Statistical Research On Carbon Emissions Of Residential Energy Consumption Based On Social Network Analysis

In 2020,General Secretary Xi Jinping proposed the double carbon target of "carbon peaking and carbon neutral" and required that it should be included in the overall layout of ecological civilization construction.How to achieve the dual carbon goal and promote low-carbon economic development has become a significant problem that needs to be solved for the economic and social development of China.The carbon emissions from residential energy consumption currently contribute 30%of the total,second only to the industrial sector.Residential energy consumption is likely to be a new growth point for carbon emissions sources in addition to industrial energy consumption.Therefore,it is significant to study the current situation of carbon emissions from residential energy consumption to promote carbon emission reduction.Firstly,we obtain the total carbon emissions and the information entropy of carbon emission structure by carbon emission coefficient method and information entropy theory.Secondly,we use spatial statistical methods to analyze the carbon emissions of residential energy consumption.Then,we construct spatial correlation networks of residential energy consumption carbon emission structure based on modified gravity model,and use SNA(social network analysis)method to study the characteristic indicators of these networks.Next,we use QAP(quadratic assignment procedure)method to explore the influencing factors of these networks.Finally,we construct the metabolic gray Markov prediction model to forecast the future trend of household energy consumption carbon emissions.This study indicates:(1)From the time perspective,Chinese household energy consumption carbon emissions are growing,and the share of coal in the carbon emission structure is falling,while the shares of oil products,electricity and natural gas are rising.(2)From the spatial perspective,carbon emissions from residential energy consumption are higher in the eastern regions and lower in the western regions,and differences exist in the balance of carbon emission structures.Both carbon emissions and carbon emission structure information entropy from residential energy consumption show positive spatial correlation.(3)The overall network construction of the spatially connected network of residential energy consumption-induced carbon emission structure is steady,with certain transferability and accessibility,while each province does not play the same role in the network.Spatial clustering analysis shows that,at the beginning of the network,the "Bidirectional spillover" block consists of Bohai Rim provinces and cities,the "Main inflow" block consists of southeastern coastal provinces and cities,the "Agent" block is mainly composed of northeastern provinces and cities,and the "Main outflow" block is mainly composed of central inland and remote western provinces and cities.This indicates that the spillover and radiation effects of the spatially correlated network of carbon emission structure of residential energy consumption are strengthening.(4)The factors that affect the spatial correlation of residential energy consumption-induced carbon emission structure in order of their impact are spatial distance,urbanization rate,per capital GDP,per capital domestic electricity consumption,population density,science and technology input and unit GDP domestic energy consumption.(5)In the next five years,Chinese residential energy consumption carbon emissions will show an increasing trend and the information entropy of residential energy consumption carbon emission structure will fluctuate less volatile,which indicates that it is urgent to curb the growth of carbon emissions from residential energy consumption.We can focus on the structure of carbon emissions and apply the method of improving the structure of carbon emissions.We propose some suggestions based on the above research and the national conditions in China.