發(fā)布時(shí)間：2018-06-05 09:04

  本文選題：省域碳排放 + 能源消費(fèi)��； 參考：《湖南科技大學(xué)》2017年碩士論文

【摘要】：全球氣候變暖是當(dāng)前可持續(xù)發(fā)展面臨的巨大挑戰(zhàn),已有研究表明人類活動(dòng)產(chǎn)生的溫室氣體特別是CO_2是導(dǎo)致全球氣候變暖的最主要原因。因此,如何減少CO_2排放已成為世界各國面臨的共同問題。自改革開放以來,中國經(jīng)濟(jì)持續(xù)快速發(fā)展,經(jīng)濟(jì)總量已躍居世界第二。然而,在經(jīng)濟(jì)快速發(fā)展的同時(shí)中國的能源消費(fèi)碳排放量也迅速增加,已超過美國成為世界最大的碳排放國。目前,中國仍是發(fā)展中國家,處于工業(yè)化、城市化中后期,經(jīng)濟(jì)高速發(fā)展,導(dǎo)致其碳排放量持續(xù)走高,面臨著巨大的減排壓力。中國各省域經(jīng)濟(jì)發(fā)展和能源消費(fèi)結(jié)構(gòu)存在很大差異,如何科學(xué)準(zhǔn)確地測算各省能源消費(fèi)碳排放量,分析中國省域能源消費(fèi)排放費(fèi)的空間格局變化及其空間依賴關(guān)系,探究各省域碳排放影響因素的空間異質(zhì)性,是明確各省減排目標(biāo)、科學(xué)制定“共同但有區(qū)別”的減排策略的基本前提。本文基于IPCC提供的參考方法,利用1995-2014年中國各省能源消費(fèi)數(shù)據(jù)估算各省域能源消費(fèi)碳排放量。在此基礎(chǔ)上,利用標(biāo)準(zhǔn)差橢圓分析法及GIS可視化方法分析中國省域能源消費(fèi)碳排放的空間格局變化,采用空間自相關(guān)分析法分析中國省域能源消費(fèi)碳排放的空間依賴關(guān)系,最后利用地理回歸加權(quán)模型分析中國省域能源消費(fèi)碳排放影響因素的空間異質(zhì)性。得出以下主要結(jié)論:(1)1995-2014年中國各省域的能源消費(fèi)碳排放量顯著增加,從整體上看,東部省域的碳排放量高于中部及西部省域碳排放量。碳排放重心位于中國幾何中心東南方向,整體上碳排放重心有向西北方向遷移的趨勢,表明雖然東部和南部省域碳排放較高,但近年來西北內(nèi)陸地區(qū)省域碳排放增速要高于其他省域,盡管如此,在制定減排策略時(shí)東部和南部省域仍需承擔(dān)更大的責(zé)任。近20年中國碳排放的標(biāo)準(zhǔn)差橢圓總體上變化幅度不大,基本上覆蓋了絕大部分碳排放較高的省域,省域碳排放的空間分布呈現(xiàn)出東北—西南格局,且有逐步向正北—正南方向轉(zhuǎn)變的態(tài)勢。(2)1995-2014年全局Moran’s I指數(shù)均為正值,且均通過5%顯著性檢驗(yàn),表明中國省域能源消費(fèi)碳排放之間具有顯著的空間依賴性。局部空間自相關(guān)分析表明中國省域能源消費(fèi)碳排放之間不但具有空間依賴性,而且具有空間異質(zhì)性。(3)LISA時(shí)間路徑分析表明,局部空間結(jié)構(gòu)具有強(qiáng)波動(dòng)性和強(qiáng)穩(wěn)定性的省區(qū)數(shù)量均呈下降趨勢。LISA時(shí)間路徑的移動(dòng)方向類型中,中國碳排放出現(xiàn)協(xié)同運(yùn)動(dòng)的省區(qū)由1995-2001年的13個(gè)下降到2002-2014年的10個(gè),表明中國碳排放空間格局變化具有一定的空間整合性,但呈減弱趨勢。1997-2001年,協(xié)同高增長的省區(qū)分別為北京、上海、河北、山西、河南、內(nèi)蒙古、福建和海南,協(xié)同低增長的省區(qū)分別為吉林、湖北、四川、甘肅和廣西。而2002-2014年,協(xié)同高增長的省區(qū)分布在西北內(nèi)陸,協(xié)同低增長的省區(qū)分別為北京、天津、重慶、河南、湖南和貴州。(4)從Moran散點(diǎn)的時(shí)空躍遷分析看,在1995~2001年和2002~2014年兩個(gè)時(shí)段中,類型Ⅳ躍遷的省域占全部省區(qū)的比例為83.3%,即Moran散點(diǎn)圖的空間穩(wěn)定性均為0.833,且兩個(gè)時(shí)段中均無發(fā)生類型Ⅲ躍遷的省域,表明中國省域碳排放的局部空間關(guān)聯(lián)模式存在較強(qiáng)的穩(wěn)定性,省域要改變自身的相對位置非常困難,即具有一定的路徑依賴或空間鎖定特征。(5)SG方法定量分析表明中國省域能源消費(fèi)碳排放之間具有正向的空間依賴性,且2002-2014年的相關(guān)性高于1995-2001年的相關(guān)性,進(jìn)而說明隨著產(chǎn)業(yè)結(jié)構(gòu)的轉(zhuǎn)移,省域之間聯(lián)系的加強(qiáng),省域之間能源消費(fèi)碳排放的空間依賴性也曾增加趨勢。(6)GWR模型分析結(jié)果表明:總體上看各驅(qū)動(dòng)因素對能源消費(fèi)碳排放的影響存在差異性,同一影響因素在不同省份對能源消費(fèi)碳排放的影響也存在差異性,而且隨著經(jīng)濟(jì)的發(fā)展、工業(yè)化和城鎮(zhèn)化的快速推進(jìn)、技術(shù)的進(jìn)步,各影響因素對碳排放影響的空間異質(zhì)性格局也會發(fā)生明顯的變化。能源強(qiáng)度、能源結(jié)構(gòu)、人均GDP和人口規(guī)模等因素與能源消費(fèi)碳排放均有正相關(guān)關(guān)系;能源結(jié)構(gòu)和其它三個(gè)因素相比,對能源消費(fèi)碳排放的影響相對較小。
[Abstract]:Global warming is a great challenge for the current sustainable development. Research has shown that the greenhouse gases produced by human activities, especially CO_2, are the main causes of global warming. Therefore, how to reduce CO_2 emissions has become a common problem facing all countries. Since the reform and opening up, China's economy has been developing rapidly, The total economic total has jumped to second in the world. However, while China's energy consumption carbon emissions are rapidly increasing in the rapid economic development, China has exceeded the United States as the largest carbon emitter in the world. At present, China is still a developing country, in the industrialization, in the middle and later period of urbanization and the rapid development of economy, which leads to the continuous high carbon emissions. There is a great pressure on emission reduction. There are great differences in the economic development and energy consumption structure of various provinces in China. How to calculate the energy consumption carbon emissions scientifically and accurately, analyze the spatial pattern and spatial dependence of the energy consumption in the province, and explore the spatial heterogeneity of the influence factors of the carbon emission in all provinces and regions, which is clear. The basic premise of the emission reduction strategy of each province is scientifically formulated. Based on the reference method provided by IPCC, this paper uses the energy consumption data of all provinces in China for 1995-2014 years to estimate the emission of energy consumption in all provinces and regions. On this basis, the standard deviation ellipse analysis and GIS visualization are used to analyze the province of China. Spatial autocorrelation analysis method is used to analyze the spatial dependence of energy consumption carbon emissions in China province by spatial autocorrelation analysis. Finally, the spatial heterogeneity of the influence factors of energy consumption in China's provincial energy consumption is analyzed by using the geographic regression weighting model. The main conclusions are as follows: (1) 1995-2014 years of China's provinces and regions As a whole, the carbon emissions in the eastern province are higher than that in the central and western provinces. The carbon emission center is located in the southeast direction of the Chinese geometric center, and the overall carbon emission center has a tendency to move north-west. It shows that although the carbon emissions from the eastern and southern provinces are higher, but in recent years, the carbon emissions are in the northwest. However, the eastern and southern provinces still need to assume greater responsibility for the emission reduction strategy in the inland areas. In the last 20 years, the standard deviation ellipse of China's carbon emissions has not changed substantially in the last 20 years, which basically covers most of the provinces with higher carbon emissions, and the spatial distribution of carbon emissions in the province is presented. The northeast and south-west pattern is presented, and there is a trend towards the direction north to the south. (2) the global Moran 's I index is positive in 1995-2014 years, and all through 5% significant tests, it shows that the energy consumption of China's provincial energy consumption has a significant spatial dependence. The local spatial autocorrelation analysis shows that the energy consumption carbon in the province of China is consumed. Emissions not only have spatial dependence, but also have spatial heterogeneity. (3) LISA time path analysis shows that the local spatial structure has strong volatility and strong stability in the number of provinces which are decreasing in the moving direction of.LISA time path, and the provinces and regions of China's carbon emission and present cooperative movement have decreased from 13 to 1995-2001 years. To 10 in 2002-2014 years, it shows that the spatial pattern of carbon emissions in China has a certain spatial integration, but it has a weakening trend.1997-2001 years. The coordinated high growth provinces are Beijing, Shanghai, Hebei, Shanxi, Henan, Inner Mongolia, Fujian and Hainan, and the provinces with low growth are Jilin, Hubei, Sichuan, Gansu and Guangxi, respectively, 2002-2. In the 014 years, the cooperative high growth provinces are distributed in the northwest inland, and the provinces with low growth are Beijing, Tianjin, Chongqing, Henan, Hunan and Guizhou. (4) from the time and space transition analysis of the Moran scatter point, in the two periods of 1995~2001 and 2002~2014, the proportion of the type IV transition in the whole province is 83.3%, that is, the space of the Moran scatter plot. The inter regional stability is 0.833, and there is no type of type III transition in the two period. It shows that the local spatial correlation model of carbon emission in China has strong stability. It is very difficult to change the relative position of the province. That is, it has a certain path dependence or spatial locking characteristics. (5) the quantitative analysis of SG method shows that China is a province. There is a positive spatial dependence between the energy consumption of the domain energy consumption, and the correlation of the 2002-2014 years is higher than the correlation of 1995-2001 years. Furthermore, with the transfer of the industrial structure and the strengthening of the relations between provinces, the spatial dependence of energy consumption carbon emissions between provinces has also increased. (6) the results of GWR model analysis show that: There are differences in the impact of the driving factors on energy consumption carbon emissions, and the impact of the same factors on energy consumption carbon emissions in different provinces is also different, and with the development of the economy, the rapid advancement of industrialization and urbanization, the progress of technology, and the influence of different factors on the carbon emissions will also be issued. The energy intensity, the energy structure, the per capita GDP and the population size have a positive correlation with the energy consumption carbon emissions, and the energy structure and the other three factors have relatively small influence on the energy consumption carbon emissions.
【學(xué)位授予單位】：湖南科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F426.2;X24

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