of 16 days We have downloaded georeferenced measures of XCO2 (the column-averaged dry air mole fraction of CO2) and computed daily mean values for each 10 km grid cell in our global database.

We use georeferenced facility-level global databases to obtain capacity measures and technology specifications for power plants (Byers et al. 2021), steel mills (GEM 2021), cement plants (McCaffrey et al. 2021), and oil refineries (Auch 2017). We use capacity estimates in the regressions because production estimates are both scanty and assigned low confidence by the database producers. Van der Werf et al. (2017) provide monthly estimates of carbon emissions from agricultural and forest burning at 25 km resolution.

The World Cities Database (2021) provides data on urban centroid locations and populations. Population density data are provided at 5 km resolution by CIESIN (2018). We use two sources to construct our georeferenced measure of income per capita. From the G-Econ database (Nordhaus et al. 2006), we obtain GDP per capita in 2005 purchasing power parity for a global grid with 100 km resolution. Each grid cell is assigned to its geographically-dominant country by G-Econ. For each cell in a country, we compute the ratio of cell GDP per capita to the national mean for all cells. We merge the results with annual World Bank estimates of GDP per capita in constant $US 2015, and use the cell ratios to estimate annual GDP per capita for each cell. We introduce another proxy for economic activity by incorporating monthly observations for VIIRS global nighttime lights (EOG 2021) at 500 m resolution. Mistry (2019) has provided global estimates of monthly heating degree days at 25 km resolution.

# **3.2 CO2 Diffusion Effects**

Space-based observations detect higher CO2 concentrations over significant emissions sources because atmospheric diffusion is not instantaneous. As emissions diffuse from their sources, deviations from background concentrations will persist for some distance. By implication, the effects of emissions sources in the model should not be constrained by arbitrarily-scaled grid structures. To allow for diffusion, we incorporate the assumption that deviations from background CO2 concentrations are inversely related to distances from emissions points. We search for best-fit inverse-distance functions in regression experiments with distance exponent values from 0.1 to 3.0. We obtain the best results with an exponent that is effectively 1.0. It does not differ significantly across model variables, which simply confirms that CO2 molecules behave identically in the atmosphere, whatever their source. For estimation, the effect of an emissions source on satellitemeasured CO2 concentration has unitary weight at its location and inverse-distance weights at the centroids of neighboring cells. For tractability, we bound the weighting radius at 100 km (where the weight is .01).

# **3.3 Wind Effects**

Our modeling approach uses grid search experiments to determine distance decay functions for CO2 emissions from all geolocated sources (power plants, steel mills, cement plants, refineries, agricultural and forest burning). To illustrate the consequence, a capacity observation for coal-fired power plants at each 10-km grid centroid is the sum of inverse-distance-weighted capacities of coalfired plants within a 100-km radius. At each point in time, the trajectory of emissions from each plant is potentially influenced by the wind direction at its location. However, we aggregate information from plants that may be widely separated, with a different wind direction at each plant.

8