Multiscale Carbon Burden of Infrastructure in the United States

Abstract

Anthropogenic greenhouse gas (GHG) emissions vary spatially with development patterns, climate, economic structure, and energy systems. Using Vulcan v4.0 fossil-fuel CO2 (FFCO2) data for the United States at 1-km resolution, this study examines how land use and infrastructure shape emissions at local and metropolitan scales. I combine doubly robust Bayesian Additive Regression Tree estimators with multi-treatment spatial regression models to identify local and spillover effects by sector. A key methodological contribution is treating transportation emissions as production-based, capturing infrastructure carbon burden rather than household-attributed travel demand. Results show strong scale dependence and spatial interaction: in the preferred heteroscedasticity-robust SLX+SEM model with a 10-km distance band, residual spatial autocorrelation declines substantially. Local roadway design is a strong negative predictor of transportation FFCO2, while neighbouring land use diversity exceeds local diversity. In residential sectors, higher local density is consistently associated with lower per-capita emissions. These findings support coordinated multi-scale mitigation policy in the United States.