本文選題：碳排放 + 脫鉤彈性��； 參考：《北方工業(yè)大學(xué)》2014年碩士論文

【摘要】：由二氧化碳排放增加導(dǎo)致的全球氣候變暖問(wèn)題嚴(yán)重影響了人類(lèi)生存和發(fā)展。2013年初,由于大量排放的污染物無(wú)法消散,肆虐的霧霾天氣再一次給中國(guó)的環(huán)境問(wèn)題敲響了警鐘,而北京的情況尤為嚴(yán)重。在這樣的大背景下,研究北京的碳排放與經(jīng)濟(jì)發(fā)展的關(guān)系以及真正驅(qū)動(dòng)北京二氧化碳排放的原因?qū)⒂兄诤饬坑绊懕本┑貐^(qū)能源碳排放的關(guān)鍵指標(biāo),從而有針對(duì)性的提出減排政策,為建設(shè)“世界城市”提供有力的理論依據(jù)。 本文首先利用Tapio和IGT脫鉤模型研究了北京地區(qū)經(jīng)濟(jì)發(fā)展與碳排放之間脫鉤特征,研究發(fā)現(xiàn)：除了1993年、1997年和2011年以外,其他年份北京地區(qū)經(jīng)濟(jì)發(fā)展與能源碳排放之間的脫鉤關(guān)系都屬于相對(duì)脫鉤；能源消費(fèi)結(jié)構(gòu)的優(yōu)化,清潔能源使用量的不斷增加,節(jié)能減排力度的加強(qiáng)以及以第三產(chǎn)業(yè)為主的產(chǎn)業(yè)結(jié)構(gòu)形式都是北京地區(qū)能夠呈現(xiàn)大部分時(shí)間相對(duì)脫鉤狀態(tài)的主要原因；Tapio方程和IGT方程在研究脫鉤狀態(tài)上各有利弊,Tapio方程測(cè)量精度更高,而IGT方程可以計(jì)算脫鉤的臨界值。 為了進(jìn)一步研究影響北京地區(qū)碳排放的脫鉤特征,本文運(yùn)用LMDI分解方法對(duì)北京地區(qū)的能源碳排放驅(qū)動(dòng)因素進(jìn)行了分解,分解為能源結(jié)構(gòu)、產(chǎn)業(yè)結(jié)構(gòu)、人均產(chǎn)出、人均收入、生產(chǎn)能源強(qiáng)度、生活能源強(qiáng)度、總?cè)丝凇⒔煌茉磸?qiáng)度、交通工具平均運(yùn)輸線(xiàn)路長(zhǎng)度、交通工具規(guī)模等10個(gè)因素,在此基礎(chǔ)上,運(yùn)用STIRPAT模型定量分析了這些因素對(duì)碳排放的影響彈性大小。結(jié)果發(fā)現(xiàn)：北京地區(qū)能源強(qiáng)度是能源碳排放最大的負(fù)向驅(qū)動(dòng)因素,能源結(jié)構(gòu)和產(chǎn)業(yè)結(jié)構(gòu)因素對(duì)減排做出了很大的貢獻(xiàn),而經(jīng)濟(jì)發(fā)展規(guī)模與人口規(guī)模是拉動(dòng)北京地區(qū)碳排放增長(zhǎng)的主要因素,交通運(yùn)輸業(yè)對(duì)北京地區(qū)的能源碳排放影響不容忽視。從影響因素彈性大小來(lái)看城市化率是對(duì)北京碳排放影響最大的因素。人口、城市化率、人均GDP和煤炭消費(fèi)比例及北京市機(jī)動(dòng)車(chē)保有量對(duì)碳排放有促進(jìn)作用,而對(duì)碳排放有抑制作用的因素是能源消耗強(qiáng)度和第三產(chǎn)業(yè)占GDP比重。 最后,本文通過(guò)GM-PLS組合預(yù)測(cè)模型和GM(1,1)單一預(yù)測(cè)模型的比較,研究得出GM-PLS組合模型的預(yù)測(cè)精度要高于GM(1,1)單獨(dú)預(yù)測(cè)的精度,進(jìn)而利用GM-PLS組合預(yù)測(cè)模型對(duì)北京地區(qū)2012-2015年的碳排放強(qiáng)度進(jìn)行了預(yù)測(cè)。從預(yù)測(cè)結(jié)果發(fā)現(xiàn),北京的節(jié)能減排目標(biāo)效果很好。
[Abstract]:Global warming caused by the increase of carbon dioxide emissions has seriously affected the survival and development of human beings. In early 2013, the rampant haze weather once again sounded the alarm for China's environmental problems because a large number of pollutants could not dissipate. The situation in Beijing is especially serious. Against this background, studying the relationship between Beijing's carbon emissions and economic development and the causes that really drive Beijing's carbon dioxide emissions will help measure the key indicators that affect energy carbon emissions in Beijing. Therefore, the emission reduction policy is put forward, which provides a powerful theoretical basis for the construction of "world city". In this paper, the characteristics of decoupling between economic development and carbon emissions in Beijing are studied by using Tapio and IGT decoupling models. In other years, the decoupling relationship between economic development and energy and carbon emissions in Beijing was relatively decoupled; the optimization of energy consumption structure and the increasing use of clean energy, The enhancement of energy saving and emission reduction and the industrial structure based on the tertiary industry are the main reasons for the relative decoupling in Beijing for most of the time. Tapio equation and IGT equation have both advantages and disadvantages in the study of decoupling state. The Tapio equation is more accurate. IGT equation can calculate the critical value of decoupling. In order to further study the decoupling characteristics of carbon emissions in Beijing area, this paper uses LMDI decomposition method to decompose the driving factors of energy carbon emissions in Beijing area, which can be divided into energy structure, industrial structure, per capita output, per capita income, energy structure, industrial structure, per capita output and per capita income. On the basis of 10 factors, such as energy intensity of production, energy intensity of daily life, total population, energy intensity of traffic, average length of transportation line and scale of transportation, The effect of these factors on carbon emissions is quantitatively analyzed by STIRPAT model. The results show that the energy intensity in Beijing is the largest negative driver of energy carbon emissions, and the energy structure and industrial structure contribute greatly to the emission reduction. The scale of economic development and population scale are the main factors to promote the growth of carbon emissions in Beijing, and the impact of transportation on energy carbon emissions in Beijing can not be ignored. Urbanization is the most important factor affecting carbon emissions in Beijing. Population, urbanization rate, per capita GDP and coal consumption ratio and motor vehicle ownership in Beijing promote carbon emissions, while energy consumption intensity and tertiary industry account for the proportion of GDP. Finally, through the comparison between the GM-PLS combined prediction model and the single prediction model, it is found that the prediction accuracy of the GM-PLS combination model is higher than that of the GM1 / 1) single prediction model. Furthermore, the carbon emission intensity of Beijing region from 2012 to 2015 is predicted by GM-PLS combined forecasting model. From the forecast results, Beijing's energy conservation and emission reduction targets are very effective.
【學(xué)位授予單位】：北方工業(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)】：X321;F127

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