一类新型节能减排系统的分析和应用
|
摘要
全球变暖被认为是人类生存的最大威胁之一,大量温室气体排放是引起气候变化,并带来严重自然灾害的诱因。其中化石能源消耗产生的二氧化碳是温室气体的主要来源。要控制气候变暖的步伐需要“全球行动”,自《联合国气候变化框架公约》(UNECCC)缔结以来,各国关于减排的目标、各自承担的任务等纷争不断。不管分歧如何,大家的认知是一致的：节能减排是控制碳排放,解决气候变暖问题的关键所在。针对自己国家的节能减排计划,建立符合实际国情的节能减排系统,协调好节能减排系统中各变量的关系,可以有效地开展节能减排,在很大程度上控制碳排放、降低能源强度。
中国正处于经济快速发展时期,经济的发展需要大量的能源做保障,大量的能源消耗势必导致碳排放不断增加。目前,中国的资源环境承载能力已接近极限,经济社会发展正经受着能源资源供给不足、利用效率低下和生态环境恶化等问题的严重困扰,粗放发展方式下的资源环境已无法保证发展的可持续性。在这种严峻的资源环境形势下,节能减排被作为一项操作层面的具体措施摆在了突出位置。中国在节能减排“十二五”规划中明确提出能源强度降低16%、碳排放强度下降17%的目标,表明中国已下决心把应对气候变化和节能减排纳入战略轨道,使之上升为国家发展战略的核心内容。
本文从实际情况出发建立节能减排、碳排放、经济增长、碳税、新能源、能源强度、低碳生活方式等相关变量的动态演化模型。借助非线性发展方程理论分析了系统的动力学行为。以中国实际情况为出发点,实证分析节能减排系统中对能源强度影响明显的向量,找到了降低能源强度的关键点,同时分析了这些变量及相应的措施对经济增长等诸多因素的影响,探讨了符合中国国情的节能减排演化理论体系及应用。
根据节能减排、碳排放、经济增长的复杂关系建立三维节能减排动态演化系统。借助李雅普诺夫指数和分岔图分析了系统的动力学行为。借助神经网络(ANN),基于中国统计数据对系统进行参数辨识,对实际系统进行实证分析,得到一些与实际符合的分析结果。结果表明：全面开展节能减排越早即碳排放的峰值越早达到,实际系统越容易控制,减少碳排放的目标越容易实现,越容易将能源强度控制在理想范围内。
节能成本(CCE)是节能减排发展的制约因素,政府调控和低碳生活方式是控制碳排放、降低能源强度的关键,节能减排新技术的投入会对经济产生一定的影响。将这三个约束条件纳入三维节能减排系统中,得到一类选择约束节能减排系统,分析了约束条件对能源强度及经济增长的影响。综合分析后演化出两种不同的节能减排开展方案,很好地协调了节能减排系统中变量与约束条件的关系,为更好地开展节能减排提供了解决方法。
在与碳排放相关的变量中,碳税作为最有效的经济手段之一,适时适度开征碳税,在含有碳税的节能减排系统中协调好碳税与节能减排、能源强度、经济发展等的关系,会在很大程度上减少碳排放、降低能源强度。通过研究碳税约束下的节能减排系统的动力学演化行为,找出碳税对节能减排系统的演化趋势,首次在四维节能减排系统框架下给出碳税对能源强度的拐点,并讨论了与实际对应的相关参数变化对拐点及能源强度最终稳定点的影响,通过动态演化分析得出在四维动态演化系统框架下中国开征碳税的最佳时间和最佳起征点,并进一步讨论了如何更好地开展碳税。
新能源的开发和利用是解决能源短缺、环境污染等问题的重要途径,是发展低碳经济的有效动力源。将新能源纳入节能减排演化系统,构建新能源约束下的节能减排系统,分析了新能源对能源强度和经济增长的影响。分析结果表明：依靠新能源自身发展或单纯加大对新能源的经济投入,并不能很好地控制能源强度,当经济投入过大时会对经济发展带来很大的阻碍作用,甚至给经济带来致命的影响。加大包括新能源在内的节能减排等的综合投入,可以很好地降低能源强度,当综合投入加大时,开始对经济的阻碍也大,但是随着系统的进一步发展,当新能源发展成熟时,这种投入对经济的促进作用也大。
本文的创新主要体现在：将系统科学分析理论、动态大系统建模方法、神经网络原理方法、博弈均衡原理、控制优化原理、复杂系统分析及决策理论应用于具有中国特色的节能减排体系的研究,以中国在新形势下开展节能减排为实际背景,建立合理、科学的定量分析理论,定性与定量结合、理论与实证分析结合。从节能减排系统中推导出随时间变化的能源强度演化公式,进而分析了在不同的约束条件下,在含有碳税、新能源等变量的节能减排系统中能源强度及经济增长的演化规律,将这些演化规律用图像形象地展示出来,对比分析,得到了一系列符合中国国情的研究结果,为在现有国情下更好地开展节能减排工作提供了很好的解决方案。
Global warming is considered as one of the most pressing threats to the existence of human race. Most of the observed changes in the climate increase and deadly natural disasters are very likely attributed to the observed increase in anthropogenic GHG (Greenhouse Gas) emissions. Among them, carbon emissions from the energy consumption of fossil fuels are the main source of anthropogenic GHG. Global action is of great necessity in controlling the pace of climate change. Since the approval of the United Nations Framework Convention on Climate Change (UNECCC), the wrangle between nations about the goal and task of carbon emission reduction has never stopped. Despite all the controversy, people have consonant opinions on the big challenge:energy-saving and emission-reduction (ESER) is the key to control carbon emissions and tackle global warming issues. Each country can establish an ESER system in agreement with their actual situation by coordinating variables in ESER system efficiently, according to the country's ESER plan. Then carbon emissions could be better controlled and energy intensity would decline to a great extent.
China is going through a rapid economic development period. Economy development needs a huge amount of energy, the use of large quantities of energy will inevitably lead to carbon emissions increases steadily. Under the circumstances, the load capacities of resource environment in China have reached the limit. China's economic and social development are suffering from strain in energy and sources supply, inefficient use of resources, zoology environment aggravation and other intractable problems. The resource environment under the extensive mode of economic development can not guarantee the sustainable development. Under the serious situation of resource environment, ESER which acts as operational concrete measure and breakthrough has been placed in the prominent place. China has put forward the ESER target clearly in the "12th Five-Year", i.e. energy consumption per unit GDP should be reduced by16%, carbon intensity should be reduced by17%. Which indicate that China has decided to incorporate climate change and ESER into strategic issue, and make them to be the core content of nation development strategy.
According to domestic actual conditions, this study brought ESER, carbon emissions, economic growth, carbon tax, new energy, energy intensity, low carbon lifestyle and other corresponding variables into a nonlinear dynamics system with the analysis of the relationship between the variables. The dynamic behavior of the system is analyzed by nonlinear development equation theory. Taking the real situation in China for instance, an empirical study is undertaken. The vectors which can significantly affect energy intensity is studied, the key point to decrease energy intensity is put forward. The impacts of these variables on economic growth energy is analyzed simultaneously, the ESER system and its application which accord with Chinese national condition are discussed.
A novel three-dimensional ESER system is proposed, which 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. 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, the easier the ESER system could be controlled and the more energy intensity could be declined, so as to achieve the goal of reducing the carbon dioxide emissions and keeping proper energy intensity.
Cost of conserved energy (CCE) is the restrictive factor of ESER, and the decision-making basis of energy saving measures. Government control and low carbon lifestyle is the key to control carbon emissions and decrease energy intensity. Investment in new technology of ESER will have certain effects on economic growth. This study develops a selective-constrained ESER dynamic evolution system further, introducing cost of conserved energy, government control, low carbon lifestyle and investment in new technology of ESER into ESER system, which act as restriction conditions. The impact of these restriction conditions on energy intensity and economic growth are Analyzed. Two different cases of ESER are proposed after a comprehensive analysis. The relations between variables and constraint conditions in the ESER system are harmonized remarkably. A better solution to carry out ESER is put forward.
Among the variables affecting carbon emissions, carbon tax as one of the most effective economic measures of controlling carbon emissions has become an area of academic interest. Levying carbon tax timely and appropriately and dealing with the relation between energy-saving and emission-reduction, energy intensity and economic growth appropriately will reduce carbon emissions and energy intensity to a great extent. By analyzing the dynamic behavior of the system, the evolution tendency of carbon tax in the ESER system is studied. The concept of turning point of energy intensity in the four-dimensional dynamic system is put forward for the first time. By adjusting the correlation coefficients of the four-dimensional system, more effective methods being performed to steadily and diligently reduce energy intensity. Take for instance the situation in China, the problem of when and how to introduce carbon tax are settled within the framework of the four-dimensional dynamic system. Furthermore, the method to improve the effects of carbon tax properly in the ESER system is discussed.
The development and utilization of new energy is an important way to solve the energy shortage, environmental pollution and so on, which is also an effective power source for the development of low carbon economy. This study brings new energy into the dynamics ESER system and analyzes the effect of new energy on energy intensity and economic growth. The result shows that, by relying on the development of new energy or simply increasing investment to develop new energy, the effect of controlling energy intensity is not conspicuous. When the financial investment is too high, the inhibition effect on economic growth is great or fatal. Increasing overall investment in energy-saving and emission-reduction system which includes new energy, energy intensity could be declined better. When the overall investment gets larger, the inhibition effect on economic growth is also getting bigger in the early stage, however, with the further development of the dynamics system and the gradual maturity of new energy, the investment's promoting effect on economic growth is also very bigger.
Innovations of this paper are as follows:applying systems science analysis theory, dynamic system modeling method, artificial neural network, game theory, contrall and optimize principle, complexity systems analysis and decision theory to study the ESER system with Chinese characteristics. Science and reasonable quantitatively analyses is established under the background of developing ESER in the new domestic situation. This study combines theory with example. Both qualitative research and quantitative research are used. Based on the time-varying energy intensity calculation formula, which is derived from the ESER system, this research has illustrated the impacts of carbon tax, new energy and other restriction conditions on energy intensity and economic growth. With the aid of simulation figures, the evolution behavior influence trends are shown vividly, and the contrast analysis of these results are put forward. A series of research results that accord with Chinese national condition are given, a good solution for further carrying out ESER is provided under China's basic conditions.
中国正处于经济快速发展时期,经济的发展需要大量的能源做保障,大量的能源消耗势必导致碳排放不断增加。目前,中国的资源环境承载能力已接近极限,经济社会发展正经受着能源资源供给不足、利用效率低下和生态环境恶化等问题的严重困扰,粗放发展方式下的资源环境已无法保证发展的可持续性。在这种严峻的资源环境形势下,节能减排被作为一项操作层面的具体措施摆在了突出位置。中国在节能减排“十二五”规划中明确提出能源强度降低16%、碳排放强度下降17%的目标,表明中国已下决心把应对气候变化和节能减排纳入战略轨道,使之上升为国家发展战略的核心内容。
本文从实际情况出发建立节能减排、碳排放、经济增长、碳税、新能源、能源强度、低碳生活方式等相关变量的动态演化模型。借助非线性发展方程理论分析了系统的动力学行为。以中国实际情况为出发点,实证分析节能减排系统中对能源强度影响明显的向量,找到了降低能源强度的关键点,同时分析了这些变量及相应的措施对经济增长等诸多因素的影响,探讨了符合中国国情的节能减排演化理论体系及应用。
根据节能减排、碳排放、经济增长的复杂关系建立三维节能减排动态演化系统。借助李雅普诺夫指数和分岔图分析了系统的动力学行为。借助神经网络(ANN),基于中国统计数据对系统进行参数辨识,对实际系统进行实证分析,得到一些与实际符合的分析结果。结果表明：全面开展节能减排越早即碳排放的峰值越早达到,实际系统越容易控制,减少碳排放的目标越容易实现,越容易将能源强度控制在理想范围内。
节能成本(CCE)是节能减排发展的制约因素,政府调控和低碳生活方式是控制碳排放、降低能源强度的关键,节能减排新技术的投入会对经济产生一定的影响。将这三个约束条件纳入三维节能减排系统中,得到一类选择约束节能减排系统,分析了约束条件对能源强度及经济增长的影响。综合分析后演化出两种不同的节能减排开展方案,很好地协调了节能减排系统中变量与约束条件的关系,为更好地开展节能减排提供了解决方法。
在与碳排放相关的变量中,碳税作为最有效的经济手段之一,适时适度开征碳税,在含有碳税的节能减排系统中协调好碳税与节能减排、能源强度、经济发展等的关系,会在很大程度上减少碳排放、降低能源强度。通过研究碳税约束下的节能减排系统的动力学演化行为,找出碳税对节能减排系统的演化趋势,首次在四维节能减排系统框架下给出碳税对能源强度的拐点,并讨论了与实际对应的相关参数变化对拐点及能源强度最终稳定点的影响,通过动态演化分析得出在四维动态演化系统框架下中国开征碳税的最佳时间和最佳起征点,并进一步讨论了如何更好地开展碳税。
新能源的开发和利用是解决能源短缺、环境污染等问题的重要途径,是发展低碳经济的有效动力源。将新能源纳入节能减排演化系统,构建新能源约束下的节能减排系统,分析了新能源对能源强度和经济增长的影响。分析结果表明：依靠新能源自身发展或单纯加大对新能源的经济投入,并不能很好地控制能源强度,当经济投入过大时会对经济发展带来很大的阻碍作用,甚至给经济带来致命的影响。加大包括新能源在内的节能减排等的综合投入,可以很好地降低能源强度,当综合投入加大时,开始对经济的阻碍也大,但是随着系统的进一步发展,当新能源发展成熟时,这种投入对经济的促进作用也大。
本文的创新主要体现在：将系统科学分析理论、动态大系统建模方法、神经网络原理方法、博弈均衡原理、控制优化原理、复杂系统分析及决策理论应用于具有中国特色的节能减排体系的研究,以中国在新形势下开展节能减排为实际背景,建立合理、科学的定量分析理论,定性与定量结合、理论与实证分析结合。从节能减排系统中推导出随时间变化的能源强度演化公式,进而分析了在不同的约束条件下,在含有碳税、新能源等变量的节能减排系统中能源强度及经济增长的演化规律,将这些演化规律用图像形象地展示出来,对比分析,得到了一系列符合中国国情的研究结果,为在现有国情下更好地开展节能减排工作提供了很好的解决方案。
Global warming is considered as one of the most pressing threats to the existence of human race. Most of the observed changes in the climate increase and deadly natural disasters are very likely attributed to the observed increase in anthropogenic GHG (Greenhouse Gas) emissions. Among them, carbon emissions from the energy consumption of fossil fuels are the main source of anthropogenic GHG. Global action is of great necessity in controlling the pace of climate change. Since the approval of the United Nations Framework Convention on Climate Change (UNECCC), the wrangle between nations about the goal and task of carbon emission reduction has never stopped. Despite all the controversy, people have consonant opinions on the big challenge:energy-saving and emission-reduction (ESER) is the key to control carbon emissions and tackle global warming issues. Each country can establish an ESER system in agreement with their actual situation by coordinating variables in ESER system efficiently, according to the country's ESER plan. Then carbon emissions could be better controlled and energy intensity would decline to a great extent.
China is going through a rapid economic development period. Economy development needs a huge amount of energy, the use of large quantities of energy will inevitably lead to carbon emissions increases steadily. Under the circumstances, the load capacities of resource environment in China have reached the limit. China's economic and social development are suffering from strain in energy and sources supply, inefficient use of resources, zoology environment aggravation and other intractable problems. The resource environment under the extensive mode of economic development can not guarantee the sustainable development. Under the serious situation of resource environment, ESER which acts as operational concrete measure and breakthrough has been placed in the prominent place. China has put forward the ESER target clearly in the "12th Five-Year", i.e. energy consumption per unit GDP should be reduced by16%, carbon intensity should be reduced by17%. Which indicate that China has decided to incorporate climate change and ESER into strategic issue, and make them to be the core content of nation development strategy.
According to domestic actual conditions, this study brought ESER, carbon emissions, economic growth, carbon tax, new energy, energy intensity, low carbon lifestyle and other corresponding variables into a nonlinear dynamics system with the analysis of the relationship between the variables. The dynamic behavior of the system is analyzed by nonlinear development equation theory. Taking the real situation in China for instance, an empirical study is undertaken. The vectors which can significantly affect energy intensity is studied, the key point to decrease energy intensity is put forward. The impacts of these variables on economic growth energy is analyzed simultaneously, the ESER system and its application which accord with Chinese national condition are discussed.
A novel three-dimensional ESER system is proposed, which 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. 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, the easier the ESER system could be controlled and the more energy intensity could be declined, so as to achieve the goal of reducing the carbon dioxide emissions and keeping proper energy intensity.
Cost of conserved energy (CCE) is the restrictive factor of ESER, and the decision-making basis of energy saving measures. Government control and low carbon lifestyle is the key to control carbon emissions and decrease energy intensity. Investment in new technology of ESER will have certain effects on economic growth. This study develops a selective-constrained ESER dynamic evolution system further, introducing cost of conserved energy, government control, low carbon lifestyle and investment in new technology of ESER into ESER system, which act as restriction conditions. The impact of these restriction conditions on energy intensity and economic growth are Analyzed. Two different cases of ESER are proposed after a comprehensive analysis. The relations between variables and constraint conditions in the ESER system are harmonized remarkably. A better solution to carry out ESER is put forward.
Among the variables affecting carbon emissions, carbon tax as one of the most effective economic measures of controlling carbon emissions has become an area of academic interest. Levying carbon tax timely and appropriately and dealing with the relation between energy-saving and emission-reduction, energy intensity and economic growth appropriately will reduce carbon emissions and energy intensity to a great extent. By analyzing the dynamic behavior of the system, the evolution tendency of carbon tax in the ESER system is studied. The concept of turning point of energy intensity in the four-dimensional dynamic system is put forward for the first time. By adjusting the correlation coefficients of the four-dimensional system, more effective methods being performed to steadily and diligently reduce energy intensity. Take for instance the situation in China, the problem of when and how to introduce carbon tax are settled within the framework of the four-dimensional dynamic system. Furthermore, the method to improve the effects of carbon tax properly in the ESER system is discussed.
The development and utilization of new energy is an important way to solve the energy shortage, environmental pollution and so on, which is also an effective power source for the development of low carbon economy. This study brings new energy into the dynamics ESER system and analyzes the effect of new energy on energy intensity and economic growth. The result shows that, by relying on the development of new energy or simply increasing investment to develop new energy, the effect of controlling energy intensity is not conspicuous. When the financial investment is too high, the inhibition effect on economic growth is great or fatal. Increasing overall investment in energy-saving and emission-reduction system which includes new energy, energy intensity could be declined better. When the overall investment gets larger, the inhibition effect on economic growth is also getting bigger in the early stage, however, with the further development of the dynamics system and the gradual maturity of new energy, the investment's promoting effect on economic growth is also very bigger.
Innovations of this paper are as follows:applying systems science analysis theory, dynamic system modeling method, artificial neural network, game theory, contrall and optimize principle, complexity systems analysis and decision theory to study the ESER system with Chinese characteristics. Science and reasonable quantitatively analyses is established under the background of developing ESER in the new domestic situation. This study combines theory with example. Both qualitative research and quantitative research are used. Based on the time-varying energy intensity calculation formula, which is derived from the ESER system, this research has illustrated the impacts of carbon tax, new energy and other restriction conditions on energy intensity and economic growth. With the aid of simulation figures, the evolution behavior influence trends are shown vividly, and the contrast analysis of these results are put forward. A series of research results that accord with Chinese national condition are given, a good solution for further carrying out ESER is provided under China's basic conditions.
引文
[1]Ma C, Stern DI. Biomass and China's carbon emissions:A missing piece of carbon decomposition [J]. Energy Policy.2008 (36):2517-2526
[2]尹政平.低碳经济与中国战略性新兴产业的发展[J].现代经济探讨.2012(5)：14-22
[3]中华人民共和国国家统计局.中国统计年鉴2012[M].北京：中国统计出版社,2012
[4]Meinshausen M, Meinshausen N, Hare W, Raper SCB, Frieler K, Knutti R, Frame DJ, Allen MR. Greenhouse-gas emission targets for limiting global warming to 20 C [J]. Nature.2009 (458):1158-1163
[5]丁一汇等.气候变化国家评估报告(Ⅰ)：中国气候变化的历史和未来趋势[J].气候变化研究进展.2006(2)：3-8
[6]Niu SW, Ding YX, Niu YZ, Li YX, Luo GH. Economic growth, energy conservation and emissions reduction:A comparative analysis based on panel data for 8 Asian-Pacific countries [J]. Energy Policy.2011(39):2121-2131
[7]Desideri U, Yan J. Clean energy technologies and systems for a sustainable world[J]. Applied Energy.2012(97):1-4
[8]Loschel A, Otto V.M. Technological uncertainty and cost effectiveness of CO2 emission reduction [J]. Energy Economics.2009(31) (Supplement 1):S4-S17
[9]Erdmenger C, Lehmann H, Muschen K, Tambke J, Mayr S, Kuhnhenn K. A climate protection strategy for Germany-40% reduction of CO2 emissions by 2020 compared to 1990 [J]. Energy Policy.2009 (37):158-165
[10]Xu Y, Masui T. Local air pollutant emission reduction and ancillary carbon benefits of SO2 control policies:Application of AIM/CGE model to China [J]. European Journal of Operational Research.2009(1):315-325
[11]Jonathan M, Cullende, Julian M, Allwood. The efficient use of energy:Tracing the global flow of energy from fuel to service [J]. Energy Policy 2010 (38): 75-81
[12]Prasad M, Munch S. State-level renewable electricity policies and reductions in carbon emissions [J]. Energy Policy.2012 (45):237-242
[13]金红光,张希良.控制CO2排放的能源科技战略综合研究[J].中国科学E辑.2008(38)：1495-1506
[14]陈红敏.中国产业部门的能耗强度特征及节能减排的分类实现途径[J].资源科学.2009(31)：1226-1232
[15]Yuan CQ, Liu SF. Research on energy-saving effect of technological progress based on Cobb-Douglas production function [J]. Energy Policy.2009 (37): 2842-2846
[16]Long RY, Yu L. Study on regulation design anbout energy-saving and emission-reduction based on game theory[J]. Procedia Earth and Planetary Science,2009(1):1641-1646
[17]杨晓妹.应对气候变化：碳税与碳排放权交易的比较分析[J].青海社会科学.2010(6)：36-39
[18]王迪,聂锐.中国一次能源需求结构与二氧化碳排放的演变情景[J].软科学.2011(25)：76-86
[19]孙梅,陶阳威.节能降耗目标分解模型研究[J].统计与决策.2011(5)：51-53
[20]Fang GC, Tian LX, Sun M, Fu M. Analysis and application of a novel three-dimensional energy-saving and emission-reduction dynamic evolution system [J]. Energy 2012 (40):291-299
[21]田立新,刘雅婷.长三角地区节能减排演化情景分析及预测[J].管理学报.2012(9)：1-4
[22]Andrews CJ, Krogmann U. Technology diffusion and energy intensity in US commercial buildings[J]. Energy Policy.2009 (37):541-553
[23]Silva F.I.A, Guerra S.M.G. Analysis of the energy intensity evolution in the Brazilian industrial sector-1995 to 2005 [J]. Renewable and Sustainable Energy Reviews.2009(13):2589-2596
[24]Mendiluce M, Perez-Arriaga I, Ocana C. Comparison of the evolution of energy intensity in Spain and in the EU15. Why is Spain different? [J]. Energy Policy. 2010(38):639-645
[25]Balezentis A, Balezentis T, Streimikiene D. The energy intensity in Lithuania during 1995-2009:A LMDI approach [J]. Energy Policy.2011(39):7322-7334
[26]Lescaroux F. Dynamics of final sectoral energy demand and aggregate energy intensity [J]. Energy Policy.2011 (39):66-82
[27]Choi K-H, Ang B.W. Attribution of changes in Divisia real energy intensity index-An extension to index decomposition analysis [J]. Energy Economics. 2012(34):171-176
[28]Hasanbeigi A, Can SR, Sathaye J. Analysis and decomposition of the energy intensity of California industries [J]. Energy Policy.2012 (46):234-245
[29]李善同,许召元.中国各地区能源强度差异的因素分解[J].中外能源.2009(14)：1-10
[30]贺灿飞,王俊松.经济转型与中国省区能源强度研究[J].地理科学.2009(29)：461-469
[31]Zhang BB, Tian LX. Analysis of China's energy intensity and its forecast [J]. International Journal of Nonlinear Science.2010(9):320-324
[32]田立新,刘晶.能源强度研究中能源价格影响的实证分析[J].自然资源学报.2010(25)：1519-1524
[33]华贲.产业结构、能效及一次能源构成对能源强度的影响分析[J].中外能源.2010(15)：1-7
[34]邵兴军,田立新.能源强度直接效应间接效应与影响路径的实证分析[J].系统工程.2011(29)：59-63
[35]邵兴军,田立新,蒋书敏.基于内生经济增长模型的短期能源回弹效应分析[J].统计与决策.2012(3)：135-137
[36]赵爱文,李东.中国碳强度与能源强度实证研究[J].干旱区资源与环境.2012(26)：1-5
[37]宋枫,王丽丽.中国能源强度变动趋势及省际差异分析[J].资源科学.2012(34)：13-19
[38]Heiskanen E, Johnson M, Robinson S, Vadovics E, Saastamoinen M. Low-carbon communities as a context for individual behavioural change [J]. Energy Policy.2010 (38):7586-7595
[39]Carvalho M.D.G, Bonifacio M, Dechamps P. Building a low carbon society [J]. Energy.2011(36):1842-1847
[40]Feliciano M, Prosperi D.C. Planning for low carbon cities:Reflection on the case of Broward County, Florida, USA [J]. Cities.2011(28):505-516
[41]Howell RA. It's not (just) "the environment, stupid!" Values, motivations, and routes to engagement of people adopting lower-carbon lifestyles[J]. Global Environmental Change.2013 (23):281-290
[42]Liu LQ, Liu CX, Sun ZY. A survey of China's low-carbon application practice-Opportunity goes with challenge [J]. Renewable and Sustainable Energy Reviews.2011(15):2895-2903
[43]Shen XL, Zhang ZX. Countermeasures on Low-carbon Economy Development in the West District of Panzhihua City [J]. Energy Procedia.2011 (5):1313-1321
[44]吉耀武.城市居民践行低碳生活方式的环境满意度评价及环境优化探讨[J].环境污染与防治.2011(33)：107-110
[45]孟禹言.依托社区推进低碳生活方式的途径.开放导报[J].2011(1)：44-47
[46]郑玉香,束正洁.消费者责任观视角下的低碳生活方式培育研究.消费经济[J].2012(28)：37-39
[47]Hammar H, Sjostrom M. Accounting for behavioral effects of increases in the carbon dioxide(co2) tax in revenue estimation in Sweden [J]. Energy Policy. 2011 (39):6672-6676
[48]Mehdi AM, Masud B. Impact of carbon tax on internal combustion engine size selection in a medium scale CHP system[J]. Applied Energy.2011(88): 5153-5163
[49]Bureau B. Distributional effects of a carbon tax on car fuels in France [J]. Energy Economics.2011(33):121-130
[50]Barua SK, Uusivuori J, Kuuluvainen J. Impacts of carbon-based policy instruments and taxes on tropical deforestation [J]. Ecological Economics.2012 (73):211-219
[51]Callan T, Lyons S, Scott S, Tol R, Verde S. The distributional implications of a carbon tax in Ireland [J]. Energy Policy.2009 (37):407-412
[52]Lin BQ, Li XH. The effect of carbon tax on per capita co2 emissions [J]. Energy Policy.2011(39):5137-5146
[53]Wang X, Li JF, Zhang YX. An analysis on the short-term sectoral competitiveness impact of carbon tax in China [J]. Energy Policy.2011 (39): 4144-4152
[54]Lu CY, Tong Q, Liu XM. The impacts of carbon tax and complementary policies on Chinese economy [J]. Energy Policy.2010 (38):7278-7285
[55]Liang QM, Wei YM. Distributional impacts of taxing carbon in China:Results from the CEEPA model [J]. Applied Energy.2012 (92):545-551
[56]苏明,傅志华,许文,王志刚,李欣,梁强.碳税的国际经验与借鉴[J].环境经济.2011(4)：10-23
[57]Raven R.P.J.M, Mourik R.M, Feenstra C.F.J, Heiskanen E. Modulating societal acceptance in new energy projects:Towards a toolkit methodology for project managers [J]. Energy.2009 (34):564-574
[58]Kellett J.2007:Community-based energy policy-A practical approach to carbon reduction [J]. Journal of Environmental Planning and Management.2007 (3): 381-396
[59]Uyterlinde MA, Junginger M, Vries HJ, Faaij APC, Turkenburg WC. Implications of technological learning on the prospects for renewable energy technologies in Europe [J]. Energy Policy.2007 (8):4072-4087
[60]Onoue K, Murakami Y, Sofronis P. Japan's energy supply:Mid-to-long-term scenario-A proposal for a new energy supply system in the aftermath of the March 11 earthquake [J]. international journal of hydrogen energy.2012 (37): 8123-8132
[61]Alzbutas R, Norvaisa E. Uncertainty and sensitivity analysis for economic optimisation of new energy source in Lithuania [J]. Progress in Nuclear Energy. 2012(61):17-25
[62]孟宪淦.中国可再生能源产业概况[J].太阳能.2006(6)：4-11
[63]马爱芳.新能源产业：机遇与挑战并存[J].湖北水利水电职业技术学院学报.2009(50)：12-15
[64]谢晶仁.对我国新能源产业发展的理性探析[J].湖南涉外经济学院学报.2010(10)：10-13
[65]曾晓斌.我国新能源投资的“冷”思考[J].价值工程.2012(1)：122-123
[66]刘舜,王伟华.新能源战略性新兴产业发展问题与对策——以河北省为例[J].中国流通经济.2012(3)：51-55
[67]史丹.我国新能源产能“过剩”的原因与解决途径[J].中国能源.2012(9)：5-9
[68]Guo JE, Chai J, Xi YM.The relationship between energy consumption structure, economic structure and energy intensity in China [J]. Chn Popu Res Envi 2008 (18):38-3
[69]He TL, Li ZD, He L. On the relationship between energy intensity and industrial structure in China [J]. Energy Procedia.2011(5):2499-2503
[70]Feng TW, Sun LY, Zhang Y. The relationship between energy consumption structure, economic structure and energy intensity in China [J]. Energy Policy. 2009(37):5475-5483
[71]Liddle B. Revisiting world energy intensity convergence for regional differences [J]. Applied Energy.2010 (87):3218-3225
[72]Duro JA, Padilla E. Inequality across countries in energy intensities:An analysis of the role of energy transformation and final energy consumption [J]. Energy Economics.2011 (33):474-479
[73]Yue T, Long RY, Zhuang YY. Analysis of the FDI Effect on Energy Consumption Intensity in Jiangsu Province [J]. Energy Procedia.2011 (5): 100-105
[74]Zhang DS, Aunan K, Seip HM, Vennemo H. The Energy intensity target in China's 11th Five-Year Plan period-Local Implementation and achievements in Shanxi Province [J]. Energy Policy.2011(39):4115-4124
[75]Abdelaziz E.A, Saidur R, Mekhilef S. A review on energy saving strategies in industrial sector [J]. Renewable and Sustainable Energy Reviews.2011 (15): 150-168
[76]Mu ST, Niu JG,2011. An analysis of the Effect of Government-Enterprise Game in the Government-leading Industrial Energy Saving Model [J]. Energy Procedia. 2011(5):633-637
[77]Hickman R, Ashiru O, Banister D. Transitions to low carbon transport futures: strategic conversations from London and Delhi [J]. Journal of Transport Geography.2011(19):1553-1562
[78]Tang BJ, Wu XF, Zhang X. Modeling the co2 emissions and energy saved from new energy vehicles based on the logistic-curve [J]. Energy Policy.2013 (57): 30-35
[79]郭四代,陈刚,杜念霜.我国新能源消费与经济增长关系的实证分析[J].企业经济.2012(5)：35-37
[80]陈文颖,齐志新.结构调整还是技术进步?—改革开放后我幽能源效率提高的因素分析[J].上海经济研究.2006(6)：8-16
[81]吴滨,李为人.中国能源强度变化因素争论与剖析[J].中国社会科学院研究生院学报.2007(2)：121-128
[82]段海艳.能源价格研究最新进展[J].会计之友.2010(4)：26-28
[83]周焯华,张宗益,欧阳.校准方法与计量经济方法的比较[J].重庆大学学报(自然科学版).2001(24)：103-106.
[84]梁朝晖.上海市碳排放的历史特征与远期趋势分析[J].上海经济研究,2009(7)：79-87
[85]Johan A, Delphine F, Koen S. A Shapley Decomposition of Carbon Emissions without Residuals [J]. Energy Policy.2002 (30):727-736
[86]徐国泉,刘则渊,姜照华.中国碳排放的因素分解模型及实证分析：1995-2004[J].中国人口·资源与环境.2006(6)：158-161
[87]田立新,张蓓蓓.中国碳排放变动的因素分解分析[J].中国人口·资源与环境.2011(11)：1-7
[88]Chen G, Dong X. From Chaos to order:Perspectives, Methodologies and Applications [M]. Singapore:World Scientific,1988
[89]Lorenz EN. Deterministic nonperiodic flow [J]. Atmos. Sci.1963(10):130-141
[90]Chen G, Ueta T. Yet anther chaotic attractor [J]. Int, J. of Bifurcation and Chaos. 1999(9):1465-1466
[91]Lu J, Chen G. A new chaotic attractor coined [J]. Int. J. of Bifurcation and Chaos. 2002(12):659-661
[92]Sun M, Tian LX, Fu Y. An energy resource demand-supply system and it's dynamical analysis [J]. Chaos Solitions & Fractals.2007(32):168-180
[93]Ott E, Grebogi C, Yorke JA. Controlling chaos [J]. Phys Rev Lett.1990 (64): 1196-1199
[94]Bai EW, Lonngren KE. Sequential synchronization of two Lorenz systems using active control [J]. Chaos. Solitons & Fractals.2000(11):1041-1044
[95]Yang XS, Chen G. Some observer-based criteria for discrete-time generalized chaos synchronization [J]. Chaos, Solitons & Fractals.2002(13):1303-1308
[96]Chen G, Dong X. On feedback control of chaotic continuous time systems [J]. IEEE Trans Circ Syst.1993(40):591-601.
[97]Just W, Bernard T, Ostheimer M, Reibold E, Benner H. Mechanism of time-delayed feedback control [J]. Phys. Rev. Lett.1997 (78):203-206
[98]Sanchez EN, Perez JP, Martinez M, Chen G. Chaos stabilization:an inverse optimal control approach [J]. Latin Am Appl Res:Int J.2002 (32):111-114
[99]Yassen MT. Adaptive control and synchronization of a modified Chua's circuit system [J]. Appl Math Comp.2001(135):113-128
[100]Saporta B, Dufour F. Numerical method for impulse control of piecewise deterministic Markov processes [J]. Automatica.2012(48):779-793
[101]孙伟,李柏年.BP神经网络在安徽省GDP预测中的应用[J].科技和产业,2009(9)：90-92
[102]吕彬,曾洁.基于BP神经网络的电力负荷预测研究[J].现代商贸工业,2009(21)：254-255
[103]刘映,张岐山.能源消费的时序神经网络预测模型[J].能源与环境,2006(5)：26-28
[104]王珏,鲍勤.基于小波神经网络的中国能源需求预测模型[J].系统科学与数 学,2009(29)：1542-1551
[105]Geem ZW, Roper WE. Energy demand estimation of South Korea using artificial neural network [J]. Energy Policy.2009(37):4049-4054
[106]董长虹Matlab神经网络与应用[M].北京：国防工业出版社,2007
[107]Diakoulaki D, Mavrotas G, Orkopoulos D, Papayannakis L. A bottom-up decomposition analysis of energy-related CO2 emissions in Greece [J]. Energy. 2006(31):2638-2651
[108]Soytas U, Sari R, Ewing BT. Energy consumption, income, and carbon emissions in the United States [J]. Ecological Economics.2007(62):482-489
[109]Zhang XP, Cheng XM. Energy consumption, carbon emissions, and economic growth in China [J]. Ecological Economics.2009 (68):2706-2712
[110]Hofer C, Dresner ME, Windle RJ. The environmental effects of airline carbon emissions taxation in the US [J].Transportation Research Part D.2010(15): 37-45
[111]Chen HJ, Li MC. Environmental tax policy, habit formation and nonlinear dynamics[J]. Nonlinear Analysis:Real World Applications.2011(12):246-253
[112]Ozturk I, Acaravci A. co2 emissions,energy consumption and economic growth inTurkey [J]. Renewable and Sustainable Energy Reviews.2010(14): 3220-3225
[113]Pao HT, Tsai CM. co2, emissions,energy consumption and economic growth in BRIC countries [J]. Energy Policy.2010 (38):7850-7860
[114]Chang-Jian CW, Chang SM. Bifurcation and chaos analysis of spur gear pair with and without nonlinear suspension.Nonlinear Analysis:Real World Applications 2011(12):979-989
[115]Silva CP. Silnikov theorem-a tutorial [J]. IEEE Trans Circ Syst I.1993 (40): 675-682
[116]Zheng Z, Chen G. Existence of heteroclinic orbits of the Silnikov type in a 3D quadratic autonomous chaotic system [J]. J Math Anal Appl.2006 (315): 106-119
[117]魏一鸣,范英,刘兰翠,吴刚等.中国能源报告(2008)：碳排放研究[M].北京：科学出版社,2008
[118]Golmohammadi H, Safdari M. Quantitative structure-property relationship prediction of gas-to-chloroformpartition coefficient using artificial neural network systems [J]. Microchemical Journal.2010 (95):140-151
[119]刘兆恒.江苏省能源消耗变动因素的实证研究[J].能源研究与利用.2008(5)：41-45
[120]江苏省统计局,国家统计局江苏调查总队.江苏省统计年鉴2012[M].北京：中国统计出版社,2012
[121]Garg A, Maheshwari J, Mahapatra D, Kumar S.2011. Economic and environmental implications of demand-side management options [J]. Energy Policy.2011(39):3076-3085
[122]Aghion P, David PA, Foray D.2009. Science, technology and innovation for economic growth:Linking policy research and practice in'STIG Systems'[J]. Research Policy.2009(38):681-693
[123]Zhang T. Strategy of City Development in Low-Carbon Economic Mode-A Case Study on Qingdao [J]. Energy Procedia 2011 (5):926-932
[124]Streimikiene D, Volochovic A. The impact of household behavioral changes on GHG emission reduction in Lithuania [J]. Renewable and Sustainable Energy Reviews 2011(15):4118-4124
[125]林伯强,牟敦国.高级能源经济学[M].北京：中国财政经济出版社,2009
[126]Murat Y.S, Ceylan H. Use of artificial neural networks for transport energy demand modeling[J]. Energy Policy.2006(34):3165-3172
[127]Jammazi R, Aloui C. Crude oil price forecasting:Experimental evidence from wavelet decomposition and neural network modeling [J]. Energy Economics. 2012 (34):828-841
[128]Lee CF, Lin SJ, Lewis C, Chang YF. Effects of carbon taxes on different industries by fuzzy goal programming:A case study of the petrochemical-related industries, Taiwan[J]. Energy Policy.2007(35):4051-4058
[129]Edenhofer O, Kalkuhl M. When do increasing carbon taxes accelerate global warming? A note on the green paradox [J]. Energy Policy.2011 (39): 2208-2212.
[130]Fang GC, Tian LX, Fu M. Dynamic analysis of a novel four-dimensional ESER system [J]. International Journal of Nonlinear Science.2013(15):10-14
[131]方国昌,田立新,傅敏.一类新型碳税约束下的节能减排系统的分析和应用[J].能源技术与管理.2013(1)：163-165
[132]Rademacher J D.M. Homoclinic orbits near heteroclinic cycles with one equilibrium and one periodic orbit [J]. J. Differential Equations.2005 (218): 390-344.
[133]Streimikiene D. The impact of international GHG trading regimes on penetration of new energy technologies and feasibility to implement EU Energy and Climate Package targets [J]. Renewable and Sustainable Energy Reviews.2012(16):2172-2177
[134]Lund P.D. Exploring past energy changes and their implications for the pace of penetration of new energy technologies [J]. Energy.2010 (35):647-656
[135]Krupa J, Burch S. A new energy future for South Africa:The political ecology of South African [J]. Energy Policy.2011 (39):6254-6261
[136]Oksay S, Iseri E. A new energy paradigm for Turkey:A political risk-inclusive cost analysis for sustainable energy [J]. Energy Policy.2011 (39):2386-2395
[137]Chai J, Guo JE, Wang SY, Lai KK. Why does energy intensity fluctuate in China? [J]. Energy Policy.2009 (37):5717-5731
[138]Xia B, Li Y. Analysis on the Impact of Tax Policy over China's New Energy Industry Development [J]. Physics Procedia.2012(25):1277-1284
[2]尹政平.低碳经济与中国战略性新兴产业的发展[J].现代经济探讨.2012(5)：14-22
[3]中华人民共和国国家统计局.中国统计年鉴2012[M].北京：中国统计出版社,2012
[4]Meinshausen M, Meinshausen N, Hare W, Raper SCB, Frieler K, Knutti R, Frame DJ, Allen MR. Greenhouse-gas emission targets for limiting global warming to 20 C [J]. Nature.2009 (458):1158-1163
[5]丁一汇等.气候变化国家评估报告(Ⅰ)：中国气候变化的历史和未来趋势[J].气候变化研究进展.2006(2)：3-8
[6]Niu SW, Ding YX, Niu YZ, Li YX, Luo GH. Economic growth, energy conservation and emissions reduction:A comparative analysis based on panel data for 8 Asian-Pacific countries [J]. Energy Policy.2011(39):2121-2131
[7]Desideri U, Yan J. Clean energy technologies and systems for a sustainable world[J]. Applied Energy.2012(97):1-4
[8]Loschel A, Otto V.M. Technological uncertainty and cost effectiveness of CO2 emission reduction [J]. Energy Economics.2009(31) (Supplement 1):S4-S17
[9]Erdmenger C, Lehmann H, Muschen K, Tambke J, Mayr S, Kuhnhenn K. A climate protection strategy for Germany-40% reduction of CO2 emissions by 2020 compared to 1990 [J]. Energy Policy.2009 (37):158-165
[10]Xu Y, Masui T. Local air pollutant emission reduction and ancillary carbon benefits of SO2 control policies:Application of AIM/CGE model to China [J]. European Journal of Operational Research.2009(1):315-325
[11]Jonathan M, Cullende, Julian M, Allwood. The efficient use of energy:Tracing the global flow of energy from fuel to service [J]. Energy Policy 2010 (38): 75-81
[12]Prasad M, Munch S. State-level renewable electricity policies and reductions in carbon emissions [J]. Energy Policy.2012 (45):237-242
[13]金红光,张希良.控制CO2排放的能源科技战略综合研究[J].中国科学E辑.2008(38)：1495-1506
[14]陈红敏.中国产业部门的能耗强度特征及节能减排的分类实现途径[J].资源科学.2009(31)：1226-1232
[15]Yuan CQ, Liu SF. Research on energy-saving effect of technological progress based on Cobb-Douglas production function [J]. Energy Policy.2009 (37): 2842-2846
[16]Long RY, Yu L. Study on regulation design anbout energy-saving and emission-reduction based on game theory[J]. Procedia Earth and Planetary Science,2009(1):1641-1646
[17]杨晓妹.应对气候变化：碳税与碳排放权交易的比较分析[J].青海社会科学.2010(6)：36-39
[18]王迪,聂锐.中国一次能源需求结构与二氧化碳排放的演变情景[J].软科学.2011(25)：76-86
[19]孙梅,陶阳威.节能降耗目标分解模型研究[J].统计与决策.2011(5)：51-53
[20]Fang GC, Tian LX, Sun M, Fu M. Analysis and application of a novel three-dimensional energy-saving and emission-reduction dynamic evolution system [J]. Energy 2012 (40):291-299
[21]田立新,刘雅婷.长三角地区节能减排演化情景分析及预测[J].管理学报.2012(9)：1-4
[22]Andrews CJ, Krogmann U. Technology diffusion and energy intensity in US commercial buildings[J]. Energy Policy.2009 (37):541-553
[23]Silva F.I.A, Guerra S.M.G. Analysis of the energy intensity evolution in the Brazilian industrial sector-1995 to 2005 [J]. Renewable and Sustainable Energy Reviews.2009(13):2589-2596
[24]Mendiluce M, Perez-Arriaga I, Ocana C. Comparison of the evolution of energy intensity in Spain and in the EU15. Why is Spain different? [J]. Energy Policy. 2010(38):639-645
[25]Balezentis A, Balezentis T, Streimikiene D. The energy intensity in Lithuania during 1995-2009:A LMDI approach [J]. Energy Policy.2011(39):7322-7334
[26]Lescaroux F. Dynamics of final sectoral energy demand and aggregate energy intensity [J]. Energy Policy.2011 (39):66-82
[27]Choi K-H, Ang B.W. Attribution of changes in Divisia real energy intensity index-An extension to index decomposition analysis [J]. Energy Economics. 2012(34):171-176
[28]Hasanbeigi A, Can SR, Sathaye J. Analysis and decomposition of the energy intensity of California industries [J]. Energy Policy.2012 (46):234-245
[29]李善同,许召元.中国各地区能源强度差异的因素分解[J].中外能源.2009(14)：1-10
[30]贺灿飞,王俊松.经济转型与中国省区能源强度研究[J].地理科学.2009(29)：461-469
[31]Zhang BB, Tian LX. Analysis of China's energy intensity and its forecast [J]. International Journal of Nonlinear Science.2010(9):320-324
[32]田立新,刘晶.能源强度研究中能源价格影响的实证分析[J].自然资源学报.2010(25)：1519-1524
[33]华贲.产业结构、能效及一次能源构成对能源强度的影响分析[J].中外能源.2010(15)：1-7
[34]邵兴军,田立新.能源强度直接效应间接效应与影响路径的实证分析[J].系统工程.2011(29)：59-63
[35]邵兴军,田立新,蒋书敏.基于内生经济增长模型的短期能源回弹效应分析[J].统计与决策.2012(3)：135-137
[36]赵爱文,李东.中国碳强度与能源强度实证研究[J].干旱区资源与环境.2012(26)：1-5
[37]宋枫,王丽丽.中国能源强度变动趋势及省际差异分析[J].资源科学.2012(34)：13-19
[38]Heiskanen E, Johnson M, Robinson S, Vadovics E, Saastamoinen M. Low-carbon communities as a context for individual behavioural change [J]. Energy Policy.2010 (38):7586-7595
[39]Carvalho M.D.G, Bonifacio M, Dechamps P. Building a low carbon society [J]. Energy.2011(36):1842-1847
[40]Feliciano M, Prosperi D.C. Planning for low carbon cities:Reflection on the case of Broward County, Florida, USA [J]. Cities.2011(28):505-516
[41]Howell RA. It's not (just) "the environment, stupid!" Values, motivations, and routes to engagement of people adopting lower-carbon lifestyles[J]. Global Environmental Change.2013 (23):281-290
[42]Liu LQ, Liu CX, Sun ZY. A survey of China's low-carbon application practice-Opportunity goes with challenge [J]. Renewable and Sustainable Energy Reviews.2011(15):2895-2903
[43]Shen XL, Zhang ZX. Countermeasures on Low-carbon Economy Development in the West District of Panzhihua City [J]. Energy Procedia.2011 (5):1313-1321
[44]吉耀武.城市居民践行低碳生活方式的环境满意度评价及环境优化探讨[J].环境污染与防治.2011(33)：107-110
[45]孟禹言.依托社区推进低碳生活方式的途径.开放导报[J].2011(1)：44-47
[46]郑玉香,束正洁.消费者责任观视角下的低碳生活方式培育研究.消费经济[J].2012(28)：37-39
[47]Hammar H, Sjostrom M. Accounting for behavioral effects of increases in the carbon dioxide(co2) tax in revenue estimation in Sweden [J]. Energy Policy. 2011 (39):6672-6676
[48]Mehdi AM, Masud B. Impact of carbon tax on internal combustion engine size selection in a medium scale CHP system[J]. Applied Energy.2011(88): 5153-5163
[49]Bureau B. Distributional effects of a carbon tax on car fuels in France [J]. Energy Economics.2011(33):121-130
[50]Barua SK, Uusivuori J, Kuuluvainen J. Impacts of carbon-based policy instruments and taxes on tropical deforestation [J]. Ecological Economics.2012 (73):211-219
[51]Callan T, Lyons S, Scott S, Tol R, Verde S. The distributional implications of a carbon tax in Ireland [J]. Energy Policy.2009 (37):407-412
[52]Lin BQ, Li XH. The effect of carbon tax on per capita co2 emissions [J]. Energy Policy.2011(39):5137-5146
[53]Wang X, Li JF, Zhang YX. An analysis on the short-term sectoral competitiveness impact of carbon tax in China [J]. Energy Policy.2011 (39): 4144-4152
[54]Lu CY, Tong Q, Liu XM. The impacts of carbon tax and complementary policies on Chinese economy [J]. Energy Policy.2010 (38):7278-7285
[55]Liang QM, Wei YM. Distributional impacts of taxing carbon in China:Results from the CEEPA model [J]. Applied Energy.2012 (92):545-551
[56]苏明,傅志华,许文,王志刚,李欣,梁强.碳税的国际经验与借鉴[J].环境经济.2011(4)：10-23
[57]Raven R.P.J.M, Mourik R.M, Feenstra C.F.J, Heiskanen E. Modulating societal acceptance in new energy projects:Towards a toolkit methodology for project managers [J]. Energy.2009 (34):564-574
[58]Kellett J.2007:Community-based energy policy-A practical approach to carbon reduction [J]. Journal of Environmental Planning and Management.2007 (3): 381-396
[59]Uyterlinde MA, Junginger M, Vries HJ, Faaij APC, Turkenburg WC. Implications of technological learning on the prospects for renewable energy technologies in Europe [J]. Energy Policy.2007 (8):4072-4087
[60]Onoue K, Murakami Y, Sofronis P. Japan's energy supply:Mid-to-long-term scenario-A proposal for a new energy supply system in the aftermath of the March 11 earthquake [J]. international journal of hydrogen energy.2012 (37): 8123-8132
[61]Alzbutas R, Norvaisa E. Uncertainty and sensitivity analysis for economic optimisation of new energy source in Lithuania [J]. Progress in Nuclear Energy. 2012(61):17-25
[62]孟宪淦.中国可再生能源产业概况[J].太阳能.2006(6)：4-11
[63]马爱芳.新能源产业：机遇与挑战并存[J].湖北水利水电职业技术学院学报.2009(50)：12-15
[64]谢晶仁.对我国新能源产业发展的理性探析[J].湖南涉外经济学院学报.2010(10)：10-13
[65]曾晓斌.我国新能源投资的“冷”思考[J].价值工程.2012(1)：122-123
[66]刘舜,王伟华.新能源战略性新兴产业发展问题与对策——以河北省为例[J].中国流通经济.2012(3)：51-55
[67]史丹.我国新能源产能“过剩”的原因与解决途径[J].中国能源.2012(9)：5-9
[68]Guo JE, Chai J, Xi YM.The relationship between energy consumption structure, economic structure and energy intensity in China [J]. Chn Popu Res Envi 2008 (18):38-3
[69]He TL, Li ZD, He L. On the relationship between energy intensity and industrial structure in China [J]. Energy Procedia.2011(5):2499-2503
[70]Feng TW, Sun LY, Zhang Y. The relationship between energy consumption structure, economic structure and energy intensity in China [J]. Energy Policy. 2009(37):5475-5483
[71]Liddle B. Revisiting world energy intensity convergence for regional differences [J]. Applied Energy.2010 (87):3218-3225
[72]Duro JA, Padilla E. Inequality across countries in energy intensities:An analysis of the role of energy transformation and final energy consumption [J]. Energy Economics.2011 (33):474-479
[73]Yue T, Long RY, Zhuang YY. Analysis of the FDI Effect on Energy Consumption Intensity in Jiangsu Province [J]. Energy Procedia.2011 (5): 100-105
[74]Zhang DS, Aunan K, Seip HM, Vennemo H. The Energy intensity target in China's 11th Five-Year Plan period-Local Implementation and achievements in Shanxi Province [J]. Energy Policy.2011(39):4115-4124
[75]Abdelaziz E.A, Saidur R, Mekhilef S. A review on energy saving strategies in industrial sector [J]. Renewable and Sustainable Energy Reviews.2011 (15): 150-168
[76]Mu ST, Niu JG,2011. An analysis of the Effect of Government-Enterprise Game in the Government-leading Industrial Energy Saving Model [J]. Energy Procedia. 2011(5):633-637
[77]Hickman R, Ashiru O, Banister D. Transitions to low carbon transport futures: strategic conversations from London and Delhi [J]. Journal of Transport Geography.2011(19):1553-1562
[78]Tang BJ, Wu XF, Zhang X. Modeling the co2 emissions and energy saved from new energy vehicles based on the logistic-curve [J]. Energy Policy.2013 (57): 30-35
[79]郭四代,陈刚,杜念霜.我国新能源消费与经济增长关系的实证分析[J].企业经济.2012(5)：35-37
[80]陈文颖,齐志新.结构调整还是技术进步?—改革开放后我幽能源效率提高的因素分析[J].上海经济研究.2006(6)：8-16
[81]吴滨,李为人.中国能源强度变化因素争论与剖析[J].中国社会科学院研究生院学报.2007(2)：121-128
[82]段海艳.能源价格研究最新进展[J].会计之友.2010(4)：26-28
[83]周焯华,张宗益,欧阳.校准方法与计量经济方法的比较[J].重庆大学学报(自然科学版).2001(24)：103-106.
[84]梁朝晖.上海市碳排放的历史特征与远期趋势分析[J].上海经济研究,2009(7)：79-87
[85]Johan A, Delphine F, Koen S. A Shapley Decomposition of Carbon Emissions without Residuals [J]. Energy Policy.2002 (30):727-736
[86]徐国泉,刘则渊,姜照华.中国碳排放的因素分解模型及实证分析：1995-2004[J].中国人口·资源与环境.2006(6)：158-161
[87]田立新,张蓓蓓.中国碳排放变动的因素分解分析[J].中国人口·资源与环境.2011(11)：1-7
[88]Chen G, Dong X. From Chaos to order:Perspectives, Methodologies and Applications [M]. Singapore:World Scientific,1988
[89]Lorenz EN. Deterministic nonperiodic flow [J]. Atmos. Sci.1963(10):130-141
[90]Chen G, Ueta T. Yet anther chaotic attractor [J]. Int, J. of Bifurcation and Chaos. 1999(9):1465-1466
[91]Lu J, Chen G. A new chaotic attractor coined [J]. Int. J. of Bifurcation and Chaos. 2002(12):659-661
[92]Sun M, Tian LX, Fu Y. An energy resource demand-supply system and it's dynamical analysis [J]. Chaos Solitions & Fractals.2007(32):168-180
[93]Ott E, Grebogi C, Yorke JA. Controlling chaos [J]. Phys Rev Lett.1990 (64): 1196-1199
[94]Bai EW, Lonngren KE. Sequential synchronization of two Lorenz systems using active control [J]. Chaos. Solitons & Fractals.2000(11):1041-1044
[95]Yang XS, Chen G. Some observer-based criteria for discrete-time generalized chaos synchronization [J]. Chaos, Solitons & Fractals.2002(13):1303-1308
[96]Chen G, Dong X. On feedback control of chaotic continuous time systems [J]. IEEE Trans Circ Syst.1993(40):591-601.
[97]Just W, Bernard T, Ostheimer M, Reibold E, Benner H. Mechanism of time-delayed feedback control [J]. Phys. Rev. Lett.1997 (78):203-206
[98]Sanchez EN, Perez JP, Martinez M, Chen G. Chaos stabilization:an inverse optimal control approach [J]. Latin Am Appl Res:Int J.2002 (32):111-114
[99]Yassen MT. Adaptive control and synchronization of a modified Chua's circuit system [J]. Appl Math Comp.2001(135):113-128
[100]Saporta B, Dufour F. Numerical method for impulse control of piecewise deterministic Markov processes [J]. Automatica.2012(48):779-793
[101]孙伟,李柏年.BP神经网络在安徽省GDP预测中的应用[J].科技和产业,2009(9)：90-92
[102]吕彬,曾洁.基于BP神经网络的电力负荷预测研究[J].现代商贸工业,2009(21)：254-255
[103]刘映,张岐山.能源消费的时序神经网络预测模型[J].能源与环境,2006(5)：26-28
[104]王珏,鲍勤.基于小波神经网络的中国能源需求预测模型[J].系统科学与数 学,2009(29)：1542-1551
[105]Geem ZW, Roper WE. Energy demand estimation of South Korea using artificial neural network [J]. Energy Policy.2009(37):4049-4054
[106]董长虹Matlab神经网络与应用[M].北京：国防工业出版社,2007
[107]Diakoulaki D, Mavrotas G, Orkopoulos D, Papayannakis L. A bottom-up decomposition analysis of energy-related CO2 emissions in Greece [J]. Energy. 2006(31):2638-2651
[108]Soytas U, Sari R, Ewing BT. Energy consumption, income, and carbon emissions in the United States [J]. Ecological Economics.2007(62):482-489
[109]Zhang XP, Cheng XM. Energy consumption, carbon emissions, and economic growth in China [J]. Ecological Economics.2009 (68):2706-2712
[110]Hofer C, Dresner ME, Windle RJ. The environmental effects of airline carbon emissions taxation in the US [J].Transportation Research Part D.2010(15): 37-45
[111]Chen HJ, Li MC. Environmental tax policy, habit formation and nonlinear dynamics[J]. Nonlinear Analysis:Real World Applications.2011(12):246-253
[112]Ozturk I, Acaravci A. co2 emissions,energy consumption and economic growth inTurkey [J]. Renewable and Sustainable Energy Reviews.2010(14): 3220-3225
[113]Pao HT, Tsai CM. co2, emissions,energy consumption and economic growth in BRIC countries [J]. Energy Policy.2010 (38):7850-7860
[114]Chang-Jian CW, Chang SM. Bifurcation and chaos analysis of spur gear pair with and without nonlinear suspension.Nonlinear Analysis:Real World Applications 2011(12):979-989
[115]Silva CP. Silnikov theorem-a tutorial [J]. IEEE Trans Circ Syst I.1993 (40): 675-682
[116]Zheng Z, Chen G. Existence of heteroclinic orbits of the Silnikov type in a 3D quadratic autonomous chaotic system [J]. J Math Anal Appl.2006 (315): 106-119
[117]魏一鸣,范英,刘兰翠,吴刚等.中国能源报告(2008)：碳排放研究[M].北京：科学出版社,2008
[118]Golmohammadi H, Safdari M. Quantitative structure-property relationship prediction of gas-to-chloroformpartition coefficient using artificial neural network systems [J]. Microchemical Journal.2010 (95):140-151
[119]刘兆恒.江苏省能源消耗变动因素的实证研究[J].能源研究与利用.2008(5)：41-45
[120]江苏省统计局,国家统计局江苏调查总队.江苏省统计年鉴2012[M].北京：中国统计出版社,2012
[121]Garg A, Maheshwari J, Mahapatra D, Kumar S.2011. Economic and environmental implications of demand-side management options [J]. Energy Policy.2011(39):3076-3085
[122]Aghion P, David PA, Foray D.2009. Science, technology and innovation for economic growth:Linking policy research and practice in'STIG Systems'[J]. Research Policy.2009(38):681-693
[123]Zhang T. Strategy of City Development in Low-Carbon Economic Mode-A Case Study on Qingdao [J]. Energy Procedia 2011 (5):926-932
[124]Streimikiene D, Volochovic A. The impact of household behavioral changes on GHG emission reduction in Lithuania [J]. Renewable and Sustainable Energy Reviews 2011(15):4118-4124
[125]林伯强,牟敦国.高级能源经济学[M].北京：中国财政经济出版社,2009
[126]Murat Y.S, Ceylan H. Use of artificial neural networks for transport energy demand modeling[J]. Energy Policy.2006(34):3165-3172
[127]Jammazi R, Aloui C. Crude oil price forecasting:Experimental evidence from wavelet decomposition and neural network modeling [J]. Energy Economics. 2012 (34):828-841
[128]Lee CF, Lin SJ, Lewis C, Chang YF. Effects of carbon taxes on different industries by fuzzy goal programming:A case study of the petrochemical-related industries, Taiwan[J]. Energy Policy.2007(35):4051-4058
[129]Edenhofer O, Kalkuhl M. When do increasing carbon taxes accelerate global warming? A note on the green paradox [J]. Energy Policy.2011 (39): 2208-2212.
[130]Fang GC, Tian LX, Fu M. Dynamic analysis of a novel four-dimensional ESER system [J]. International Journal of Nonlinear Science.2013(15):10-14
[131]方国昌,田立新,傅敏.一类新型碳税约束下的节能减排系统的分析和应用[J].能源技术与管理.2013(1)：163-165
[132]Rademacher J D.M. Homoclinic orbits near heteroclinic cycles with one equilibrium and one periodic orbit [J]. J. Differential Equations.2005 (218): 390-344.
[133]Streimikiene D. The impact of international GHG trading regimes on penetration of new energy technologies and feasibility to implement EU Energy and Climate Package targets [J]. Renewable and Sustainable Energy Reviews.2012(16):2172-2177
[134]Lund P.D. Exploring past energy changes and their implications for the pace of penetration of new energy technologies [J]. Energy.2010 (35):647-656
[135]Krupa J, Burch S. A new energy future for South Africa:The political ecology of South African [J]. Energy Policy.2011 (39):6254-6261
[136]Oksay S, Iseri E. A new energy paradigm for Turkey:A political risk-inclusive cost analysis for sustainable energy [J]. Energy Policy.2011 (39):2386-2395
[137]Chai J, Guo JE, Wang SY, Lai KK. Why does energy intensity fluctuate in China? [J]. Energy Policy.2009 (37):5717-5731
[138]Xia B, Li Y. Analysis on the Impact of Tax Policy over China's New Energy Industry Development [J]. Physics Procedia.2012(25):1277-1284