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Driving Forces And Decoupling States Of Carbon Emissions At Provincial Level

Posted on:2021-05-10Degree:MasterType:Thesis
Country:ChinaCandidate:H Q GeFull Text:PDF
GTID:2381330614458075Subject:Environmental Science
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As the largest carbon emitter,China is under the international and domastic pressure to urgently develop a low-carbon economy.Its vast territory and unbalanced regional development also have led to large differences in the economic development and carbon emissions among the provinces.Therefore,it is of great practical significance to systematically analyze the carbon emissions at the provincial level and the underlying driving forces to unlock the relationship between carbon emissions and economic development-the largest carbon emission-promoting factor which are essential to design and implement targeted and scientifically sound emission reduction policies.This study first calculated the energy-related carbon emissions of China's economic sector at the provincial level and analyzed the emission characteristics from 2000 to 2015.The K-mean cluster analysis was used to divide 30 provinces according to the emission characteristics.The results show that the total national emissions increased rapidly before 2011,then grew slowly,and even showed a slight decline in 2015.Compared with the 11 th Five-Year Plan(FYP)period,some provinces in the east and central China shifted from a high-emission growth mode to a low-emission growth mode in the 12 th FYP,indicating that the 12 th FYP may be a turning point in China's emission control effort.The Logarithmic Mean Divisia Index(LMDI)was combined with Cobb-Douglas production function to analyze the driving force of provincial carbon emissions in the three complete periods of 10 th FYP,11 th FYP and 12 th FYP.The results show that energy intensity reduction has consistently leading the inhibition of emissions,especially during the 12 th FYP period.The energy structure and industrial structure show little impact on carbon emissions until the 12 th FYP period,suggesting large potential for reducing structural emissions in the future.Economic development is the biggest factor in promoting carbon emissions,and when further decomposted into the capital investment,labor investment,and total factor productivity,all three factors appear to have positive influence on carbon emissions.The emission-promoting role of capital input and total factor productivity is significantly higher than that of the labor investment.during the 11 th FYP period than that of the 10 th FYP period,but only slightly less than that of the 12 th FYP period.Slowing of the economy's contribution to carbon emissions in the 12 th FYP could be caused by the role transformation of capital input and total factor productivity from simply expanding production scale and improving production efficiency to investing in low-carbon equipment and improving energy efficiency.Finally,the Tapio decoupling model is used to analyze the decoupling status of carbon emissions and economy,and the club convergence is used to analyze the convergence trend of the decoupling index among the provinces.The results demonstrate that emissions-economy decoupling status has been improving during the three FYPs and provinces in east China show better than those in the central and western regions.When the decoupling state is further decomposed into emission intensity factors and energy consumption factors,it is found that the decoupling state is donimanted by energy consumption decoupling.Half of the provinces achieved the weakly decoupling status of carbon emissions-capital input during the 10 th FYP period,while the decoupling status of carbon emissions from labor input and total factor productivity only improved during the 12 th FYP period.The overall club convergence of all decoupling indicators indicates that the correlation between the economy and carbon emissions has been relatively consistent across the provinces.
Keywords/Search Tags:provincial carbon emissions, driving force, LMDI decomposition model, Tapio decoupling model, club convergence analysis
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