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Study On Spatial-temporal Differentiation Of Energy Consumption Carbon Emissions And The Affecting Factors In Three Northeast Provinces

Posted on:2018-12-25Degree:MasterType:Thesis
Country:ChinaCandidate:D S FanFull Text:PDF
GTID:2321330542483383Subject:Physical geography
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With the development of economic and the improvement of people’s living standard.human’s demand for fossil fuels is increasing heavily.The produced greenhouse gases such as carbon dioxide not only pollute the environment,but also threaten the survival security of human.Three provinces in the Northeast of China,as China’s old.industry bases,start to develop the industry early.And they have made outstanding contributions to China’s economic development.At the same time,they also bring much more emissions of Carbon.So this paper will study and analysis the regulations of spatial–temporal variation on carbon dioxide emission in three Northern provinces and influencing factors.It also expect to provide scientific basis for making policies on the region of energy–saving and emission–reduction and sustainable development.The thesis focus on the study of carbon emissions from energy consumption in 36prefecture-level cities,which are from three provinces in the Northeast of China.Through the IPCC,carbon emissions factors method and the parameter of carbon emissions to estimate the amount of carbon emissions and analysis the spatial–temporal variation factors of carbon emission from energy consumption in 36 prefecture-level cities in ten years,the influencing factors,as well as the influencing of major factors in different degree in the way of ESDA,GWR and GIS.Firstly,this paper summarizes the background and significance of the research.And the research on carbon emissions from home and abroad,research methods and impact factors are also summarized.Then there are some main work and conclusions as follows:(1)The thesis concluded that the speed of carbon emissions in the Northeast China increased rapidly.From 2005 to 2014,the amount of carbon emissions increased from257.33 million ton to 365.91 million ton.The extent of increasing was 4.2%,and there was a negative growth in 2014.The order of regional carbon emissions from large to small are: Liaoning,Heilongjiang and Jilin provinces.Compared to the carbon emissions in Heilongjiang and Jilin province,the carbon emissions in Liaoning province is the most,but the increasing speed is not so fast.From 2005-2014,in these ten years,the main part of carbon emissions shifted to the North-East.Compared to the annual average moving distance was 64.97 km,in a word,the moving distance of carbon emissions was enlarging gradually.(2)On the carbon emission of energy consumption,it exists a positive correlation among 36 prefecture-level cities in the Northeast of China and there is a downward trend on this relationship for nearly 10 years,the space aggregation characteristics of the carbon emissions of energy consumption in the northeast provinces of China was weakening.And in 2005,it reached a maximum 0.3345.The fluctuant decline of Moran’s I index of carbon emissions reflects that in the process of regional energy conservation and emissions reduction,the overall differences of carbon emissions between different places is widening gradually.From 2005 to 2014,Shenyang,Jinzhou,Panjin,Lliaoyang,Anshan,Yingkou,and Dalian were at the high value of carbon emissions,this was called spatial agglomeration.In 2005,Daqing and Jixi were in high-low area,but Qaqing was out of the high-low area in 2010.This was called otherness.The charge of energy consumption in the Northeast of China is characterized by the stability of hot-spots,the shrink of secondary hot-spots,the agglomeration status of the expand of cold-spots and the primary stability of cold-spots.It shows the energy–saving and emission–reduction in the Northeast of China has achieved progress in a degree,but it also needs further effort to achieve the energy–saving and emission–reduction in areas with high carbon emission of energy consumption.(3)The analysis of influencing factors on carbon emissions showed that: The per capital GDP plays an important role in the promotion of carbon emissions of energy consumption and it also shows a gradual increased pattern from northwest to southeast.The regression coefficient of population is positive value,and it explains that the population in 36 prefecture-level cities in the Northeast of China plays a positive role in the carbon emissions of energy consumption,and it also means that the increase of population will bring more carbon emissions of energy consumption.Besides,the sensitivity of Liaoning province is higher than Jilin and Hei longjiang.From 2005 to2014,the regression coefficient in the model of GWR was gradually reducing by making use of the foreign capital,but the deviation between the maximum and the minimum is bigger and bigger,it means that the use of foreign capital to the promoting function ofcarbon emissions is reducing,but in the study area,the utilization of foreign capital in the affecting factors of carbon emissions space difference is bigger and bigger.Urbanization rate coefficient at the year of the study,Heilongjiang province is in low area and the coefficient is negative,at the same time liaoning province is in high value area and the coefficient is positive,Shows that Heilongjiang province urbanization rate is negatively correlated with carbon emissions,but liaoning province urbanization rate is positively correlated with carbon emissions,Further illustrates the steady progress of urbanization today,out of a to improve the quality and efficiency of urbanization development of green low carbon is a new path of urbanization of Heilongjiang province urbanization way;The influence of industrial structure on carbon emissions in the study area are bigger,to further increase the intensity of industrial structure adjustment,to develop the emerging of the third industry,give full play to the advantage of the third industry in low carbon,promote the upgrading of industrial structure and carbon emissions by advancing together.
Keywords/Search Tags:carbon emissions, energy consumption, spatial differentiation pattern, affecting factors
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