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Study On The Spatial-temporal Evolution And Driving Factors Of Carbon Emission Efficiency In Thermal Power Industry

Posted on:2019-01-26Degree:MasterType:Thesis
Country:ChinaCandidate:F JiangFull Text:PDF
GTID:2371330548489352Subject:Quantitative Economics
Abstract/Summary:PDF Full Text Request
With the deepening of the reform of the power industry,thermal power industry is facing a comprehensive integration,and the thermal power plants with low power generation efficiency are gradually replaced.How to reduce carbon emissions of thermal power has become the focus of scholars at home and abroad.Increasing carbon emission efficiency of thermal power is the key to reduce carbon emissions from thermal power plants.Scientific measurement of carbon efficiency and its influencing factors in different regions of China is of great significance to formulate relevant policies and achieve national carbon reduction strategy goals.In the use of the stochastic frontier model to 2003-2014 years of China's 30 provinces(excluding Hong Kong and Macao,Tibet).The spatial correlation test is carried out.Based on this,and Dubin model is used to explore the influence of the related driving factors on the space.Finally,through the endogeny division,the threshold effect of environmental regulation index on carbon emission efficiency of thermal power is discussed.The research finds that:(1)During the survey period,the static electricity carbon efficiency in China has a slow rising trend with the vertical dimension of time,but its rise is decreasing.In a specific time period,the growth of efficiency value is decreasing.(2)In 2004-2014 years,the overall trend of power carbon efficiency in the whole country has been fluctuating,and the growth is not smooth.Among them,the technological efficiency of decomposition factors is slow down,and the phenomenon of technological retrogression appears.(3)The power efficiency of carbon in 30 provinces of China is of spatial correlation and spatial dependence in spatial distribution.In the period of 2003-2014,the spatial correlation of power carbon showed a downward trend,and the spatial agglomeration and dependence were weakening.(4)The results of spatial econometric analysis show that there is space interaction and significant spatial effect on power efficiency between provinces.The spatial spillover effect of urbanization rate,per capita GDP,coal consumption and state-owned assets proportion has a spatial spillover effect,and the spatial spillover effect has greatly affected the change of regional agglomeration.(5)The endogenous environmental regulation has a significant double threshold effect on power carbon efficiency.Based on the above conclusions,puts forward the driving factors of carbon emission reduction power policy recommendations:(1)To increase the thermal power generation technology,human capital investment,optimize the utilization rate of coal combustion;adjusting the structure of power type,make full use of geographical advantages and local resources to increase the power plant construction of clean energy based wind power,hydropower,reduce dependence on the electric fire.(2)We should speed up the process of deepening the reform of China's power grid,integrate the power grid of the power industry,improve the west east power transmission project,optimize the allocation of electric power resources,form complementary among regions.(3)We should pay close attention to the direct and indirect effects of economic development level,population structure and environmental regulation on electricity carbon emissions,and optimize the carbon emission efficiency policy of environmental regulation.(4)Scientific formulation of environmental regulation policies,regulation of environmental regulations,increasing research and development of environmental technology and training of relevant talents,and establishing awareness of environmental protection are of great significance for the implementation of power carbon emission reduction.
Keywords/Search Tags:Thermal power industry, carbon emission efficiency, random frontier, spatial measurement, threshold regression
PDF Full Text Request
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