本文選題：碳排放 + 省域差異　； 參考：《江西財經(jīng)大學》2017年碩士論文

【摘要】：隨著經(jīng)濟的快速發(fā)展,全球的氣候問題也越來越突出,吸引了全國人民的廣泛關(guān)注,其中突出的是二氧化碳等溫室氣體,這也是造成地球溫度普遍升高的原因之一。因此實現(xiàn)碳的減排是應(yīng)對氣候變化的重中之重。首先,對1995-2015年我國30個省區(qū)碳排放量進行核算,構(gòu)建面板數(shù)據(jù)庫,并將30個省區(qū)按碳排放、人均碳排放、碳排放強度分別聚類,研究了各類別所屬省區(qū)的碳排放差異。研究發(fā)現(xiàn)碳排放量、人均碳排放總體上升,碳排放強度總體下降,但不同省域在不同時期有較小的波動。通過對碳排放、能源消耗和GDP三個變量建立多變量聚類模型,得到了3類不同省域。從該3類不同省域來看,第3類省域中山東、遼寧、廣東等省區(qū)碳排放量及人均碳排放量遠遠高于其他地區(qū),而碳排放強度卻最低,第1類省域中內(nèi)蒙古、新疆等省區(qū)碳排放強度最高。其次,研究了3類省域碳排放、人均碳排放、碳排放強度的異同。在此基礎(chǔ)上,利用擴展的STIRPAT模型對我國總體影響因素進行靜態(tài)面板模型和動態(tài)GMM模型估計。在靜態(tài)面板模型當中,除技術(shù)水平外其余因素對碳排放均有顯著的正向影響,而技術(shù)水平對碳減排有顯著的正向影響。經(jīng)濟彈性系數(shù)最大,即人均GDP變動對碳排放影響最明顯。在動態(tài)面板模型當中,上一期碳排放量對當期具有顯著的路徑依賴特征。人口規(guī)模、經(jīng)濟水平對碳排放也顯著為正,但其彈性系數(shù)有所下降。另外,經(jīng)濟水平依然是幾個彈性中最大的,且只有人口、經(jīng)濟水平上一期的值對當前碳排放量有明顯的負向抑制作用。再次,對3類不同省域的碳排放影響因素作分解。在靜態(tài)面板模型中,3類省域回歸結(jié)果與總體一致,其中第1類省域模型中的人口彈性最大,第2類和第3類省域模型中的經(jīng)濟彈性最大。在動態(tài)面板模型中,滯后一期的碳排放回歸系數(shù)按從大到小排列的省域依次為第1類(1.129)、第3類(1.101)和第2類(1.087)省域。最后,根據(jù)實證結(jié)果對全國和3類省域提出相應(yīng)的碳減排建議。其中,對于全國而言,應(yīng)控制人口過度增長,倡導低碳生活,提高能源利用率,廣泛開展節(jié)能工作,并且出臺能源消費的有關(guān)法律政策,保證政策得以有力實施。對各類省域而言,第1類省域應(yīng)控制人口增長、加快城鎮(zhèn)化進程,第2類省域應(yīng)促進經(jīng)濟增長、加快城鎮(zhèn)化進程和提高技術(shù)水平,第3類省域應(yīng)持續(xù)控制人口增長、提高技術(shù)水平。
[Abstract]:With the rapid development of economy, the global climate problem is becoming more and more prominent, attracting the widespread attention of the whole country, especially carbon dioxide and other greenhouse gases, which is one of the causes of the global warming. Therefore, carbon emission reduction is the top priority in tackling climate change. Firstly, the carbon emissions of 30 provinces and regions in China from 1995 to 2015 are calculated, and the panel database is constructed, and the carbon emissions of 30 provinces and autonomous regions are clustered according to carbon emissions, per capita carbon emissions and carbon emission intensities, respectively, and the differences of carbon emissions between the provinces and autonomous regions of each category are studied. It is found that the carbon emissions per capita increase and the intensity of carbon emissions decrease, but there are small fluctuations in different provinces in different periods. Through the establishment of multi-variable clustering model for three variables, carbon emission, energy consumption and GDP, three kinds of different provincial regions are obtained. From the perspective of these three different provinces, the carbon emissions and per capita carbon emissions of Shandong, Liaoning, Guangdong and other provinces in the third category are far higher than those in other regions, but the intensity of carbon emissions is the lowest. Xinjiang and other provinces and regions the highest intensity of carbon emissions. Secondly, the similarities and differences of three kinds of provincial carbon emission, per capita carbon emission and carbon emission intensity are studied. On this basis, the static panel model and dynamic GMM model are used to estimate the total influencing factors in China by using the extended STIRPAT model. In the static panel model, except for the technical level, the other factors have significant positive effects on carbon emissions, while the technical level has a significant positive impact on carbon emission reduction. The coefficient of economic elasticity is the largest, that is, the change of GDP per capita has the most obvious effect on carbon emissions. In the dynamic panel model, the last period of carbon emissions has a significant path-dependent characteristics of the current period. Population size and economic level were also significantly positive for carbon emissions, but its elastic coefficient decreased. In addition, the economic level is still the largest of several elasticity, and only the population, the economic level of the last period of the value of the current carbon emissions have a significant negative inhibition. Thirdly, the factors affecting carbon emission in three different provinces are decomposed. In the static panel model, the regression results of three types of provincial domain are consistent with the whole. The population elasticity of the first type of provincial model is the largest, and the economic elasticity of the second and the third type of provincial model is the largest. In the dynamic panel model, the regression coefficients of carbon emissions in the lag period are the first (1.129), the third (1.101) and the second (1.087) provincial domains according to the provincial range from large to small. Finally, according to the empirical results, the corresponding carbon emission reduction recommendations for the whole country and three kinds of provinces are put forward. Among them, for the whole country, we should control the excessive growth of population, advocate low carbon living, improve energy efficiency, widely carry out energy conservation work, and introduce the relevant laws and policies on energy consumption to ensure that the policies can be carried out effectively. For all kinds of provinces, category 1 should control population growth and speed up the process of urbanization, category 2 should promote economic growth, speed up the process of urbanization and raise the level of technology, and category 3 should continue to control population growth. Raise the technical level.
【學位授予單位】：江西財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：X321

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