Using data of prefecture-level cities in Shandong province from 2004 to 2012 and the Stochastic Impacts by Regression on Population,Affluence,and Technology framework,this paper builds the geographically weighted regr...Using data of prefecture-level cities in Shandong province from 2004 to 2012 and the Stochastic Impacts by Regression on Population,Affluence,and Technology framework,this paper builds the geographically weighted regression(GWR)model of carbon emissions and its influencing factors.Unlike traditional econometric methods,such as ordinary least squares(OLS),the spatial econometrics models of spatial lag model(SLM)and spatial error model(SEM)are often estimate parameters constantly,namely these methods just estimate parameters in "average" or "globally" and can not reflect the parameters' nonstationary in different spaces.So in this paper,the influencing factors of carbon emissions are estimated by GWR,and the influencing factors of carbon emissions are estimated to be more realistic.The results indicates that the local GWR model is better than OLS,SLM and SEM,and there is spatial heterogeneity between the factors involved in economic growth,population status,industrial structure,energy price and carbon emissions across cities in Shandong province.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.71373079)
文摘Using data of prefecture-level cities in Shandong province from 2004 to 2012 and the Stochastic Impacts by Regression on Population,Affluence,and Technology framework,this paper builds the geographically weighted regression(GWR)model of carbon emissions and its influencing factors.Unlike traditional econometric methods,such as ordinary least squares(OLS),the spatial econometrics models of spatial lag model(SLM)and spatial error model(SEM)are often estimate parameters constantly,namely these methods just estimate parameters in "average" or "globally" and can not reflect the parameters' nonstationary in different spaces.So in this paper,the influencing factors of carbon emissions are estimated by GWR,and the influencing factors of carbon emissions are estimated to be more realistic.The results indicates that the local GWR model is better than OLS,SLM and SEM,and there is spatial heterogeneity between the factors involved in economic growth,population status,industrial structure,energy price and carbon emissions across cities in Shandong province.
