Expected Health Effects of Reduced Air Pollution from Covid-19 Social Distancing

The COVID-19 pandemic resulted in stay-at-home policies and other social distancing behaviors in the United States in spring of 2020. This paper examines the impact that these actions had on emissions and expected health effects through reduced personal vehicle travel and electricity consumption. Using daily cell phone mobility data for each U.S. county, we find that vehicle travel dropped about 40% by mid-April across the nation. States that imposed stay-at-home policies before March 28 decreased travel slightly more than other states, but travel in all states decreased significantly. Using data on hourly electricity consumption by electricity region (e.g., balancing authority), we find that electricity consumption fell about six percent on average by mid-April with substantial heterogeneity. Given these decreases in travel and electricity use, we estimate the county-level expected improvements in air quality, and therefore expected declines in mortality. Overall, we estimate that, for a month of social distancing, the expected premature deaths due to air pollution from personal vehicle travel and electricity consumption declined by approximately 360 deaths, or about 25% of the baseline 1500 deaths. In addition, we estimate that CO2 emissions from these sources fell by 46 million metric tons (a reduction of approximately 19%) over the same time frame.


Introduction
The novel coronavirus outbreak, along with measures intended to contain the spread of COVID-19, resulted in significant, and in some cases, unprecedented, changes in society.
Because of these alterations to daily life, demand for numerous goods and services fell precipitously from late February through the present. Evidence of the far-reaching effects of social distancing, retail closures, and, generally, reductions in economic activity, is perhaps most obvious in weekly unemployment claims which exceed those during the Great Recession by a factor of ten. Equally evocative is the collapse of oil prices.
In a fossil fuel-based economy such as the U.S., a large adverse demand shock is likely to have appreciable repercussions for emissions and ambient pollution levels. Though long-run outcomes are not yet discernible, it is feasible to assess near term changes in certain measures of environmental quality. Further, because there is an established literature linking exposure to ambient pollution to various health outcomes, it is possible to gauge the effects of such changes on public health. The goal of this analysis is to quantify the health effects of these unprecedented changes from two channels: reduced travel and electricity consumption. This quantification is an important input in an economic analysis of social distancing.
Our analysis uses cell phone data, which are reported daily for every U.S. county, to measure changes in mobility, and by extension, vehicle-miles traveled, over the February to April, 2020 period. For electricity, we employ hourly data by electricity region (e.g., balancing authority) to estimate the changes in electricity consumption, and the corresponding emissions, over the same time period controlling for observable factors such as temperature and a battery of temporal fixed effects. We focus on reductions in emissions that contribute to the formation of fine particulate matter (PM 2.5 ). 1 In recent years, emissions from travel and electricity generation account for between 25% and 50% of national totals depending on pollution species. 2 We use integrated assessment modeling to connect emissions to changes in ambient PM 2.5 and the associated reductions in expected adverse health effects from exposure to pollution. Of particular interest are reductions in PM 2.5 -associated mortality 1 Emitted pollutants tracked in this study include primary PM 2.5 , sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), and volatile organic compounds (VOCs). 2 See Table A in the Appendix. 1 risk, as this health endpoint contributes the largest share of air pollution damages (Muller, Mendelsohn, and Nordhaus, 2011).
The cell phone data indicates that personal mobility declined about 40% on average, with larger decreases in states that were early in implementing social distancing measures.
The reduction in electricity generation, in contrast, is just 6% on average. While behavioral responses to COVID-19 and stay-at-home orders induce a large decline in travel, power consumption decreases are smaller, perhaps due to a shift from commercial to residential applications.
We find that reduced travel and electricity generation are associated with a monthly decrease of about 360 deaths from PM 2.5 exposure in the contiguous U.S. As a means of Note that these estimates are a function of the behavioral changes in mobility in response to COVID-19, the implied changes in vehicle miles traveled, population exposure per ton of emissions, and demographics of the exposed population.
Location-specific reductions in deaths from electricity are much lower given the smaller declines in consumption. Emission and reductions in deaths also display a different geographic pattern, with the largest reduction in deaths due to demand changes in the Southeast and Midwest. These estimates of reduction in deaths from electricity generation depend on behavioral changes in power use in response to COVID-19, the likely changes in the op-eration of power plants across the country, population exposure per ton of emissions, and demographics of the exposed population. Section 2 describes the data sources and methods for our estimation of the reduction in deaths from reduced travel and reduced electricity consumption. Section 3 describes the results, and Section 4 concludes.

Methods
Calculating the expected health effects of the reductions in personal vehicle travel and electricity consumption from social distancing has three components: first, estimating the re-

Personal Vehicle travel
To estimate the health effects of reduced vehicle travel, we first need an estimate of how much travel decreased. Comprehensive data on vehicle miles traveled (VMT) is reported by a variety of state agencies and collected at the national level. However, our analysis requires high frequency data to estimate the effect of social distancing that has only been in effect for a short time. For high-frequency travel data, we turn to Unacast (2020). 4 Unacast, which specializes in mobility data analysis, created a pro bono COVID-19 Toolkit to help researchers and to raise public awareness of social distancing. Unacast analyzed cell phone mobility data to calculate a percentage reduction in distance traveled for each county. 5,6 We combine these percentage reductions with county-level estimates of light duty vehicle VMT from the US EPA MOVES model to determine the reduction in VMT in each county.
Light duty vehicles include cars, mini-vans, sport utility vehicles (SUVs), and some pick-up trucks. By applying the Unacast percentage reduction to all light duty vehicles, we are assuming that reductions in travel are proportional across the vehicle classes.
The resulting reduction in light-duty vehicle travel is summarized in Figure 1a which shows the seven-day moving average of the VMT-weighted average reduction across counties for two groups: counties in states that had an early stay-at-home policy in place by March 28 and counties in states that did not (some of which imposed a stay-at-home policy at a later date). 7 Before early March there is no reduction in VMT, but by the end of March, VMT fell by approximately 40%. States with early stay-at-home policies reduced travel more than others, however, there is a substantial reduction in travel in all the states. 8 Since early April, the VMT reduction seems to have stabilized at around a 40% average reduction. We use the last week of data (from April 11 to April 17) to calculate the reduction in light duty VMT for each county relative to the baseline.
The dramatic reduction in VMT is corroborated by a simultaneous reduction in the consumption of gasoline. Figure Table 4-43 in NTS (2018). 9 The emissions rate for SO 2 assumes 22.3 fleet average mpg and 10 ppm sulfur in gasoline, which reflects the latest gasoline sulfur content regulations. 10 The resulting emissions rates (in grams per mile) are shown in Table 1.

Emissions of different pollutants have different effects in different locations. The AP3
model accounts for dispersion of air pollution, atmospheric chemistry, dose-response relationships, and demographics of the affected populations to calculate the premature deaths from 7 The robust standard errors for the confidence intervals are clustered at the state level and account for serial correlation and correlations across counties within a state. 8 An F-test of an equal reduction during the last week of our data is rejected at the 5% level. 9 We use the average of emission rates for light duty vehicles and light duty trucks. 10 The fleet average mpg is for U.S. light duty vehicles in 2017 (BTS 2020). Carbon emissions per mile can be calculated from this mpg and the carbon content of gasoline.  To calculate the reduction in expected deaths through reduced travel in a county because of social distancing, we simply multiply the county-level reduction in miles traveled (summarized in Figure 1a) by the county-specific estimates of expected deaths per billion miles (summarized in Table 1). The reduction in expected deaths is mapped in Figure C in the Appendix. The reductions in deaths are the greatest in California's urban areas.

Electricity use
To estimate the health effects of reduced electricity usage, we combine estimates of the reduction in electricity use with estimates of the marginal health effects (marginal damage) per unit of power produced.
The reduction in electricity usage is estimated from data from individual Independent System Operators (ISOs) and the Energy Information Administration (EIA) on hourly electricity consumption, referred to as 'system load'. System load is the aggregate of all power taken from the grid, including residential, commercial, and residential customers, as well as line loses. ISOs and the EIA vary in the geographic specificity of their reporting, ranging from zones covering local municipal utilities to the entire Tennessee Valley Authority. We refer to each reported unit as a Power Control Area (PCA) to simplify the distinction between types of load zones and balancing authorities. 12 We match hourly load data to local temperature readings from the National Weather  The results show that there are not reductions in electricity usage before early March but by mid-April reductions in electricity usage average about 6%. 14 We estimate the health effects of these reductions in electricity consumption using a twostep procedure similar to that in Holland et al. (2020) for estimating marginal damages.
The first step is to determine hourly expected deaths from pollution from power plants. 12 In total there are 105 PCAs in our data. 13 The robust standard errors for the confidence intervals are clustered at the PCA to account for serial correlation.
14 Because PCA's can cross state boundaries, we do not break out the reduction by state stay-at-home policy.

8
The second step is to determine the change in expected deaths from a change in electricity consumption.
In the first step, we use data reported from EPA's Continuous In the second step, we regress hourly expected deaths on hourly electricity load in each interconnection: East, West, and Texas. 17 More specifically, let D t be the expected deaths in the interconnection due to emissions of all pollutants from all power plants in an interconnnection in hour t. Our estimating equation is where Load t is electricity usage in the interconnnection in hour t and α mh are month of sample times hour fixed effects (1 year * 12 months * 24 hours fixed effects). The coefficient β is the change in expected deaths from a change in electricity consumption in the interconnection. 15 SO 2 and NO x are directly reported, and we impute hourly PM 2.5 emissions based on average emissions rates and observed hourly generation. 16 CEMS also reports carbon emissions. We use a similar procedure to estimate marginal carbon emissions from a change in electricity usage. 17 We aggregate deaths and load to the interconnection because electricity generally flows throughout an interconnection and PCA loads are highly correlated. See Holland et al. (2020). To calculate the reduction in expected deaths through reduced electricity consumption from social distancing, we simply multiply the estimated reduction in electricity consumption at a PCA (summarized in Figure 1b) by the expected deaths per TWh in Table 2 for the appropriate interconnection. The reduction in expected deaths is mapped in Figure D in the Appendix. The reductions are the greatest in the Midwest and Southeast, but are much smaller than from reduced travel.

Results
Social distancing due to the COVID-19 outbreak led to reduced personal vehicle travel and electricity consumption which in turn lowered emissions of pollution and expected deaths.
The overall effect of these changes, aggregated to the contiguous U.S., are shown in Table 3.
Our baseline estimated number of expected deaths per month from air pollution from all light-duty vehicle travel is 666 expected deaths. Our estimated 40 percent average reduction in travel implies that the expected deaths is reduced by 314 deaths per month due to reduced travel. 18 The table breaks the reduction in deaths into the precursor pollutant to which they can be attributed. Over half of the reduction in deaths are due to reduced NO x emissions but reductions in other pollutants such as VOCs and PM 2.5 also contributed substantially.
For electricity consumption, our baseline estimated number of expected deaths per month from air pollution from electricity consumption is 859 deaths. This is a higher baseline than for travel, but the six percent reduction in electricity consumption implies that expected deaths are only reduced by 49 deaths (about 15% of the reduction in deaths from travel).
The primary reduction in deaths from electricity consumption can be attributed to reduced SO 2 emissions. Combining the results for the reduction in travel and electricity usage gives a reduction of 363 expected deaths. The preceding analysis focuses on the expected health benefits from local pollutants of the reductions in personal vehicle travel and electricity consumption due to social distancing.
Additionally, these reductions imply reductions in CO 2 emissions which we can calculate using similar procedures. In particular, for travel we can use the carbon content of gasoline and the fleet mpg together with our estimated reduction in VMT to estimate the reduction in carbon emissions. Applying this methodology, we estimate that CO tons. This is approximately 19% of the 242 million metric tons that are emitted monthly from driving and using electricity.
Social distancing was not evenly distributed across the country as some states and cities implemented stay-at-home policies while others did not. In addition, behavioral changes differed across regions, and mortality risks (as specified by the AP3 model) differ across counties. Table 4  Although California had one of the larger percent reductions in electricity consumption (an 8 percent reduction), this reduction led to smaller declines in expected deaths and CO 2 emissions due to cleaner electricity generation in the West.

Conclusion
Social distancing to control the spread of the novel coronavirus resulted in unprecedented changes in society and in economic activity. Among these are substantial changes in vehicle travel and in electricity usage. This paper quantifies reductions in travel and electricity usage relative to counterfactuals using highly-resolved data. We find that, at the county  Using observed behavioral changes, our paper demonstrates the degree to which reduced reliance on fossil-fuel based transport and power generation yields public health benefits. In the long run these findings are, perhaps, most interesting when interpreted in the context of a post-COVID-19 economy in which remote working and retail delivery are more common.
In this state of the world as observed in early April 2020, power demand is only marginally affected, whereas personal travel declines appreciably. The paper shows significant local health benefits from this adjustment. The extent to which consumption habits revert to their pre-COVID-19 levels remains to be seen.

Appendices
Emissions from all sources  Weekly gasoline sales  A.2

Streetlight mobility data
In the main text, we applied the travel reduction percentages from Unacast to the EPA's MOVES estimates of VMT. Alternatively, Streetlight (2020) uses cell phone mobility data to directly estimate reductions in VMT. An analogous figure to Figure 1a made using the Streetlight data is shown in Figure B. The results from using the Streetlight data to estimate the reduction in deaths from decreased air pollution are given in Table B. Compared to the results in the main text, the Streetlight data gives a greater decrease in VMT and hence a greater reduction in deaths. However, the decrease in the Streetlight VMT is larger than we would expect from the reduction in gasoline sales documented in Figure   Notes: Average travel reduction is weighted by VMT. Baseline monthly deaths from travel is slightly lower than in Table 3 because there are more counties with missing data. A.3

Supplementary information about reductions in expected deaths
Figures C shows the reduction in deaths from reduced travel at the county level. The spatial distribution of the reduction in deaths depends on reduced travel from COVID-19, observed vehicle miles traveled, population exposure per ton of emissions, and demographics of the exposed population. Figure D shows the reduction in deaths from reduced electricity consumption at the PCA level. The spatial distribution depends on the reduction in electricity usage from COVID-19, the regional mix of fuels used to produce power, population exposure per ton of emissions, and demographics of the exposed population. These figures also illustrate that data is missing for a small number of counties. Table C shows the reduction in deaths aggregated to geographic regions based on a combination of ISO and NERC regions.

COVID-19 deaths and Total Respiratory Deaths
There are aspects of PM 2.5 and COVID-19 that require an important qualification, or caveat, to our findings. The epidemiological literature that establishes the association between PM 2.5 and premature mortality repeatedly finds that risk from exposure is proportional to baseline mortality rates (Krewski et al., 2009;Lepeule et al., 2012). Because of this, our benefit estimates may significantly understate actual benefits. The estimated ambient pollution reductions have occurred during a period of time when baseline risks are elevated. We modeled the link between emissions and monetary damages with data from the most recent year comprehensive economy-wide emissions data are available, the 2014 model year. If risk from exposure is proportional to mortality rates in a given period, then it is quite likely that exposure during a period when mortality rates are elevated will yield a larger relative risk.
Thus, damages will be higher in the elevated risk period.
To gauge how large this effect might be we gathered daily COVID-19 mortality data.