cities in the sample, providing a consistent mid-day activity benchmark for comparing CO2 concentration anomalies.<sup>3</sup> OCO-2 has an observation repeat time of 16 days. We have downloaded georeferenced measures of XCO2 (the column-averaged dry air mole fraction of CO2).

We filter the XCO2 data for local concentration anomalies, or differences between observed and background CO2 at each point. We calculate background CO2 using the methodology of Hakkarainen et al. (2019), which incorporates both temporal and geographic elements. As Hakkarainen notes, the available data are insufficient for estimating daily medians at resolutions higher than 10 degrees of latitude. We compute the daily median XCO2 for each 10-degree latitude band and linearly interpolate the result to each OCO-2 observation with 1-degree resolution. Following Hakkarainen, we use the median as the representative value because it is not skewed by extreme observations. We subtract this background value to compute the local anomaly for each observation. Then we compute monthly mean values of concentration anomalies for the 25-km grid cells in our 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 convert capacity measures to annual CO2 emissions using standard emissions factors for power production by fuel source (USEIA 2021), steel mills (World Steel Association 2021), cement (IEA 2020), and refineries (Jing et al. 2020). Van der Werf et al. (2017) provide monthly estimates of carbon emissions from agricultural and forest burning at 25 km resolution.

Mistry (2019) has provided global estimates of monthly heating and cooling degree days at 25 km resolution.<sup>4</sup> We compute population at 25 km resolution by aggregating data from CIESIN (2021) at 5 km resolution. Monthly estimates are interpolated from data provided for 2010, 2015, and 2020. 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 UN estimates of GDP per capita in constant $US 2015 (UN 2021), and use the cell ratios to estimate annual GDP per capita for each cell. We resample these cells to 25 km for compatibility with the rest of our database.

We draw our subway data from two sources. The first is a global subway survey by Turner and Gonzalez-Navarro (2018), which includes 137 systems installed prior to 2011.<sup>5</sup> The survey includes digital subway maps at five-year intervals from 1930 to 2010. The second source is our own survey of 55 subway systems installed since 2010. We have constructed digital maps for these systems using

> 3 CO2 measurement during the full daily activity cycle will improve as systems like OCO-3 observe each area at more widely varying times.

> 4 Mistry’s data terminate in December 2019. We extend the domain for regression analysis by computing monthly means for each 25 km cell using the data for 2014–19.

> 5  Gendron-Carrier et al. (2020) provide the following definition: “These data define a ‘subway’ as an electric powered urban rail system isolated from interactions with automobile traffic and pedestrians. This excludes most streetcars because they interact with vehicle and pedestrian traffic at stoplights and crossings, but underground streetcar segments are counted as subways. The data do not distinguish between surface, underground, or aboveground subway lines as long as the exclusive right of way condition is satisfied. To focus on intraurban subway transportation systems, the data exclude heavy rail commuter lines (which tend not to be electric powered). For the most part, these data describe public transit systems that would ordinarily be described as ‘subways’, e.g., the Paris metro and the New York city subway, and only such systems.”

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