Abstract

Prospective demographic information of the United States is limited to national-level analyses and subnational analyses of the total population. With nearly 40% of the U.S. population being residents of coastal areas, understanding the anticipated demographic changes in coastal counties is important for long-range planning purposes. In this research note, we use long-range, county-level population projections based on a simplified cohort-component method to discuss demographic changes by age, sex, and race and ethnicity for coastal counties between 2020 and the end of the century, and we compare these changes to inland counties. Presently, coastal counties are statistically significantly different from inland counties by race and ethnicity (more diverse) and sex (more women) but not by age, yet by 2025, we expect coastal counties to become significantly older than inland counties. We note several important trajectories of predicted demographic outcomes in coastal counties across the remainder of the century: (1) the non-Hispanic White population is expected to decrease, both numerically and as a percentage of the population; (2) the population older than 65 is projected to increase, both numerically and as a percentage of the population; and (3) the ratio of women to men remains constant over the century at 1.03. These trends combine to suggest that the future U.S. coastline will likely be both increasingly diverse racially and ethnically and significantly older than it is today.

Introduction

Nearly 40% of the U.S. population lives in a coastal community (Crossett et al. 2014), and coastal areas are among the most rapidly growing areas in the United States and across the world (Neumann et al. 2015). Descriptions of historical demographic change abound in both the academic and the gray literature (Creel 2003; Crossett et al. 2014; Hinrichsen 1999; Wilson and Fischetti 2010), yet reports describing future demographic changes are limited to national-level analyses (Gerland et al. 2014; Jiang et al. 2020; Vespa et al. 2018) or to descriptions of total population in coastal areas (Jones and O'Neill 2016; Neumann et al. 2015). Secular demographic trends in the United States suggest growth of both communities of color and older populations (Vespa et al. 2018) but, to our knowledge, no analyses of anticipated demographic change in coastal areas presently exist. It is widely recognized that coastal areas are at or near the forefront of climate change impacts—impacts that are unequally distributed across primary demographic classifications such as age, sex, and race and ethnicity (Barros and Field 2014; Harlan et al. 2015). Understanding the future demographic changes in coastal communities can help illuminate important, broad demographic patterns and inform decision-making for climate change, disaster, and environmental justice planning.

To investigate the anticipated demographic changes in the coastal United States, we use recently published, detailed county-level population projections (Hauer 2019) for the 258 counties the U.S. Census Bureau identifies as coastal to describe the anticipated demographic changes along age, sex, and race/ethnicity from 2020 until the end of the century.1 These data project populations for 18 five-year age-groups (0–85+), two sex groups (male and female), and four race/ethnicity groups (non-Hispanic White, non-Hispanic Black, non-Hispanic Other, and Hispanic). These demographic population projections employ cohort-change ratios (CCRs) and cohort-change differences (CCDs) in Leslie matrix projection models. We note that the population projections do not account for potential racial identity changes over the life course—changes that are likely to occur (Alba 2018)—nor for adaptation, and this is a limitation in our analysis. See Hauer (2019) for a detailed description of the methods.

We choose 2100 as a terminal date, despite its long temporal distance, because of that year's importance in the calculations of the U.S. Climate Assessment and the Intergovernmental Panel on Climate Change. We compare the differences between coastal and inland counties along age/sex/race/ethnicity to determine if coastal counties experience secular demographic trends (i.e., become more diverse and older) (Vespa et al. 2018). Then, we describe the anticipated demographic changes in coastal counties along these primary demographic classifications.

Results

Comparison to the Inland United States

Current demographic trends show both coastal and noncoastal areas becoming more racially and ethnically diverse, having a larger older population, and being relatively even across sex categories; however, the coastal trends far outpace the rest of the country. Table 1 reports summary metrics for these trends in the coastal and inland United States, aggregated to their respective geographies.

Both the coastal and inland regions are projected to become increasingly non-White, with non-Hispanic White population shares declining by 20 and 27 percentage points, respectively. The aging expected to occur in coastal areas is accelerated compared to that in inland areas, with median ages increasing by 14 and 11 years, respectively. Finally, the ratio of women to men in coastal areas stays relatively constant at 1.03 but approaches 1.01 in 2100 in inland areas.

Figure 1 shows the results of Welch's unequal variances t test (Welch 1947), a nonparametric test of means, between coastal and inland areas for the year 2100 for the percentage of the population that is non-Hispanic White, the median age, and the ratio of women to men. In 2020, coastal counties are significantly different from inland counties by race/ethnicity and sex ratio but not by age. But by 2050, coastal counties are significantly different than inland counties under all three metrics, suggesting that coastal counties will continue to be more diverse, have more women than men residents, and become significantly older. We estimate that coastal counties will begin to become significantly older than inland areas around 2025 and continue aging faster than inland areas through 2100 (2025, p = .0409; 2030, p = .0064; 2050, p = .0001; 2065, p = .0000).

Race/Ethnicity

We find that U.S. coastal communities will be considerably more diverse through the remainder of the century than they are today (Table 2 and Figure 2). Currently, the coastline is majority non-White, with less than 50% of the population being non-Hispanic White. Demographic change in coastal communities will likely lead to a dwindling of this population—both in absolute numbers and as a proportion of the total population—through 2100.

All non-White racial/ethnic groups are projected to increasein absolute numbers and as proportions of the total populationas the century progresses. By 2050, the non-Hispanic White population is projected to account for less than 40% of the U.S. coastal populationa decline of nearly 10 percentage points and a change of −0.83 million (between −8.3 million and +7.2 million). Conversely, the Hispanic population is likely to increase by more than 50% by 2050 (between 26% and 83%), when it will account for more than 30% of the U.S. coastal population.

This shift toward an increasingly diverse U.S. coastline is not limited to specific areas. Figure 3 shows the geographic distribution of race and ethnicity in the coastal United States in 2020 and projected in 2100 as a bivariate choropleth. The dominant racial/ethnic group in each county is uniquely colored, and the deepness of the color corresponds with the percentage of the population that the dominant group represents. Even though the overall U.S. coast is majority non-White, most coastal counties contain 75%+ non-Hispanic White population, as exhibited by the dark blue color in the majority of counties. Only pockets of counties in parts of California, southern Texas, and south Florida are dominantly Hispanic—shown by the light reds—but they are decidedly less dominant than the non-Hispanic White populations. Alaska and Hawaii contain significant populations that are Native American, Pacific Islander, and Asian racial/ethnic groups. Isolated pockets of non-Hispanic Black dominance are also found in parts of the U.S. South.

By 2100, however, the dark blue counties indicating a non-Hispanic White dominance are considerably reduced in number and in hue (suggesting a smaller non-Hispanic White dominance by 2100), and many areas across the entire coastline contain significant Hispanic majorities—especially in the Northeast and California.

Age

Through the remainder of the century, the coastal communities are likely to be considerably older than at present (Figure 4, panel a). Today, the U.S. coast contains approximately 16.1 million people over the age of 65, representing 16% of the coastal population. However, by 2100, the population older than 65 is projected to more than triple to 51.5 million people (30 million–70 million) and will account for 37% (33%–42%) of the coastal population. This would increase the proportion of the 65+ age-group from approximately 1 in every 6 coastal residents to more than 1 in every 3. This represents a significant demographic shift from a relatively youthful coastal population to a decidedly more elderly population.

For nearly every age-group below age 40, the projected populations exhibit very little change. On the other hand, each age-group above age 40 shows much larger growth. The coastal communities for the remainder of the century are likely to be populated by considerably older residents, with the open-ended age interval (85+) showing the greatest numerical and percentage increase in population. Older women (in particular, women aged 85+) represent the largest population increase. Broad demographic groupings of young (0–19), working age (20–64), and old (65+) help illuminate the aging trend by midcentury (Figure 4, panel b). Despite the fact that the coastal United States is known as a retirement destination, the young population in coastal communities is presently larger than the old population. However, within 20 years, the older population will numerically outnumber the young population.

This demographic shift to older populations is especially profound in some of the largest U.S. coastal counties. Figure 5 presents the median ages in 2020, 2050, and 2100 for the 25 most populous coastal counties. Pinellas County, Florida, is expected to be the oldest by 2100 with a median age of 53.5—an increase of more than 10 years. We expect some counties to age much faster than others: Miami-Dade, Florida, and Alameda, California, in particular, will see considerable aging by 2050, while other counties, such as New York, New York, and Fairfax, Virginia, will see relatively little aging by 2050.

Sex

Our results regarding sex composition in the coastal United States are markedly different from the results for race/ethnicity and age (see Figure S1 in the online supplement). We find that coastal counties today contain approximately 1.3 million more women than men (49.9 million women to 48.6 million men), and this disparity grows by more than 0.5 million women to 1.9 million more women than men by 2100 (71.4 million women to 69.5 million men). However, the ratio of women to men remains constant over the next 80 years at 1.03 women per man.

Differential Vulnerability to Sea-Level Rise

Most analyses concerning vulnerability to sea-level rise focus on total populations in the future (e.g., Hauer et al. 2016; Neumann et al. 2015) or examine vulnerability within present demographic conditions (e.g., Emrich and Cutter 2011; Spanger-Siegfried et al. 2017). Here, we combine the counties most at risk to inundation from sea-level rise with our demographic projections to examine differential vulnerability to sea-level rise.

We stratify U.S. coastal counties by the percentage of the population at risk to 3 feet of sea-level rise in 2100 into five quartiles (Figure 6). We find that race/ethnicity compositions follow an upside-down U shape based on sea-level rise risk, where counties with the most and least risk are likely to have lower non-Hispanic White percentages of the population, with the counties most at risk to sea-level rise having the lowest such percentage (Figure 6, panel a). We also note that most counties—regardless of sea-level rise—are likely to be increasingly non-White as the century progresses. Regarding aging, we find that the oldest counties tend to have the lowest risk of sea-level rise and the counties with the highest risk tend to have the youngest populations (Figure 6, panel b).

Discussion

Two major, secular trends will come to a head in this century: demographic change and climate change. As we demonstrate, people of color and the elderly will likely predominate in coastal regions in the future and, at the same time, climate impacts due to intensified warming are expected to worsen as we approach the end of the century. Our results suggest that climate change hazards in coastal regions will likely threaten an increasingly diverse and older coastal U.S. population, potentially amplifying already existing inequalities. Environmental justice and related disaster planning issues are likely to become more important in the future.

While many of the trends are rather homogeneous (i.e., aging and diversification), these trends are not uniform across the U.S. coastline nor within areas more or less threatened by sea-level rise. In fact, considerable heterogeneity exists across the coastline in terms of the speed of demographic change and the ultimate amount of demographic change. The implications of an aging and diverse coastal population are also markedly different for different regions of the United States, given the historical twin legacies of racism/segregation (Massey 1990) and long-term depopulation (Johnson and Lichter 2019). However, our results emphasize the importance of understanding localized demographic change within the broader context of climate change. Marginalized groups could face the brunt of climate change in unexpected ways.

We recognize that over long time frames any number of circumstances may arise—not the least of which involves climate change—that may alter the composition of coastal populations and therefore acknowledge important limitations in using these projections in our analysis. First and foremost, our projections do not account for climate change impacts in the projections themselves; these population projections are strictly demographic projections. Climate change broadly, and sea-level rise specifically, is likely to spur migration in this century (Black et al. 2011). Sea-level rise could alter residential mobility patterns (Hauer et al. 2020), fertility rates (Grace 2017), and mortality rates (Huang et al. 2011). Government-sponsored managed retreat programs to relocate individuals away from risky coastal areas (Siders et al. 2019) could also markedly alter the underlying demographic processes.

Additionally, it is entirely possible that the demographic changes alone predicted here could be different. For example, aging in coastal areas could attract more working-age people as salaries rise for scarce elder-care labor. The COVID pandemic, unforeseen at the time Hauer (2019) published his projections, almost certainly will alter the future demographic trajectory of the United States, though the extent of this alteration remains to be seen. Alternative demographic futures could naturally arise over the course of the century, thus it is possible that our findings will not fully capture the projected demographic change in the U.S. coastal population over this time frame.

Second, the population projections are limited to the county level. These projections cannot distinguish who resides in the most environmentally vulnerable locations in coastal communities and, presumably, those who might be most impacted. While it is true that those closest to the shoreline are most at risk to sea-level rise hazards, it is also true that those more distant from the shoreline will still experience impacts. Besides complete inundation (the conversion of habitable dry land to inhabitable water), many sea-level rise impacts affect areas broader than neighborhoods and may spread over the entire coastal region (McAlpine and Porter 2018). In this note, we are not interested in any specific environmental hazard that would necessitate the projections of individual coastal hazards, but rather we situate the changing demographics of the coastal United States within the contexts of broader vulnerability to hazards associated with climate change and invite future research into more specific, localized, and regionalized patterns of climate impacts.

Finally, the projections we employ in our analysis explicitly assume that past demographic rates predict future demographic rates. This assumption likely holds over a few decades, but any deviation in predicted rates could result in sizable errors over long time horizons. While it is possible that any single finding we describe might not manifest in the future (i.e., particular differentials between coastal and inland areas), the general trends we describe are likely to unfold.

Note

1

Hauer (2019) controlled his population projections to the Shared Socioeconomic Pathways (SSPs) and performed an ex-post-facto error analysis against multiple published population projections (Raftery et al. 2012; Rayer 2008; Smith and Tayman 2003; Sprague 2012; Wilson 2016). The SSPs represent possible socioeconomic scenarios that couple potential challenges for reducing carbon emissions with challenges for adapting to climate change impacts. For a detailed description of the SSPs, see KC and Lutz (2017). Throughout our results, we derived upper and lower bounds in parentheses based on the upper/lower bounds of all five SSPs. Point estimates came from SSP2: Middle of the Road.

References

Alba, R. (
2018
).
What majority-minority society? A critical analysis of the Census Bureau's projections of America's demographic future
.
Socius
,
4
. https://doi.org/10.1177/2378023118796932
Barros, V. R., & Field, C. B. (Eds.). (
2014
).
Climate change 2014–Impacts, adaptation and vulnerability: Regional aspects
.
Cambridge, UK
:
Cambridge University Press
.
Black, R., Bennett, S. R. G., Thomas, S. M., & Beddington, J. R. (
2011
).
Climate change: Migration as adaptation
.
Nature
,
478
,
447
449
.
Creel, L. (
2003
).
Ripple effects: Population and coastal regions
(Making the Link report).
Washington, DC
:
Population Reference Bureau
.
Crossett, K., Ache, B., Pacheco, P., & Haber, K. (
2014
).
National coastal population report: Population trends from 1970 to 2020
(NOAA's State of the Coast report).
Washington, DC
:
National Oceanic and Atmospheric Administration, Department of Commerce, U.S. Census Bureau
. Retrieved from https://coast.noaa.gov/digitalcoast/training/population-report.html
Emrich, C. T., & Cutter, S. L. (
2011
).
Social vulnerability to climate-sensitive hazards in the southern United States
.
Weather, Climate, and Society
,
3
,
193
208
.
Gerland, P., Raftery, A. E., Ševčíková, H., Li, N., Gu, D., Spoorenberg, T., . . . Wilmoth, J. (
2014
).
World population stabilization unlikely this century
.
Science
,
346
,
234
237
.
Grace, K. (
2017
).
Considering climate in studies of fertility and reproductive health in poor countries
.
Nature Climate Change
,
7
,
479
485
.
Harlan, S. L., Pellow, D, N., Roberts, J. T., Bell, S. E., Holt, W. G., & Nagel, J. (
2015
).
Climate justice and inequality
. In Dunlap, R. E. & Brulle, R. J. (Eds.),
Climate change and society: Sociological perspectives
(pp.
127
163
).
New York, NY
:
Oxford University Press
.
Hauer, M. E. (
2019
).
Population projections for U.S. counties by age, sex, and race controlled to shared socioeconomic pathway
.
Scientific Data
,
6
,
190005
. https://doi.org/10.1038/sdata.2019.5
Hauer, M. E., Evans, J. M., & Mishra, D. R. (
2016
).
Millions projected to be at risk from sea-level rise in the continental United States
.
Nature Climate Change
,
6
,
691
695
.
Hauer, M. E., Fussell, E., Mueller, V., Burkett, M., Call, M., Abel, K., . . . Wrathall, D. (
2020
).
Sea-level rise and human migration
.
Nature Reviews Earth & Environment
,
1
,
28
39
. https://doi.org/10.1038/s43017-019-0002-9
Hinrichsen, D. (
1999
).
The coastal population explosion
. In Cicin-Sain, V., Knecht, R. W., & Foster, N. (Eds.),
Trends and future challenges for U.S. national ocean and coastal policy: Proceedings
(Vol.
22
, pp.
27
29
).
Washington, DC
:
National Oceanic and Atmospheric Administration
.
Huang, C., Barnett, A. G., Wang, X., Vaneckova, P., FitzGerald, G., & Tong, S. (
2011
).
Projecting future heat-related mortality under climate change scenarios: A systematic review
.
Environmental Health Perspectives
,
119
,
1681
1690
.
Jiang, L., O'Neill, B. C., Zoraghein, H., & Dahlke, S. (
2020
).
Population scenarios for U.S. states consistent with shared socioeconomic pathways
.
Environmental Research Letters
,
15
,
094097
. https://doi.org/10.1088/1748-9326/aba5b1
Johnson, K. M., & Lichter, D. T. (
2019
).
Rural depopulation: Growth and decline processes over the past century
.
Rural Sociology
,
84
,
3
27
.
Jones, B., & O'Neill, B. C. (
2016
).
Spatially explicit global population scenarios consistent with the shared socioeconomic pathways
.
Environmental Research Letters
,
11
,
084003
. https://doi.org/10.1088/1748-9326/11/8/084003
KC, S., & Lutz, W. (
2017
).
The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100
.
Global Environmental Change
,
42
,
181
192
.
Massey, D. S. (
1990
).
American apartheid: Segregation and the making of the underclass
.
American Journal of Sociology
,
96
,
329
357
.
McAlpine, S. A., & Porter, J. R. (
2018
).
Estimating recent local impacts of sea-level rise on current real-estate losses: A housing market case study in Miami-Dade, Florida
.
Population Research and Policy Review
,
37
,
871
895
.
Neumann, B., Vafeidis, A. T., Zimmermann, J., & Nicholls, R. J. (
2015
).
Future coastal population growth and exposure to sea-level rise and coastal flooding—A global assessment
.
PloS One
,
10
,
e0118571
. https://doi.org/10.1371/journal.pone.0118571
Raftery, A. E., Li, N., Ševčíková, H., Gerland, P., & Heilig, G. K. (
2012
).
Bayesian probabilistic population projections for all countries
.
Proceedings of the National Academy of Sciences
,
109
,
13915
13921
.
Rayer, S. (
2008
).
Population forecast errors: A primer for planners
.
Journal of Planning Education and Research
,
27
,
417
430
.
Siders, A. R., Hino, M., & Mach, K. J. (
2019
).
The case for strategic and managed climate retreat
.
Science
,
365
,
761
763
.
Smith, S. K., & Tayman, J. (
2003
).
An evaluation of population projections by age
.
Demography
,
40
,
741
757
.
Spanger-Siegfried, E., Dahl, K., Caldas, A., Udvardy, S., Cleetus, R., Worth, P., & Hernandez Hammer, N. (
2017
).
When rising seas hit home: Hard choices ahead for hundreds of U.S. coastal communities
(Union of Concerned Scientists report). Retrieved from https://www.ucsusa.org/resources/when-rising-seas-hit-home
Sprague, W. W. (
2012
).
Automatic parametrization of age/ sex Leslie matrices for human populations
(q-bio ArXiv papers). https://doi.org/10.48550/arXiv.1203.2313
Vespa, J., Medina, L., & Armstrong, D. M. (
2018
).
Demographic turning points for the United States: Population projections for 2020 to 2060
(Current Population Reports, No. P25-1144).
Washington, DC
:
U.S. Department of Commerce, Economics and Statistics Administration, U.S. Census Bureau
.
Welch, B. L. (
1947
).
The generalization of ‘student's’ problem when several different population variances are involved
.
Biometrika
,
34
,
28
35
.
Wilson, S. G., & Fischetti, T. R. (
2010
).
Coastline population trends in the United States: 1960 to 2008
(Current Population Reports, No. P25-1139).
Washington, DC
:
U.S. Department of Commerce, Economics and Statistics Administration, U.S. Census Bureau
.
Wilson, T. (
2016
).
Evaluation of alternative cohort-component models for local area population forecasts
.
Population Research and Policy Review
,
35
,
241
261
.
This is an open access article distributed under the terms of a Creative Commons license (CC BY-NC-ND 4.0).

Supplementary data