中国能源消费低碳化发展模型研究
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摘要
化石能源消费是经济增长的重要驱动力量,但其燃烧产生的CO2是造成温室效应及其衍生灾害加剧的主要原因之一。近年来,中国的化石能源CO2排放量长期高居世界第一位,且所占份额逐年增加。因此,研究中国能源消费的低碳化发展问题,对于改善生态环境,实现社会经济的可持续发展具有重要意义。当前,对于能源消费低碳化发展模型的研究尚缺乏系统性,且多数并不适合中国的需要。鉴于此,本文根据中国化石能源消费及CO2排放的特点,设计了一套包括路径分解、边际影响分析以及排放量预测功能的模型系统,并以此对中国的能源消费低碳化问题进行了实证研究,主要工作如下：
(1)提出了碳生产率三维分解模型组。该模型组以碳生产率对时间的导数形式为起点,通过设计不同的积分算法、对数中值算法和影响力累计算法,最终得到碳生产率绝对分解模型和相对分解模型。将该模型组应用于中国的实证分析表明,工业部门淘汰落后产能、优化出口结构以及破除地方保护主义对于中国碳生产率的提高具有重要意义。
(2)设计了分解方程将中国的GDP增长分解为碳排放总量增加、各省碳排放结构调整以及各省碳生产率提高三者的贡献,并采用Laspeyres指数分解算法实现了上述分解因素的定量测算。研究结果表明：地方保护主义的影响得到了进一步证实；迄今为止,中国的经济发展依然以粗放型增长为主；在考虑碳排放的情况下,各省的经济增长模式与其经济发达程度以及经度位置(东部、中部和西部)无关,而与纬度位置(北部、中部和南部)密切相关。
(3)以不同种类能源消费量作为输入,以GDP产出和碳排放量作为输出定义了广义能源效率。采用DEA-MPI分解算法对中国各省的广义能源效率的相对变化进行了分解研究。研究结果表明：第一,中国能源效率的提高以中央政府的推动起主导作用,地方政府的推动作用较弱；第二,第二产业的发展模式和能源消费模式变化,对于各省相对能源效率的变化具有决定性影响；第三,与经济发展模式类似,各省能源效率变化模式与其经济发达程度以及经度位置无关,而与纬度位置密切相关。
(4)将42部门投入产出表合并为27部门,结合各产业部门的进出口额和碳排放量数据,通过IO模型测算出各产业部门的完全碳密度和国际贸易的完全碳转移量,并在此基础上预测了未来的碳转移趋势。研究结果表明,由于国际贸易环境的变化,中国各产业部门国际贸易的总净碳转移量在2014年之后将成为负值。
(5)以对数STIRPAT模型框架为基础,采用PLS方法克服变量间多重共线性的影响,对影响中国C02排放的各因素解释能力进行了测算。研究结果表明,对中国C02排放量增长而言,人口增长的解释能力最强,人民生活水平提高的解释能力次之,技术进步的解释能力最弱。此外,以PLS方法建立的较为稳定的对数线性模型为基础,设计了8种发展情景,对中国2012-2020年的C02排放量进行了情景预测。
(6)在广泛研究世界各国CO2排放规律的基础上,提出了用于拟合长期CO2排放趋势的Logistic方程。在解释其科学性的基础上,总结了四种参数估计算法。此外,考虑到中国等发展中国家的需要,提出了采用混合模型对未来少数年份CO2排放量进行预测的算法。
(7)考虑到中国等发展中国家C02排放EKC所处的阶段和特点,提出了采用分段二次函数对其进行拟合的算法。并采用小样本建模方法预测了中国未来的人均C02排放量的可行域及达到峰值的时间。
(8)考虑到中国等发展中国家月度CO2排放趋势的特点,提出了变动振幅趋势外推模型。该模型采用小波变换对月度CO2排放数据进行分解,在剔除随机性波动后,用RBF网络和混合模型分别对规律性波动趋势和长期增长趋势进行建模预测。实证分析表明,该模型对于中国月度CO2排放量的预测精度要高于传统算法和当前主要算法。
本文的主要研究成果,已作为论文被本领域内多种有重要影响的期刊出版或录用。部分研究成果被《Nature》网站进行介绍并由其专业期刊《Nature Climate Change》发表文章进行评论和肯定。部分实证研究的结论已经被国务院相关部门采纳,用于中国能源及产业政策的调整。
Fossil energy consumption is an important driving force factor of economic growth. However, CO2emissions from it are one of the main causes of aggravating the greenhouse effects and their derivative disasters. In recent years, China's CO2emissions have long ranked the country first and its share has also increased year by year. Research on China's low-carbon oriented development of energy consumption is of great importance in improving the ecological environment and realizing the sustainable development of socio-economy. At present, researches on low-carbon oriented development model of energy consumption usually still lack systematization and, more importantly, not adapt to China's needs. In view of the above, and considering the characteristics of China's fossil energy consumption and CO2emissions, this paper has designed a model system which has the function of roadmap decomposition, marginal influence analysis and emission amount forecasting. Using the said model system, China's energy consumption and CO2emissions were analyzed. More specifically, the main contents of this paper are as follows:
(1) The three-dimensional decomposition model group of carbon productivity was proposed. The derivative form of carbon productivity was used to indicate the beginning of decomposition. After designing the integrating algorithms, the logarithmic mean algorithms and the influence accumulating algorithms, the absolute and relative decomposition models for carbon productivity were derived. Empirical analysis to China showed that eliminating backward industrial capacity, optimizing the structure of exports, and break down the local protectionism were the key factors to improve its carbon productivity.
(2) An equation is designed to decompose the GDP growth into the contributions of total carbon emission increase, emission structure adjustment and carbon productivity improvement of each province. Using Laspeyres index decomposition algorithm to quantitatively analyze China's data, the following conclusions were drew: the influence of local protectionism is further verified; Till now, China's economic development still mainly depends on the extensive mode; Considering carbon emissions, economic development mode of each province is closely related to its latitudinal location (the north, middle or south), and not related to its longitudinal location (the east, middle and west) and economic development level..
(3) Selecting different kinds of energy consumption values as input, and the GDP output and carbon emissions as output, the energy efficiency in the broader sense is defined. Using the DEA-MPI decomposition algorithm to analysis the relative changes of energy efficiency in the broader sense of China's provinces, the following conclusions were drew:Firstly, the central government is more important than the local ones in propelling the improvement of China's energy efficiency; Secondly, changes of economic development and energy consumption mode of the secondary industry have decisive effects on the relative changes of energy efficiency of each province; Thirdly, similar to economic development mode, change mode of energy efficiency of each province is closely related to its latitudinal location, and not related to its longitudinal location and economic development level.
(4) The42industrial sectors in input-output table are combined into27ones. The total carbon intensity and carbon transmission amount of each industrial sector is measured by using the adjusted input-output table, the import and export values, and the carbon emission amount of each industrial sector. Results show that because of the change of international trade environment, China's net carbon transmissions of all the industrial sectors embedded in international trade will be less than0after year2014.
(5) Based on the logarithmic STIRPAT model, the PLS algorithm was used to overcome the multicollinearity between variables, and at last, the explanatory ability to the CO2emissions of each variable was measured. Results show that the population increase has the strongest ability, the improvement of living standard the medium, and the technological innovation the weakest. Moreover, based on the logarithmic linear equation which is built by the PLS algorithm, eight development scenarios were designed to forecast China's CO2emissions of2012-2020.
(6) Based on the survey to the CO2emission trends of main emitters in the world, a Logistic equation was proposed to simulate the long-term CO2emission curve. After explaining its scientificity, four parameter estimation algorithms were offered. Further, considering the needs of China and other developing countries, a hybrid model was also proposed to forecast the CO2emissions of the next several years.
(7) Considering the characteristics and stages of EKC of China and other developing countries' CO2emissions, a segmented quadratic equation is proposed to simulate the EKCs of these countries. Furthermore, the small-sample algorithm is used to forecast the future feasible region and time to reach the peak point of China's CO2emissions per capita.
(8) Considering the characteristics of monthly CO2emissions of China and other developing countries, a dynamic amplitude trend extrapolation model is proposed. This model used the wavelet transform to decompose the trend data. After eliminating the stochastic series, the rising trend and the periodic waves were modeled by the hybrid model and the RBF neural networks, separately. Empirical results show that this model has more precise forecasting results for China's monthly CO2emissions than the traditional and present algorithms.
Most of the aforementioned researches have been published or accepted as papers by many influential journals in this area. Nature has introduced part of them in its website and also reviewed them in Nature Climate Change, a professional journal of it. Furthermore, the relevant departments of the State Council of China have adopted part of the aforementioned empirical results to adjust the energy and industrial policies.
(1)提出了碳生产率三维分解模型组。该模型组以碳生产率对时间的导数形式为起点,通过设计不同的积分算法、对数中值算法和影响力累计算法,最终得到碳生产率绝对分解模型和相对分解模型。将该模型组应用于中国的实证分析表明,工业部门淘汰落后产能、优化出口结构以及破除地方保护主义对于中国碳生产率的提高具有重要意义。
(2)设计了分解方程将中国的GDP增长分解为碳排放总量增加、各省碳排放结构调整以及各省碳生产率提高三者的贡献,并采用Laspeyres指数分解算法实现了上述分解因素的定量测算。研究结果表明：地方保护主义的影响得到了进一步证实；迄今为止,中国的经济发展依然以粗放型增长为主；在考虑碳排放的情况下,各省的经济增长模式与其经济发达程度以及经度位置(东部、中部和西部)无关,而与纬度位置(北部、中部和南部)密切相关。
(3)以不同种类能源消费量作为输入,以GDP产出和碳排放量作为输出定义了广义能源效率。采用DEA-MPI分解算法对中国各省的广义能源效率的相对变化进行了分解研究。研究结果表明：第一,中国能源效率的提高以中央政府的推动起主导作用,地方政府的推动作用较弱；第二,第二产业的发展模式和能源消费模式变化,对于各省相对能源效率的变化具有决定性影响；第三,与经济发展模式类似,各省能源效率变化模式与其经济发达程度以及经度位置无关,而与纬度位置密切相关。
(4)将42部门投入产出表合并为27部门,结合各产业部门的进出口额和碳排放量数据,通过IO模型测算出各产业部门的完全碳密度和国际贸易的完全碳转移量,并在此基础上预测了未来的碳转移趋势。研究结果表明,由于国际贸易环境的变化,中国各产业部门国际贸易的总净碳转移量在2014年之后将成为负值。
(5)以对数STIRPAT模型框架为基础,采用PLS方法克服变量间多重共线性的影响,对影响中国C02排放的各因素解释能力进行了测算。研究结果表明,对中国C02排放量增长而言,人口增长的解释能力最强,人民生活水平提高的解释能力次之,技术进步的解释能力最弱。此外,以PLS方法建立的较为稳定的对数线性模型为基础,设计了8种发展情景,对中国2012-2020年的C02排放量进行了情景预测。
(6)在广泛研究世界各国CO2排放规律的基础上,提出了用于拟合长期CO2排放趋势的Logistic方程。在解释其科学性的基础上,总结了四种参数估计算法。此外,考虑到中国等发展中国家的需要,提出了采用混合模型对未来少数年份CO2排放量进行预测的算法。
(7)考虑到中国等发展中国家C02排放EKC所处的阶段和特点,提出了采用分段二次函数对其进行拟合的算法。并采用小样本建模方法预测了中国未来的人均C02排放量的可行域及达到峰值的时间。
(8)考虑到中国等发展中国家月度CO2排放趋势的特点,提出了变动振幅趋势外推模型。该模型采用小波变换对月度CO2排放数据进行分解,在剔除随机性波动后,用RBF网络和混合模型分别对规律性波动趋势和长期增长趋势进行建模预测。实证分析表明,该模型对于中国月度CO2排放量的预测精度要高于传统算法和当前主要算法。
本文的主要研究成果,已作为论文被本领域内多种有重要影响的期刊出版或录用。部分研究成果被《Nature》网站进行介绍并由其专业期刊《Nature Climate Change》发表文章进行评论和肯定。部分实证研究的结论已经被国务院相关部门采纳,用于中国能源及产业政策的调整。
Fossil energy consumption is an important driving force factor of economic growth. However, CO2emissions from it are one of the main causes of aggravating the greenhouse effects and their derivative disasters. In recent years, China's CO2emissions have long ranked the country first and its share has also increased year by year. Research on China's low-carbon oriented development of energy consumption is of great importance in improving the ecological environment and realizing the sustainable development of socio-economy. At present, researches on low-carbon oriented development model of energy consumption usually still lack systematization and, more importantly, not adapt to China's needs. In view of the above, and considering the characteristics of China's fossil energy consumption and CO2emissions, this paper has designed a model system which has the function of roadmap decomposition, marginal influence analysis and emission amount forecasting. Using the said model system, China's energy consumption and CO2emissions were analyzed. More specifically, the main contents of this paper are as follows:
(1) The three-dimensional decomposition model group of carbon productivity was proposed. The derivative form of carbon productivity was used to indicate the beginning of decomposition. After designing the integrating algorithms, the logarithmic mean algorithms and the influence accumulating algorithms, the absolute and relative decomposition models for carbon productivity were derived. Empirical analysis to China showed that eliminating backward industrial capacity, optimizing the structure of exports, and break down the local protectionism were the key factors to improve its carbon productivity.
(2) An equation is designed to decompose the GDP growth into the contributions of total carbon emission increase, emission structure adjustment and carbon productivity improvement of each province. Using Laspeyres index decomposition algorithm to quantitatively analyze China's data, the following conclusions were drew: the influence of local protectionism is further verified; Till now, China's economic development still mainly depends on the extensive mode; Considering carbon emissions, economic development mode of each province is closely related to its latitudinal location (the north, middle or south), and not related to its longitudinal location (the east, middle and west) and economic development level..
(3) Selecting different kinds of energy consumption values as input, and the GDP output and carbon emissions as output, the energy efficiency in the broader sense is defined. Using the DEA-MPI decomposition algorithm to analysis the relative changes of energy efficiency in the broader sense of China's provinces, the following conclusions were drew:Firstly, the central government is more important than the local ones in propelling the improvement of China's energy efficiency; Secondly, changes of economic development and energy consumption mode of the secondary industry have decisive effects on the relative changes of energy efficiency of each province; Thirdly, similar to economic development mode, change mode of energy efficiency of each province is closely related to its latitudinal location, and not related to its longitudinal location and economic development level.
(4) The42industrial sectors in input-output table are combined into27ones. The total carbon intensity and carbon transmission amount of each industrial sector is measured by using the adjusted input-output table, the import and export values, and the carbon emission amount of each industrial sector. Results show that because of the change of international trade environment, China's net carbon transmissions of all the industrial sectors embedded in international trade will be less than0after year2014.
(5) Based on the logarithmic STIRPAT model, the PLS algorithm was used to overcome the multicollinearity between variables, and at last, the explanatory ability to the CO2emissions of each variable was measured. Results show that the population increase has the strongest ability, the improvement of living standard the medium, and the technological innovation the weakest. Moreover, based on the logarithmic linear equation which is built by the PLS algorithm, eight development scenarios were designed to forecast China's CO2emissions of2012-2020.
(6) Based on the survey to the CO2emission trends of main emitters in the world, a Logistic equation was proposed to simulate the long-term CO2emission curve. After explaining its scientificity, four parameter estimation algorithms were offered. Further, considering the needs of China and other developing countries, a hybrid model was also proposed to forecast the CO2emissions of the next several years.
(7) Considering the characteristics and stages of EKC of China and other developing countries' CO2emissions, a segmented quadratic equation is proposed to simulate the EKCs of these countries. Furthermore, the small-sample algorithm is used to forecast the future feasible region and time to reach the peak point of China's CO2emissions per capita.
(8) Considering the characteristics of monthly CO2emissions of China and other developing countries, a dynamic amplitude trend extrapolation model is proposed. This model used the wavelet transform to decompose the trend data. After eliminating the stochastic series, the rising trend and the periodic waves were modeled by the hybrid model and the RBF neural networks, separately. Empirical results show that this model has more precise forecasting results for China's monthly CO2emissions than the traditional and present algorithms.
Most of the aforementioned researches have been published or accepted as papers by many influential journals in this area. Nature has introduced part of them in its website and also reviewed them in Nature Climate Change, a professional journal of it. Furthermore, the relevant departments of the State Council of China have adopted part of the aforementioned empirical results to adjust the energy and industrial policies.
引文
[1]Palmer R. R., Colton J., Kramer L.工业革命：变革世界的引擎[M].苏中友,周鸿临,范丽萍.美：世界图书出版公司,2010：1-62.
[2]Robert E. L. The Industrial Revolution:Past and Future [EB/OL]. (2004-5-1). [2012-7-13]. http://www.minneapolisfed.org/publications_papers/pub_display.cfm?id=3333.
[3]Ohyama A., Tsujimura M. Induced effects and technological innovation with strategic environmental policy [J]. European Journal of Operational Research,2008, 190(3):834-854.
[4]Nakata T., Sato T., Wang H., et al. Modeling technological learning and its application for clean coal technologies in Japan [J]. Applied Energy,2011,88(1): 330-336.
[5]Lim H. J., Yoo S. H., Kwak S. J. Industrial CO2 emissions from energy use in Korea:A structural decomposition analysis [J]. Energy Policy,2009,37(2): 686-698.
[6]Gingrich S., Kuskova P., Steinberger J. K. Long-term changes in CO2 emissions in Austria and Czechoslovakia-Identifying the drivers of environmental pressures [J]. Energy Policy,2011,39(2):535-543.
[7]Kraft, J. and Kraft A. On the Relationship between Energy and GNP [J], The Journal of Energy and Development,1978,3(2):401-403.
[8]Nachanea D. M., Nadkarnia R. M., Karnika A. V. Co-Integration and Causality Testing of the Energy-GDP Relationship:A Cross-Country Study [J]. Applied Economics,1988,20(11):1511-1531.
[9]Glasure Y. U., Lee A. R. Cointegration, error-correction, and the relationship between GDP and energy:The case of South Korea and Singapore [J]. Resource and Energy Economics,1998,20(1):17-25.
[10]Asafu-Adjaye J. The relationship between energy consumption, energy prices and economic growth:time series evidence from Asian developing countries [J]. Energy Economics,2000,22(6):615-625.
[11]Yang H. Y. A note on the causal relationship between energy and GDP in Taiwan [J]. Energy Economics,2000,22(3):309-317.
[12]Yang H. Y. Coal Consumption and Economic Growth in Taiwan [J]. Energy Sources,2000,22(2):109-115.
[13]林伯强.电力消费与中国经济增长：基于生产函数的研究[J].管理世界,2003,11：18-27.
[14]Ghali K. H., El-Sakka M. I. T. Energy use and output growth in Canada:a multivariate cointegration analysis [J]. Energy Economics,2004,26(2):225-238.
[15]赵进文,范继涛.经济增长与能源消费内在依从关系的实证研究[J].经济研究,2007,8：31-42.
[16]尹建华,王兆华.中国能源消费与经济增长间关系的实证研究——基于1953-2008年数据的分析[J].科研管理,2011,32(7)：122-129.
[17]王世进,周敏.基于面板数据的中国工业能源消费与产出协整分析[J].生态经济,2012,3：33-35.
[18]BP. BP Statistical Review of World Energy 2012 [EB/OL]. [2012-7-14]. http://www.bp.com/sectionbodycopy.do?categoryld=7500&contentld=7068481.
[19]Ranganathan V. Hydropower and environment in India [J]. Energy Policy,1997, 25(4):435-438.
[20]Sternberg R. Hydropower's future, the environment, and global electricity systems [J]. Renewable and Sustainable Energy Reviews,2010,14(2):713-723.
[21]Berkun M. Hydroelectric potential and environmental effects of multidam hydropower projects in Turkey [J]. Energy for Sustainable Development,2010, 14(4):320-329.
[22]Cioffi F., Gallerano F. Multi-objective analysis of dam release flows in rivers downstream from hydropower reservoirs [J]. Applied Mathematical Modelling, 2012,36(7):2868-2889.
[23]Hamada N., Ogino H. Food safety regulations:what we learned from the Fukushima nuclear accident [J]. Journal of Environmental Radioactivity,2012,111: 83-99.
[24]伍浩松.日本核事故之后各国的核政策(一)[J].国外核新闻,2011,11：1-9.
[25]伍浩松.日本核事故之后各国的核政策(二)[J].国外核新闻,2011,12：1-6.
[26]王冬利,宋宏坤,黎洪声.电力需求侧管理使用技术[M].北京：中国电力出版社,2005：4.
[27]Fourier J. Remarques Generales Sur Les Temperatures Du Globe Terrestre Et Des Espaces Planetaires [J]. Annales de Chimie et de Physique,1824,27:136-167.
[28]Arrhenius A. On the Influence of Carbonic Acid in the Air upon the Temperature of the Ground [J]. Philosophical Magazine,1896,41:237-76.
[29]IPCC (Intergovernmental Panel on Climate Change). IPCC《第三次评估报告》使用的术语[EB/OL]. [2012-7-15]. http://www.ipcc.ch/pdf/glossary/tar-ipcc-terms-ch.pdf.
[30]UMFCCC (United Nations Framework Conventionon Climate Change). Kyoto Protocol to the United Nations Framework Convention on Climate Change [EB/OL]. [2012-7-15]. http://unfccc.int/essential_background/kyoto_protocol/ items/1678.php.
[31]Taseska V., Markovska N., Causevski A., Bosevski T., Pop-Jordanov J. Greenhouse gases (GHG) emissions reduction in a power system predominantly based on lignite [J]. Energy,2011,36(4):2266-2270.
[32]EESC (Earth and Environmental Sciences Center). Solar Radiation and the Earth's Energy Balance [EB/OL]. [2012-7-17]. http://eesc.columbia.edu/courses/ees/ climate/lectures/radiation/.
[33]NASA (National Aeronauticsand Space Administration). GISS Surface Temperature Analysis [EB/OL]. [2012-7-17]. http://data.giss.nasa.gov/gistemp/ abs_temp.html.
[34]Jain P. C. Greenhouse effect and climate change:scientific basis and overview [J]. Renewable Energy,1993,3(4-5):403-420.
[35]Stern N. The Economics of Climate Change [J]. American Economic Review, 2008,98(2):1-37.
[36]Yamano H., Kayanne H., Yamaguchi T., Kuwahara Y, Yokoki H., et al. Atoll island vulnerability to flooding and inundation revealed by historical reconstruction: Fongafale Islet, Funafuti Atoll, Tuvalu [J]. Global and Planetary Change,2007, 57(3-4):407-416
[37]Farbotko C., Lazrus H. The first climate refugees? Contesting global narratives of climate change in Tuvalu [J]. Global Environmental Change,2012,22(2): 382-390.
[38]Titus J. G. Greenhouse Effect, Sea Level Rise, and Barrier Islands:Case Study of Long Beach Island, New Jersey [J]. Coastal Management,1990,8:65-90.
[39]IPCC (Intergovernmental Panel on Climate Change). Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,2007 [EB/OL]. [2012-7-18]. http://www.ipcc.ch/publications_and_data/ar4/wg2/en/contents.html.
[40]Walker M. Back from the dead [J]. New Scientist,1999,2202:4.
[41]Ramanathan V., Feng Y. Air pollution, greenhouse gases and climate change: Global and regional perspectives [J]. Atmospheric Environment,2009,43(1): 37-50.
[42]Lozano I., Devoy R. J. N., May W. Storminess and vulnerability along the Atlantic coastlines of Europe:analysis of storm records and of a greenhouse gases induced climate scenario [J]. Marine Geology,2004,210(1-4):205-225.
[43]Henderson-Sellers A., Irannejad P., McGuffie K. Future desertification and climate change:The need for land-surface system evaluation improvement [J]. Global and Planetary Change,2008,64(3-4):129-138.
[44]Charles D. Stimulus Gives DOE Billions for Carbon-Capture Projects [J]. Science,2009,323(5918):1158.
[45]IPCC (Intergovernmental Panel on Climate Change). Organization of 1PCC [EB/OL]. [2012-7-18]. http://www.ipcc.ch/organization/organization.shtml.
[46]Hohne N., Wartmann S., Herold A, Freibauer A. The rules for land use, land use change and forestry under the Kyoto Protocol-lessons learned for the future climate negotiations [J]. Environmental Science & Policy,2007,10(4):353-369.
[47]Coskun A. A., Gencay G. Kyoto Protocol and "deforestation":A legal analysis on Turkish environment and forest legislation [J]. Forest Policy and Economics,2011, 13(5):366-377.
[48]Jaehn F., Letmathe P. The emissions trading paradox [J]. European Journal of Operational Research,2010,202(1):248-254.
[49]Elzen M. G J., Hof A. F., Beltran A. M., Grassi G, Roelfsema M., et al. The Copenhagen Accord:abatement costs and carbon prices resulting from the submissions [J]. Environmental Science & Policy,2011,14(1):28-39.
[50]世界银行.世界银行数据-GDP(现价美元)[EB/OL]. [2012-7-19]. http://data.worldbank.org.cn/indicator/NY.GDP.MKTP.CD/countries.
[51]中华人民共和国国家统计局.中国统计年鉴(2000-2012)[M].中国统计出版社,2012.
[52]UN (United Nations). GDP by Type of Expenditure at current prices-National currency. [EB/OL]. [2012-7-19]. http://data.un.org/Data.aspx?q=GDP&d= SNAAMA&f=grID%3a101%3bcurrID%3aNCU%3bpcFlag%3a0.
[53]EIA (U.S.Energy Information Administration). Energy Intensity-Total Primary Energy Consumption per Dollar of GDP [EB/OL]. [2012-7-20]. http://www.eia.gov /cfapps/ipdbproject/iedindex3.cfm?tid=92&pid=46&aid=2&cid=regions&syid=1980&e yid=2009&unit=BTUPUSDM.
[54]EIA (U.S.Energy Information Administration). Carbon Intensity using Market Exchange Rates (Metric Tons of Carbon Dioxide per Thousand Year 2005 U.S. Dollars) [EB/OL]. [2012-7-20]. http://www.eia.gov/cfapps/ipdbproject/iedindex3.cfin? tid=91&pid=46&aid=31&cid=regions&syid=1980&eyid=2010&unit=MTCDPUSD.
[55]Mitchell W. E. Progress and Problems From Interconnection in Southeastern States [J]. Transactions of the American Institute of Electrical Engineers,1928, 47(2):382-392.
[56]Rowson R. B. Electricity supply:a statistical approach to some particular problems [A]. Proceedings of the IEE-Part Ⅱ:Power Engineering,1952,99(68): 151-167.
[57]Gillies D. K. A., Bernholtz B., Sandiford P. J. A New Approach to Forecasting Daily Peak Loads [J]. Power Apparatus and Systems, Part Ⅲ. Transactions of the American Institute of Electrical Engineers,1956,75(3):382-387.
[58]King J. I. F. Greenhouse effect in a semi-infinite atmosphere [J]. Icarus,1963,2: Pages 359-363.
[59]Pollack J. B. Greenhouse models of the atmosphere of titan [J]. Icarus,1973, 19(1):43-58.
[60]Marx G., Miskolci F. The CO2 greenhouse effect and the thermal history of the atmosphere [J]. Advances in Space Research,1981,1(14):5-18.
[61]Bruun P. Sea Level Rise as a Cause if Shore Erosion [J]. Journal of the Waterways and Harbors Division,1962,1:116-130.
[62]Barth M. C., Titus J. G. Greenhouse Effect and Sea Level Rise:A Challenge For This Generation [M]. New York:Van Nostrand Reinhold,1984.
[63]Rose A., Chen C. Y. Sources of change in energy use in the U.S. economy, 1972-1982:A structural decomposition analysis [J]. Resources and Energy,1991, 13(1):1-21.
[64]Liu X.Q., Ang B. W., Ong H. L. Interfuel substitution and decomposition of changes in industrial energy consumption [J]. Energy,1992,17(7):689-696.
[65]Torvanger A. Manufacturing sector carbon dioxide emissions in nine OECD countries,1973-87:A Divisia index decomposition to changes in fuel mix, emission coefficients, industry structure, energy intensities and international structure [J]. Energy Economics,1991,13(3):168-186.
[66]Abdel-Aal R. E., Al-Garni A. Z., Al-Nassar Y. N. Modelling and forecasting monthly electric energy consumption in eastern Saudi Arabia using abductive networks [J]. Energy,1997,22(9):911-921.
[67]Wang S., Yu L., Tang L., Wang S. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China [J]. Energy,2011,36(11):6542-6554.
[68]Gonzalez-Romera E., Jaramillo-Moran M. A., Carmona-Fernandez D. Monthly Electric Energy Demand Forecasting Based on Trend Extraction [J]. IEEE Transactions on Power Systems,2006,21(4):1946-1953.
[69]Gonzalez-Romera E., Jaramillo-Moran M. A., Carmona-Fernandez D. Forecasting of the electric energy demand trend and monthly fluctuation with neural networks [J]. Computers & Industrial Engineering,2007,52(3):336-343.
[70]Gonzalez-Romera E., Jaramillo-Moran M. A., Carmona-Fernandez D. Monthly electric energy demand forecasting with neural networks and Fourier series [J]. Energy Conversion and Management,2008,49(11):3135-3142.
[71]中华人民共和国国民经济和社会发展第十个五年计划纲要[EB/OL]. (2005-10-19). [2012-7-22]. http://theory.people.com.cn/GB/40557/54239/54243/ 3783806.html.
[72]中华人民共和国国民经济和社会发展第十一个五年规划纲要[EB/OL]. [2012-7-22]. http://www.gov.cn/gongbao/content/2006/content_268766.htm.
[73]中华人民共和国国民经济和社会发展第十二个五年规划纲要[EB/OL]. (20011-3-16). [2012-7-22]. http://www.gov.cn/20111h/content_1825838.htm.
[74]Divisia F. L'indice monetaire et la theorie de la monnaie [J]. Revue d'econ,1926, 1:49-81.
[75]Barnett W. A. Recent Monetary Policy and the Divisia Monetary Aggregation [J]. The American Statistician,1984,38(2):165-172.
[76]Ang B. W., Zhang F. Q. A survey of index decomposition analysis in energy and environmental studies [J]. Energy,2000,25(12):1149-1176.
[77]Hatzigeorgiou E., Polatidis H., Haralambopoulos D. CO2 emissions in Greece for 1990-2002:A decomposition analysis and comparison of results using the Arithmetic Mean Divisia Index and Logarithmic Mean Divisia Index techniques [J]. Energy,2008,33(3):492-499.
[78]Ang B. W. Decomposition methodology in industrial energy demand analysis [J]. Energy 1995; 20(11):1081-95.
[79]Ang B. W., Choi K. H. Decomposition of aggregate energy and gas emission intensities for industry:a refined Divisia index method [J]. The Energy Journal 1997; 18(3):59-73.
[80]Wang W. W., Zhang M., Zhou M. Using LMDI method to analyze transport sector CO2 emissions in China [J]. Energy,2011,36(10):5909-5915.
[81]Chung H. S., Rhee H. C. A residual-free decomposition of the sources of carbon dioxide emissions:a case of the Korean industries [J]. Energy,2001,26(1):15-30.
[82]Liu L. C., Fan Y., Wu G, Wei Y. Using LMDI method to analyze the change of China's industrial CO2 emissions from final fuel use:An empirical analysis [J]. Energy Policy,2007,35(11):5892-5900.
[83]Zhao M., Tan L., Zhang W., Ji M., Liu Y., et al. Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method [J]. Energy,2010,35(6):2505-2510.
[84]Mahony T. O., Zhou P., Sweeney J. The driving forces of change in energy-related CO2 emissions in Ireland:A multi-sectoral decomposition from 1990 to 2007 [J]. Energy Policy,2012,44:256-267.
[85]Zhang J. Y, Zhang Y, Yang Z. F., Li S. S. An Estimation and Factor Decomposition Analysis of Energy-related Carbon Emissions in Beijing [J]. Procedia Environmental Sciences,2012,13:1602-1608.
[86]Ediger V. S., Huvaz O. Examining the sectoral energy use in Turkish economy (1980-2000) with the help of decomposition analysis [J]. Energy Conversion and Management,2006,47(6):732-745.
[87]Achao C., Schaeffer R. Decomposition analysis of the variations in residential electricity consumption in Brazil for the 1980-2007 period:Measuring the activity, intensity and structure effects [J]. Energy Policy,2009,37(12):5208-5220.
[88]Mairet N., Decellas F. Determinants of energy demand in the French service sector:A decomposition analysis [J]. Energy Policy,2009,37(7):2734-2744.
[89]Zhao X., Li N., Ma C. Residential energy consumption in urban China:A decomposition analysis [J]. Energy Policy,2012,41:644-653.
[90]Rogan F., Cahill C. J., Gallachoir B. P.O. Decomposition analysis of gas consumption in the residential sector in Ireland [J]. Energy Policy,2012,42:19-36.
[91]Shrestha R. M., Timilsina G R. SO2 emission intensities of the power sector in Asia:Effects of generation-mix and fuel-intensity changes [J]. Energy Economics, 1997,19(3):355-362.
[92]Bhattacharyya S. C., Ussanarassamee A. Decomposition of energy and CO2 intensities of Thai industry between 1981 and 2000 [J]. Energy Economics,2004, 26(5):765-781.
[93]Bhattacharyya S. C., Ussanarassamee A. Changes in energy intensities of Thai industry between 1981 and 2000:a decomposition analysis [J]. Energy Policy, 2005,33(8):995-1002.
[94]宋承先,许强.现代西方经济学(宏观经济学)(第三版)[M].上海：复旦大学出版社,2004：56-57.
[95]Hankinson G. A., Rhys J. M. W. Electricity consumption, electricity intensity and industrial structure [J]. Energy Economics,1983,5(3):146-152.
[96]Reitler W., Rudolph M., Schaefer H. Analysis of the factors influencing energy consumption in industry:A revised method [J]. Energy Economics,1987,9(3): 145-148.
[97]Boyd G. A., Hanson D. A., Sterner T. Decomposition of changes in energy intensity:A comparison of the Divisia index and other methods [J]. Energy Economics.1988.10(4):309-312.
[98]Doblin C. P. Declining Energy Intensity in the U.S. Manufacturing Sector [J]. The Energy Journal,1988,9(2):109-135.
[99]Howarth R. B., Schipper L. Manufacturing Energy Use in Eight OECD Countries:Trends through 1988 [J]. The Energy Journal,1991,12(4):15-40.
[100]Park S. H. Decomposition of industrial energy consumption:An alternative method [J]. Energy Economics,1992,14(4):265-270.
[101]Park S. H., Dissmann B., Nam K. Y. A cross-country decomposition analysis of manufacturing energy consumption [J]. Energy,1993,18(8):843-858.
[102]Sun J. W. Changes in energy consumption and energy intensity:A complete decomposition model [J]. Energy Economics,1998,20(1):85-100.
[103]Ebohon O. J., Ikeme A. J. Decomposition analysis of CO2 emission intensity between oil-producing and non-oil-producing sub-Saharan African countries [J]. Energy Policy,2006,34(18):3599-3611.
[104]曾琳,张天柱.循环经济与节能减排政策对我国环境压力影响的研究[J].清华大学学报(自然科学版),2012,52(4)：478-482.
[105]Kumbaroglu G. A sectoral decomposition analysis of Turkish CO2 emissions over 1990-2007 [J]. Energy,2011,36(5):2419-2433.
[106]Ren S., Hu Z. Effects of decoupling of carbon dioxide emission by Chinese nonferrous metals industry [J]. Energy Policy,2012,43:407-414.
[107]Gonzalez D., Martinez M. Decomposition analysis of CO2 emissions in the Mexican industrial sector [J]. Energy for Sustainable Development,2012,16(2): 204-215.
[108]Farrell M. J. The Measurement of Productive Efficiency [J]. Journal of the Royal Statistical Society,1957,120(3):253-290.
[109]Charnes A., Cooper W. W., Rhodes E. Measuring the efficiency of decision making units [J]. European Journal of Operational Research,1978,2(6):429-444.
[110]Caves D. W., Christensen L. R., Diewert W. E. Multilateral Comparisons of Output, Input, and Productivity Using Superlative Index Numbers [J]. The Economic Journal,1982,92(365):73-86.
[111]Fare R., Grosskopf S., Lovell C. A. K. Production Frontiers [M]. London: Cambridge University Press,1994.
[112]Fare R., Grosskopf S., Norris M., Zhang Z. Productivity growth, technical progress, and efficiency change in industrialized countries [J]. American Economic Review,1994,84 (1):66-83.
[113]Odeck J. Assessing the relative efficiency and productivity growth of vehicle inspection services:An application of DEA and Malmquist indices [J]. European Journal of Operational Research,2000,126(3):501-514.
[114]Odeck J. Identifying traffic safety best practice:an application of DEA and Malmquist indices [J]. Omega,2006,34(1):28-40.
[115]Kortelainen M. Dynamic environmental performance analysis:A Malmquist index approach [J]. Ecological Economics,2008,64(4):701-715.
[116]Liu F. H. F., Wang P. DEA Malmquist productivity measure:Taiwanese semiconductor companies [J]. International Journal of Production Economics,2008, 112(1):367-379.
[117]Kao C. Malmquist productivity index based on common-weights DEA:The case of Taiwan forests after reorganization [J]. Omega,2010,38(6):484-491.
[118]Sun C. C. Evaluating and benchmarking productive performances of six industries in Taiwan Hsin Chu Industrial Science Park [J]. Expert Systems with Applications,2011,38(3):2195-2205.
[119]Gitto S., Mancuso P. Bootstrapping the Malmquist indexes for Italian airports [J]. International Journal of Production Economics,2012,135(1):403-411.
[120]Ang B. W. Decomposition analysis for policymaking in energy:which is the preferred method? [J]. Energy Policy,2004,32(9):1131-1139.
[121]Andrews-Speed P. China's ongoing energy efficiency drive:Origins, progress and prospects [J]. Energy Policy,2009,37(4):1331-1344.
[122]Yuan X., Zuo J. Transition to low carbon energy policies in China-from the Five-Year Plan perspective [J]. Energy Policy,2011,39(6):3855-3859.
[123]Zhang X., Han J., Zhao H., Deng S., Xiao H., et al. Evaluating the interplays among economic growth and energy consumption and CO2 emission of China during 1990-2007 [J]. Renewable and Sustainable Energy Reviews,2012,16(1): 65-72.
[124]Zhang N., Lior N., Jin H. The energy situation and its sustainable development strategy in China [J]. Energy,2011,36(6):3639-3649.
[125]Hu Y. Energy conservation assessment of fixed-asset investment projects:An attempt to improve energy efficiency in China [J]. Energy Policy,2012,43: 327-334.
[126]彭斯震,张九天.中国2020年碳减排目标下若干关键经济指标研究[J].中国人口.资源与坏境,2012,22(5)：27-31.
[127]Yan X., Crookes R. J. Energy demand and emissions from road transportation vehicles in China [J]. Progress in Energy and Combustion Science,2010,36(6): 651-676.
[128]徐钢,田龙虎,刘彤,黄其励.中国电力工业CO2减排战略分析[J].中国电机工程学报,2011,31(17)：1-8.
[129]潘雄锋,舒涛,徐大伟.中国制造业碳排放强度变动及其因素分解[J].中国人口.资源与环境,2011,21(5)：101-105.
[130]Xiao H., Wei Q., Jiang Y. The reality and statistical distribution of energy consumption in office buildings in China [J]. Energy and Buildings,2012,50: 259-265.
[131]Wang Z. H., Zeng H. L., Wei Y. M, Zhang Y. X. Regional total factor energy efficiency:An empirical analysis of industrial sector in China [J]. Applied Energy, 2012,97:115-123.
[132]陈卓淳,姚遂.中国电力系统低碳转型的路径探析——基于社会技术转型思路[J].中国人口.资源与环境,2012,22(2)：62-68.
[133]Li G., Niu S., Ma L., Zhang X. Assessment of environmental and economic costs of rural household energy consumption in Loess Hilly Region, Gansu Province, China [J]. Renewable Energy,2009,34(6):1438-1444.
[134]Dianshu F., Sovacool B. K., Vu K. M. The barriers to energy efficiency in China:Assessing household electricity savings and consumer behavior in Liaoning Province [J]. Energy Policy,2010,38(2):1202-1209.
[135]Liu X., Niu D., Bao C., Suk S., Shishime T. A survey study of energy saving activities of industrial companies in Taicang, China [J]. Journal of Cleaner Production,2012,26:79-89.
[136]Yao C., Chen C., Li M. Analysis of rural residential energy consumption and corresponding carbon emissions in China [J]. Energy Policy,2012,41:445-450.
[137]张乐勤,李荣富,荣慧芳,许信旺.安徽省近10年能源足迹测度及驱动因子分析[J].环境科学研究,25(4)：474-480.
[138]Zhang M., Wang W. Using an energy flow chart to analyze Jiangsu Province's energy balance [J]. Renewable Energy,2012,39(1):307-312.
[139]Lenzen M. Primary energy and greenhouse gases embodied in Australian final consumption:an input-output analysis [J]. Energy Policy,1998,26(6):495-506.
[140]Battjes J. J., Noorman K. J., Biesiot W. Assessing the energy intensities of imports [J], Energy Economics,1998,20(1):67-83.
[141]Machado G, Schaeffer R., Worrell E. Energy and carbon embodied in the international trade of Brazil:an input-output approach [J]. Ecological Economics, 2001,39(3):409-424.
[142]Hayami H., Nakamura M. CO2 Emission of Alternative Technologies and Bilateral Trade between Japan and Canada:Technology Option and Implication for Joint Implementation [R]. Montreal:The 14th International Conference on Input-Output Techniques,2002.
[143]Mongelli I., Tassielli G, Notarnicola B. Global warming agreements, international trade and energy/carbon embodiments:an input-output approach to the Italian case [J]. Energy Policy,2006,34(1):88-100.
[144]席酉民,刘洪涛,郭菊娥.能源投入产出分式规划模型的构建与应用[J].科学学研究,2009,27(4)：535-540.
[145]Wei Y. M., Liu L. C., Zou L. L. Energy Economics:CO2 Emissions in China [M]. Heidelberg:Springer,2010.
[146]Liu H., Xi Y., Guo J., Li X. Energy embodied in the international trade of China: An energy input-output analysis [J]. Energy Policy,2010,38(8):3957-3964.
[147]李锴,齐绍洲.贸易开放、经济增长与中国二氧化碳排放[J].经济研究,2011,11：60-72.
[148]Ehrlich P. R., Holdren J. P. Impact of population growth [J]. Science 1971, 171(3977):1212-1217.
[149]Ehrlich P. R., Holdren J. P. A bulletin dialogue on the'closing circle'critique: one-dimensional ecology [J]. Bulletin of the Atomic Scientists,1972,28 (5):16-27.
[150]Waggoner P. E., Ausubel J. H. A framework for sustainability science:a renovated IPAT identity [J]. Proceedings of the National Academy of Science,2002, 99:7860-7865.
[151]Geoffrey G. P., Hammond P. Towards sustainability:energy efficiency, thermodynamic analysis, and the'two cultures'[J]. Energy Policy,2004,32(16): 1789-1798.
[152]Saikku L., Rautiainen A., Kauppi P. E. The sustainability challenge of meeting carbon dioxide targets in Europe by 2020 [J]. Energy Policy,2008,36(2):730-742.
[153]Ma C., Stern D. I. Biomass and China's carbon emissions:A missing piece of carbon decomposition [J]. Energy Policy,2008,36(7):2517-2526.
[154]Dietz T., Rosa E. A. Rethinking the environmental impacts of population, affluence and technology [J]. Human Ecology Review,1994,1:277-300.
[155]Fan Y, Liu L.-C., Wu G, et al. Analyzing impact factors of CO2 emissions using the STIRPAT model [J]. Environmental Impact Assessment Review,2006, 26(4):377-395.
[156]王惠文,吴载斌,孟洁.偏最小二乘回归的线性与非线性方法[M].北京：国防工业出版社,2005.
[157]Kuznets, S. Economic growth and income inequality. American Economic Review [J].1955,49(1):1-28.
[158]Grossman, GM.; Krueger, A.B. Environmental impacts of a North American Free Trade Agreement [EB/OL]. [2012-8-1]. http://www.nber.org/papers/w3914 .pdf.
[159]Grossman GM., Krueger A. B. The US-Mexico Free Trade Agreement [M]. MIT Press,1993.
[160]Panayotou, T. Empirical tests and policy analysis of environmental degradation at different stages of economic development [EB/OL]. [2012-8-1]. http://www. econis.eu/PPNSET?PPN=258294558.
[161]李海鹏,叶慧,张俊飚.中国收入差距与环境质量关系的实证检验-基于对环境库兹涅茨曲线的扩展[J].中国人口·资源与环境,2006,16(2)：46-50.
[162]Wagner, M. The carbon Kuznets curve:A cloudy picture emitted by bad econometrics [J]. Resource and Energy Economics,2008,30(3):388-408.
[163]Iwata H., Okada K., Samreth, S. A note on the environmental Kuznets curve for CO2:Apooled mean group approach [J]. Applied Energy,2011,88(5):1986-1996.
[164]钟茂初,孔元,宋树仁.发展追赶过程中收入差距与环境破坏的动态关系[J].软科学,2011,25(2)：1-6.
[165]张为付,周长富.我国碳排放轨迹呈现库兹涅茨倒U型吗?基于不同区域经济发展与碳排放关系分析[J].经济管理,2011,33(6)：14-23.
[166]Esteve V., Tamarit C. Threshold cointegration and nonlinear adjustment between CO2 and income:The Environmental Kuznets Curve in Spain,1857-2007 [J]. Energy Economics,2012,34(6):2148-2156.
[167]Baek J., Kim H. S. Is economic growth good or bad for the environment? Empirical evidence from Korea [J]. Energy Economics,2013,36:744-749.
[168]杜婷婷,毛锋,罗锐.中国经济增长与C02排放演化探析[J].中国人口·资源与环境,2007,17(2)：94-99.
[169]He J., Richard P. Environmental Kuznets curve for CO2 in Canada [J]. Ecological Economics,2010,69(5):1083-1093.
[170]王曾.人力资本、技术进步与C02排放关系的实证研究-基于中国1953-2008年时间序列数据的分析[J].科技进步与对策,2010,27(22)：4-8.
[171]Tevie J., Grimsrud K. M., Berrens R. P. Testing the Environmental Kuznets Curve Hypothesis for Biodiversity Risk in the US:A Spatial Econometric Approach [J]. Sustainability,2011,3(11):2182-2199.
[172]高静,黄繁华.贸易视角下经济增长和环境质量的内在机理研究-基于中国30个省市环境库兹涅茨曲线的面板数据分析[J].上海财经大学学报,2011,13(5)：66-74.
[173]李卫兵,陈思.我国东中西部二氧化碳排放的驱动因素研究[J].华中科技大学学报：社会科学版,2011,25(3)：111-116.
[174]Saboori B., Sulaiman J., Mohd S. Economic growth and CO2 emissions in Malaysia:A cointegration analysis of the Environmental Kuznets Curve [J]. Energy Policy,2012,51:184-191.
[175]Fosten J., Morley B., Taylor T. Dynamic misspecification in the environmental Kuznets curve:Evidence from CO2 and SO2 emissions in the United Kingdom [J]. Ecological Economics,2012,76:25-33.
[176]Shahbaz M., Mutascu M., Azim P. Environmental Kuznets curve in Romania and the role of energy consumption [J]. Renewable and Sustainable Energy Reviews,2013,18:165-173.
[177]Denafas G, Sitnikovas D., Galinis A., Kudrenickis I., Klavs G, et al. Predicting CO2 and SO2 emissions in the Baltic States through reorganization of energy infrastructure [J]. Environment International,2004,30(8):1045-1053.
[178]Hunt L. C., Ninomiya Y. Primary energy demand in Japan:an empirical analysis of long-term trends and future CO2 emissions [J]. Energy Policy,2005, 33(11):1409-1424.
[179]Kwon T.-H. A scenario analysis of CO2 emission trends from car travel:Great Britain 2000-2030 [J]. Transport Policy,2005,12(2):175-184.
[180]Lu I. J., Lewis C, Lin S. J. The forecast of motor vehicle, energy demand and CO2 emission from Taiwan's road transportation sector [J]. Energy Policy,2009, 37(8):2952-2961.
[181]Chitnis M., Hunt L. C. What drives the change in UK household energy expenditure and associated CO2 emissions? Implication and forecast to 2020 [J]. Applied Energy,2012,94:202-214.
[182]Kristrom B., Lundgren T. Swedish CO2-emissions 1900-2010:an exploratory note [J]. Energy Policy,2005,33(9):1223-1230.
[183]He K., Huo H., Zhang Q., He D., An F., et al. Oil consumption and CO2 emissions in China's road transport:current status, future trends, and policy implications [J]. Energy Policy,2005,33(12):1499-1507.
[184]Lin C.-S., Liou F.-M., Huang C.-P. Grey forecasting model for CO2 emissions: A Taiwan study [J]. Applied Energy,2011,88(11):3816-3820.
[185]Pao H.-T., Fu H.-C., Tseng C.-L. Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model [J]. Energy,2012,40(1):400-409.
[186]Kone A. C., Buke T. Forecasting of CO2 emissions from fuel combustion using trend analysis [J]. Renewable and Sustainable Energy Reviews,2010,14(9): 2906-2915.
[187]郝明途,林天佳,刘焱.我国PM2.5的污染状况和污染特征[J].环境科学与管理,2006,31(2)：58-61.
[188]殷永文,程金平,段玉森,魏海平,嵇若旭,等.上海市霾期间PM2.5、 PM10污染与呼吸科、儿呼吸科门诊人数的相关分析[J].环境科学,2011,32(7)：1894-1898.
[189]戴海夏,宋伟民,高翔,陈立民,胡敏.上海市A城区大气PM10、PM2.5污染与居民日死亡数的相关分析[J].卫生研究,2004,33(3)：293-297.
[190]GB 3095-2012,环境空气质量标准[S].北京：中国环境出版社,2012.
[191]徐敬,丁国安,颜鹏,王淑凤,孟昭阳,等.北京地区PM2.5的成分特征及来源分析[J].应用气象学报,2007,18(5)：645-654.
[192]刘彦飞,邵龙义,王彦彪,李卫军.哈尔滨春季大气PM2.5物理化学特征及来源解析[J].环境科学与技术,2010,33(2)：131-134.
[193]姚振坤,冯满,吕森林,张锦平,王青躍,等.上海城区和临安本底站PM2.5的物化特征及来源解析[J].中国环境科学,2010,30(3)：289-295.
[194]牛东晓,乞建勋,邢棉.二重趋势性季节型电力负荷预测组合灰色神经网络模型[J].中国管理科学,2001,9(6)：15-20.
[195]牛东晓,陈志业,邢棉,谢宏.具有二重趋势性的季节型电力负荷预测组合优化灰色神经网络模型[J].中国电机工程学报,2002,22(1)：29-32.
[196]宋仙磊,刘业政,陈思凤.二重趋势时间序列的灰色组合预测模型[J].计算机工程与应用,2011,47(8)：115-117.
[197]Abdel-Aal R. E., Al-Garni A. Z. Forecasting monthly electric energy consumption in Eastern Saudi Arabia using univariate time-series analysis [J]. Energy,1997,22(11):1059-1069.
[198]Saab S., Badr E., Nasr G Univariate modeling and forecasting of energy consumption:the case of electricity in Lebanon [J]. Energy,2001,26(1):1-14.
[199]Hong W. C., Dong Y, Lai C. Y, Chen L. Y, Wei S. Y. SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting [J]. Energies, 2011,4(6):960-977.
[200]吕筱萍.市场预测与决策[M].北京：中国财政经济出版社,1998.
[201]Kaya Y, Yokobori K. Environment, Energy and Economy:Strategies for Sustainability [M]. Delhi:Bookwell Publications,1999.
[202]He J., Deng J., Su M. CO2 emission from China's energy sector and strategy for its control [J]. Energy,2010,35(11):4494-4498.
[203]Stern D. I., Jotzo F. How ambitious are China and India's emissions intensity targets? [J]. Energy Policy,2010,38(11):6776-6783.
[204]Bhattacharyya S. C., Matsumura W. Changes in the GHG emission intensity in EU-15:Lessons from a decomposition analysis [J]. Energy,2010,35(8): 3315-3322.
[205]Davidsdottir B., Fisher M. The odd couple:The relationship between state economic performance and carbon emissions economic intensity [J]. Energy Policy, 2011,39(8):4551-4562.
[206]Taylor R. P., Draugelis G. J., Zhang Y., Ang A. U. Accelerating energy conservation in China's provinces.2010 [EB/OL]. [2012-8-8]. http://www. worldbank.org/research/2010/06/12720925/accelerating-energy-conservation-china s-provinces.
[207]Persson T. A., Colpier U. C., Azar C. Adoption of carbon dioxide efficient technologies and practices:An analysis of sector-specific convergence trends among 12 nations [J]. Energy Policy,2007,35(5):2869-2878.
[208]Andrews C. J., Krogmann U. Explaining the adoption of energy-efficient technologies in U.S. commercial buildings [J]. Energy and Buildings,2009,41(3): 287-294.
[209]Ou X., Yan X., Zhang X., Liu Z. Life-cycle analysis on energy consumption and GHG emission intensities of alternative vehicle fuels in China [J]. Applied Energy,2012,90(1):218-224.
[210]Gardner D. T., Elkhafif M. A. T. Understanding industrial energy use:structural and energy intensity changes in Ontario industry [J]. Energy Economics,1998, 20(1):29-41.
[211]Zhang Z. Why did the energy intensity fall in China's industrial sector in the 1990s? The relative importance of structural change and intensity change [J]. Energy Economics,2003,25(6):625-638.
[212]Wing I. S. Explaining the declining energy intensity of the U.S. economy [J]. Resource and Energy Economics,2008,30(1):21-49.
[213]GB/T2589-2008,综合能耗计算通则[S].北京：中国标准出版社,2008.
[214]中国可持续发展能源暨碳排放情景分析综合报告[EB/OL]. [2012-8-11]. http://web.cenet.org.cn/upfile/77222.pdf.
[215]Wang C., Chen J., Zou J. Decomposition of energy-related CO2 emission in China:1957-2000 [J]. Energy,2005,30(1):73-83.
[216]Martinez C. I. P. Energy efficiency developments in the manufacturing industries of Germany and Colombia,1998-2005 [J]. Energy for Sustainable Development,2009,13(3):189-201.
[217]国家统计局能源统计司.中国能源统计年鉴(2000-2010)[M].北京：中国统计出版社,2011.
[218]张立群.论我国经济增长方式的转换[J].管理世界,1995,5：41-50.
[219]中华人民共和国国民经济和社会发展“九五”计划和二〇一〇年远景目标纲要[EB/OL]. [2012-8-24]. http://cpc.people.com.cn/GB/64184/64186/66686/ 4494253.html.
[220]燕凌.学习中共中央关于发展农业生产合作社的决议[M].北京：华北人民出版社,1954.
[221]Cornillie J., Fankhauser S. The energy intensity of transition countries. Energy Economics [J],2004,26(3):283-295.
[222]Lescaroux F. Decomposition of US manufacturing energy intensity and elasticities of components with respect to energy prices [J]. Energy Economics, 2008,30(3):1068-1080.
[223]Mendiluce M., Perez-Arriaga I., Ocana C. Comparison of the evolution of energy intensity in Spain and in the EU15 [J]. Why is Spain different? Energy Policy,2010,38(1):639-645.
[224]滕玉华.自主研发、技术引进与能源消耗强度——基于中国工业行业的实证分析[J].中国人口·资源与环境,2011,21(7)：169-174.
[225]张敏,张娜,王文.不同来源地FDI对我国能源消费强度的影响[J].西安 交通大学学报(社会科学版),2012,32(2)：13-18.
[226]EIA (U.S. Energy Information Administration). Energy Consumption, Expenditures, and Emissions Indicators Estimates,1949-2010 [EB/OL]. [2012-9-1]. http://www.eia.gov/totalenergy/data/annual/showtext.cfin?t=ptb0105.
[227]Farla J., Cuelenaere R., Blok K. Energy efficiency and structural change in the Netherlands,1980-1990 [J]. Energy Economics,1998,20(1):1-28.
[228]Hasanbeigi A., Can S. R., Sathaye J. Analysis and decomposition of the energy intensity of California industries [J]. Energy Policy,2012,46:234-245.
[229]Wu D. A note on DEA efficiency assessment using ideal point:An improvement of Wang and Luo's model [J]. Applied Mathematics and Computation,2006, 183(2):819-830.
[230]Asmild M., Paradi J. C., Reese D. N., Tam F. Measuring overall efficiency and effectiveness using DEA [J]. European Journal of Operational Research,2007, 178(1):305-321.
[231]Andre F. J., Herrero I., Riesgo L. A modified DEA model to estimate the importance of objectives with an application to agricultural economics [J]. Omega, 2010,38(5):371-382.
[232]Fukuyama H., Mirdehghan S. M. Identifying the efficiency status in network DEA [J]. European Journal of Operational Research,2012,220(1):85-92.
[233]Chen J. X. A comment on DEA efficiency assessment using ideal and anti-ideal decision making units [J]. Applied Mathematics and Computation,2012,219(2): 583-591.
[234]Liang L., Wu J., Cook W. D., Zhu J. The DEA Game Cross-Efficiency Model and Its Nash Equilibrium [J]. Operations Research,2008,56(5):1278-1288
[235]Cook W. D., Zhu J. Within-group common weights in DEA:An analysis of power plant efficiency [J]. European Journal of Operational Research,2007,178(1): 207-216.
[236]高莹,李卫东,尤笑宇.基于网络DEA的我国铁路运输企业效率评价研 究[J].中国软科学,2011,5：176-182.
[237]夏琼,杨锋,梁樑,吴华清.非独立并联生产系统的DEA效率评价研究[J].管理科学学报,2012,15(7)：20-25.
[238]Sueyoshi T., Goto M. DEA-DA for bankruptcy-based performance assessment: Misclassification analysis of Japanese construction industry [J]. European Journal of Operational Research,2009,199(2):576-594.
[239]赵荫.中国制造业生产效率评价：基于并联决策单元的动态DEA方法[J].系统工程理论与实践,2012,36(6)：1251-1260.
[240]Azadeh A., Amalnick M. S., Ghaderi S. F., Asadzadeh S. M. An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors [J]. Energy Policy,2007,35(7):3792-3806.
[241]Hernandez-Sancho F., Molinos-Senante M, Sala-Garrido R. Energy efficiency in Spanish wastewater treatment plants:A non-radial DEA approach [J]. Science of The Total Environment,2011,409(14):2693-2699.
[242]Estache A., Fe B. T., Trujillo L. Sources of efficiency gains in port reform:a DEA decomposition of a Malmquist TFP index for Mexico [J]. Utilities Policy, 2004,12(4):221-230.
[243]Gonzalez E., Gascon F. Sources of productivity growth in the Spanish pharmaceutical industry (1994-2000) [J]. Research Policy,2004,33(5):735-745.
[244]Kashani H. A. State intervention causing inefficiency:an empirical analysis of the Norwegian Continental Shelf [J]. Energy Policy,2005,33(15),1998-2009.
[245]Estache A., Tovar B., Trujillo L. How efficient are African electricity companies? Evidence from the Southern African countries [J]. Energy Policy,2008, 36(6):1969-1979.
[246]Pastor J. Y., Asmild M., Lovell C. A. K. The biennial Malmquist productivity change index [J]. Socio-Economic Planning Sciences,2011,45(1):10-15.
[247]Worthington A. C. Malmquist indices of productivity change in Australian financial services [J]. Journal of International Financial Markets, Institutions and Money,1999,9(3):303-320.
[248]Mukherjee K., Ray S. C., Miller S. M. Productivity growth in large US commercial banks:The initial post-deregulation experience [J]. Journal of Banking & Finance,2001,25(5):913-939.
[249]Barros C. P., Alves C. An empirical analysis of productivity growth in a Portuguese retail chain using Malmquist productivity index [J]. Journal of Retailing and Consumer Services,2004,11(5):269-278.
[250]Oh D., Heshmati A. A sequential Malmquist-Luenberger productivity index: Environmentally sensitive productivity growth considering the progressive nature of technology [J]. Energy Economics,2010,32(6):1345-1355.
[251]Uri N. D. Measuring productivity change in telecommunications [J]. Telecommunications Policy,2000,24(5):439-452.
[252]Hseu J. S., Shang J. K. Productivity changes of pulp and paper industry in OECD countries,1991-2000:a non-parametric Malmquist approach [J]. Forest Policy and Economics,2005,7(3):411-422.
[253]Odeck J. How efficient and productive are road toll companies? Evidence from Norway [J]. Transport Policy,2008,15(4):232-241.
[254]Assaf A. G., Barros C. Performance analysis of the Gulf hotel industry:A Malmquist index with bias correction [J]. International Journal of Hospitality Management,2011,30(4):819-826.
[255]贵州省政府.贵州概况-自然资源-能源资源[EB/OL]. [2012-9-25]. http://www.gzgov.gov.cn/gz/288796628934983680/20111010/322539.html.
[256]河南省统计局,国家统计局河南调查总队.河南统计年鉴(2000-2010)[M].中国统计出版社,2010.
[257]青海省统计局,国家统计局青海调查总队.青海统计年鉴(2000-2010)[M].中国统计出版社,2010.
[258]广东省统计局,国家统计局广东调查总队.广东统计年鉴 (2000-2010)[M].中国统计出版社,2010.
[259]江苏省统计局,国家统计局江苏调查总队.江苏统计年鉴(2000-2010)[M].中国统计出版社,2010.
[260]刘起运,陈璋,苏汝劫.投入产出分析[M].北京：中国人民大学出版社,2011.
[261]陈锡康,杨翠红.投入产出技术[M].北京：科学技术出版社,2011.
[262]李艳梅,付加锋.中国出口贸易中隐含碳排放增长的结构分解分析[J].中国人口.资源与环境,2010,20(8)：53-57.
[263]中华人民共和国中央政府.国务院办公厅关于进行全国投入产出调查的通知[EB/OL]. [2012-9-28]. http://www.gov.cn/xxgk/pub/govpublic/mrlm/201109 /t20110907 64052.html.
[264]国家统计局国民经济核算司.2007中国地区投入产出表[M].北京：中国统计出版社,2011.
[265]国家统计局贸易外经统计司.中国贸易外经统计年鉴(2008)[M].北京：中国统计出版社,2010.
[266]孙小羽,臧新.中国出口贸易的能耗效应和环境效应的实证分析——基于混合单位投入产出模型[J].数量经济技术经济研究,2009,4：33-44.
[267]1997-2009年中国服务贸易进口／出口分项目表[EB/OL]. [2012-11-05]. http://tradeinservices.mofcom.gov.cn/c/index.shtml?method=history&mod_index=1319.
[268]Peters G. P., Hertwich G E. CO2 Embodied in International Trade with Implications for Global Climate Policy [J]. Environmental Science & Technology, 2008,42(5):1401-1407.
[269]Deng J. L. Control problems of grey systems [J]. Systems & Control Letters, 1982,1(5):288-294.
[270]牛东晓,曹树华,卢建昌,赵磊.电力负荷预测技术及其应用(第二版)[M].北京：中国电力出版社,2009.
[271]Deng J. L. Introduction to Grey Mathematical Resource Seience.华中科技大学出版社,2010.
[272]孙敬水,马淑琴.计量经济学[M].北京：清华大学出版社,2004.
[273]王惠文.偏最小二乘回归方法及其应用[M].北京：国防工业出版社,1999.
[274]Tracy N. D., Young J. C., Mason R. L. Multivariate control charts for individual observations [J]. Journal of Quality Technology,1992,24:88-95.
[275]Wang X., Westerdahl D., Chen L. C., Wu Y., Hao J., et al. Evaluating the air quality impacts of the 2008 Beijing Olympic Games:On-road emission factors and black carbon profiles [J]. Atmospheric Environment,2009,43(30):4535-4543.
[276]Vernon R. International Investment and International Trade in the Product Cycle [J]. Quarterly Journal of Economics,1966,80(2):190-207.
[277]Leach D. Re-evaluation of the logistic curve for human populations [J]. Journal of the Royal Statistical Society, Series A,1981,144:94-103.
[278]曾波,孟伟.面向特殊序列的灰色预测建模方法[M].重庆大学出版社,2011.
[279]谢乃明,刘思峰.近似非齐次指数序列的离散灰色模型特性研究[J].系统工程与电子技术,2008,30(5)：863-867.
[280]刘思峰,党耀国,方志耕,谢乃明.灰色系统理论及其应用(第五版)[M].北京：科学出版社,2010.
[281]Hyndman R. J., Koehler A. B. Another look at measures of forecast accuracy [J]. International Journal of Forecasting,2006,22(4):679-688.
[282]Armstrong J. S., Collopy F. Error measures for generalizing about forecasting methods:Empirical comparisons [J]. International Journal of Forecasting,1992, 8(1):69-80.
[283]Grossman G. M., Krueger A. B. Economic Growth and the Environment [J]. The Quarterly Journal of Economics,1995,110(2):353-377.
[284]Achelis S. Technical Analysis from A to Z,2nd Edition [M]. The McGraw-Hill Companies,2000.
[285]Zhao S., Wei G. W. Jump process for the trend estimation of time series [J]. Computational Statistics & Data Analysis,2003,42(1-2):219-241.
[286]McCulloch W. S., Pitts W. A logical calculus of the ideas immanent in nervous activity [J]. Bulletin of Mathematical Biophysics,1943,5:115-133.
[287]Stavrou E. T., Charalambous C., Spiliotis S. Human resource management and performance:A neural network analysis [J]. European Journal of Operational Research,2007,181(1):453-467.
[288]Lee J., Sanmugarasa K., Blumenstein M., Loo Y-C. Improving the reliability of a Bridge Management System (BMS) using an ANN-based Backward Prediction Model (BPM) [J]. Automation in Construction,2008,17(6):758-772.
[289]Padhi S. S., Aggarwal V. Competitive revenue management for fixing quota and price of hotel commodities under uncertainty [J]. International Journal of Hospitality Management,2011,30(3):725-734.
[290]Zhang H., Song J., Su C., He M. Human attitudes in environmental management:Fuzzy Cognitive Maps and policy option simulations analysis for a coal-mine ecosystem in China [J]. Journal of Environmental Management,2013, 115(30):227-234.
[291]Ming A., Furukawa S., Teshima T., Shimojo M., Kajitani M. A new golf swing robot to simulate human skill-Learning control based on direct dynamics model using recurrent ANN [J]. Mechatronics,2006,16(7):443-449.
[292]Moon J. W. Performance of ANN-based predictive and adaptive thermal-control methods for disturbances in and around residential buildings [J]. Building and Environment,2012,48:15-26.
[293]Abdel-Khalik A., Elserougi A., Massoud A., Ahmed S. A power control strategy for flywheel doubly-fed induction machine storage system using artificial neural network [J]. Electric Power Systems Research,2013,96:267-276.
[294]Abbass H. A. An evolutionary artificial neural networks approach for breast cancer diagnosis [J]. Artificial Intelligence in Medicine,2002,25(3):265-281.
[295]Dietzel M., Baltzer P. A. T., Dietzel A., Zoubi R., Groschel T. et al. Artificial Neural Networks for differential diagnosis of breast lesions in MR-Mammography: A systematic approach addressing the influence of network architecture on diagnostic performance using a large clinical database [J]. European Journal of Radiology,2012,81(7):1508-1513.
[296]Atkov O. Y., Gorokhova S. G., Sboev A. G, Generozov E. V., Muraseyeva E. V., et al. Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters [J]. Journal of Cardiology,2012, 59(2):190-194.
[297]韩力群.人工神经网络理论、设计及应用[M].北京：化学工业出版社,2002.
[298]张青贵.人工神经网络导论[M].北京：中国水利水电出版社,2004.
[299]高隽.人工神经原理及仿真实例[M].北京：机械工业出版杜,2005.
[300]胡昌华,李国华,刘涛,周志杰.基于MATLAB 6.X的系统分析与设计-小波分析(第二版)[M].西安：西安电子科技大学出版社,2004.
[301]Boggess A., Narcowich F. J.小波与傅里叶分析基础(第2版)[M].芮国胜,康健.北京：电子工业出版社,2010.
[2]Robert E. L. The Industrial Revolution:Past and Future [EB/OL]. (2004-5-1). [2012-7-13]. http://www.minneapolisfed.org/publications_papers/pub_display.cfm?id=3333.
[3]Ohyama A., Tsujimura M. Induced effects and technological innovation with strategic environmental policy [J]. European Journal of Operational Research,2008, 190(3):834-854.
[4]Nakata T., Sato T., Wang H., et al. Modeling technological learning and its application for clean coal technologies in Japan [J]. Applied Energy,2011,88(1): 330-336.
[5]Lim H. J., Yoo S. H., Kwak S. J. Industrial CO2 emissions from energy use in Korea:A structural decomposition analysis [J]. Energy Policy,2009,37(2): 686-698.
[6]Gingrich S., Kuskova P., Steinberger J. K. Long-term changes in CO2 emissions in Austria and Czechoslovakia-Identifying the drivers of environmental pressures [J]. Energy Policy,2011,39(2):535-543.
[7]Kraft, J. and Kraft A. On the Relationship between Energy and GNP [J], The Journal of Energy and Development,1978,3(2):401-403.
[8]Nachanea D. M., Nadkarnia R. M., Karnika A. V. Co-Integration and Causality Testing of the Energy-GDP Relationship:A Cross-Country Study [J]. Applied Economics,1988,20(11):1511-1531.
[9]Glasure Y. U., Lee A. R. Cointegration, error-correction, and the relationship between GDP and energy:The case of South Korea and Singapore [J]. Resource and Energy Economics,1998,20(1):17-25.
[10]Asafu-Adjaye J. The relationship between energy consumption, energy prices and economic growth:time series evidence from Asian developing countries [J]. Energy Economics,2000,22(6):615-625.
[11]Yang H. Y. A note on the causal relationship between energy and GDP in Taiwan [J]. Energy Economics,2000,22(3):309-317.
[12]Yang H. Y. Coal Consumption and Economic Growth in Taiwan [J]. Energy Sources,2000,22(2):109-115.
[13]林伯强.电力消费与中国经济增长：基于生产函数的研究[J].管理世界,2003,11：18-27.
[14]Ghali K. H., El-Sakka M. I. T. Energy use and output growth in Canada:a multivariate cointegration analysis [J]. Energy Economics,2004,26(2):225-238.
[15]赵进文,范继涛.经济增长与能源消费内在依从关系的实证研究[J].经济研究,2007,8：31-42.
[16]尹建华,王兆华.中国能源消费与经济增长间关系的实证研究——基于1953-2008年数据的分析[J].科研管理,2011,32(7)：122-129.
[17]王世进,周敏.基于面板数据的中国工业能源消费与产出协整分析[J].生态经济,2012,3：33-35.
[18]BP. BP Statistical Review of World Energy 2012 [EB/OL]. [2012-7-14]. http://www.bp.com/sectionbodycopy.do?categoryld=7500&contentld=7068481.
[19]Ranganathan V. Hydropower and environment in India [J]. Energy Policy,1997, 25(4):435-438.
[20]Sternberg R. Hydropower's future, the environment, and global electricity systems [J]. Renewable and Sustainable Energy Reviews,2010,14(2):713-723.
[21]Berkun M. Hydroelectric potential and environmental effects of multidam hydropower projects in Turkey [J]. Energy for Sustainable Development,2010, 14(4):320-329.
[22]Cioffi F., Gallerano F. Multi-objective analysis of dam release flows in rivers downstream from hydropower reservoirs [J]. Applied Mathematical Modelling, 2012,36(7):2868-2889.
[23]Hamada N., Ogino H. Food safety regulations:what we learned from the Fukushima nuclear accident [J]. Journal of Environmental Radioactivity,2012,111: 83-99.
[24]伍浩松.日本核事故之后各国的核政策(一)[J].国外核新闻,2011,11：1-9.
[25]伍浩松.日本核事故之后各国的核政策(二)[J].国外核新闻,2011,12：1-6.
[26]王冬利,宋宏坤,黎洪声.电力需求侧管理使用技术[M].北京：中国电力出版社,2005：4.
[27]Fourier J. Remarques Generales Sur Les Temperatures Du Globe Terrestre Et Des Espaces Planetaires [J]. Annales de Chimie et de Physique,1824,27:136-167.
[28]Arrhenius A. On the Influence of Carbonic Acid in the Air upon the Temperature of the Ground [J]. Philosophical Magazine,1896,41:237-76.
[29]IPCC (Intergovernmental Panel on Climate Change). IPCC《第三次评估报告》使用的术语[EB/OL]. [2012-7-15]. http://www.ipcc.ch/pdf/glossary/tar-ipcc-terms-ch.pdf.
[30]UMFCCC (United Nations Framework Conventionon Climate Change). Kyoto Protocol to the United Nations Framework Convention on Climate Change [EB/OL]. [2012-7-15]. http://unfccc.int/essential_background/kyoto_protocol/ items/1678.php.
[31]Taseska V., Markovska N., Causevski A., Bosevski T., Pop-Jordanov J. Greenhouse gases (GHG) emissions reduction in a power system predominantly based on lignite [J]. Energy,2011,36(4):2266-2270.
[32]EESC (Earth and Environmental Sciences Center). Solar Radiation and the Earth's Energy Balance [EB/OL]. [2012-7-17]. http://eesc.columbia.edu/courses/ees/ climate/lectures/radiation/.
[33]NASA (National Aeronauticsand Space Administration). GISS Surface Temperature Analysis [EB/OL]. [2012-7-17]. http://data.giss.nasa.gov/gistemp/ abs_temp.html.
[34]Jain P. C. Greenhouse effect and climate change:scientific basis and overview [J]. Renewable Energy,1993,3(4-5):403-420.
[35]Stern N. The Economics of Climate Change [J]. American Economic Review, 2008,98(2):1-37.
[36]Yamano H., Kayanne H., Yamaguchi T., Kuwahara Y, Yokoki H., et al. Atoll island vulnerability to flooding and inundation revealed by historical reconstruction: Fongafale Islet, Funafuti Atoll, Tuvalu [J]. Global and Planetary Change,2007, 57(3-4):407-416
[37]Farbotko C., Lazrus H. The first climate refugees? Contesting global narratives of climate change in Tuvalu [J]. Global Environmental Change,2012,22(2): 382-390.
[38]Titus J. G. Greenhouse Effect, Sea Level Rise, and Barrier Islands:Case Study of Long Beach Island, New Jersey [J]. Coastal Management,1990,8:65-90.
[39]IPCC (Intergovernmental Panel on Climate Change). Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,2007 [EB/OL]. [2012-7-18]. http://www.ipcc.ch/publications_and_data/ar4/wg2/en/contents.html.
[40]Walker M. Back from the dead [J]. New Scientist,1999,2202:4.
[41]Ramanathan V., Feng Y. Air pollution, greenhouse gases and climate change: Global and regional perspectives [J]. Atmospheric Environment,2009,43(1): 37-50.
[42]Lozano I., Devoy R. J. N., May W. Storminess and vulnerability along the Atlantic coastlines of Europe:analysis of storm records and of a greenhouse gases induced climate scenario [J]. Marine Geology,2004,210(1-4):205-225.
[43]Henderson-Sellers A., Irannejad P., McGuffie K. Future desertification and climate change:The need for land-surface system evaluation improvement [J]. Global and Planetary Change,2008,64(3-4):129-138.
[44]Charles D. Stimulus Gives DOE Billions for Carbon-Capture Projects [J]. Science,2009,323(5918):1158.
[45]IPCC (Intergovernmental Panel on Climate Change). Organization of 1PCC [EB/OL]. [2012-7-18]. http://www.ipcc.ch/organization/organization.shtml.
[46]Hohne N., Wartmann S., Herold A, Freibauer A. The rules for land use, land use change and forestry under the Kyoto Protocol-lessons learned for the future climate negotiations [J]. Environmental Science & Policy,2007,10(4):353-369.
[47]Coskun A. A., Gencay G. Kyoto Protocol and "deforestation":A legal analysis on Turkish environment and forest legislation [J]. Forest Policy and Economics,2011, 13(5):366-377.
[48]Jaehn F., Letmathe P. The emissions trading paradox [J]. European Journal of Operational Research,2010,202(1):248-254.
[49]Elzen M. G J., Hof A. F., Beltran A. M., Grassi G, Roelfsema M., et al. The Copenhagen Accord:abatement costs and carbon prices resulting from the submissions [J]. Environmental Science & Policy,2011,14(1):28-39.
[50]世界银行.世界银行数据-GDP(现价美元)[EB/OL]. [2012-7-19]. http://data.worldbank.org.cn/indicator/NY.GDP.MKTP.CD/countries.
[51]中华人民共和国国家统计局.中国统计年鉴(2000-2012)[M].中国统计出版社,2012.
[52]UN (United Nations). GDP by Type of Expenditure at current prices-National currency. [EB/OL]. [2012-7-19]. http://data.un.org/Data.aspx?q=GDP&d= SNAAMA&f=grID%3a101%3bcurrID%3aNCU%3bpcFlag%3a0.
[53]EIA (U.S.Energy Information Administration). Energy Intensity-Total Primary Energy Consumption per Dollar of GDP [EB/OL]. [2012-7-20]. http://www.eia.gov /cfapps/ipdbproject/iedindex3.cfm?tid=92&pid=46&aid=2&cid=regions&syid=1980&e yid=2009&unit=BTUPUSDM.
[54]EIA (U.S.Energy Information Administration). Carbon Intensity using Market Exchange Rates (Metric Tons of Carbon Dioxide per Thousand Year 2005 U.S. Dollars) [EB/OL]. [2012-7-20]. http://www.eia.gov/cfapps/ipdbproject/iedindex3.cfin? tid=91&pid=46&aid=31&cid=regions&syid=1980&eyid=2010&unit=MTCDPUSD.
[55]Mitchell W. E. Progress and Problems From Interconnection in Southeastern States [J]. Transactions of the American Institute of Electrical Engineers,1928, 47(2):382-392.
[56]Rowson R. B. Electricity supply:a statistical approach to some particular problems [A]. Proceedings of the IEE-Part Ⅱ:Power Engineering,1952,99(68): 151-167.
[57]Gillies D. K. A., Bernholtz B., Sandiford P. J. A New Approach to Forecasting Daily Peak Loads [J]. Power Apparatus and Systems, Part Ⅲ. Transactions of the American Institute of Electrical Engineers,1956,75(3):382-387.
[58]King J. I. F. Greenhouse effect in a semi-infinite atmosphere [J]. Icarus,1963,2: Pages 359-363.
[59]Pollack J. B. Greenhouse models of the atmosphere of titan [J]. Icarus,1973, 19(1):43-58.
[60]Marx G., Miskolci F. The CO2 greenhouse effect and the thermal history of the atmosphere [J]. Advances in Space Research,1981,1(14):5-18.
[61]Bruun P. Sea Level Rise as a Cause if Shore Erosion [J]. Journal of the Waterways and Harbors Division,1962,1:116-130.
[62]Barth M. C., Titus J. G. Greenhouse Effect and Sea Level Rise:A Challenge For This Generation [M]. New York:Van Nostrand Reinhold,1984.
[63]Rose A., Chen C. Y. Sources of change in energy use in the U.S. economy, 1972-1982:A structural decomposition analysis [J]. Resources and Energy,1991, 13(1):1-21.
[64]Liu X.Q., Ang B. W., Ong H. L. Interfuel substitution and decomposition of changes in industrial energy consumption [J]. Energy,1992,17(7):689-696.
[65]Torvanger A. Manufacturing sector carbon dioxide emissions in nine OECD countries,1973-87:A Divisia index decomposition to changes in fuel mix, emission coefficients, industry structure, energy intensities and international structure [J]. Energy Economics,1991,13(3):168-186.
[66]Abdel-Aal R. E., Al-Garni A. Z., Al-Nassar Y. N. Modelling and forecasting monthly electric energy consumption in eastern Saudi Arabia using abductive networks [J]. Energy,1997,22(9):911-921.
[67]Wang S., Yu L., Tang L., Wang S. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China [J]. Energy,2011,36(11):6542-6554.
[68]Gonzalez-Romera E., Jaramillo-Moran M. A., Carmona-Fernandez D. Monthly Electric Energy Demand Forecasting Based on Trend Extraction [J]. IEEE Transactions on Power Systems,2006,21(4):1946-1953.
[69]Gonzalez-Romera E., Jaramillo-Moran M. A., Carmona-Fernandez D. Forecasting of the electric energy demand trend and monthly fluctuation with neural networks [J]. Computers & Industrial Engineering,2007,52(3):336-343.
[70]Gonzalez-Romera E., Jaramillo-Moran M. A., Carmona-Fernandez D. Monthly electric energy demand forecasting with neural networks and Fourier series [J]. Energy Conversion and Management,2008,49(11):3135-3142.
[71]中华人民共和国国民经济和社会发展第十个五年计划纲要[EB/OL]. (2005-10-19). [2012-7-22]. http://theory.people.com.cn/GB/40557/54239/54243/ 3783806.html.
[72]中华人民共和国国民经济和社会发展第十一个五年规划纲要[EB/OL]. [2012-7-22]. http://www.gov.cn/gongbao/content/2006/content_268766.htm.
[73]中华人民共和国国民经济和社会发展第十二个五年规划纲要[EB/OL]. (20011-3-16). [2012-7-22]. http://www.gov.cn/20111h/content_1825838.htm.
[74]Divisia F. L'indice monetaire et la theorie de la monnaie [J]. Revue d'econ,1926, 1:49-81.
[75]Barnett W. A. Recent Monetary Policy and the Divisia Monetary Aggregation [J]. The American Statistician,1984,38(2):165-172.
[76]Ang B. W., Zhang F. Q. A survey of index decomposition analysis in energy and environmental studies [J]. Energy,2000,25(12):1149-1176.
[77]Hatzigeorgiou E., Polatidis H., Haralambopoulos D. CO2 emissions in Greece for 1990-2002:A decomposition analysis and comparison of results using the Arithmetic Mean Divisia Index and Logarithmic Mean Divisia Index techniques [J]. Energy,2008,33(3):492-499.
[78]Ang B. W. Decomposition methodology in industrial energy demand analysis [J]. Energy 1995; 20(11):1081-95.
[79]Ang B. W., Choi K. H. Decomposition of aggregate energy and gas emission intensities for industry:a refined Divisia index method [J]. The Energy Journal 1997; 18(3):59-73.
[80]Wang W. W., Zhang M., Zhou M. Using LMDI method to analyze transport sector CO2 emissions in China [J]. Energy,2011,36(10):5909-5915.
[81]Chung H. S., Rhee H. C. A residual-free decomposition of the sources of carbon dioxide emissions:a case of the Korean industries [J]. Energy,2001,26(1):15-30.
[82]Liu L. C., Fan Y., Wu G, Wei Y. Using LMDI method to analyze the change of China's industrial CO2 emissions from final fuel use:An empirical analysis [J]. Energy Policy,2007,35(11):5892-5900.
[83]Zhao M., Tan L., Zhang W., Ji M., Liu Y., et al. Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method [J]. Energy,2010,35(6):2505-2510.
[84]Mahony T. O., Zhou P., Sweeney J. The driving forces of change in energy-related CO2 emissions in Ireland:A multi-sectoral decomposition from 1990 to 2007 [J]. Energy Policy,2012,44:256-267.
[85]Zhang J. Y, Zhang Y, Yang Z. F., Li S. S. An Estimation and Factor Decomposition Analysis of Energy-related Carbon Emissions in Beijing [J]. Procedia Environmental Sciences,2012,13:1602-1608.
[86]Ediger V. S., Huvaz O. Examining the sectoral energy use in Turkish economy (1980-2000) with the help of decomposition analysis [J]. Energy Conversion and Management,2006,47(6):732-745.
[87]Achao C., Schaeffer R. Decomposition analysis of the variations in residential electricity consumption in Brazil for the 1980-2007 period:Measuring the activity, intensity and structure effects [J]. Energy Policy,2009,37(12):5208-5220.
[88]Mairet N., Decellas F. Determinants of energy demand in the French service sector:A decomposition analysis [J]. Energy Policy,2009,37(7):2734-2744.
[89]Zhao X., Li N., Ma C. Residential energy consumption in urban China:A decomposition analysis [J]. Energy Policy,2012,41:644-653.
[90]Rogan F., Cahill C. J., Gallachoir B. P.O. Decomposition analysis of gas consumption in the residential sector in Ireland [J]. Energy Policy,2012,42:19-36.
[91]Shrestha R. M., Timilsina G R. SO2 emission intensities of the power sector in Asia:Effects of generation-mix and fuel-intensity changes [J]. Energy Economics, 1997,19(3):355-362.
[92]Bhattacharyya S. C., Ussanarassamee A. Decomposition of energy and CO2 intensities of Thai industry between 1981 and 2000 [J]. Energy Economics,2004, 26(5):765-781.
[93]Bhattacharyya S. C., Ussanarassamee A. Changes in energy intensities of Thai industry between 1981 and 2000:a decomposition analysis [J]. Energy Policy, 2005,33(8):995-1002.
[94]宋承先,许强.现代西方经济学(宏观经济学)(第三版)[M].上海：复旦大学出版社,2004：56-57.
[95]Hankinson G. A., Rhys J. M. W. Electricity consumption, electricity intensity and industrial structure [J]. Energy Economics,1983,5(3):146-152.
[96]Reitler W., Rudolph M., Schaefer H. Analysis of the factors influencing energy consumption in industry:A revised method [J]. Energy Economics,1987,9(3): 145-148.
[97]Boyd G. A., Hanson D. A., Sterner T. Decomposition of changes in energy intensity:A comparison of the Divisia index and other methods [J]. Energy Economics.1988.10(4):309-312.
[98]Doblin C. P. Declining Energy Intensity in the U.S. Manufacturing Sector [J]. The Energy Journal,1988,9(2):109-135.
[99]Howarth R. B., Schipper L. Manufacturing Energy Use in Eight OECD Countries:Trends through 1988 [J]. The Energy Journal,1991,12(4):15-40.
[100]Park S. H. Decomposition of industrial energy consumption:An alternative method [J]. Energy Economics,1992,14(4):265-270.
[101]Park S. H., Dissmann B., Nam K. Y. A cross-country decomposition analysis of manufacturing energy consumption [J]. Energy,1993,18(8):843-858.
[102]Sun J. W. Changes in energy consumption and energy intensity:A complete decomposition model [J]. Energy Economics,1998,20(1):85-100.
[103]Ebohon O. J., Ikeme A. J. Decomposition analysis of CO2 emission intensity between oil-producing and non-oil-producing sub-Saharan African countries [J]. Energy Policy,2006,34(18):3599-3611.
[104]曾琳,张天柱.循环经济与节能减排政策对我国环境压力影响的研究[J].清华大学学报(自然科学版),2012,52(4)：478-482.
[105]Kumbaroglu G. A sectoral decomposition analysis of Turkish CO2 emissions over 1990-2007 [J]. Energy,2011,36(5):2419-2433.
[106]Ren S., Hu Z. Effects of decoupling of carbon dioxide emission by Chinese nonferrous metals industry [J]. Energy Policy,2012,43:407-414.
[107]Gonzalez D., Martinez M. Decomposition analysis of CO2 emissions in the Mexican industrial sector [J]. Energy for Sustainable Development,2012,16(2): 204-215.
[108]Farrell M. J. The Measurement of Productive Efficiency [J]. Journal of the Royal Statistical Society,1957,120(3):253-290.
[109]Charnes A., Cooper W. W., Rhodes E. Measuring the efficiency of decision making units [J]. European Journal of Operational Research,1978,2(6):429-444.
[110]Caves D. W., Christensen L. R., Diewert W. E. Multilateral Comparisons of Output, Input, and Productivity Using Superlative Index Numbers [J]. The Economic Journal,1982,92(365):73-86.
[111]Fare R., Grosskopf S., Lovell C. A. K. Production Frontiers [M]. London: Cambridge University Press,1994.
[112]Fare R., Grosskopf S., Norris M., Zhang Z. Productivity growth, technical progress, and efficiency change in industrialized countries [J]. American Economic Review,1994,84 (1):66-83.
[113]Odeck J. Assessing the relative efficiency and productivity growth of vehicle inspection services:An application of DEA and Malmquist indices [J]. European Journal of Operational Research,2000,126(3):501-514.
[114]Odeck J. Identifying traffic safety best practice:an application of DEA and Malmquist indices [J]. Omega,2006,34(1):28-40.
[115]Kortelainen M. Dynamic environmental performance analysis:A Malmquist index approach [J]. Ecological Economics,2008,64(4):701-715.
[116]Liu F. H. F., Wang P. DEA Malmquist productivity measure:Taiwanese semiconductor companies [J]. International Journal of Production Economics,2008, 112(1):367-379.
[117]Kao C. Malmquist productivity index based on common-weights DEA:The case of Taiwan forests after reorganization [J]. Omega,2010,38(6):484-491.
[118]Sun C. C. Evaluating and benchmarking productive performances of six industries in Taiwan Hsin Chu Industrial Science Park [J]. Expert Systems with Applications,2011,38(3):2195-2205.
[119]Gitto S., Mancuso P. Bootstrapping the Malmquist indexes for Italian airports [J]. International Journal of Production Economics,2012,135(1):403-411.
[120]Ang B. W. Decomposition analysis for policymaking in energy:which is the preferred method? [J]. Energy Policy,2004,32(9):1131-1139.
[121]Andrews-Speed P. China's ongoing energy efficiency drive:Origins, progress and prospects [J]. Energy Policy,2009,37(4):1331-1344.
[122]Yuan X., Zuo J. Transition to low carbon energy policies in China-from the Five-Year Plan perspective [J]. Energy Policy,2011,39(6):3855-3859.
[123]Zhang X., Han J., Zhao H., Deng S., Xiao H., et al. Evaluating the interplays among economic growth and energy consumption and CO2 emission of China during 1990-2007 [J]. Renewable and Sustainable Energy Reviews,2012,16(1): 65-72.
[124]Zhang N., Lior N., Jin H. The energy situation and its sustainable development strategy in China [J]. Energy,2011,36(6):3639-3649.
[125]Hu Y. Energy conservation assessment of fixed-asset investment projects:An attempt to improve energy efficiency in China [J]. Energy Policy,2012,43: 327-334.
[126]彭斯震,张九天.中国2020年碳减排目标下若干关键经济指标研究[J].中国人口.资源与坏境,2012,22(5)：27-31.
[127]Yan X., Crookes R. J. Energy demand and emissions from road transportation vehicles in China [J]. Progress in Energy and Combustion Science,2010,36(6): 651-676.
[128]徐钢,田龙虎,刘彤,黄其励.中国电力工业CO2减排战略分析[J].中国电机工程学报,2011,31(17)：1-8.
[129]潘雄锋,舒涛,徐大伟.中国制造业碳排放强度变动及其因素分解[J].中国人口.资源与环境,2011,21(5)：101-105.
[130]Xiao H., Wei Q., Jiang Y. The reality and statistical distribution of energy consumption in office buildings in China [J]. Energy and Buildings,2012,50: 259-265.
[131]Wang Z. H., Zeng H. L., Wei Y. M, Zhang Y. X. Regional total factor energy efficiency:An empirical analysis of industrial sector in China [J]. Applied Energy, 2012,97:115-123.
[132]陈卓淳,姚遂.中国电力系统低碳转型的路径探析——基于社会技术转型思路[J].中国人口.资源与环境,2012,22(2)：62-68.
[133]Li G., Niu S., Ma L., Zhang X. Assessment of environmental and economic costs of rural household energy consumption in Loess Hilly Region, Gansu Province, China [J]. Renewable Energy,2009,34(6):1438-1444.
[134]Dianshu F., Sovacool B. K., Vu K. M. The barriers to energy efficiency in China:Assessing household electricity savings and consumer behavior in Liaoning Province [J]. Energy Policy,2010,38(2):1202-1209.
[135]Liu X., Niu D., Bao C., Suk S., Shishime T. A survey study of energy saving activities of industrial companies in Taicang, China [J]. Journal of Cleaner Production,2012,26:79-89.
[136]Yao C., Chen C., Li M. Analysis of rural residential energy consumption and corresponding carbon emissions in China [J]. Energy Policy,2012,41:445-450.
[137]张乐勤,李荣富,荣慧芳,许信旺.安徽省近10年能源足迹测度及驱动因子分析[J].环境科学研究,25(4)：474-480.
[138]Zhang M., Wang W. Using an energy flow chart to analyze Jiangsu Province's energy balance [J]. Renewable Energy,2012,39(1):307-312.
[139]Lenzen M. Primary energy and greenhouse gases embodied in Australian final consumption:an input-output analysis [J]. Energy Policy,1998,26(6):495-506.
[140]Battjes J. J., Noorman K. J., Biesiot W. Assessing the energy intensities of imports [J], Energy Economics,1998,20(1):67-83.
[141]Machado G, Schaeffer R., Worrell E. Energy and carbon embodied in the international trade of Brazil:an input-output approach [J]. Ecological Economics, 2001,39(3):409-424.
[142]Hayami H., Nakamura M. CO2 Emission of Alternative Technologies and Bilateral Trade between Japan and Canada:Technology Option and Implication for Joint Implementation [R]. Montreal:The 14th International Conference on Input-Output Techniques,2002.
[143]Mongelli I., Tassielli G, Notarnicola B. Global warming agreements, international trade and energy/carbon embodiments:an input-output approach to the Italian case [J]. Energy Policy,2006,34(1):88-100.
[144]席酉民,刘洪涛,郭菊娥.能源投入产出分式规划模型的构建与应用[J].科学学研究,2009,27(4)：535-540.
[145]Wei Y. M., Liu L. C., Zou L. L. Energy Economics:CO2 Emissions in China [M]. Heidelberg:Springer,2010.
[146]Liu H., Xi Y., Guo J., Li X. Energy embodied in the international trade of China: An energy input-output analysis [J]. Energy Policy,2010,38(8):3957-3964.
[147]李锴,齐绍洲.贸易开放、经济增长与中国二氧化碳排放[J].经济研究,2011,11：60-72.
[148]Ehrlich P. R., Holdren J. P. Impact of population growth [J]. Science 1971, 171(3977):1212-1217.
[149]Ehrlich P. R., Holdren J. P. A bulletin dialogue on the'closing circle'critique: one-dimensional ecology [J]. Bulletin of the Atomic Scientists,1972,28 (5):16-27.
[150]Waggoner P. E., Ausubel J. H. A framework for sustainability science:a renovated IPAT identity [J]. Proceedings of the National Academy of Science,2002, 99:7860-7865.
[151]Geoffrey G. P., Hammond P. Towards sustainability:energy efficiency, thermodynamic analysis, and the'two cultures'[J]. Energy Policy,2004,32(16): 1789-1798.
[152]Saikku L., Rautiainen A., Kauppi P. E. The sustainability challenge of meeting carbon dioxide targets in Europe by 2020 [J]. Energy Policy,2008,36(2):730-742.
[153]Ma C., Stern D. I. Biomass and China's carbon emissions:A missing piece of carbon decomposition [J]. Energy Policy,2008,36(7):2517-2526.
[154]Dietz T., Rosa E. A. Rethinking the environmental impacts of population, affluence and technology [J]. Human Ecology Review,1994,1:277-300.
[155]Fan Y, Liu L.-C., Wu G, et al. Analyzing impact factors of CO2 emissions using the STIRPAT model [J]. Environmental Impact Assessment Review,2006, 26(4):377-395.
[156]王惠文,吴载斌,孟洁.偏最小二乘回归的线性与非线性方法[M].北京：国防工业出版社,2005.
[157]Kuznets, S. Economic growth and income inequality. American Economic Review [J].1955,49(1):1-28.
[158]Grossman, GM.; Krueger, A.B. Environmental impacts of a North American Free Trade Agreement [EB/OL]. [2012-8-1]. http://www.nber.org/papers/w3914 .pdf.
[159]Grossman GM., Krueger A. B. The US-Mexico Free Trade Agreement [M]. MIT Press,1993.
[160]Panayotou, T. Empirical tests and policy analysis of environmental degradation at different stages of economic development [EB/OL]. [2012-8-1]. http://www. econis.eu/PPNSET?PPN=258294558.
[161]李海鹏,叶慧,张俊飚.中国收入差距与环境质量关系的实证检验-基于对环境库兹涅茨曲线的扩展[J].中国人口·资源与环境,2006,16(2)：46-50.
[162]Wagner, M. The carbon Kuznets curve:A cloudy picture emitted by bad econometrics [J]. Resource and Energy Economics,2008,30(3):388-408.
[163]Iwata H., Okada K., Samreth, S. A note on the environmental Kuznets curve for CO2:Apooled mean group approach [J]. Applied Energy,2011,88(5):1986-1996.
[164]钟茂初,孔元,宋树仁.发展追赶过程中收入差距与环境破坏的动态关系[J].软科学,2011,25(2)：1-6.
[165]张为付,周长富.我国碳排放轨迹呈现库兹涅茨倒U型吗?基于不同区域经济发展与碳排放关系分析[J].经济管理,2011,33(6)：14-23.
[166]Esteve V., Tamarit C. Threshold cointegration and nonlinear adjustment between CO2 and income:The Environmental Kuznets Curve in Spain,1857-2007 [J]. Energy Economics,2012,34(6):2148-2156.
[167]Baek J., Kim H. S. Is economic growth good or bad for the environment? Empirical evidence from Korea [J]. Energy Economics,2013,36:744-749.
[168]杜婷婷,毛锋,罗锐.中国经济增长与C02排放演化探析[J].中国人口·资源与环境,2007,17(2)：94-99.
[169]He J., Richard P. Environmental Kuznets curve for CO2 in Canada [J]. Ecological Economics,2010,69(5):1083-1093.
[170]王曾.人力资本、技术进步与C02排放关系的实证研究-基于中国1953-2008年时间序列数据的分析[J].科技进步与对策,2010,27(22)：4-8.
[171]Tevie J., Grimsrud K. M., Berrens R. P. Testing the Environmental Kuznets Curve Hypothesis for Biodiversity Risk in the US:A Spatial Econometric Approach [J]. Sustainability,2011,3(11):2182-2199.
[172]高静,黄繁华.贸易视角下经济增长和环境质量的内在机理研究-基于中国30个省市环境库兹涅茨曲线的面板数据分析[J].上海财经大学学报,2011,13(5)：66-74.
[173]李卫兵,陈思.我国东中西部二氧化碳排放的驱动因素研究[J].华中科技大学学报：社会科学版,2011,25(3)：111-116.
[174]Saboori B., Sulaiman J., Mohd S. Economic growth and CO2 emissions in Malaysia:A cointegration analysis of the Environmental Kuznets Curve [J]. Energy Policy,2012,51:184-191.
[175]Fosten J., Morley B., Taylor T. Dynamic misspecification in the environmental Kuznets curve:Evidence from CO2 and SO2 emissions in the United Kingdom [J]. Ecological Economics,2012,76:25-33.
[176]Shahbaz M., Mutascu M., Azim P. Environmental Kuznets curve in Romania and the role of energy consumption [J]. Renewable and Sustainable Energy Reviews,2013,18:165-173.
[177]Denafas G, Sitnikovas D., Galinis A., Kudrenickis I., Klavs G, et al. Predicting CO2 and SO2 emissions in the Baltic States through reorganization of energy infrastructure [J]. Environment International,2004,30(8):1045-1053.
[178]Hunt L. C., Ninomiya Y. Primary energy demand in Japan:an empirical analysis of long-term trends and future CO2 emissions [J]. Energy Policy,2005, 33(11):1409-1424.
[179]Kwon T.-H. A scenario analysis of CO2 emission trends from car travel:Great Britain 2000-2030 [J]. Transport Policy,2005,12(2):175-184.
[180]Lu I. J., Lewis C, Lin S. J. The forecast of motor vehicle, energy demand and CO2 emission from Taiwan's road transportation sector [J]. Energy Policy,2009, 37(8):2952-2961.
[181]Chitnis M., Hunt L. C. What drives the change in UK household energy expenditure and associated CO2 emissions? Implication and forecast to 2020 [J]. Applied Energy,2012,94:202-214.
[182]Kristrom B., Lundgren T. Swedish CO2-emissions 1900-2010:an exploratory note [J]. Energy Policy,2005,33(9):1223-1230.
[183]He K., Huo H., Zhang Q., He D., An F., et al. Oil consumption and CO2 emissions in China's road transport:current status, future trends, and policy implications [J]. Energy Policy,2005,33(12):1499-1507.
[184]Lin C.-S., Liou F.-M., Huang C.-P. Grey forecasting model for CO2 emissions: A Taiwan study [J]. Applied Energy,2011,88(11):3816-3820.
[185]Pao H.-T., Fu H.-C., Tseng C.-L. Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model [J]. Energy,2012,40(1):400-409.
[186]Kone A. C., Buke T. Forecasting of CO2 emissions from fuel combustion using trend analysis [J]. Renewable and Sustainable Energy Reviews,2010,14(9): 2906-2915.
[187]郝明途,林天佳,刘焱.我国PM2.5的污染状况和污染特征[J].环境科学与管理,2006,31(2)：58-61.
[188]殷永文,程金平,段玉森,魏海平,嵇若旭,等.上海市霾期间PM2.5、 PM10污染与呼吸科、儿呼吸科门诊人数的相关分析[J].环境科学,2011,32(7)：1894-1898.
[189]戴海夏,宋伟民,高翔,陈立民,胡敏.上海市A城区大气PM10、PM2.5污染与居民日死亡数的相关分析[J].卫生研究,2004,33(3)：293-297.
[190]GB 3095-2012,环境空气质量标准[S].北京：中国环境出版社,2012.
[191]徐敬,丁国安,颜鹏,王淑凤,孟昭阳,等.北京地区PM2.5的成分特征及来源分析[J].应用气象学报,2007,18(5)：645-654.
[192]刘彦飞,邵龙义,王彦彪,李卫军.哈尔滨春季大气PM2.5物理化学特征及来源解析[J].环境科学与技术,2010,33(2)：131-134.
[193]姚振坤,冯满,吕森林,张锦平,王青躍,等.上海城区和临安本底站PM2.5的物化特征及来源解析[J].中国环境科学,2010,30(3)：289-295.
[194]牛东晓,乞建勋,邢棉.二重趋势性季节型电力负荷预测组合灰色神经网络模型[J].中国管理科学,2001,9(6)：15-20.
[195]牛东晓,陈志业,邢棉,谢宏.具有二重趋势性的季节型电力负荷预测组合优化灰色神经网络模型[J].中国电机工程学报,2002,22(1)：29-32.
[196]宋仙磊,刘业政,陈思凤.二重趋势时间序列的灰色组合预测模型[J].计算机工程与应用,2011,47(8)：115-117.
[197]Abdel-Aal R. E., Al-Garni A. Z. Forecasting monthly electric energy consumption in Eastern Saudi Arabia using univariate time-series analysis [J]. Energy,1997,22(11):1059-1069.
[198]Saab S., Badr E., Nasr G Univariate modeling and forecasting of energy consumption:the case of electricity in Lebanon [J]. Energy,2001,26(1):1-14.
[199]Hong W. C., Dong Y, Lai C. Y, Chen L. Y, Wei S. Y. SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting [J]. Energies, 2011,4(6):960-977.
[200]吕筱萍.市场预测与决策[M].北京：中国财政经济出版社,1998.
[201]Kaya Y, Yokobori K. Environment, Energy and Economy:Strategies for Sustainability [M]. Delhi:Bookwell Publications,1999.
[202]He J., Deng J., Su M. CO2 emission from China's energy sector and strategy for its control [J]. Energy,2010,35(11):4494-4498.
[203]Stern D. I., Jotzo F. How ambitious are China and India's emissions intensity targets? [J]. Energy Policy,2010,38(11):6776-6783.
[204]Bhattacharyya S. C., Matsumura W. Changes in the GHG emission intensity in EU-15:Lessons from a decomposition analysis [J]. Energy,2010,35(8): 3315-3322.
[205]Davidsdottir B., Fisher M. The odd couple:The relationship between state economic performance and carbon emissions economic intensity [J]. Energy Policy, 2011,39(8):4551-4562.
[206]Taylor R. P., Draugelis G. J., Zhang Y., Ang A. U. Accelerating energy conservation in China's provinces.2010 [EB/OL]. [2012-8-8]. http://www. worldbank.org/research/2010/06/12720925/accelerating-energy-conservation-china s-provinces.
[207]Persson T. A., Colpier U. C., Azar C. Adoption of carbon dioxide efficient technologies and practices:An analysis of sector-specific convergence trends among 12 nations [J]. Energy Policy,2007,35(5):2869-2878.
[208]Andrews C. J., Krogmann U. Explaining the adoption of energy-efficient technologies in U.S. commercial buildings [J]. Energy and Buildings,2009,41(3): 287-294.
[209]Ou X., Yan X., Zhang X., Liu Z. Life-cycle analysis on energy consumption and GHG emission intensities of alternative vehicle fuels in China [J]. Applied Energy,2012,90(1):218-224.
[210]Gardner D. T., Elkhafif M. A. T. Understanding industrial energy use:structural and energy intensity changes in Ontario industry [J]. Energy Economics,1998, 20(1):29-41.
[211]Zhang Z. Why did the energy intensity fall in China's industrial sector in the 1990s? The relative importance of structural change and intensity change [J]. Energy Economics,2003,25(6):625-638.
[212]Wing I. S. Explaining the declining energy intensity of the U.S. economy [J]. Resource and Energy Economics,2008,30(1):21-49.
[213]GB/T2589-2008,综合能耗计算通则[S].北京：中国标准出版社,2008.
[214]中国可持续发展能源暨碳排放情景分析综合报告[EB/OL]. [2012-8-11]. http://web.cenet.org.cn/upfile/77222.pdf.
[215]Wang C., Chen J., Zou J. Decomposition of energy-related CO2 emission in China:1957-2000 [J]. Energy,2005,30(1):73-83.
[216]Martinez C. I. P. Energy efficiency developments in the manufacturing industries of Germany and Colombia,1998-2005 [J]. Energy for Sustainable Development,2009,13(3):189-201.
[217]国家统计局能源统计司.中国能源统计年鉴(2000-2010)[M].北京：中国统计出版社,2011.
[218]张立群.论我国经济增长方式的转换[J].管理世界,1995,5：41-50.
[219]中华人民共和国国民经济和社会发展“九五”计划和二〇一〇年远景目标纲要[EB/OL]. [2012-8-24]. http://cpc.people.com.cn/GB/64184/64186/66686/ 4494253.html.
[220]燕凌.学习中共中央关于发展农业生产合作社的决议[M].北京：华北人民出版社,1954.
[221]Cornillie J., Fankhauser S. The energy intensity of transition countries. Energy Economics [J],2004,26(3):283-295.
[222]Lescaroux F. Decomposition of US manufacturing energy intensity and elasticities of components with respect to energy prices [J]. Energy Economics, 2008,30(3):1068-1080.
[223]Mendiluce M., Perez-Arriaga I., Ocana C. Comparison of the evolution of energy intensity in Spain and in the EU15 [J]. Why is Spain different? Energy Policy,2010,38(1):639-645.
[224]滕玉华.自主研发、技术引进与能源消耗强度——基于中国工业行业的实证分析[J].中国人口·资源与环境,2011,21(7)：169-174.
[225]张敏,张娜,王文.不同来源地FDI对我国能源消费强度的影响[J].西安 交通大学学报(社会科学版),2012,32(2)：13-18.
[226]EIA (U.S. Energy Information Administration). Energy Consumption, Expenditures, and Emissions Indicators Estimates,1949-2010 [EB/OL]. [2012-9-1]. http://www.eia.gov/totalenergy/data/annual/showtext.cfin?t=ptb0105.
[227]Farla J., Cuelenaere R., Blok K. Energy efficiency and structural change in the Netherlands,1980-1990 [J]. Energy Economics,1998,20(1):1-28.
[228]Hasanbeigi A., Can S. R., Sathaye J. Analysis and decomposition of the energy intensity of California industries [J]. Energy Policy,2012,46:234-245.
[229]Wu D. A note on DEA efficiency assessment using ideal point:An improvement of Wang and Luo's model [J]. Applied Mathematics and Computation,2006, 183(2):819-830.
[230]Asmild M., Paradi J. C., Reese D. N., Tam F. Measuring overall efficiency and effectiveness using DEA [J]. European Journal of Operational Research,2007, 178(1):305-321.
[231]Andre F. J., Herrero I., Riesgo L. A modified DEA model to estimate the importance of objectives with an application to agricultural economics [J]. Omega, 2010,38(5):371-382.
[232]Fukuyama H., Mirdehghan S. M. Identifying the efficiency status in network DEA [J]. European Journal of Operational Research,2012,220(1):85-92.
[233]Chen J. X. A comment on DEA efficiency assessment using ideal and anti-ideal decision making units [J]. Applied Mathematics and Computation,2012,219(2): 583-591.
[234]Liang L., Wu J., Cook W. D., Zhu J. The DEA Game Cross-Efficiency Model and Its Nash Equilibrium [J]. Operations Research,2008,56(5):1278-1288
[235]Cook W. D., Zhu J. Within-group common weights in DEA:An analysis of power plant efficiency [J]. European Journal of Operational Research,2007,178(1): 207-216.
[236]高莹,李卫东,尤笑宇.基于网络DEA的我国铁路运输企业效率评价研 究[J].中国软科学,2011,5：176-182.
[237]夏琼,杨锋,梁樑,吴华清.非独立并联生产系统的DEA效率评价研究[J].管理科学学报,2012,15(7)：20-25.
[238]Sueyoshi T., Goto M. DEA-DA for bankruptcy-based performance assessment: Misclassification analysis of Japanese construction industry [J]. European Journal of Operational Research,2009,199(2):576-594.
[239]赵荫.中国制造业生产效率评价：基于并联决策单元的动态DEA方法[J].系统工程理论与实践,2012,36(6)：1251-1260.
[240]Azadeh A., Amalnick M. S., Ghaderi S. F., Asadzadeh S. M. An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors [J]. Energy Policy,2007,35(7):3792-3806.
[241]Hernandez-Sancho F., Molinos-Senante M, Sala-Garrido R. Energy efficiency in Spanish wastewater treatment plants:A non-radial DEA approach [J]. Science of The Total Environment,2011,409(14):2693-2699.
[242]Estache A., Fe B. T., Trujillo L. Sources of efficiency gains in port reform:a DEA decomposition of a Malmquist TFP index for Mexico [J]. Utilities Policy, 2004,12(4):221-230.
[243]Gonzalez E., Gascon F. Sources of productivity growth in the Spanish pharmaceutical industry (1994-2000) [J]. Research Policy,2004,33(5):735-745.
[244]Kashani H. A. State intervention causing inefficiency:an empirical analysis of the Norwegian Continental Shelf [J]. Energy Policy,2005,33(15),1998-2009.
[245]Estache A., Tovar B., Trujillo L. How efficient are African electricity companies? Evidence from the Southern African countries [J]. Energy Policy,2008, 36(6):1969-1979.
[246]Pastor J. Y., Asmild M., Lovell C. A. K. The biennial Malmquist productivity change index [J]. Socio-Economic Planning Sciences,2011,45(1):10-15.
[247]Worthington A. C. Malmquist indices of productivity change in Australian financial services [J]. Journal of International Financial Markets, Institutions and Money,1999,9(3):303-320.
[248]Mukherjee K., Ray S. C., Miller S. M. Productivity growth in large US commercial banks:The initial post-deregulation experience [J]. Journal of Banking & Finance,2001,25(5):913-939.
[249]Barros C. P., Alves C. An empirical analysis of productivity growth in a Portuguese retail chain using Malmquist productivity index [J]. Journal of Retailing and Consumer Services,2004,11(5):269-278.
[250]Oh D., Heshmati A. A sequential Malmquist-Luenberger productivity index: Environmentally sensitive productivity growth considering the progressive nature of technology [J]. Energy Economics,2010,32(6):1345-1355.
[251]Uri N. D. Measuring productivity change in telecommunications [J]. Telecommunications Policy,2000,24(5):439-452.
[252]Hseu J. S., Shang J. K. Productivity changes of pulp and paper industry in OECD countries,1991-2000:a non-parametric Malmquist approach [J]. Forest Policy and Economics,2005,7(3):411-422.
[253]Odeck J. How efficient and productive are road toll companies? Evidence from Norway [J]. Transport Policy,2008,15(4):232-241.
[254]Assaf A. G., Barros C. Performance analysis of the Gulf hotel industry:A Malmquist index with bias correction [J]. International Journal of Hospitality Management,2011,30(4):819-826.
[255]贵州省政府.贵州概况-自然资源-能源资源[EB/OL]. [2012-9-25]. http://www.gzgov.gov.cn/gz/288796628934983680/20111010/322539.html.
[256]河南省统计局,国家统计局河南调查总队.河南统计年鉴(2000-2010)[M].中国统计出版社,2010.
[257]青海省统计局,国家统计局青海调查总队.青海统计年鉴(2000-2010)[M].中国统计出版社,2010.
[258]广东省统计局,国家统计局广东调查总队.广东统计年鉴 (2000-2010)[M].中国统计出版社,2010.
[259]江苏省统计局,国家统计局江苏调查总队.江苏统计年鉴(2000-2010)[M].中国统计出版社,2010.
[260]刘起运,陈璋,苏汝劫.投入产出分析[M].北京：中国人民大学出版社,2011.
[261]陈锡康,杨翠红.投入产出技术[M].北京：科学技术出版社,2011.
[262]李艳梅,付加锋.中国出口贸易中隐含碳排放增长的结构分解分析[J].中国人口.资源与环境,2010,20(8)：53-57.
[263]中华人民共和国中央政府.国务院办公厅关于进行全国投入产出调查的通知[EB/OL]. [2012-9-28]. http://www.gov.cn/xxgk/pub/govpublic/mrlm/201109 /t20110907 64052.html.
[264]国家统计局国民经济核算司.2007中国地区投入产出表[M].北京：中国统计出版社,2011.
[265]国家统计局贸易外经统计司.中国贸易外经统计年鉴(2008)[M].北京：中国统计出版社,2010.
[266]孙小羽,臧新.中国出口贸易的能耗效应和环境效应的实证分析——基于混合单位投入产出模型[J].数量经济技术经济研究,2009,4：33-44.
[267]1997-2009年中国服务贸易进口／出口分项目表[EB/OL]. [2012-11-05]. http://tradeinservices.mofcom.gov.cn/c/index.shtml?method=history&mod_index=1319.
[268]Peters G. P., Hertwich G E. CO2 Embodied in International Trade with Implications for Global Climate Policy [J]. Environmental Science & Technology, 2008,42(5):1401-1407.
[269]Deng J. L. Control problems of grey systems [J]. Systems & Control Letters, 1982,1(5):288-294.
[270]牛东晓,曹树华,卢建昌,赵磊.电力负荷预测技术及其应用(第二版)[M].北京：中国电力出版社,2009.
[271]Deng J. L. Introduction to Grey Mathematical Resource Seience.华中科技大学出版社,2010.
[272]孙敬水,马淑琴.计量经济学[M].北京：清华大学出版社,2004.
[273]王惠文.偏最小二乘回归方法及其应用[M].北京：国防工业出版社,1999.
[274]Tracy N. D., Young J. C., Mason R. L. Multivariate control charts for individual observations [J]. Journal of Quality Technology,1992,24:88-95.
[275]Wang X., Westerdahl D., Chen L. C., Wu Y., Hao J., et al. Evaluating the air quality impacts of the 2008 Beijing Olympic Games:On-road emission factors and black carbon profiles [J]. Atmospheric Environment,2009,43(30):4535-4543.
[276]Vernon R. International Investment and International Trade in the Product Cycle [J]. Quarterly Journal of Economics,1966,80(2):190-207.
[277]Leach D. Re-evaluation of the logistic curve for human populations [J]. Journal of the Royal Statistical Society, Series A,1981,144:94-103.
[278]曾波,孟伟.面向特殊序列的灰色预测建模方法[M].重庆大学出版社,2011.
[279]谢乃明,刘思峰.近似非齐次指数序列的离散灰色模型特性研究[J].系统工程与电子技术,2008,30(5)：863-867.
[280]刘思峰,党耀国,方志耕,谢乃明.灰色系统理论及其应用(第五版)[M].北京：科学出版社,2010.
[281]Hyndman R. J., Koehler A. B. Another look at measures of forecast accuracy [J]. International Journal of Forecasting,2006,22(4):679-688.
[282]Armstrong J. S., Collopy F. Error measures for generalizing about forecasting methods:Empirical comparisons [J]. International Journal of Forecasting,1992, 8(1):69-80.
[283]Grossman G. M., Krueger A. B. Economic Growth and the Environment [J]. The Quarterly Journal of Economics,1995,110(2):353-377.
[284]Achelis S. Technical Analysis from A to Z,2nd Edition [M]. The McGraw-Hill Companies,2000.
[285]Zhao S., Wei G. W. Jump process for the trend estimation of time series [J]. Computational Statistics & Data Analysis,2003,42(1-2):219-241.
[286]McCulloch W. S., Pitts W. A logical calculus of the ideas immanent in nervous activity [J]. Bulletin of Mathematical Biophysics,1943,5:115-133.
[287]Stavrou E. T., Charalambous C., Spiliotis S. Human resource management and performance:A neural network analysis [J]. European Journal of Operational Research,2007,181(1):453-467.
[288]Lee J., Sanmugarasa K., Blumenstein M., Loo Y-C. Improving the reliability of a Bridge Management System (BMS) using an ANN-based Backward Prediction Model (BPM) [J]. Automation in Construction,2008,17(6):758-772.
[289]Padhi S. S., Aggarwal V. Competitive revenue management for fixing quota and price of hotel commodities under uncertainty [J]. International Journal of Hospitality Management,2011,30(3):725-734.
[290]Zhang H., Song J., Su C., He M. Human attitudes in environmental management:Fuzzy Cognitive Maps and policy option simulations analysis for a coal-mine ecosystem in China [J]. Journal of Environmental Management,2013, 115(30):227-234.
[291]Ming A., Furukawa S., Teshima T., Shimojo M., Kajitani M. A new golf swing robot to simulate human skill-Learning control based on direct dynamics model using recurrent ANN [J]. Mechatronics,2006,16(7):443-449.
[292]Moon J. W. Performance of ANN-based predictive and adaptive thermal-control methods for disturbances in and around residential buildings [J]. Building and Environment,2012,48:15-26.
[293]Abdel-Khalik A., Elserougi A., Massoud A., Ahmed S. A power control strategy for flywheel doubly-fed induction machine storage system using artificial neural network [J]. Electric Power Systems Research,2013,96:267-276.
[294]Abbass H. A. An evolutionary artificial neural networks approach for breast cancer diagnosis [J]. Artificial Intelligence in Medicine,2002,25(3):265-281.
[295]Dietzel M., Baltzer P. A. T., Dietzel A., Zoubi R., Groschel T. et al. Artificial Neural Networks for differential diagnosis of breast lesions in MR-Mammography: A systematic approach addressing the influence of network architecture on diagnostic performance using a large clinical database [J]. European Journal of Radiology,2012,81(7):1508-1513.
[296]Atkov O. Y., Gorokhova S. G., Sboev A. G, Generozov E. V., Muraseyeva E. V., et al. Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters [J]. Journal of Cardiology,2012, 59(2):190-194.
[297]韩力群.人工神经网络理论、设计及应用[M].北京：化学工业出版社,2002.
[298]张青贵.人工神经网络导论[M].北京：中国水利水电出版社,2004.
[299]高隽.人工神经原理及仿真实例[M].北京：机械工业出版杜,2005.
[300]胡昌华,李国华,刘涛,周志杰.基于MATLAB 6.X的系统分析与设计-小波分析(第二版)[M].西安：西安电子科技大学出版社,2004.
[301]Boggess A., Narcowich F. J.小波与傅里叶分析基础(第2版)[M].芮国胜,康健.北京：电子工业出版社,2010.