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Carbon Emission Accounting And Analysis Of Its Driving Factors In Typical Cities Of China

Posted on:2020-11-21Degree:MasterType:Thesis
Country:ChinaCandidate:Z H GongFull Text:PDF
GTID:2381330575460396Subject:Human Geography
Abstract/Summary:PDF Full Text Request
For nearly a century,climate change is gradually destroying the environment we live on.While the city brings convenience to people,it also brings a series of environmental problems,such as more and more greenhouse gases are emitted into the atmosphere.As an important city in China,Beijing,Guangzhou and Shenzhen have always been the guides and focus of China's low-carbon economy development,and the responsibility for carbon emission reduction is even more important.Therefore,this paper takes these cities as examples to calculate the carbon emissions of energy consumption in typical cities in China,discusses the influencing factors of their carbon emissions,and compares the differences in carbon emission characteristics and influencing factors among the three cities.Combined with the actual situation,some opinions on low carbon emission reduction were put forward.The details are as follows.Firstly,the IPCC checklist nuclear algorithm was adopted to construct a carbon emission accounting framework from the perspectives of three major industries,light and heavy industries and eight major industrial sub-sectors,and the energy consumption carbon emissions of Beijing,Guangzhou and Shenzhen in the past ten years were accounted for.The results show that the energy consumption carbon emission curves of Beijing,Guangzhou and Shenzhen all show similar shapes of inverted U or inverted W.In addition,Beijing's energy consumption is increasing,Shenzhen's energy consumption is declining,and Guangzhou's energy consumption is first increased and then declined.The energy consumption density of the three cities is declining.Heavy industry or secondary industry is the biggest factor in energy consumption and carbon emissions.Among them,Beijing's three major industrial energy consumption carbon emissions rose from 95 million tons in 2005 to 105 million tons in 2010,and then fell to 90 million tons in 2015,the trend is similar to the inverted "W" shape.Guangzhou's industrial energy consumption carbon emissions rose from 41 million tons in 2004 to 52 million tons in 2006,and then fell to 27 million tons in 2016.The trend is like a "U" shape.The carbon emissions of industrial energy consumption in Shenzhen increased from 20 million tons in 2009 to 22 million tons in 2010,and then fell to 13 million tons in 2016.The trend is also in a "U" shape.Then,the logarithmic mean Division exponential decomposition method(LMDI)is used to decompose the corresponding factors of energy consumption carbon emissions in Beijing,Guangzhou and Shenzhen.Studies have shown that the economies of scale are the biggest driving factors for the growth of energy consumption in Beijing,Guangzhou and Shenzhen.The energy intensity effect is the biggest mitigating factor for carbon emissions from energy consumption in Beijing,Guangzhou and Shenzhen.The industrial structure effect only has a reduced impact on Beijing's energy consumption carbon emissions,but has an increased impact on Guangzhou and Shenzhen.This is because Beijing's industrial structure is better than Guangzhou and Shenzhen.The effect of industrial emission factors has caused a reduction in carbon emissions in Beijing and Guangzhou,which has caused an increase in Shenzhen.This is because the energy structure of Beijing and Guangzhou is better than that of Shenzhen.From the perspective of sub-industries,heavy industry,secondary industry,hydropower production and supply are the main contributors to China's typical urban energy consumption carbon emissions.Finally,based on the research results,this paper proposes policy recommendations for building a low-carbon industry economy,adjusting and optimizing the energy structure,and strengthening international cooperation.
Keywords/Search Tags:Carbon Emissions, LMDI, Energy Consumption, Factor Decomposition, Influencing Factors
PDF Full Text Request
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