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Study On Decoupling Effect And Peak Prediction Of Carbon Emission In Anhui Province

Posted on:2023-04-28Degree:MasterType:Thesis
Country:ChinaCandidate:L ZhangFull Text:PDF
GTID:2531306815466954Subject:Public administration and urban culture
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Extreme climate problems such as floods and droughts caused by climate warming have gradually intensified,while the climate problem of sea-level rise caused by ice melting has become more and more serious.Climate warming caused by increasing carbon emission has become a hot topic of concern to the global community and an important challenge to the construction of ecological civilization in human society.As a major energy province in central China,how to coordinate the relationship between economic development and energy consumption while realizing the constant increase of GDP is not only related to the transformation of economic development mode of Anhui Province,but also plays an exemplary role for other cities with the same level of development to carry out carbon emission reduction.Based on the Energy-Economy-Environment(3E)theory,low-carbon economy theory,sustainable development theory and ecological civilization construction theory,first of all,this paper systematically analyzes the current situation of economic and social development,energy consumption and carbon emission in Anhui Province,using carbon emission coefficient method to calculate the carbon emission of Anhui province from 2010 to 2019,so as to provide data support for exploring the decoupling effect of carbon emission and predicting the peak of carbon emission in Anhui Province.Province.Secondly,Tapio decoupling model is used to explore the decoupling effect between economic development,energy consumption and carbon emission in Anhui Province.Thirdly,the entropy weight method is used to evaluate the low-carbon development index system of Anhui province and the prediction indexes are selected according to the evaluation results of entropy weight method and the correlation analysis results of SPSS 21.0.Then,using scenario analysis method to set low,medium and high speed development of three contextual model and using the STIRPAT model fitting to carbon emission data in Anhui province.According to the fitting equation of the model,the carbon emission of Anhui province in 2020-2035 under different development scenarios are predicted,as well as the peak carbon emission and the specific time of peak carbon emission under different development scenarios.Finally,based on the peak prediction results,strategies and suggestions are put forward for controlling carbon emission in Anhui Province from different aspects.The results show that the carbon emission of Anhui province shows a trend of phased change.In the study period,the total decoupling effect of carbon emission and energy saving decoupling effect of Anhui Province are mainly weak decoupling,and the decoupling effect of emission reduction is more complex.Therefore,the low-carbon development of Anhui Province should focus on emission reduction.Combined with the evaluation results of entropy weight method and correlation analysis results,urbanization rate,industrial structure,proportion of scientific research funds and energy intensity have the greatest impact on carbon emission in Anhui Province.Under the different development model,carbon emission peak in Anhui province as well as the peak time is different,which in low speed development mode in Anhui province carbon emission peak time is 2025,and in the medium speed development mode and the high speed development mode no peak occurred in the prediction of time scope,but medium speed development mode in Anhui province significantly more slow emission growth.Finally,suggestions are put forward for carbon emission peak control strategy in Anhui province from four aspects:coordinating regional development,adjusting industrial structure,increasing scientific research investment and improving technological level.Figure [9] Table [23] Reference [96]...
Keywords/Search Tags:Carbon emission, Decoupling effect, STIRPAT model, Scenario analysis, Peak prediction
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