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.
This paper extends the resource drag studies by empirically investigating how spatial factors affect the regional economic growth. Using spatial panel econometric models, this paper estimates the dragging effect of energy resources of the Yangtze River Delta metropolitan areas. We fi nd that the growth drag of energy in the Yangtze River Delta is about 6% on average, which means that energy constraints decrease the economic growth by 6% annually, higher than the national level that has been previously measured in the literature. This result has taken into account the impact of neighboring cities' economic development, so as to obtain a more accurate estimate. Based on these measurement results, we propose some policy recommendations.
