我国区域碳排放效率测度及影响因素分析

发布时间：2018-05-10 15:12

本文选题：碳排放效率 + 数据包络分析　；参考：《中国矿业大学》2014年硕士论文

【摘要】：近年来，我国作为经济高速增长的发展中大国，也面临着资源生态环境承载不足的压力和挑战，尤其是温室气体排放量的快速增长成为国内外关注的焦点，2012年，我国的碳排放量占全球的比重达到28.8%，是世界第一大碳排放国，而同年我国的经济总量占全球比重仅为10%左右。我国的经济发展水平远没有达到发达国家之列，但减排压力更剧，2009年我国宣布到2020年碳排放强度相比2005年要减少40-45%。本文正是基于这样的背景，从基于数据包络分析的全要素碳排放效率和碳排放强度的两个角度开展对碳排放效率的测度和影响因素研究，论文的主要内容有：（1）基于数据包络分析方法的全要素碳排放效率分析。选用人力资本投入、资本存量、二氧化碳排放量3个要素作为投入变量，选取GDP作为产出变量，区域碳排放效率均呈上升态势，呈现东、中、西的由高到低排列顺序。将碳排放效率进行分解：全国和东部地区的技术效率处于有效水平，中、西部地区规模效率呈现不断上升趋势；将碳排放生产率进行分解：东部地区科学技术也处于较为领先的水平，中、西部全要素生产率指数的提升则更多依赖于技术效率的提高，科技出现了退化现象。用Tobit模型研究的全要素碳排放效率影响因素作用结果表明：经济发展水平和产业结构存在显著正相关的影响，而城镇化水平、能源结构、能源强度存在显著负相关的影响，科技进步和对外开放存在正的影响，但不十分显著。用空间计量经济模型分析结果表明，全要素碳排放效率存在显著地空间相关性。（2）区域碳排放强度及其影响因素的协整分析。运用面板协整分析方法探析了2002-2011年各区域碳排放强度和能源强度、能源结构、城镇化水平三个影响因素的长期均衡关系，并运用协整估计和误差修正模型。检验结果表明：在全国及东、中、西部区域，变量均存在长期协整关系。长期均衡协整方程估计结果表明：能源强度在全国及各区域对碳排放强度均为正的影响，影响程度从大到小依次为东、中、西部；能源结构影响均为正，影响程度从大到小依次为中、西、东部；城镇化水平在全国及中、西区域对碳排放强度影响为正的，，在东部地区影响为负，其中在西部地区的相关性不十分显著，正影响程度依次为中部、西部。误差修正模型结果表明：西部调整到均衡的速度最快，东部次之，中部最慢。（3）省域碳排放强度及其影响因素空间计量分析。运用空间计量经济模型对2007-2011年的省域碳排放强度和能源强度、能源结构、城镇化水平三个影响因素进行实证检验。空间自相关检验结果发现：29省域的碳排放强度空间相关性显著，即其空间分布存在着区域间的溢出效应，相邻地区存在着类似的特性。空间计量回归结果发现：采用空间误差模型，能源强度（EI）弹性系数为1.06，能源结构（EB）弹性系数为0.63，人口城镇化水平（URB）弹性系数为0.21，能源强度是我国碳排放强度的最重要影响因素，能源结构、城镇化水平也具有显著的影响性，这个结论与协整检验的结论基本一致。
[Abstract]:In recent years, China, as a developing country with high speed of economic growth, also faces the pressure and challenge of carrying out the shortage of resources and ecological environment, especially the rapid growth of greenhouse gas emissions has become the focus of attention at home and abroad. In 2012, China's carbon emissions accounted for 28.8% of the global proportion, the world's largest carbon emission country, and the same year in the same year. The total economic total of the country is only about 10% of the global proportion. China's economic development level is far from the developed countries, but the pressure of emission reduction is more dramatic. In 2009, China announced that the carbon emission intensity in 2020 was reduced by 40-45%. than in 2005. This is based on this background, from the total factor carbon emission efficiency and carbon based on data envelopment analysis. From the two angles of emission intensity, the measurement and influencing factors of carbon emission efficiency are studied.
(1) the total factor carbon emission efficiency analysis based on the data envelopment analysis method. The investment of human capital, capital stock and carbon dioxide emissions are selected as input variables, and GDP is selected as the output variable, and the efficiency of regional carbon emission shows an upward trend, showing the order of East, middle and West from high to low. The carbon emission efficiency is decomposed. The technical efficiency of the national and eastern regions is at an effective level. In the western region, the scale efficiency of the western region is rising constantly; the carbon emission productivity is decomposed: the science and technology in the eastern region is also in the leading level. In the middle, the promotion of the Western total factor productivity index is more dependent on the improvement of technical efficiency and the emergence of science and technology. Degeneracy.
The effect of all factors carbon emission efficiency influenced by the Tobit model shows that there is a significant positive correlation between the level of economic development and the industrial structure, while the level of urbanization, energy structure and energy intensity have a significant negative correlation, and there is a positive impact on the progress of science and technology and the opening to the outside world, but it is not very significant. The results of economic model analysis show that there is a significant spatial correlation of total factor carbon emission efficiency.
(2) the co integration analysis of regional carbon emission intensity and its influencing factors. Using the panel cointegration analysis method, the long-term equilibrium relationship between the 2002-2011 years' carbon emission intensity and energy intensity, energy structure and the urbanization level of three influencing factors is analyzed, and the cointegration estimation and error correction model are used. The results show that in the country and the East, The results of long-term equilibrium cointegration equation show that energy intensity has a positive influence on carbon emission intensity throughout the country and regions, and the influence degree from large to small is East, middle and West; the influence of energy structure is positive, and the influence degree is in the order of middle, West, East and city from large to small. The influence of the western region on the carbon emission intensity is positive in the whole country and in the western region. The correlation of the eastern region is negative, and the correlation in the western region is not very significant. The positive influence degree is in the middle and West. The error correction model shows that the western region is the fastest in adjusting to equilibrium, Higashibe Jinno and the slowest in the middle.
(3) the spatial econometric analysis of the intensity of carbon emission and its influencing factors in the province. Using the spatial econometric model, this paper empirically tests the three factors affecting the intensity of carbon emission and energy intensity, energy structure and the level of urbanization in 2007-2011 years. The spatial autocorrelation test results show that the spatial correlation of carbon emission intensity in the 29 provinces is significant, that is, The spatial distribution has an inter regional spillover effect, and the adjacent areas have similar characteristics. The spatial error regression results show that the spatial error model, the energy intensity (EI) elastic coefficient are 1.06, the energy structure (EB) elastic coefficient is 0.63, the population urbanization level (URB) elastic coefficient is 0.21, the energy intensity is the carbon emission intensity of China. The most important influencing factors, the energy structure and the level of urbanization also have significant influence. This conclusion is basically consistent with the conclusion of co integration test.

【学位授予单位】：中国矿业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F124.5

【参考文献】