reducing the demand for motor vehicle transport and promoting denser settlements that are more easily served by utility-scale energy sources.

We specify the econometric model as follows: Expected signs: β1, β2, ... β7 > 0, β8 < 0 For grid cell _i_ in period _t_ , _CO2it_ is the satellite-measured mean CO2 concentration anomaly. _Iit_ stands for CO2 emissions from industrial sources; _DIit_ represents wind-displaced industrial CO2 emissions from other cells; _Fit_ equals CO2 emissions from agricultural and forest fires; and _DFit_ is wind-displaced fire CO2 emissions from other cells. _Pit_ stands for population and _Yit_ represents income per capita. _Hit_ and _Cit_ represent heating and cooling degree days, respectively. _Sit_ is the subway impact index, which is a function of system scale (L) and age (A); _ε_ is a random error term.

In this equation, the atmospheric CO2 anomaly is related to emissions from industrial sources, fires, and non-industrial population sources. Spatially-referenced variables in the model are translated to consistent measures by resampling to centroids for our 25-km grid cells. The core model is additive because emissions from the three sources contribute separately to the accumulation of CO2 molecules in the atmosphere. The anomaly recorded for a grid cell by a satellite platform includes emissions from sources within the cell and the “spillover” emissions created by wind displacement from sources in neighboring cells. For industry and fires, the model includes both cell-specific emissions ( _Iit_ , _Fit_ ) and wind-displaced emissions ( _DIit_ , _DFit_ ).

In the population-related component of the model, the marginal impact of population ( _Pit_ ) is a function of heating degree days ( _Hit_ ), cooling degree days ( _Cit_ ), and income per capita ( _Yit_ ). For a subway city, their composite effect is conditioned by an exponential function of the scale ( _Lit_ ) and age ( _Ait_ ) of the subway system. The exponential constrains the multiplier to a range from 1 (no subway: _Lit_ = 0, _Ait_ = 0) to 0. The multiplier value should decline with both age and scale, the latter measured by the length of operating subway lines.

# **2.2 Data**

Data from several satellite platforms that provide CO2 measures have been collected by various instruments over different periods, with different resolutions and observation repeat cycles and widths of area coverage along orbital paths (Pan, Yuan, and Jieqi 2021). The data are also accessible in varying degrees. Combining observations from multiple sources could present difficulties that are as yet littleexplored. For this exercise, prudence has dictated the choice of one platform, NASA’s OCO (Orbiting Carbon Observatory)-2, because it offers open access (JPL/NASA 2021); a long panel of consistently measured, daily observations (beginning on September 6, 2014); 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 sun-synchronous near-polar orbit, crossing the equator in ascending mode around 1330 hours local time. This means that the OCO-2 observations for our study are collected between 1200 and 1500 local time for all 4