Outdoor workers perform critical societal functions, often despite higher-than-average on-the-job risks and below-average pay. Climate change is expected to increase the frequency of days when it is too hot to safely work outdoors, compounding risks to workers and placing new stressors on the personal, local, state, and federal economies that depend on them. After quantifying the number of outdoor workers in the contiguous United States and their median earnings, we couple heat-based work reduction recommendations from the U.S. Centers for Disease Control and Prevention with an analysis of hourly weather station data to develop novel algorithms for calculating the annual number of unsafe workdays due to extreme heat. We apply these algorithms to projections of the frequency of extreme heat days to quantify the exposure of the outdoor workforce to extreme heat and the associated earnings at risk under different emissions scenarios and, for the first time, different adaptation measures. With a trajectory of modest greenhouse gas emissions reductions, outdoor worker exposure to extreme heat would triple that of the late 20th-century baseline by mid-century, and earnings at risk would reach an estimated $39.3 billion annually. By the late century with that same trajectory, exposure would increase four-fold compared to the baseline with an estimated$49.2 billion in annual earnings at risk. Losses are considerably higher with a limited-mitigation trajectory. While universal adoption of 2 specific adaptation measures in conjunction could reduce mid-century and late-century economic risks by roughly 90% and 93%, respectively, practical limitations to their adoption suggest that emissions mitigation policies will be critical for ensuring the well-being and livelihoods of outdoor workers in a warming climate.

Outdoor workers are among the most vulnerable people to heat-related illness—a condition in which the body is unable to successfully thermoregulate heat stress and, as a result, the core body temperature increases. Heat-related illness includes a range of conditions, from the relatively mild (e.g., heat cramps) to those more severe such as heat stroke and can even lead to death (Gauer and Meyers, 2019). For outdoor workers, chronic exposure to extreme heat can also lead to other adverse health outcomes, such as acute kidney injury (Mix et al., 2018; Wesseling et al., 2020). In the United States, outdoor workers face a disproportionate risk of heat-related death (Gubernot et al., 2015), and among outdoor workers, heat-related fatalities occur disproportionately among Black and Hispanic people (Gubernot et al., 2015).

Currently, there are few mandatory protections in place to prevent heat-related illnesses and deaths in the workplace at either the federal or state level. The National Institute for Occupational Safety and Health (NIOSH) under the Centers for Disease Control and Prevention (CDC) has published a detailed set of recommendations for employers to follow to protect employees from heat-related illness (Jacklitsch et al., 2016). However, only a small number of states—including California (Heat Illness Prevention in Outdoor Places of Employment, 2015) and Washington (Washington Department of Environmental & Occupational Health Sciences, 2021)—have enacted regulations requiring employers to take specific measures to prevent heat-related illness among employees.

Moving forward, the hot and humid conditions that can lead to heat-related illness and death are projected to increase dramatically across the United States as a result of human-caused climate change (Vose et al., 2017; Dahl et al., 2019). Dahl et al. (2019) found that the frequency of days with maximum daily heat index values above 100°F (37.8°C) increases four-fold nationally by the end of the 21st century under a high-emissions scenario relative to late 20th-century conditions. Despite the likely increase in risks outdoor workers will face due to continued climate change in the coming decades, their disproportionate exposure to extreme heat, and their importance to U.S. society, few studies have attempted to quantify the impacts of future extreme heat on the well-being and livelihoods of outdoor workers. As a result, several critical knowledge gaps remain.

First among these gaps is a lack of knowledge regarding where outdoor jobs are concentrated in the United States and how those patterns intersect with areas where extreme heat conditions are projected to occur more frequently as a result of human-caused climate change. Critically, most studies examining the effect of increasing extreme heat conditions on outdoor workers use industry-level rather than occupation-level data (Zivin and Neidell, 2015; Neidell et al., 2021; Tigchelaar et al., 2020) or only examine one sector of workers (e.g., Tigchelaar et al., 2020).

Second, understanding local, state, and regional variability in outdoor worker exposure and vulnerability is critical for designing effective climate resilience policies, as is understanding the range of potential climate conditions we face. However, many studies examining the effect of increasing extreme heat on outdoor workers to date have used coarse-resolution models, a single emissions scenario, or constrained estimates of the heat-humidity combination (Dunne et al., 2013; Tigchelaar et al., 2020).

A third knowledge gap for addressing the scope of the problem includes the economic impacts of climate change on outdoor workers. Previous studies (Dunne et al., 2013; Zivin and Neidell, 2015; Neidell et al., 2021) have given little attention to the consequences of climate change for the earnings of individual workers in a range of outdoor occupations. Finally, while efforts have quantified the economic benefits of greenhouse gas emission reductions on the outdoor work sector (Dunne et al., 2013; Zivin and Neidell, 2015; Neidell et al., 2021), none, to our knowledge, quantify the economic benefits of implementing adaptation measures that could enhance worker safety.

Given the gaps in our understanding of how heat is likely to impact outdoor workers as a result of human-caused climate change, this study focuses on 3 primary research objectives. First, this study aims to intersect spatial patterns of outdoor work across the contiguous United States with 21st-century extreme heat projections to identify outdoor worker populations at particular risk of increasing heat exposure. Within this objective, we couple public health guidelines with an analysis of weather station data to develop novel algorithms for quantifying the number of workdays that could become unsafe under different emissions scenarios. Second, this study aims to quantify the individual and collective earnings at risk due to future extreme heat across a comprehensive suite of outdoor occupations. Third, this research aims to evaluate some of the economic benefits of both emissions reductions and adaptation measures by analyzing 2 emissions scenarios (Representative Concentration Pathway [RCP] 4.5 and RCP8.5) as well as 2 commonsense adaptation policies.

To achieve these objectives, we couple fine-resolution extreme heat frequency projections for the contiguous United States from Dahl et al. (2019) with county-level data from the U.S. Census’s American Community Survey (ACS) to quantify changes in the frequency of unsafe workdays—defined here as the number of days per year with a heat index above 100°F (37.8°C, D100)—over the 21st century using 2 different emissions scenarios. We defined unsafe workdays as such following 2016 recommendations from NIOSH, which specify that moderate work should be reduced when a heat stress metric equivalent to the heat index rises above 100°F. It is important to note that heat-related illness can occur at heat index values significantly lower than 100°F (Morris et al., 2019); however, we focus here on the implications of existing U.S. government recommendations. We consider 2 emissions scenarios (RCP4.5 and 8.5, see Methods for details) utilized by Dahl et al. (2019) and 2 time periods (mid-century, 2036–2065, and late century, 2070–2099) compared to late 20th-century (1971–2000) conditions.

We further examine the economic impacts to the livelihoods of outdoor workers by calculating the earnings at risk of being lost due to unsafe workdays. We then apply our methodology to 2 potential adaptation options—using an adjusted work schedule that shifts work hours to cooler times of day and lightening workloads—to assess their potential benefits. We use these results to consider regulatory gaps that should be filled to protect worker health as well as the livelihoods of workers and their employers in order that no individual is faced with choosing between income and their health.

### 2.1. Identification of outdoor worker occupations

We used data from the U.S. Bureau of Labor Statistics’ (BLS) Outdoor Requirements Survey to identify occupations for which a significant portion (defined here by approximately two thirds or 65.2% or more) of jobs require outdoor work (BLS, n.d.). We use occupational data as they characterize the nature of work that people do, allowing us to better identify outdoor workers. Industry data provide information on the type of business a person is employed in; these data can include a range of occupations (e.g., the construction industry can include construction workers in addition to office administrators). Information on occupations was available at different levels of specificity. For example, protective service occupations included police officers and firefighters. We selected the level for which county-level data were consistently available. This method yielded 7 outdoor-work occupational categories: protective service; buildings and grounds cleaning and maintenance; farming, fishing, and forestry; construction and extraction; installation, maintenance, and repair; transportation; and materials moving.

### 2.2. Outdoor worker data

We determined the number of workers in each occupational category as well as their associated median annual earnings for each county using 5-year average data (2013–2017) from the U.S. Census Bureau’s (2017a, 2017b) ACS. This was the only data source for which occupation and earnings data were available at the county level for most of the U.S. civilian workforce, including self-employed individuals.

In order to focus on the economic consequences of climate change on its own, we assume no change in the size of the U.S. population or the outdoor workforce over time. While various population scenarios were considered, each involved assumptions with similar repercussions to holding population constant. For instance, applying the contemporary fraction of outdoor workers per county to future time periods assumes no future inflection points in the automation of outdoor jobs or environmentally caused shifts in where and by whom outdoor work takes place.

### 2.3. Extreme heat data

To quantify the annual frequency of extreme-heat days historically and in the future, we utilized data developed by Dahl et al. (2019). Dahl et al. (2019) developed fine-resolution, 21st-century projections of the heat index—a heat stress index used by the U.S. National Weather Service that combines temperature and relative humidity to produce a “feels like” temperature. In their study, Dahl et al. (2019) used statistically downscaled data (4-km grid resolution; Abatzoglou and Brown, 2012) covering the contiguous United States from 18 climate models from the 5th Coupled Model Intercomparison Project to calculate a daily maximum heat index from April through October between 1971 and 2099. They aggregated counts of days with maximum heat index values above various thresholds up to county levels across the contiguous United States.

The heat index calculation was performed using the National Weather Service’s heat index algorithm (National Oceanic and Atmospheric Administration, 2014) with daily maximum temperature and daily minimum relative humidity as the 2 input variables. This pairing provides a conservative estimate of the daily maximum heat index as daily maximum temperature does not always coincide with the daily minimum in relative humidity. The authors then tallied the number of days when the daily maximum heat index exceeded a suite of heat index thresholds relevant to both the National Weather Service and human health including 100°F (37.8°C; D100), 105°F (40.6°C; D105) and “off-the-charts” (OTC) conditions (Dotc). The latter refers to days where the combination of temperature and relative humidity exceeds the bounds of the National Weather Service heat index algorithm. It should be noted that the heat index calculation is designed to represent apparent temperatures in the shade, with notably higher sensible temperatures in direct sun (U.S. Department of Commerce, n.d.).

We utilized Dahl et al.’s (2019) results from the RCP4.5 and RCP8.5 scenarios to analyze conditions during 2 time periods, mid-century (2036–2065) and late century (2070–2099), in addition to the historical period (1971–2000; Meinshausen et al., 2011). These scenarios were constructed in order to examine the changes in climate induced by future changes in global greenhouse gas emissions. Under RCP4.5, emissions peak near 2040 then begin to decline, resulting in a global mean temperature change of roughly 2°C relative to a 1986–2005 baseline by the end of the century. Under RCP8.5, emissions continue to rise through the end of the century, causing global mean temperature to rise by approximately 4°C (IPCC, 2014). It is important to note that recent studies suggest that the RCP8.5 trajectory is unrealistically dependent on coal as a future energy source (Ritchie and Dowlatabadi, 2017); however, the late-century warming such as that projected by RCP8.5 has not been completely ruled out, given the increased climate sensitivity of some of the climate models in CMIP6 (Zelinka et al., 2020) and uncertainties in carbon cycle feedbacks.

### 2.4. Calculating unsafe workdays, earnings at risk, and worker heat exposure

We examined the effect of increasing extreme heat on outdoor work conditions and worker earnings using an array of climate mitigation and adaptation options (Table 1). As described in greater detail below, we quantify unsafe workdays and related risks to outdoor worker earnings in counties across the United States for RCP 4.5 and 8.5 at both mid- and late century. We also quantify the benefits of shifting work schedules to cooler parts of the day by examining how this adaptation would affect the number of unsafe workdays and worker earnings under both a normal work schedule, in which work is carried out during daytime hours, and under a so-called adjusted work schedule, in which work is carried out during the coolest contiguous 8-h daytime period, typically between 5:00 and 13:00 local standard time in the weather station data described below. Finally, we consider the benefits of reducing workloads from moderate to light levels (described below).

Table 1.

Array of climate mitigation and adaptation scenarios for which unsafe workdays and earnings at risk were calculated. DOI: https://doi.org/10.1525/elementa.2021.00048.t1

RCP 4.5 Normal Moderate
RCP 4.5 Normal Light
RCP 8.5 Normal Moderate
RCP 8.5 Normal Light
RCP 4.5 Normal Moderate
RCP 4.5 Normal Light
RCP 8.5 Normal Moderate
RCP 8.5 Normal Light

We developed algorithms to calculate the work time at risk of being lost as a result of extreme heat using an analysis of weather station data in concert with heat-based guidance from the CDC’s NIOSH (Table 2; Jacklitsch et al., 2016) and assumed this guidance would be followed. NIOSH recommends reducing work time for moderate levels of work when a heat stress metric equivalent to the heat index rises above 100°F (37.8°C; Jacklitsch et al., 2016). These recommendations are intended to estimate another commonly used indicator of heat stress conditions—the Wet Bulb Globe Temperature (WBGT, Morris et al., 2019)—using commonly available meteorological data. Similar to the WBGT and the heat index, the NIOSH recommendations are based on air temperature with suggestions for how to adjust those temperatures for higher or lower relative humidity conditions and, similar to the WBGT but unlike the heat index, the recommendations also include suggestions for how to adjust air temperature depending on sun exposure. However, the guidance provides only a gross estimate of how to adjust the air temperature based on whether conditions are sunny or partly cloudy to account for the WBGT’s radiant heat term. As the U.S. Occupational Safety and Health Administration (OSHA) notes in its guidance on the use of the heat index for heat illness prevention, the heat index could be up to 15°F (8.3°C) higher in direct sunlight (U.S. OSHA, n.d.).

Table 2.

Work schedule reduction recommendations from the Center for Disease Control and Prevention’s National Institutes for Occupational Health and Safety based on moderate and light levels of work (Jacklitsch et al. 2016). DOI: https://doi.org/10.1525/elementa.2021.00048.t2

Adjusted Temperature or Heat Index (°F)aWork/Rest Minutes Per Hour; Moderate Workloads (% Hourly Reduction)Work/Rest Minutes Per Hour; Light Workloads (% Hourly Reduction)
90 Normal (0%) Normal (0%)
100 45/15 (25%) Normal (0%)
104 30/30 (50%) Normal (0%)
105 25/35 (58.3%) Normal (0%)
106 20/40 (66.6%) 45/15 (25%)
108+ 0/60 (100%) 35/25 (41.6%)
111+ 0/60 (100%) 0/60 (100%)
Adjusted Temperature or Heat Index (°F)aWork/Rest Minutes Per Hour; Moderate Workloads (% Hourly Reduction)Work/Rest Minutes Per Hour; Light Workloads (% Hourly Reduction)
90 Normal (0%) Normal (0%)
100 45/15 (25%) Normal (0%)
104 30/30 (50%) Normal (0%)
105 25/35 (58.3%) Normal (0%)
106 20/40 (66.6%) 45/15 (25%)
108+ 0/60 (100%) 35/25 (41.6%)
111+ 0/60 (100%) 0/60 (100%)

These recommendations assume workers are “physically fit, well-rested, fully hydrated, under age 40, and have adequate water intake,” as well as assuming there is “natural ventilation with perceptible air movement” (Jacklitsch et al. 2016).

aFor the purposes of this study and given the strong correlation between the two, we use heat index as a stand-in for adjusted temperature in this study.

Recent research found that both the adjusted temperature variable featured in the NIOSH guidance and the heat index are suitable surrogates for WBGT (Bernard and Iheanacho, 2015). For example, Bernard and Ihanacho (2015) suggest that heat index values are within 1.4°F (0.8°C) of the adjusted temperatures for heat index values exceeding 100°F (37.8°C). For adjusted temperatures between 105°F (40.6°C) and 108°F (42.4°C), when NIOSH recommends the cessation of work, heat index values are, on average, 2.5°F (1.4°C) higher than adjusted temperatures. Given uncertainties around applying adjustments to either adjusted temperatures or the heat index based on sun exposure and given the fact that physiological responses to heat exposure vary greatly from person to person, for the purposes of this study, we consider heat index an adequate stand-in for adjusted temperature.

The Dahl et al. (2019)’s data provide information on the number of days per year in which heat indices exceed select thresholds: 100°F, 105°F, and OTC conditions. To translate the NIOSH guidance into algorithms that use climate data to estimate the portion of a workday that is unsafe as a result of extreme heat, we first needed to determine the number of hours for which heat indices remain above these 3 thresholds when they are traversed. We thus analyzed hourly temperature and humidity observations from 16 Automated Surface Observing Systems (ASOS) from airports across the United States during 2001–2020 (National Oceanic and Atmospheric Administration National Centers for Environmental Information, 2021). For days in the ASOS data set with a maximum heat index above 100°F, 105°F, and OTC conditions, we tabulate the average number of hours spent above these 3 thresholds across the full set of weather stations (Table 2).

We then used the work/rest guidance from NIOSH (Table 3) to calculate the number of hours that would be unsafe to work during a typical day in which the maximum heat index exceeds 100°F, 105°F, and OTC conditions under different work scenarios (described in detail below). Finally, we coupled these findings with the annual average number of days projected to exceed these 3 thresholds at mid- and late century under RCP 4.5 and 8.5 from the Dahl et al.’s (2019) data sets to estimate the amount of unsafe worktime that occurs in an average year under these different time frames and emissions scenarios in counties across the contiguous United States.

Table 3.

Hours (and fraction of an 8-h daytime shift) above heat index thresholds necessitating work reductions as per NIOSH guidance (Jacklitsch et al., 2016). DOI: https://doi.org/10.1525/elementa.2021.00048.t3

Work Schedule and LoadDaily Maximum HI > 100°FDaily Maximum HI > 105°FDaily Maximum HI Off the Charts
Hours > 100°FaHours > 100°FHours > 105°FHours > 106°FbHours > 100°FHours > 105°FHours > 106°FbHours > 108°FHours > 111°Fb
Normal schedule; moderate workload 4.7 (0.588) 3.6 (0.525) 4.4 (0.550) N/A 0.4 (0.05) 2.2 (0.275) N/A 5.4 (0.675) N/A
Adjusted schedule; moderate workload 1.6 (0.200) 2 (0.250) 1.3 (0.163) N/A 1.1 (0.138) 1.1 (0.138) N/A 1.9 (0.238) N/A
Normal schedule; light workload N/A N/A 0 (0) 3.1 (0.388) N/A NA 4.1 (0.513) N/A 3.9 (0.488)
Adjusted schedule; light workload N/A N/A 0 (0) 0.9 (0.113) N/A NA 2.9 (0.363) N/A 1.4 (0.175)
Work Schedule and LoadDaily Maximum HI > 100°FDaily Maximum HI > 105°FDaily Maximum HI Off the Charts
Hours > 100°FaHours > 100°FHours > 105°FHours > 106°FbHours > 100°FHours > 105°FHours > 106°FbHours > 108°FHours > 111°Fb
Normal schedule; moderate workload 4.7 (0.588) 3.6 (0.525) 4.4 (0.550) N/A 0.4 (0.05) 2.2 (0.275) N/A 5.4 (0.675) N/A
Adjusted schedule; moderate workload 1.6 (0.200) 2 (0.250) 1.3 (0.163) N/A 1.1 (0.138) 1.1 (0.138) N/A 1.9 (0.238) N/A
Normal schedule; light workload N/A N/A 0 (0) 3.1 (0.388) N/A NA 4.1 (0.513) N/A 3.9 (0.488)
Adjusted schedule; light workload N/A N/A 0 (0) 0.9 (0.113) N/A NA 2.9 (0.363) N/A 1.4 (0.175)

Values in parentheses are fractions of 8-hr workdays that are used as inputs to the equations above. HI = heat index; NIOSH = National Institute for Occupational Safety and Health.

aThe 100°F and 108°F thresholds only apply to work reductions under moderate workloads.

bThe 106°F and 111°F thresholds only apply to work reductions under light workloads.

To calculate worker heat exposure, we calculated the total D100 for each of the 7 occupational categories included in this study and for each model and scenario from Dahl et al. (2019). We then multiplied D100 by the number of people in each occupational category (e.g., protective service) and refer to this exposure metric as “person–days” per year.

#### 2.4.1. Unsafe workdays with no adaptation measures implemented

While there is anecdotal evidence that employers in some occupations and in some places will shift workers’ hours to cooler times of the day (Holloway and Etheredge, 2019), one recent survey of outdoor workers’ indicated that workers are typically outdoors for most or all of the entire 10 a.m.—4 p.m. window that was evaluated in their study (Peters et al., 2016), which according to our analysis of weather station data, overlaps with the majority of the work hours included in the normal work schedule scenario of our study. Put another way, it is therefore reasonable to assume a no-adaptation baseline in which workers are outdoors exposed to heat during the hottest hours of the day.

For moderate levels of exertion, following the NIOSH guidance for the discrete temperature thresholds available in the Dahl et al.’s (2019) data set, we calculate the average number of hourly observations of heat indices above 100°F (37.8°C) on days when daily maximum heat indices were between 100°F and 104°F (37.8°C–40.0°C), the number of hourly observations of heat indices above 100°F and 105°F (40.6°C) on days when daily maximum heat indices were greater than 105°F (40.6°C) but not off the chart (OTC), and the number of hourly heat indices above 100°F, 105°F, and 108°F (42.2°C) on days when daily maximum heat indices were OTC for the 16 ASOS stations (Figure 1). Hourly observations covered the period 2001–2020. As our study assumes an 8-h workday, we capped the number of hours above the extreme heat thresholds used to estimate work schedule reductions at 8. We did so by subtracting any extra time from the number of hours spent above the lowest temperature threshold in a given calculation, as we assumed that the normal work schedule will occur during the daytime when peak heat conditions occur (this measure was not necessary for the adjusted work schedule scenarios described below). Table 3 shows the average number of hours across the ASOS stations corresponding to thresholds from Table 2. These data are used to calculate the annual number of unsafe workdays (U) assuming a normal work schedule and moderate workload following the NIOSH recommendations. The calculation was therefore:

1
Figure 1.

For a normal daytime work schedule (A) and an adjusted daytime work schedule (B), average number of hourly observations of heat indices above 100°F (37.8°C) on days when daily maximum heat indices were between 100°F and 104°F (37.8°F–40.0°C), the number of hourly observations of heat indices above 100°F and 105°F (40.6°C) on days when daily maximum heat indices were greater than 105°F (40.6°C) but not off the chart (OTC), and the number of hourly heat indices above 100°F, 105°F, and 108°F (42.2°C) on days when daily maximum heat indices were OTC for the 16 different ASOS stations. Hourly observations covered the period 2001–2020. DOI: https://doi.org/10.1525/elementa.2021.00048.f1

Figure 1.

For a normal daytime work schedule (A) and an adjusted daytime work schedule (B), average number of hourly observations of heat indices above 100°F (37.8°C) on days when daily maximum heat indices were between 100°F and 104°F (37.8°F–40.0°C), the number of hourly observations of heat indices above 100°F and 105°F (40.6°C) on days when daily maximum heat indices were greater than 105°F (40.6°C) but not off the chart (OTC), and the number of hourly heat indices above 100°F, 105°F, and 108°F (42.2°C) on days when daily maximum heat indices were OTC for the 16 different ASOS stations. Hourly observations covered the period 2001–2020. DOI: https://doi.org/10.1525/elementa.2021.00048.f1

Estimates are scaled by 5/7 to account for the typical 5-day work week; that is, we assume that outdoor workers are exposed to on-the-job heat 5 days per week rather than 7. Instead of reporting our findings in terms of the number of unsafe work hours, we calculate the number of workday equivalents that could become unsafe due to extreme heat exposure (that is, 8 h of unsafe work). For instance, if work needs to be reduced by 50% during 2 separate days, we tally this as 1 full unsafe workday.

#### 2.4.2. Unsafe workdays with adaptation options implemented

We modified the algorithms described above to calculate how effectively two different adaptation options—shifting work hours to cooler times of day and reducing physical workloads from moderate to light—would reduce the number of unsafe workdays and, in turn, earnings at risk due to extreme heat.

To simulate an adjusted schedule in which work is shifted to cooler times of day, we again utilized the ASOS data described above (Figure 1). After identifying the coolest contiguous 8-h period during daylight hours for each station (5:00–13:00 LST), we determined the number of hours within that period at or above the NIOSH thresholds and modified Equation 1 appropriately using the number of above-threshold hours for each heat index category (Table 3). Thus, the calculation for annual unsafe workdays with a schedule adjusted to the coolest 8-h daytime shift (A) became:

2

We also simulated the potential benefits of reducing physical workloads from moderate levels to light levels. Because light work can be done in hotter conditions than moderate work, NIOSH guidance for reducing work time based on light levels of work relies on different heat thresholds than those described above for moderate levels of work (Table 2). Applying these thresholds to the ASOS data and using the number of hours above each of the thresholds (Table 3), the calculation for annual unsafe workdays with light levels of work and a normal schedule (L) became:

3

Finally, in addition to simulating shifted work schedules and reduced workloads individually, we simulated the benefit of implementing these 2 adaptation options in conjunction, again using the ASOS data and the values in Tables 2 and 3. The equation for the annual number of unsafe workdays with both light levels of work and an adjusted schedule (LA) then became:

4

#### 2.4.3. Earnings at risk

To calculate earnings at risk of being lost due to extreme heat exposure for all combinations of time period, emissions scenario, and adaptation option, we assumed annual wages reported by the U.S. Census Bureau are based on a 40-h work week spread over 5 workdays, and 50 work weeks per year (250 days per year). We calculated earnings at risk for productivity loss estimates (E) as described above:

$E=W*M/T,$
5

where E is earnings at risk, W is unsafe workdays, M is annual median earnings, and T is total workdays per year.

### 3.1. Characterizing outdoor workers

Using data from the ACS, we identified 31.7 million workers across the contiguous United States in the 7 occupational categories the BLS identified as requiring outdoor work (Table 4; BLS, n.d.). Males made up 83% of the workers included in this analysis. BLS statistics at the national level indicate that 29% of outdoor workforce identified as Hispanic or Latino, disproportionately higher than that of the 19% of the general population (U.S. Census Bureau, 2017c; BLS, 2019). According to the BLS, people identifying as Hispanic or Latino are disproportionately represented within all outdoor occupation categories with the exception of protective service relative to their representation in the U.S. population as a whole. Similarly, African Americans comprise 13% of the general population but represent roughly 20% of workers in specific outdoor occupations such as protective service and transportation (U.S. Census Bureau, 2017c; BLS, 2019).

Table 4.

Summary wage and demographic statistics for the occupational categories included in this study (U.S. Census Bureau, 2017a, 2017b; Bureau of Labor Statistics, 2019, n.d.). DOI: https://doi.org/10.1525/elementa.2021.00048.t4

Occupational CategoryTotal WorkersPercent of Jobs Requiring Outdoor WorkWages (as Percent of Median)Percent MalePercent Black or African AmericanPercent Hispanic or LatinoPercent White
Protective service 3,301,545 89.6 128.0 87.9 20.3 15.3 73.9
Buildings and grounds cleaning and maintenance 5,936,527 65.2 57.1 58.0 14.9 38.2 77.3
Farming, fishing, and forestry 1,073,820 71.1 77.3 74.8 4.4 47.6 89.3
Construction and extraction 7,629,904 92.3 116.1 96.5 7.3 36.4 87.1
Installation, maintenance, and repair 4,764,507 74.9 132.6 96.1 9.1 20.3 84.0
Transportationa 5,564,429 70.6 115.4 81.8 22.0 22.9 72.2
Materials movinga 3,971,288 70.6 81.9 81.8 22.0 22.9 72.2
Occupational CategoryTotal WorkersPercent of Jobs Requiring Outdoor WorkWages (as Percent of Median)Percent MalePercent Black or African AmericanPercent Hispanic or LatinoPercent White
Protective service 3,301,545 89.6 128.0 87.9 20.3 15.3 73.9
Buildings and grounds cleaning and maintenance 5,936,527 65.2 57.1 58.0 14.9 38.2 77.3
Farming, fishing, and forestry 1,073,820 71.1 77.3 74.8 4.4 47.6 89.3
Construction and extraction 7,629,904 92.3 116.1 96.5 7.3 36.4 87.1
Installation, maintenance, and repair 4,764,507 74.9 132.6 96.1 9.1 20.3 84.0
Transportationa 5,564,429 70.6 115.4 81.8 22.0 22.9 72.2
Materials movinga 3,971,288 70.6 81.9 81.8 22.0 22.9 72.2

aWhile the American Community Survey breaks transportation and materials moving into 2 separate categories, the Bureau of Labor Statistics reports data for the 2 categories combined, thus all values except those for wages are identical for these 2 categories.

Overall, median earnings for some outdoor occupational categories (e.g., protective service) were above the median income for all occupations nationally, but workers in several outdoor occupational categories earned notably less (Table 4). For example, building and grounds cleaning and maintenance workers earned, on average, 43% less than the U.S. workforce as a whole. Median earnings within each occupational category level reflect the range of earnings associated with each specific occupation within that category.

### 3.2. Heat exposure

Using the metric of person–days per year and assuming no growth or change in population, the nationwide exposure of the United States’ outdoor workers to days with a heat index above 100°F (37.8°C) would increase 3- or 4-fold by mid-century and 4- to 7-fold by late century depending on the emissions scenario (Table 5). Historically, 442 counties have had 100,000 or more person–days of heat exposure per year (Figure 2). By mid-century, expansions in the frequency and intensity of days with a heat index above 100°F (37.8°C) increase the number of counties in that category to 1,264 under the RCP4.5 scenario and 1,557—more than half of all counties—under the RCP8.5 scenario. These shifts grow substantially between mid-century and late century; however, as would be expected by the trajectory of emissions modeled by RCP8.5, exposure ramps up more steeply during the second half of the century under RCP8.5 than under RCP4.5.

Table 5.

Summary of results for each time period and scenario evaluated in this study. DOI: https://doi.org/10.1525/elementa.2021.00048.t5

Annual Earnings (Billions USD) at Risk (Percent)
Historical 315 Million $8.6 (0.8%)$1.0 (0.1%) $3.0 (0.3%)$0.3 (0%)
Mid-century (RCP4.5) 1.1 Billion $39.3 (3.7%)$7.7 (0.7%) $14.2 (1.3%)$2.4 (0.2%)
Mid-century (RCP8.5) 1.4 Billion $55.4 (5.2%)$12.3 (1.2%) $20.1 (1.9%)$4.0 (0.4%)
Late century (RCP4.5) 1.2 Billion $49.2 (4.7%)$10.4 (1.0%) $17.8 (1.7%)$3.3 (0.3%)
Late century (RCP8.5) 2.1 Billion $107.5 (10.2%)$33.1 (3.1%) $39.8 (3.8%)$11.7 (1.1%)
Annual Earnings (Billions USD) at Risk (Percent)
Historical 315 Million $8.6 (0.8%)$1.0 (0.1%) $3.0 (0.3%)$0.3 (0%)
Mid-century (RCP4.5) 1.1 Billion $39.3 (3.7%)$7.7 (0.7%) $14.2 (1.3%)$2.4 (0.2%)
Mid-century (RCP8.5) 1.4 Billion $55.4 (5.2%)$12.3 (1.2%) $20.1 (1.9%)$4.0 (0.4%)
Late century (RCP4.5) 1.2 Billion $49.2 (4.7%)$10.4 (1.0%) $17.8 (1.7%)$3.3 (0.3%)
Late century (RCP8.5) 2.1 Billion $107.5 (10.2%)$33.1 (3.1%) $39.8 (3.8%)$11.7 (1.1%)

### 3.4. Benefits of implementing adaptation measures

Results presented thus far indicate that without new policies, protecting worker health by implementing temperature-appropriate work/rest schedules could come at a significant financial cost both to individual workers and to the broader economy. While maintaining work/rest schedules aimed at protecting worker health, the 2 adaptation measures simulated in this analysis—adjusting work schedules to cooler hours of the day and reducing workloads from moderate to light levels—were both found to reduce the number of unsafe workdays and earnings at risk due to extreme heat (Table 5). Most effective, however, was the combination of the 2 measures when implemented in conjunction.

Compared to a baseline of maintaining a normal work schedule, adjusting work hours to cooler times of day while maintaining moderate workloads would reduce the number of workers with 7 or more workdays at risk annually due to extreme heat from 14.0 million to 6.5 million under RCP4.5 and from 18.4 million to 9.2 million under RCP8.5 in the mid-century time period (Figure 5). Compared to a baseline of maintaining a normal work schedule and moderate workloads, reducing workloads to light levels again would reduce the number of workers with 7 or more workdays at risk annually. In this case, the number of workers carrying this level of risk would decline from 14.0 million to 0.7 million under RCP4.5 and from 18.4 million to 4.9 million under RCP8.5 in the mid-century time period. If work schedule adjustments and work-level reductions were implemented together, virtually no workers would risk losing 7 or more workdays per year by mid-century with either emissions scenario. By late century, universal implementation of both adaptation measures combined with emissions reductions consistent with the RCP4.5 pathway would reduce the number of workers experiencing 7 or more unsafe workdays per year to virtually none compared with 27.7 million workers who would experience such losses with the higher emissions RCP8.5 scenario and no adaptation measures implemented. These adaptation measures have significant benefits for preserving workers’ earnings as well: If both measures were implemented in conjunction, virtually no outdoor workers in the United States would be at risk of losing 5% or more of their earnings annually even by late century and with the high-emissions RCP8.5 scenario.

Figure 5.

Workers at risk of significant losses in workdays or earnings as a result of extreme heat with the implementation of different adaptation measures: a “normal” work schedule with moderate workloads (normal/moderate); a “normal” work schedule with light workload (normal/light); an adjusted work schedule with moderate workloads (adjusted/moderate); and an adjusted schedule with light workloads (adjusted/light). Graphs show the number of workers nationwide experiencing 7 or more unsafe workdays per year by mid-century (a) and late century (b) as well as the number of workers for whom 5% or more of annual earnings are at risk by mid-century (c) and late century (d). DOI: https://doi.org/10.1525/elementa.2021.00048.f5

Figure 5.

Workers at risk of significant losses in workdays or earnings as a result of extreme heat with the implementation of different adaptation measures: a “normal” work schedule with moderate workloads (normal/moderate); a “normal” work schedule with light workload (normal/light); an adjusted work schedule with moderate workloads (adjusted/moderate); and an adjusted schedule with light workloads (adjusted/light). Graphs show the number of workers nationwide experiencing 7 or more unsafe workdays per year by mid-century (a) and late century (b) as well as the number of workers for whom 5% or more of annual earnings are at risk by mid-century (c) and late century (d). DOI: https://doi.org/10.1525/elementa.2021.00048.f5

### 4.1. Comparisons to previously published studies

These results show that increasingly frequent extreme heat could heavily burden the health and livelihoods of outdoor workers as well as the livelihoods of their employers vis-à-vis a decline in the number of safe working hours or days. As there are no mandatory heat protection standards for workers across much of the United States, the implementation of heat-related work time or workload reductions or shifts in work schedules such as those quantified here is predicated on the notions that (a) worker health will be the top priority in deciding whether or not work will be carried out; and (b) employers will follow NIOSH’s health-based recommendations. If not coupled to income guarantees, health-focused reductions in the amount of time outdoor workers spend working could put workers’ earnings in jeopardy. This analysis also shows, however, that both emissions reductions and, in particular, adaptation measures have the potential to mitigate the number of unsafe workdays as well as the potential losses to workers’ earnings over this century.

Our findings are directionally consistent with a growing body of literature indicating that extreme heat is already impacting worker health, capacity, and productivity around the world and will increasingly do so as our climate continues to warm (Dunne et al., 2013; Zander et al., 2015; Kjellstrom et al., 2016; Takakura et al., 2017). For U.S. agricultural workers specifically, work by Tigchelaar et al. (2020) has shown that the frequency of unsafe working conditions would double given a global mean warming of 2°C above preindustrial temperatures and would triple with a global mean warming of 4°C (Tigchelaar et al., 2020). Such warming levels are roughly consistent with mid-century and late-century warming projections, respectively, under RCP8.5. Given differences in the Tigchelaar et al.’s methodology—including their use of lower resolution climate models, a simplified methodology for heat index projections, and industry-based classifications as opposed to the occupation-based classifications used here—the broad consistency between our results and theirs is notable.

Coupling American Time of Use Survey (ATUS) data by industry to observations and projections of temperature, Neidell et al. (2021) find that during periods of economic growth in the United States, outdoor workers measurably reduce their work time when temperatures rise. Similarly, Hsiang et al. (2017) used data based on the ATUS (Zivin and Neidell, 2015) to project the change in labor supply due to climate change over the course of this century and found a roughly 0.5% °C–1 decline in labor supply for high-heat-exposure jobs, which implies a smaller change by late century than our findings suggest absent the implementation of adaptation measures (Hsiang et al., 2017). The differences in our results may be due to Neidell et al.’s inclusion of periods of economic contraction, the heat stress metrics used, or functional differences between what workers and their employers do in reality in response to heat versus the breaks in work that employees should be afforded. It is also possible that workers and their employers are already shifting work schedules and workloads somewhat such that Neidell et al.’s results would reflect a reality that is closer to one of the adaptation scenarios we analyzed.

In terms of the efficacy of potential adaptation measures, Tigchelaar et al. (2020) conclude that while increasing workers’ rest time and decreasing the level of effort associated with their work would reduce workers’ heat exposure, such measures would come with costs to productivity, earnings, and labor costs for employers. Our results suggest that without guarantees of payment for rest periods, simply adding additional rest periods to workers’ schedules without shifting work hours to cooler times of day or reducing workloads could provide health benefits but would also come at a significant cost to workers and the national economy. In contrast, shifting work to cooler times of day and reducing workloads while continuing to provide the necessary rest breaks could reduce heat stress and lessen financial repercussions for workers, though reductions in productivity could create challenges for employers and the broader economy that would need to be taken into consideration in any policy measure meant to address this issue.

Given that Black or African American and Hispanic or Latino workers are disproportionately represented in many outdoor occupations, losses in outdoor workers’ earnings could exacerbate existing inequities in health outcomes, poverty rates, and economic mobility, all of which have accumulated from centuries of systemic racism. The health and lives of undocumented and migrant workers, who are likely underrepresented in the data underlying this study, could also be disproportionately affected by increases in extreme heat owing to the fact that fear of deportation and payment practices for these workers often discourage them from taking breaks, reporting symptoms of heat illness, or reporting employers’ negligence to provide a safe working environment (Gubernot et al., 2014; Moyce and Schenker, 2017). A climate-altered future could also necessitate radical shifts in outdoor work, such as increased replacement of outdoor workers by technology, as well as shifts in where and when certain occupations are performed. Without attention to justice and equity, such changes could fall especially hard on the working class.

Communities—particularly those where outdoor workers make up a large proportion of the workforce—would likely experience adverse outcomes as a result of reduced outdoor worker labor and earnings. A loss in the amount of time outdoor workers can safely perform their jobs could disrupt the essential services they provide, from building maintenance and construction to law enforcement and the harvesting of food crops. Further, if employer costs rise due to changes needed to cope with extreme heat, costs could ultimately be borne on the shoulders of consumers. Reduced earnings for outdoor workers could also reduce local revenue from income taxes in some communities, affecting the public services dependent on that revenue.

The results of our analysis also point to the unique vulnerabilities of different regions based on both the composition of their outdoor workforce and the extent to which extreme heat is likely to increase. For example, we project that Louisiana would see among the highest (34) number of unsafe workdays on average by mid-century under RCP8.5. We found that approximately 29% of the state’s outdoor workers are employed in construction and extraction jobs. In Florida, we project a similar number (33) of unsafe workdays per year under RCP8.5 and found that building and grounds cleaning and maintenance occupations account for one of the greatest portions (22%) of outdoor workers in the state, alongside construction and extraction workers (23%). The outdoor workforce needs with respect to heat safety thus may differ from state to state and occupation to occupation.

While beyond the scope of the present study, if emissions continue to rise and/or if employers fail to implement worker protection measures, the impacts to the health of outdoor workers and to the U.S. healthcare system could be significant. For example, the 2018 U.S. National Climate Assessment found that under RCP8.5, annual heat- and cold-related mortalities across large cities in the United States would reach 9,000 by late century (Ebi et al., 2018). Considering their higher risk of heat-related fatalities among outdoor workers, outdoor workers could disproportionately bear that burden.

In addition to those studied here, many additional factors could influence outdoor workers’ schedules or the nature of their work as the climate warms. For example, one could imagine certain types of outdoor work, such as planting crops, being shifted largely to predawn hours. Other types of outdoor work, such as roofing, cannot be done at such times because of the disruption it would cause to homeowners and communities during sleeping hours. For the workers themselves, previous studies suggest that performing “shift work,” or work that is done outside standard daytime working hours, can be associated with poorer diet (Souza et al., 2019) as well other negative health outcomes (Hansen, 2017; Shan et al., 2018). Thus, while our results simulate the benefit of shifting work to cooler hours of the day, in practical terms, the extent to which work hours for certain types of work can be shifted may be limited and shifting work hours could have drawbacks for worker health.

Similarly, there may be limits to how much physical workloads can be reduced. While we have simulated a shift from moderate to light workloads in the present analysis, barring advances in the automation of the tasks typically associated with moderate workloads, the fact remains that there are work-related functions that will continue to necessitate at least moderate levels of physical exertion. As a result, the potential benefits of workload adjustments and/or work schedule shifts quantified here are likely overestimates in some instances but provide useful comparisons with typical work conditions.

### 4.3. Limitations and areas for future research

This study has several limitations that should be noted. The ACS data set used here do not fully capture all outdoor workers because it focuses only on occupations for which outdoor work is essential. Each occupational category in the analysis contains a number of subcategories. For example, protective service includes firefighters and police officers. Some subcategories are not clearly outdoor occupations; in other instances, subcategories could be listed under other occupational categories that largely do not conduct work outdoors and would thus be excluded from our analysis. Furthermore, workers in some occupations (e.g., preschool and elementary school teachers) typically conduct work outdoors, but outdoor work is not necessarily essential for conducting those jobs. This analysis does not include those occupations. Similarly, the COVID-19 pandemic necessitated people in a broad variety of occupations to shift their work at least partially outdoors; those occupations are not included here. The analysis also does not include such occupations as agricultural and construction managers, as ACS includes these workers into a broader category of managers. Finally, ACS data lack precision at smaller geographic areas. Total outdoor worker counts should therefore be taken with caution at small geographic areas (e.g., counties) as well as for the reasons listed above.

This study assumes that outdoor workers are evenly distributed over the area of each county and that there is no change, redistribution, or growth in population of outdoor workers over time. Nor does it include many additional adaptation measures that could lessen future heat exposure, such as the greater use of protective clothing or the potential for human acclimatization to hotter conditions. In this sense, the study is focused on changes in exposure and risk resulting exclusively from climate change.

The extreme heat data underlying this study have some limitations as well. For example, daily minimum heat index values and multiday heat waves are known to affect heat-health outcomes but are not considered. In addition, we utilize county average heat statistics and do not consider their spatial variability within a county. For much of the United States counties are small enough that this spatial variability is likely to not be important. However, in counties with a larger area, such as in parts of the Western United States, such variability could be important and is not considered. The data also do not capture current or future urban heat island dynamics or other land cover changes that can affect the intensity of heat at the local level. Following our analysis of weather station data, we applied our assumptions about the persistence of extreme heat uniformly across the country, though conditions do vary from region to region. We note that our calculations only considered the April–October; warming is likely to create unsafe workdays during the other 5 months in the year in some locations, further increasing the burden. Lastly, improved climate projections that allow for proper estimates of WBGT may be useful for refining exposure estimates used herein based on the heat index.

Recent research has shown that cases of heat-related illness in the United States begin to rise when the heat index reaches 80°F, which is well below the 100°F (37.8°C) threshold identified by the CDC and used in this study (Morris et al., 2019; Vaidyanathan et al., 2019). A lower heat index threshold (e.g., 80°F [26.7°C]) is particularly justified when outdoor workers must wear protective clothing, such as when applying pesticides to crops (Ferguson et al., 2019). Given that our study only considers work reductions on days when the heat index is above 100°F (37.8°C) as well as light and moderate (but never heavy exertion), our estimates of unsafe workdays and earnings at risk may be conservative. On the other hand, because the heat index tends to be higher than the adjusted temperature, particularly for adjusted temperatures above 105°F (40.6°C), our application of the heat index to the NIOSH work reduction guidance could lead to a slight overestimation of the number of hours necessitating work reductions on days with a heat index above 105°F (40.6°C).

### 4.4. Policy implications

In all but two U.S. states—California and Washington—there are no enforceable heat protection standards for outdoor workers. While the Occupational Safety and Health Act of 1970 (OSH Act) requires that employers provide employees with a workplace that is free of hazards that could cause serious physical harm or death (OSHA, 2004), there are no federal measures that employers are mandated to follow to ensure that preventable heat-related illnesses and deaths are in fact prevented. Rather, employers are provided with recommendations from OSHA and NIOSH. The lack of standards enforceable under state or federal law is a clear gap in heat-health policies in the United States.

This research may provide data useful for workers and advocates for workers’ rights as well as for policymakers seeking to understand how climate mitigation and adaptation measures could affect their jurisdictions and constituents. The results of our research show that under ideal circumstances, adaptation measures can prevent the majority of outdoor worker exposure to unsafe work time as well as the majority of earnings losses. However, as discussed above, reducing work schedules and lightening workloads will not be possible in many instances, and in some instances, such adaptation measures can have their own adverse consequences for outdoor worker well-being. As a result, it is critical that mitigation measures also be taken to limit the increase in extreme heat conditions.

All novel data including unsafe workday, earnings at risk, and worker exposure data are publicly available through the Union of Concerned Scientists website: https://www.ucsusa.org/resources/too-hot-to-work#ucs-report-downloads.

The authors would like to thank Linda McCauley, Matías Scaglione, Brenda Ekwurzel, and Erika Spanger-Siegfried for their critical input and important insights that helped shape this research.

This research was funded by grants from the Barr Foundation, the Common Sense Fund, the Energy Foundation, the Farvue Foundation, the Fresh Sound Foundation, the MacArthur Foundation, the Martin Foundation, New York Community Trust, the Rauch Foundation, Sand County Charitable Trust, the Scherman Foundation, three anonymous funders, and Union of Concerned Scientist members (RL and KD) as well as NOAA awards NA15OAR4310145 and NA17OAR4310284 (JTA).

The authors declare no competing interests.

Contributed to conception and design: RL, KD.

Contributed to acquisition of data: RL, KD, JTA.

Contributed to analysis and interpretation of data: RL, KD, JTA.

Drafted and/or revised the article: RL, KD, JTA.

Approved the submitted version for publication: RL, KD, JTA.

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How to cite this article: Licker, R, Dahl, K, Abatzoglou, JT. 2022. Quantifying the impact of future extreme heat on the outdoor work sector in the United States. Elementa: Science of Anthropocene 10(1). DOI: https://doi.org/10.1525/elementa.2021.00048

Domain Editor-in-Chief: Anne R. Kapuscinski, University of California, Santa Cruz, CA, USA

Associate Editor: Selena Ahmed, The Food and Health Lab, Department of Health and Human Development, Montana State University, Bozeman, MT, USA

Knowledge Domain: Sustainability Transitions

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.