Human migration is both motivated and constrained by a multitude of
socioeconomic and environmental factors, including climate-related factors.
Climatic factors exert an influence on local and regional population
density. Here, we examine the implications of future motivation for humans to
migrate by analyzing today's relationships between climatic factors and
population density, with all other factors held constant. Such “all other
factors held constant” analyses are unlikely to make quantitatively accurate
predictions, but the order of magnitude and spatial pattern that come out of
such an analysis can be useful when considering the influence of climate
change on the possible scale and pattern of future incentives to migrate.
Our results indicate that, within decades, climate change may provide
hundreds of millions of people with additional incentive to migrate, largely from
warm tropical and subtropical countries to cooler temperate countries, with
India being the country with the greatest number of people with additional
incentive to migrate. These climate-driven incentives would be among the
broader constellation of incentives that influence migration decisions.
Areas with the highest projected population growth rates tend to be areas
that are likely to be most adversely affected by climate change.
Introduction
Human migration is a complex socioeconomic phenomena driven by mixture of
historical, political, cultural, economic, and geographical factors (Black et al., 2011; Boas et al., 2019; Foresight: Migration and Global Environmental Change, 2011; Greenwood, 1985) – often by the need to adapt to environmental
stressors (Adger et al., 2014) including those caused by
climate change (Missirian and Schlenker, 2017; Myers, 1993; Núñez et al., 2002; Stapleton et al., 2017). Climate change is expected to lead to higher temperatures and an
altered hydrological cycle in the coming decades (McLeman and
Hunter, 2010), and temperature and precipitation changes have been shown to
influence human migration at the local to regional scale (Barrios et al., 2006; Black et al., 2011; Bohra-Mishra et al., 2014; Gray and
Bilsborrow, 2013; Hsiang et al., 2013; Kelley et al., 2015; Marchiori et
al., 2012; Mueller et al., 2014). Hsiang and Sobel (2016) examined the consequences of migration if everyone moved to remain at
the same annual global mean temperature under a climate change scenario.
We apply a simple and transparent approach to estimate the number and
geographical distribution of people for whom temperature and precipitation
changes may provide an additional incentive migrate. Of course, people are
subject to a wide range of incentives and constraints; therefore, actual
future migration will depend on a much broader set of factors
(Adger et al., 2014; Boas et al., 2019; Greenwood, 1985). Ideally, projections of future human migration patterns
would involve the consideration of a wide range of factors that are difficult to quantify, such as future wealth, the efficacy of the adaptive response, cultural factors, and
nonlinear interactions between climate change and population growth (Boas et al., 2019; Holobinko, 2012; Suweis, 2018), although this is a topic with ongoing debate
(Afifi, 2011; Bettini, 2013; Boas et al., 2019; Mortreux and Barnett, 2009; Piguet et al., 2011; Suhrke, 1994). For example, Milan et al. (2015) pointed out that household vulnerability could impact human
migration patterns in the mountainous areas of the “Global South”. Nevertheless,
our goal is to identify what a continuance of the current relationships between
climate variables and global human population density would imply for future
incentives to migrate. While these relationships will not remain fixed in
time, it is nonetheless useful to understand what the direct application of
current relationships to future climate would contribute to the set of
incentives that will influence future human migration.
MethodsOverview
Nordhaus (2006) applied a regression analysis
on geographical and economic data to estimate the influence of climate
variables on the areal density of the gross domestic product (GDP). Samson et
al. (2011) used a
weighted regression model to identify the ideal temperature and precipitation
ranges for human habitation (as measured by population density) and studied
how those ideal temperature and precipitation ranges may change in the
future owing to climate change. Here we apply similar methods to the same
dataset, the Geographically based Economic data (G-Econ), to estimate the
influence of climate variables on population density.
To estimate the influence of climate on the attractiveness of different
locations, we apply the historical relationship between climate variables
and population density along with projections (Taylor et al., 2012) of future climate change
from the output of the Coupled Model Intercomparison Project Phase 5 (CMIP5)
under Representative Concentration Pathways (RCPs; Vuuren et al., 2011), including RCP2.6, RCP4.5, RCP6.0, and RCP8.5, incorporating future
country-scale demographic population projections from the United Nations'
World Population Prospects 2015 (United Nations, 2015). Details are given in Sect. 2.3, but the basic idea is that if, for example,
historical relationships between population density and climate change would
predict a 10 % decrease in population density for a grid cell in a climate
change scenario, we would estimate that there would be incentive for 10 %
of the future population (as estimated by the UN) to migrate from that grid
cell. Of course, many other factors including family ties, linguistic
barriers, lack of resources, employments relations, and so on would be
expected to influence migration decisions. When we report country-level
results, we integrate across all grid cells within a country and report the
net value; thus, our methodology would not predict the incentive to migrate from a
country, when that country had some
grid cells that indicated incentives for emigration, if other grid
cells indicated an even greater incentive to migrate into that country.
When we report country-level results, we consider only cross-border migration with our study and do not consider movement within a country (Rigaud
et al., 2018).
Data
This research uses the Geographically based Economic data (G-Econ) dataset (Nordhaus, 2006) for the historical climate and
population data. The G-Econ dataset was originally developed for analyzing
global economic activities and provides gridded (1∘×1∘) economic (e.g., gross cell product and population) and
geographical (e.g., climate, location, country, distance from seacoasts,
soils, and vegetation cover) information covering all terrestrial regions. In
total, there are 27 445 grid cells in the dataset. Climatology
data from G-Econ, including annual mean air temperature (T, in ∘C) and annual
precipitation (P, in mm yr-1), were derived from the Climate Research
Unit average climatology high-resolution datasets (New et al., 2002). The gridded
population (N) was adapted from the Gridded Population of the World (GPW)
dataset (http://sedac.ciesin.columbia.edu/data/collection/gpw-v3, last access: 2 October 2020). More
details and the data download link are available at http://gecon.yale.edu/, last access: 2 October 2020).
In this study, we used the population density (D)
and the geographical data from the G-Econ dataset, including T, P, distance to lake (DL, in km), distance to
major river (DMR, in km), distance to river (DR, in km), distance to ocean (DO, in km), elevation
(E, in m), and surface roughness (roughness, in m).
To make our projections, we used T and P in historical (i.e., 1960–2005) climate
as well as future (2006–2100) climate scenarios from the output of the Coupled
Model Intercomparison Project Phase 5 (CMIP5), which produces a state-of-the-art
multi-model dataset to advance the knowledge of climate change. We collected
the model projected T and P (20 model projections; see Table S1 in the Supplement) under all
RCPs, including RCP2.6, RCP4.5, RCP6.0, and RCP8.5, from the CMIP5 dataset to represent the range of future climate
projections. We regridded the CMIP5 data to a 1∘×1∘ common grid using bilinear interpolation.
We used the historical and predicted (median variant) country-level
population data from the “World Population Prospects: The 2015 Revision” by
the United Nations Department of Economic and Social Affairs (United
Nations, 2015). We use Wi,y to denote the population estimated by the UN
for grid cell i in year y, and we use Wc,y to denote the population estimated
by the UN for country c in year y.
AnalysisYear 2005 population density and within-country distribution
Areal population density for the year 2005 in each grid cell i (Di) was
calculated from the population (Ni) of 2005, grid area (Ai, in
km2), and the land fraction of the grid (Li, no unit) from the G-Econ
dataset:
Di=Ni/Ai×Li
We denote the fraction of the population of country c living in grid cell i with
the symbol di,c:
di=Ni/∑i∈cNi,
where i∈c indicates that the summation is performed over all grid cells
in country c. The distributional parameter, di,c, is considered to be
constant in time.
Linear regression model
Our methods for estimating climate influence on the population density parallels
methods previously applied (Nordhaus, 2006) to estimate
climate influence on the areal density of the GDP. The basic idea is to find a
single set of coefficients that explain within-country relationships between
population, climatic, and geographical variables. For our regressions, we used
data from the G-Econ dataset. The
Climate Research Unit average climatology high-resolution datasets were used to
fill the missing data in the G-Econ dataset. To estimate the logarithm of
the population density from both geographical (G) and climatic variables (C), we
used the following equation:
log10D=β0+GβG+CβC,
where D is a vector of grid-scale population densities (i.e., Di for grid cell i). Specifically,
4G=countrysoilDLDMRDRDOEroughness5C=TT2T3pp2p3TpT2pp2T
Here, T is as defined above, and p is log10P. “country” and “soil” are
categorical variables, and βG and βC are the numerical
coefficient vectors on geographical and climatic variables, respectively.
βG=transposeβG,countryβG,soilβG,DLβG,DMRβG,DRβG,DOβG,EβG,roughness
and
βC=transposeβC,TβC,T2βC,T3βC,pβC,p2βC,p3βC,TpβC,T2pβC,p2T
Antarctica, Greenland, and grid cells with zero precipitation were excluded
from this analysis.
The values for the β coefficients are determined by an area-weighted
ordinary least squares curve fit to log10D. Fitting of the above linear
regression model was conducted in MATLAB R2017a
(http://www.mathworks.com/products/matlab/, last access: 2 October 2020). In total, 20 503 grid cells had
data for all of the parameters needed for the fitting procedure. Variability that
is not explained by Eq. (3) is assumed to be the result of unknown
factors which we treat as invariant with time.
Population change projections
We first calculated the ratio of the population in the changed climate relative
to the base-state climate (here taken to be the climate in the period
preceding 2005) in region i for the climate in year y considering climate
factors alone (ri,y):
ri,y=Di,yDi,2005
For each grid, we calculated ri,y for each year from 2006 to 2100 using
Eq. (8) and the 30-year moving average of T and P projected by each CMIP5
model. (The 30-year moving average ends on the period under consideration so
that decisions are made on past but not future climate states.)
In the absence of climate change, we would estimate the population in grid
cell i in country c for year y (Wi,y) to be di,c×Wc,y,
where c is the country containing grid cell i. If we directly apply the
population change ratio under climate change (ri,y) to the population
estimates, the population considering climate change would be
ri,y×Wi,y. However, this estimate must be scaled to
conserve total population. Thus, the population Ni,y of grid cell i in
year y can be estimated to be
Ni,y=ri,y×Wi,y×∑i∈cdi,c×Wc,y∑i∈cri,y×di,c×Wc,y
By carrying this adjustment out, we conserve the total global population but also consider
climate change when estimating the spatial distribution of the
population.
We then estimate the number of people for whom climate change is projected
to provide additional incentive to migrate for grid cell i and year y
(indicated by ΔNi,y) as
ΔNi,y=Ni,y-Wi,y
Negative ΔNi,y values are interpreted as indicating areas
where climate change provides additional incentive to emigrate; positive
values indicate areas that are projected to increase in relative
attractiveness. (Even if every location were to decrease in absolute
attractiveness due to climate change, places with a smaller absolute
decrease would increase in relative attractiveness.)
We define fi,y=Ni,y/Wi,y, so that fi,y-1 indicates
the fractional change in the population that would be required to offset the
influence of climate change on the attractiveness of grid cell i in year y.
When fi,y-1<0, it means that grid cell i has become less
attractive.
We integrated Ni,y for grid cells in each country c to yield
Nc,y and define fc,y=Nc,y/Wc,y. We calculate results
independently for each of the CMIP5 models' simulations
(Taylor et al., 2012) and present median results.
Where a range is reported, it encompasses results for 68 % of the CMIP5
models.
We report results to two significant figures. The computer scripts written
in MATLAB R2017a used to perform our analyses are available upon request.
Results
The regression of population density against geographical and climate
variables as described above (see also Sect. 2 and the Supplement)
explains 72 % of the geographical variance in the logarithm of the population
density. Parameter values and their uncertainties are shown in Table S2;
p values, based on a Student's t test on coefficients for all temperature- and
precipitation-related variables, are <0.0005, indicating that these
results are unlikely to have been obtained by chance.
Applying our regression equation to climate model and demographic
projections, we find that ΔNi,y is negative (i.e., indicating
decreased attractiveness) in regions that are already hot and are projected
to experience substantial additional warming under climate change (primarily
tropical and subtropical regions), whereas ΔNi,y is
positive (i.e., indicating increased attractiveness) in cooler regions
(primarily in the temperate regions of the Northern Hemisphere; Figs. 1a
and S1a, b, c).
The number of people for whom climate change is projected to
provide additional incentive to migrate under RCP8.5 per 1∘×1∘ grid cell: (a)ΔNi,2100 (in thousands of people per grid cell) and (b)ΔNc,2100 (in billions people per country). (c) The fractional change in the population that would be required to offset the
influence of climate change on the relative attractiveness of living in a
particular location for the year 2100 (fi,2100) under scenario RCP8.5 .
To isolate the effect of climate change on incentives to migrate, all
factors are held constant, except for climate and country-level population.
Of course, many other factors influence migration decisions.
Under RCP8.5, India has the largest negative ΔNc,2100 value
among countries (0.89 billion, range from 0.77 to 1.10 billion; Fig. 1b), followed by
Nigeria (0.46 billion, range from 0.38 to 0.58 billion). The other countries with the largest
negative ΔNi,2100 values are the Democratic Republic of Congo
(0.20 billion), Indonesia (0.18 billion), Niger (0.14 billion), Sudan (0.11
billion), the Philippines (0.10 billion), Bangladesh (0.09 billion), Tanzania
(0.09 billion), and Pakistan (0.08 billion). In contrast, China, Russia, and
the United States all have positive ΔNc,2100 values.
The fi,2100 metric is less than 0.3 in parts of the northern African
tropical savanna, tropical South America, and tropical Asia under RCP8.5,
indicating that future incentives to migrate from those areas may be
substantial. The fi,2100 metric is >5 in much of Canada,
Russia, and Scandinavia, as well as parts of the United States and China (Fig. 1c), which could indicate that – in the absence of other barriers – these
regions could become migration destinations. Results for RCP2.6, RCP4.5, and
RCP6.0 show similar spatial patterns but at a lower magnitude (Fig. S1).
The countries with the largest projected population growth by the year 2100 tend
to be countries with the largest negative ΔNc,2100 values
(Fig. 2). The equation ΔNc,2100=(1.79±0.06)ΔWc,2100+(0.21±0.02) explains 79 % of the
variation in population-weighted ΔNc,2100 (best estimate ±1 standard error). Figure 2 shows that the average projected population increase from
2005 to 2100 (ΔWc,2100; on the x axis) is negatively
correlated with the number of people in each country with additional incentive
to emigrate (ΔNc,2100; on the y axis). About 70 % of the projected global population in the year 2100 lives in a country that is expected
to experience population growth and for which ΔNc,2100 is
<0 (lower right quadrant in Fig. 2). In contrast, 14 % of the
global population in 2100 is projected to live in a country with a population lower than today and for which ΔNc,2100 is
>0 (upper left quadrant in Fig. 2). Similar patterns are found
under other scenarios (Fig. S2).
Country-level projections for population increase in the year 2100
relative to the year 2005 (ΔWc,2100=Wc,2100-Wc,2005,
x axis) and the number of people for whom climate change is
projected to provide additional incentive to migrate under RCP8.5
(ΔNc,2100; y axis). The areas of the circles are proportional to
the year 2100 population. The color scale is as per Fig. 1b. The line shows the
population-weighted linear trend. Negative values on the y axis
indicate additional incentive to emigrate; positive values indicate
countries that increase in relative attractiveness. The results hold all factors
constant, except for climate and country-level population. The data used to produce this figure are provided in Table S3.
Figure 3 shows ΔNi,y values integrated over all grid cells
with ΔNi,y<0, indicating the number of people for whom
climate change may produce an additional incentive
to migrate. Under all of the RCP scenarios, this integrated value increases
over the next few decades (Fig. 3), reaching 0.6 to 1.9 billion people by
2050 (depending on RCP scenario). By the year 2100 under RCP8.5, this number
increases to about 3.8 billion people (range from 3.3 to 4.9 billion people), which is about one-third
of the projected global population in 2100.
(a) The number of people projected to experience additional
climate-related incentive to emigrate under four Representative
Concentration Pathways (RCPs) against years, and (b) the number of people projected to experience additional
climate-related incentive to emigrate against the change in the 30-year moving mean
temperature over global land relative to 2005. The lines show the
median value across CMIP5 models with results from 66 % of the models
falling within the shaded area. The results hold all factors constant, except
for climate and country-level population.
Discussion and conclusions
In this section, we discuss some of the relevance of the results of our
calculations for the real world. We intend our quantitative results to
indicate possible orders of magnitude and global-scale spatial patterns of
people with changed incentives; we do not intend our results to be
interpreted as quantitative predictions of future climate-induced human
migration.
It is clear that population distributions are related to climate variables.
Population densities tend be very low in both very hot areas (e.g., Death
Valley) and very cold areas (e.g., Alaska) and tend to be relatively high in areas
with intermediate temperatures (e.g., New York City). Similarly, population
densities tend to be low in very dry areas (e.g., central Australia) and
very wet areas (e.g., northern Australia) and relatively high where there is
an intermediate amount of precipitation (e.g., Sydney, Australia). Our
calculations consider changes in temperature and precipitation
only, under the artificial assumption that all other factors remain
constant. Further, our calculation treats the relationship between climate
and incentive to move as constant in time. However, factors such as the
availability of indoor work in air-conditioned environments would surely
modify these relationships. This study isolates a narrow range of factors
under ceteris paribus assumptions. We hope that our study motivates efforts to quantitatively
address the panoply of factors that can influence migration decisions.
Our highly idealized calculations are intended to indicate the scale and
geographical distribution of people for whom climate change might provide an
additional incentive to migrate. Our calculations also indicate the
regions that climate change might make more attractive to potential migrants.
Clearly, migration decisions are influenced by a wide range of factors
(Fussell et al., 2014; McLeman and Hunter, 2010).
Further, there is often a substantial incentive to avoid migration entirely; thus, additional incentive to migrate does not imply an overall positive net
incentive to migrate. Consequently, the number of people who will have positive net
incentive to migrate as a result of climate change is less than the
number of people for whom climate change will provide an additional
incentive to migrate. Migration is one of many possible adaptive responses
to climate change. For example, people might choose to cool interior spaces
with air conditioners (Barreca et al., 2016). Another
response could be a shift from agricultural work in rural environments to
industrial or service-sector jobs in more urbanized environments
(Jiang and O'Neill, 2017; Neill et al., 2010). Thus, migration flows can be influenced by differences in the types of
development and not only climatic factors.
Our results indicate that India may be the country that will contain the
largest number of people for whom climate change may provide an additional
incentive to emigrate. West Africa, in particular Nigeria, may be the
second most important area in this regard (Fig. 1a, b). This is largely a
consequence of the high population densities in areas that are already warm and are
projected to get warmer. Our results indicate that many people living in the
Amazon region would have additional incentive to emigrate, but the population
density is generally low. More generally, climate change may provide
many people living in the tropics with additional incentive to emigrate
(Fig. 1c). In contrast, our regression equations indicate that, from a
purely climatic perspective, climate change may increase the attractiveness
of northern countries, such as China, Russia, Canada, Norway, Sweden, and
Finland, relative to most other parts of the world.
There is a country-level correlation between projected population increase
and the degree to which climate change is projected to provide additional
incentive to emigrate. This correlation suggests that population increases
have the potential to exacerbate the negative effects of climate change in
much of the world. Over two-thirds of the global population in 2100 is
projected to live in a country with a greater population than today and for
which climate change may provide additional incentive to emigrate. In
contrast, about one in seven people are projected to live in a country
with a lower population and where climate change may cause the location to become
relatively more attractive. China is the largest country that is expected to
experience both a decrease in population and an increase in climate-related
relative attractiveness. Moreover, our calculations suggest that India could
be the largest potential source of climate emigrants and that China could
potentially be the largest potential destination for climate immigrants
(Fig. 1b). However, immigration in China is currently very limited
(Abel and Sander, 2014). Thus, barriers to migration
in Southeast Asia could potentially become an important source of future
climate-related conflict (Hsiang et al., 2013).
Climate change may provide hundreds of
millions of people with additional incentive to migrate over the coming decades and potentially billions of
people with additional incentive to migrate by the end of this century (Fig. 3). Approximately 0.8 billion
people are provided with additional incentive to migrate per 1 ∘C increase in
air temperature over global land (Fig. 3b). The number of people projected
to have additional incentive to migrate by the year 2100 under RCP4.5 or RCP6.0 is
about half that projected under RCP8.5, and the number projected under RCP2.6 is about half that projected under RCP4.5 or RCP6.0. This result points to
the important role that emissions reductions may play in reducing
climate-related incentives to migrate. Successful local adaptation measures
could greatly reduce incentives to migrate (Adger et al., 2014).
Climate change is likely to induce a complex web of dynamical interactions
at a range of spatial and temporal scales, and these interactions are not
well represented by our model. A more complete treatment of migration, and
not simply an examination of one possible set of incentives as we have done
here, would require embedding our results in the broader context of
incentives that could influence migration decisions
(Piguet et al., 2011). For example, factor such as language, work, and family ties can provide strong incentive not to migrate.
Therefore, projections of how climate change might affect migration are
fraught with uncertainty. Nevertheless, the results of our calculations may
indicate areas where climate change can be expected to provide large numbers of
people with an additional incentive to migrate – primarily from the tropics to the middle and high latitudes of the Northern Hemisphere. This
change in climate-driven incentives to migrate is one factor among many that
need to be included in a comprehensive understanding of possible future
migration flows.
Code and data availability
The data used in this study are publicly available. The CMIP5 climate
projections are available at https://pcmdi.llnl.gov/mips/cmip5/data-portal.html (Program for Climate Model Diagnosis & Intercomparison, the CMIP5 output data, last access: 2 October 2020). The G-Econ dataset is available at http://gecon.yale.edu/sites/default/files/files/Gecon40_post_final.xls (Nordhaus et al., last access: 2 October 2020) and the WPP2015 (World Population Prospects: The 2015 Revision by the United Nations
Department of Economic and Social Affairs) data are available at
https://population.un.org/wpp/Download/Files/5_Archive/WPP2015-Excel-files.zip (United Nations, World Population Prospects: The 2015 Revision by the United Nations Department of Economic and Social Affairs, last access: 2 October 2020). The computer scripts written
in MATLAB R2017a that were used to perform our analyses are available upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/esd-11-875-2020-supplement.
Author contributions
MC and KC conceived and designed the project and performed the
computational analysis. MC wrote the first draft of the paper with
subsequent development by MC and KC.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank Bill Hayes for his efforts with respect to processing CMIP5 data. We
appreciate comments from Kate Ricke and Juan Moreno-Cruz on earlier drafts
of this paper. This work was supported by the Carnegie Institution for
Science endowment and the Fund for Innovative Climate and Energy Research. While no financial support was provided specifically for this project, Min Chen was supported by NASA's Terrestrial Ecology program Arctic-Boreal Vulnerability Experiment (ABoVE; grant no. NNH18ZDA001N-TE). Ken Caldeira was supported primarily by the Carnegie Institution for Science.
Review statement
This paper was edited by Valerio Lucarini and reviewed by two anonymous referees.
ReferencesAbel, G. J. and Sander, N.: Quantifying global international migration
flows., Science (New York), 343, 1520–1522,
10.1126/science.1248676, 2014.
Adger, W. N., Pulhin, J. M., Barnett, J., Dabelko, G. D., Hovelsrud, G. K.,
Levy, M., Ú. Oswald, S., and Vogel, C. H.: Human security, in: Climate
Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and
Sectoral Aspects, Contribution of Working Group II to the Fifth Assessment
Report of the Intergovernmental Panel of Climate Change, edited by:
Field, C. B., Barros, V. R., Dokken, D. J., Mach, K. J., Mastrandrea, M. D., Bilir, T. E., Chatterjee, Ebi, M., K. L., Estrada, Y. O., Genova, R. C., Girma, B., Kissel, E. S., Levy, A. N., MacCracken, S., Mastrandrea, P. R., and White, L. L.,
pp. 755–791, Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, ISBN 978-1-107-05807-1 Hardback
ISBN 978-1-107-64165-5 Paperback, 2014.Afifi, T.: Economic or Environmental Migration? The Push Factors in Niger,
Int. Migr., 49, e95–e124,
10.1111/j.1468-2435.2010.00644.x, 2011.Barreca, A., Clay, K., Deschenes, O., Greenstone, M., and Shapiro, J. S.:
Adapting to Climate Change: The Remarkable Decline in the US
Temperature-Mortality Relationship over the Twentieth Century, J. Polit. Econ., 124, 105–159, 10.1086/684582, 2016.Barrios, S., Bertinelli, L., and Strobl, E.: Climatic change and rural-urban
migration: The case of sub-Saharan Africa, J. Urban Econ.,
60, 357–371, 10.1016/j.jue.2006.04.005, 2006.Bettini, G.: Climate Barbarians at the Gate? A critique of apocalyptic
narratives on “climate refugees”, Geoforum, 45, 63–72,
10.1016/j.geoforum.2012.09.009, 2013.Black, R., Adger, W. N., Arnell, N. W., Dercon, S., Geddes, A., and Thomas,
D.: The effect of environmental change on human migration, Global
Environ. Change, 21, Supplement 1, S3–S11,
10.1016/j.gloenvcha.2011.10.001, 2011.Boas, I., Farbotko, C., Adams, H., Sterly, H., Bush, S., van der Geest, K.,
Wiegel, H., Ashraf, H., Baldwin, A., Bettini, G., Blondin, S., de Bruijn,
M., Durand-Delacre, D., Fröhlich, C., Gioli, G., Guaita, L., Hut, E.,
Jarawura, F. X., Lamers, M., Lietaer, S., Nash, S. L., Piguet, E., Rothe,
D., Sakdapolrak, P., Smith, L., Tripathy Furlong, B., Turhan, E., Warner,
J., Zickgraf, C., Black, R., and Hulme, M.: Climate migration myths, Nat.
Clim. Change, 9, 901–903, 10.1038/s41558-019-0633-3, 2019.Bohra-Mishra, P., Oppenheimer, M., and Hsiang, S. M.: Nonlinear permanent
migration response to climatic variations but minimal response to disasters,
P. Natl. Acad. Sci., 111, 9780–9785,
10.1073/pnas.1317166111, 2014.Davis, K. F., Bhattachan A., D'Odorico P., and Suweis S.: A universal model for predicting human migration under climate change: examining future sea level rise in Bangladesh, Environ. Res. Lett., 13, 64030, 10.1088/1748-9326/aac4d4, 2018.Foresight: Migration and Global Environ. Change: Final Project Report,
GOV.UK, available at:
https://www.gov.uk/government/publications/migration-and-global-environmental-change-future-challenges-and-opportunities
(last access: 15 July 2020), 2011.Fussell, E., Hunter, L. M., and Gray, C. L.: Measuring the environmental
dimensions of human migration: The demographer's toolkit, Global
Environ. Change, 28, 182–191,
10.1016/j.gloenvcha.2014.07.001, 2014.Gray, C. and Bilsborrow, R.: Environmental Influences on Human Migration in
Rural Ecuador, Demography, 50, 1217–1241, 10.1007/s13524-012-0192-y,
2013.Greenwood, M. J.: HUMAN MIGRATION: THEORY, MODELS, AND EMPIRICAL STUDIES*,
J. Regional Sci., 25, 521–544,
10.1111/j.1467-9787.1985.tb00321.x, 1985.Holobinko, A.: Theoretical and Methodological Approaches to Understanding
Human Migration Patterns and their Utility in Forensic Human Identification
Cases, Societies, 2, 42–62, 10.3390/soc2020042, 2012.Hsiang, S. M. and Sobel, A. H.: Potentially Extreme Population Displacement
and Concentration in the Tropics Under Non-Extreme Warming, Sci Rep.-UK, 6, 25697, 10.1038/srep25697, 2016.
Hsiang, S. M., Burke, M., and Miguel, E.: Quantifying the Influence of
Climate on Human Conflict, Science, 341, 1235367, 2013.Jiang, L. and O'Neill, B. C.: Global urbanization projections for the Shared
Socioeconomic Pathways, Global Environ. Change, 42, 193–199,
10.1016/j.gloenvcha.2015.03.008, 2017.Kelley, C. P., Mohtadi, S., Cane, M. A., Seager, R., and Kushnir, Y.: Climate
change in the Fertile Crescent and implications of the recent Syrian
drought, P. Natl. Acad. Sci., 112,
3241–3246, 10.1073/pnas.1421533112, 2015.Marchiori, L., Maystadt, J.-F., and Schumacher, I.: The impact of weather
anomalies on migration in sub-Saharan Africa, J. Environ. Econ. Manag., 63, 355–374,
10.1016/j.jeem.2012.02.001, 2012.McLeman, R. A. and Hunter, L. M.: Migration in the context of vulnerability
and adaptation to climate change: insights from analogues, Wiley
Interdisciplinary Reviews: Climate Change, 1, 450–461,
10.1002/wcc.51, 2010.Milan, A., Gioli, G., and Afifi, T.: Migration and global environmental change: methodological lessons from mountain areas of the global South, Earth Syst. Dynam., 6, 375–388, 10.5194/esd-6-375-2015, 2015.
Missirian, A. and Schlenker, W.: Asylum applications respond to temperature
fluctuations, Science, 358, 1610–1614, 2017.Mortreux, C. and Barnett, J.: Climate change, migration and adaptation in
Funafuti, Tuvalu, Global Environ. Change, 19, 105–112,
10.1016/j.gloenvcha.2008.09.006, 2009.
Mueller, V., Gray, C., and Kosec, K.: Heat stress increases long-term human
migration in rural Pakistan, Nat. Clim. Change, 4, 182–185, 2014.Myers, N.: Environmental Refugees in a Globally Warmed World: Estimating the
scope of what could well become a prominent international phenomenon,
Bioscience, 43, 752–761, 10.2307/1312319, 1993.
Neill, B. C., Dalton, M., Fuchs, R., Jiang, L., Pachauri, S., and Zigova, K.:
Global demographic trends and future carbon emissions, P. Natl. Acad. Sci., 107, 17521–17526, 2010.
New, M., Lister, D., and Hulme, M.: A high-resolution data set of surface
climate over global land areas, Clim. Res., 21, 1–25, 2002.Nordhaus, W. D.: Geography and macroeconomics: new data and new findings.,
P. Natl. Acad. Sci. USA, 103, 3510–3517, 10.1073/pnas.0509842103, 2006.Nordhaus, W., Azam, Q., Corderi, D., Hood, K., Victor, N. M., Mohammed, M., Miltner, A., and Weiss, J.: The G-Econ data, http://gecon.yale.edu/sites/default/files/files/Gecon40_post_final.xls, last access: 2 October 2020.
Núñez, L., Grosjean, M., and Cartajena, I.: Human Occupations and
Climate Change in the Puna de Atacama, Chile, Science, 298, 821–824,
2002.Piguet, E., Pécoud, A., and de Guchteneire, P.: Migration and
Climate Change: An Overview, Refugee Survey Quarterly, 30, 1–23,
10.1093/rsq/hdr006, 2011.Program for Climate Model Diagnosis & Intercomparison, the CMIP5 output data, https://pcmdi.llnl.gov/mips/cmip5/data-portal.html, last access: 2 October 2020.Rigaud, K. K., de Sherbinin, A., Jones, B., Bergmann, J., Clement, V., Ober,
K., Schwe, J., Adamo, S., McCusker, B., Heuser, S., and Midgley, A.:
Groundswell: Preparing for Internal Climate Migration, World Bank,
Washington, DC, available at:
https://openknowledge.worldbank.org/handle/10986/29461 (last access: 15 July 2020), 2018.Samson, J., Berteaux, D., McGill, J. B., and Humphries, M. M.: Geographic
disparities and moral hazards in the predicted impacts of climate change on
human populations, Global Ecol. Biogeogr., 20, 532–544,
10.1111/j.1466-8238.2010.00632.x, 2011.Stapleton, S. O., Nadin, R., Watson, C., and Kellett, J.: Climate change, migration and displacement and coherent approach, Research reports and studies, Overseas Development Institute, London, UK. pp. 35 https://www.odi.org/sites/odi.org.uk/files/resource-documents/11874.pdf, 2017.
Suhrke, A.: Environmental Degradation and Population Flows, J. Int. Aff., 47, 473–496, 1994.Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and
the Experiment Design, B. Am. Meteorol. Soc.,
93, 485–498, 10.1175/BAMS-D-11-00094.1, 2012.United Nations, World Population Prospects: The 2015 Revision by the United Nations Department of Economic and Social Affairs, https://population.un.org/wpp/Download/Files/5_Archive/WPP2015-Excel-files.zip, last access: 2 October 2020.Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard,
K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T.,
Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The
representative concentration pathways: an overview, Climatic Change, 109,
5–31, 10.1007/s10584-011-0148-z, 2011.