【作者】 范丹；

【导师】 王维国；

【作者基本信息】 东北财经大学 ， 管理科学与工程， 2013， 博士

【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。更多还原

【Abstract】 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.更多还原