摘要：Recent sustainability research has focused on urban systems given their high share of environmental impacts and potential for centralized impact mitigation. Recent research emphasizes descriptive statistics from place-based case studies to argue for policy action. This limits the potential for general insights and decision support. Here, we implement generalized linear and multiple linear regression analyses to obtain more robust insights on the relationship between urbanization and greenhouse gas (GHG) emissions in the US We used consistently derived county-level scope 1 and scope 2 GHG inventories for our response variable while predictor variables included dummy-coded variables for county geographic type (central, outlying, and nonmetropolitan), median household income, population density, and climate indices (heating degree days (HDD) and cooling degree days (CDD)). We find that there is not enough statistical evidence indicating per capita scope 1 and 2 emissions differ by geographic type, ceteris paribus. These results are robust for different assumed electricity emissions factors. We do find statistically significant differences in per capita emissions by sector for different county types, with transportation and residential emissions highest in nonmetropolitan (rural) counties, transportation emissions lowest in central counties, and commercial sector emissions highest in central counties. These results indicate the importance of regional land use and transportation dynamics when planning local emissions mitigation measures.
