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The Impacts of Energy Construction Adjustment on Energy Intensity and Economic Growth—A Case Study of China
Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 104 (2016) 239 – 244
CUE2016-Applied Energy Symposium and Forum 2016: Low carbon cities & urban energy systems
The impacts of energy construction adjustment on energy intensity and economic growth—A case study of China Guochang Fanga,b , Lixin Tian a,c, *, Min Fu a, Mei Sun a , Ruijin Du a a
Center for Energy Development and Environmental Protection, Jiangsu University, Zhenjiang, Jiangsu, 212013, China b School of Economics, Nanjing University of Finance and Economics, Nanjing, Jiangsu, 210023, China c School of Mathematical Sciences, Nanjing Normal University, Nanjing, Jiangsu, 210023, China
Abstract This paper attempts to explore the impacts of energy construction adjustment on energy intensity and economic growth based on the 3D energy-saving and emission-reduction (ESER) system, which has not yet been discussed in present literature. The dynamic behavior of the system is discussed. The results of the scenario study show that, energy construction adjustment could effectively control energy intensity. When the investment of economic to energy construction adjustment gets bigger, energy intensity becomes larger. This is because this practice could control energy intensity while brings inhibiting impact on economic growth. Rationally and efficiently utilizing the investment of economic to energy construction adjustment is the key in the current ESER system. © 2016 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility the organizing committee of CUE 2016 Peer-review under responsibility of theofscientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems. Keywords: energy construction adjustment; energy-saving and emission-reduction; energy intensity; economic growth.
1. Introduction Control analysis of greenhouse gas emissions has attracted a great deal of attention from various fields of researchers [1]. Over the course of the study, people are gradually aware that energy-saving and emission-reduction (ESER) is the ideal choice of attaining the low-carbon targets [2]. The ESER system should be constructed according to the current situation. The measure index of ESER system should be made clear, and the key variables of the system should be explored [3], all of these are the determining factors to promote the development of ESER system. The smooth development of ESER can achieve the objectives of protecting the environment and achieving an emission trajectory to a low emission economy [4], which could build a resource conserving and environment-friendly society [5].
*
Corresponding author. Tel.: +86 (511) 88797671; fax: +86 (511) 88791467. E-mail addresses: [email protected] (L. Tian); [email protected] (G. Fang).
1876-6102 © 2016 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems. doi:10.1016/j.egypro.2016.12.041
240
Guochang Fang et al. / Energy Procedia 104 (2016) 239 – 244
ESER system is a complex nonlinear coupling system, which includes many variables. How to find these sensitive variables of reducing greenhouse gas emissions in ESER system is becoming a hot topic of relative academic researches [6]. Energy construction adjustment is an efficient measure among the policy instruments of ESER system, which could promote ESER targets to be more easily achieved to a certain extent. Coordinating the relationship between energy construction adjustment, technological progress and other variables are helpful to overcome constraints of the internal alternative energy and energy supply structure [7]. The scientific and effective utilization of energy construction adjustment could perfectly control energy intensity, and there is no obvious suppression effect on economic growth. Growing attention has been focused on chaos analysis and applications. Fang and Tian proposed a novel 3D ESER chaotic system [8], which provide sound theoretical basis for the present study. This paper introduces energy construction adjustment into the three-dimensional ESER dynamics system. The dynamic evolution behavior of the energy intensity and economic growth are put forward vividly, the coefficients which affect the peak value and stable value are discussed systemically. The way to better develop ESER is investigated further. Existing literatures on energy construction adjustment studies are multifarious, while lack the theory strut. The dynamic method is an innovation research method for energy construction adjustment. Compared with the previous researches, evolution analysis and theoretical basis in this paper are more persuasive. The findings of this study are more consistent with the reality of energy construction adjustment. The rest of this paper is organized as follows. Section 2 provides a brief description of the model. Section 3 is about parameter identification of the actual system based on China’s statistical data. A scenario analysis of the actual system is presented in Section 4. Conclusions and further perspectives are presented in Section 5. 2. Establishment of the model Energy construction adjustment is one of the effective methods to promote the development of ESER, which is the nub of solving the current energy, resources and environmental issues. Study on the ESER problem in China should comply with the situation of China. Energy construction adjustment is driven by economic means under the current circumstances, and the investment of economic to energy construction adjustment has effect on economic growth. The dynamic evolution system with energy construction adjustment constraints can be described by the following differential equations: x a1 x y M 1 a2 y a3 z °° t ® y b1 x b2 y 1 y C b3 z 1 z E b4c z d 1 1 d ° °¯ z c1 x x N 1 c2 y c3 z c4 z
(1)
where x t is the time-dependent variable of ESER, y t , of carbon emissions, z t , of
t
economic growth (GDP). For the explanation of the formulas, see [9, 10]. b4c z d 1 1 d is timec dependent energy construction adjustment, b4 is the coefficient of energy construction adjustment, d is effective rate of discount, t is the period, t I ,
I is a given economic period. To facilitate the
t discussions, let b4c d 1 1 d =b4 . Based on the reality of China, energy construction adjustment
is driven by economic means under the current circumstances. So energy construction adjustment is simplified as the direct effect of economic growth ( b4 z ). The investment in energy construction adjustment will have a certain effect on z t , and c4 z represents the effect.
In Eq. (1), integrate y dy dt and z dz dt about t , the energy consumption and the GDP could
t I1 x, ky, z, t , z t I2 x, y, z, t . The time-dependent energy during a given period can be depicted as U t I1 x, ky, z , t I2 x, y , z , t , t I . be deduced as y
*
intensity
When the coefficients of Eq. (1) are given different values, the system presented in Eq. (1) will show different dynamic behavior. In order to research Eq. (1) further, parameters are fixed as table 1.
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Guochang Fang et al. / Energy Procedia 104 (2016) 239 – 244
When the parameters of Eq. (1) are given in table 1 and initials are given [0.015 0.758 1.83], a chaotic attractor could be observed as shown in Fig. 1, the bifurcation diagram of state variable y with respect to parameter c3 is shown in Fig. 2, the Lyapunov exponent spectrum of parameter c3 is shown in Fig. 3. Table 1. Parameters of Eq. (1).
a1
a2
a3
b1
b2
b3
b4
c1
0.09
0.003
0.012
0.412
0.08
0.8
0.0064
0.035
c2
c3
c4
M
C
E
N
0.0062
0.08
0.0015
0. 9
1.6
2.88
0.35
1.6 1.4 2.5
1.2
2
1
1.5
y
x
0.8
1
0.6
0.5
0.4
0 4
0.2 2 0 Ŧ2
y
0
1
0.5
1.5
2
2.5
0 Ŧ0.2 0.08
z
0.082
0.084
0.086
0.088
0.09
c3
Fig.1. ESER attractor ( z
y x ).
Fig.2. Bifurcation diagram of
y.
3. Parameter identification The source data of carbon emissions and economic growth originate from China National Statistics Yearbook, and calculation of ESER is referred to the algorithm in reference [8]. The data of ESER, carbon emissions and economic growth of the years 2000 to 2014 can be shown in Table 2. The data of the year 2013 and the previous years had been revised systematically, so the data in table 2 have slight change compared with the data in reference [8, 9]. Table 2. The data of ESER, carbon emissions and economic growth (2000-2014, 1999 is the base). year x y z year x y z 2000 2.2183 1.0455 1.1063 2008 6.3104 2.2808 3.5121 2001 3.9786 1.1066 1.2227 2009 3.5778 2.3912 3.8323 2002 2.5602 1.2064 1.3417 2010 4.1914 2.5656 4.5339 2003 1.4457 1.4020 1.5142 2011 4.0169 2.7534 5.3680 2004 2.6127 1.6382 1.7820 2012 4.6762 2.8608 5.9223 2005 2.1059 1.8594 2.0612 2013 6.0923 2.9659 6.5199 2006 2.2948 2.0379 2.4134 2014 7.1510 3.0305 7.0535 2007 4.3497 2.2156 2.9718 Based on the Genetic Algorithm-Back Propagation (GA-BP) neural network [10], let crossover rate be 0.85 and mutation rate be 0.06. When the error e d 2.9231e 003 , the parameters of the actual system are shown in Table 3. Table 3. The parameters of the actual system.
a1
a2
a3
b1
b2
b3
b4
c1
0.2893
0.4313
0.3427
0.0439
0.5257
0.1035
0.0772
0.1247
c2
c3
c4
M
C
E
N
0.0914
0.0903
0.1024
0.8132
0.5812
0.9513
0.5051
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Guochang Fang et al. / Energy Procedia 104 (2016) 239 – 244
Choose the parameters of Eq. (1) shown in Table 3, select the data of the year 2000 as the initial 9 condition (standard coal/ 10 ton, [0.00000096 0.615 1.73]), and the corresponding stable solutions are observed as shown in Fig. 4. The phase diagram in Fig. 4 indicates that the actual system is steady. 0.04 0.02
1 0.5
Ŧ0.02
0
x
lyapunov exponents
0
Ŧ0.04
Ŧ0.5 Ŧ0.06
Ŧ1 0.7
Ŧ0.08
0.6
2
Ŧ0.1
1.5
0.5
1
0.4 Ŧ0.12 0.08
0.082
0.084
0.086
0.088
0.09
0.5 0
y
c
z
3
Fig.3. Lyapunov exponent spectrum. Fig.4. The phase diagram of the actual system. 4. A scenario analysis Energy construction adjustment could solve the plight of resources and the environment. And of course, energy construction adjustment has strong impact on the overall variables of ESER system. Research and experience show that energy intensity constraint is more suitable for China’s ESER targets. It must be noted that, economic growth should not be ignored in the progress of ESER. So the dynamical evolution behaviors of energy intensity and economic growth are studied pointedly in this paper. The evolution trend of energy intensity and economic growth are diagramed. By observing the evolution plots of energy intensity, the error value and evolution trend of economic growth, a comprehensive analysis of ESER system under energy construction adjustment constraints is put forward. b4 is the coefficient of energy construction adjustment, the change of b4 reflects the confinement strength of energy construction adjustment. Fig.5 shows the evolution curve of energy intensity when b4 growing. The red curve is the energy intensity evolution diagram of 3D ESER system [8], the blue curve corresponds to the curve when b4 =0.0772 , the green curve, b4 =0.0972 . It can be observed that, the blue and green curves are lower than the red one obviously, which indicates that energy construction adjustment can actually control energy intensity. When b4 becomes big gradually, the stable value of energy intensity become small gradually. The analysis shows that, the successful implementation of energy construction adjustment could control energy intensity effectively. The more effectively energy construction adjustment plays in the system, the more energy intensity declines. 1
0.44 Peak value 1 0.42
The error value of economic growth
0.5
Energy intensity
0.4 Peak value 2
0.38
Peak value 3
Stable value 1
0.36 Stable value 2 0.34
0.32
0.3
0
0.5
Ŧ0.5
5
10
15 t (year)
20
25
0.5
1
1.5
2
2.5
0
Stable value 3
0
0
Case 1
Case 2 30
Ŧ0.5
0
5
10
15 t (year)
20
25
30
Fig.6. The error value of economic growth. Fig.5. Energy intensity ( b4 ). Fig.6 shows the impacts of energy construction adjustment on economic growth when b4 is given the above values. Case 1 corresponds to the conditions when b4 =0.0772 , and case 2, b4 =0.0972 . Comparative observation of Fig.6 shows that, the two curves are all under the zero line (red line), the
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Guochang Fang et al. / Energy Procedia 104 (2016) 239 – 244
inhibitory impacts on economic growth appears when b4 becomes larger gradually. Further observation, the spacing between case 1 and case 2 is not very big. In one sense, the inhibitory impact on economic growth is smaller than the promoting effect on controlling energy intensity. This also indicates that, energy construction adjustment is appropriate for ESER system from another angle. The error value of carbon emissions and economic growth
0.44 Peak value 1 0.42
Energy intensity
0.4
0.38
Stable value 1
0.36
Peak value 3,2
Stable value 3
0.34 Stable value 2 0.32
0.3
0
5
10
15 t (year)
20
25
30
1 0.1 0 Ŧ0.1 0.5 Ŧ0.2 Ŧ0.3 0
2
4
6
8
0
The error value of carbon emissions The error value of economic growth Ŧ0.5
0
5
10
15 t (year)
20
25
30
Fig.7. Energy intensity ( c4 ). Fig.8. The error value of y t and z t . Energy construction adjustment is driven by economic means under current circumstances in China, and the investment of economic to energy construction adjustment are bound to have effect on economic growth. Fig.7 shows the evolution curve of energy intensity when c4 growing. The red curve is the energy intensity evolution diagram of 3D ESER system [8], the blue curve corresponds to the curve when c4 =0.1024 ( b4 =0.0772 ), the green curve, c4 =0.1224 ( b4 =0.0772 ). It can be observed that, because of the existence of b4 , the blue and green curves are lower than the red one, i.e. energy intensity could still be controlled under this kind of situation. However, it is important to realize that the green curve is higher than the blue one, i.e. the bigger c4 brings higher energy intensity. The reason could be explained commendably by Fig.8. Fig.8 describes the error value of carbon emissions and economic growth when c4 changing from 0.1024 to 0.1224. The blue curve is the error value of carbon emissions, and the green curve is the error value of economic growth. The growing c4 has both inhibition effect on carbon emissions and economic growth at the same time. The effect is greater than the one on carbon emissions, i.e. the green curve is always under the blue line after an infinitesimal fluctuation. In the formula of U t , by comparison with the numerator, the denominator gets smaller, so energy intensity U t gets bigger. Comparative observation can be found that, energy construction adjustment plays an important role in ESER system, which can control energy intensity to an ideal level. However, it should be noted that energy construction adjustment is driven by economic means, so this measure inevitably has inhibition effect on economic growth during a given economic period (the inhibition effect will change into promoting effect when ESER system is mature). There is an urgent need to address the rationally and efficiently utilizing energy construction adjustment. 5. Conclusions and further perspectives On the basis of the 3D ESER system, this paper has discussed the impacts of energy construction adjustment on energy intensity and economic growth. The nonlinear dynamics behavior of the system is discussed by means of system dynamics method, and a novel ESER attractor is achieved. The quantitative coefficients of the actual system are obtained based on the GA-BP network. The time-dependent energy intensity calculation formula and the evolution tendency of economic growth are made as the measurements. The effect of energy construction adjustment and the corresponding investment on energy intensity and economic growth are put forward. A comprehensive study is carried out providing results in perfect agreement with actual conditions in China. Energy intensity changes during the varying of ESER policy, and the deep level reason is revealed by analyzing the error value of carbon emissions and economic growth. By analysis, energy construction adjustment could effectively control energy intensity, which plays an important role in ESER system. The
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Guochang Fang et al. / Energy Procedia 104 (2016) 239 – 244
role of energy construction adjustment in China’s ESER system is driven by economic means. It is exactly because of this, this measure has dampening effect on economic growth. Energy construction adjustment should be used rationally and efficiently. Energy construction adjustment should first get rid of the high dependence on coal-fired power. Meanwhile, wind energy, solar energy, nuclear energy and other clean energy should be vigorously developed in accordance with local conditions. According to the results of this paper, the driving effect of economic growth on energy construction adjustment should be used to the full. Strengthening the commercial operation of nuclear energy, perfecting photovoltaic industry fiscal subsidy, stepping up spending on research and development of windmill generator and fan blades, all of these combined with policy measures will consolidate the effects of energy construction adjustment and ESER. There might be some influence on the output parameters for the sake of lack of statistical data during the process of parameter identification. This paper mainly examines the evolution behavior of ESER, carbon emissions, economic growth and energy construction adjustment. However, more variables should be included in the actual ESER system, such as technical progress, economic growth rates and so on. The ESER system contains more variables should be constructed, and a further research on the dynamic behavior of the system will be explored in the future research. Acknowledgements The research is supported by the National Natural Science Foundation of China (Nos.71303205, 71340024, 71303095, 71403105, 61403171), Major Research plan of the National Natural Science Foundation of China (No. 91546118), China Postdoctoral Science Foundation (2015T80519, 2014M551524, 2015M581738), Jiangsu Planned Projects for Postdoctoral Research Funds (1401049C), sponsored by Qing Lan Project (JS201423). References [1] Marucci A, Cappuccini A. Dynamic photovoltaic greenhouse: Energy efficiency in clear sky conditions. Appl Energy 2016; 170: 362–76. [2] Chen QQ, Tang ZY, Lei Y, Sun YH, Jiang MH. Feasibility analysis of nuclear–coal hybrid energy systems from the perspective of low-carbon development. Appl Energy 2015; 158: 619–30. [3] Pablo-Romero MP, Jesús JD. Economic growth and energy consumption: The Energy-Environmental Kuznets Curve for Latin America and the Caribbean. Renew Sustain Energy Rev 2016; 60:1343–50. [4] Christensen A, Hobbs B. A model of state and federal biofuel policy: Feasibility assessment of the California Low Carbon Fuel Standard. Appl Energy 2016; 169: 799–812. [5] Desideri U, Yan J. Clean energy technologies and systems for a sustainable world. Appl Energy 2012; 97:1–4. [6] Haro P, Aracil C, Vidal-Barrero F, Ollero P. Balance and saving of GHG emissions in thermochemical biorefineries. Appl Energy 2015; 147: 444–55. [7] Carta JA, González J, Cabrera P, Subiela VJ. Preliminary experimental analysis of a small-scale prototype SWRO desalination plant, designed for continuous adjustment of its energy consumption to the widely varying power generated by a stand-alone wind turbine. Appl Energy 2015; 137: 222–39. [8] Fang GC, Tian LX, Sun M, Fu M. Analysis and application of a novel three-dimensional energy-saving and emission-reduction dynamic evolution system. Energy 2012; 40: 291–9. [9] Fang GC, Tian LX, Fu M, Sun M. The impacts of carbon tax on energy intensity and economic growth—A dynamic evolution analysis on the case of China. Appl Energy 2013; 110: 17–28. [10] Yeo IA, Yee JJ. A proposal for a site location planning model of environmentally friendly urban energy supply plants using an environment and energy geographical information system (E-GIS) database (DB) and an artificial neural network (ANN). Appl Energy 2014; 119: 99–117.
Biography Guochang Fang is an Associate Professor in Nanjing University of Finance and Economics. His current work involves the theory and application of energy-saving and emission-reduction system, carbon tax, carbon trading, and published more than 20 academic articles in journals, which are indexed by SSCI, SCI, or EI.
ScienceDirect Energy Procedia 104 (2016) 239 – 244
CUE2016-Applied Energy Symposium and Forum 2016: Low carbon cities & urban energy systems
The impacts of energy construction adjustment on energy intensity and economic growth—A case study of China Guochang Fanga,b , Lixin Tian a,c, *, Min Fu a, Mei Sun a , Ruijin Du a a
Center for Energy Development and Environmental Protection, Jiangsu University, Zhenjiang, Jiangsu, 212013, China b School of Economics, Nanjing University of Finance and Economics, Nanjing, Jiangsu, 210023, China c School of Mathematical Sciences, Nanjing Normal University, Nanjing, Jiangsu, 210023, China
Abstract This paper attempts to explore the impacts of energy construction adjustment on energy intensity and economic growth based on the 3D energy-saving and emission-reduction (ESER) system, which has not yet been discussed in present literature. The dynamic behavior of the system is discussed. The results of the scenario study show that, energy construction adjustment could effectively control energy intensity. When the investment of economic to energy construction adjustment gets bigger, energy intensity becomes larger. This is because this practice could control energy intensity while brings inhibiting impact on economic growth. Rationally and efficiently utilizing the investment of economic to energy construction adjustment is the key in the current ESER system. © 2016 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility the organizing committee of CUE 2016 Peer-review under responsibility of theofscientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems. Keywords: energy construction adjustment; energy-saving and emission-reduction; energy intensity; economic growth.
1. Introduction Control analysis of greenhouse gas emissions has attracted a great deal of attention from various fields of researchers [1]. Over the course of the study, people are gradually aware that energy-saving and emission-reduction (ESER) is the ideal choice of attaining the low-carbon targets [2]. The ESER system should be constructed according to the current situation. The measure index of ESER system should be made clear, and the key variables of the system should be explored [3], all of these are the determining factors to promote the development of ESER system. The smooth development of ESER can achieve the objectives of protecting the environment and achieving an emission trajectory to a low emission economy [4], which could build a resource conserving and environment-friendly society [5].
*
Corresponding author. Tel.: +86 (511) 88797671; fax: +86 (511) 88791467. E-mail addresses: [email protected] (L. Tian); [email protected] (G. Fang).
1876-6102 © 2016 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems. doi:10.1016/j.egypro.2016.12.041
240
Guochang Fang et al. / Energy Procedia 104 (2016) 239 – 244
ESER system is a complex nonlinear coupling system, which includes many variables. How to find these sensitive variables of reducing greenhouse gas emissions in ESER system is becoming a hot topic of relative academic researches [6]. Energy construction adjustment is an efficient measure among the policy instruments of ESER system, which could promote ESER targets to be more easily achieved to a certain extent. Coordinating the relationship between energy construction adjustment, technological progress and other variables are helpful to overcome constraints of the internal alternative energy and energy supply structure [7]. The scientific and effective utilization of energy construction adjustment could perfectly control energy intensity, and there is no obvious suppression effect on economic growth. Growing attention has been focused on chaos analysis and applications. Fang and Tian proposed a novel 3D ESER chaotic system [8], which provide sound theoretical basis for the present study. This paper introduces energy construction adjustment into the three-dimensional ESER dynamics system. The dynamic evolution behavior of the energy intensity and economic growth are put forward vividly, the coefficients which affect the peak value and stable value are discussed systemically. The way to better develop ESER is investigated further. Existing literatures on energy construction adjustment studies are multifarious, while lack the theory strut. The dynamic method is an innovation research method for energy construction adjustment. Compared with the previous researches, evolution analysis and theoretical basis in this paper are more persuasive. The findings of this study are more consistent with the reality of energy construction adjustment. The rest of this paper is organized as follows. Section 2 provides a brief description of the model. Section 3 is about parameter identification of the actual system based on China’s statistical data. A scenario analysis of the actual system is presented in Section 4. Conclusions and further perspectives are presented in Section 5. 2. Establishment of the model Energy construction adjustment is one of the effective methods to promote the development of ESER, which is the nub of solving the current energy, resources and environmental issues. Study on the ESER problem in China should comply with the situation of China. Energy construction adjustment is driven by economic means under the current circumstances, and the investment of economic to energy construction adjustment has effect on economic growth. The dynamic evolution system with energy construction adjustment constraints can be described by the following differential equations: x a1 x y M 1 a2 y a3 z °° t ® y b1 x b2 y 1 y C b3 z 1 z E b4c z d 1 1 d ° °¯ z c1 x x N 1 c2 y c3 z c4 z
(1)
where x t is the time-dependent variable of ESER, y t , of carbon emissions, z t , of
t
economic growth (GDP). For the explanation of the formulas, see [9, 10]. b4c z d 1 1 d is timec dependent energy construction adjustment, b4 is the coefficient of energy construction adjustment, d is effective rate of discount, t is the period, t I ,
I is a given economic period. To facilitate the
t discussions, let b4c d 1 1 d =b4 . Based on the reality of China, energy construction adjustment
is driven by economic means under the current circumstances. So energy construction adjustment is simplified as the direct effect of economic growth ( b4 z ). The investment in energy construction adjustment will have a certain effect on z t , and c4 z represents the effect.
In Eq. (1), integrate y dy dt and z dz dt about t , the energy consumption and the GDP could
t I1 x, ky, z, t , z t I2 x, y, z, t . The time-dependent energy during a given period can be depicted as U t I1 x, ky, z , t I2 x, y , z , t , t I . be deduced as y
*
intensity
When the coefficients of Eq. (1) are given different values, the system presented in Eq. (1) will show different dynamic behavior. In order to research Eq. (1) further, parameters are fixed as table 1.
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Guochang Fang et al. / Energy Procedia 104 (2016) 239 – 244
When the parameters of Eq. (1) are given in table 1 and initials are given [0.015 0.758 1.83], a chaotic attractor could be observed as shown in Fig. 1, the bifurcation diagram of state variable y with respect to parameter c3 is shown in Fig. 2, the Lyapunov exponent spectrum of parameter c3 is shown in Fig. 3. Table 1. Parameters of Eq. (1).
a1
a2
a3
b1
b2
b3
b4
c1
0.09
0.003
0.012
0.412
0.08
0.8
0.0064
0.035
c2
c3
c4
M
C
E
N
0.0062
0.08
0.0015
0. 9
1.6
2.88
0.35
1.6 1.4 2.5
1.2
2
1
1.5
y
x
0.8
1
0.6
0.5
0.4
0 4
0.2 2 0 Ŧ2
y
0
1
0.5
1.5
2
2.5
0 Ŧ0.2 0.08
z
0.082
0.084
0.086
0.088
0.09
c3
Fig.1. ESER attractor ( z
y x ).
Fig.2. Bifurcation diagram of
y.
3. Parameter identification The source data of carbon emissions and economic growth originate from China National Statistics Yearbook, and calculation of ESER is referred to the algorithm in reference [8]. The data of ESER, carbon emissions and economic growth of the years 2000 to 2014 can be shown in Table 2. The data of the year 2013 and the previous years had been revised systematically, so the data in table 2 have slight change compared with the data in reference [8, 9]. Table 2. The data of ESER, carbon emissions and economic growth (2000-2014, 1999 is the base). year x y z year x y z 2000 2.2183 1.0455 1.1063 2008 6.3104 2.2808 3.5121 2001 3.9786 1.1066 1.2227 2009 3.5778 2.3912 3.8323 2002 2.5602 1.2064 1.3417 2010 4.1914 2.5656 4.5339 2003 1.4457 1.4020 1.5142 2011 4.0169 2.7534 5.3680 2004 2.6127 1.6382 1.7820 2012 4.6762 2.8608 5.9223 2005 2.1059 1.8594 2.0612 2013 6.0923 2.9659 6.5199 2006 2.2948 2.0379 2.4134 2014 7.1510 3.0305 7.0535 2007 4.3497 2.2156 2.9718 Based on the Genetic Algorithm-Back Propagation (GA-BP) neural network [10], let crossover rate be 0.85 and mutation rate be 0.06. When the error e d 2.9231e 003 , the parameters of the actual system are shown in Table 3. Table 3. The parameters of the actual system.
a1
a2
a3
b1
b2
b3
b4
c1
0.2893
0.4313
0.3427
0.0439
0.5257
0.1035
0.0772
0.1247
c2
c3
c4
M
C
E
N
0.0914
0.0903
0.1024
0.8132
0.5812
0.9513
0.5051
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Guochang Fang et al. / Energy Procedia 104 (2016) 239 – 244
Choose the parameters of Eq. (1) shown in Table 3, select the data of the year 2000 as the initial 9 condition (standard coal/ 10 ton, [0.00000096 0.615 1.73]), and the corresponding stable solutions are observed as shown in Fig. 4. The phase diagram in Fig. 4 indicates that the actual system is steady. 0.04 0.02
1 0.5
Ŧ0.02
0
x
lyapunov exponents
0
Ŧ0.04
Ŧ0.5 Ŧ0.06
Ŧ1 0.7
Ŧ0.08
0.6
2
Ŧ0.1
1.5
0.5
1
0.4 Ŧ0.12 0.08
0.082
0.084
0.086
0.088
0.09
0.5 0
y
c
z
3
Fig.3. Lyapunov exponent spectrum. Fig.4. The phase diagram of the actual system. 4. A scenario analysis Energy construction adjustment could solve the plight of resources and the environment. And of course, energy construction adjustment has strong impact on the overall variables of ESER system. Research and experience show that energy intensity constraint is more suitable for China’s ESER targets. It must be noted that, economic growth should not be ignored in the progress of ESER. So the dynamical evolution behaviors of energy intensity and economic growth are studied pointedly in this paper. The evolution trend of energy intensity and economic growth are diagramed. By observing the evolution plots of energy intensity, the error value and evolution trend of economic growth, a comprehensive analysis of ESER system under energy construction adjustment constraints is put forward. b4 is the coefficient of energy construction adjustment, the change of b4 reflects the confinement strength of energy construction adjustment. Fig.5 shows the evolution curve of energy intensity when b4 growing. The red curve is the energy intensity evolution diagram of 3D ESER system [8], the blue curve corresponds to the curve when b4 =0.0772 , the green curve, b4 =0.0972 . It can be observed that, the blue and green curves are lower than the red one obviously, which indicates that energy construction adjustment can actually control energy intensity. When b4 becomes big gradually, the stable value of energy intensity become small gradually. The analysis shows that, the successful implementation of energy construction adjustment could control energy intensity effectively. The more effectively energy construction adjustment plays in the system, the more energy intensity declines. 1
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Fig.6. The error value of economic growth. Fig.5. Energy intensity ( b4 ). Fig.6 shows the impacts of energy construction adjustment on economic growth when b4 is given the above values. Case 1 corresponds to the conditions when b4 =0.0772 , and case 2, b4 =0.0972 . Comparative observation of Fig.6 shows that, the two curves are all under the zero line (red line), the
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inhibitory impacts on economic growth appears when b4 becomes larger gradually. Further observation, the spacing between case 1 and case 2 is not very big. In one sense, the inhibitory impact on economic growth is smaller than the promoting effect on controlling energy intensity. This also indicates that, energy construction adjustment is appropriate for ESER system from another angle. The error value of carbon emissions and economic growth
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Fig.7. Energy intensity ( c4 ). Fig.8. The error value of y t and z t . Energy construction adjustment is driven by economic means under current circumstances in China, and the investment of economic to energy construction adjustment are bound to have effect on economic growth. Fig.7 shows the evolution curve of energy intensity when c4 growing. The red curve is the energy intensity evolution diagram of 3D ESER system [8], the blue curve corresponds to the curve when c4 =0.1024 ( b4 =0.0772 ), the green curve, c4 =0.1224 ( b4 =0.0772 ). It can be observed that, because of the existence of b4 , the blue and green curves are lower than the red one, i.e. energy intensity could still be controlled under this kind of situation. However, it is important to realize that the green curve is higher than the blue one, i.e. the bigger c4 brings higher energy intensity. The reason could be explained commendably by Fig.8. Fig.8 describes the error value of carbon emissions and economic growth when c4 changing from 0.1024 to 0.1224. The blue curve is the error value of carbon emissions, and the green curve is the error value of economic growth. The growing c4 has both inhibition effect on carbon emissions and economic growth at the same time. The effect is greater than the one on carbon emissions, i.e. the green curve is always under the blue line after an infinitesimal fluctuation. In the formula of U t , by comparison with the numerator, the denominator gets smaller, so energy intensity U t gets bigger. Comparative observation can be found that, energy construction adjustment plays an important role in ESER system, which can control energy intensity to an ideal level. However, it should be noted that energy construction adjustment is driven by economic means, so this measure inevitably has inhibition effect on economic growth during a given economic period (the inhibition effect will change into promoting effect when ESER system is mature). There is an urgent need to address the rationally and efficiently utilizing energy construction adjustment. 5. Conclusions and further perspectives On the basis of the 3D ESER system, this paper has discussed the impacts of energy construction adjustment on energy intensity and economic growth. The nonlinear dynamics behavior of the system is discussed by means of system dynamics method, and a novel ESER attractor is achieved. The quantitative coefficients of the actual system are obtained based on the GA-BP network. The time-dependent energy intensity calculation formula and the evolution tendency of economic growth are made as the measurements. The effect of energy construction adjustment and the corresponding investment on energy intensity and economic growth are put forward. A comprehensive study is carried out providing results in perfect agreement with actual conditions in China. Energy intensity changes during the varying of ESER policy, and the deep level reason is revealed by analyzing the error value of carbon emissions and economic growth. By analysis, energy construction adjustment could effectively control energy intensity, which plays an important role in ESER system. The
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role of energy construction adjustment in China’s ESER system is driven by economic means. It is exactly because of this, this measure has dampening effect on economic growth. Energy construction adjustment should be used rationally and efficiently. Energy construction adjustment should first get rid of the high dependence on coal-fired power. Meanwhile, wind energy, solar energy, nuclear energy and other clean energy should be vigorously developed in accordance with local conditions. According to the results of this paper, the driving effect of economic growth on energy construction adjustment should be used to the full. Strengthening the commercial operation of nuclear energy, perfecting photovoltaic industry fiscal subsidy, stepping up spending on research and development of windmill generator and fan blades, all of these combined with policy measures will consolidate the effects of energy construction adjustment and ESER. There might be some influence on the output parameters for the sake of lack of statistical data during the process of parameter identification. This paper mainly examines the evolution behavior of ESER, carbon emissions, economic growth and energy construction adjustment. However, more variables should be included in the actual ESER system, such as technical progress, economic growth rates and so on. The ESER system contains more variables should be constructed, and a further research on the dynamic behavior of the system will be explored in the future research. Acknowledgements The research is supported by the National Natural Science Foundation of China (Nos.71303205, 71340024, 71303095, 71403105, 61403171), Major Research plan of the National Natural Science Foundation of China (No. 91546118), China Postdoctoral Science Foundation (2015T80519, 2014M551524, 2015M581738), Jiangsu Planned Projects for Postdoctoral Research Funds (1401049C), sponsored by Qing Lan Project (JS201423). References [1] Marucci A, Cappuccini A. Dynamic photovoltaic greenhouse: Energy efficiency in clear sky conditions. Appl Energy 2016; 170: 362–76. [2] Chen QQ, Tang ZY, Lei Y, Sun YH, Jiang MH. Feasibility analysis of nuclear–coal hybrid energy systems from the perspective of low-carbon development. Appl Energy 2015; 158: 619–30. [3] Pablo-Romero MP, Jesús JD. Economic growth and energy consumption: The Energy-Environmental Kuznets Curve for Latin America and the Caribbean. Renew Sustain Energy Rev 2016; 60:1343–50. [4] Christensen A, Hobbs B. A model of state and federal biofuel policy: Feasibility assessment of the California Low Carbon Fuel Standard. Appl Energy 2016; 169: 799–812. [5] Desideri U, Yan J. Clean energy technologies and systems for a sustainable world. Appl Energy 2012; 97:1–4. [6] Haro P, Aracil C, Vidal-Barrero F, Ollero P. Balance and saving of GHG emissions in thermochemical biorefineries. Appl Energy 2015; 147: 444–55. [7] Carta JA, González J, Cabrera P, Subiela VJ. Preliminary experimental analysis of a small-scale prototype SWRO desalination plant, designed for continuous adjustment of its energy consumption to the widely varying power generated by a stand-alone wind turbine. Appl Energy 2015; 137: 222–39. [8] Fang GC, Tian LX, Sun M, Fu M. Analysis and application of a novel three-dimensional energy-saving and emission-reduction dynamic evolution system. Energy 2012; 40: 291–9. [9] Fang GC, Tian LX, Fu M, Sun M. The impacts of carbon tax on energy intensity and economic growth—A dynamic evolution analysis on the case of China. Appl Energy 2013; 110: 17–28. [10] Yeo IA, Yee JJ. A proposal for a site location planning model of environmentally friendly urban energy supply plants using an environment and energy geographical information system (E-GIS) database (DB) and an artificial neural network (ANN). Appl Energy 2014; 119: 99–117.
Biography Guochang Fang is an Associate Professor in Nanjing University of Finance and Economics. His current work involves the theory and application of energy-saving and emission-reduction system, carbon tax, carbon trading, and published more than 20 academic articles in journals, which are indexed by SSCI, SCI, or EI.