Driving factors analysis of residential electricity expenditure using a multi-scale spatial regression analysis: A case study

The driving factors of electricity consumption have been widely explored globally, especially in the U.S. However, few studies have quantified the determinants of electricity expenditure and its spatial heterogeneity at the county level. This paper employs 3142 county-level data from the U.S. to examine the spatially-varying influence of climate, socio-economic, housing type, and demographic factors on residential electricity expenditure through a multi-scale geographically weighted regression model (MGWR). As expected, the multi-scale geographically weighted regression model provides significantly better goodness of fit on the spatial analysis and gets an adjusted R2 of 0.585, which is 3.4% higher than the geographically weighted regression (GWR) model. The results demonstrate that poverty is positively related to electricity expenditure, followed by income and the percentage of mobile homes, whereas the population of white and housing structures was negative. Notably, significant regional heterogeneity of electricity expenditure is related to socio-economic and demographic characteristics (F = 360.057), while there are indications that the climate zones matter. Our empirical findings could be references to national, regional, and county-level policymakers to optimize the design of energy policies. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).