中国省域能源消费碳排放空间依赖及其影响因素分析

发布时间：2018-06-05 09:04

本文选题：省域碳排放 + 能源消费　；参考：《湖南科技大学》2017年硕士论文

【摘要】：全球气候变暖是当前可持续发展面临的巨大挑战,已有研究表明人类活动产生的温室气体特别是CO_2是导致全球气候变暖的最主要原因。因此,如何减少CO_2排放已成为世界各国面临的共同问题。自改革开放以来,中国经济持续快速发展,经济总量已跃居世界第二。然而,在经济快速发展的同时中国的能源消费碳排放量也迅速增加,已超过美国成为世界最大的碳排放国。目前,中国仍是发展中国家,处于工业化、城市化中后期,经济高速发展,导致其碳排放量持续走高,面临着巨大的减排压力。中国各省域经济发展和能源消费结构存在很大差异,如何科学准确地测算各省能源消费碳排放量,分析中国省域能源消费排放费的空间格局变化及其空间依赖关系,探究各省域碳排放影响因素的空间异质性,是明确各省减排目标、科学制定“共同但有区别”的减排策略的基本前提。本文基于IPCC提供的参考方法,利用1995-2014年中国各省能源消费数据估算各省域能源消费碳排放量。在此基础上,利用标准差椭圆分析法及GIS可视化方法分析中国省域能源消费碳排放的空间格局变化,采用空间自相关分析法分析中国省域能源消费碳排放的空间依赖关系,最后利用地理回归加权模型分析中国省域能源消费碳排放影响因素的空间异质性。得出以下主要结论:(1)1995-2014年中国各省域的能源消费碳排放量显著增加,从整体上看,东部省域的碳排放量高于中部及西部省域碳排放量。碳排放重心位于中国几何中心东南方向,整体上碳排放重心有向西北方向迁移的趋势,表明虽然东部和南部省域碳排放较高,但近年来西北内陆地区省域碳排放增速要高于其他省域,尽管如此,在制定减排策略时东部和南部省域仍需承担更大的责任。近20年中国碳排放的标准差椭圆总体上变化幅度不大,基本上覆盖了绝大部分碳排放较高的省域,省域碳排放的空间分布呈现出东北—西南格局,且有逐步向正北—正南方向转变的态势。(2)1995-2014年全局Moran’s I指数均为正值,且均通过5%显著性检验,表明中国省域能源消费碳排放之间具有显著的空间依赖性。局部空间自相关分析表明中国省域能源消费碳排放之间不但具有空间依赖性,而且具有空间异质性。(3)LISA时间路径分析表明,局部空间结构具有强波动性和强稳定性的省区数量均呈下降趋势。LISA时间路径的移动方向类型中,中国碳排放出现协同运动的省区由1995-2001年的13个下降到2002-2014年的10个,表明中国碳排放空间格局变化具有一定的空间整合性,但呈减弱趋势。1997-2001年,协同高增长的省区分别为北京、上海、河北、山西、河南、内蒙古、福建和海南,协同低增长的省区分别为吉林、湖北、四川、甘肃和广西。而2002-2014年,协同高增长的省区分布在西北内陆,协同低增长的省区分别为北京、天津、重庆、河南、湖南和贵州。(4)从Moran散点的时空跃迁分析看,在1995~2001年和2002~2014年两个时段中,类型Ⅳ跃迁的省域占全部省区的比例为83.3%,即Moran散点图的空间稳定性均为0.833,且两个时段中均无发生类型Ⅲ跃迁的省域,表明中国省域碳排放的局部空间关联模式存在较强的稳定性,省域要改变自身的相对位置非常困难,即具有一定的路径依赖或空间锁定特征。(5)SG方法定量分析表明中国省域能源消费碳排放之间具有正向的空间依赖性,且2002-2014年的相关性高于1995-2001年的相关性,进而说明随着产业结构的转移,省域之间联系的加强,省域之间能源消费碳排放的空间依赖性也曾增加趋势。(6)GWR模型分析结果表明:总体上看各驱动因素对能源消费碳排放的影响存在差异性,同一影响因素在不同省份对能源消费碳排放的影响也存在差异性,而且随着经济的发展、工业化和城镇化的快速推进、技术的进步,各影响因素对碳排放影响的空间异质性格局也会发生明显的变化。能源强度、能源结构、人均GDP和人口规模等因素与能源消费碳排放均有正相关关系;能源结构和其它三个因素相比,对能源消费碳排放的影响相对较小。
[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.
【学位授予单位】：湖南科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F426.2;X24

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