中国化石能源碳排放统计数据跨尺度空间化方法研究

发布时间：2018-07-05 13:20

本文选题：化石能源 + 碳排放量　；参考：《华中师范大学》2017年硕士论文

【摘要】：统计(属性)数据是国家统计部门或机构以行政区为单元,采用普查、抽样等统计方法,搜集、整理、编制的各种统计资料,能展示出该区域在自然、经济、社会等方面所具有的属性特征。现存统计数据存在的问题主要包括:统计时间间隔跨度大,即时间分辨率低;市县级尺度统计数据较少,即空间分辨率低;并且统计结果多以文字表格等形式显示,空间属性表现方式均一化,数据内部差异特征不明显。通过将地理单元划分为一定尺寸格网,选取适宜的指标,构建模型结构,实现统计数据在地理空间上分布的过程,即统计数据空间化,该方法可以有效规避现存统计数据存在的问题。近年来,全球气候变化问题日益严峻,其中,人们最关心的是全球气候变暖这一严峻问题,其产生的主要原因在于化石能源燃烧产生的温室气体——二氧化碳排放含量的大幅增加。因此,减少化石能源燃烧产生的碳排放量,是世界各国面临的一个严峻问题,需要各国承担相应的责任,中国政府也积极加入了这一行列。目前中国关于化石能源统计数据资料较少,且能源统计数据主要以省级层面数据为主,市县级能源消费数据较难获取,同时,不同的统计部门采用不同的计算方法、统计尺度、统计口径,致使不同省市之间化石能源统计数据存在计算偏差等问题。通过对具有空间属性的化石能源碳排放统计数据进行空间化,可更好地分析温室气体减排问题。因此,研究碳排放统计数据空间化对于我们国家的长远发展具有重要的意义。本文以化石能源消费产生的碳排放统计数据为例,通过融合多源数据,构建统计数据与影响因素之间的关系模型,提出了模拟中国碳排放统计数据跨尺度空间化的方法。本文研究的主要内容是融合夜间灯光数据、人口数据、GDP等多源数据,将化石能源碳排放的统计数据进行空间化,具体是利用多个尺度(省-县-城市)的化石能源碳排放统计数据空间化方法,选择能够兼顾碳排放数据地域尺度差异的面板回归模型,分别构建省级-县级-城市跨尺度的空间化模型,进而模拟出省级(中国30个省)-县级(河南省128个县)-城市(郑州市)跨尺度碳排放空间化示意图。分析中国化石能源碳排放跨尺度空间化的方法,不仅有利于中国政府了解行政区内碳排量,从而科学合理的应对气候变化问题;还有利于各级政府明确行政管辖范围内的减排责任,为政府政策的制定提供合理的依据,为我国在国际上争取碳减排权提供科学支撑。
[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.
【学位授予单位】：华中师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X24

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