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LETTER
Spatiotemporal dynamics of global population and heat exposure
(2020–2100): based on improved SSP-consistent population
projections
Mengya Li1, Bing-Bing Zhou2, Minyi Gao1, Yimin Chen3,∗, Ming Hao1, Guohua Hu4and Xia Li4,∗
1Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities (Ministry of Natural
Resources), School of Geographic Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241,
People’s Republic of China
2School of International Affairs and Public Administration, Ocean University of China, 238 Songling Road, Qingdao 266100,
People’s Republic of China
3School of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University,
135 West Xingang Road, Guangzhou 510275, People’s Republic of China
4Key Lab of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University,
500 Dongchuan Road, Shanghai 200241, People’s Republic of China
∗Authors to whom any correspondence should be addressed.
E-mail: chenym49@mail.sysu.edu.cn and lixia@geo.ecnu.edu.cn
Keywords: gridded global population projections, Shared Socioeconomic Pathways (SSPs), extreme heat,
climate variability and global change, sustainable development
Supplementary material for this article is available online
Abstract
To address future environmental change and consequent social vulnerability, a better
understanding of future population (FPOP) dynamics is critical. In this regard, notable progress
has been made in producing FPOP projections that are consistent with the Shared Socioeconomic
Pathways (SSPs) at low resolutions for the globe and high resolutions for specific regions. Building
on existing endeavors, here we contribute a new set of 1 km SSP-consistent global population
projections (FPOP in short for the dataset) under a machine learning framework. Our approach
incorporates a recently available SSP-consistent global built-up land dataset under the Coupled
Model Intercomparison Project 6, with the aim to address the misestimation of future built-up
land dynamics underlying existing datasets of future global population projections. We show that
the overall accuracy of our FPOP outperforms five existing datasets at multiple scales and especially
in densely-populated areas (e.g. cities and towns). Followingly, FPOP-based assessments of future
global population dynamics suggest a similar trend by population density and a spatial Matthew
effect of regional population centralization. Furthermore, FPOP-based estimates of global heat
exposure are around 300 billion person-days in 2020 under four SSP-Representative Concentration
Pathway (RCPs), which by 2100 could increase to as low as 516 billion person-days under
SSP5-RCP4.5 and as high as 1626 billion person-days under SSP3-RCP8.5—with Asia and Africa
contributing 64%–68% and 21%–25%, respectively. While our results shed lights on proactive
policy interventions for addressing future global heat hazard, FPOP will enable future-oriented
assessments of a wide range of environmental hazards, e.g. hurricanes, droughts, and flooding.
1. Introduction
In recent years, five Shared Socioeconomic Path-
ways (SSPs) were proposed by the Intergovern-
mental Panel on Climate Change for navigating the
uncertainties in addressing future climate change
(Riahi et al 2017) and advancing our common
journey toward sustainability (Szetey et al 2021).
Essentially, SSPs 1–5 depict five plausible future scen-
arios of socioeconomic development (Kriegler et al
2014) which can be used to derive greenhouse gas
emissions scenarios with different climate policies.
SSPs 1–5 correspond respectively to sustainability
(SSP1), middle of the road (SSP2), regional rivalry
© 2022 The Author(s). Published by IOP Publishing Ltd
Environ. Res. Lett. 17 (2022) 094007 M Li et al
(SSP3), inequality (SSP4), and fossil-fueled develop-
ment (SSP5), and have been gaining increasing pop-
ularity within the global change and sustainability
research community (Maury et al 2017, Van Vuuren
et al 2017). These SSPs are distinguished by a few
socioeconomic variables (e.g. population, GDP, urb-
anization, and education level). Among these fun-
damental variables, particularly, the arguably most
critical is population. Population data are often the
basis for addressing a wide range of social concerns,
e.g. epidemics (Coccia 2020), heat waves (Liu et al
2017, Huang et al 2019), floods (Gu 2019), droughts
(Liu and Chen 2021), and sea-level rise (Kulp and
Strauss 2019), and for monitoring 73 indicators of
the UN SDGs (Freire et al 2018) that require pop-
ulation data as an input (Dahmm 2021). In this
vein, an urgent need is to downscale the SSP pro-
jections of future global population over the 21st
century to spatially explicit projections—i.e. to pro-
duce SSP-consistent gridded population data.
Notable progress has been made in producing
such SSP-consistent gridded population projections
at the global and regional scales. For global projec-
tions, Jones and O’Neill (2016) at the National Cen-
ter for Atmospheric Research (NCAR) developed a
widely-used global population dataset under SSPs
1–5. They predicted grid-level population under the
SSPs’ constraints of population and urbanization. Yet,
the dataset was found that approximately 30%–43%
of the estimated population were in uninhabited areas
with cropland, forest, or pasture in 2050 (Chen et al
2020c) and is limited by its relatively low resolution
(i.e. 0.125 degree). Subsequently, Gao (2020) down-
scaled the NCAR dataset from 0.125 degree to 1 km.
There are other global forecasting efforts, albeit sim-
ilarly they suffer from low resolution, poor accur-
acy for certain areas, and/or sometimes considering
only partial SSPs (Murakami and Yamagata 2019). For
regional projections, high-resolution gridded popu-
lation datasets under the SSPs have been developed
for Africa (1 km) (Boke-Olén et al 2017), China
(1 km (Chen et al 2020a) and 100 m (Chen et al
2020b)), the United States (1 km) (McKee et al 2015,
Zoraghein and O’Neill 2020), global coastal areas
(1 km) (Merkens et al 2016), and the Mediterranean
coastal zone (1 km) (Reimann et al 2018). However,
due to their different input data and methods, com-
bining these regional population datasets for global
applications would induce unknown uncertainties.
As of now, the improved NCAR dataset by Gao
(2020) is perhaps the best source of SSP-consistent
high-resolution global population projections. For
further improvements, needed are additional auxil-
iary data, novel downscaling models, and/or greater
computational capacity. Thanks to the reducing cost
of super computers and servers, what really hinders
is to develop better models with hopefully the aid
of additional predictive data. Existing studies have
included historical population (Leyk et al 2019),
urban fraction under Representative Concentration
Pathways (RCPs) (Boke-Olén et al 2017, Chen et al
2020a, Boke-Olén and Lehsten 2022), and other aux-
iliary variables (Leyk et al 2019, Murakami and
Yamagata 2019) as the predictors, involving down-
scaling methods like areal weighting techniques (Gao
2020), the share-of-growth model (McKee et al
2015), and gravity-type models (Grübler et al 2007,
Jones and O’Neill 2016). Note that population often
presents nonlinear relationships with the various pre-
dictive variables (Rohat 2018, Leyk et al 2019), such
as historical population distribution (Xu et al 2021),
biophysical or environmental factors (Stevens et al
2015, Yao et al 2017), and particularly, built-up land
pattern—a decisive factor that shapes population dis-
tribution (Nieves et al 2017, Reed et al 2018). Yet,
built-up land pattern remains insufficiently incorpor-
ated in the existing studies.
Here we incorporate a newly available fine-
resolution future built-up land dataset to train
a non-linear machine learning (ML) model, cre-
ating a new dataset of 1 km resolution global
population projections under SSPs 1–5 throughout
the 21st century for every ten years (the future
population dataset, FPOP for short; available at
www.geosimulation.cn/FPOP.html). Specifically, we
take advantage of an SSP-consistent 1 km resolu-
tion global built-up land dataset for 2020–2100 by
Chen et al (2020c), which itself is not informed by
FPOP distribution. Further, we adopt the random
forest (RF) algorithm to better capture the nonlinear
relationships between population and various pre-
dictive variables. The remainder is organized as fol-
low: section 2presents methodology and raw data.
Section 3validates and evaluates FPOP’s accuracy,
following which we assess the spatiotemporal dynam-
ics of future global population under SSPs 1–5 and
associated heat exposure dynamics under contrast-
ing SSP-RCP scenarios. Section 4discusses some key
findings that deserve research and policy attention.
2. Data and methodology
2.1. Population projection using machine learning
The core of our methodology is recursive projections
for every ten years from 2010 to 2100 by using an
ML framework (i.e. RF) to capture the potentially
nonlinear relationships between a range of predict-
ive factors and gridded population (figure 1). The
exceptional accuracy of our projections, as detailed
below, results from a combination of three meth-
odological peculiarities. First, we adopted a recurs-
ive approach—a conventional practice in existing
population projection studies (Chen et al 2020a,
2020b)—to extend gridded projections at an earlier
time Tionto the next time point Ti+10, i.e. ten years
later. The underlying assumption is that population
distribution is path-dependent, influenced by a leg-
acy effect. Second, in addition to a few commonly
2
Environ. Res. Lett. 17 (2022) 094007 M Li et al
Figure 1. Methodological flowchart of FPOP projection including two main parts: (1) modeling and (2) projection. All input data
were processed to train the RF model and then perform the recursive projections based on the trained model.
used predictive factors—i.e. slope, distance to city
center, distance to roads, and distance to water, as
highlighted in previous studies (Stevens et al 2015,
Nieves et al 2017)—we further incorporated the SSP-
consistent 1 km future global built-up land dataset
by Chen et al (2020c) to improve prediction (see
figure S1 for a comparative illustration justifying our
use of the 1 km projection by Chen et al (2020c)
instead of a 0.125 degree data by Gao and O’Neill
2020). The underlying assumption is that popula-
tion distribution is closely related to built-up land.
Third, similar to the idea of Cellular Automaton,
we used derived data from population and built-up
land grids by applying 3 ×3, 5 ×5, and 7 ×7
moving windows—following Chen et al (2020b)—
to account for multi-scale neighborhood effects. The
underlying assumption is Tobler’s First Law of Geo-
graphy that ‘[e]verything is related to everything
else, but near things are more related than distant
things.’
For each projection, the SSP-consistent global
population count and ten gridded layers (figure 1;
table 1) were used as inputs of the predictive RF
algorithm, which was trained and tested at the year
2010 (see supplementary material for critical meth-
odological details). The prediction performance of
the trained RF algorithm was evaluated at multi-
scales against the adjusted WorldPop 2020 data
(version: unconstrained individual countries 2000–
2020 UN adjusted-1 km resolution) and further, com-
pared with five existing gridded FPOP datasets that
are SSP-consistent, including NCAR, CoastalZone,
AFRICA, China_CAI, and China_CHEN (table 2).
The accuracy assessment and comparison were based
on the percent root relative squared error (%RRSE)
(Raji and Vinod Chandra 2016, Khan et al 2020,
Kumar and Susan 2020) for comparison across scales
and regions. It is calculated as follows:
%RRSE =
v
u
u
u
u
u
t
n
P
i=1Pred(i)−Act(i)2
n
P
i=1Act(i)−Act(i)2
×100%(1)
where nis the total number of grids, Pred(i)denotes
the predicted population at the ith grid, and Act(i)
denotes the corresponding adjusted WorldPop popu-
lation in 2020, while Act(i)denotes the mean of Act(i)
(i.e. the gridded adjusted WorldPop population in
2020 averaged over the total of ngrids). Besides,
seven representative urban regions—Cairo, Egypt;
Melboume, Australia; New York, USA; Paris, France;
Sao Pabulo, Brazil; Tokyo, Japan; and Yangtze River
Delta, China—were mapped in terms of their 2020
WorldPop populations and the corresponding pro-
jections of the five existing datasets and our study.
In so doing, the projection accuracy can be visually
contrasted.
2.2. Extreme heat exposure assessment
Accurate population data is critical for a wide range
of sustainability issues, of which exposure to extreme
heat is a typical concern. Following the method adop-
ted by Liu et al (2017), Jones et al (2018) and Chen
et al (2020b), we calculated extreme heat exposure as
the product of population times the yearly frequency
of extreme heat days (unit: person per day), as follows:
ET
(i),pop =PT
(i)×HT
(i)(2)
where ET
(i),pop and PT
(i)denote respectively the extreme
heat exposure and population at the ith grid in year T,
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Environ. Res. Lett. 17 (2022) 094007 M Li et al
Table 1. Data used for SSP-consistent population modeling in this study.
Dataset Year Resolution Source
Global Population Maps 2000 1 km Lloyd et al (2019)
2010
Environmental Variables Maps City center — 1 km Natural Earth
Roads
Water
Slope
Historical Global Built-up Land 2010 30 m Liu et al (2020)
Global Built-up Land Scenarios 2020–2100 1 km Chen et al (2020c)
Global Population Scenarios 2010–2100 — Riahi et al (2017)
Table 2. Existing gridded population datasets for comparison with our product.
Scenario Dataset Scale Year Resolution Source
SSPs WorldPop Global-scale 2020 1 km Lloyd et al (2019)
NCAR 2010–2100 1 km Gao (2020)
Coastal Zone 2005–2100 1 km Merkens et al (2016)
AFRICA Regional-scale 2010–2100 1 km Boke-Olén et al (2017)
China_CAI 2010–2100 1 km Chen et al (2020a)
China_CHEN 2015–2050 100 m Chen et al (2020b)
and HT
(i)is the frequency of extreme heat days at the
ith grid during year T. Here, an extreme heat day is
defined as one when its highest daily temperature is
no less than 35 ◦C (see supplementary material for
methodological details).
Following the scenario setting rationale in Jones
et al (2018) and Chen et al (2020b), population
dynamics under SSP3 and SSP5 were considered for
estimating future extreme heat exposure; SSP3 rep-
resents a world with rapid population growth in most
regions and low urbanization, while SSP5 depicts low
population growth and high urbanization (see table 2
in O’Neill et al 2017 for detail). Besides, to quantify
the impact of population dynamics on extreme heat
exposure, the Thiel-sen slope (Sen 1968) was cal-
culated to measure the rate of change in extreme
heat exposure for each continent (except Antarctica)
from 2020 to 2100. The same method was applied to
study the population dynamics from 2020 to 2100 in
regions of different population densities.
3. Results
3.1. Accuracy comparison between FPOP and
existing datasets
We compared the projection accuracy of FPOP
and five existing gridded population datasets, i.e.
NCAR, CoastalZone, AFRICA, China_CAI, and
China_CHEN (table 2). As noted in section 2.1, the
%RRSE was used as the accuracy measure of the pro-
jections against the adjusted WorldPop population
in 2020 for the globe, major continents and regions
(see figure 2(a) for grid-accuracy’s mean and table
S1 for standard deviation). FPOP has a %RRSE of
34.10% at the global scale, contrasting to 84.24% for
NCAR and 139.42% for CoastalZone. The improved
accuracy of our population projections also applies to
the six populated continents. Our accuracy outper-
forms the existing datasets by up to 54% in Asia, 97%
in Africa, 57% in Europe, 53% in North America,
47% in Oceania, and 47% in South America. For
China, particularly, the relative accuracy improve-
ment of our projections is up to 112%. To quantify
our accuracy at the country scale in the absolute sense,
we further regressed our national projections against
the population counts of 227 countries in 2020 based
on globally comparable data of the World Popula-
tion Prospects 2019 by United Nations (2019), which
shows a near-perfect R-square of 0.99 (figure 2(b)).
The multi-scale accuracy assessments suggest that
our data would provide the best available population
projections at the global, continental, and national
scales.
The accuracy improvement of FPOP as compared
with the existing datasets seems to also hold across
various populated metropolitan areas (figure 3). We
made a visual comparison based on the spatial dis-
tribution of adjusted WorldPop and projected pop-
ulation densities in 2020 in seven typical metropol-
itan areas at the same spatial resolution of 1 km. The
comparison consistently illustrates the qualitatively
better accuracy of FPOP. Quantitatively, FPOP again
has the lowest %RRSE for all the seven metropol-
itan areas as compared with the five existing data-
sets. For one, our %RRSE for Cairo, Egypt is 32.28%,
while those of NCAR, CoastalZone, and AFRICA
are 87.24%, 90.04%, and 72.36%, respectively. For
another, our %RRSE for the Yangtze River Delta,
China is 25.45%, while those of NCAR, CoastalZone,
AFRICA, China_CAI, and China_CHEN are 88.83%,
82.22%, 71.31%, and 134.60%, respectively. The
comparison for the other five metropolitan areas
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Environ. Res. Lett. 17 (2022) 094007 M Li et al
Figure 2. (a) Accuracy comparison of existing gridded population datasets and FPOP. The accuracy was measured by %RRSE of
predicted population against adjusted WorldPop population in 2020. FPOP has consistently higher grid-average accuracy. See
table S1 for the standard deviation of %RRSE. (b) Validation of our population dataset in 2020 at the country scale (227 countries
in total). The circle size is proportional to the ratio of population over built-up areas in a country. The subfigure at the bottom
right shows the top three countries by population for better presentation. Importantly, the population count constraint on spatial
allocation in FPOP is SSP-consistent global population counts instead of the conventionally-used national counts. See
supplementary material for methodological details and discussion.
(i.e. Melbourne, Australia; New York, United States;
Paris, France; Sao Paulo, Brazil; and Tokyo, Japan)
also confirms that FPOP has quantitatively substan-
tial accuracy improvement over the existing datasets.
3.2. Projected future population dynamics by SSP
and population density
Based on the above accuracy assessment of our
population projection algorithm, we then simu-
lated future global population under SSPs 1–5 at
a 1 km resolution for 2000–2100 at ten-year inter-
vals, with the projections starting from 2010 (see
section 2for details). The projected populations in
high-, medium-, low-density areas show a similar
trend under all SSPs, except for SSP3 when the
global population—particularly the population of
high-density areas—was projected to increase all the
way to 2100 (figures 4(a)–(e)). Contrarily, the trend
under the other SSPs is consistent that the popu-
lation would first increase until some point dur-
ing the second half of the 21st century and then
decrease till 2100. Relatedly, the spatial pattern of
the global population is also similar, except that the
populous (high- and medium-density) areas in places
such as India, China, and parts of central Africa
expand remarkably under SSP3 compared with the
other SSPs (figures 4(A)–(E)). Generally speaking, the
whole nation of India and the southeast half of China
would be the most populous across the globe, fol-
lowed by west and central Europe, southeast Asia,
west and central Africa, and lastly, the fragmentedly-
distributed built-up areas in east America and Latin
America. The spatial pattern and temporal trend of
the global population under SSP3 show stark con-
trast to those under the other four SSPs, indicat-
ing the critical need of enhancing proactive research
and strategic policy interventions for an increasingly
likely future where regional rivals and competitions
divide the global community (i.e. SSP3)—as signaled
by trade wars and travel bans in the recent years.
The trends of population change, like the trends
of population per se, seem to show the strongest
Matthew effect under SSP3, which is followed by
SSP2 and SSP4, and lastly SSP5 and SSP1 (figure 5).
To be specific, those high- and medium-density
areas (e.g. India, southeast China, southeast Asia,
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Environ. Res. Lett. 17 (2022) 094007 M Li et al
Figure 3. Comparison of population predictions in 2020 against the adjusted WorldPop population distributions across typical
regions. The %RRSE for each metropolitan area is shown at the bottom left corner of each subfigure. Despite the 1 km resolution,
other datasets have a coarser population distribution than ours. Our projections are more uniform and closer to the adjusted
WorldPop population distributions.
west and central Europe, central Africa, and east
America) appear to experience more drastic popu-
lation growth during 2020–2100, while those low-
density or population-sparse areas (e.g. southw-
est China, west Sub-Saharan Africa, and central
America) are more inclined to have population
decline (figures 5(A)–(E)). However, this qualitat-
ive observation of spatial variations is not in line
with the contrast of decadal population changes by
different type density areas. In most cases, actu-
ally, the decadal population change rate in each
of three density areas ranks as follows: population-
sparse, low-density, medium-density, high-density
(figures 5(a)–(e)). In other words, the Matthew effect
that densely-populated grids seem to have more pop-
ulation growth (or less population decline) holds
only at the grid level yet not at the category level.
It suggests notable variations within each population-
density category, and may indicate that FPOP would
become regionally polarized/centralized.
3.3. Projected dynamics of future heat exposure by
SSP-RCP and continent
The SSP-consistent, spatially-explicit future global
population projections make it possible to do pro-
gnostic assessments of alternative socioeconomic
development and climate policies. As an example
of global concern, here future global heat exposure
and dynamics are estimated under different scen-
ario settings of SSP and RCP (see section 2for
details) by combining FPOP and future daily max-
imum temperature projections (figure S3). Differ-
ent SSP and RCP settings would remarkably affect
future global heat exposure (figures 6(a)–(d)). The
6
Environ. Res. Lett. 17 (2022) 094007 M Li et al
Figure 4. Spatial patterns of projected global population in 2100 under SSPs 1–5 (A)–(E) and temporal trends between 2020 and
2100 under corresponding SSPs (a)–(e). The projected 2100 global population shows similar patterns under SSPs 1–5; yet the
populous areas in places such as India, China, and parts of central Africa expand remarkably under SSP3 compared with other
SSPs. Relatedly, the global population—particularly the population of high-density areas—was projected to increase steadily from
2020 to 2100 under SSP3, contrasting to the consistent trend under other SSPs that the population would first increase up to the
second half of the 21st century and then decrease till 2100.
worst situation would occur under SSP3-RCP8.5—a
future scenario featured by low urbanization, expo-
nential population growth, and high greenhouse
gas emission. Under this very scenario, the global
heat exposure would increase almost exponentially,
i.e. from 315 billion person-days at 2020 to over
1626 billion person-days in 2100, an increase by
416% (figure 6(c)). Under the contrary scenario,
SSP5-RCP4.5, which is featured by high urbanization,
slow population growth, and low emission, the global
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Environ. Res. Lett. 17 (2022) 094007 M Li et al
Figure 5. Spatial patterns of projected population change from 2020 to 2100 under SSPs 1–5 (A)–(E) and the rates of change per
decade under corresponding SSPs (a)–(e). Negative rates denote population decline while positive values mean population
growth. The projected population change pattern is similar among the five SSP scenarios, following the Matthew effect of
different degrees (SSP3 > SSP2 ≈SSP4 > SSP5 ≈SSP1). The decadal change rate shows that, while the global population would
increase steadily under SSP3, it would increase up to 2070 and then decrease under SSP2, SSP4, and SSP5 and have the year 2050
as the tipping point under SSP1. Negative rates denote population decline while positive values mean population growth.
heat exposure would increase by only 62% from
318 billion person-days at 2020–516 billion person-
days in 2100. From a spatial perspective, the future
heat exposure shows a similar global pattern under
different SSP-RCPs, i.e. typical areas with high
exposure are the Ganges River Basin, Indus val-
ley, eastern China, southeast Asia, and Sub-Saharan
Africa, while those with much lower exposure include
South America, North America, Europe, and Oceania
(figures 6(A)–(D)). In general, Asia and Africa
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Environ. Res. Lett. 17 (2022) 094007 M Li et al
Figure 6. Global patterns of projected heat extreme exposure in 2100 under SSP3-RCP4.5 (A), SSP5-RCP4.5 (B), SSP3-RCP8.5
(C), and SSP5-RCP8.5 (D), as well as temporal trends between 2020 and 2100 under corresponding scenarios (a)–(d). Results
show that both SSP and RCP settings would affect the projected heat exposure, with the most dramatic exposure occurring under
SSP3-RCP8.5. The contrast seems to be dominated by the different trends of exposure in Asia—it would increase almost
exponentially under SSP3-RCP8.5 while contrastingly, decrease after 2080 or experience lower increase in the second half of 21st
century under the other three scenarios.
account respectively for 64%–68% and 21%–25%
of the global exposure, while the remaining areas
together contributing to 8%–15%.
Like the patterns of population change under dif-
ferent SSP-RCPs, the global patterns of heat expos-
ure change during 2020–2100 also show the Matthew
effect—where is more heat-stricken is also projec-
ted to suffer from more incremental heat exposure
(figures 7(A)–(D)). The least heat-stricken regions
largely overlap with the global population-sparse
areas, e.g. deserts in Africa, Oceania, and northwest
China as well as the Arctic. From a temporal perspect-
ive, the projected exposure would increase in each
region during 2020–2100 (figures 7(a), (c) and (d)),
except for the last two decades of the 21st cen-
tury under SSP5-RCP4.5 (figure 7(b)). Generally,
the decadal increase rate of heat exposure would
decrease over time during 2020–2100. Under RCP4.5,
the decrease is approximately 35% in North Amer-
ica, 20% in Oceania, 12% in Europe and Africa, and
10% in Asia. Under RCP8.5, the decrease is about 36%
in North America, 25% in Oceania, 35% in Europe,
and 15% ∼20% in Africa and Asia. It should be
noted that the exposure change rate of South Amer-
ica shows dramatic fluctuations after 2070 under RCP
4.5 and after 2050 under RCP 8.5. Notably, under the
9
Environ. Res. Lett. 17 (2022) 094007 M Li et al
Figure 7. Global patterns of projected heat exposure change from 2020 to 2100 under SSP3-RCP4.5 (A), SSP5-RCP4.5 (B),
SSP3-RCP8.5 (C), and SSP5-RCP8.5 (D), as well as temporal trends between 2020 and 2100 under corresponding scenarios
(a)–(d). The projected exposure change pattern is similar among the four scenarios, with the largest increase in India and China
under SSP3-RCP8.5 and the largest decrease occurring in central Africa under SSP5-RCP4.5. The decadal change rate shows that
overall the projected exposure would increase in each region during 2020–2100, except for the last two decades of the 21st century
under SSP3-RCP8.5. Besides, the exposure in South America shows dramatic fluctuations, especially at around 2090.
same RCPs, different SSP settings that reflect oppos-
ite population growth and urbanization trends could
have a similar trend of heat exposure change. This
observation indicates that urbanization and popula-
tion growth contribute to a lesser degree than climate
policies at the continent scale.
4. Discussion and conclusions
The significance of long-term trends and global pat-
terns of population dynamics has been made clear in
the existing literature (e.g. public health and envir-
onmental protection (Kii 2021), floods (Tate et al
2021), droughts (Liu and Chen 2021)). In this vein,
our demonstration study that applies the improved
FPOP (see supplementary material for methodolo-
gical discussion) to assess future global heat expos-
ure during 2020–2100 makes at least three contribu-
tions. First, from a spatial perspective, FPOP projects
a consistent global pattern of future heat exposure
under SSP3-RCP4.5, SSP3-RCP8.5, SSP5-RCP4.5,
and SSP5-RCP8.5 (figures 6(A)–(E)), confirming the
findings by Liu et al (2017, figure 1therein) and Jones
et al (2018, figure 1therein). Our results also depict
that future increment of high exposure areas would
most likely occur in the tropical and sub-tropical
regions (figures 7(A)–(E)), and that the worst scen-
ario is SSP3-RCP8.5 (figures 6(a)–(e)). Our study
10
Environ. Res. Lett. 17 (2022) 094007 M Li et al
additionally provides the continental shares of the
global exposure, highlighting the primary contrib-
utor of Asia (64%–68%) and secondary role of
Africa (21%–25%). Our study also hints on the Mat-
thew effect of future exposure dynamics at the grid
scale (figures 7(A)–(E)), which has important policy
implications deserving further investigation.
Second, the contrast of global/continental expos-
ures under different SSP-RCPs suggests that cli-
mate policies (the RCP setting regarding greenhouse
gas emission) seems slightly more influential than
socioeconomic policies (the SSP setting regarding
population and urbanization) (figures 6(a)–(e)). Our
preliminary conclusion is consistent with Jones et al
(2015) in the context of the U.S. and the obser-
vation by Jones et al (2018) in their global-scale
study. However, there are contradictory studies. For
example, Liu et al (2017) conclude the contribu-
tions of future climate change and global population
dynamics would respectively account for 28% and
9%, with the main contribution coming from their
interaction (66%). Huang et al (2018) show that in the
context of China the contributions of climate change
and population dynamics are 60%–70% and 20%–
40%, respectively. Besides, a recent historical study by
Tuholske et al (2021) suggests that the impact of pop-
ulation dynamics on global urban population expos-
ure to extreme heat is about two times that of warm-
ing. Despite these inconclusive findings, it seems cli-
mate policies would be relatively more effective than
population policies in addressing future global heat
exposure.
Last, our estimates of future global heat expos-
ure under the various SSP-RCPs are substantially lar-
ger than those of existing findings, though consist-
ent in terms of the magnitude. FPOP-based global
heat exposure under the four SSP-RCPs is around
300 billion person-days in 2020, and by the end of
the 21st century, could increase to as high as 1626
billion person-days under SSP3-RCP8.5 and as low
as 516 billion person-days under SSP5-RCP4.5. Con-
trastingly, Liu et al (2017) report an annual aver-
age of 57 billion person-days for 1971–2000 and
1200–2300 billion person-days for 2071–2100. Jones
et al (2018) document a median global annual heat
exposure of 15 billion person-days for 1981–2005 and
for 2061–2080, project an upper boundary of 800
billion person-days under SSP3-RCP8.5 and a lower
boundary of 150 billion person-days under SSP5-
RCP4.5. Relatedly, Tuholske et al (2021) report an
average annual increase rate of 2.1 billion person-days
for the historical global urban heat exposure during
1983–2016, and a total of 119 billion person-days in
2016. The differences notwithstanding, our estimates
and the noted values are not directly comparable in
at least two aspects. One is that they are based on
various population and temperature datasets, and the
other is that they are average or median values of
multiple years. With regards to the latter, moreover,
our study illustrates the generally increasing trend of
FPOP heat exposure under the examined SSP-RCPs
(figures 6(a)–(e)) and reveals the generally declining
trend of the exposure increase rate (figures 7(a)–(e)).
In light of these two findings, it is possible that the
snapshot-style assessments which ignore temporal
dynamics may lead to severe underestimation or even
misleading conclusions.
To be rigorous, we made supplementary analyses
to disentangle the difference/sensitivity in estimat-
ing heat exposure due to the variety of population
datasets (figures S4–S7). The estimates of global heat
exposure at 2020 are 306.80–326.36, 313.68–337.81,
and 312.04–335.44 billion person-days based respect-
ively on FPOP, NCAR, and CoastalZone, and for
2100, are 516.17–1626.21, 518.36–1975.14, and
504.50–1935.97 billion person-days under the four
SSP-RCPs (table S2). Against the FPOP-based res-
ults, NCAR underestimates the exposure in densely-
populated areas (e.g. India, southeast China, south-
east Asia, west and central Europe, South Africa, east
and central America) by up to 3.77%, and overes-
timates in sparsely-populated areas (e.g. southwest
China, North and central Africa, west America) by
as much as 67.42% (figure S6); CoastalZone overes-
timates the exposure in densely-populated areas (e.g.
India, China coast, southeast Asia coast, east Europe,
and east America) by as much as 52.89%, and under-
estimates in inland areas (e.g. central China, central
America, central Europe, central southeast Asia and
Sub-Saharan Africa) by as much as 89.02% (figure
S7). The mis-estimation and spatial distribution due
to inaccurate population projections are far from
trivial. A recent study by Burkart and colleagues
(Burkart et al 2021) reports that 356 000 deaths
worldwide in 2019 were linked to heat extremes.
The death number may seem not so alarming, yet
with the projected increase by 2100 and the mis-
/underestimation due to inaccurate population pro-
jections, can translate into more dramatic deaths and
devastating loss for the families involved. As none of
the existing datasets are perfect including FPOP, con-
tinuing efforts should be invested to keep improving
gridded future global population projections and to
reexamine future exposures and vulnerabilities asso-
ciated with other widely-concerned hazards, such as
flooding (Kirezci et al 2020), extreme cold (Batibeniz
et al 2020, Broadbent et al 2020), and typhoon (Yin
et al 2021).
Data availability statement
Any data that support the findings of this study are
included within the article (and any supplementary
files). The historical global population maps are avail-
able at www.worldpop.org/. The United Nations Pop-
ulation can be obtained at https://population.un.org/
wpp/Download/Standard/Population/. The histor-
ical global built-up land maps can be retrieved from
11
Environ. Res. Lett. 17 (2022) 094007 M Li et al
https://figshare.com/articles/dataset/High_spatio
temporal_resolution_mapping_of_global_urban_ch
ange_from_1985_to_2015/11513178/1. The official
SSP database can be obtained at https://tntcat.iiasa.
acat/SspDb/. The environmental variables maps
are calculated from www.naturalearthdata.com/.
The five existing gridded population data-
sets for comparing with FPOP are available
from https://sedac.ciesin.columbia.edu/data/
set/popdynamics-1-km-downscaled-pop-base-
year-projection-ssp-2000-2100-rev01 (NCAR),
https://figshare.com/s/9a94ae958d6a45684382
(Coastal Zone), https://dataguru.lu.se/doi:10.18161/
popcount.201610 (AFRICA), https://springernature.
figshare.com/collections/Provincial_and_gridded_
population_projection_for_China_under_shared_
socioeconomic_pathways_from_2010_to_2100/
4605713 (China_CAI), https://figshare.com/articles/
dataset/Mainland_China_SSP_Population_Grids/
11634372 (China_CHEN), respectively.
Acknowledgments
This study was financially supported by the Key
National Natural Science Foundation of China (Grant
No. 42130107). We are grateful to two anoym-
ous reviewers for their critical and constructive
comments.
Author contributions
X L and Y C conceived research and planed ana-
lysis; X L, B Z, M L, and Y C improved experiment
design; M L performed computational experiments
and statistical analysis; M G, M H, Y C, and G H
provided technical and data supports; M L and B Z
drafted manuscript; B Z, M L, X L, and Y C revised
manuscript.
Conflict of interest
The authors declare they have no actual or potential
competing financial interests.
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