本文選題：化石能源 + 碳排放量�。� 參考：《華中師范大學(xué)》2017年碩士論文

【摘要】：統(tǒng)計(屬性)數(shù)據(jù)是國家統(tǒng)計部門或機構(gòu)以行政區(qū)為單元,采用普查、抽樣等統(tǒng)計方法,搜集、整理、編制的各種統(tǒng)計資料,能展示出該區(qū)域在自然、經(jīng)濟、社會等方面所具有的屬性特征。現(xiàn)存統(tǒng)計數(shù)據(jù)存在的問題主要包括:統(tǒng)計時間間隔跨度大,即時間分辨率低;市縣級尺度統(tǒng)計數(shù)據(jù)較少,即空間分辨率低;并且統(tǒng)計結(jié)果多以文字表格等形式顯示,空間屬性表現(xiàn)方式均一化,數(shù)據(jù)內(nèi)部差異特征不明顯。通過將地理單元劃分為一定尺寸格網(wǎng),選取適宜的指標,構(gòu)建模型結(jié)構(gòu),實現(xiàn)統(tǒng)計數(shù)據(jù)在地理空間上分布的過程,即統(tǒng)計數(shù)據(jù)空間化,該方法可以有效規(guī)避現(xiàn)存統(tǒng)計數(shù)據(jù)存在的問題。近年來,全球氣候變化問題日益嚴峻,其中,人們最關(guān)心的是全球氣候變暖這一嚴峻問題,其產(chǎn)生的主要原因在于化石能源燃燒產(chǎn)生的溫室氣體——二氧化碳排放含量的大幅增加。因此,減少化石能源燃燒產(chǎn)生的碳排放量,是世界各國面臨的一個嚴峻問題,需要各國承擔(dān)相應(yīng)的責(zé)任,中國政府也積極加入了這一行列。目前中國關(guān)于化石能源統(tǒng)計數(shù)據(jù)資料較少,且能源統(tǒng)計數(shù)據(jù)主要以省級層面數(shù)據(jù)為主,市縣級能源消費數(shù)據(jù)較難獲取,同時,不同的統(tǒng)計部門采用不同的計算方法、統(tǒng)計尺度、統(tǒng)計口徑,致使不同省市之間化石能源統(tǒng)計數(shù)據(jù)存在計算偏差等問題。通過對具有空間屬性的化石能源碳排放統(tǒng)計數(shù)據(jù)進行空間化,可更好地分析溫室氣體減排問題。因此,研究碳排放統(tǒng)計數(shù)據(jù)空間化對于我們國家的長遠發(fā)展具有重要的意義。本文以化石能源消費產(chǎn)生的碳排放統(tǒng)計數(shù)據(jù)為例,通過融合多源數(shù)據(jù),構(gòu)建統(tǒng)計數(shù)據(jù)與影響因素之間的關(guān)系模型,提出了模擬中國碳排放統(tǒng)計數(shù)據(jù)跨尺度空間化的方法。本文研究的主要內(nèi)容是融合夜間燈光數(shù)據(jù)、人口數(shù)據(jù)、GDP等多源數(shù)據(jù),將化石能源碳排放的統(tǒng)計數(shù)據(jù)進行空間化,具體是利用多個尺度(省-縣-城市)的化石能源碳排放統(tǒng)計數(shù)據(jù)空間化方法,選擇能夠兼顧碳排放數(shù)據(jù)地域尺度差異的面板回歸模型,分別構(gòu)建省級-縣級-城市跨尺度的空間化模型,進而模擬出省級(中國30個省)-縣級(河南省128個縣)-城市(鄭州市)跨尺度碳排放空間化示意圖。分析中國化石能源碳排放跨尺度空間化的方法,不僅有利于中國政府了解行政區(qū)內(nèi)碳排量,從而科學(xué)合理的應(yīng)對氣候變化問題;還有利于各級政府明確行政管轄范圍內(nèi)的減排責(zé)任,為政府政策的制定提供合理的依據(jù),為我國在國際上爭取碳減排權(quán)提供科學(xué)支撐。
[Abstract]:Statistical (attribute) data are all kinds of statistical data collected, collated and compiled by national statistical departments or agencies, which take the administrative region as the unit, using statistical methods such as census, sampling, etc., to show the natural and economic situation of the region. Social and other aspects of the attributes of the characteristics. The problems of the existing statistical data mainly include: the statistical interval is large, that is, the time resolution is low; the city and county scale statistical data are less, that is, the spatial resolution is low; and the statistical results are often displayed in the form of text tables, etc. The spatial attribute expression is uniform, and the internal difference of data is not obvious. By dividing the geographical unit into a certain size grid and selecting the appropriate index, the model structure is constructed to realize the process of statistical data distribution in geographical space, that is, the spatial distribution of statistical data. This method can effectively avoid the existing problems of statistical data. In recent years, the problem of global climate change has become increasingly serious, among which, the most serious concern is the serious problem of global warming. The main reason for this is a sharp increase in greenhouse gas-carbon dioxide emissions from fossil energy combustion. Therefore, reducing carbon emissions from fossil energy combustion is a serious problem facing all countries in the world, which requires all countries to bear the corresponding responsibilities, and the Chinese government has also actively joined the ranks. At present, there are few data on fossil energy statistics in China, and the energy statistics are mainly at the provincial level, and energy consumption data at the city and county levels are difficult to obtain. At the same time, different statistical departments adopt different calculation methods and statistical scales. Statistical caliber, resulting in different provinces and cities between fossil energy statistics there are problems such as calculation deviation. Through the spatialization of carbon emission statistics of fossil energy with spatial attributes, the problem of greenhouse gas emission reduction can be better analyzed. Therefore, it is of great significance to study the spatialization of carbon emission statistics for the long-term development of our country. Taking the carbon emission statistics from fossil energy consumption as an example, a cross-scale spatial simulation method of carbon emission statistics in China is proposed by combining the multi-source data and the relationship between the statistical data and the influencing factors. The main content of this paper is to integrate night lighting data, population data and other multi-source data, so as to spatialize the statistical data of carbon emissions from fossil energy. Specifically, using the spatial method of carbon emission statistics of fossil energy based on multiple scales (province, county and city), we choose a panel regression model that can take into account the regional scale difference of carbon emission data. The spatialization models of provincial, county-level and urban scale are constructed, and the spatial map of cross-scale carbon emissions from provincial (30 provinces in China) to county-level (128 counties in Henan Province) to cities (Zhengzhou) is simulated. Analyzing the method of carbon emission from fossil energy in China is not only helpful for the Chinese government to understand the amount of carbon emission in the administrative region, but also to deal with the problem of climate change scientifically and reasonably. It also helps governments at all levels to make clear the responsibility of emission reduction within the scope of administrative jurisdiction, to provide a reasonable basis for the formulation of government policies, and to provide scientific support for our country to fight for the right to reduce carbon emissions internationally.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：X24

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