# **1.  Introduction**

The World Meteorological Organization forecasts that the current greenhouse gas (GHG) emissions trend will increase global temperature 3-5 degrees C by 2100 (Reuters 2018). This would far overshoot the 2-degree limit pledged by the 2015 Paris Climate Accords (COP 21) and might have a catastrophic impact (Steffen et al. 2018; World Bank 2012). In response, several industrial nations pledged very steep emissions reductions at the recent Leaders’ Summit on Climate (April 22-23, 2021).

Unfortunately, these pledges confront a striking information shortfall at the outset: near-total absence of directly-measured local and regional GHG data for problem diagnosis, program design and performance assessment. Recently, the advent of satellite-based GHG measurement has greatly expanded the potential for empirical assessment. High-resolution observations of atmospheric GHG concentrations are now available from several platforms, including NASA’s OCO-2 and OCO-3 instruments, the European Space Agency’s METOP-A and TROPOMI (Sentinel-5P) platforms, China’s TANSAT and the Japan Space Exploration Agency’s GOSAT and GOSAT-2. Detailed technical assessments of measures from these platforms have verified that they provide useful and comprehensive information for global carbon emissions analysis (Weir et al. 2021; Nassar et al. 2021; Pan et al. 2021; Wu et al. 2020; Hakkarainen et al.; Labzovskii et al. 2019).

This paper extends recent research on satellite-based measurement to an easily-updated template for tracking changes in atmospheric CO2 concentrations at local and regional scales. Using observations from NASA’s OCO-2 platform, we develop the template from the data filtering techniques and econometric analysis employed by Dasgupta, Lall and Wheeler (2022). Our prior work estimates an econometric model that relates satellite-based CO2 measures to georeferenced emissions sources. In this paper, we develop and compare two versions of the CO2 tracking template. The first tracks changes in residuals after fitting the econometric model to satellite-based observations, while the second version simply tracks changes in the observations themselves. Using data from thousands of urban areas, we find an extremely close correspondence between results for the two versions. We opt for simplicity and select the second version for template development. We also introduce a technique for identifying city-level changes that are distinct from regional changes induced by broader atmospheric circulation patterns. We provide several illustrative applications for urban areas, while noting that the same approach could be used for any other areas of interest.

In an additional exercise, we use our regression model results to compute expected emissions from urban areas. The regression residuals identify the directions and relative magnitudes of departures from expected values for individual areas. We convert the residuals to their emissions equivalents using high-resolution gridded information from the EDGAR global database (Crippa et al. 2020). For 1,306 urban areas with populations greater than 500,000, we find a rough balance between cities whose emissions are higher and lower than their expected values. Converting deviations to percentages of expected values, we find that percent deviations are typically greater in absolute value for cities with lower-than-expected emissions.

The remainder of the paper is organized as follows. Section 2 motivates the comparison of tracking models by reviewing the econometric model and supporting data from Dasgupta, Lall and Wheeler (2022). Section 3 develops the candidate tracking models and Section 4 compares their results for a large sample of urban areas. Section 5 applies the selected model to illustrative urban cases, while 2