Hemisphere. The third component is local, reflecting the time lag between local emissions of CO2 molecules and their full dispersion into the global mix. In this paper, we use the term “concentration anomaly” for the local component because it measures the deviation from the global background CO2 concentration.

Our model incorporates the two global components in a global time trend and controls for seasonality and hemisphere location. The local component comprises variables in three broad categories. The first includes activities in the most critical CO2-emitting sectors. The Intergovernmental Panel on Climate Change (IPCC) (Gale et al. 2005) has identified four dominant industrial sources of CO2 emissions: power plants, steel mills, cement plants, and oil refineries. Another potentially-important factor is atmospheric “spillover” from agricultural and forest burning in neighboring areas. Traffic emissions are also important, but we do not have reliable measures of motor vehicle operation for most of the cities in our sample.<sup>1</sup> The model therefore absorbs motor vehicle operations into the non-industrial income effects that are described below.

The second category includes demographic and economic variables. CO2 emissions should increase with urban population, ceteris paribus, because each resident accounts for some emissions. The spatial distribution of population may also play a significant role. In high-income areas that rely heavily on mechanized transport, increased population density may lower aggregate emissions by reducing travel requirements. On the other hand, higher population density in low-income areas may translate to higher CO2 emissions because small reductions in sparse mechanized transport do not offset increased CO2 emissions from factors like more concentrated household cooking and heating.

Economic development also has countervailing effects on CO2 emissions intensity. Higher-income urbanites use more goods and services, some of which are CO2-intensive (e.g., home heating, motor vehicles). However, locational economics inhibit CO2-intensive industrial activities in higherincome urban areas with higher land costs, fewer active resource mining sites, and stricter control of local air pollutants that are emitted along with CO2. In consequence, higher-income cities tend to import CO2-intensive goods and services from areas where the converse conditions hold. The net effect of urban economic development on CO2 emissions depends on the relative strength of direct income effects and indirect displacement and pollution control effects. Empirical research on the relationship between CO2 emissions and income has relied almost entirely on country-level emissions inventories. The results are mixed; some studies find a linear relationship, while others identify an inverse U-shaped relationship, or Environmental Kuznets Curve (EKC) (Ben Youssef, Hammoudeh and Omri 2016; Dasgupta et al. 2002). Where the EKC holds, domination passes from direct effects to displacement and pollution control effects as income rises. With satellitebased CO2 measures, the EKC investigation in this paper departs from conventional practice by avoiding emissions inventories and focusing on local rather than national CO2 emissions.

Climate comprises the third category. Other things equal, we would expect greater annual heatingrelated CO2 emissions from cities in cold climates.

We specify a linear estimation model because the atmospheric CO2 load should be additive in CO2 emissions from different sources. Spatially-referenced variables in the model are translated to

> 1 Recent research has used Google Traffic to produce high-resolution traffic congestion data for individual cities (Dasgupta et al. 2021; Heger et al. 2018).  Future research could extend this approach to all global cities.

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