing Normal University, Nanjing, Jiangsu 210023,
6
China
7
Abstract: This paper attempts to explore the influence mechanism of carbon trading on new
8
energy based on a novel nonlinear energy-saving and emission-reduction system. The dynamics
9
behavior of the novel system is discussed, and
Pr e-
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School of Economics, Nanjing University of Finance and Economics, Nanjing, Jiangsu, 210023,
the existence
of chaotic motion
is approved by using digital simulation. With the aid of genetic algorithm-back propagation neural
11
network, the quantitative coefficients of the actual system are identified according to
12
Chinese statistics. The result of scenario analysis shows that carbon trading can drive the
13
development of new energy under given conditions, the driving effect is closely related to a
14
threshold. The mature carbon trading can control carbon emissions and energy intensity effectively.
15
The more perfect carbon trading market can better promote the development of new energy when
16
meets such constraints. Carbon trading and new energy are very sensitive to government control.
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*
Corresponding author. Tel.: +86 (025) 84028202; fax: +86 (025) 84028202.E-mail addresses: [email protected] (G. Fang).
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Grasping the reasonable boundary of government and market is the key to realize sustainable
18
development of carbon trading and new energy.
19
Keywords: Carbon trading; new energy; carbon emissions; energy intensity
20
1. Introduction
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Carbon trading, as a kind of market mechanism, whose main commodity is emission rights of
22
greenhouse gas [1, 2]. The trading opportunities will exist if the allocation of carbon emissions
23
quotas is not optimal. When all trading opportunities are fully utilized, carbon trading achieves
24
optimal efficiency [3, 4]. From this perspective, carbon trading can reduce the total cost of
25
emission reduction and achieve the unification of environmental and economic benefits [5-7].
26
Objectively speaking, to achieve the maximum efficiency of emission reduction, carbon trading
27
needs to be coordinated with other environmental policies [8]. Meanwhile, the implementation of
28
carbon trading will promote the development of new energy [9].
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As a substitute for traditional energy, new energy has an impact on all aspects of business
30
production and household life [10, 11]. Developing new energy can effectively reduce greenhouse
31
gas emissions and environmental pollution [12]. For developing countries, new energy also brings
32
opportunities to eliminate energy poverty, stimulate production and improve environmental
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benefits [13-15]. The use of new energy means a cleaner environment, especially for today's
34
large-scale frequent outbreaks of haze [16]. A survey showed that 80% of residents were willing to
35
pay an extra fee to support the use of new energy buses to get cleaner air [17]. Therefore, it is an
36
irreversible trend for new energy to replace traditional fossil energy [18].
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To achieve energy conservation and emission reduction, both "open-source" and "cut-costs" are
38
needed. We can cut costs by reasonably distributing existing energy resources and improving
39
energy efficiency [19]. We can also open source by increasing the types of energy available and
40
developing new sources of energy. Reducing the reliance on fossil energy is even more important
41
under the current resource situation [20, 21]. The use of new energy contributes to reducing the
42
share of fossil energy consumption [22, 23]. The implementation of carbon trading can cut the
43
cost of emission reduction and improve energy efficiency [24, 25]. Therefore, both carbon trading
44
and new energy are effective measures to open source and cut cost. They are of great significance
45
for energy saving and emission reduction [26].
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There are many research achievements in the field of carbon trading and new energy. But there
47
is little research on the interaction between them. Fisher and Preonas [27] believed that the
48
compatibility of various environmental policies in emission reduction targets, mechanism and
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effectiveness needs to be systematically studied. Wang and Guo [28] studied the asymmetric
50
spillover effect between carbon trading market and energy market on the yield rate and volatility.
51
Then they analyzed the information and risk transmission mechanism between the two markets.
52
Müsgens [29] took the European electricity market as an example to analyze the relationship
53
between European emission trading schemes and renewable energy subsidies. Mo et al. [30] used
54
the real option model to evaluate the impact of China's carbon trading system on wind power
55
investment and analyzed the coordination of the two environmental policies. They found that
56
unstable carbon price would dampen investment in new energy. But the stable carbon price could
57
reduce the risk of investment in new energy.
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Based on the current situation of carbon trading and new energy in China, this paper constructs
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a carbon trading system with multi-variable constraints and discusses the driving mechanism of
60
carbon trading to new energy. There are few discussions on the interaction between carbon trading
61
and new energy in previous studies. The variables in these studies were relatively single, failing to
62
reflect the nonlinear properties and make comprehensive analysis. This paper establishes a
63
nonlinear coupled system and strengthens the theoretical foundation of the interaction between
64
carbon trading and new energy. This paper visualizes the evolution by dynamics method and gives
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suitable suggestions in line with national conditions. Compared with previous studies, this paper is
66
more vivid and the conclusions are more applicable.
The rest of this paper is organized as follows. Section 2 provides a brief description of the
68
model developed for this study. Section 3 is about parameter identification of the actual system
69
based on the statistical data in China. Section 4 is about a scenario study of the actual system.
70
Implications of the research for government policy are presented in Section 5. Conclusions and
71
further perspectives are discussed in Section 6.
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2. ESER Model
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Both new energy and carbon trading policies are important components of environmental
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policy tools. The combination of them will expand their respective impact on the energy-saving
75
and emission-reduction (ESER) system. The implementation of carbon trading promotes the
76
development of new energy, and then promotes the process of energy conservation and emission
77
reduction. At the same time, carbon emissions, economic growth and other variables in ESER
78
system also affect carbon trading and new energy.
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Based on the evolutionary relationship among new energy, carbon emission and economic
growth, a nonlinear three-dimensional ESER system can be constructed as following:
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81
(1)
82
Where
83
new energy;
84
period. Explanation of other coefficients please refer to the nomenclature.
is abbreviated as , the same as
, of economic growth.
Nomenclature development coefficient of
influence coefficient of
to
influence coefficient of
to
influence coefficient of
to
development coefficient of to
influence coefficient of
to
influence coefficient of
to
inflection of
to
to
to
inflection of
to
inflection of
to
inflection of
to
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Abbreviations
BP
to
back propagation
GDP
gross domestic product
influence coefficient of
ESER
energy-saving and emission-reduction
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influence coefficient of investment to
to
development coefficient of
influence coefficient of
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is a given economic
threshold value of
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influence coefficient of
inflection of
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influence coefficient of
influence coefficient of
,
p ro
, of carbon emissions;
and ) is the time-dependent variable of
of
(
In the first equation,
Subscript
to
component labels
indicates that the potential of new energy will affect the
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( x in short, the same as y and
z ). When
86
development speed of new energy
87
the total amount of carbon emission is less than a certain threshold and the energy consumption is
88
small. External pressure for the development of new energy is small, so the development speed of
89
new energy is slow. When
90
urgent to develop new energy to alleviate the energy gap and control carbon emission. The
91
economic input will promote the development of new energy ( a2 z ).
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, with high energy consumption and large carbon emission, it is
In the second equation, the development of new energy can inhibit carbon emissions
93
means when
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92
,
b1 x
, the threshold of carbon emissions has not
94
arrived, the growth rate of
95
gradually slows down after reaching the threshold (the system (1) is a highly coupling and
96
nonlinear system. At this point,
99
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, the growth rate of
is affected by other variables in the system. So
slows down after reaching the threshold).
means when
, before reaching the inflection point, industry is the main driver of economic growth. The large
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is fast. When
consumption of fossil energy causes a lot of carbon emissions by industry, the growth rate of
100
is fast. When
101
upgraded. The demand for fossil energy decreases and the growth rate of
, after reaching the inflection point, the industrial structure is
7
slows down.
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103
In the third equation,
106
, the large
early investment to new energy plays a negative role in economic development. When
104
, with the maturity of new energy industry, investment in new energy promotes
of
105
indicates that when
economic development. c2 y represents the effect of carbon emissions on economic growth.
is the influence coefficient of new energy economic input to
p ro
102
, and the value of
will
107
change over time.
108
of new energy economic input to
109
represents the restraining function on
110
energy development, the huge economic input to new energy will stifle economic growth.
,
Pr e-
, which can be divided into two parts
.
, promoting one. In the initial stage of new
;
. When the new energy (system (1)) is developed to a certain level, the input
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gives a comprehensive reflection of promoting function and restraining one
can generate higher economic profits, and create new sources of economic growth. The effect
113
becomes positive.
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112
,
. The same as
.
The implementation of carbon trading will bring a series of effects to ESER system. On the
115
basis of the complex evolutionary relationships between new energy, carbon emission, economic
116
growth and carbon trading, the nonlinear four-dimensional ESER system is constructed under the
117
comprehensive consideration.
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118
(2)
119
Where
120
coefficients please refer to the nomenclature. The comparative analysis between system (1) and
121
system (2) can highlight the impacts of carbon trading on ESER system. Compared with system
122
(1), the effects of carbon trading on the corresponding variables are shown in the following
123
chapters. The evolution analysis of the two systems can be found in section 4.
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p ro
Pr e-
124
is the time-dependent variable of carbon trading volume. Explanation of the
In the first equation,
indicates that when
, the scope
125
of carbon trading is small, or the market mechanism is immature. Its driving effect on new energy
126
is not significant. When
127
which has a good driving effect on new energy. In the second equation, b4u indicates that the
128
implementation of carbon trading can reduce carbon emissions. In the third equation, c4u
129
represents the comprehensive effect of carbon trading on economic growth. c4 can be divided
130
into two parts
131
function.
132
performance of c4u is depressing the economy. The comprehensive effect will be positive
, the development of carbon trading is mature,
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(
outstrips
.
represents the restraining function on
in the initial stage of carbon trading
9
;
, promoting
, the
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133
134
(
) when the carbon trading market is mature.
In the fourth equation,
reflects the impact of changes in supply and demand
135
on carbon trading. When carbon emission exceeds the threshold value (
136
emission rights is larger than the supply. In order to meet the government's emission target, carbon
137
trading is active and the volume will naturally increase (
138
carbon emission is low (
139
demand, the carbon trading volume will decrease (
140
trading can all promote the process of ESER. When new energy develops to some extent, it can
141
substitute the role of carbon trading in promoting ESER ( d 2 x ). Economic inputs can speed up
142
carbon trading development ( d3 z ).
p ro
of
), the demand for
). On the contrary, when
). New energy and carbon
al
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), the supply of emission rights in the market is greater than the
System (2) will show different dynamic behaviors with various coefficients. After a lot of
144
debugging and numerical simulation, it is found that when the parameters of system (2) are taken
145
as Table 1 and given the initial value as [0.015 0.758 1.83 0.01] (it has no practical meaning,
146
which might just mean that the system will have chaos phenomenon in this case), the system will
147
show very interesting dynamic behavior. A chaos attractor can be obtained. Fig. 1 shows the
148
three-dimensional graph of the attractor.
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149 150
Table 1
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System parameter.
a2
a3
b1
b2
b3
b4
c1
c2
c3
0.1
0.005
0.012
0.412
0.088
0.8
0.072
0.035
0.008
0.1
c4
d1
d2
d3
M
C
E
0.025
0.016
0.002
0.0012
1.0
1.6
3.46
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a1
F
P
2.58
0.35
0.9222
p ro
N
A deterministic system is constrained by deterministic conditions. A certain solution or process
153
must be obtained if the initial value is given. Chaos is a seemingly random irregular motion in a
154
deterministic system, which is characterized by uncertainty, unpredictability and sensitivity to the
155
initial value. A minimal change of initial value will lead to the great change of solution. The most
156
intuitionistic method to judge the existence of chaos in a deterministic system is Lyapunov
157
exponent. Lyapunov exponent can measure the sensitivity of system motion to initial values.
158
When the maximum Lyapunov exponent is greater than zero, the existence of chaos in the system
159
can be proved.
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160 161
Let
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Fig. 1. ESER attractor with carbon trading constraints (4D ESER system).
be arbitrary and the other parameters are fixed as Table 1. Two diagrams can be
obtained. Fig. 2 is maximum Lyapunov exponent spectrum. Fig. 3 is sensitivity diagram to the
164
initial value. The maximum Lyapunov index is greater than zero in Fig. 2. Besides, Fig. 3 shows
165
that a very small change in the initial value can make a big difference. It means system (2) is very
166
sensitive to initial values. Both Fig. 2 and Fig. 3 can prove the existence of chaos in system (2)
167
from the view of numerical simulation.
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Fig. 2. Lyapunov exponent spectrum of the 4D ESER system (b2).
170 171
172
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Fig. 3. The error value with changing initial conditions.
Take the parameters of system (1) as Table 1, let the initial value be [0.015 0.758 1.83]. Fig. 4
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shows the maximum Lyapunov exponent spectrum (from the perspective of simulation, if the
174
maximum Lyapunov exponent is greater than zero, it is concluded that the system has chaos
175
phenomenon). From the above analysis, it can be concluded that the three-dimensional ESER
176
system (1) is also a chaotic system.
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177
Fig. 4. Lyapunov exponent spectrum of the 3D ESER system (b2).
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3. Parameter identification
180
3.1. Statistical data
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The new energy evolution system is established on the complex relationship of mutual
182
promotion and mutual restriction among new energy, carbon emission, economic growth and
183
carbon trading. The identification of system parameters is of great realistic importance. To
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calculate the parameters of actual system, this paper collected the monthly statistical data of new
185
energy, carbon emission, economic development and carbon trading in China. The range of data is
186
from Jan. 2014 to Dec. 2017. The data in Dec. 2013 is taken as the base period. The processed
187
data is shown in Table 2.
188
Table 2
189
Data of new energy, carbon emissions, economic growth and carbon price.
p ro
z
u
time
x
y
z
u
201401 0.8826
1.1144
0.8900
0.1693
201601
1.3439
0.9853
1.0689
2.0896
201402 0.7651
1.0789
0.8616
0.3241
201602
1.3940
1.0330
1.1207
4.4415
201403 0.9663
1.0433
0.8332
0.6704
201603
1.4441
1.0806
1.1724
12.7726
201404 1.0527
1.0315
0.9341
0.7004
201604
1.4655
0.9535
1.2354
2.0147
201405 1.2822
1.0433
0.9449
3.5256
201605
1.7437
0.9535
1.2354
7.3397
201406 1.4222
1.0907
0.9878
15.6288
201606
1.8943
0.9853
1.2766
20.5928
201407 1.7667
1.0670
1.1275
6.9629
201607
2.1117
0.9535
1.2810
5.0603
201408 1.8256
0.8180
0.8644
1.2239
201608
1.9315
1.0012
1.3451
2.3358
201409 1.7811
0.9485
201410 1.5757
0.9129
al 1.0022
1.1128
201609
1.6978
0.9694
1.3024
1.4192
1.1200
0.9314
201610
1.7239
0.9694
1.4308
1.1768
1.3057
0.8536
1.0472
1.3562
201611
1.6149
0.9853
1.4543
1.9722
201412 1.1599
0.9366
1.1491
3.4795
201612
1.4269
0.9535
1.4074
4.0383
201501 1.0285
1.1550
1.0326
2.0007
201701
1.4770
0.9873
1.1080
4.2503
201502 0.8970
1.0308
0.9216
1.3504
201702
1.5270
1.0679
1.1984
1.0177
201503 1.2437
0.9067
0.8107
3.8656
201703
1.5770
1.1485
1.2889
2.4172
201504 1.2834
0.9553
0.9625
3.9643
201704
1.6256
0.9823
1.2640
5.6588
201505 1.3852
0.9877
0.9952
4.6740
201705
1.8202
0.9823
1.2640
10.1373
201506 1.7315
1.1010
1.1094
8.5942
201706
1.9493
1.1485
1.4779
20.7179
201507 1.7929
0.9715
1.0784
15.9105
201707
2.2364
0.9672
1.4139
7.7271
201508 1.7217
0.9877
1.0964
1.3099
201708
2.1351
0.9067
1.3255
0.7615
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y
time
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0.9229
1.0245
4.4812
201709
2.0846
0.9974
1.4581
2.6918
201510 1.6787
0.9067
1.1207
2.5047
201710
2.0405
0.9370
1.5357
2.9146
201511
1.3941
1.0038
1.2408
4.8729
201711
1.8718
0.9219
1.5109
3.3352
201512 1.2937
0.9553
1.1807
4.2504
201712
1.6730
0.9370
1.5357
1.5656
The main utilizing form of new energy is electricity generation. Therefore, this paper chose the
191
sum of the four new energy — hydro-energy, nuclear energy, wind energy and solar energy to
192
measure the development of new energy in China. Electricity generation of new energy is derived
193
from the sum of hydroelectricity, nuclear, wind and solar power. The data comes from the website
194
of China's national bureau of statistics. The development of carbon trading can be measured by the
195
turnover in China’s carbon trading market. The larger the turnover, the better the development of
196
carbon trading. Total trading volume of China is the sum of eight carbon trading markets—Beijing,
197
Fujian, Guangdong, Hubei, Shanghai, Shenzhen, Tianjin and Chongqing. The data comes from
198
China carbon trading network (http://k.tanjiaoyi.com).
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Monthly data of new energy and carbon trading can be collected directly. However, monthly
200
data of economic growth and carbon emission cannot be collected directly. The data of economic
201
growth and carbon emissions needs to be split. Gross domestic product (GDP) represents the level
202
of economic development. But the minimum statistical frequency of GDP is quarterly. We can
203
split GDP into monthly data weighted by year-on-year growth rate of industrial added value. Both
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of them come from the website of China's national bureau of statistics. The data of carbon
205
emission is downloaded from Statistical Review of World Energy 2018 published by British
206
Petroleum. The frequency of this data is year，so we also split carbon emission into monthly data
207
weighted by year-on-year growth rate of industrial added value.
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There are two reasons to choose year-on-year growth rate of industrial added value to split
209
data. On the one hand, industry plays a leading role in national economy and is the most important
210
driving force for economic growth. On the other hand, the industrial sector is the largest consumer
211
of fossil energy. Energy consumption and carbon emission can be roughly estimated by the growth
212
of the industry. Using the same economic index to split GDP and carbon emission can maximize
213
the consistency and rationality. Splitting annual data into monthly data can enlarge the sample size,
214
which will help to improve the accuracy of identification parameters.
215
3.2. Parameter acquisition
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208
Neural network is a nonlinear system composed of large number of simple computing units. It
217
imitates the function of information storage and processing from human brain’s nervous system
218
[31]. Back propagation (BP) neural network is one of the most widely used neural networks. BP
219
neural network is simple in structure and calculation. It has significant advantages in dealing with
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220
nonlinear problems. It is widely used in function approximation, intelligent control, economic
221
prediction and other areas.
Using BP neural network to identify parameters, the first step is data processing. The former
223
n-1 group data is divided into input data; the latter n-1 group data is divided into output data. All
224
the variables are normalized. Then choose appropriate feedforward neural network and set all
225
adjustable parameters as random numbers. Finally, compare the result with the target and calculate
226
the error— . After several times of running and debugging, when error
227
value, the parameters of the actual system can be determined.
p ro
Pr e-
is less than a specific
x k 1 x k T a1 x k y k M a2 z k a3u k u k N 1 y k 1 y k T b1 x k b2 y k 1 y k C b3 z k 1 z k E b4u k z k +1 z k T c1 x k x k F 1 c2 y k c3 z k c4u k u k 1 u k T d1u k y k P 1 d 2 x k d 3 z k
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of
222
(3)
This paper uses BP neural network to calculate the actual parameters. First, discretize system
230
(2) and get the difference equation (3). Then, take the first 47 groups of data in Table 2 as input
231
data and the last 47 groups of data as output data. Finally, calculate the actual parameters shown in
232
Table 3,
233
acquisition, small error and stable system. Objectively speaking, we can get the smaller
234
However, system (2) will be unstable under this smaller error
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. The principles should be followed in the process of parameter
18
.
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Table 3
236
Parameters of the actual system.
a3
b1
b2
b3
b4
c1
c2
c3
0.3927
0.2037
0.8952
0.0139
0.1229
0.7954
0.3133
0.3110
0.9192
0.0035
c4
d1
d2
d3
M
C
E
N
F
P
0.7012
0.4818
0.1125
0.1277
1.955
0.2576
0.7085
0.5871
0.1417
0.9222
of
a2
p ro
237
a1
4. Scenario analysis
To analyze the dynamic evolution relationship among new energy, carbon emission, economic
239
growth and carbon trading, this section will start from the changes in key parameters of the system.
240
In this section, we will analyze the dynamic evolution behavior and combine it with the actual
241
scenario. The data of Dec. 2013 are selected as initial conditions. The initial value can be
242
converted into [0.0027 0.0323 0.311 0.00000021] in units of
Pr e-
238
al
tons of standard coal.
represents the influence coefficient of carbon trading to new energy. Change the value of
244
and fix the remaining parameters as Table 3. Fig. 5 is the evolution diagram of new energy
urn
243
245
when
246
evolution tendency of new energy more concisely and clearly, an optimization method is adopted.
247
Take case 1 in Fig.5 as example, the timeline of case 1 is divided equally. The maximum value in
248
every small range is singled out, and these values are connected by a curve which is as smooth as
Jo
gradually increases. In fact, the evolution curves in Fig.5 are oscillatory. To reflect the
19
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249
possible. Repeat the previous steps until the evolution curve is monotonic (the same as
250
Fig.6-Fig.9). The red curve (case 1) in Fig. 5 represents the evolution trend of new energy when
=0.6952; the blue curve (case 2),
=0.7952; the green curve (case 3),
=0.8952. The brown
of
251
curve (case 4) is the new energy evolution diagram of the 3D ESER system (1) (the same as Fig. 6
253
and Fig. 9). The 2020 is the end of the "13th Five-Year Plan" (the same as Fig. 6-Fig. 9). For
254
system (1), when
255
economic inputs (bigger
) to guarantee ESER system reliability. So, case 4 in Fig. 5
256
corresponds to
=0.1235. The other parameters are the same as the ones in Table 3.
p ro
252
257 258
259
Jo
urn
al
=0.5041,
Pr e-
<0.5041, it will crash. Compared with system (2), new energy needs more
Fig. 5. The evolution diagram of new energy (
).
By observing Fig. 5, we find that the red curve is the highest, followed by the blue curve, and
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260
then the green curve. The three curves are higher than case 4, i.e. carbon trading brings new
261
energy better development. As for case 1-3, the increase in
262
indicates that the development of carbon trading is out of step with the development of new energy.
263
This is obviously contradictory to economic theory. Usually, the development of carbon trading
264
will raise the cost of fossil energy and increase the competitiveness of new energy. Carbon trading
265
encourages producers to continually improve new energy technologies and sell surplus carbon
266
quotas on the market. It can help producers reduce production costs and achieve additional
267
economic benefits. Therefore, in theory, carbon trading plays a role in promoting new energy.
270
of
p ro
Pr e-
a very important parameter.
is
represents the inflexion of carbon trading to new energy. When
al
269
What is the cause of the conflict in Fig. 5? Look back at the first equation in system (2),
, the development of carbon trading is not mature. Its driving effect on new energy is not
urn
268
leads to the lower curve. It
271
significant or even inhibiting. When
272
effect on new energy. Therefore, we take the change of
, carbon trading is mature and has a good driving
Jo
into consideration. Change the value of
273
and fix the remaining parameters as Fig. 5. Fig. 6 is the evolution diagram of new energy when
274
increases and
275
new energy when
decreases. The red curve (case 1) in Fig. 6 represents the evolution trend of
=0.6952 and
=0.5871; the blue curve (case 2),
21
=0.7952 and
=0.2871;
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the green curve (case 3),
=0.8952 and
=0.0871.
Pr e-
p ro
of
276
277
279
Fig. 6. The evolution diagram of new energy (
By observing Fig. 6, we find that when
,
).
becomes smaller, the green curve is obviously
al
278
higher than the one in Fig. 5. The value is 109% bigger than the green curve in Fig. 5 at the year
281
of 2020. The green curve is higher than the red and blue curve. It means carbon trading
282
significantly promotes the development of new energy. Only when
283
threshold, can the change in carbon trading keep pace with the development of new energy. In this
284
case, carbon trading can play a role in promoting new energy. Fig. 6 perfectly explains the
285
contradiction in Fig. 5. It is the threshold value
286
very important for the development of carbon trading and new energy to reach this threshold value
is less than a certain
Jo
urn
280
22
that causes the contradiction in Fig. 5. It is
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287
288
as soon as possible.
is the influence coefficient measuring the impact of carbon trading development on carbon
289
emissions, which reflects the emission reduction efficiency of carbon trading. Vary
290
other coefficients unchanged, Fig. 7 shows the evolution diagram of carbon emissions when
291
gradually increases. The red curve (case 1) in Fig. 7 represents the evolution trend of carbon
292
emissions when
of
p ro
=0.3133; the blue curve (case 2),
=0.5133. The curve is getting lower and lower as
=0.4133; the green curve (case 3),
increasing, the value of case 3 is 59.7%
Pr e-
293
and keep
lower than the one of case 1 at the year of 2020. Carbon trading can effectively restrain the
295
increase of carbon emissions. The more mature carbon trading system is, the more it can inhibit
296
the increase of carbon emissions.
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urn
al
294
297
23
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298
299
Fig. 7. The evolution diagram of carbon emissions
).
Energy intensity is an important index to measure the utilization effect of energy resource,
300
which can reflect the effect of energy saving. Fixed the value of
301
diagram of energy intensity is shown in Fig. 8. Case 1-3 are the same as the ones in Fig. 7 (case 1,
of
=0.3133; case 2,
=0.4133; case 3,
p ro
302
as above, the evolution
=0.5133). When
increases gradually, the curve is
getting lower and lower. Energy intensity in case 3 is 55.7% lower than the one in case 1 by the
304
year of 2020. Result shows that carbon trading really can reduce energy intensity. Carbon trading
305
adds the cost of using fossil energy to some extent. These can help the development and
306
popularization of new energy, thereby lowering energy intensity.
Jo
urn
al
Pr e-
303
307 308
Fig. 8. The evolution diagram of energy intensity ( ).
24
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309
represents the development coefficient of carbon trading under the influence of supply and
310
demand. The larger
311
the remaining parameters as Table 3. Fig. 9 is the evolution diagram of new energy when
312
gradually increases. The red curve (case 1) in Fig. 9 represents the evolution trend of new energy
313
when
314
the increase of
315
40.2% bigger than the one of case 1, and 99.2% bigger than case 4 by the year of 2020. It means
316
that the faster the carbon trading develops, the greater the promotion to new energy.
and fix
=0.4818; the blue curve (case 2),
p ro
of
, the faster development of carbon trading. Change the value of
=0.5818; the green curve (case 3),
=0.6818. With
317 318
319
Jo
urn
al
Pr e-
, the evolution curve of new energy increases gradually. The value of case 3 is
Fig. 9. The evolution diagram of new energy (
On the contrary, Fig. 10 shows the evolution diagram of new energy when
25
gradually
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320
decreases. The green curve (case 1) represents the evolution trend of new energy when
321
the blue curve (case 2),
322
sharply. When
323
fluctuate below the zero line and once rise above the zero line. Eventually, the blue curve stops
324
running at
325
less than a certain threshold value, the system will crash (system (2) will stop running under this
326
situation, and only when the constraint conditions are changed, the system can run again). The
327
smaller the parameter is, the earlier the system will crash. Therefore, the slow development of
328
carbon trading will hinder the development of new energy.
=0.1100. Unlike Fig. 9, the green and blue curves in Fig. 10 fluctuate
, two curves
p ro
of
, two curves fluctuate around the zero line. When
. This indicates that if
Jo
urn
al
Pr e-
, the green curve stops running at
329 330
=0.1200;
Fig. 10. The evolution diagram of new energy (
26
gradually decreases).
is
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331
represents the influence coefficient of economic growth to carbon trading. The government
332
mainly controls carbon price and consummates carbon trading system through economic means,
333
such as financial support. So, to some extent,
334
carbon trading. Since system (2) is a nonlinear coupled system, any change in the coefficient will
335
cause the change in the entire system. The change of
336
affects the evolution trend of new energy. Fig. 11 shows the evolution diagram of new energy
337
when
338
energy when
p ro
of
can also measure the government's regulation to
not only affects carbon trading, but also
Pr e-
gradually increases. The green curve (case 1) represents the evolution trend of new
=0.9906.
339 340
341
Jo
urn
al
=0.9306; the blue curve (case 2),
Fig. 11. The evolution diagram of new energy (
gradually increases).
When the system only runs to around t=0.05, two curves begin to drop below zero and
27
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342
fluctuate greatly. Eventually, the blue curve stops running at
, the green curve stops
343
running at
, the system will crash.
344
With the increase of
345
market is still in its infancy. Carbon trading market is a policy market with low degree of
346
marketization. Therefore, the carbon trading market cannot be separated from the regulation and
347
support from government. However, through the above analysis, we can see that the government's
348
regulation and control should be moderate. The government should keep its control within a
349
reasonable range for sustainable development. Policy makers should not only give full play to the
350
function of macro-control, but also grasp the reasonable boundary between the government and
351
the market.
. Simulation results show that when
al
Pr e-
p ro
of
, the time of crash will be earlier. At present, China's carbon trading
New energy evolution system is based on the complex relationship between new energy,
353
carbon emissions, economic growth and carbon trading. The study of the complex relationship
354
between variables is helpful to further clarify the key problems in ESER system. It also helps
355
reveal the key to the evolution of these variables. Fig. 12 shows the relationship between carbon
356
trading, new energy, carbon emissions, economic growth and energy intensity. The two-way arrow
357
in the diagram shows the interaction between the five variables, forming an organic whole. Any
Jo
urn
352
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variable will affect the other variables through the conduction of the system. Therefore, Fig. 12 is
359
a visual representation of system (2). System (2) is the abstract and mathematical expression of
360
Fig. 12.
In the first equation, the relationship between carbon trading and new energy is discussed by
. The direct influence path corresponds to ① in Fig. 12.
p ro
361
of
358
362
analyzing the variation of
363
Carbon trading can also indirectly affect the new energy through other paths. When
364
a certain threshold, the change of
365
trading promotes new energy. When
366
appears. Carbon trading inhibits the development of new energy. In the fourth equation, we
367
analyze the influence of
368
carbon trading. The faster the carbon trading develops, the greater the contribution to new energy.
369
Conversely, the lagging development of carbon trading will hinder the development of new energy
370
and even lead to the collapse of the whole system.
371
energy. It corresponds to ③ in Fig. 12. When government intervention exceeds a certain
372
threshold, it will lead to the collapse of the system.
is less than
is consistent with the development of new energy. Carbon
Pr e-
and
on new energy.
reflects the development speed of
Jo
urn
al
and
is greater than this threshold, the contradiction to reality
29
measures government regulation on new
p ro
of
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373 374
Pr e-
375
Fig. 12. Interaction diagram.
In fact, carbon trading can also drive variables through other conduction pathways. As in the
376
second equation, changes in
377
trading, more and more enterprises participate in carbon trading. The development of carbon
378
trading has effectively curbed the growth of carbon emissions, corresponding to ② in Fig. 12. As
379
the system develops, carbon trading will affect energy intensity. The conduction mechanism of
380
carbon trading's impact on energy intensity is complex. This is the combined effect of ① ⑦
381
⑥ ⑤, ⑧ ③ ⑦ ④ and other paths. Similarly,
382
affecting carbon emissions. Carbon trading can also indirectly promote the development of new
383
energy through
Jo
urn
al
will affect carbon emissions. With the improvement of carbon
also affects new energy by
in the third equation. Due to space constraints, these driving effects will be
30
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384
further explored in future studies.
385
5. Policy enlightenment
China's total energy consumption has been rising steadily, ranking first in global energy
387
growth for many consecutive years. In terms of energy consumption and energy structure, China is
388
facing enormous pressure to address these thorny issues. In this sense, carbon trading is expected
389
to solve the present bottle-neck of resource and environment. Evolution results show that mature
390
carbon trading market can effectively control carbon emissions (Fig. 7). Carbon trading can urge
391
the related enterprises to improve energy utilization efficiency by technology, management and
392
other means. Carbon trading started late in China but is growing fast. As of December 31, 2018,
393
total carbon trading volume reached 163.35 million tons, total turnover reached 3.53963 billion
394
yuan. These numbers, however, are far from enough compared with large carbon emissions. To
395
cope with climate change and achieve emission reduction targets, China must accelerate the
396
construction of carbon trading market. Given the size of China's energy consumption and the
397
status of ESER, there is still huge room for carbon trading in the future.
Jo
urn
al
Pr e-
p ro
of
386
398
Developing new energy is the important way to promote the process of energy conservation
399
and emission reduction. At present, China's new energy resources have the conditions for
31
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large-scale development. China accounts for about 30 percent of the world's hydropower capacity
401
and a sixth of global geothermal resources. The control effect of new energy on energy intensity
402
can be predicted after a series of evolutions (Fig. 12), it can reduce energy intensity through
403
carbon trading (Fig. 8). It must be noted that, the lagging carbon trading market is not conducive
404
to the development of new energy. It is foreseeable that coal and oil will play an unshakable role
405
in China's energy production and consumption for a long time to come. However, new energy
406
such as natural gas, hydropower, solar energy, nuclear energy and wind energy also have a strong
407
momentum and broad space for development. By vigorously developing new energy, we will be
408
able to diversify the energy structure. In this way, energy conservation and emission reduction
409
targets can be achieved as soon as possible.
al
Pr e-
p ro
of
400
As important parts of environmental policy tools, carbon trading and new energy promotion
411
policies have been introduced simultaneously in many countries [32]. There are both synergies
412
and conflicts between carbon trading and new energy [33]. Through the above analysis, it is found
413
that carbon trading will inhibit the development of new energy when carbon trading system is
414
immature (Fig. 5). Only when carbon trading reaches a certain scale can carbon trading promote
415
the development of new energy. In the ideal economy without market failures, carbon trading
Jo
urn
410
32
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participants make the most of every trading opportunity to minimize the cost of emission
417
reduction. So, it would be superfluous to implement other emission reduction policies in the ideal
418
economy [34]. However, there are market failures and transaction costs in reality. Carbon trading
419
fails to achieve theoretical efficiency. So, carbon trading must be coordinated with other
420
environmental policies. New energy promotion policies can alleviate market failure. New energy
421
promotion policies can improve the design of carbon trading mechanism and reduce its uncertainty.
422
Policy makers should coordinate carbon trading and new energy to maximize their synergies in
423
energy saving and emission reduction.
Pr e-
p ro
of
416
Long-term since, the development of new energy is closely related to government control. As
425
an emerging industry, the initial investment to new energy industry is huge and the investment
426
cycle is long. New energy industry has high risks of technology and market. Therefore, support by
427
government and market is one of the most direct and effective ways to promote new energy
428
development [35]. On the contrary, when the government cancels the support policy, it may cause
429
adverse impact on new energy development. The experience of photovoltaic industry in Germany,
430
Italy and Switzerland had proven this point [36]. To realize the sustainable development of new
431
energy, the government should formulate new energy development strategy and implement
Jo
urn
al
424
33
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reasonable industrial policies. On the other hand, policy makers should also grasp the reasonable
433
boundary between government and market. Appropriate government regulation is beneficial. But
434
too much government control is counterproductive (Fig. 11). The government should support new
435
energy within a reasonable range.
436
6. Conclusions and further perspectives
p ro
of
432
Based on the theory of nonlinear dynamic system, this paper constructs a multi-variable
438
constrained four-dimensional (4D) carbon trading system with combining the current operation of
439
carbon trading and the development of new energy in China. After fully considering the complex
440
relationship among new energy, carbon trading, carbon emission and economic growth, this paper
441
discusses the nonlinear dynamical behavior of the 4D system and obtain an ESER attractor. Then,
442
the relevant statistics of China from 2014 to 2017 are collected, and BP neural network is used to
443
identify the actual system parameters. Finally, the key parameters are changed to analyze the
444
dynamic evolution behavior of the actual system, and the influence mechanism of carbon trading
445
on new energy under various constraints is explored.
Jo
urn
al
Pr e-
437
446
The study finds that carbon trading can drive the development of new energy within the
447
framework of 4D ESER system. But the driving effect is closely related to a threshold. Only when
34
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the carbon trading market is mature, can carbon trading promote the development of new energy.
449
The aggressive carbon trading in immature markets may stifle the development of new energy.
450
Carbon trading can control carbon emissions and energy intensity effectively. The development of
451
new energy is closely related to government regulation. The government can regulate the carbon
452
trading market through carbon price. However, when the regulation goes beyond a certain scope,
453
it will have a fatal impact on the 4D ESER system. Therefore, the government needs to regulate
454
new energy and carbon trading appropriately for sustainable development.
Pr e-
p ro
of
448
The analysis of this paper also has some shortcomings. In terms of model construction, only
456
four variables are introduced in system (2). But the reality is more complicated. Technological
457
progress, international trade and other factors also affect the evolution of carbon trading and new
458
energy. ESER system will further be improved in the future. In terms of data, economic growth
459
and carbon emission data are not available on monthly basis. Instead, we split them into monthly
460
data weighted by year-on-year growth rate of industrial added value. The ways of crunching data
461
will affect the data precision to some degree. It is believed that with the development of carbon
462
trading and new energy in China, the quality of data will improve in the future. This paper
463
analyzes the influence mechanism of carbon trading on new energy. But not all the relationships
Jo
urn
al
455
35
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among the five variables in Fig. 12 are analyzed. The evolution of carbon emission, economic
465
growth and energy intensity under various constraints will be given in the further study.
466
Acknowledgements
of
464
The research is supported by the National Natural Science Foundation of China (Nos.
468
71774077, 71690242, 71774087, 51976085), Jiangsu “Qing Lan” Project (No. JS20190401),
469
Jiangsu “Six Talent Peaks” High level Talent Project (No. JNHB-026), Jiangsu “333” Project
470
Research projects subsidy scheme (No. BRA2017447), Major Research plan of the National
471
Natural Science Foundation of China (No. 91546118), Jiangsu Social Science Foundation Project
472
(No. 18EYB020).
473
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*Declaration of Interest Statement
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To the best of our knowledge, the named authors have no conflict of interest, financial or otherwise. All authors declare that: (i) no support, financial or otherwise, has been received from any
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organization that may have an interest in the submitted work; and (ii) there are no other relationships
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or activities that could appear to have influenced the submitted work.