Third, we use the results to estimate the expected CO2 concentration for each city, given its geographic, demographic and economic characteristics. We explore the implications using regression residuals as performance indicators that identify cities whose CO2 emissions are less than or greater than model-based expectations. This provides the first empirical scorecard of city performance in CO2 emissions management.

Fourth, we use the econometric results to address a critical policy question on the role of public investment as a supplement to Pigouvian policy in mitigating CO2 emissions. Economists generally support emissions reduction via emissions taxation or permit trading (Stiglitz and Stern 2021; Jacobs and van der Ploeg 2019; King et al. 2019; Klenert et al. 2018). Many policy analysts who support Pigouvian pricing also argue for a non-Pigouvian supplement: coordinated public investment in low-carbon land development, energy and transport that will accelerate the transition to low-carbon economies (van der Ploeg and Venables 2020). Investments in subway systems provide an interesting test case for the non-Pigouvian approach. Reduced motor vehicle emissions and energy efficiencies associated with higher-density development are frequently cited as carbon-saving advantages of mass transit systems. By implication, cities with subways should have followed lowercarbon development paths, other things equal. We explore this proposition with our regression residuals, drawing on a recent global survey of subway systems (Turner and Gonzalez-Navarro 2018).

The remainder of the paper is organized as follows. Section 2 introduces the econometric model, while Section 3 describes the data. Section 4 performs an exploratory analysis of the data and the econometric results are presented in Section 5. Section 6 explores the implications, Section 7 offers a prospectus for future research, and Section 8 summarizes and concludes the paper.

# **2.  Modeling Urban CO2 Concentrations**

An extensive empirical literature has explored the determinants of CO2 emissions growth. Most of the attention has focused on drivers of CO2 emissions from fossil fuel combustion and cement production (e.g. Raupach et al., 2007; Jotzo et al., 2012). Land-use change also produces large CO2 emissions, which have been estimated more precisely by recent research (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 both industrial and non-industrial sectors, previous studies have relied almost exclusively on estimates from CO2 emissions inventories that apply engineering parameters to measures of activity in industry, transport, land-clearing, residential heating and other sectors. Standard engineering estimates are particularly suspect for developing countries, because many of the parameters have been calibrated using databases and models developed for high-income economies.

This study takes a completely different approach, employing only direct CO2 observations from satellites. The dependent variable in our model is the atmospheric CO2 concentration above an urban area. For climate change analysis, the dominant concentration component is the global cumulative stock of long-duration CO2 molecules that have been emitted by human activity since the Industrial Revolution. Another global component is seasonal, reflecting differential CO2 absorption and respiration by vegetation over the annual cycle. The seasonal CO2 component is more pronounced in the Northern Hemispheric because it has more plant life than the Southern 4