(1), we have estimated regressions that exclude either industrial emissions sources or income. When we exclude the income terms, we find a very small effect (median change of 3%) on parameter estimates for power plants, steel mills, cement plants and refineries. When we exclude the industrial facilities, we find that the income parameters change slightly. For case (2), we have estimated regressions that exclude all righthand variables except income, time trend and seasonal controls (by hemisphere). As expected, with all collinear variables dropped, we find an increase in the significance and size of the EKC parameters.

Figure 8 displays the estimated EKC with industrial emissions sources, without them (case 1), and without any righthand variables except the EKC terms (and the trend and seasonal controls) (case 2). With industrial sources excluded, the EKC peaks sooner and declines more sharply at higher incomes. At present, we cannot say how much of this change reflects estimation bias and how much reflects the collinearity-related upward bias that would accompany exclusion of properlyinstrumented facility variables. In any case, all three estimates carry the same basic message: The EKC reaches a peak in the range [$40,000 - $50,000] which is above the 90<sup>th</sup> percentile internationally.

Case (3) warrants more detailed attention because of potential simultaneity bias in the relationship between income and CO2 emissions. A substantial literature has used inventory-based emissions estimates to study this relationship at the country level (e.g., Apergis and Payne 2014). Results have differed substantially by country and time period (Ben Youssef et al. 2016). The research presented in this paper is different from previous work in at least three relevant ways. First, it uses direct, satellite-based observations of CO2 rather than estimated emissions inventories. Previous studies have risked introducing some technical element of simultaneity _by construction_ , because their emissions inventories derive entirely from measures of activities that provide components of income. Second, the present study uses local, spatially-referenced data rather than national or regional aggregates. Third, we take a different approach to energy as a link between income and CO2 emissions. Most previous studies of the simultaneity issue have focused on the energy sector as the key determinant of simultaneity between income and CO2 emissions, because CO2 emissions increase with energy use and energy use contributes to economic growth. Researchers’ views have differed substantially on the potential importance of this problem. Csereklyei and Stern (2015) argue that the bias is fairly small, so estimated emissions-income elasticities will be close to effects for exogenous changes in income. In any case, the modeling exercise in this paper takes an entirely different approach to the energy sector, incorporating georeferenced data for the coal-, gas- and oil-fired power plants that create sectoral CO2 emissions while excluding other power facilities (e.g., nuclear, solar, wind, hydro) that do not. In the previous subsection, we have discussed and illustrated the implications of our approach to energy facilities.

As previously noted, the EKC component of our model relates to non-industrial, income-related activities (e.g., motor transport) that cannot be observed directly. The relationship between income and CO2 emissions from these activities may also include elements of simultaneity, such as the joint emission by some activities of CO2 and local air pollutants (NO2, SO2, CO) that affect health, productivity, and therefore income (Van Ewijk and Van Wijnbergen 1995).

For econometric estimation, instrumental variables (IV) provide the standard correction for such simultaneity problems. In recent work on EKC estimation for local pollutants, Lawell and Liscow (2013) have identified the age dependency ratio as a plausibly-exogenous instrument that affects 17