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Low Carbon Path In Anhui Province Under The Background Of "Double Carbon" Goal

Posted on:2024-02-06Degree:MasterType:Thesis
Country:ChinaCandidate:L Y JiangFull Text:PDF
GTID:2531307082961909Subject:Business Administration
Abstract/Summary:PDF Full Text Request
The rapid economic development leads to the rapid growth of energy consumption,and the massive emission of carbon dioxide leads to the increasingly serious greenhouse effect.As the world’s largest developing country and the world’s largest carbon emitter,General Secretary Xi Jinping set a target of reaching a carbon peak by 2030 and becoming carbon neutral by 2060 at the 75 th UN General Assembly.Under the overall requirements of the "two-carbon" goal,understanding the regional carbon emission level and carbon sink potential is the basis and core of promoting regional low-carbon development.Hefei,Huaibei,Huangshan,Lu ’an and Xuancheng were selected as the third batch of national pilot low-carbon cities,which is a signal that Anhui is actively promoting low-carbon development.By measuring the overall carbon emission level of Anhui Province,analyzing its main sources and change rules,and exploring the influencing factors of carbon emission,objective suggestions can be provided for Anhui Province to achieve the goal of "double carbon" as soon as possible.In this paper,the carbon emission inventory of Anhui Province from 2000 to 2020 was compiled from five sources of carbon emission,namely energy consumption,food consumption,cultivated land,ruminants and waste,and three carbon sink sources,namely forest,grassland and crops.On this basis,the change of carbon emissions in different departments is analyzed,and the reasons leading to their differences are analyzed.The STIRPAT model is used to analyze the relevant influencing factors of carbon emission in Anhui Province,and to predict its carbon emission in the period of "14th Five-Year Plan".We will combine the Tapio decoupling model,the LMDI model and Attribution Analysis to the industry,which is the main source of carbon emissions on the carbon inventory,and make a systematic analysis of changes in carbon emissions and the main driving factors from the Tenth Five-Year Plan to the Thirteenth Five-Year Plan,so as to find a path to low-carbon development in Anhui Province.The empirical results show that:(1)From the perspective of carbon emission inventory of Anhui Province,its carbon emissions increase rapidly from 2000 to 2020,and carbon emissions caused by energy consumption is its main source.Energy structure gradually adjusts from single coal consumption to coordinated development of coal,oil,electricity and other energy sources;There are significant differences between urban and rural energy consumption and energy structure.The carbon emission from waste is growing rapidly,and the carbon emission from waste incineration is the main source.(2)From the perspective of industry in Anhui Province,the relationship between economic benefits and carbon emissions of the other four industrial sectors in Anhui Province is gradually weakened,except transportation,warehousing and postal industry.Through the decomposition model of LMDI index,it is found that economic efficiency is the main driving factor of industrial carbon emissions in Anhui Province,and its driving effect gradually decreases.Energy intensity is the main inhibiting factor of industrial carbon emissions,which plays a large inhibiting role in the early stage of the decline of industrial carbon emissions.Through attribution analysis,it can be found that the major contributor of industrial carbon emissions is industry,and the major contributor of industrial carbon emissions is mining,electricity,heat,gas and water production and supply industry and resource processing industry.Finally,according to the empirical results,suggestions were put forward to optimize the energy structure,increase the proportion of clean energy,steadily promote the level of urbanization in Anhui province,and improve the garbage disposal mechanism.
Keywords/Search Tags:Carbon emission inventory, STIRPAT model, Decoupling index, Attribution analysis
PDF Full Text Request
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