Section 6 computes expected emissions values and deviations from those values for a large number of urban areas. Section 7 summarizes and concludes the paper. The Appendix describes the World Bank’s development of an online platform to support future work.

# **2.  The CO2 Emissions Model**

# **2.1 Model Specification**

An extensive body of empirical literature has explored the determinants of growth in CO2 emissions. Attention has focused primarily on the drivers of CO2 emissions from fossil-fuel combustion and cement production (e.g., Raupach et al. 2007; Jotzo et al. 2012). Recent research has also estimated more precisely the CO2 emissions from fires associated with agriculture and land-use change (Gasser et al. 2020; Winkler et al. 2021). However, emissions drivers in this sector have received less attention than work on industrial determinants (Sanchez and Stern 2016). For the industrial sectors, most of the available estimates are inferred from survey-based activity measures that may be incomplete, particularly for developing countries.

This study takes a completely different approach, employing direct CO2 observations from satellites. The dependent variable in our model is the atmospheric CO2 concentration above a cell in a global grid with 25 km resolution. We employ 25-km grid cells defined by Van der Werf et al. (2017), who provide the data on CO2 emissions from fires for this analysis. The dominant component of the atmospheric concentration is the global stock of CO2 molecules that have accumulated since the Industrial Revolution. The second component is seasonal CO2, reflecting differential absorption and release by vegetation over the annual cycle. The seasonal CO2 component is latitudinal, differing by hemisphere because the Northern Hemisphere has more plant life than the Southern Hemisphere.

The third component is local CO2 emissions, reflecting the time lag between local emissions of CO2 molecules and their full dispersion into the global mix. We follow convention by terming this the “concentration anomaly” since it measures the local deviation from the global background CO2 concentration. In our global emissions model, we classify the determinants of local concentration anomalies in three categories. The first comprises activities in the most significant CO2-emitting industry sectors. The Intergovernmental Panel on Climate Change (IPCC) (Gale et al. 2005) has identified four dominant industrial sources of CO2 emissions: (i) power plants, (ii) steel mills, (iii) cement plants, and (iv) oil refineries. The second category includes CO2 emissions from agricultural and forest fires. The third category comprises population-related emissions other than those directly associated with CO2-intensive industrial activity. These include motor vehicle emissions, which are not measured reliably at the spatial resolution required for our analysis.<sup>1</sup> We would expect traffic emissions per capita to increase with income per capita, all else being equal. Population-related factors also include CO2 emissions from household heating and cooling, which are not captured by data for central power plants.<sup>2</sup> The subway component of the model affects population-related emissions by

> 1 Recent research has used Google Traffic to infer vehicular emissions from high-resolution traffic congestion data for some cities (Heger et al. 2018; Dasgupta, Lall, and Wheeler 2021). However, no currently available technology enables direct estimation of global vehicular emissions at 25 km resolution.

> 2 Household air conditioning is powered by fossil-fired home generators in many hot low-income areas where utility-scale power is either nonexistent or unreliable. For a detailed assessment, see Lam et al. (2019).

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