Spatial effect of factors affecting household CO2 emissions at the provincial level in China: a geographically weighted regression model

Abstract

ABSTRACTThe size of household CO2 emissions (HCE) has drawn increasing attention recently. Due to differences in geographical location, traditional models do not provide a valid basis or countermeasures for CO2 emissions reduction in different provinces, leading to biased estimation. This paper uses a geographical weighted regression (GWR) model to examine the spatial effect of urbanization, energy intensity, energy structure and income on HCE. The results indicate an obvious spatial effect on carbon emissions in the provinces. The impact of urbanization on household CO2 emissions presented an increasing trend from the southeastern coast to the northwest from 2000 to 2015. Energy intensity had a remarkably positive effect on HCE in 2000 and 2015, although it had a negative effect in all provinces in 2005 and in some provinces in 2010. The elasticity coefficient of energy structure on HCE was negative in most provinces in all four years, indicating that more use of natural gas and electricity decreased HCE. Income was a powerful explanatory factor for growth in household CO2 carbon emissions in all years. The effect of income on HCE was positive and showed an increasing tendency year by year.