The recent shift in the United States from coal to natural gas as a primary feedstock for the production of electric power has reduced the intensity of sectoral carbon dioxide emissions, but—due to gaps in monitoring—its downstream pollution-related effects have been less well understood. Here, I analyse old units that have been taken offline and new units that have come online to empirically link technology switches to observed aerosol and ozone changes and subsequent impacts on human health, crop yields and regional climate. Between 2005 and 2016 in the continental United States, decommissioning of a coal-fired unit was associated with reduced nearby pollution concentrations and subsequent reductions in mortality and increases in crop yield. In total during this period, the shutdown of coal-fired units saved an estimated 26,610 (5%–95% confidence intervals (CI), 2,725–49,680) lives and 570 million (249–878 million) bushels of corn, soybeans and wheat in their immediate vicinities; these estimates increase when pollution transport-related spillovers are included. Changes in primary and secondary aerosol burdens also altered regional atmospheric reflectivity, raising the average top of atmosphere instantaneous radiative forcing by 0.50 W m−2. Although there are considerable benefits of decommissioning older coal-fired units, the newer natural gas and coal-fired units that have supplanted them are not entirely benign.
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All data used in these analyses are publicly available, as described above. Processed, compiled datasets to replicate these analyses are available at https://doi.org/10.7910/DVN/RIZQUN.
Code to generate compiled data and to replicate all of the analyses here (results, figures, tables) is available at https://github.com/jaburney/naturalgastransition.
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I thank the Policy Design and Evaluation Lab and Big Pixel Initiative (both at UC San Diego), and the National Science Foundation (CNH Award no. 1715557) for funding.
The author declares no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Temporal and geographic distribution of old coal units taken offline, new natural gas units brought online, and new coal units brought online in the United States, 2005-2016.
These technological changes (closing and opening of new and old electric power generation units) occur at discrete moments in time, resulting in changes in emissions fluxes into the surrounding area. These changes are assumed to be as-if random in space and time, vis-à-vis each other, and discontinuities in ambient conditions are estimated across the sample for these natural experiments (see Methods).
Extended Data Fig. 2 Emissions of CO2, SO2, and NOx associated with shutdown of old coal-fired units, and start-up of new natural gas-fired and coal-fired units.
(Left Column) Coal-fired units approaching decommissioning are often ‘ramped down’ prior to shutdown, as reflected in decreasing gross load and emissions. (Centre and Right Columns) Conversely, as they ramp up after commissioning, new units may take some time to settle into steady-state. Further downstream impacts of a coal unit shut-down are thus likely to begin to manifest in the year prior to final closure, and impacts of new units may change over time. Boxes show the 25th-75th percentiles, with the median indicated by the bar, with whiskers indicating the 2.5 to 97.5 percentile confidence interval; values outside of this range are not shown. (Note the different scales for new coal unit generation and CO2 emissions, and for new natural gas generation and NOx and SO2 emissions, marked with asterisks).
Extended Data Fig. 3 Starting (2005) levels and trends over the study period for PM2.5, O3, SO2, and NO2.
Dots in the trends plots show locations of coal-fired units taken offline (red) and natural gas-fired units brought online (blue) during the study period. As shown in this analysis, part of these changes is attributable to shifts in electric power production feedstock, but other policies and regulations (for example fuel efficiency standards) and technology changes (for example emissions controls technologies) have contributed as well. Coal and diesel combustion are responsible for most SO2 emissions, while NO2 (a portion of NOx) comes from transportation as well as combustion of coal and natural gas. NO2 concentrations are more tightly associated with urban areas and transportation corridors. Ozone production is nonlinear, based on reactions of NOx and volatile organic compounds in the presence of sunlight. Particulate Matter includes aerosols from many sources, including primary carbonaceous aerosols, sulfates (from SO2), nitrates (in part from NOx), dust, and sea salt (see Supplementary Fig. 1).
(a,c,e) As in Fig. 2b: Average near-surface (Planetary Boundary Layer) SO2, tropospheric NO2, and surface O3 surrounding operating electric power plants, by fuel type. SO2 and NO2 decrease radially around plants. Although SO2 is not a main byproduct of natural gas combustion, some plants have a combination of gas and coal-fired units, and others may use different types of fuels. Ozone dynamics are more complicated around an emissions source, consistent with previous studies. (b,d,f) As in Fig. 2a: Raw average changes in ambient O3, SO2, and NO2 in the time leading up to, and after, a coal-fired unit shutdown. Estimates include location-level fixed effects (that is concentrations for each location are de-meaned to show changes from baseline). Error bars show the 5th-95th% confidence interval, based on standard errors clustered at the location level.
As in Fig. 2c. Comparison of different models relating a change in number of units of a given feedstock within a given radius of a county, and average levels of O3 in that county. Addition of a natural gas-fired unit (and to a lesser extent, a coal-fired unit) is associated with increased ozone levels (likely via increased NOx production).
Data from a coal-fired unit shut down in Georgia, showing the changes in ambient PM2.5, O3, SO2, and NO2. The thick blue line shows power generation (gross load), with the shutdown marked by the grey bar. Black lines show pollutant concentrations: the solid line shows concentration of each pollutant in the immediate region around the power plant, with dashed lines out to a 100km radius. This is an individual instance of the aggregate averages presented in the main text (Fig. 2, as well as Figures ED2 and ED5, and all Supplementary Tables).
As in Fig. 3a,b, only using an instrumental variables approach to estimate the effect of a 1 µg m-3 increase in PM2.5 on mortality and crop yields. In this approach, coal unit shutdowns are first related to PM2.5 concentrations; those predicted PM2.5 values are then related to mortality and crop yields. This approach strips out the variation in aerosol PM2.5 and other covarying pollutants not associated with unit shutdown. Central estimates are similar to Fig. 3a,b (but with larger error bars) indicating robustness of the approach of relating unit shutdowns directly to downstream outcomes. However, results should be interpreted as the impact of all pollutants covarying with PM2.5, and not PM2.5 alone.
Extended Data Fig. 8 A summary of impacts results estimated from different models at the county level.
The top row shows reduced form results for pollution, mortality, and crop impacts for 3 county-based models. The 25km and 200km coal models are shown in Fig. 2 and Fig. 3 in the main text. The third (top set of points) model includes natural-gas fired units. Red dots indicate coal unit impacts, and blue dots indicate natural gas unit impacts. The bottom row shows a comparison of instrumental variables (IV) results, whereby the number of units within a given radius is first related to changes in ambient pollution; those changes in pollution are then related in a second step to outcomes. Although results are cast as per μg m-3, they should be interpreted as the impacts of all pollution that covaries with PM2.5. The robustness of these IV results across models highlights the need for more causally-identified impacts studies and provides evidence that natural gas-fired units are not benign. Error bars show the 5th-95th percentile confidence interval; all estimates include county and year fixed effects, with standard errors clustered at the county level. Small grey bars show the average of the three models for each outcome.
The left column shows the results presented in Fig. 3c-f, with mortality and crop yield impacts integrated over the study period for plants within (a) a 200km radius, and (b) a 25km radius from each county. Right column shows the calculation described for impacts of remaining coal-fired units still operating, assuming that their impacts are the same as those that have been decommissioned.
Central mortality estimates in this study are similar to previous empirical exposure studies, for both total mortality and infant mortality. The Thurston et al, Eftim et al, and Zeger et al studies all focused on adults; Chay & Greenstone and Knitell et al focus on infant mortality. GBD results (2005-2013) are derived by combining PM2.5 reduction estimates from and pollution mortality from the GBD web interface. Apte et al results are for the lowest quartile (U.S. in that category), cast as percentages, and Burnett et al are estimated from the GEMM total mortality curve provided in the paper. Although not statistically significant (error bars show 5th-95th percentile confidence interval) the instrumental variables estimates from this analysis nevertheless highlight the importance of future causally-identified observational studies, as well as the critical role more comprehensive monitoring may play in reducing measurement errors (see Figure ED8).
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Burney, J.A. The downstream air pollution impacts of the transition from coal to natural gas in the United States. Nat Sustain (2020) doi:10.1038/s41893-019-0453-5