dominates for low incomes. We use inverse log income because the log transformation increases our ability to differentiate effects at very low income levels.

For the relationship between CO2 concentration and income per capita, we expect (β12 > 0). In the income-squared term, we expect β13 < 0 (the EKC case) or β13 = 0 (a linear relationship). Heating requirements are greater in colder climates, so we expect the effect of heating degree days on CO2 emissions (β14) to be positive.

Clearly-exogenous variables in the model include fires in neighboring areas and the demographic and climate factors. We include georeferenced data on CO2-intensive industrial facilities because they should have important effects on observed CO2 concentrations above their locations. Their inclusion serves our principal objective, a relatively complete econometric accounting of city-level CO2 emissions that can be used for comparative performance benchmarking and exploration of residuals. However, we recognize the possibility of some estimation bias from interactions with income and local air pollutants. Industrial facilities emit locally-significant air pollutants (e.g., NO2, SO2, fine particulates) along with CO2. Technical measures to reduce local air pollutants (e.g., stack scrubbers for SO2 emissions) do not reduce CO2 emissions directly, but the associated costs may have significant indirect effects by altering facility location decisions. Stricter environmental regulation in higher-income cities may enhance this effect. In principle, we could treat the potential bias problem by using measures for input costs to instrument the facility-level variables. In practice, however, we have no prospect of measuring the relevant variables at the requisite spatial resolution. Joint determination of income and CO2 emissions could also be a significant problem for the EKC component of the model. Our results discussion will include some estimation exercises that shed further light on these issues.

# **3.  Data**

# **3.1 Sources**

Several satellite platforms provide CO2 measures (Pan et al. 2021, Table 1). Data from these platforms have been collected by different instruments, over different periods, with different resolutions, observation repeat cycles and widths of area coverage along orbital paths. The data are also accessible in varying degrees. Combining observations from multiple sources could present difficulties that are as yet little-explored. For this exercise, prudence has dictated the choice of one platform, NASA’s OCO (Orbiting Carbon Observatory)-2. We have chosen OCO-2 because it offers open access (JPL/NASA 2021); a long panel of consistently-measured daily observations (Sept. 6, 2014 – Dec. 31, 2020); and the highest spatial resolution among the available sources (1.29 × 2.25 km).

The design of OCO-2 supports comparative exercises like our analysis. It follows a sunsynchronous near-polar orbit, crossing the equator in ascending mode around 1330 hours local time. In practice, this means that the OCO-2 observations for our study are collected between 1200 and 1500 local time for all cities in the sample. This provides a consistent mid-day activity benchmark for comparing CO2 concentration anomalies across cities.<sup>2</sup> OCO-2 has an observation repeat time

> 2  CO2 measurement during the full daily activity cycle will improve as systems like OCO-3 observe each area at more widely-varying times.

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