Research On The Reduction Of CO₂ Emission And Aggregate Carbon Intensity In China's Thermal Electricity Generation

According to the Global Energy&CO2 Status Report 2018,the global energy-related CO2 emission grew by 1.7%to a historic high of 33.1 Gt1 CO2 in 201 8.The report specially pointed out that coal-fired power plants were the single largest contributor for the growth of emissions observed in 201 8,with an increase of 2.9%,or 280 Mt2,in comparison with 2017 levels,outnumbering 10 Gt for the first time.As the world's largest CO2 emitter,As the largest carbon emitter in the world,China's coal-based resource endowment determines its coal-based energy consumption structure and power generation structure.China's power generation mainly relies on the coal-based thermal electricity generation.At present,thermal electricity generation still has significant advantages over new energy in China,such as stable supply,peaking regulation,central heating services and lower electricity generation costs,which makes it hard to replace thermal electricity generation entirely by new energy.As outlined in The Electric Power Development Planning in 13th Five-Year',the average annual growth rate of coal-fired thermal electricity generation is supposed to maintain 4.1%during 2015-2020,and 10.8%for the gas-fired thermal electricity generation.Specifically,a jump of over 5%in China's electricity generation from coal-fired power plants in 2018 drove up emissions by 250 Mt,which more than offset the decrease of CO2 emissions outside of the electricity sector.The reduction of CO2 emission and ACI(Aggregate Carbon Intensity,ACI)is becoming increasingly prominent,thus China's thermal electricity generation facing great pressure to control CO2 emission and ACI.Based on this,this paper takes the CO2 emission and ACI of China's thermal electricity generation as the research object,focusing on the driving factors of CO2 emission changes,environmental efficiency of CO2 emission,spatial decomposition of ACI variance,inequality analysis of ACI,and emission reduction feasibility of China's thermal electricity generation.The main research contents and conclusions are as follows:(1)Analyzed the basic situation of CO2 emission in China's thermal electricity generation.First,according to the State Power System Reform Project(No.5 document of the State Council),China's 30's provincial thermal power grids are divided into six regional power grids,and the research scope and data sources of the empirical analysis are clarified.Then,based on the calculation method of CO2 emission provided by Intergovernmental Panel on Climate Change(IPCC),this paper calculated the provincial and regional CO2 emission of thermal electricity generation from 2000 to 2017.Finally,analyzed the change trends of CO2 emission,absolute indicators(i.e.economic activity,energy consumption and electricity output)and relative indicators(i.e.carbon factor,energy mix,electricity intensity and et al.)of thermal electricity generation in China as a whole and its six regions.(2)Constructed the decomposition model of driving factors of CO2 emission changes in China's thermal electricity generation based on Generalized Divisia Index Method(GDIM).Different from the traditional factor decomposition methods,through the new Jacobian matrix,this paper put forward the general form of eight factor and five factor GDIM model and constructed the CO2 emission changes decomposition model of China's thermal electricity generation based on GDIM.The changes of CO2 emission in thermal electricity generation is divided into eight factors,i.e.thermal power demand,energy consumption,economic development,carbon intensity,energy structure,technical level,power output efficiency and energy efficiency.Based on the historical data of carbon emissions,this study predicted the evolution path and peak trend of CO2 emissions for China's six regional thermal electricity generation in 2020?2040 under the benchmark scenario.(3)Constructed Slack-based Model(SBM)to measure the CO2 emission efficiency and shadow price of China's thermal electricity generation.Taking installed capacity,energy consumption of coal and other energy consumption as input variables,thermal electricity output as desirable output,and energy related carbon emissions as undesirable output,a SBM model for calculating the environmental efficiency of CO2 emissions of provincial thermal electricity generation is constructed.The scores of environmental efficiency of CO2 emissions from thermal electricity generation in China's 30 provinces from 2000 to 2017 are studied,thus identifying the provinces with the lowest average score of environmental efficiency.Besides,based on the dual theory,the shadow price model of CO2 emission in the provincial thermal electricity generation is constructed to study the marginal cost of carbon emission reduction and its convergence,thus analyzing the convergence of provincial shadow price.(4)Constructed the Multi-Region&Multi-Province(M-R&M-P)spatial decomposition model of ACI to decompose the variances of ACI.Based on the traditional M-R(Multi-Region,M-R)spatial decomposition model,proposed the framework of nested M-R spatial decomposition model,and constructed the nested M?R model i.e.M-R&M-P model.The spatial variances of ACI between regions(or provinces)are divided into three factors,i.e.CO2 emission coefficient,energy structure and energy intensity.Analyzed the key factors that cause the spatial differences of ACI in different regions/provinces,thus identifying the regions and provinces with poor performance of ACI.(5)Analyzed the inequality of ACI based on Theil index.First,this study measured the inequality of ACI during 2000-2017 at both national(provinces within China)and regional(provinces within region)level based on Theil index.Then,the paper constructed the ACI inequality decomposition model and decomposed the ACI inequality of China's thermal electricity generation into inter-regional and intra-regional inequality.Finally,the concept of spatial autocorrelation is introduced based on the traditional Markov chain,and the provincial ACI traditional and spatial Markov chain transfer probability matrixes are constructed.Analyzed evolution characteristics of provincial ACI during 2000 to 2017 from the perspective of time and space.