低碳视角下的中国能源效率研究
|
摘要
面对能源约束趋紧、环境污染严重、生态系统退化的严峻形势,以不断消耗能源和排放二氧化碳为代价的传统发展模式已经难以持续。从全球范围看,节能、循环、低碳正成为新的发展方式,“绿色工业革命”已经悄然兴起。国际能源署IEA(2010)指出改善能源效率对碳减排的潜力要超过电力部门的脱碳潜力,成为减排的第一大来源。作为快速发展中的中国,推动能源效率水平提高,加强节能降耗,支持节能低碳产业发展,形成节约能源和保护环境的空间格局,是从源头上扭转生态环境恶化趋势和实现中国绿色经济可持续发展的重要且有效途径。
本文通过将能源效率的各种理论和方法进行整合,将二氧化碳排放纳入能源效率的评价体系中,以经济增长理论、生产理论、能源经济学、环境经济学相关理论为基础,分别从宏观层面和中观层面考察了碳排放约束下中国能源效率问题,从而整合了能源投入、经济产出与环境污染三部分。全文以研究、解决现实问题为出发点,在实际可得数据的基础上建立相应经济模型,尝试综合运用计量经济分析、数理经济分析和实证检验等分析方法,系统、全面地对低碳视角下的中国能源效率进行科学地评价。本文的主要内容和结论如下：
(1)基于空间动态面板数据,建立了中国能源效率影响因素及二氧化碳排放的EKC扩展曲线的空问计量模型。实证结果可知：我国各邻近省域能源效率之间存在着显著的空间相关特征,有着显著的集聚效应和相似性；相对于经济距离,地理距离对地区经济活动的空间相关性影响更大；经济增长、技术进步与对外开放度对能源强度的回归系数为负,而能源价格、产业结构与能源强度具有显著的正相关性；中国人均二氧化碳排放与经济增长之间基本满足EKC假定的倒U型曲线关系；我国人均二氧化碳排放的环境库兹涅茨曲线受相邻地域与地域间的经济发展的双重影响；人均二氧化碳排放的经济拐点为101276元,结合中国经济实际发展水平,中国目前正处于二氧化碳排放的库兹涅茨曲线的左端。
(2)运用非径向、非角度的SBM模型测度了碳排放约束下中国省域全要素能源效率及节能减排潜力。与考虑碳排放测算的全要素能源效率相比,不考虑碳排放约束的各省份全要素能源效率被高估。省际全要素能源效率的评价表明,在考察期内上海、广东一直处于最优生产前沿上；从区域全要素能源效率的差异分析来看,中国全要素能源效率的区域格局按照由东向西递减,并呈现收敛趋势。由聚类结果可知,处于高效区的省份全部为东部沿海省份,中效区的省份大多是中部省市及东北老工业基地,而西部区域的各省份多数处于低效区；通过节能减排评价模型的测算分析可知,按照区域进行比较,西部区域的节能减排潜力最高,其次为中部和东北部,东部的节能减排潜力最低。
(3)基于序列DEA的方向性距离函数、环境规制强度指数、Malmqulist-Luenberger指数,测度了环境规制下我国省域全要素能源效率与生产率的动态变化、分解变量及环境规制成本。主要结论如下：绿色生产率大于传统生产率,绿色生产率呈现W型波动趋势,主要的转折点出现在2005年与2009年；从区域差异来看,绿色生产率东部区域最高,其次为东北部、中部,西部最低,各区域的全要素生产率存在趋同的趋势。“创新者”地区集中在北京、上海、广东、海南这4个省市；考虑碳排放约束后我国的产业结构得到了优化调整,呈现出规模效率的提升；从经济增长的分解效应来看,全国的经济增长驱动力量主要来自投入要素的增长效应,全要素生产率的平均贡献比例仅为5%。其中东部区域正处在由“外延型”向“内涵型”的绿色经济增长模式过渡时期,而东北、中、西部区域经济增长模式仍以“粗放型”为主；环境规制强度与全要素生产率有显著的正相关性,这支持了波特假说的存在。
(4)基于四阶段DEA和Bootstrapped DEA方法,在控制了外生环境变量和随机冲击的影响下,对中国省域规模以上工业企业的全要素能源效率进行了实证分析。主要结论如下：初始DEA模型、四阶段DEA模型以及Bootstrapped DEA模型计算得到的效率得分存在显著差异；tobit回归模型显示：各地区工业企业的技术研发投入是全要素能源率提高的有利因素,且对能源和碳排放减少的贡献比例最大,环境保护支出对全要素能源效率影响微弱,国有化程度的下降是全要素能源效率提高的有利因素；剔除环境变量影响后,全国工业企业的平均纯技术效率得到了改善,而平均综合技术效率、规模效率均出现下降。规模报酬递减省份均调整为规模报酬递增状态；运用Bootstrapped DEA对四阶段DEA得到的效率得分进行偏误修正后,所有地区工业企业的全要素能源效率得分均有所下降。
(5)基于SBM方向距离函数和Luenberger生产率指标测度了碳排放约束下中国工业36个行业的全要素能源效率及其分解变量。主要结论为：碳排放约束下的工业行业能源技术效率高于传统能源技术效率。考虑碳排放约束后,制造业的能源技术效率最高,其次为电力、煤气及水生产供应业,采掘业最低；工业行业的最优生产前沿面发生了不断的外移,绿色技术边界越来越偏离规模报酬不变技术；核密度分析可知：累积全要素能源效率分布的波峰逐渐向右偏移且高度明显下降,这说明在考察期内,多数工业行业由于生产率的提高使得全要素能源效率得到了不同程度的改善；影响因素分析结果显示：工业行业在一定程度上存在能源使用的规模经济；能源结构对全行业的能源效率及生产率的具有显著的抑制作用；资本深化对工业行业能源效率的影响为正,而对绿色生产率具有显著的负面影响；马歇尔外部性与工业行业的能源效率及生产率存在U型关系；本研究框架里不支持污染天堂假说。
(6)将DEA中基于能源投入的Shephard距离函数引入到LMDI分解模型中,建立了六大产业能源消费的碳排放七因素分解模型(即LMDI-PDA分解模型),并从抑制我国碳排放增长的关键因素出发,考察了潜在能源强度,能源绩效以及能源技术进步对我国碳排放下降的作用大小。研究结果显示：产业结构效应、经济产出效应、人口规模效应、能源绩效效应对碳排放的增加具有一定的拉动作用,其中经济产出效应的累积贡献率最大为135%,产业结构效应、人口规模效应、能源绩效效应对碳排放累积贡献率分别为10.74%、9.39%、0.65%；潜在能源强度效应对碳排放下降的累积贡献率最大为54.6%,说明产业能源强度的调整空间较大,且抑制效应逐年增强；能源结构效应、能源技术进步效应对我国碳减排的累积贡献率分别为0.2%和1%,贡献微弱,亟待提高；从产业层面研究发现,农林牧渔业、建筑业、批发零售和住宿餐饮业和其他行业的低碳发展较好,工业、交通运输仓储和邮政业低碳发展不佳,工业始终是我国碳排放的主要来源。
目前,中国正处于经济由“外延型”向“内涵型”经济增长方式的转变时期。在经济持续增长的同时伴随着能源消耗、碳排放总量的增加是无法避免的。基于本文的定量分析结果,提出如下政策建议：(1)提高全要素生产率对经济增长的贡献率,改变以能源和资本要素投入为支撑的经济增长方式,向主要依靠技术进步、低碳经济带动的发展方式转变。(2)发展现代产业结构体系,加快能源消费结构的调整。重视高能耗产业的内部结构升级,提高战略性新兴产业、低碳产业比重和国际竞争实力。(3)推动能源利用的技术创新。促进各区域的纯技术效率的提高,推进生产前沿面的不断外移。(4)推进中、西部区域的“追赶效应”,逐步缩小全要素能源效率的区域差异。(5)因地制宜的制定碳排放规制政策或激励手段,加强碳排放市场的监管,扩大碳排放交易的试点城市。
Facing the severe situation of tight energy constraints, serious environmental pollution and ecosystem degradation, the traditional development mode to constantly consume energy and produce carbon dioxide has been difficult to sustain. From a global perspective, energy conservation, recycling, low-carbon has become a new development way,"green Industrial Revolution" has already begun. IEA (2010) points out that the potential for carbon emissions reduction by improving energy efficiency exceeds the electricity sector, becoming the largest source of emission reduction. As the rapid development of China, promoting the level of energy efficiency, strengthening energy conservation, supporting the development of low-carbon industry, making the formation of spatial pattern of saving energy and protecting environment are an important and effective way from the source to reverse the trend of ecological environment deterioration and to achieve the sustainable development of China's green economy.
In this paper, we integrate the various theories and method of energy efficiency, carbon emissions are taken into energy efficiency evaluation system, and based on economic growth theory, production theory, energy economics, environmental economics theory, we study the energy efficiency of carbon emission constraints in China from macro and meso level, thereby integrate of the three parts of the energy inputs, economic outputs and environmental pollution. According to the research, solve practical problems for the starting point, establish the appropriate economic model based on the actual available data, try to make comprehensive use of quantitative analysis method, the mathematical economic analysis and empirical analysis, systematically, comprehensively and scientifically evaluate the energy efficiency of China under low-carbon economy. The main contents and conclusions of this paper are as follows:
1. Based on spatial dynamic panel data, we establish the spatial econometric model of energy efficiency of China and EKC expansion curve of CO2. The empirical results show that:there are significant spatial correlation characteristics between the neighboring provincial energy efficiency in China, there is a significant agglomeration effect and similarity. Relative to the geographical distance, the influence of economic distance to spatial correlation on regional economic activity is greater. Economic growth, technological progress and the opening degree to the outside world are negative regression coefficient of the energy intensity, and energy prices, the industrial structure and energy intensity has a significant positive correlation. China's per capita carbon dioxide emissions and economic growth basically meet inverted U curve relationship of EKC assumption. Environmental Kuznets curve of China's per capita carbon dioxide have dual influence between adjacent geographical and regional economic development. The economic turning point of per capita carbon dioxide is101276yuan. Combined with the actual level of development of China's economy, China is currently on the left of the Kuznets curve of carbon dioxide.
2. Using the non-radial, non-point of the SBM model, we measure total-factor energy relative efficiency and energy-saving emission potential reduction under the constraint of provinces carbon emissions. Compared with total factor energy efficiency without consideration of carbon emissions, total factor energy efficiency with consideration of the provinces of carbon emissions constraints is underestimated. The evaluations of provincial total factor energy efficiency show that Shanghai and Guangdong have been at the optimal production frontier in the period investigated. From the analysis of variance about regional total factor energy efficiency, the regional pattern of total factor energy efficiency in accordance with decreasing from east to west, and presents the trend of convergence. By the clustering analysis results, the provinces of the high efficiency area are the eastern coastal provinces. The provinces of the Medium efficiency are mostly in central regions and the northeast old industrial base, while most of provinces in western are low efficiency. Calculating and analyzing by the energy saving and emission reduction evaluation model, accordance with the regional comparison, the potential of energy-saving and emission-abating in western is the highest, followed by central and northeastern, eastern is minimum.
3. Based on the sequence of DEA, directional distance function, environmental regulation intensity index, Malmqulist Luenberger index, we measure the dynamic changes of provincial total factor energy productivity, the decomposition variant and the cost of environmental regulation. The main conclusions are as follows:the green productivity is higher than traditional, green productivity shows W-type fluctuation trend, major turning point came in2005and2009. From the regional differences, the green highest productivity is in the eastern area, followed by the northeast, central, western lowest. The regional total factor energy efficiency exists the trend of convergence. The "innovator areas" mainly concentrate in the four provinces:Beijing, Shanghai, Guangdong and Hainan. After considering carbon emissions, China's industrial structure has been optimized and adjusted, showing the promotion of the scale efficiency. Analyzing from the decomposition of the effect of economic growth, the driving force of national economic growth is mainly from the growth effects of the input factors, ratio of the average contribution of total factor productivity is only5%. The eastern region is in the green economy growth mode transition period from "extensive" to "intensive", and the northeast, central and western regions, economic growth mode is still "extensive"; intensity of environmental regulation has a significant positive correlation with the total factor energy productivity, which supports the hypothesis of the existence of Potter.
4. Based on four-stage DEA and Bootstrapped DEA method, in the control of the effects of exogenous environmental variables and random shocks, we analyze the total factor energy efficiency and its decomposition variables of industrial enterprises above provincial domain scale. The main conclusions are as follows:the initial DEA model, four-stage DEA model and Bootstrapped DEA model to calculate the efficiency score have significant difference. Tobit regression model show that R&D investment is favorable factors to improve the total factor energy efficiency, and contribution proportion is the largest about reduction of CO2emission; environmental protection expenditures on the effect about the total factor energy efficiency are weak. The improvement in the level of nationalization is the adverse factors about the improvement of the total factor energy efficiency. Excluding the impact of environmental variable, the average pure technological efficiency of industrial enterprises has been improved, while the average technical efficiency, scale efficiency declined. Decreasing returns to scale provinces are adjusted for increasing returns to scale. After the bias correction by Bootstrapped DEA method in four-stage DEA efficiency scores, the total factor energy efficiency of all regions has declined.
5. Based on the SBM directional distance function and Luenberger productivity index, we measure total factor energy efficiency and productivity and decomposition of variables under the constraint of36industries. The results show that:The green industries energy efficiency is higher than the traditional energy efficiency; in the measure of green energy efficiency, energy efficiency of manufacturing industry are the highest, The energy technology efficiency of power, gas and water production and supply industry is higher than the extractive industries; The optimal production frontier has been constantly shifting, green technology boundary is further away from the constant returns to scale technology; The analysis of the kernel density shows that:the cumulative distribution of total factor energy efficiency peaks gradually shifted to the right and the height significantly decreased, indicating that total factor energy efficiency of the overall industry has been improved. Influencing factors analysis show:the industry exist the economic scale of energy use; energy structure has significant inhibition impact on energy efficiency and productivity of the industry; capital deepening impact on the green energy efficiency in industry positively, while has a significant negative impact on the green productivity; between green energy efficiency, productivity and Marshall externalities show U-shaped relationship. This research framework does not support the pollution haven hypothesis.
6. Shephard distance function based on the energy input in DEA introduced into LMDI decomposition model, we build six major industries of the Chinese energy consumption carbon emissions seven factor decomposition model. The results show that: industrial structure, economic output, population size, energy performance of the increase in carbon emissions has a certain stimulus, and the cumulative effect of economic output was the maximum contribution135%, Industrial structure, population size, energy efficiency of cumulative effect of contribution rate to carbon emissions were10.74%,9.39%,0.65%. Potential energy intensity of cumulative effects on the decreasing of carbon emissions had the maximum of54.6%contribution, the adjustment of the industrial energy intensity is larger, and the inhibitory effect of increased year by year; The effect of energy structure, energy technology progress cumulative contributions to China's carbon emission reduction rates were0.2%and1.04%, contribution is weak and needs to be improved; finding from the study of industry level, the development of low-carbon is better about agriculture forestry animal husbandry and fishery, construction, wholesale and retail and catering industry, industrial, transportation, storage and postal industry is poor about low-carbon development, the industry has always been the main source of China's carbon emissions.
At present, China is in the transformation of economic growth mode by "extensive" to "intensive". While sustained economic growth, energy consumption, carbon emissions increasing are inevitable.In this paper, based on the quantitative analysis result, we propose the following policy recommendations.(1) To improve the TFP contribution to economic growth, changing the economic growth mode of energy and capital investment to mainly rely on technological progress, low carbon economy development mode.(2) To develop the modern industrial system, accelerating the adjustment of energy consumption structure. Pay attention to upgrade high energy consumption industry structure and enhance strategic emerging industries, the proportion of low carbon industry and the international competition strength.(3) To promote energy utilization of technology innovation, pushing forward the improvement of the pure technical efficiency and the continuous relocation of production frontier.(4) To promote the "catch-up effect" of the central and western regions, gradually narrowing the regional differences of the total factor energy efficiency.(5) To formulate carbon emissions regulation policy or incentive means according to local conditions, enchancing the supervision of the carbon market and expanding the pilot city of carbon emission trading.
本文通过将能源效率的各种理论和方法进行整合,将二氧化碳排放纳入能源效率的评价体系中,以经济增长理论、生产理论、能源经济学、环境经济学相关理论为基础,分别从宏观层面和中观层面考察了碳排放约束下中国能源效率问题,从而整合了能源投入、经济产出与环境污染三部分。全文以研究、解决现实问题为出发点,在实际可得数据的基础上建立相应经济模型,尝试综合运用计量经济分析、数理经济分析和实证检验等分析方法,系统、全面地对低碳视角下的中国能源效率进行科学地评价。本文的主要内容和结论如下：
(1)基于空间动态面板数据,建立了中国能源效率影响因素及二氧化碳排放的EKC扩展曲线的空问计量模型。实证结果可知：我国各邻近省域能源效率之间存在着显著的空间相关特征,有着显著的集聚效应和相似性；相对于经济距离,地理距离对地区经济活动的空间相关性影响更大；经济增长、技术进步与对外开放度对能源强度的回归系数为负,而能源价格、产业结构与能源强度具有显著的正相关性；中国人均二氧化碳排放与经济增长之间基本满足EKC假定的倒U型曲线关系；我国人均二氧化碳排放的环境库兹涅茨曲线受相邻地域与地域间的经济发展的双重影响；人均二氧化碳排放的经济拐点为101276元,结合中国经济实际发展水平,中国目前正处于二氧化碳排放的库兹涅茨曲线的左端。
(2)运用非径向、非角度的SBM模型测度了碳排放约束下中国省域全要素能源效率及节能减排潜力。与考虑碳排放测算的全要素能源效率相比,不考虑碳排放约束的各省份全要素能源效率被高估。省际全要素能源效率的评价表明,在考察期内上海、广东一直处于最优生产前沿上；从区域全要素能源效率的差异分析来看,中国全要素能源效率的区域格局按照由东向西递减,并呈现收敛趋势。由聚类结果可知,处于高效区的省份全部为东部沿海省份,中效区的省份大多是中部省市及东北老工业基地,而西部区域的各省份多数处于低效区；通过节能减排评价模型的测算分析可知,按照区域进行比较,西部区域的节能减排潜力最高,其次为中部和东北部,东部的节能减排潜力最低。
(3)基于序列DEA的方向性距离函数、环境规制强度指数、Malmqulist-Luenberger指数,测度了环境规制下我国省域全要素能源效率与生产率的动态变化、分解变量及环境规制成本。主要结论如下：绿色生产率大于传统生产率,绿色生产率呈现W型波动趋势,主要的转折点出现在2005年与2009年；从区域差异来看,绿色生产率东部区域最高,其次为东北部、中部,西部最低,各区域的全要素生产率存在趋同的趋势。“创新者”地区集中在北京、上海、广东、海南这4个省市；考虑碳排放约束后我国的产业结构得到了优化调整,呈现出规模效率的提升；从经济增长的分解效应来看,全国的经济增长驱动力量主要来自投入要素的增长效应,全要素生产率的平均贡献比例仅为5%。其中东部区域正处在由“外延型”向“内涵型”的绿色经济增长模式过渡时期,而东北、中、西部区域经济增长模式仍以“粗放型”为主；环境规制强度与全要素生产率有显著的正相关性,这支持了波特假说的存在。
(4)基于四阶段DEA和Bootstrapped DEA方法,在控制了外生环境变量和随机冲击的影响下,对中国省域规模以上工业企业的全要素能源效率进行了实证分析。主要结论如下：初始DEA模型、四阶段DEA模型以及Bootstrapped DEA模型计算得到的效率得分存在显著差异；tobit回归模型显示：各地区工业企业的技术研发投入是全要素能源率提高的有利因素,且对能源和碳排放减少的贡献比例最大,环境保护支出对全要素能源效率影响微弱,国有化程度的下降是全要素能源效率提高的有利因素；剔除环境变量影响后,全国工业企业的平均纯技术效率得到了改善,而平均综合技术效率、规模效率均出现下降。规模报酬递减省份均调整为规模报酬递增状态；运用Bootstrapped DEA对四阶段DEA得到的效率得分进行偏误修正后,所有地区工业企业的全要素能源效率得分均有所下降。
(5)基于SBM方向距离函数和Luenberger生产率指标测度了碳排放约束下中国工业36个行业的全要素能源效率及其分解变量。主要结论为：碳排放约束下的工业行业能源技术效率高于传统能源技术效率。考虑碳排放约束后,制造业的能源技术效率最高,其次为电力、煤气及水生产供应业,采掘业最低；工业行业的最优生产前沿面发生了不断的外移,绿色技术边界越来越偏离规模报酬不变技术；核密度分析可知：累积全要素能源效率分布的波峰逐渐向右偏移且高度明显下降,这说明在考察期内,多数工业行业由于生产率的提高使得全要素能源效率得到了不同程度的改善；影响因素分析结果显示：工业行业在一定程度上存在能源使用的规模经济；能源结构对全行业的能源效率及生产率的具有显著的抑制作用；资本深化对工业行业能源效率的影响为正,而对绿色生产率具有显著的负面影响；马歇尔外部性与工业行业的能源效率及生产率存在U型关系；本研究框架里不支持污染天堂假说。
(6)将DEA中基于能源投入的Shephard距离函数引入到LMDI分解模型中,建立了六大产业能源消费的碳排放七因素分解模型(即LMDI-PDA分解模型),并从抑制我国碳排放增长的关键因素出发,考察了潜在能源强度,能源绩效以及能源技术进步对我国碳排放下降的作用大小。研究结果显示：产业结构效应、经济产出效应、人口规模效应、能源绩效效应对碳排放的增加具有一定的拉动作用,其中经济产出效应的累积贡献率最大为135%,产业结构效应、人口规模效应、能源绩效效应对碳排放累积贡献率分别为10.74%、9.39%、0.65%；潜在能源强度效应对碳排放下降的累积贡献率最大为54.6%,说明产业能源强度的调整空间较大,且抑制效应逐年增强；能源结构效应、能源技术进步效应对我国碳减排的累积贡献率分别为0.2%和1%,贡献微弱,亟待提高；从产业层面研究发现,农林牧渔业、建筑业、批发零售和住宿餐饮业和其他行业的低碳发展较好,工业、交通运输仓储和邮政业低碳发展不佳,工业始终是我国碳排放的主要来源。
目前,中国正处于经济由“外延型”向“内涵型”经济增长方式的转变时期。在经济持续增长的同时伴随着能源消耗、碳排放总量的增加是无法避免的。基于本文的定量分析结果,提出如下政策建议：(1)提高全要素生产率对经济增长的贡献率,改变以能源和资本要素投入为支撑的经济增长方式,向主要依靠技术进步、低碳经济带动的发展方式转变。(2)发展现代产业结构体系,加快能源消费结构的调整。重视高能耗产业的内部结构升级,提高战略性新兴产业、低碳产业比重和国际竞争实力。(3)推动能源利用的技术创新。促进各区域的纯技术效率的提高,推进生产前沿面的不断外移。(4)推进中、西部区域的“追赶效应”,逐步缩小全要素能源效率的区域差异。(5)因地制宜的制定碳排放规制政策或激励手段,加强碳排放市场的监管,扩大碳排放交易的试点城市。
Facing the severe situation of tight energy constraints, serious environmental pollution and ecosystem degradation, the traditional development mode to constantly consume energy and produce carbon dioxide has been difficult to sustain. From a global perspective, energy conservation, recycling, low-carbon has become a new development way,"green Industrial Revolution" has already begun. IEA (2010) points out that the potential for carbon emissions reduction by improving energy efficiency exceeds the electricity sector, becoming the largest source of emission reduction. As the rapid development of China, promoting the level of energy efficiency, strengthening energy conservation, supporting the development of low-carbon industry, making the formation of spatial pattern of saving energy and protecting environment are an important and effective way from the source to reverse the trend of ecological environment deterioration and to achieve the sustainable development of China's green economy.
In this paper, we integrate the various theories and method of energy efficiency, carbon emissions are taken into energy efficiency evaluation system, and based on economic growth theory, production theory, energy economics, environmental economics theory, we study the energy efficiency of carbon emission constraints in China from macro and meso level, thereby integrate of the three parts of the energy inputs, economic outputs and environmental pollution. According to the research, solve practical problems for the starting point, establish the appropriate economic model based on the actual available data, try to make comprehensive use of quantitative analysis method, the mathematical economic analysis and empirical analysis, systematically, comprehensively and scientifically evaluate the energy efficiency of China under low-carbon economy. The main contents and conclusions of this paper are as follows:
1. Based on spatial dynamic panel data, we establish the spatial econometric model of energy efficiency of China and EKC expansion curve of CO2. The empirical results show that:there are significant spatial correlation characteristics between the neighboring provincial energy efficiency in China, there is a significant agglomeration effect and similarity. Relative to the geographical distance, the influence of economic distance to spatial correlation on regional economic activity is greater. Economic growth, technological progress and the opening degree to the outside world are negative regression coefficient of the energy intensity, and energy prices, the industrial structure and energy intensity has a significant positive correlation. China's per capita carbon dioxide emissions and economic growth basically meet inverted U curve relationship of EKC assumption. Environmental Kuznets curve of China's per capita carbon dioxide have dual influence between adjacent geographical and regional economic development. The economic turning point of per capita carbon dioxide is101276yuan. Combined with the actual level of development of China's economy, China is currently on the left of the Kuznets curve of carbon dioxide.
2. Using the non-radial, non-point of the SBM model, we measure total-factor energy relative efficiency and energy-saving emission potential reduction under the constraint of provinces carbon emissions. Compared with total factor energy efficiency without consideration of carbon emissions, total factor energy efficiency with consideration of the provinces of carbon emissions constraints is underestimated. The evaluations of provincial total factor energy efficiency show that Shanghai and Guangdong have been at the optimal production frontier in the period investigated. From the analysis of variance about regional total factor energy efficiency, the regional pattern of total factor energy efficiency in accordance with decreasing from east to west, and presents the trend of convergence. By the clustering analysis results, the provinces of the high efficiency area are the eastern coastal provinces. The provinces of the Medium efficiency are mostly in central regions and the northeast old industrial base, while most of provinces in western are low efficiency. Calculating and analyzing by the energy saving and emission reduction evaluation model, accordance with the regional comparison, the potential of energy-saving and emission-abating in western is the highest, followed by central and northeastern, eastern is minimum.
3. Based on the sequence of DEA, directional distance function, environmental regulation intensity index, Malmqulist Luenberger index, we measure the dynamic changes of provincial total factor energy productivity, the decomposition variant and the cost of environmental regulation. The main conclusions are as follows:the green productivity is higher than traditional, green productivity shows W-type fluctuation trend, major turning point came in2005and2009. From the regional differences, the green highest productivity is in the eastern area, followed by the northeast, central, western lowest. The regional total factor energy efficiency exists the trend of convergence. The "innovator areas" mainly concentrate in the four provinces:Beijing, Shanghai, Guangdong and Hainan. After considering carbon emissions, China's industrial structure has been optimized and adjusted, showing the promotion of the scale efficiency. Analyzing from the decomposition of the effect of economic growth, the driving force of national economic growth is mainly from the growth effects of the input factors, ratio of the average contribution of total factor productivity is only5%. The eastern region is in the green economy growth mode transition period from "extensive" to "intensive", and the northeast, central and western regions, economic growth mode is still "extensive"; intensity of environmental regulation has a significant positive correlation with the total factor energy productivity, which supports the hypothesis of the existence of Potter.
4. Based on four-stage DEA and Bootstrapped DEA method, in the control of the effects of exogenous environmental variables and random shocks, we analyze the total factor energy efficiency and its decomposition variables of industrial enterprises above provincial domain scale. The main conclusions are as follows:the initial DEA model, four-stage DEA model and Bootstrapped DEA model to calculate the efficiency score have significant difference. Tobit regression model show that R&D investment is favorable factors to improve the total factor energy efficiency, and contribution proportion is the largest about reduction of CO2emission; environmental protection expenditures on the effect about the total factor energy efficiency are weak. The improvement in the level of nationalization is the adverse factors about the improvement of the total factor energy efficiency. Excluding the impact of environmental variable, the average pure technological efficiency of industrial enterprises has been improved, while the average technical efficiency, scale efficiency declined. Decreasing returns to scale provinces are adjusted for increasing returns to scale. After the bias correction by Bootstrapped DEA method in four-stage DEA efficiency scores, the total factor energy efficiency of all regions has declined.
5. Based on the SBM directional distance function and Luenberger productivity index, we measure total factor energy efficiency and productivity and decomposition of variables under the constraint of36industries. The results show that:The green industries energy efficiency is higher than the traditional energy efficiency; in the measure of green energy efficiency, energy efficiency of manufacturing industry are the highest, The energy technology efficiency of power, gas and water production and supply industry is higher than the extractive industries; The optimal production frontier has been constantly shifting, green technology boundary is further away from the constant returns to scale technology; The analysis of the kernel density shows that:the cumulative distribution of total factor energy efficiency peaks gradually shifted to the right and the height significantly decreased, indicating that total factor energy efficiency of the overall industry has been improved. Influencing factors analysis show:the industry exist the economic scale of energy use; energy structure has significant inhibition impact on energy efficiency and productivity of the industry; capital deepening impact on the green energy efficiency in industry positively, while has a significant negative impact on the green productivity; between green energy efficiency, productivity and Marshall externalities show U-shaped relationship. This research framework does not support the pollution haven hypothesis.
6. Shephard distance function based on the energy input in DEA introduced into LMDI decomposition model, we build six major industries of the Chinese energy consumption carbon emissions seven factor decomposition model. The results show that: industrial structure, economic output, population size, energy performance of the increase in carbon emissions has a certain stimulus, and the cumulative effect of economic output was the maximum contribution135%, Industrial structure, population size, energy efficiency of cumulative effect of contribution rate to carbon emissions were10.74%,9.39%,0.65%. Potential energy intensity of cumulative effects on the decreasing of carbon emissions had the maximum of54.6%contribution, the adjustment of the industrial energy intensity is larger, and the inhibitory effect of increased year by year; The effect of energy structure, energy technology progress cumulative contributions to China's carbon emission reduction rates were0.2%and1.04%, contribution is weak and needs to be improved; finding from the study of industry level, the development of low-carbon is better about agriculture forestry animal husbandry and fishery, construction, wholesale and retail and catering industry, industrial, transportation, storage and postal industry is poor about low-carbon development, the industry has always been the main source of China's carbon emissions.
At present, China is in the transformation of economic growth mode by "extensive" to "intensive". While sustained economic growth, energy consumption, carbon emissions increasing are inevitable.In this paper, based on the quantitative analysis result, we propose the following policy recommendations.(1) To improve the TFP contribution to economic growth, changing the economic growth mode of energy and capital investment to mainly rely on technological progress, low carbon economy development mode.(2) To develop the modern industrial system, accelerating the adjustment of energy consumption structure. Pay attention to upgrade high energy consumption industry structure and enhance strategic emerging industries, the proportion of low carbon industry and the international competition strength.(3) To promote energy utilization of technology innovation, pushing forward the improvement of the pure technical efficiency and the continuous relocation of production frontier.(4) To promote the "catch-up effect" of the central and western regions, gradually narrowing the regional differences of the total factor energy efficiency.(5) To formulate carbon emissions regulation policy or incentive means according to local conditions, enchancing the supervision of the carbon market and expanding the pilot city of carbon emission trading.
引文
[1]白仲林、尹长斌.中国省际全要素生产率动态行为的经验研究[J].西北师大学报(社会科学版),2008,(4)：124-129.
[2]曹建华.能源经济与环境政策的理论基础研究[M].上海财经大学出版社,2012,5.
[3]陈德敏、张瑞、谭志雄.全要素能源效率与中国经济增长收敛性——基于动态面板数据的实证检验[J].中国人口·资源与环境,2012,22(1)：130-137.
[4]成金华、吴巧生.中国工业化进程中的环境问题与“环境成本内化”发展模式[J].管理世界,2007,(1)：147-148.
[5]陈青青、龙志和.中国区域C02排放收敛的空间计量分析[J].资源与产业,2011,13(6)：128-134.
[6]陈诗一.中国的绿色工业革命：基于环境全要素生产率视角的解释(1980-2008)[J].经济研究,2010,(11)：21-58.
[7]陈诗一.能源消耗、二氧化碳排放与中国工业的可持续发展[J].经济研究,2009,(4)：41-55.
[8]陈诗一.节能减排与中国工业的双赢发展：2009-2049[J].经济研究,2012,(3)：129-143.
[9]崔文田、高宇、张博Malmquist指数与Malmquist-Luenberger指数的比较研究[J].西安工程科技学院学报,2005,19(2)：152-156.
[10]笪凤媛、吴军.环境约束下中国地区工业增长效率研究[J].河北科技大学学报,2011,11(2)：1-8.
[11]蒂莫西·J·科埃利.效率与生产率分析引论[M].中国人民大学出版社,2008,8.
[12]段文斌、余泳泽.全要素生产率增长有利于提升我国能源效率吗?——基于35个工业行业面板数据的实证研究[J].产业经济研究,2011,(4)：78-88.
[13]冯蕾.2005-2007年我国省际能源效率研究[J].统计研究,2009,26(11)：31-35.
[14]范丹、王维国.中国产业能源消费碳排放变化的因素分解——基于广义GFI的指数分解[J].系统工程,2012,(11)：48-54.
[15]高铁梅.计量经济分析方法与建模Eviews应用及实例[M].清华大学出版社,2006,1.
[16]国涓、王玲、孙平.中国区域能源消费强度的影响因素分析[J].资源科学,2009,31(2)：205-213.
[17]国涓、项吉宁、郭崇慧.空间影响与环境库兹涅茨曲线：基于空间经济计量方法的实证分析[J].数理统计与管理,2009,28(4)：678-684.
[18]龚锋.地方公共安全服务供给效率评估——基于四阶段DEA和Bootstrapped DEA的实证研究[J].管理世界,2008,(4)：80-90.
[19]郭军华、倪明、李帮义.基于三阶段DEA模型的农业生产效率研究[J].数量经济技术经济研究,2010,(12)：27-38.
[20]韩颖、马萍、刘璐.一种能源消耗强度影响因素分解的新方法[J].数量经济技术经济研究,2010,(4)：137-146.
[21]胡鞍钢、郑京海、高宇宁.考虑环境因素的省级技术效率排名(1999-2005)[J].经济学,2008,7(3)：933-960.
[22]胡玉敏、杜纲.中国各省区能源消耗强度趋同的空间计量研究[J].经济纵横,2009,(11)：95-96.
[23]蒋金荷.提高能源效率与经济结构调整的策略分析[J].数量经济技术经济研究,2004,(10)：16-23.
[24]李国璋、江金荣、周彩云.全要素能源效率与环境污染关系研究[J].中国人口·资源与环境,2010,20(4)：50-56.
[25]李国璋、王双.区域能源强度变动：基于GFI的因素分解分析[J].中国人口·资源与环境,2008,18(4)：62-66.
[26]李建中、武铁梅、谢威.我国能源效率与经济增长关系分析[J].生产力研究,2010,(9)：1-3.
[27]李静.中国区域环境效率的差异与影响因素研究[J].南方经济,2009,(12)：24-35.
[28]李静、沈伟.环境规制对中国工业绿色生产率的影响——基于波特假说的再检验[J].山西财经大学学报,2012,34(2)：56-65.
[29]刘畅、孔宪丽、高铁梅.中国工业行业能源消耗强度变动及影响因素的实证分析[J].资源科学,2008,30(9)：1290-1299.
[30]林伯强、蒋竺均.中国二氧化碳的环境库兹涅茨曲线预测及影响因素分析[J].管理世界,2009,(4)：28-36.
[31]林伯强.能源经济学理论与政策实践[M].中国财政经济出版社,2008,7.
[32]李双杰、范超.随机前沿分析与数据包络分析方法的评析与比较[J].统计与决策,2009,(7)：25-28.
[33]李涛、傅强.中国省际碳排放效率研究[J].统计研究,2011,28(7)：62-71.
[34]李伟娜、金晓雨.中国制造业的环境技术效率研究[J].中国科技论坛,2011,(2)：33-38.
[35]李艳梅、张雷、程晓凌.中国碳排放变化的因素分解与减排途径分析[J].资源科学,2010,32(2)：218-222.
[36]吕健.中国经济增长与环境污染关系的空间计量分析[J].财贸研究,2011,(4)：1-7.
[37]美丽中国：五年撬动8万亿绿色投资[DB/OL].http://www.21 cbh.com/HTML/2012-11-13/1MNjYxXzU2MTE1Mg.html,2011-11-13.
[38]马树才,李国柱.中国经济增长与环境污染关系的Kuzenes曲线[J].统计研究2006,(8)：37-40.
[39]潘祺志.我国工业能耗强度变动与节能路径选择[D].东北财经大学,2010.
[40]庞瑞芝、李鹏、路永刚.转型期间我国新型工业化增长绩效及其影响因素研究[J].中国工业经济,2011,(4)：64-73.
[41]庞瑞芝、李鹏.中国工业增长模式转型绩效研究——基于1998-2009年省际工业企业数据的实证考察[J].数量经济技术经济研究,2011,(9)：36-46.
[42]屈小娥.中国省际全要素能源效率变动分解[J].数量经济技术经济研究,2009,(8)：29-43.
[43]谌伟、诸大建.中国二氧化碳排放效率低么?——基于福利视角的国际比较[J].经济与管理研究,2011,(1)：56-63.
[44]沈满洪、吴文博、魏楚.近二十年低碳经济研究进展及未来趋势[J].浙江大学学报(人文社会科学版),2011,(3)：1-12.
[45]史丹、吴利学、傅晓霞.中国能源效率地区差异及其成因研究——基于随机前沿生产函数的方差分解[J].管理世界,2008,(2)：35-43.
[46]宋德勇、卢忠宝.中国碳排放影响因素分解及其周期性波动研究[J].中国人口·资源与环境,2009,19(3)：18-24.
[47]宋杰鲲.基于LMDI的山东省能源消费碳排放因素分解[J].资源科学,2012,34(1)：35-41.
[48]孙辉、支大林.对中国各省资本存量的估计及典型性事实：1978-2008[J].广东金融学院学报,2010,25(3)：103-116.
[49]孙巍.效率与生产率的非参数分析——方法、软件与应用[M].社会科学文献出版社,2012,5.
[50]孙作人、周德群、周鹏.工业碳排放驱动因素研究：一种生产分解分析新方法[J].数量经济技术经济研究,2012,(5)：63-74.
[51]唐玲、杨正林.能源效率与工业经济转型——基于中国1998-2007年行业数据的实证分析[J].数量经济技术经济研究,2009,(10)：34-48.
[52]谭忠富、张金良.中国能源效率与其影响因素的动态关系研究[J].中国人口·资源与环境,2010,20(4)：43-49.
[53]田立新、张蓓蓓.中国碳排放变动的因素分解分析[J].中国人口·资源与环境,2011,21(11)：1-7.
[54]涂正革.全要素生产率与区域经济增长的动力[J].南开经济研究,2007,(4)：14-36.
[55]涂正革.环境、资源与工业增长的协调性[J].经济研究,2008,(2)：93-105.
[56]涂正革、肖耿.环境约束下的中国工业增长模式研究[J].世界经济,2009,(11)：41-54.
[57]涂正革、刘磊珂.考虑能源、环境因素的中国工业效率评价[J].经济评论,2011,(2)：55-65.
[58]涂正革、肖耿.非参数成本前沿模型与中国工业增长模式研究[J].经济学,2007,7(1)：185-210.
[59]涂正革、肖耿.中国工业增长模式的转变——大中型企业劳动生产率的非参数生产前沿动态分析[J].管理世界,2006,(10)：57-67.
[60]涂正革.区域经济和谐发展的全要素生产率研究——基于对1995-2004年28个省市大中型工业企业的非参数生产前沿分析[J].经济评论,2008,(1)：29-35.
[61]王兵、吴延瑞、颜鹏飞.中国区域环境效率与环境全要素生产率增长[J].经济研究,2010,(5)：95-109.
[62]王兵、王丽.环境约束下中国区域工业技术效率与生产率及其影响因素实证研究[J].南方经济,2010,(11)：3-19.
[63]王兵、梁淑珍.节能减排约束下中国制造业行业的效率和生产率的研究[J].工业技术经济,2012,(3)：124-133.
[64]王国印、王动.波特假说、环境规制与企业技术创新——对中东部地区的比较分析[J].中国软科学,2011,(1)：100-112.
[65]王海宁、陈媛媛.产业集聚效应与工业能源效率研究[J].财经研究,2010,36(9)：69-79.
[66]汪克亮、杨宝臣、杨力.考虑环境效应的中国省际全要素能源效率研究[J].管理科学,2010,23(6)：100-111.
[67]王立平、管杰、张纪东.中国环境污染与经济增长：基于空间动态面板数据模型的实证分析[J].地理科学,2010,30(6)：818-825.
[68]王秋彬.工业行业能源效率与工业结构优化升级[J].数量经济技术经济研究,2010,(10)：49-63.
[69]王群伟、周德群.中国全要素能源效率变动的实证研究[J].系统工程,2008,26(7)：74-80.
[70]王群伟、周鹏、周德群.我国二氧化碳排放绩效的动态变化、区域差异及影响因素[J].中国工业经济,2010,(1)：45-54.
[71]王群伟、周德群、周鹏.中国全要素二氧化碳排放绩效的区域差异——考虑非期望产出共同前沿函数的研究[J].财贸经济,2010,(9)：112-117.
[72]王群伟、周德群、葛世龙.环境规制下的投入产出效率及规制成本研究[J].管理科学,2009,22(6)：111-119.
[73]王少平、杨继生.中国工业能源调整的长期战略与短期措施[J].中国社会科学,2006,(4)：88-96.
[74]王维国、范丹.中国区域全要素能源效率收敛性及影响因素分析——基于Malmquist-luenberger指数方法[J].资源科学,2012,(10)：1816-1824.
[75]王维国、马越越.中国区域物流产业效率——基于三阶段DEA模型的Malmquist-luenberger指数方法[J].系统工程,2012,30(3)：66-75.
[76]王维国、潘祺志.辽宁省工业行业技术及配置无效率的测度[J].中国人口·资源与环境,2009,19(4)：145-150.
[77]王喜平、姜晔.碳排放约束下我国工业行业全要素能源效率及其影响因素研究[J].软科学,2012,26(2)：73-78.
[78]王亚华、吴凡、王争.交通行业生产率变动的Boot st rap-Malmquist指数分析(1980--2005)[J].经济学,2008,7(3)：891-912.
[79]魏楚.中国能源效率问题研究[D].浙江大学,2009.
[80]魏楚、杜立民、沈满洪.中国能否实现节能减排目标：基于DEA方法的评价与模拟[J].世界经济,2010,(3)：141-160.
[81]魏楚、沈满洪.能源效率及其影响因素：基于DEA的实证分析[J].管理世界,2007,(8)：66-76.
[82]魏楚、沈满洪.能源效率研究发展及趋势：一个综述[J].浙江大学学报,2009,39(3)：56-63.
[83]魏楚、沈满洪.规模效率与配置效率：一个对中国能源低效的解释[J].世界经济,2009,(4)：84-96.
[84]魏楚、沈满洪.结构调整能否改善能源效率：基于中国省级数据的研究[J].世界经济,2008,(11)：77-85.
[85]魏东、岳杰.低碳经济模式下的碳排放权效率探析[J].山东社会科学,2010,(8)：90-92.
[86]魏一鸣、廖华.能源效率的七类测度指标及其测度方法[J].中国软科学,2010,(1)：128-137.
[87]魏一鸣、廖华等.中国能源报告(2010)：能源效率研究[M].科学出版社,2010,8.
[88]吴军、笪凤媛、张建华.环境管制与中国区域生产率增长[J].统计研究,2010,27(1)：83-89.
[89]吴军.环境约束下中国地区工业全要素生产率增长及收敛分析[J].数量经济技术经济研究,2009,(11)：17-27.
[90]吴琦、武春友.基于DEA的能源效率评价模型研究[J].管理科学,2009,22(1)：103-112.
[91]吴琦、武春友.我国能源效率关键影响因素的实证研究[J].科研管理,2010,31(5)：164-171.
[92]吴琦.我国省域能源效率评价研究[D].2010.
[93]吴巧生,成金华.能源约束与中国工业化发展研究[M].科学出版社,2009,4.
[94]吴巧生、成金华.中国工业化中的能源消耗强度变动及因素分析——基于分解模型的实证分析[J].财经研究,2006,32(6)：75-85.
[95]吴玉鸣、李建霞.中国区域工业全要素生产率的空间计量经济分析[J].地理科学,2006,26,(4)：386-391.
[96]吴玉鸣、李建霞.中国省域能源消费的空间计量经济分析[J].中国人口·资源与环境,2008,18(3)：93-98.
[97]吴玉鸣、田斌.省域环境库兹涅茨曲线的扩展及其决定因素[J].地理研究,2012,31(4)：627-640.
[98]徐国泉、刘则渊、姜照华.中国碳排放的因素分解模型及实证分析：1995-2004[J].中国人口·资源与环境,2006,16(6)：158-161.
[99]许士春、习蓉、何正霞.中国能源消耗碳排放的影响因素分析及政策启示[J].资源科学,2012,34(1)：2-12.
[100]许广月.中国能源消费、碳排放与经济增长关系的研究[D].2010.
[101]颜云云、佘元冠.能源效率、二氧化碳排放效率与经济增长的关系分析[J].中国管理信息化,2011,14(9)：83-88.
[102]姚先国.能源效率与能源安全——基于浙江省的分析[J].浙江社会科学,2008,(4)：33-39.
[103]姚奕、倪勤.中国地区碳强度与FDI的空间计量分析[J].经济地理,2011,31(9)：1432-1438.
[104]叶祥松、彭良燕.我国环境规制下的规制效率与全要素生产率研究：1999-2008[J].财贸经济,2011,(2)：102-110.
[105]袁晓玲、张宝山、杨万平.基于环境污染的中国全要素能源效率研究[J].中国工业经济,2009,(2)：76-86.
[106]余晓泓、张超.中国工业部门的能源效率与减排潜力分析[J].产经评论,2012,(2)：5-15.
[107]余泳泽.我国节能减排潜力、治理效率与实施路径研究[J].中国工业经济,2011,(5)：58-68.
[108]曾贤刚.我国能源效率、CO2减排潜力及影响因素分析[J].中国环境科学,2010,30(10)：1432-1440.
[109]张江雪、朱磊.基于绿色的我国各地区工业企业技术创新效率研究[J].数量经济技术经济研究,2012,(2)：113-125.
[110]张纪录.区域碳排放因素分解及最优低碳发展情景分析——以中部地区为例[J].经济学问题,2012,(7)：126-129.
[111]张瑞、丁日佳.我国能源效率与能源消费结构的协整分析[J].煤炭经济研究,2006,(12)：8-10.
[112]赵奥、武春友.中国CO2排放量变化的影响因素分解研究[J].软科学,2010,24(12)：55-59.
[113]赵湘莲、李岩岩、陆敏.我国能源消费与经济增长的空间计量分析[J].软科学,2012,26(3)：33-38.
[114]赵欣、龙如银.考虑全要素生产率的中国碳排放影响因素分析[J].资源科学,2010,32(10)：1863-1870.
[115]周建.我国区域经济增长与能源利用效率改进的动态演化机制研究[J].数量经济技术经济研究,2008,(9)：3-16.
[116]朱勤、彭希哲、陆志明.中国能源消费碳排放变化的因素分解及实证分析[J].资源科学,2009,31(12)：2072-2079.
[117]Andres J. Picazo-Tadeo,2005,"Directional Distance Functions and Environmental Regulation",Resource and Energy Economics,Vol.27,PP131-142.
[118]B.W. Ang,2004,"Decomposition Analysis for Policymaking in Energy:Which Is the Preferredmethod?",Energy Policy,Vol.32,PP1131-1139.
[119]B.W. Ang, A.R. Mu, P. Zhou,2010,"Accounting Frameworks for Tracking Energy Efficiency Trends",Energy Economics,Vol.32,PP1209-1219.
[120]B.W. Ang, F.L. Liu, Hyun-Sik Chung,2004,"A Generalized Fisher Index Approach to Energy Decomposition Analysis",Energy Economics,Vol.26,PP757-763.
[121]B.W. Ang, Na Liu,2007,"Energy Decomposition Analysis:IEA Model Versus other Methods",Energy Policy,Vol.35,PP 1426-1432.
[122]Bernhard Mahlberg, Mikulas Luptacik, Biresh K. Sahoo,2011,"Examining the Drivers of Total Factor Productivity Change with an Illustrative Example of 14 EU Countries",Ecological Economics,Vol.72,PP60-69.
[123]Bin Su, B.W. Ang,2012,"Structural Decomposition Analysis Applied to Energy and Emissions:some Methodological Developments",Energy Economics,Vol.34, PP 177-188.
[124]Bin Su, B.W. Ang,2011,"Multi-Region Input-Output Analysis of CO2 Emissions Embodied in Trade:the Feedback Effects",Ecological Economics,Vol.71,PP42-53.
[125]Bin Su, H.C. Huang, B.W. Ang, P. Zhou,2010,"Input-Output Analysis of CO2 Emissions Embodied in Trade:the Effects of Sector Aggregation",Energy Economics,Vol.32,PP 166-175.
[126]Bo Hsiao, Ching-Chin Chern, Yung-Ho Chiu, Ching-Ren Chiu,2011,"Using Fuzzy Super-Efficiency Slack-Based Measure Data Envelopment Analysis to Evaluate Taiwan's Commercial Bank Efficiency",Expert Systems with Applications,Vol. 38,PP9147-9156.
[127]Cheng-Zhong Chen, Zhen-Shan Lin,2008,"Multiple Timescale Analysis and Factor Analysis of Energy Ecological Footprint Growth in China 1953-2006",Energy Policy,Vol.36,PP1666-1678.
[128]Chu Wei, Jinlan Ni, Manhong Sheng,2011,"China's Energy Inefficiency:a Cross-Country Comparison",The Social Science Journal,Vol.48,PP478-488.
[129]Chunhong Zhang, Haiying Liu, Hans Th.A. Bressers, Karen S. Buchanan,2011, "Productivity Growth and Environmental Regulations-Accounting for Undesirable Outputs:Analysis of China's Thirty Provincial Regions Using the Malmquist-Luenberger Index",Energy Economics,Vol.70,PP2369-2379.
[130]DONG Feng, LI Xiao-hui LONG Ru-yin,2011,"Laspeyres Decomposition of Energy Intensity Including Household-Energy Factors",Energy Procedia,Vol.5, PP 1482-1487.
[131]F. Wua, L.W.Fan, P.Zhou, D.Q.Zhou,2012,"Industrial Energy Efficiency with CO2 Emissions in China:a Nonparametric Analysis",Energy Policy,Vol.11,PP164-172.
[132]Fuh-Hwa Franklin Liu, Peng-hsiang Wang,2008,"DEA Malmquist Productivity Measure:Taiwanese Semiconductor Companies",Int. J. Production Economics,Vol. 112,PP367-379.
[133]Guang-Ming Shi, JunBi, Jin-NanWang,2010,"Chinese Regional Industrial Energy Efficiency Evaluation Based on a DEA Model of Fixing Non-Energy Inputs",Energy Policy,Vol.38,PP6172-6179.
[134]Hu JL, Wang SC,2006, "Total-Factor Energy Efficiency of Regions in China", Energy Policy,Vol.17, PP 3206-3217.
[135]H. O. Fried,2002,"Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis",Journal of Productivity Analysis,Vol.17,PP157-174.
[136]Hirofumi Fukuyama, William L. Weber,2009,"A Directional Slacks-Based Measure of Technical Inefficiency",Socio-Economic Planning Sciences,Vol.43,PP274-287.
[137]Huayi Yu,2012,"The Influential Factors of China's Regional Energy Intensity and its Spatial Linkages:1988-2007",Energy Policy,Vol.45,PP583-593.
[138]Isabela Butnar, Maria Llop,2011,"Structural Decomposition Analysis and Input-Output Subsystems:Changes in CO2 Emissions of Spanish Service Sectors (2000-2005)",Ecological Economics,Vol.70,PP2012-2019.
[139]Jan Ondrich, John Ruggiero,2001,"E(?) Ciency Measurement in the Stochastic Frontier Model",European Journal of Operational Research,Vol.129,PP434-442.
[140]Jin-Li Hu, Shih-Chuan Wang,2006,"Total-Factor Energy Efficiency of Regions in China",Energy Policy,Vol.34,PP3206-3217.
[141]Jin-Li Hua, Chih-Hung Kao,2007,"Efficient Energy-Saving Targets for APEC Economies",Energy Policy,Vol.35,PP373-382.
[142]John Ruggiero,1998, "Non-Discretionary Inputs in Data Envelopment Analysis ",European Journal of Operational Research,Vol.111,PP461-469.
[143]John Ruggiero,1999,"Nonparametric Analysis of Educational Costs",European Journal of Operational Research,Vol.119,PP605-612.
[144]John Ruggiero,1996,"On the Measurement of Technical Efficiency in the Public Sector",European Journal of Operational Research,Vol.90,PP553-565.
[145]John Ruggiero,2004,"Performance Evaluation When Non-Discretionary Factors Correlate with Technical Efficiency",European Journal of Operational Research,Vol. 159,PP250-257.
[146]John Ruggiero, Jerry Miner, Lloyd Blanchard,2002,"Measuring Equity of Educational Outcomes in the Presence of Inefficiency",European Journal of Operational Research,Vol.142,PP642-652.
[147]Juan Antonio Duro, Vicent Alcantara, Emilio Padilla,2010,"International Inequality in Energy Intensity Levels and the Role of Production Composition and Energy Efficiency:an Analysis of OECD Countries",Ecological Economics,Vol.69, PP2468-2474.
[148]Juan Du, Liang Liang, Joe Zhu,2010,"A Slacks-Based Measure of Super-Efficiency in Data Envelopment Analysis:a Comment",European Journal of Operational Research,Vol.204,PP694-697.
[149]Jun Bi, RongrongZhang, HaikunWang, MiaomiaoLiu, YiWu,2011,"The Bench Marks of Carbon Emissions and Policy Implications for China's Cities:Case of Nanjing",Energy Policy,Vol.39,PP4785-4794.
[150]Jun Li, Michel Colombier,2009,"Managing Carbon Emissions in China Through Building Energy Efficiency",Journal of Environmental Management,Vol.90, PP2436-2447.
[151]Kaoru Tone,2001,"A Slacks-Based Measure of Efficiency in Data Envelopment Analysis",European Journal of Operational Research, Vol.130,PP498-509.
[152]Kaoru Tone,2002,"A Slacks-Based Measure of Super-Efficiency in Data Envelopment Analysis",European Journal of Operational Research,Vol.143, PP 32-41.
[153]Kaoru Tone,2010, "Variations on the Theme of Slacks-Based Measure of Efficiency in DEA",European Journal of Operational Research,Vol.200,PP901-907.
[154]Kaoru Tone,2003,"Dealing with Undesirable Outputs in DEA:a Slacks-Based Measure (SBM) Approach",National Graduate Institute for Policy Studies,I-2003-0005.
[155]Karen Turner, Max Munday, Peter McGregor, Kim Swales,2012,"How Responsible Is a Region for Its Carbon Emissions? an Empirical General Equilibrium Analysis",Ecological Economics,Vol.76,PP70-78.
[156]Ke Wang, Shiwei Yu, Wei Zhang,2011,"China's Regional Energy and Environmental Efficiency:a DEA Window Analysis Based Dynamic Evaluation",Mathematical and Computer Modelling,Vol.,PPdoi:10.1016/j.mcm.2011.11.067.
[157]Ke Wang, Yi-MingWei, XianZhang,2012,"A Comparative Analysis of China's Regional Energy and Emission Performance:Which Is the Better Way to Deal with Undesirable Outputs?",Energy Policy,Vol.46,PP574-584.
[158]Ki-Hong Choi, B.W. Ang,2012,"Attribution of Changes in Divisia Real Energy Intensity Index——an Extension to Index Decomposition Analysis",Energy Economics,Vol.34,PP 171-176.
[159]Kyunam Kim, Yeonbae Kim,2012,"International Comparison of Industrial CO2 Emission Trends and the Energy Efficiency Paradox Utilizing Production-Based Decomposition",Energy Economics,Vol.34,PP 1724-1741.
[160]L'eopold Simar,2000,"Statistical Inference in Nonparametric Frontier Models:the State of the Art",Journal of Productivity Analysis, Vol.13,PP49-78.
[161]Lan-Bing Li, Jin-LiHu,2012,"Ecological Total-Factor Energy Efficiency of Regions in China",Energy Policy,Vol.46,PP216-224.
[162]Le'opold Simar j Paul W. Wilson,1998,"Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models",Management Science.Vol. 44,PP506-1.
[163]Luca Chiari,Antonio Zecca,2011,"Constraints of Fossil Fuels Depletion on Global Warming Projections",Energy Policy,Vol.39,PP5026-5034.
[164]Luciano Charlita de Freitas, Shinji Kaneko,2011,"Decomposing the Decoupling of CO2 Emissions and Economic Growth in Brazil",Ecological Economics,Vol. 70,PP1459-1469.
[165]Manuel Muniz, Joseph Paradi, John Ruggiero, et al,2006,"Evaluating Alternative DEA Models Used to Control for Non-Discretionary Inputs",Computers & Operations Research,Vol.33,PP1173-1183.
[166]Mian Yang, Fu-XiaYang, Xing-Peng Chen,2011,"Effects of Substituting Energy with Capital on China'saggregated Energy and Environmental Efficiency",Energy Policy,Vol.39,PP6065-6072.
[167]Mika Kortelainen,2008,"Dynamic Environmental Performance Analysis:a Malmquist Index Approach",Ecological Economics,Vol.64,PP701-715.
[168]Mikko J. Syrj€anen,2004,"Non-Discretionary and Discretionary Factors and Scale in Data Envelopment Analysis",European Journal of Operational Research,Vol. 158,PP20-33.
[169]Murray G Patterson,1996,"What Is Energy Efficiency?",Energy Policy.,Vol. 24,PP377-390.
[170]O. Zaim and F. Taskin,2000,"Environmental Efficiency in Carbon Dioxide Emissions in the OECD:A Non-Parametric Approach",Journal of Environmental Management, Vol.58,PP95-107.
[171]P. Zhou, B.W. Ang, H. Wang,2012,"Energy and CO2 Emission Performance in Electricity Generation:a Non-Radial Directional Distance Function Approach", European Journal of Operational Research, Vol.221,PP625-635.
[172]P. Zhou, B.W. Ang, K.L. Poh,2008,"Measuring Environmental Performance Under Different Environmental DEA Technologies",Energy Economics,Vol.30,PP1-14.
[173]P. Zhou, B.W. Ang,2008,"Linear Programming Models for Measuring Economy Wide Energy Efficiency Performance",Energy Policy.,Vol.36,PP2911-2916.
[174]P.Zhou, B.W. Ang,2008,"Decomposition of Aggregate CO2 Emissions:a Production-Theoretical Approach",Energy Economics,Vol.30,PP 1054-1067.
[175]P. Zhou, B.W. Ang, J.Y. Han,2010,"Total Factor Carbon Emission Performance:a Malmquist Index Analysis",Energy Economics,Vol.32,PP194-201.
[176]P. Zhou, B.W. Ang, K.L. Poh,2006,"Slacks-Based Efficiency Measures for Modeling Environmental Performance",Ecological Economics,Vol.60,PP111-118.
[177]Philip Andrews-Speed,2009,"China's Ongoing Energy Efficiency Drive:Origins, Progress and Prospects",Energy Policy,Vol.37,PP 1331-1344.
[178]R. Alton Gilbert and Paul W. Wilson,1998,"Effects of Deregulation on the Productivity of Korean Banks",Journal of Economics and Business,Vol.50, PP133-155.
[179]Ramakrishnan Ramanathan,2006,"A Multi-Factor Efficiency Perspective to the Relationships Among World GDP, Energy Consumption and Carbon Dioxide Emissions",Technological Forecasting & Social Change,Vol.73,PP483-494.
[180]Rolf Fare, Shawna Grosskopf,2010,"Directional Distance Functions and Slacks-Based Measures of Efficiency",European Journal of Operational Research,Vol. 200,PP320-322.
[181]Run Wang, WenjuanLiu, LishanXiao, JianLiu, WilliamKao,2011,"Path Towards achieving of china's 2020 carbon emissionreduction Target—a Discussion of Low-Carbon Energy Policies at Province Level",Energy Policy,Vol.39, PP 2740-2747.
[182]S. Monni, S.Syri,2011,"Weekly Greenhouse Gas Emissions of Municipalities: Methods and Comparisons",Energy Policy,Vol.39,PP4755-4765.
[183]Simar, L. and Wilson, R. P. W.,1998, "SensitivityAnalysis of Efficiency Scores: How to Bootstrap in Nonparametri Frontier Models", Management Science, Vol.44, PP 49-61.
[184]S.S. Wang, D.Q.Zhou, P.Zhou, Q.W.Wang,2011,"CO2 Emissions, Energy Consumption and Economic Growth in China:a Panel Data Analysis",Energy Policy,Vol.39,PP4870-4875.
[185]Sabuj KumarMandal,2010,"Do Undesirable Output and Environmental Regulation Matter in Energy Efficiency Analysis?Evidence From Indian Cement Industry ",Energy Policy.,Vol.38,PP6076-6083.
[186]SarahM.Estelle, AndyL.Johnson, JohnRuggiero,2010,"Three-Stage DEA Models for Incorporating Exogenous Inputs",Computers &OperationsResearch,Vol.37, PP 1087-1090.
[187]SatoshiHonma, Jin-LiHu,2009,"Total-Factor Energy Productivity Growth of Regions in Japan",Energy Policy.,Vol.37,PP3941-3950.
[188]Scheel H.,2001,"Undesirable Outputs in Eciency Valuations ",European Journal of Operational Research,Vol.132,PP400-410.
[189]Sebastian Lozano, Ester Gutierrez,2008,"Non-Parametric Frontier Approach to Modelling the Relationships Among Population, GDP, Energy Consumption and CO2 Emissions",Ecological Economics,Vol.66,PP687-699.
[190]Shinichiro Okushima, Makoto Tamura,2007,"Multiple Calibration Decomposition Analysis:Energy Use and Carbon Dioxide Emissions in the Japanese Economy, 1970-1995",Energy Policy,Vol.35,PP5156-5170.
[191]Sonia Ben Kheder and Natalia Zugravu,2008,"The Pollution Haven Hypothesis:a Geographic Economy Model in a Comparative Study", Working Paper.
[192]Stijn Reinhard, C.A. Knox Lovell, Geert J. Thijssen,2000,"Environmental Effciency with Multiple Environmentally Detrimental Variables; Estimated with SFA and DEA",European Journal of Operational Research,Vol.121,PP287-303.
[193]Taichen Chien, Jin-Li Hu,2007,"Renewable Energy and Macroeconomic Efficiency of OECD and Non-OECD Economies",Energy Policy,Vol.35,PP3606-3615.
[194]Trevor Collier, Andrew L. Johnson, John Ruggiero,2011,"Technical Efficiency Estimation with Multiple Inputs and Multiple Outputs Using Regression Analysis",European Journal of Operational Research,Vol.208,PP 153-160.
[195]Tsai-lien Yeh, Tser-yiethChen, Pei-yingLai,2010,"A Comparative Study of Energy Utilization Efficiency Between Taiwan and China",Energy Policy.,Vol.38, PP 2386-2394.
[196]V. Oikonomou, F.Becchis, L.Steg, D.Russolillo,2009,"Energy Saving and Energy Efficiency Concepts for Policy Making",Energy Policy.,Vol.37,PP4787-4796.
[197]Valeria Costantini, Salvatore Monni,2008,"Environment, Human Development and Economic Growth",Ecological Economics,Vol.64,PP867-880.
[198]Valeria Costantini,Chiara Martini,2009,"The Causality Between Energy Consumption and Economic Growth:a Multi-Sectoral Analysis Using Non-Stationary Cointegrated Panel Data",Energy Economics,Vol.32,PP591-603.
[199]Wang Hongwu, He Xiaoli, Ma Junhai,2011,"The Analysis of the Energy Efficiency and Its Influence Factors in Tianjin",Energy Procedia,Vol.5,PP1671-1675.
[200]Wolfgang Keller, Arik Levinson,2002,"Pollution Abatement Costs and Foreign Direct Investment Inflows to U.S. States",The Review of Economics and Statistics,Vol.84,PP691-703.
[201]Xia Yan-Qing,2011, "The impact of technology and structure change on GHG emissions in China:an analysis based on LMDI model", International Journal of Global Warming, Vol.3, No.3, PP307-327.
[202]Xia Yanqing,2012,"An empirical research on the interactions of China's energy consumption, pollution emissions and economic growth", International Journal of Global Energy Issues, Vol.35, No.5, PP411-425.
[203]Xia Yan-Qing,2012,"An input-output analysis of regional CO2 emissions from the service sector:an application to Liaoning Province of China, International Journal of Global Warming", Vol.4, No.2, PP187-214.
[204]X.H. Xia, G.T.Huang, G.Q.Chen, BoZhang, Z.M.Chen, Q.Yang,2011,"Energy Security,Efficiency and Carbon Emission of Chinese Industry",Energy Policy,Vol. 39,PP3520-3528.
[205]Xi Rao, Jun Wu, Zongyi Zhang, Bin Liu,2012,"Energy Efficiency and Energy Saving Potential in China:an Analysis Based on Slacks-Based Measure Model",Computers & Industrial Engineering,Vol.63,PP578-584.
[206]Xiao-Dan Guo, LeiZhu, YingFan, Bai-ChenXie,2011,"Evaluation of Potential Reductions in Carbon Emissions in Chinese Provinces Based on Environmental DEA",Energy Policy,Vol.39,PP2352-2360.
[207]Xiaojin Li, Linyu Xu,2012,"Simple Analysis of Energy Utilization and Sustainable Development in China",Procedia Environmental Sciences,Vol.13,PP873-878.
[208]XiaoLi Han,1997,"Impacts of Growth and Structural Change on CO2 Emissions of Developing Countries",World Development,Vol.25,PP39-5407.
[209]Xin Zhou, Hidefumi Imura,2011,"How Does Consumer Behavior Influence Regional Ecological Footprints? an Empirical Analysis for Chinese Regions Based on the Multi-Region Input-Output Model",Ecological Economics,Vol.71, PP 171-179.
[210]Xing-Ping Zhang, Xiao-Mei Cheng,2009,"Energy Consumption, Carbon Emissions. and Economic Growth in China",Ecological Economics,Vol.68,PP2706-2712.
[211]Xing-Ping Zhang, Xiao-Mei Cheng,Jia-HaiYuan,Xiao-JunGao,2011,"Total-Factor Energy Efficiency in Developing Countries",Energy Policy.,Vol.39,PP644-650.
[212]Yao-Chun Lee, Jin-Li Hu, Chih-Hung Kao,2011,"Efficient Saving Targets of Electricity and Energy for Regions in China",Electrical Power and Energy Systems,Vol.33,PP1211-1219.
[213]Ying Fan, Lan-Cui Liu, Gang Wu, Hsien-Tang Tsai, Yi-Ming Wei,2007,"Changes in Carbon Intensity in China:Empirical Findings From 1980-2003",Ecological Economics,Vol.62,PP683-691.
[214]Yiwen Bian, FengYang,2010,"Resource and Environment Efficiency Analysis of Provinces in China:A DEA Approach Based on Shannon'sen tropy",Energy Policy.,Vol.38,PP1909-1917.
[215]Yongping BAI, Jianping NIU, Yongpei HAO,2012,"Research of Regional Energy Efficiency Based on Undesirable Outputs and Its Influential Factors:a Case of Western China",Energy Procedia,Vol.16,PP802-809.
[216]Z.M. Chen, G.Q. Chen, B. Chen,2010,"Embodied Carbon Dioxide Emissions of the World Economy:a Systems Input-Output Simulation for 2004", Procedia Environm-ental Sciences,Vol.2,PP 1827-1840.
[2]曹建华.能源经济与环境政策的理论基础研究[M].上海财经大学出版社,2012,5.
[3]陈德敏、张瑞、谭志雄.全要素能源效率与中国经济增长收敛性——基于动态面板数据的实证检验[J].中国人口·资源与环境,2012,22(1)：130-137.
[4]成金华、吴巧生.中国工业化进程中的环境问题与“环境成本内化”发展模式[J].管理世界,2007,(1)：147-148.
[5]陈青青、龙志和.中国区域C02排放收敛的空间计量分析[J].资源与产业,2011,13(6)：128-134.
[6]陈诗一.中国的绿色工业革命：基于环境全要素生产率视角的解释(1980-2008)[J].经济研究,2010,(11)：21-58.
[7]陈诗一.能源消耗、二氧化碳排放与中国工业的可持续发展[J].经济研究,2009,(4)：41-55.
[8]陈诗一.节能减排与中国工业的双赢发展：2009-2049[J].经济研究,2012,(3)：129-143.
[9]崔文田、高宇、张博Malmquist指数与Malmquist-Luenberger指数的比较研究[J].西安工程科技学院学报,2005,19(2)：152-156.
[10]笪凤媛、吴军.环境约束下中国地区工业增长效率研究[J].河北科技大学学报,2011,11(2)：1-8.
[11]蒂莫西·J·科埃利.效率与生产率分析引论[M].中国人民大学出版社,2008,8.
[12]段文斌、余泳泽.全要素生产率增长有利于提升我国能源效率吗?——基于35个工业行业面板数据的实证研究[J].产业经济研究,2011,(4)：78-88.
[13]冯蕾.2005-2007年我国省际能源效率研究[J].统计研究,2009,26(11)：31-35.
[14]范丹、王维国.中国产业能源消费碳排放变化的因素分解——基于广义GFI的指数分解[J].系统工程,2012,(11)：48-54.
[15]高铁梅.计量经济分析方法与建模Eviews应用及实例[M].清华大学出版社,2006,1.
[16]国涓、王玲、孙平.中国区域能源消费强度的影响因素分析[J].资源科学,2009,31(2)：205-213.
[17]国涓、项吉宁、郭崇慧.空间影响与环境库兹涅茨曲线：基于空间经济计量方法的实证分析[J].数理统计与管理,2009,28(4)：678-684.
[18]龚锋.地方公共安全服务供给效率评估——基于四阶段DEA和Bootstrapped DEA的实证研究[J].管理世界,2008,(4)：80-90.
[19]郭军华、倪明、李帮义.基于三阶段DEA模型的农业生产效率研究[J].数量经济技术经济研究,2010,(12)：27-38.
[20]韩颖、马萍、刘璐.一种能源消耗强度影响因素分解的新方法[J].数量经济技术经济研究,2010,(4)：137-146.
[21]胡鞍钢、郑京海、高宇宁.考虑环境因素的省级技术效率排名(1999-2005)[J].经济学,2008,7(3)：933-960.
[22]胡玉敏、杜纲.中国各省区能源消耗强度趋同的空间计量研究[J].经济纵横,2009,(11)：95-96.
[23]蒋金荷.提高能源效率与经济结构调整的策略分析[J].数量经济技术经济研究,2004,(10)：16-23.
[24]李国璋、江金荣、周彩云.全要素能源效率与环境污染关系研究[J].中国人口·资源与环境,2010,20(4)：50-56.
[25]李国璋、王双.区域能源强度变动：基于GFI的因素分解分析[J].中国人口·资源与环境,2008,18(4)：62-66.
[26]李建中、武铁梅、谢威.我国能源效率与经济增长关系分析[J].生产力研究,2010,(9)：1-3.
[27]李静.中国区域环境效率的差异与影响因素研究[J].南方经济,2009,(12)：24-35.
[28]李静、沈伟.环境规制对中国工业绿色生产率的影响——基于波特假说的再检验[J].山西财经大学学报,2012,34(2)：56-65.
[29]刘畅、孔宪丽、高铁梅.中国工业行业能源消耗强度变动及影响因素的实证分析[J].资源科学,2008,30(9)：1290-1299.
[30]林伯强、蒋竺均.中国二氧化碳的环境库兹涅茨曲线预测及影响因素分析[J].管理世界,2009,(4)：28-36.
[31]林伯强.能源经济学理论与政策实践[M].中国财政经济出版社,2008,7.
[32]李双杰、范超.随机前沿分析与数据包络分析方法的评析与比较[J].统计与决策,2009,(7)：25-28.
[33]李涛、傅强.中国省际碳排放效率研究[J].统计研究,2011,28(7)：62-71.
[34]李伟娜、金晓雨.中国制造业的环境技术效率研究[J].中国科技论坛,2011,(2)：33-38.
[35]李艳梅、张雷、程晓凌.中国碳排放变化的因素分解与减排途径分析[J].资源科学,2010,32(2)：218-222.
[36]吕健.中国经济增长与环境污染关系的空间计量分析[J].财贸研究,2011,(4)：1-7.
[37]美丽中国：五年撬动8万亿绿色投资[DB/OL].http://www.21 cbh.com/HTML/2012-11-13/1MNjYxXzU2MTE1Mg.html,2011-11-13.
[38]马树才,李国柱.中国经济增长与环境污染关系的Kuzenes曲线[J].统计研究2006,(8)：37-40.
[39]潘祺志.我国工业能耗强度变动与节能路径选择[D].东北财经大学,2010.
[40]庞瑞芝、李鹏、路永刚.转型期间我国新型工业化增长绩效及其影响因素研究[J].中国工业经济,2011,(4)：64-73.
[41]庞瑞芝、李鹏.中国工业增长模式转型绩效研究——基于1998-2009年省际工业企业数据的实证考察[J].数量经济技术经济研究,2011,(9)：36-46.
[42]屈小娥.中国省际全要素能源效率变动分解[J].数量经济技术经济研究,2009,(8)：29-43.
[43]谌伟、诸大建.中国二氧化碳排放效率低么?——基于福利视角的国际比较[J].经济与管理研究,2011,(1)：56-63.
[44]沈满洪、吴文博、魏楚.近二十年低碳经济研究进展及未来趋势[J].浙江大学学报(人文社会科学版),2011,(3)：1-12.
[45]史丹、吴利学、傅晓霞.中国能源效率地区差异及其成因研究——基于随机前沿生产函数的方差分解[J].管理世界,2008,(2)：35-43.
[46]宋德勇、卢忠宝.中国碳排放影响因素分解及其周期性波动研究[J].中国人口·资源与环境,2009,19(3)：18-24.
[47]宋杰鲲.基于LMDI的山东省能源消费碳排放因素分解[J].资源科学,2012,34(1)：35-41.
[48]孙辉、支大林.对中国各省资本存量的估计及典型性事实：1978-2008[J].广东金融学院学报,2010,25(3)：103-116.
[49]孙巍.效率与生产率的非参数分析——方法、软件与应用[M].社会科学文献出版社,2012,5.
[50]孙作人、周德群、周鹏.工业碳排放驱动因素研究：一种生产分解分析新方法[J].数量经济技术经济研究,2012,(5)：63-74.
[51]唐玲、杨正林.能源效率与工业经济转型——基于中国1998-2007年行业数据的实证分析[J].数量经济技术经济研究,2009,(10)：34-48.
[52]谭忠富、张金良.中国能源效率与其影响因素的动态关系研究[J].中国人口·资源与环境,2010,20(4)：43-49.
[53]田立新、张蓓蓓.中国碳排放变动的因素分解分析[J].中国人口·资源与环境,2011,21(11)：1-7.
[54]涂正革.全要素生产率与区域经济增长的动力[J].南开经济研究,2007,(4)：14-36.
[55]涂正革.环境、资源与工业增长的协调性[J].经济研究,2008,(2)：93-105.
[56]涂正革、肖耿.环境约束下的中国工业增长模式研究[J].世界经济,2009,(11)：41-54.
[57]涂正革、刘磊珂.考虑能源、环境因素的中国工业效率评价[J].经济评论,2011,(2)：55-65.
[58]涂正革、肖耿.非参数成本前沿模型与中国工业增长模式研究[J].经济学,2007,7(1)：185-210.
[59]涂正革、肖耿.中国工业增长模式的转变——大中型企业劳动生产率的非参数生产前沿动态分析[J].管理世界,2006,(10)：57-67.
[60]涂正革.区域经济和谐发展的全要素生产率研究——基于对1995-2004年28个省市大中型工业企业的非参数生产前沿分析[J].经济评论,2008,(1)：29-35.
[61]王兵、吴延瑞、颜鹏飞.中国区域环境效率与环境全要素生产率增长[J].经济研究,2010,(5)：95-109.
[62]王兵、王丽.环境约束下中国区域工业技术效率与生产率及其影响因素实证研究[J].南方经济,2010,(11)：3-19.
[63]王兵、梁淑珍.节能减排约束下中国制造业行业的效率和生产率的研究[J].工业技术经济,2012,(3)：124-133.
[64]王国印、王动.波特假说、环境规制与企业技术创新——对中东部地区的比较分析[J].中国软科学,2011,(1)：100-112.
[65]王海宁、陈媛媛.产业集聚效应与工业能源效率研究[J].财经研究,2010,36(9)：69-79.
[66]汪克亮、杨宝臣、杨力.考虑环境效应的中国省际全要素能源效率研究[J].管理科学,2010,23(6)：100-111.
[67]王立平、管杰、张纪东.中国环境污染与经济增长：基于空间动态面板数据模型的实证分析[J].地理科学,2010,30(6)：818-825.
[68]王秋彬.工业行业能源效率与工业结构优化升级[J].数量经济技术经济研究,2010,(10)：49-63.
[69]王群伟、周德群.中国全要素能源效率变动的实证研究[J].系统工程,2008,26(7)：74-80.
[70]王群伟、周鹏、周德群.我国二氧化碳排放绩效的动态变化、区域差异及影响因素[J].中国工业经济,2010,(1)：45-54.
[71]王群伟、周德群、周鹏.中国全要素二氧化碳排放绩效的区域差异——考虑非期望产出共同前沿函数的研究[J].财贸经济,2010,(9)：112-117.
[72]王群伟、周德群、葛世龙.环境规制下的投入产出效率及规制成本研究[J].管理科学,2009,22(6)：111-119.
[73]王少平、杨继生.中国工业能源调整的长期战略与短期措施[J].中国社会科学,2006,(4)：88-96.
[74]王维国、范丹.中国区域全要素能源效率收敛性及影响因素分析——基于Malmquist-luenberger指数方法[J].资源科学,2012,(10)：1816-1824.
[75]王维国、马越越.中国区域物流产业效率——基于三阶段DEA模型的Malmquist-luenberger指数方法[J].系统工程,2012,30(3)：66-75.
[76]王维国、潘祺志.辽宁省工业行业技术及配置无效率的测度[J].中国人口·资源与环境,2009,19(4)：145-150.
[77]王喜平、姜晔.碳排放约束下我国工业行业全要素能源效率及其影响因素研究[J].软科学,2012,26(2)：73-78.
[78]王亚华、吴凡、王争.交通行业生产率变动的Boot st rap-Malmquist指数分析(1980--2005)[J].经济学,2008,7(3)：891-912.
[79]魏楚.中国能源效率问题研究[D].浙江大学,2009.
[80]魏楚、杜立民、沈满洪.中国能否实现节能减排目标：基于DEA方法的评价与模拟[J].世界经济,2010,(3)：141-160.
[81]魏楚、沈满洪.能源效率及其影响因素：基于DEA的实证分析[J].管理世界,2007,(8)：66-76.
[82]魏楚、沈满洪.能源效率研究发展及趋势：一个综述[J].浙江大学学报,2009,39(3)：56-63.
[83]魏楚、沈满洪.规模效率与配置效率：一个对中国能源低效的解释[J].世界经济,2009,(4)：84-96.
[84]魏楚、沈满洪.结构调整能否改善能源效率：基于中国省级数据的研究[J].世界经济,2008,(11)：77-85.
[85]魏东、岳杰.低碳经济模式下的碳排放权效率探析[J].山东社会科学,2010,(8)：90-92.
[86]魏一鸣、廖华.能源效率的七类测度指标及其测度方法[J].中国软科学,2010,(1)：128-137.
[87]魏一鸣、廖华等.中国能源报告(2010)：能源效率研究[M].科学出版社,2010,8.
[88]吴军、笪凤媛、张建华.环境管制与中国区域生产率增长[J].统计研究,2010,27(1)：83-89.
[89]吴军.环境约束下中国地区工业全要素生产率增长及收敛分析[J].数量经济技术经济研究,2009,(11)：17-27.
[90]吴琦、武春友.基于DEA的能源效率评价模型研究[J].管理科学,2009,22(1)：103-112.
[91]吴琦、武春友.我国能源效率关键影响因素的实证研究[J].科研管理,2010,31(5)：164-171.
[92]吴琦.我国省域能源效率评价研究[D].2010.
[93]吴巧生,成金华.能源约束与中国工业化发展研究[M].科学出版社,2009,4.
[94]吴巧生、成金华.中国工业化中的能源消耗强度变动及因素分析——基于分解模型的实证分析[J].财经研究,2006,32(6)：75-85.
[95]吴玉鸣、李建霞.中国区域工业全要素生产率的空间计量经济分析[J].地理科学,2006,26,(4)：386-391.
[96]吴玉鸣、李建霞.中国省域能源消费的空间计量经济分析[J].中国人口·资源与环境,2008,18(3)：93-98.
[97]吴玉鸣、田斌.省域环境库兹涅茨曲线的扩展及其决定因素[J].地理研究,2012,31(4)：627-640.
[98]徐国泉、刘则渊、姜照华.中国碳排放的因素分解模型及实证分析：1995-2004[J].中国人口·资源与环境,2006,16(6)：158-161.
[99]许士春、习蓉、何正霞.中国能源消耗碳排放的影响因素分析及政策启示[J].资源科学,2012,34(1)：2-12.
[100]许广月.中国能源消费、碳排放与经济增长关系的研究[D].2010.
[101]颜云云、佘元冠.能源效率、二氧化碳排放效率与经济增长的关系分析[J].中国管理信息化,2011,14(9)：83-88.
[102]姚先国.能源效率与能源安全——基于浙江省的分析[J].浙江社会科学,2008,(4)：33-39.
[103]姚奕、倪勤.中国地区碳强度与FDI的空间计量分析[J].经济地理,2011,31(9)：1432-1438.
[104]叶祥松、彭良燕.我国环境规制下的规制效率与全要素生产率研究：1999-2008[J].财贸经济,2011,(2)：102-110.
[105]袁晓玲、张宝山、杨万平.基于环境污染的中国全要素能源效率研究[J].中国工业经济,2009,(2)：76-86.
[106]余晓泓、张超.中国工业部门的能源效率与减排潜力分析[J].产经评论,2012,(2)：5-15.
[107]余泳泽.我国节能减排潜力、治理效率与实施路径研究[J].中国工业经济,2011,(5)：58-68.
[108]曾贤刚.我国能源效率、CO2减排潜力及影响因素分析[J].中国环境科学,2010,30(10)：1432-1440.
[109]张江雪、朱磊.基于绿色的我国各地区工业企业技术创新效率研究[J].数量经济技术经济研究,2012,(2)：113-125.
[110]张纪录.区域碳排放因素分解及最优低碳发展情景分析——以中部地区为例[J].经济学问题,2012,(7)：126-129.
[111]张瑞、丁日佳.我国能源效率与能源消费结构的协整分析[J].煤炭经济研究,2006,(12)：8-10.
[112]赵奥、武春友.中国CO2排放量变化的影响因素分解研究[J].软科学,2010,24(12)：55-59.
[113]赵湘莲、李岩岩、陆敏.我国能源消费与经济增长的空间计量分析[J].软科学,2012,26(3)：33-38.
[114]赵欣、龙如银.考虑全要素生产率的中国碳排放影响因素分析[J].资源科学,2010,32(10)：1863-1870.
[115]周建.我国区域经济增长与能源利用效率改进的动态演化机制研究[J].数量经济技术经济研究,2008,(9)：3-16.
[116]朱勤、彭希哲、陆志明.中国能源消费碳排放变化的因素分解及实证分析[J].资源科学,2009,31(12)：2072-2079.
[117]Andres J. Picazo-Tadeo,2005,"Directional Distance Functions and Environmental Regulation",Resource and Energy Economics,Vol.27,PP131-142.
[118]B.W. Ang,2004,"Decomposition Analysis for Policymaking in Energy:Which Is the Preferredmethod?",Energy Policy,Vol.32,PP1131-1139.
[119]B.W. Ang, A.R. Mu, P. Zhou,2010,"Accounting Frameworks for Tracking Energy Efficiency Trends",Energy Economics,Vol.32,PP1209-1219.
[120]B.W. Ang, F.L. Liu, Hyun-Sik Chung,2004,"A Generalized Fisher Index Approach to Energy Decomposition Analysis",Energy Economics,Vol.26,PP757-763.
[121]B.W. Ang, Na Liu,2007,"Energy Decomposition Analysis:IEA Model Versus other Methods",Energy Policy,Vol.35,PP 1426-1432.
[122]Bernhard Mahlberg, Mikulas Luptacik, Biresh K. Sahoo,2011,"Examining the Drivers of Total Factor Productivity Change with an Illustrative Example of 14 EU Countries",Ecological Economics,Vol.72,PP60-69.
[123]Bin Su, B.W. Ang,2012,"Structural Decomposition Analysis Applied to Energy and Emissions:some Methodological Developments",Energy Economics,Vol.34, PP 177-188.
[124]Bin Su, B.W. Ang,2011,"Multi-Region Input-Output Analysis of CO2 Emissions Embodied in Trade:the Feedback Effects",Ecological Economics,Vol.71,PP42-53.
[125]Bin Su, H.C. Huang, B.W. Ang, P. Zhou,2010,"Input-Output Analysis of CO2 Emissions Embodied in Trade:the Effects of Sector Aggregation",Energy Economics,Vol.32,PP 166-175.
[126]Bo Hsiao, Ching-Chin Chern, Yung-Ho Chiu, Ching-Ren Chiu,2011,"Using Fuzzy Super-Efficiency Slack-Based Measure Data Envelopment Analysis to Evaluate Taiwan's Commercial Bank Efficiency",Expert Systems with Applications,Vol. 38,PP9147-9156.
[127]Cheng-Zhong Chen, Zhen-Shan Lin,2008,"Multiple Timescale Analysis and Factor Analysis of Energy Ecological Footprint Growth in China 1953-2006",Energy Policy,Vol.36,PP1666-1678.
[128]Chu Wei, Jinlan Ni, Manhong Sheng,2011,"China's Energy Inefficiency:a Cross-Country Comparison",The Social Science Journal,Vol.48,PP478-488.
[129]Chunhong Zhang, Haiying Liu, Hans Th.A. Bressers, Karen S. Buchanan,2011, "Productivity Growth and Environmental Regulations-Accounting for Undesirable Outputs:Analysis of China's Thirty Provincial Regions Using the Malmquist-Luenberger Index",Energy Economics,Vol.70,PP2369-2379.
[130]DONG Feng, LI Xiao-hui LONG Ru-yin,2011,"Laspeyres Decomposition of Energy Intensity Including Household-Energy Factors",Energy Procedia,Vol.5, PP 1482-1487.
[131]F. Wua, L.W.Fan, P.Zhou, D.Q.Zhou,2012,"Industrial Energy Efficiency with CO2 Emissions in China:a Nonparametric Analysis",Energy Policy,Vol.11,PP164-172.
[132]Fuh-Hwa Franklin Liu, Peng-hsiang Wang,2008,"DEA Malmquist Productivity Measure:Taiwanese Semiconductor Companies",Int. J. Production Economics,Vol. 112,PP367-379.
[133]Guang-Ming Shi, JunBi, Jin-NanWang,2010,"Chinese Regional Industrial Energy Efficiency Evaluation Based on a DEA Model of Fixing Non-Energy Inputs",Energy Policy,Vol.38,PP6172-6179.
[134]Hu JL, Wang SC,2006, "Total-Factor Energy Efficiency of Regions in China", Energy Policy,Vol.17, PP 3206-3217.
[135]H. O. Fried,2002,"Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis",Journal of Productivity Analysis,Vol.17,PP157-174.
[136]Hirofumi Fukuyama, William L. Weber,2009,"A Directional Slacks-Based Measure of Technical Inefficiency",Socio-Economic Planning Sciences,Vol.43,PP274-287.
[137]Huayi Yu,2012,"The Influential Factors of China's Regional Energy Intensity and its Spatial Linkages:1988-2007",Energy Policy,Vol.45,PP583-593.
[138]Isabela Butnar, Maria Llop,2011,"Structural Decomposition Analysis and Input-Output Subsystems:Changes in CO2 Emissions of Spanish Service Sectors (2000-2005)",Ecological Economics,Vol.70,PP2012-2019.
[139]Jan Ondrich, John Ruggiero,2001,"E(?) Ciency Measurement in the Stochastic Frontier Model",European Journal of Operational Research,Vol.129,PP434-442.
[140]Jin-Li Hu, Shih-Chuan Wang,2006,"Total-Factor Energy Efficiency of Regions in China",Energy Policy,Vol.34,PP3206-3217.
[141]Jin-Li Hua, Chih-Hung Kao,2007,"Efficient Energy-Saving Targets for APEC Economies",Energy Policy,Vol.35,PP373-382.
[142]John Ruggiero,1998, "Non-Discretionary Inputs in Data Envelopment Analysis ",European Journal of Operational Research,Vol.111,PP461-469.
[143]John Ruggiero,1999,"Nonparametric Analysis of Educational Costs",European Journal of Operational Research,Vol.119,PP605-612.
[144]John Ruggiero,1996,"On the Measurement of Technical Efficiency in the Public Sector",European Journal of Operational Research,Vol.90,PP553-565.
[145]John Ruggiero,2004,"Performance Evaluation When Non-Discretionary Factors Correlate with Technical Efficiency",European Journal of Operational Research,Vol. 159,PP250-257.
[146]John Ruggiero, Jerry Miner, Lloyd Blanchard,2002,"Measuring Equity of Educational Outcomes in the Presence of Inefficiency",European Journal of Operational Research,Vol.142,PP642-652.
[147]Juan Antonio Duro, Vicent Alcantara, Emilio Padilla,2010,"International Inequality in Energy Intensity Levels and the Role of Production Composition and Energy Efficiency:an Analysis of OECD Countries",Ecological Economics,Vol.69, PP2468-2474.
[148]Juan Du, Liang Liang, Joe Zhu,2010,"A Slacks-Based Measure of Super-Efficiency in Data Envelopment Analysis:a Comment",European Journal of Operational Research,Vol.204,PP694-697.
[149]Jun Bi, RongrongZhang, HaikunWang, MiaomiaoLiu, YiWu,2011,"The Bench Marks of Carbon Emissions and Policy Implications for China's Cities:Case of Nanjing",Energy Policy,Vol.39,PP4785-4794.
[150]Jun Li, Michel Colombier,2009,"Managing Carbon Emissions in China Through Building Energy Efficiency",Journal of Environmental Management,Vol.90, PP2436-2447.
[151]Kaoru Tone,2001,"A Slacks-Based Measure of Efficiency in Data Envelopment Analysis",European Journal of Operational Research, Vol.130,PP498-509.
[152]Kaoru Tone,2002,"A Slacks-Based Measure of Super-Efficiency in Data Envelopment Analysis",European Journal of Operational Research,Vol.143, PP 32-41.
[153]Kaoru Tone,2010, "Variations on the Theme of Slacks-Based Measure of Efficiency in DEA",European Journal of Operational Research,Vol.200,PP901-907.
[154]Kaoru Tone,2003,"Dealing with Undesirable Outputs in DEA:a Slacks-Based Measure (SBM) Approach",National Graduate Institute for Policy Studies,I-2003-0005.
[155]Karen Turner, Max Munday, Peter McGregor, Kim Swales,2012,"How Responsible Is a Region for Its Carbon Emissions? an Empirical General Equilibrium Analysis",Ecological Economics,Vol.76,PP70-78.
[156]Ke Wang, Shiwei Yu, Wei Zhang,2011,"China's Regional Energy and Environmental Efficiency:a DEA Window Analysis Based Dynamic Evaluation",Mathematical and Computer Modelling,Vol.,PPdoi:10.1016/j.mcm.2011.11.067.
[157]Ke Wang, Yi-MingWei, XianZhang,2012,"A Comparative Analysis of China's Regional Energy and Emission Performance:Which Is the Better Way to Deal with Undesirable Outputs?",Energy Policy,Vol.46,PP574-584.
[158]Ki-Hong Choi, B.W. Ang,2012,"Attribution of Changes in Divisia Real Energy Intensity Index——an Extension to Index Decomposition Analysis",Energy Economics,Vol.34,PP 171-176.
[159]Kyunam Kim, Yeonbae Kim,2012,"International Comparison of Industrial CO2 Emission Trends and the Energy Efficiency Paradox Utilizing Production-Based Decomposition",Energy Economics,Vol.34,PP 1724-1741.
[160]L'eopold Simar,2000,"Statistical Inference in Nonparametric Frontier Models:the State of the Art",Journal of Productivity Analysis, Vol.13,PP49-78.
[161]Lan-Bing Li, Jin-LiHu,2012,"Ecological Total-Factor Energy Efficiency of Regions in China",Energy Policy,Vol.46,PP216-224.
[162]Le'opold Simar j Paul W. Wilson,1998,"Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models",Management Science.Vol. 44,PP506-1.
[163]Luca Chiari,Antonio Zecca,2011,"Constraints of Fossil Fuels Depletion on Global Warming Projections",Energy Policy,Vol.39,PP5026-5034.
[164]Luciano Charlita de Freitas, Shinji Kaneko,2011,"Decomposing the Decoupling of CO2 Emissions and Economic Growth in Brazil",Ecological Economics,Vol. 70,PP1459-1469.
[165]Manuel Muniz, Joseph Paradi, John Ruggiero, et al,2006,"Evaluating Alternative DEA Models Used to Control for Non-Discretionary Inputs",Computers & Operations Research,Vol.33,PP1173-1183.
[166]Mian Yang, Fu-XiaYang, Xing-Peng Chen,2011,"Effects of Substituting Energy with Capital on China'saggregated Energy and Environmental Efficiency",Energy Policy,Vol.39,PP6065-6072.
[167]Mika Kortelainen,2008,"Dynamic Environmental Performance Analysis:a Malmquist Index Approach",Ecological Economics,Vol.64,PP701-715.
[168]Mikko J. Syrj€anen,2004,"Non-Discretionary and Discretionary Factors and Scale in Data Envelopment Analysis",European Journal of Operational Research,Vol. 158,PP20-33.
[169]Murray G Patterson,1996,"What Is Energy Efficiency?",Energy Policy.,Vol. 24,PP377-390.
[170]O. Zaim and F. Taskin,2000,"Environmental Efficiency in Carbon Dioxide Emissions in the OECD:A Non-Parametric Approach",Journal of Environmental Management, Vol.58,PP95-107.
[171]P. Zhou, B.W. Ang, H. Wang,2012,"Energy and CO2 Emission Performance in Electricity Generation:a Non-Radial Directional Distance Function Approach", European Journal of Operational Research, Vol.221,PP625-635.
[172]P. Zhou, B.W. Ang, K.L. Poh,2008,"Measuring Environmental Performance Under Different Environmental DEA Technologies",Energy Economics,Vol.30,PP1-14.
[173]P. Zhou, B.W. Ang,2008,"Linear Programming Models for Measuring Economy Wide Energy Efficiency Performance",Energy Policy.,Vol.36,PP2911-2916.
[174]P.Zhou, B.W. Ang,2008,"Decomposition of Aggregate CO2 Emissions:a Production-Theoretical Approach",Energy Economics,Vol.30,PP 1054-1067.
[175]P. Zhou, B.W. Ang, J.Y. Han,2010,"Total Factor Carbon Emission Performance:a Malmquist Index Analysis",Energy Economics,Vol.32,PP194-201.
[176]P. Zhou, B.W. Ang, K.L. Poh,2006,"Slacks-Based Efficiency Measures for Modeling Environmental Performance",Ecological Economics,Vol.60,PP111-118.
[177]Philip Andrews-Speed,2009,"China's Ongoing Energy Efficiency Drive:Origins, Progress and Prospects",Energy Policy,Vol.37,PP 1331-1344.
[178]R. Alton Gilbert and Paul W. Wilson,1998,"Effects of Deregulation on the Productivity of Korean Banks",Journal of Economics and Business,Vol.50, PP133-155.
[179]Ramakrishnan Ramanathan,2006,"A Multi-Factor Efficiency Perspective to the Relationships Among World GDP, Energy Consumption and Carbon Dioxide Emissions",Technological Forecasting & Social Change,Vol.73,PP483-494.
[180]Rolf Fare, Shawna Grosskopf,2010,"Directional Distance Functions and Slacks-Based Measures of Efficiency",European Journal of Operational Research,Vol. 200,PP320-322.
[181]Run Wang, WenjuanLiu, LishanXiao, JianLiu, WilliamKao,2011,"Path Towards achieving of china's 2020 carbon emissionreduction Target—a Discussion of Low-Carbon Energy Policies at Province Level",Energy Policy,Vol.39, PP 2740-2747.
[182]S. Monni, S.Syri,2011,"Weekly Greenhouse Gas Emissions of Municipalities: Methods and Comparisons",Energy Policy,Vol.39,PP4755-4765.
[183]Simar, L. and Wilson, R. P. W.,1998, "SensitivityAnalysis of Efficiency Scores: How to Bootstrap in Nonparametri Frontier Models", Management Science, Vol.44, PP 49-61.
[184]S.S. Wang, D.Q.Zhou, P.Zhou, Q.W.Wang,2011,"CO2 Emissions, Energy Consumption and Economic Growth in China:a Panel Data Analysis",Energy Policy,Vol.39,PP4870-4875.
[185]Sabuj KumarMandal,2010,"Do Undesirable Output and Environmental Regulation Matter in Energy Efficiency Analysis?Evidence From Indian Cement Industry ",Energy Policy.,Vol.38,PP6076-6083.
[186]SarahM.Estelle, AndyL.Johnson, JohnRuggiero,2010,"Three-Stage DEA Models for Incorporating Exogenous Inputs",Computers &OperationsResearch,Vol.37, PP 1087-1090.
[187]SatoshiHonma, Jin-LiHu,2009,"Total-Factor Energy Productivity Growth of Regions in Japan",Energy Policy.,Vol.37,PP3941-3950.
[188]Scheel H.,2001,"Undesirable Outputs in Eciency Valuations ",European Journal of Operational Research,Vol.132,PP400-410.
[189]Sebastian Lozano, Ester Gutierrez,2008,"Non-Parametric Frontier Approach to Modelling the Relationships Among Population, GDP, Energy Consumption and CO2 Emissions",Ecological Economics,Vol.66,PP687-699.
[190]Shinichiro Okushima, Makoto Tamura,2007,"Multiple Calibration Decomposition Analysis:Energy Use and Carbon Dioxide Emissions in the Japanese Economy, 1970-1995",Energy Policy,Vol.35,PP5156-5170.
[191]Sonia Ben Kheder and Natalia Zugravu,2008,"The Pollution Haven Hypothesis:a Geographic Economy Model in a Comparative Study", Working Paper.
[192]Stijn Reinhard, C.A. Knox Lovell, Geert J. Thijssen,2000,"Environmental Effciency with Multiple Environmentally Detrimental Variables; Estimated with SFA and DEA",European Journal of Operational Research,Vol.121,PP287-303.
[193]Taichen Chien, Jin-Li Hu,2007,"Renewable Energy and Macroeconomic Efficiency of OECD and Non-OECD Economies",Energy Policy,Vol.35,PP3606-3615.
[194]Trevor Collier, Andrew L. Johnson, John Ruggiero,2011,"Technical Efficiency Estimation with Multiple Inputs and Multiple Outputs Using Regression Analysis",European Journal of Operational Research,Vol.208,PP 153-160.
[195]Tsai-lien Yeh, Tser-yiethChen, Pei-yingLai,2010,"A Comparative Study of Energy Utilization Efficiency Between Taiwan and China",Energy Policy.,Vol.38, PP 2386-2394.
[196]V. Oikonomou, F.Becchis, L.Steg, D.Russolillo,2009,"Energy Saving and Energy Efficiency Concepts for Policy Making",Energy Policy.,Vol.37,PP4787-4796.
[197]Valeria Costantini, Salvatore Monni,2008,"Environment, Human Development and Economic Growth",Ecological Economics,Vol.64,PP867-880.
[198]Valeria Costantini,Chiara Martini,2009,"The Causality Between Energy Consumption and Economic Growth:a Multi-Sectoral Analysis Using Non-Stationary Cointegrated Panel Data",Energy Economics,Vol.32,PP591-603.
[199]Wang Hongwu, He Xiaoli, Ma Junhai,2011,"The Analysis of the Energy Efficiency and Its Influence Factors in Tianjin",Energy Procedia,Vol.5,PP1671-1675.
[200]Wolfgang Keller, Arik Levinson,2002,"Pollution Abatement Costs and Foreign Direct Investment Inflows to U.S. States",The Review of Economics and Statistics,Vol.84,PP691-703.
[201]Xia Yan-Qing,2011, "The impact of technology and structure change on GHG emissions in China:an analysis based on LMDI model", International Journal of Global Warming, Vol.3, No.3, PP307-327.
[202]Xia Yanqing,2012,"An empirical research on the interactions of China's energy consumption, pollution emissions and economic growth", International Journal of Global Energy Issues, Vol.35, No.5, PP411-425.
[203]Xia Yan-Qing,2012,"An input-output analysis of regional CO2 emissions from the service sector:an application to Liaoning Province of China, International Journal of Global Warming", Vol.4, No.2, PP187-214.
[204]X.H. Xia, G.T.Huang, G.Q.Chen, BoZhang, Z.M.Chen, Q.Yang,2011,"Energy Security,Efficiency and Carbon Emission of Chinese Industry",Energy Policy,Vol. 39,PP3520-3528.
[205]Xi Rao, Jun Wu, Zongyi Zhang, Bin Liu,2012,"Energy Efficiency and Energy Saving Potential in China:an Analysis Based on Slacks-Based Measure Model",Computers & Industrial Engineering,Vol.63,PP578-584.
[206]Xiao-Dan Guo, LeiZhu, YingFan, Bai-ChenXie,2011,"Evaluation of Potential Reductions in Carbon Emissions in Chinese Provinces Based on Environmental DEA",Energy Policy,Vol.39,PP2352-2360.
[207]Xiaojin Li, Linyu Xu,2012,"Simple Analysis of Energy Utilization and Sustainable Development in China",Procedia Environmental Sciences,Vol.13,PP873-878.
[208]XiaoLi Han,1997,"Impacts of Growth and Structural Change on CO2 Emissions of Developing Countries",World Development,Vol.25,PP39-5407.
[209]Xin Zhou, Hidefumi Imura,2011,"How Does Consumer Behavior Influence Regional Ecological Footprints? an Empirical Analysis for Chinese Regions Based on the Multi-Region Input-Output Model",Ecological Economics,Vol.71, PP 171-179.
[210]Xing-Ping Zhang, Xiao-Mei Cheng,2009,"Energy Consumption, Carbon Emissions. and Economic Growth in China",Ecological Economics,Vol.68,PP2706-2712.
[211]Xing-Ping Zhang, Xiao-Mei Cheng,Jia-HaiYuan,Xiao-JunGao,2011,"Total-Factor Energy Efficiency in Developing Countries",Energy Policy.,Vol.39,PP644-650.
[212]Yao-Chun Lee, Jin-Li Hu, Chih-Hung Kao,2011,"Efficient Saving Targets of Electricity and Energy for Regions in China",Electrical Power and Energy Systems,Vol.33,PP1211-1219.
[213]Ying Fan, Lan-Cui Liu, Gang Wu, Hsien-Tang Tsai, Yi-Ming Wei,2007,"Changes in Carbon Intensity in China:Empirical Findings From 1980-2003",Ecological Economics,Vol.62,PP683-691.
[214]Yiwen Bian, FengYang,2010,"Resource and Environment Efficiency Analysis of Provinces in China:A DEA Approach Based on Shannon'sen tropy",Energy Policy.,Vol.38,PP1909-1917.
[215]Yongping BAI, Jianping NIU, Yongpei HAO,2012,"Research of Regional Energy Efficiency Based on Undesirable Outputs and Its Influential Factors:a Case of Western China",Energy Procedia,Vol.16,PP802-809.
[216]Z.M. Chen, G.Q. Chen, B. Chen,2010,"Embodied Carbon Dioxide Emissions of the World Economy:a Systems Input-Output Simulation for 2004", Procedia Environm-ental Sciences,Vol.2,PP 1827-1840.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |