The influencing factors of urban household embedded carbon emissions based on quantile regression

摘要

The embedded carbon emissions produced by human consumption are an important part of residential carbon emissions.With large sample data and high-resolution remote sensing images,we explored the spatial differentiation and influencing factors of household embedded carbon emissions within the city fine scale using the EIO-LCA model and quantile regression.The results indicate that the factors of family characteristics,housing characteristics,lifestyles,and consumption concept have significant effects on the embedded carbon emissions of each person.The influencing intensity of most factors showed an increasing trend with increased carbon emissions.The application of the quantile regression(QR)method can more accurately reveal the different mechanisms of various influencing factors under different distribution functions and overcome the estimation error of the least square method on the mean level.The estimated results show that the influence strength of the remaining factors on embedded carbon emissions per capita showed a trend of increase with the increase in carbon emissions,but the influencing strength of housing types and frugal purpose for saving money have the tendency of zigzag fluctuation.To a certain extent,the result emphasized the necessity of specific emissions reduction measures for high carbon emissions groups.