Analysis and application of a novel three-dimensional energy-saving and emission-reduction dynamic evolution system

A novel three-dimensional energy-saving and emission-reduction chaotic system is proposed, which has not yet been reported in present literature. The system is established in accordance with the complicated relationship between energy-saving and emission-reduction, carbon emissions and economic growth. The dynamic behavior of the system is analyzed by means of Lyapunov exponents and bifurcation diagrams. With undetermined coefficient method, expressions of homoclinic orbits of the system are obtained. The Silnikov theorem guarantees that the system has Smale horseshoes and the horseshoes chaos. Artificial neural network (ANN) is used to identify the quantitative coefficients in the simulation models according to the statistical data of China, and an empirical study of the real system is carried out with the results in perfect agreement with actual situation. It is found that the sooner and more perfect energy-saving and emission-reduction is started, the easier and sooner the maximum of the carbon emissions will be achieved so as to reduce carbon emissions and energy intensity. Numerical simulations are presented to demonstrate the results.

[1]  G. Cornelis van Kooten,et al.  How costly are carbon offsets? A meta-analysis of carbon forest sinks , 2004 .

[2]  Ting Wang,et al.  Benefit Evaluation on Energy Saving and Emission Reduction of National Small Hydropower Ecological Protection Project , 2011 .

[3]  Min Zhao,et al.  Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method , 2010 .

[4]  Guanrong Chen,et al.  On a Generalized Lorenz Canonical Form of Chaotic Systems , 2002, Int. J. Bifurc. Chaos.

[5]  Chen Mingsheng,et al.  The Mechanism and Measures of Adjustment of Industrial Organization Structure: the Perspective of Energy Saving and Emission Reduction , 2011 .

[6]  Mei Sun,et al.  The energy resources system with parametric perturbations and its hyperchaos control , 2009 .

[7]  Ignacio J. Pérez-Arriaga,et al.  Comparison of the evolution of energy intensity in Spain and in the EU15. Why is Spain different , 2010 .

[8]  Cai-Wan Chang-Jian,et al.  Bifurcation and chaos analysis of spur gear pair with and without nonlinear suspension , 2011 .

[9]  W. Kurz,et al.  Accounting of forest carbon sinks and sources under a future climate protocol—factoring out past disturbance and management effects on age–class structure , 2008 .

[10]  Xi Youmin,et al.  Analysis of Influences between the Energy Structure Change and Energy Intensity in China , 2008 .

[11]  Brantley Liddle.  Revisiting World Energy Intensity Convergence for Regional Differences , 2010 .

[12]  Long Yin,et al.  Analysis of the FDI Effect on Energy Consumption Intensity in Jiangsu Province , 2011 .

[13]  D. W. Thompson,et al.  Developing forest sinks in Australia and the United States — A forest owner's prerogative , 2011 .

[14]  Li Yang,et al.  Evolution of China’s power dispatch principle and the new energy saving power dispatch policy , 2010 .

[15]  G. Mavrotas,et al.  A bottom-up decomposition analysis of energy-related CO2 emissions in Greece , 2006 .

[16]  Zhancheng Guo,et al.  Current situation of energy consumption and measures taken for energy saving in the iron and steel industry in China , 2010 .

[17]  N. K. Bansal,et al.  Nuclear and clean coal technology options for sustainable development in India , 2010 .

[18]  J. Duro,et al.  Inequality across countries in energy intensities: An analysis of the role of energy transformation and final energy consumption , 2011 .

[19]  Guanrong Chen,et al.  Existence of heteroclinic orbits of the Shil'nikov type in a 3D quadratic autonomous chaotic system , 2006 .

[20]  Huang Xian-jin,et al.  Characteristics of Carbon Emission in China and Analysis on Its Cause , 2008 .

[21]  Zhufeng Yu,et al.  Policy study on development and utilization of clean coal technology in China , 2008 .

[22]  Meichen Fu,et al.  Energy saving and emission reduction: A project of coal-resource integration in Shanxi Province, China , 2011 .

[23]  Hao Wang,et al.  Modeling technological learning and its application for clean coal technologies in Japan , 2011 .

[24]  Linyan Sun,et al.  The relationship between energy consumption structure, economic structure and energy intensity in China , 2009 .

[25]  Tunisia.  CO 2 emissions , energy consumption and economic growth in , 2008 .

[26]  Gwo-Ching Liao,et al.  A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reducti , 2011 .

[27]  I. Ozturk,et al.  CO2 emissions, energy consumption and economic growth in Turkey , 2010 .

[28]  Kristin Aunan,et al.  The energy intensity target in China's 11th Five-Year Plan period--Local implementation and achievements in Shanxi Province , 2011 .

[29]  Mei Sun,et al.  The impacts of carbon tax on energy intensity and economic growth – A dynamic evolution analysis on the case of China , 2013 .

[30]  R. Rudramoorthy,et al.  Assessment of petroleum saving and greenhouse gas emission reduction from two-wheeler segment: 2011–2021 , 2011 .

[31]  Yetis Sazi Murat,et al.  Use of artificial neural networks for transport energy demand modeling , 2006 .

[32]  Ying Chen,et al.  Energy resources demand-supply system analysis and empirical research based on non-linear approach , 2011 .

[33]  Boqiang Lin,et al.  Electricity tariff reform and rebound effect of residential electricity consumption in China , 2013 .

[34]  Ernest Orlando Lawrence.  Analysis of Potential Energy Saving and CO 2 Emission Reduction of Home Appliances and Commercial Equipments in , 2010 .

[35]  L. Xiuli.  Probing the Guiding Role of Taxation in Energy-Saving and Emission-Reducing Technology , 2011 .

[36]  Kyung Hwan Yoon,et al.  Energy price uncertainty, energy intensity and firm investment , 2011 .

[37]  Daizhan Cheng,et al.  Bridge the Gap between the Lorenz System and the Chen System , 2002, Int. J. Bifurc. Chaos.

[38]  Hassan Golmohammadi,et al.  Quantitative structure–property relationship prediction of gas-to-chloroform partition coefficient using artificial neural network , 2010 .

[39]  Lixin Tian,et al.  Dynamics and adaptive synchronization of the energy resource system , 2007 .