發(fā)布時(shí)間：2018-05-10 15:12

  本文選題：碳排放效率 + 數(shù)據(jù)包絡(luò)分析��； 參考：《中國(guó)礦業(yè)大學(xué)》2014年碩士論文

【摘要】：近年來(lái)，我國(guó)作為經(jīng)濟(jì)高速增長(zhǎng)的發(fā)展中大國(guó)，也面臨著資源生態(tài)環(huán)境承載不足的壓力和挑戰(zhàn)，尤其是溫室氣體排放量的快速增長(zhǎng)成為國(guó)內(nèi)外關(guān)注的焦點(diǎn)，2012年，我國(guó)的碳排放量占全球的比重達(dá)到28.8%，是世界第一大碳排放國(guó)，而同年我國(guó)的經(jīng)濟(jì)總量占全球比重僅為10%左右。我國(guó)的經(jīng)濟(jì)發(fā)展水平遠(yuǎn)沒(méi)有達(dá)到發(fā)達(dá)國(guó)家之列，但減排壓力更劇，2009年我國(guó)宣布到2020年碳排放強(qiáng)度相比2005年要減少40-45%。本文正是基于這樣的背景，從基于數(shù)據(jù)包絡(luò)分析的全要素碳排放效率和碳排放強(qiáng)度的兩個(gè)角度開(kāi)展對(duì)碳排放效率的測(cè)度和影響因素研究，論文的主要內(nèi)容有： （1）基于數(shù)據(jù)包絡(luò)分析方法的全要素碳排放效率分析。選用人力資本投入、資本存量、二氧化碳排放量3個(gè)要素作為投入變量，選取GDP作為產(chǎn)出變量，區(qū)域碳排放效率均呈上升態(tài)勢(shì)，呈現(xiàn)東、中、西的由高到低排列順序。將碳排放效率進(jìn)行分解：全國(guó)和東部地區(qū)的技術(shù)效率處于有效水平，中、西部地區(qū)規(guī)模效率呈現(xiàn)不斷上升趨勢(shì)；將碳排放生產(chǎn)率進(jìn)行分解：東部地區(qū)科學(xué)技術(shù)也處于較為領(lǐng)先的水平，中、西部全要素生產(chǎn)率指數(shù)的提升則更多依賴(lài)于技術(shù)效率的提高，科技出現(xiàn)了退化現(xiàn)象。 用Tobit模型研究的全要素碳排放效率影響因素作用結(jié)果表明：經(jīng)濟(jì)發(fā)展水平和產(chǎn)業(yè)結(jié)構(gòu)存在顯著正相關(guān)的影響，而城鎮(zhèn)化水平、能源結(jié)構(gòu)、能源強(qiáng)度存在顯著負(fù)相關(guān)的影響，科技進(jìn)步和對(duì)外開(kāi)放存在正的影響，但不十分顯著。用空間計(jì)量經(jīng)濟(jì)模型分析結(jié)果表明，全要素碳排放效率存在顯著地空間相關(guān)性。 （2）區(qū)域碳排放強(qiáng)度及其影響因素的協(xié)整分析。運(yùn)用面板協(xié)整分析方法探析了2002-2011年各區(qū)域碳排放強(qiáng)度和能源強(qiáng)度、能源結(jié)構(gòu)、城鎮(zhèn)化水平三個(gè)影響因素的長(zhǎng)期均衡關(guān)系，并運(yùn)用協(xié)整估計(jì)和誤差修正模型。檢驗(yàn)結(jié)果表明：在全國(guó)及東、中、西部區(qū)域，變量均存在長(zhǎng)期協(xié)整關(guān)系。長(zhǎng)期均衡協(xié)整方程估計(jì)結(jié)果表明：能源強(qiáng)度在全國(guó)及各區(qū)域?qū)μ寂欧艔?qiáng)度均為正的影響，影響程度從大到小依次為東、中、西部；能源結(jié)構(gòu)影響均為正，影響程度從大到小依次為中、西、東部；城鎮(zhèn)化水平在全國(guó)及中、西區(qū)域?qū)μ寂欧艔?qiáng)度影響為正的，，在東部地區(qū)影響為負(fù)，其中在西部地區(qū)的相關(guān)性不十分顯著，正影響程度依次為中部、西部。誤差修正模型結(jié)果表明：西部調(diào)整到均衡的速度最快，東部次之，中部最慢。 （3）省域碳排放強(qiáng)度及其影響因素空間計(jì)量分析。運(yùn)用空間計(jì)量經(jīng)濟(jì)模型對(duì)2007-2011年的省域碳排放強(qiáng)度和能源強(qiáng)度、能源結(jié)構(gòu)、城鎮(zhèn)化水平三個(gè)影響因素進(jìn)行實(shí)證檢驗(yàn)�？臻g自相關(guān)檢驗(yàn)結(jié)果發(fā)現(xiàn)：29省域的碳排放強(qiáng)度空間相關(guān)性顯著，即其空間分布存在著區(qū)域間的溢出效應(yīng)，相鄰地區(qū)存在著類(lèi)似的特性�？臻g計(jì)量回歸結(jié)果發(fā)現(xiàn)：采用空間誤差模型，能源強(qiáng)度（EI）彈性系數(shù)為1.06，能源結(jié)構(gòu)（EB）彈性系數(shù)為0.63，人口城鎮(zhèn)化水平（URB）彈性系數(shù)為0.21，能源強(qiáng)度是我國(guó)碳排放強(qiáng)度的最重要影響因素，能源結(jié)構(gòu)、城鎮(zhèn)化水平也具有顯著的影響性，這個(gè)結(jié)論與協(xié)整檢驗(yàn)的結(jié)論基本一致。
[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.

【學(xué)位授予單位】：中國(guó)礦業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F124.5

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