A spatial uncertainty metric for anthropogenic CO2 emissions

Large point sources account for as much as 60% of the anthropogenic carbon dioxide emissions for some countries. Because CO2 emissions are seldom measured directly, but are generally estimated from other data, we need to understand the uncertainty of these emissions estimates. Simply stated, for any given geographical and temporal location, we would like to quantify the emissions and the associated uncertainty with as fine a resolution as possible. While US data on point sources are largely assumed to be among the best available globally, the reported locations of these sources, based on the data set used in this analysis, are estimated to differ by 0.84 km on average from their actual locations. This paper presents a metric to quantify spatial uncertainty in point sources and explains why the uncertainty in point source data cannot be described with traditional methods. A Monte Carlo simulation is used to calculate expected emissions values for each point source and the associated spatial uncertainty is derived from these expected values. The uncertainty metric can be used to define and calibrate appropriate levels of resolution for regions with more or less reliable data sets. Gridded data are output to be incorporated into other data products reporting spatially explicit emissions estimates.