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Sustainability Science (2022) 17:1873–1897
https://doi.org/10.1007/s11625-022-01116-z
ORIGINAL ARTICLE
Sub‑Saharan Africa’s international migration constrains its sustainable
development under climate change
Qirui Li1,2 · Cyrus Samimi1,2,3
Received: 19 May 2021 / Accepted: 9 February 2022 / Published online: 18 March 2022
© The Author(s) 2022
Abstract
Sub-Saharan Africa (SSA) is seen as a region of mass migration and population displacement caused by poverty, violent
conflict, and environmental stress. However, empirical evidence is inconclusive regarding how SSA’s international migration
progressed and reacted during its march to achieving the Sustainable Development Goals (SDGs). This article attempts to
study the patterns and determinants of SSA’s international migration and the cause and effects on sustainable development
by developing a Sustainability Index and regression models. We find that international migration was primarily intra-SSA
to low-income but high-population-density countries. Along with increased sustainability scores, international migration
declined, but emigration rose. Climate extremes tend to affect migration and emigration but not universally. Dry extremes
propelled migration, whereas wet extremes had an adverse effect. Hot extremes had an increasing effect but were insignifi-
cant. SSA’s international migration was driven by food insecurity, low life expectancy, political instability and violence, high
economic growth, unemployment, and urbanisation rates. The probability of emigration was mainly driven by high fertility.
SSA’s international migration promoted asylum seeking to Europe with the diversification of origin countries and a motive
for economic wellbeing. 1% more migration flow or 1% higher probability of emigration led to a 0.2% increase in asylum
seekers from SSA to Europe. Large-scale international migration and recurrent emigration constrained SSA’s sustainable
development in political stability, food security, and health, requiring adequate governance and institutions for better migra-
tion management and planning towards the SDGs.
Handled by Takanori Matsui, Osaka University, Japan.
* Qirui Li
leolee8612@gmail.com; qirui.li@uni-bayreuth.de
Cyrus Samimi
cyrus.samimi@uni-bayreuth.de
1
Africa Multiple Cluster of Excellence, University
of Bayreuth, 95440 Bayreuth, Germany
2
Climatology Research Group, University of Bayreuth,
95447 Bayreuth, Germany
3
Bayreuth, Centre of Ecology and Environmental Research,
University of Bayreuth, 95448 Bayreuth, Germany
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Graphical abstract
Keywords Sustainable Development Goals · Climate extremes · Migration and development · Adaptation of social-
ecological systems · Feedback loop · Impact assessment
Introduction
Sub-Saharan Africa (SSA) has rich and diverse environmen-
tal and demographic potentials and has experienced rapid
economic growth over ten years (Ahmed et al. 2016; Jayne
et al. 2018). Nevertheless, the region still faces myriad and
formidable challenges for sustainable development, like
unemployment (Ackah-Baidoo 2016), the lack of health
care and education (Appleton et al. 1996), underinvestment
in infrastructure (Kodongo and Ojah 2016), debt crisis
(Battaile et al. 2015), and the failure of national governance
(Ndulu and O’Connell 1999; Davis 2017). As one of the
world’s most vulnerable areas to climate change (Niang et al.
2014a; Serdeczny et al. 2017), SSA might get the highest
population rise and considerable displaced persons in Africa
while making up a significant portion of migrant flows to
Europe (van Ittersum et al. 2016; Hoffmann et al. 2020;
Cottier and Salehyan 2021). Therefore, scholars and poli-
cymakers search for a more positive and proactive migra-
tion management process and planning for a better and more
sustainable future.
The United Nations adopted the Sustainable Develop-
ment Goals (SDGs) in 2015 as the developmental agenda
for global sustainable development covering 17 goals with
231 indicators that 193 countries have committed to, includ-
ing SSA (United Nations 2015; United Nations Statistics
Division 2017). ‘Orderly, safe, regular and responsible
migration’ is a critical issue mentioned in the SDGs, with 11
out of 17 goals being migration related (United Nations Sta-
tistics Division 2017; IOM's GMDAC 2019). However, the
role of migration in sustainable development and their inter-
actions are still unclear. In addition, migration is influenced
by a mix of climatic and environmental, socioeconomic,
demographic, cultural, and political factors in the intercon-
nected world (Black et al. 2011). It remains challenging
to capture the diverse forms and multiplicities of human
migration, monitor and estimate the migration flows and
directions, and clarify main migration drivers and predict
migration effects on sustainable development (Boas et al.
2019). Reliable data and measurable indicators and theoreti-
cal and analytical frameworks are thus needed to integrate
interrelated concepts into logical thinking for sophisticated
measurement and understanding.
To fill these gaps, by following the resilience thinking
of coupled social-ecological systems (SESs) (Folke et al.
2010, 2016; Folke 2016; Cumming et al. 2017; Marchese
et al. 2018), migration is considered a strategy to adapt live-
lihoods in response to change (Chapin et al. 2010; Bardsley
and Hugo 2010; Kniveton et al. 2012; Steffen et al. 2015;
Grêt-Regamey et al. 2019; Adger et al. 2020). Change is
defined as external and internal by placing people in the
centre of analysis (Fig. 1). The internal change is from the
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difference and variation in human decision and propensity,
whereas the external one derives from nature and social-
economic-political environments. It indicates that two types
of drivers shall at least be considered for the adaptation to
change: exogenous and endogenous.
Exogenous drivers usually initiate adaptation when
people cannot avoid or prevent the change but conduct
actions and strategies to deal with stress and disturbance.
It refers more to reactive and autonomous adaptation. By
contrast, endogenous drivers come from people's intention to
improve their livelihoods, living conditions, environments,
and wellbeing. People often take a proactive and planned
adaptation to cope with the change and/or manage the sys-
tem to prevent crisis or disaster. As one form of adapta-
tion, migration depicts the movement of people from one
place to another (ex situ) along with their livelihoods and
outcomes evolving from one status to another (in situ) over
time. Migration is associated with the flow and redistribution
of resources, such as human (e.g. labour), financial (e.g. cash
and cattle), natural (e.g. land and water), and social (e.g.
networks) capitals. Thus, it represents not only individual
behaviour (e.g. proactive and reactive adaptation or auton-
omous-private and public-planned adaptation) (Grothmann
and Patt 2005) but also the process of resource allocation or
reallocation and utilisation in the entire social-ecological
system (Holling 2001; Folke et al. 2010).
In coupled SESs, migration may affect the system state
(Higgins 2017) by altering the identity of single agents (or
actors) and functional groups as well as the structure (i.e.
linkages, relations, and interactions between agents and
groups through institutions and infrastructure) and function
(i.e. socioeconomic values and bio-physical outputs gener-
ated in the process of system development and evolution
through various forms of individual and collective activities
as well as material exchange and knowledge and information
diffusion) of system components. In turn, such dynamics and
resultant outcomes, create feedback and fine-tune the exter-
nal and internal change, exerting further cascading effects
within the system and spill-over effects on another system.
Therefore, this paper attempts to assess SSA's interna-
tional migration patterns and the linkage to sustainable
development through observable data and quantitative met-
rics. It is assumed that SSA countries with varying degrees
of resource endowment might have divergent migration
patterns, influencing livelihood security and achievements
of the SDGs. We acknowledge that our selected indicators
cannot comprehensively measure and predict climate-migra-
tion-sustainability interlinkages, but quantitative approaches
were first illustrated due to the data availability (See details
in “Materials and methods”). This work entails a range of
questions: (1) What are SSA’s international migration pat-
terns and sustainable development under climate change?
(2) What are the determinants of international migration and
emigration probability? (3) What are the cascading effects of
SSA's international migration on emigrants within SSA and
Europe? (4) What are the feedback effects of SSA's inter-
national migration on sustainable development? By doing
so, this study illustrates new approaches for understanding
Fig. 1 Migration as an adapta-
tion in the feedback loop of cou-
pled social-ecological systems
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SSA's international migration patterns and the interlinkage
with sustainable development. It paves the way for integrat-
ing reliable data and measurable indicators related to migra-
tion and the SDGs.
In the remainder of this paper, we first describe materials
and methods. An analytical framework shows our study on
migration patterns, sustainability assessment, and the envi-
ronmental and socioeconomic drivers, cascading, and feed-
back effects of international migration. The empirical analy-
sis explains how we defined migration patterns, composed
a sustainability index, and developed models for migration
drivers and cascading and feedback effects. After that, we
discuss our results and compare them with previous studies.
Finally, the conclusions of this research are demonstrated.
Materials and methods
Analytical framework
According to the research hypotheses and questions, we
designed an analytical framework (Fig. 2) which consists of
the analysis for international migration patterns, a composite
index of sustainability, and regression models for the drivers
of international migration and its cascading effects on emi-
grants and feedback effects on the sustainability index. The
framework integrates reliable data from different sources to
study SSA’s international migration and sustainable devel-
opment, providing means to investigate the interlinkage
under climatic and demographic changes. It may facilitate
measuring and predicting the system adaptation and trans-
formation towards achieving the SDGs through the lens of
international migration.
Fig. 2 Analytical framework. The framework consists of five analy-
ses: the migration patterns estimate the international migration, expa-
triates, and asylum seekers of forty SSA countries (Table S1). The
sustainability index enables an assessment of sustainability and its
five specific goals for those forty countries over the research period.
Endogenous and exogenous drivers of SSA's international migration
were explored from selected variables concerning climate extremes,
demography, and SDG indicators. The cascading effect of SSA's
international migration on its emigrants in terms of expatriates and
asylum seekers. The feedback effect of international migration on
SSA's achievement of the SDGs
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International migration patterns
As the core of the framework, migration is defined as the
adaption of SSA countries to climatic, demographic, and
socioeconomic changes. It depicts population movement
associated with resource distribution and utilisation, gen-
erating various outcomes, and effects on system sustain-
ability. Herein, migration patterns are estimated through
the international migration, expatriates, and asylum seek-
ers (Table S1). Although migration is a dynamic process
(Diamantides 1994; Massey and Zenteno 1999), we did not
consider internal, return, circular, or other forms of migra-
tion due to the lack of quality data for SSA countries in
time series. It limited our capability to dissect the migra-
tory process dynamics, especially for climate change-related
migration (McLeman 2013). Despite this, our model cap-
tured the dynamics of international migration regarding the
time, place, direction, and circumstances of its occurrence.
It derived an unambiguous quantitative measure for investi-
gating the climate-migration-sustainability interlinkages of
SSA countries.
International migration is measured by the absolute value
of net international migration of an SSA country concerning
the latitude (i.e. maximum change) of a coupled system (i.e.
an individual SSA country, See Eq. (1)). It depicts the dis-
tance of the evolving system from its steady state where the
number of immigrants is equal to the number of emigrants
(i.e. net migration = 0) throughout the period. A higher value
of international migration depicts more changes in the sys-
tem (or farther from the steady state).
where Lit is the latitude of a coupled system or country i dur-
ing timet,m , mI, and mE represent the international migra-
tion, immigration, and emigration, respectively, and the
inverse D is the forcing factors that drive population move-
ment and the changes in livelihood outcomes and system
status in countryi . The latitude (i.e. maximum change) can
be considered constant for a given system or country at a
particular time or moment. Thus, the international migra-
tion can be estimated in a reduced form with corresponding
coefficients 훽 and the error term휀:
Although the drivers of international migration differ
across countries and contexts, it may take permanent migra-
tion into other countries or continents, which changes the
number of expatriates and asylum seekers in the receiving
countries. This work built a dataset of forty SSA countries
(Table S2), referring to data integrity and consistency. The
(1)
Lit = ||mI,it −mE,it||∕Dit = mit∕Dit,
(2)
dmi
dt = 훽dDi
dt + 휀.
measurement of temporary and return migration is over-
looked due to a lack of data.
Empirical analysis
This study first built a sustainability index of twelve indi-
cators. It is followed by a couple of regression models
exploring the environmental and socioeconomic drivers of
SSA’s international migration. Another regression model
was developed by assuming that SSA's international migra-
tion might affect its expatriates in Europe and within SSA
and influence its asylum seekers to Europe. Besides, a set
of regression modes was composed to estimate the feed-
back effect of SSA's international migration on sustainable
development.
Sustainability Index
Indicators are selected for the composite Sustainability Index
(Table 1) from the United Nations' Global indicator frame-
work for the Sustainable Development Goals and targets
of the 2030 Agenda for Sustainable Development (United
Nations 2015; United Nations Statistics Division 2017).
According to the description of the SDGs and indicators
and the data availability, observable variables were chosen to
calculate sustainability scores by taking an equal weighting
scheme. We took the mean value of these indicator scores
as the goal score if there is more than one indicator under
that goal. So does the overall score of sustainability, given
that different indicators (or variables) under the same goal
(or indicator) reflect different dimensions of sustainability.
All scores are transformed into values ranging from 0 to
100. The approach is consistent with the U.N. Sustainable
Development Goals reports (https://www.sdgindex.org).
In terms of ‘Food security and sustainable agriculture’
in the index, we chose SDG 2 and its indicators 2.1 and 2.4
to guide the measurement via variables concerning dietary
energy supply, livestock and crop production, arable land,
and irrigation. For ‘Healthy lives’, life expectancy was
selected referring to the description of SDG 3. Similarly, the
urbanisation rate was chosen to reflect SDG 11.1 on access
to adequate housing and essential services in cities. Vari-
ables, including per capita gross domestic product (GDP),
Agro-GDP share, and unemployment rate, were employed
to represent ‘Sustainable economy’ under SDG 8.1 and 8.5.
The share of agricultural GDP indicates economic diversi-
fication, while the unemployment rate is defined as subtrac-
tive. ‘Peaceful societies’ are measured by variables about
homicide and political stability under SDG 16.1, where rates
of homicides are also defined as subtractive. Data were col-
lected and transformed for those variables by taking the
average of every five consecutive years. Missing values
were supplemented by alternative data or dismissed with the
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Table 1 Descriptive statistics of variables used for the Sustainability Index of sub-Saharan African countries
SDGs
Indicators
Variables
Description
Mean ± standard deviation
Food security and sustainable agri-
culture: Goal 2. End hunger, achieve
food security and improved nutrition
and promote sustainable agriculture
2.1 Food security: By 2030, end hun-
ger and ensure access by all people to
safe, nutritious and sufficient food all
year round
Average dietary energy supply
adequacy
Dietary Energy Supply (DES) as a
percentage of the Average Dietary
Energy Requirement (ADER). Each
country's or region's average supply
of calories for food consumption is
normalised by the average dietary
energy requirement estimated for its
population to provide an index of
adequacy of the food supply in terms
of calories (Food and Agriculture
Organization of the United Nations,
Rome, Italy 2020)
103.39 ± 15.83
2.4 Sustainable agriculture: By 2030,
ensure sustainable food production
systems and implement resilient
agricultural practices
Livestock production index
Net per capita Livestock Production
Index Number (2004–2006 = 100)
(Food and Agriculture Organization
of the United Nations, Rome, Italy
2020)
101.21 ± 16.82
Crop production index
Net per capita Crop Production Index
Number (2004–2006 = 100)(Food
and Agriculture Organization of the
United Nations, Rome, Italy 2020)
100.17 ± 17.96
Arable land per capita
Per capita area of arable land (Food
and Agriculture Organization of the
United Nations, Rome, Italy 2020)
0.23 ± 0.11
Irrigation share
Share of land area equipped for irriga-
tion in total land area, % (Food and
Agriculture Organization of the
United Nations, Rome, Italy 2020)
1.42 ± 3.89
Healthy lives: Goal 3. Ensure healthy
lives and promote wellbeing for all at
all ages
Healthy lives: To promote physical and
mental health and wellbeing and to
extend life expectancy for all
Life expectancy
Average time people in a country are
expected to live, based on the year of
their birth, in years (United Nations
2019)
55.27 ± 7.05
Sustainable economy: Goal 8.
Promote sustained, inclusive and
sustainable economic growth, full and
productive employment and decent
work for all
8.1 Economic growth: Sustain per
capita economic growth in accord-
ance with national circumstances and
achieve higher levels of economic
productivity through diversification,
technological upgrading and innova-
tion
GDP per capita
Per capita gross domestic product, in
current US$ (The World Bank 2020a)
1379.32 ± 2266.34
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Descriptions of the Sustainable Development Goals and indicators are adapted from the United Nations’ SDG indicators (United Nations Statistics Division 2017); the number of observations
is 240 for forty SSA countries in six periods
Table 1 (continued)
SDGs
Indicators
Variables
Description
Mean ± standard deviation
8.5 Productive employment: By 2030,
achieve full and productive employ-
ment and decent work for all women
and men
Agro GDP share
Share of agriculture in the total gross
domestic product, % (The World
Bank 2020a)
24.47 ± 15.31
Unemployment rate
Share of the labour force without work
but available for and seeking employ-
ment, % (The World Bank 2020a).
(subtractive)
7.24 ± 7.09
Urbanisation: Goal 11. Make cities
and human settlements inclusive,
safe, resilient, and sustainable
11.1 By 2030, ensure access for all to
adequate, safe and affordable housing
and basic services and upgrade slums
Urbanisation rate
Share of the urban population in the
total population of a country, % (The
World Bank 2020a)
37.67 ± 16.28
Peaceful societies: Goal 16. Promote
peaceful and inclusive societies for
sustainable development, provide
access to justice for all, and build
effective, accountable and inclusive
institutions at all levels
16.1 Violence and related death:
Significantly reduce all forms of
violence and related death rates
everywhere
Homicide
Rates of homicides per 100,000 popu-
lation of a country (World Health
Organization 2020). (subtractive)
11.82 ± 7.95
Political stability and absence of
violence
Perceptions of the likelihood that the
government will be destabilised or
overthrown by unconstitutional or
violent means, including politically
motivated violence and terrorism
(The World Bank 2020a)
-0.54 ± 0.88
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entire case (or country). Eventually, twelve variables were
derived for the Sustainability Index of the forty countries
from 1990 to 2018. Although we chose those observable
variables referring to the SDGs, the scope and extent are
limited by data availability and measuring difficulty. It could
be elaborated on in a future study.
Drivers of international migration
Based on Eq. (2), a Negative Binomial regression model
(NBM) was built, given that the international migration of
those forty countries is over-dispersed count data whose
conditional variance exceeds its conditional mean. NBM is
a generalisation of the Poisson regression model address-
ing the over-dispersion issue by including a disturbance or
error term (see Eq. (3)). We chose exploratory variables by
combining climate extremes and demographic drivers with
the variables used for the Sustainability Index (Table 2).
Previous studies found that positive temperature extremes,
rainfall variability, and food insecurity are migration driv-
ers with agriculture as a transmission channel (Mastrorillo
et al. 2016; Sadiddin et al. 2019; Carney and Krause 2020).
Income opportunities, drought, and violence and armed con-
flict are found to increase emigration flow (Chort and de la
Rupelle 2016; Abel et al. 2019), while the country's living
population and fertility, health services, economic growth,
and urbanisation play a significant role (Mayda 2010; Cas-
telli 2018).
where mit are the expected values of international migra-
tion from origin country i at year t = 1995, 2000, 2005,
2010, 2015, and 2020, 휓t are time fixed effects, 휙i are origin
fixed effects, 훽 are corresponding regression coefficients,
휎휀it is the error term, and xikt′ are the values of kth explora-
tory variable for the country i calculated over time inter-
vals t
= 1990–1994, 1995–1999, 2000–2004, 2005–2009,
2010–2014, and 2015–2018 given endogeneity and reverse
causality concerns (except 'life expectancy' that was calcu-
lated over time intervals t
= 1990, 1995, 2000, 2005, 2010,
and 2015). The lagged time intervals may help reduce model
bias by assuming that exploratory variables are predeter-
mined so that international migration and the error term
might only affect their contemporaneous and future values.
As described above, international migration in this work
is the absolute value of net international migration to reflect
the distance of an evolving system from its steady state. Nev-
ertheless, it shows that those forty countries (Table S2) can
be defined as immigration counties (i.e. net migration > 0,
e.g. South Africa) and emigration countries (i.e. net migra-
tion < 0, e.g. Zimbabwe). They vary over time and have sig-
nificant differences in their demographic, socioeconomic,
(3)
logmit = 휓t + 휙i + 훽0 + 훽kxikt + 휎휀it,
and climatic conditions (Table S3). Emigration countries
take the majority in SSA, including twenty-nine out of the
forty countries, accounting for 68% of the total international
migration. It is hence necessary to study the determinants
of emigration probability and flow. A Heckman Selection
model (Table 2) was developed for the censored subset 'emi-
gration countries' of the data addressing the induced non-
random selection bias (Heckman 1979). At the first stage,
a Logit regression model was used to predict the likelihood
of being an emigration country throughout the research
period (see Eq. (4)), in the sense that a binary variable was
given as a dependent variable that was assigned a value of
1 if the value of net migration is negative and a value of 0
if positive. In the second stage, another NBM was set up
to estimate the international migration from those emigra-
tion countries (censored subset) in an unbiased way (see
Eq. (5)). The approach attempts to give more insights into
SSA's international migration drivers.
where E
′
i is the likelihood of origin country i predicted to be
an emigration country from explanatory variables Ximt′ that
are the values of mth exploratory variable for the country
i calculated over time intervals t
′ , p is the probability of a
negative value of net international migration, 휓t are time
fixed effects, 휙i are origin fixed effects, 훽 are corresponding
regression coefficients, and 휀it is the error term.
where Mlt are the expected values of international migra-
tion from emigration country l(l < i) at year t from lagged
explanatory variables xlnt′ that are the values of nth explora-
tory variable for the country l calculated over time intervals
t
′ , IMR is the inverse Mills' ratio derived from the standard
normal and cumulative density functions of Eq. (4), 휓t are
time fixed effects, 휙i are origin fixed effects, 훽n are corre-
sponding regression coefficients, and 휎휀lt is the error term.
The model coefficients were interpreted as incidence rate
ratios (UCLA: Statistical Consulting Group 2006) and NBM
coefficients. Moreover, robust standard errors were taken
to obtain unbiased standard errors of coefficients under
heteroscedasticity.
Cascading effects
SSA's international migration may affect its expatriates (See
definition in Table S1) in Europe and within SSA and make
up a significant portion of asylum seekers to Europe. Hence,
a set of Tobit regression models (Table 3) was employed
to estimate the cascading effects given the right censoring
(≥ 0) in expatriates and asylum seekers from SSA countries
(4)
E
i = 푙푛
p
1 −p = 휓t + 휙i + 훽0 + 훽mXimt + 휀it,
(5)
logMlt = 휓t + 휙l + 훽0 + 훽nxlnt + IMR + 휎휀lt,
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Table 2 Driving variables of international migration of sub-Saharan African countries
Drivers
Variables
Description
Descriptive
Model 1: International migration
from SSA countries
Model 2: International migration from
emigration countries
Mean
Standard
deviation
Incidence rate
ratio (IRR)
Coefficients
Emigration
selection
Migration coefficients:
IRR
Coefficients
Demography
Fertility
Live births per woman represent the
average number of live births a
hypothetical cohort of women would
have at the end of their reproductive
period if they were subject during
their whole lives to the fertility rates
of a given period and if they were
not subject to mortality (United
Nations 2019)
5.24
1.10
1.2331
0.2095 (0.2062)
6.4584
(1.8245) ***
–
–
Population density
Number of people per square km of
land area (United Nations 2019;
Food and Agriculture Organization
of the United Nations, Rome, Italy
2020)
77.02
89.35
0.9988
− 0.0012 (0.0029) − 0.0076
(0.0167)
–
–
Climate change
Dry extremes
Count of dry extremes (i.e. self-
calibrating Palmer Drought Severity
Index < -4) within one country of
every five-year intervals (van der
Schrier et al. 2013; Blunden and
Arndt 2020)
618.45
1881.10
1.0001
0.0002 (0.0001)**
− 0.0001
(0.0003)
1.0001
0.0001
(0.00004)
**
Wet extremes
Count of wet extremes (self-calibrating
Palmer Drought Severity Index > 4)
within one country of every five-year
intervals (van der Schrier et al. 2013;
Blunden and Arndt 2020)
916.35
4131.46
0.9999
− 0.0001
(0.00002)*
0.0012
(0.0003)***
0.9999
− 0.0001
(0.00002)
***
Temperature
extremes
Maximum value of the FAO tempera-
ture change of one country of every
five-year intervals, corresponding
to the period 1951–1980 (Food and
Agriculture Organization of the
United Nations, Rome, Italy 2020),
in °C
1.11
0.41
1.3470
0.2979 (0.2326)
− 1.0048
(1.5301)
1.1065
0.1012
(0.1526)
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Table 2 (continued)
Drivers
Variables
Description
Descriptive
Model 1: International migration
from SSA countries
Model 2: International migration from
emigration countries
Mean
Standard
deviation
Incidence rate
ratio (IRR)
Coefficients
Emigration
selection
Migration coefficients:
IRR
Coefficients
Food security and
agriculture
Average dietary
energy supply
adequacy
Dietary Energy Supply (DES) as a
percentage of the Average Dietary
Energy Requirement (ADER). Each
country’s or region’s average supply
of calories for food consumption is
normalised by the average dietary
energy requirement estimated for its
population to provide an index of
adequacy of the food supply in terms
of calories (Food and Agriculture
Organization of the United Nations,
Rome, Italy 2020)
103.39
15.83
0.9720
− 0.0284
(0.0106)**
–
0.9946
− 0.0054
(0.0085)
Livestock produc-
tion index
Net per capita Livestock Production
Index Number (2004–2006 = 100)
101.21
16.82
0.9995
− 0.0005 (0.0043) 0.1431
(0.0346) ***
1.0036
0.0036
(0.0032)
Crop production
index
Net per capita Crop Production Index
Number (2004–2006 = 100)
100.17
17.96
0.9973
− 0.0027 (0.0031) − 0.0052
(0.0174)
0.9898
− 0.0102
(0.0031)
***
Arable land per
capita
Per capita area of arable land, in km2
per capita
0.23
0.11
0.0161
− 4.1287
(1.1693)***
–
0.0172
− 4.0657
(1.2015)
***
Irrigation share
Share of land area equipped for irriga-
tion in total land area, %
1.42
3.89
1.0189
0.0187 (0.0260)
–
1.0241
0.0238
(0.0228)
Healthy lives
Life expectancy
Average time people in a country are
expected to live, based on the year of
their birth, in years
55.27
7.05
0.9768
− 0.0235 (0.0130) –
0.9606
− 0.0402
(0.0121)
***
Sustainable economy GDP per capita
Per capita gross domestic product, in
current US$
1379.32
2266.34
1.0000
0.00001
(0.00003)
–
1.0003
0.0003
(0.0001) **
Agro GDP share
Share of agriculture in total gross
domestic product, %
24.47
15.31
1.0187
0.0185 (0.0097)
− 0.0125
(0.0459)
0.9994
− 0.0006
(0.0120)
Unemployment rate
Share of the labour force that is
without work but available for and
seeking employment, %
7.24
7.09
1.0430
0.0421 (0.0234)
–
1.0797
0.0767
(0.0260) **
Urbanisation
Urbanisation rate
Share of the urban population in the
total population of a country, %
37.67
16.30
1.0600
0.0583 (0.0164)
***
–
1.0085
0.0085
(0.0152)
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Table 2 (continued)
Drivers
Variables
Description
Descriptive
Model 1: International migration
from SSA countries
Model 2: International migration from
emigration countries
Mean
Standard
deviation
Incidence rate
ratio (IRR)
Coefficients
Emigration
selection
Migration coefficients:
IRR
Coefficients
Peacefulsocieties
Homicide
Rates of homicides per100,000 popu-
lation of a country
11.82
7.95
1.0266
0.0261 (0.0126) *
–
1.0426
0.0417
(0.0259)
Political stability
and absence of
violence
Perceptions of the likelihood that the
government will be destabilised or
overthrown by unconstitutional or
violent means, including politically
motivated violence and terrorism
− 0.54
0.88
0.6994
− 0.3575 (0.1067)
***
–
0.5274
− 0.6398
(0.0963)
***
IMR
Inverse of Mills'
ratio
Ratio of the standard normal density
divided by the standard normal
cumulative distribution function
–
–
–
0.5130
− 0.6675
(0.2265) **
Year fixed effect
Yes
Yes
Yes
Origin fixed effect
Yes
Yes
Yes
Constant
100.45
4.6096 (2.0660) *
-83.2790
(16.5590)
***
182.3596
5.2060
(1.4286)
***
Pseudo R2 (Nagel-
kerke)
0.9752
0.7802
0.9986
Count (N)
240
240
167
–, *, **, *** = 0.1, 0.05, 0.01, and 0.001 levels of significance, respectively; figures in parenthesis indicate robust standard errors; N depicts the number of observations
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Table 3 Marginal effects of international migration on sub-Saharan African expatriates and asylum seekers
Variables
Description
Descriptive
Expatriates in:
Asylum seekers in
EU-14 countries
Mean
Standard deviation EU-14 countries
Sub-Saharan Africa
Population density
Differential of
population density
between an origin
country and the
destination coun-
try, in capita per
km2
22.91
131.17
− 0.0116 (0.0073)
0.0412 (0.0195)***
0.0080 (0.0174)
Distance
Great-circle distance
between an origin
country to the des-
tination country,
in km
3,925,599 2,139,808
− 1.3352
(0.2354)***
− 0.4737
(0.1046)***
0.3756 (0.5493)
Language
Origin country
has the same
colonial language
as the destination
country or not, 1/0
(Exploring Africa
2021)
0.32
0.47
0.0499 (0.0635)
0.1251 (0.0888)**
1.2630 (0.1588) ***
Border sharing
Origin country
shares its land
border with the
destinationcountry
or not, 1/0
0.07
0.25
–
− 0.5990
(0.1623)***
–
Historical migrants
Number of the
migrant stock orig-
inated from SSA
in the destination
country in 1990
5042.93
39,881.96
0.9339 ( 0.0140)***
0.6005 (0.0136)***
0.4344 (0.0320) ***
Migrant ratio
Ratio of the migrant
stock originated
from an SSA
country to the total
migrant stock of
the destination
country in 1990
0.01
0.07
− 2.0554 (0.9553)*
− 2.1974
(0.4608)***
− 9.8029 (2.0457) ***
Urbanisation
Differential of
urbanisation rate
between an origin
country and the
destination country
10.27
27.25
− 0.0399 (0.0232)
0.0062 (0.0299)
− 0.0132 (0.0499)
GDP
Differential of per
capita GDP (PPP)
between an origin
country and the
destination coun-
try, in current US$
7974.53
14,976.52
− 0.0825 (0.1915)
− 0.0021 (0.0094)
2.3217 (0.5709) ***
Emigration country
A country with a
negative value of
net international
migration (i.e. per-
manent movement
of people from one
country to another)
or not, 1/0
0.69
0.46
− 0.0138 (0.0411)
0.0484 (0.1078)
0.2302(0.1145) *
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to destination countries. The following equation is given for
two separate studies on the cascading effects on expatriates
and asylum seekers:
where Yidt are the expected values of expatriates and asylum
seekers from SSA country i to destination country d at year
t = 2000, 2005, 2010, 2015, and 2019 for expatriates and t =
2005, 2010, and 2015 for asylum seekers, respectively. 휓t are
time fixed effects, 휙i are origin fixed effects, Vd are destina-
tion fixed effects, 훼, 훽, and γ are corresponding regression
coefficients, and 휀idt is the error term. xid(t
−1) is a vector of
dyadic exploratory variables representing the differential and
connection between the origin i and destination d in geo-
graphical, demographic, cultural, and socioeconomic fac-
tors over time intervals ( t
−1). Given data consistency and
endogeneity concerns, exploratory variables took the lagged
values at (t
−1) = 1990–1994, 1995–1999, 2000–2004,
2005–2009, and 2010–2014 for expatriates and (t
−1) =
1995–1999, 2000–2004, and 2005–2009 for asylum seekers,
respectively. Previous studies claimed that bilateral migra-
tion flows are affected by the growing population, geographi-
cal distance, a common land border and language, networks,
and per capita GDP (Chort and de la Rupelle 2016; Abel
et al. 2019). The population-density differential (Table 3)
was used to measure agglomeration and the resultant effect
of population growth. Great-circle distance and a dummy
variable for land border sharing were included to capture the
geographic information between origins and destinations. A
dummy variable for colonial language was added to reflect
(6)
Yidt = 휓t + 휙i + Vd + 훼+ 훽xid(t −1) + 훾mi(t−1) + 휆Ei(t−1) + 휀idt,
the cultural and colonial ties. We chose historical migrants
and migrant ratio to represent social foundations due to
the cost-alleviating effect and leading role of networks in
international migration (Massey 1988, 1990; Rockenbauch
and Sakdapolrak 2017) and the increasing diversification
of migration origins and motives (Garcés-Mascareñas and
Penninx 2016). Differentials of urbanisation rates and per
capita GDP between origins and destinations were added
to represent socioeconomic development. Urban and eco-
nomic growth may increase migration in the short run but
gradually eliminate the incentives for movement in the long
term (Massey 1988), exerting variant effects on the impetus
of migration. mi(t−1) is a vector of variables computed for
the international migration flow from origin country i at
year (t −1) = 1995, 2000, 2005, 2010, and 2015 for expatri-
ates and (t −1) = 2000, 2005, and 2010 for asylum seekers,
respectively. A dummy variable Ei(t−1) was given for origin
country i being an emigration country at year (t −1).
Inverse Hyperbolic Sine transformation was applied for
all dependent and exploratory variables to deal with skew-
ness, remaining zero values, and to avoid stacking and dis-
proportionate misrepresentation, given the unique properties
of international migration. Marginal effects were presented
to explain how dependent variables change when a specific
exploratory variable changes while other covariates are con-
stant. Robust standard errors were taken to obtain unbiased
standard errors of coefficients under heteroscedasticity.
Table 3 (continued)
Variables
Description
Descriptive
Expatriates in:
Asylum seekers in
EU-14 countries
Mean
Standard deviation EU-14 countries
Sub-Saharan Africa
International migra-
tion
Absolute value of
net international
migration (i.e. the
difference between
the number of
immigrants and
the number of
emigrants) of an
origin country, in
thousands
177.47
256.49
0.0108 (0.0247)
0.0113 (0.0447)
0.1877 (0.0553) ***
Year fixed effect
Yes
Yes
Yes
Origin fixed effect
Yes
Yes
Yes
Destination fixed effect
Yes
Yes
Yes
McFadden's pseudo-R2
0.5335
0.5048
0.3452
Count (N)
10,600
2800
7800
1560
– ,*, **, *** = 0.1, 0.05, 0.01, and 0.001 levels of significance, respectively; figures in parenthesis indicate robust standard errors; McFadden's
values from 0.2 to 0.4 indicate excellent model fit; N depicts the number of observations. The EU-14 grouping includes Austria, Belgium, Den-
mark, Finland, France, Germany, Greece, Republic of Ireland, Italy, the Netherlands, Portugal, Spain, Sweden, and the United Kingdom
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Feedback effects
A set of Ordinary Least Squares regression models (Table 4)
was developed to estimate the feedback effect of SSA's inter-
national migration on sustainable development. The calcu-
lated sustainability scores were estimated in the following
equation:
where Sit′ are the expected scores of sustainability indexes
of country i over time intervals t
= 1995–1999, 2000–2004,
2005–2009, 2010–2014, and 2015–2018,휓t′ are time fixed
effects, 휙i are origin fixed effects, 훼, 훽, 휔, and μ are corre-
sponding regression coefficients, 휀it′ is the error term, Ci(t −1)
is a vector of control variables concerning climate extremes
and demography over time intervals (t
–1) = 1990–1994,
1995–1999, 2000–2004, 2005–2009, and 2010–2014, mi(t−1)
is the lagged value of international migration at year (t −1) =
1995, 2000, 2005, 2010, and 2015, Ei(t−1) is the lagged likeli-
hood of country i being an emigration country at year (t −1) .
The lagged values guarantee data consistency and reduce
model bias given endogeneity and reverse causality issues.
Robust standard errors were taken to interpret model results.
(7)
Sit = 휓t + 휙i + 훼+ 훽Ci(t −1) + 휔1mi(t−1) + 휇1Ei(t−1) + 휀it ,
Model validation
Multicollinearity, residual normality, and robustness tests
were conducted for the corresponding regression models
(See S2 for details).
Results and discussion
Here, we analysed the dynamics of international migration,
expatriates, and asylum seeking among SSA countries,
and their progress in sustainable development under cli-
mate change from 1995 to 2020. After that, we identified
the primary migration drivers regarding climate extremes,
food security and agriculture, urbanisation, and peace-
ful societies, investigated the effect of SSA’s international
migration on its expatriates within SSA countries and in
EU-14 countries and on its asylum seekers in EU-14 coun-
tries, and examined the migration effect on the computed
sustainability score and its five aspects (i.e. food security
and agriculture (SDG2), healthy lives (SDG3), sustainable
economy (SDG8), urbanisation (SDG11), and peaceful soci-
eties (SDG16)).
Table 4 Effects of international migration on sustainability indexes
–, *, **, *** = 0.1, 0.05, 0.01, and 0.001 levels of significance, respectively; figures in parenthesis indicate robust standard errors; N depicts the
number of observations
Variables
Sustainability indexes
Overall score
Food security and
agriculture: SDG2
Healthy lives:
SDG3
Sustainable
economy: SDG8
Urbanisation:
SDG11
Peaceful societies:
SDG16
Fertility
− 1.5400 (1.9114)
− 9.8620
(3.7251)**
2.2640 (1.5275)
0.2253 (1.6490)
0.0454 (1.1449)
− 2.0530 (2.9495)
Population density
− 0.0379 (0.0326)
− 0.0845 (0.0426)* 0.0187 (0.0254)
− 0.0529
(0.0194)**
− 0.0313 (0.0146)* 0.0160 (0.0433)
Dry extremes
− 0.0001 (0.0004)
0.0001 (0.0005)
− 0.00005
(0.0004)
0.0003 (0.0003)
0.00001 (0.0001)
− 0.0006 (0.0007)
Wet extremes
− 0.00005
(0.0001)
0.0001 (0.0001)
− 0.00003
(0.0001)
0.00004 (0.0001)
− 0.00003
(0.00004)
− 0.0001 (0.0002)
Temperature
extremes
0.3716 (2.2420)
0.9123 (4.3868)
4.6280 (2.0445)*
− 4.9230 (1.9432)* 0.6517 (1.1401)
− 2.9990 (2.8833)
Emigration
country
− 1.7480 (1.2711)
− 0.6014 (1.9074)
0.1103 (0.9406)
0.2085 (0.9852)
0.1547 (0.5001)
− 5.1420 (1.7727)**
International
migration
− 0.0071 (0.0028)* − 0.0110 (0.0047)* − 0.0079
(0.0020)***
0.0080 (0.0020)***
0.0022 (0.0008)*
− 0.0119 (0.0042)**
Year fixed effect
Yes
Yes
Yes
Yes
Yes
Yes
Origin fixed effect
Yes
Yes
Yes
Yes
Yes
Yes
Adjusted R2
0.8926
0.5952
0.8834
0.8958
0.9787
0.7781
Constant
61.9810
(15.2560)***
112.2200
(27.5060)***
35.1980
(11.2960)**
50.4180
(12.7910)***
52.9790
(8.6561)***
72.3630 (23.1940)**
Count (N)
200
200
200
200
200
200
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International migration and sustainable
development under climate change
From 1995 to 2020, the international migration from SSA
was up to 26 million, with around 6.3 million people moving
out. Among those forty countries, South Africa, Zimbabwe,
Ethiopia, Guinea, Nigeria, Malawi, Angola, United Repub-
lic of Tanzania, Senegal, and Mali were the top ten, which
account for 68% of the total SSA migration (Fig. 3A). South
Africa, Ethiopia, and Angola were the key immigration
countries (Fig. 3B), with a substantial number (about 7.6
million) accounting for 77% of the total immigration flow.
The major emigration countries like Zimbabwe, Guinea,
Nigeria, Malawi, the United Republic of Tanzania, Senegal,
and Mali took up around 63% of the total emigration flow
(Fig. 3B). It implies that the hotspots of emigration coun-
tries are in West Africa and the junction of South, East, and
Central Africa, and that significant immigration countries
are of low and lower-middle incomes (World Bank Data
Team 2019; The World Bank 2020b). The UN estimates
(United Nations 2020) indicate that around 17 of the total 26
million migrants will move within SSA by 2020. Previous
studies have also stated that SSA migrants, who represent
most African migrants, have moved predominantly within
the African continent (Abel and Sander 2014), primarily to
low- and middle-income countries (Hoffmann et al. 2020).
International migration may generate changes in SSA
expatriates within and outside of Africa (Fig. 3C). The
low- and lower-middle-income African countries, like Côte
d’Ivoire, South Africa, Nigeria, the Democratic Republic of
the Congo, and Burkina Faso, alone accommodated around
7 million SSA migrants. Outside of Africa, the United States
of America, the United Kingdom, France, Italy, and Canada
were the major destination countries that accommodated
1.6 million, 1.3 million, 979,000, 426,000, and 373,000
people. Besides, part of the outflow may take the form of
asylum seeking waves into developed countries. From 2001
to 2015, there were around 1.6 million asylum seekers from
SSA to OECD countries. The Horn of Africa, West Africa,
and Central Africa (Fig. 3D), comprising Nigeria, Eritrea,
the Democratic Republic of the Congo, Guinea, Ethiopia,
Mali, Côte d’Ivoire, Gambia, Cameroon, and Zimbabwe,
take up almost 71% (OECD 2015). EU-14 countries accom-
modated 1.2 million people, with Italy, France, Germany,
and the United Kingdom alone receiving 56%. Overall,
SSA’s international migration seems to be mainly internal to
low-and lower-middle-income SSA countries and externally
to certain high-income OECD countries. The emigrants are
primarily from the Horn of Africa, West Africa, and East-
Central Africa. Such international migration patterns under-
line the complexity and heterogeneity of SSA’s international
migration and its linkage to the socioeconomic development
of SSA and European societies.
Throughout the research period, SSA experienced a slight
decline in dry extremes, a wide variability of wet extremes,
and a sharp increase in temperature extremes (Fig. 4A).
It aligns with the global warming trend, producing more
intense and frequent extreme precipitation over West Africa
and eastern Africa and more frequent droughts and floods
over southern Africa (Niang et al. 2014b; Serdeczny et al.
2017). Southern Africa and the African Sahel are also
expected to become warmer and wetter outside the range of
their historical year-to-year variability (Mahony and Cannon
2018). Under climate change, SSA’s international migra-
tion reduced from about 11 million in 1995 to 4.5 million
in 2020. However, it seems that people were increasingly
leaving SSA due to the shift of net migration from positive
to negative and an increasing share of emigration countries
(Fig. 4B).
Meanwhile, there was a significant increase in the score
of the Sustainability Index with substantial growth in 'SDG3
healthy lives' (i.e. life expectancy) and 'SDG11 urbanisa-
tion' (i.e. urbanisation rates (Fig. 4C). The score of 'SDG16
peaceful societies' slightly rose, whereas 'SDG2 food secu-
rity and sustainable agriculture' and 'SDG8 sustainable
economy' suffered slight decreases. Moreover, West Africa
(i.e. Gabon, Ghana and Cameroon) had a higher mean value
of the computed sustainability score (Fig. 4D and Table S2).
Increases occurred in at least two out of the five computed
SDGs and more in southern SSA countries (Fig. 4E) with
minor asylum seeking (Fig. 3D). The data demonstrate that
the computed sustainability score positively correlated with
temperature extremes and negatively correlated with inter-
national migration (Table S4 and Figure S1). It also shows
significant variations in the international migration, demog-
raphy, climate extremes, and sustainability indicators across
those forty countries over time (Table S5). The results indi-
cate that a higher sustainability score accompanied more sig-
nificant temperature extremes but less international migra-
tion. SSA countries demonstrated different international
migration patterns with various degrees of resource endow-
ment and sustainable development under climate warming.
Drivers of international migration
For SSA's international migration, eleven of the seventeen
variables had significant effects, of which seven key drivers
(ρ value < 0.05) were identified regarding climate extremes,
food security and agriculture, urbanisation, and peaceful
societies (see Eq. 3 and Model 1 in Table 2). As the pri-
mary stress in SSA for agricultural productivity and food
security, the results indicate that drought increased inter-
national migration. Previous studies have stated that persis-
tent droughts and land degradation threatened food security
and aggravated humanitarian conditions while promoting
migration in Africa (Gray 2012; Maystadt and Ecker 2014).
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Fig. 3 International migration patterns of sub-Saharan Africa. A
Aggregate international migration from 1995 to 2020. International
migration is measured by the absolute value of net international
migration (United Nations 2019), reflecting the distance of the
evolving system from its steady state (see Eq. (1)). B Aggregate net
international migration of SSA from 1995 to 2020. A negative value
means that people are moving out than moving in and vice versa. C
Top thirty destination countries for SSA’s expatriates (i.e. migrant
stock in Table S1 (United Nations 2020)) in 2019. D Aggregate num-
ber of SSA's asylum seekers (OECD 2015) in the EU-14 countries
from 2001 to 2015. The EU-14 grouping includes Austria, Belgium,
Denmark, Finland, France, Germany, Greece, Republic of Ireland,
Italy, Netherlands, Portugal, Spain, Sweden, and the United Kingdom
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In contrast, floods and wet extremes had adverse effects on
the migration flow. As a new challenge, for local people
who lack resources or capacity it would be difficult or even
unable for them to make country-to-country moves (Ayeb-
Karlsson et al. 2018; Hoffmann et al. 2020). Instead, floods
and wet extremes might induce migration within the country.
In addition, high temperatures had an insignificant positive
effect. It is probably due to the differential effects between
middle-income countries and low-income countries and
across African regions. Higher temperatures may increase
Fig. 3 (continued)
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Fig. 4 International migration
and sustainable development
of sub-Saharan Africa under
climate change. A Climate
extremes in SSA countries from
1990 to 2018. Dry and wet
extremes count self-calibrating
Palmer Drought Severity Index
less than − 4 and greater than
4 in a SSA country of every
five-year intervals, respectively.
Temperature extreme is the
maximum value of the FAO
temperature change in a SSA
country of every five-year
interval, corresponding to the
reference period 1951–1980.
Data sources and descriptions
are presented in Table 2. B
SSA’s international migration
from 1995 to 2020. Interna-
tional migration is the absolute
value of net international migra-
tion (United Nations 2019),
reflecting the change of an
evolving system from its steady
state of population movement
(see Eq. (1)). Net international
migration is the difference
between immigrants and
emigrants of each SSA country.
Share of emigration countries
represents the percentage of
SSA countries that had a nega-
tive value of net international
migration in all forty SSA coun-
tries. C Sustainability scores
of SSA from 1990 to 2018. D
Mean sore of sustainability
calculated based on the SDG
indicators for the SSA countries
from 1995 to 2018. E Number
of SDGs with increased scores
for each SSA country from 1990
to 2018
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international migration in middle-income countries but
decrease the probability in low-income countries (Cattaneo
and Peri 2016). Besides, the international migration was
stimulated by low dietary energy supply and arable land per
capita, high urbanisation and homicide rates, and political
instability and violence. Food security has been a big chal-
lenge for SSA, pushing populations to move for a sufficient
food supply and adequate arable land. The significant urban
growth in SSA may promote its international migration not
only by attracting immigrants from least-developed coun-
tries with rival economies and employment opportunities but
also increasing emigration due to associated socioeconomic
and environmental issues, like pollution, inadequate infra-
structure and services, and crime (Cumming et al. 2014; Li
2020). Civil conflict and violence have also been significant
challenges for Africa, affecting livelihood opportunities and
Fig. 4 (continued)
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propelling migration (Maystadt and Ecker 2014; Kelley et al.
2015; Schleussner et al. 2016).
For SSA's emigration probability (see Eq. 4 and ‘Emi-
gration selection’ of Model 2 in Table 2), a high level of
fertility, wet extremes, and livestock production had positive
effects. High fertility has been observed in SSA countries for
a long time, contributing to the large flow and probability
of internal and international migration. The positive effect
of wet extremes on SSA's emigration probability is con-
sistent with the wetting trends and increasing emigration
in SSA. Wet extremes as a new challenge, especially for
vulnerable groups, like females and children, may acceler-
ate the propensity for environmental emigration or asylum
seeking from SSA countries. The animal stock in livestock
production, often considered savings and wealth, may enable
emigration for better living conditions and opportunities,
especially under hardship conditions or disasters. Besides,
the results about emigration countries (see Eq. 5 and ‘Migra-
tion coefficients’ of Model 2 in Table 2) indicate that climate
extremes exerted similar effects on the international migra-
tion flow. High levels of crop production, arable land per
capita, life expectancy, political stability, and the absence of
violence deceased the outflow of SSA migrants. In contrast,
high GDP per capita and unemployment rates promoted
the outflow. It implies that economic growth in SSA may
help emigrants afford their emigration costs while people
are moving abroad for better employment opportunities and
economic wellbeing. In SSA, emigration countries had a
smaller migration flow than immigration countries (i.e. IMR
coefficient = − 0.64). It aligns with the above statement that
international migration was primarily within SSA.
The results of these two models (Table 2) reveal that cli-
mate extremes affected SSA’s international migration, along
with population growth, food security, urban and economic
growth, and conflict. The effect of climate extremes dem-
onstrated significant differences between the migration
flow and direction. For instance, wet extremes decreased
SSA’s international migration flow but increased the pro-
pensity for emigration. Several studies have also claimed
that adverse climatic conditions tended to prompt human
displacement and migration but not universally, i.e. in some
cases, it reduced migration (Gray 2011, 2012; Mueller and
Binder 2015; Challinor et al. 2018). It is thus worth noting
that the effect of climate extremes might differ across SSA
countries (e.g. low- and middle-income countries), across
migration patterns (e.g. internal and international), and
between migration flow and direction. The underlying ‘scale
issues’ (Eklund et al. 2016) and spatio-temporal processes
(Schapendonk et al. 2020) of migration shall be studied in
the future.
Cascading effects of international migration
on expatriates and asylum seeking
SSA's international migration had no significant influence on
its expatriates within SSA countries nor in EU-14 countries
but significantly increased the number of asylum seekers
in EU-14 countries (see Eq. 6 and Table 3). 1% more inter-
national migration or 1% higher probability of emigration
would result in an increase of 0.2% in SSA's asylum seeking
to Europe. Historical migrants and the migrant ratio appear
to be the most critical drivers. The results indicate that inter-
national migration had a small positive effects on SSA's asy-
lum seeking to Europe. In contrast, historical migrants who
settled in the destination country before 1990 seemed the
primary stimulus to SSA’s expatriates and asylum-seeking
growth. It can be explained by network effects that can
reduce migration costs and the implementation of visas and
other migration restrictions since the 1990s. It also implies
that those migration restrictions in Europe might not corre-
spond to less SSA emigration but more asylum seeking and
unauthorised migration (Beauchemin et al. 2020). Besides,
the negative effect of the migrant ratio and the positive effect
of the GDP differential between the EU-14 destination coun-
try and SSA origin country convey an increasing diversi-
fication of migration origins and motives. The increase in
expatriates and asylum seeking appeared to be found more
in the SSA country that used to be a minor migrant origin in
the destination country. Family reunification might not be
the primary motive anymore. Instead, income opportuni-
ties and economic wellbeing became the primary driving
force for SSA's asylum seeking to Europe. SSA’s interna-
tional migration diversification might derive from the rapid
economic growth since Africa's reforms in the 1990s. The
economic growth allowed more SSA people to afford inter-
national migration to Europe, Asia, or intra-Africa for secu-
rity and adequate living conditions (Flahaux and De Haas
2016; Nour et al. 2020).
Moreover, SSA’s expatriates increased along with a short
geographical distance between origins and destinations,
which often means a shorter travel time and lower migra-
tion costs. However, the impact of short distance did not play
a significant role in asylum seeking. By contrast, a shared
colonial language promoted asylum seeking to EU-14 coun-
tries and the expatriates within SSA. These results imply that
a common language and relevant cultural and social ties built
through a colonial relationship could facilitate the emigra-
tion and integration within SSA and Europe. Translocal and
transnational social networks that embed people in sending
and receiving countries may connect migrants and facilitate
the flow of resources, information, and knowledge between
places (Rockenbauch and Sakdapolrak 2017; Schapendonk
et al. 2020). In addition, the intra-SSA expatriates appeared
to be found more in countries with a higher population
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Sustainability Science (2022) 17:1873–1897
1 3
density and no common land border. Contiguity might con-
tribute more to temporary, internal, or return migration than
international migration in SSA, given relevant costs and ben-
efits (Barkin 1967). High population density often means
a concentration of economic activities and urban markets
which provide job opportunities and attract immigrants. It
depicts a manifestation of agglomeration that is often taken
as a pathway out of poverty, improving economic returns and
social benefits (Fujita et al. 1999; Jacques-François 2000;
Borck 2005), especially in less-developed areas, like SSA.
Nevertheless, the agglomeration of intra-SSA expatriates
might also have adverse effects on sustainable development,
in the form of increasing pollution and degradation (e.g. air
and soil), pressures on scarce resources (e.g. irrigated farm-
land and skilled labour), class stratification and inequity (e.g.
social exclusion and high-cost of housing), and conflict and
violence (e.g. crime and homicide rates). Therefore, SSA’s
international migration and resultant agglomeration impacts
may influence its achievement of sustainable development.
Feedback effects of international migration
on sustainable development
An increase in SSA's international migration significantly
decreased the computed sustainability score (ρ < 0.05). The
international migration contributed to SDG8 sustainable
economy and SDG11 urbanisation but undermined SDG2
food security and agriculture, SDG3 healthy lives, and
SDG16 peaceful societies (see Eq. 7 and Table 4). It can be
explained by the ‘agglomeration impacts’ of international
migration, primarily to low-income but high-population-
density countries. The agglomeration may provoke large-
scale resource extraction in SSA, leading to land grabbing,
competition, and conflict while increasing losses of biodi-
versity and ecosystem services, climate vulnerability, and
trade-offs in the water-food-energy nexus (Biggs et al. 2018).
Good governance with efficient institutions and management
strategies could enhance positive agglomeration impacts and
curtail those negative ones. However, poor governance and
the declining institutions facing SSA are formidable, stymie-
ing local capacity building in design and execution while
challenging the foundations for sustainable and equitable
growth (Sesay 1977; World Bank 1989; United Nations
Office on Drugs and Crime 2005).
In SSA, emigration countries gained a lower score of
SDG16 peaceful societies (Table 4). SSA has been plagued
with political instability, violent conflict, famine, and high
crime rates (Adepoju 1995). Recurrent emigration and a
significant exodus of skilled labour may exacerbate weak
economies and the endemic political instability and violent
conflict in countries like Nigeria, Eritrea, and the Demo-
cratic Republic of the Congo. Previous studies have claimed
violent conflict as an outcome of significant emigration in
less-developed areas (Reuveny 2007, 2008) and that migra-
tion may adversely affect the political stability of countries
(Gebremedhin and Mavisakalyan 2013). In addition, high
fertility and population density undermined SDG2 food
security and sustainable agriculture. High population den-
sity also decreased the scores of SDG8 sustainable economy
and SDG11 urbanisation. These results convey that the rapid
population growth and agglomeration in SSA increased food
demand and pressures on resources and the environment.
Since SSA has the lowest cereal self-sufficiency, this may
put the region at the most significant food security risk (van
Ittersum et al. 2016). The agglomeration of international
migration stressed not only food security but also socioeco-
nomic development. Besides, temperature extremes impaired
economic sustainability (SDG8) but was associated with an
increase in life expectancy (SDG3). The decline of economic
growth and productivity can be explained by the wide-rang-
ing negative effect of hot extremes on agricultural output,
industrial output, and political stability (Dell et al. 2009,
2012; Moore and Diaz 2015; Burke et al. 2015). From 1990
to 2018, the mean temperature extreme in SSA was around
1.11 °C (Table 2) which was still within the 2 °C catastrophe
limit (Huang 2012; Sewe et al. 2018). Although the tempera-
ture increase could result in loss of life, SSA still presented
significant gains in life expectancy from its continued efforts
to improve access to sanitation and clean water and allevi-
ate poverty, malnourishment, and child mortality (Challinor
et al. 2018; United Nations 2019; McMaken 2019).
Conclusion
We conclude that SSA countries demonstrated different
international migration patterns with various degrees of
resource endowment and sustainable development under
climate change. SSA experienced a wide variability of wet
extremes and a sharp increase in temperature throughout
the research period. Dry extremes increased SSA’s interna-
tional migration, whereas wet extremes had adverse effects.
Temperature extremes had a positive effect but were insig-
nificant, probably due to the differential effects between
middle-income countries and low-income countries and
across different African regions. The international migration
was primarily within SSA to low- and lower-middle-income
countries and externally to certain high-income OECD
countries. It was driven by low dietary energy supply and
arable land per capita, high urbanisation and homicide rates,
low political stability, and absence of violence.
Along with the progress in sustainable development,
SSA's international migration reduced, but emigration rose
in terms of emigrant flows and the number of emigration
countries. The probability of emigration was driven by a high
level of fertility, wet extremes, and livestock production. In
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Sustainability Science (2022) 17:1873–1897
1 3
addition to the similar effect of climate extremes mentioned
above, the emigration flows were decreased by high crop
production, arable land per capita, life expectancy, political
stability, and absence of violence but increased by GDP per
capita and unemployment rates. SSA’s international migra-
tion stimulated asylum seeking in EU-14 countries with the
diversification of origin countries and a motive for income
opportunities and economic wellbeing. However, the inter-
national migration and resultant agglomeration in SSA con-
strained sustainable development by impairing SDG2 food
security and agriculture, SDG3 healthy lives, and SDG16
peaceful societies.
Our work developed a Sustainability Index and regression
models that help investigate international migration patterns
and drivers, the cascading effects of SSA's international
migration on the emigrants within SSA and Europe, and
the feedback effects on sustainable development. By using a
systematic and evidence-based approach that integrates reli-
able data, measurable indicators, multidisciplinary concepts,
and analytical frameworks, we have provided insights into
the feedback loops (Liu et al. 2007) between international
migration and sustainable development based on systems
thinking, which is not yet well presented in a single migra-
tion study. It lays a foundation for further investigating the
climate-migration-sustainability interlinkages across multi-
ple dimensions. Nevertheless, internal, return, and circular
migration was not considered because of the widespread lack
of quality data for SSA countries in time series. It limited the
definition of migration and relevant studies in this article.
Thus, we call for attention to improving the availability of
quality data on migration and ensuring the monitoring of all
migrants and migration flows which are essential to improve
migration management and policy. Comprehensive and data-
available indicators are also required to measure and predict
migration propensity, flow, and capacity and the trade-offs
and synergies between achieving different SDGs, given the
emerging challenges from COVID-19 (Lambert et al. 2020;
Forster et al. 2020; Ottersen and Engebretsen 2020).
Supplementary Information The online version contains supplemen-
tary material available at https://doi.org/10.1007/s11625-022-01116-z.
Acknowledgements This paper is the outcome of research conducted
within the Africa Multiple Cluster of Excellence at the University of
Bayreuth, funded by the Deutsche Forschungsgemeinschaft (DFG, Ger-
man Research Foundation) under Germany's Excellence Strategy—
EXC 2052/1—390713894. The authors thank the support of Steven
Higgins who is co-principal investigator, Julia Blauhut for graphics
editing, and also Amjath T.S. Babu and Martin Doevenspeck for their
comments during the initial review.
Author contributions QL designed the study; QL assembled the data,
performed the analysis, and wrote the manuscript; and CS commented
and reviewed the manuscript.
Funding Open Access funding enabled and organized by Projekt
DEAL.
Declarations
Conflict of interests The authors declare no competing financial in-
terests.
Supplemental information Supplemental information can be found
online at https://doi.org/XXX.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
References
Abel GJ, Brottrager M, Crespo Cuaresma J, Muttarak R (2019) Cli-
mate, conflict and forced migration. Glob Environ Change
54:239–249. https://doi.org/10.1016/j.gloenvcha.2018.12.003
Ackah-Baidoo P (2016) Youth unemployment in resource-rich Sub-
Saharan Africa: a critical review. Extr Ind Soc 3:249–261. https://
doi.org/10.1016/j.exis.2015.11.010
Adepoju A (1995) Emigration dynamics in Sub-Saharan Africa. Int
Migr 33:313–390. https://doi.org/10.1111/j.1468-2435.1995.
tb00032.x
Adger WN, Crépin A-S, Folke C et al (2020) Urbanization, migration,
and adaptation to climate change. One Earth 3:396–399. https://
doi.org/10.1016/j.oneear.2020.09.016
Ahmed SA, Cruz M, Go DS et al (2016) How significant is Sub-
Saharan Africa’s demographic dividend for its future growth and
poverty reduction? Rev Dev Econ 20:762–793. https://doi.org/
10.1111/rode.12227
Appleton S, Hoddinott J, Mackinnon J (1996) Education and health
in sub-Saharan. Africa 8:307–339. https://doi.org/10.1002/(SICI)
1099-1328(199605)8:3%3c307::AID-JID394%3e3.0.CO;2-D
Ayeb-Karlsson S, Smith CD, Kniveton D (2018) A discursive review
of the textual use of ‘trapped’ in environmental migration stud-
ies: the conceptual birth and troubled teenage years of trapped
populations. Ambio 47:557–573. https://doi.org/10.1007/
s13280-017-1007-6
Bardsley DK, Hugo GJ (2010) Migration and climate change: examin-
ing thresholds of change to guide effective adaptation decision-
making. Popul Environ 32:238–262. https://doi.org/10.1007/
s11111-010-0126-9
Barkin S (1967) The economic costs and benefits and human gains and
disadvantages of international migration. J Hum Resour 2:495.
https://doi.org/10.2307/144768
Battaile B, Hernández FL, Norambuena V (2015) Debt sustainability
in Sub-Saharan Africa: unraveling country-specific Risks. The
World Bank
1895
Sustainability Science (2022) 17:1873–1897
1 3
Beauchemin C, Flahaux M-L, Schoumaker B (2020) Three sub-Saha-
ran migration systems in times of policy restriction. Comp Migr
Stud 8:19. https://doi.org/10.1186/s40878-020-0174-y
Biggs R, Kizito F, Adjonou K, et al (2018) Current and future interac-
tions between nature and society. In: King N, Peterson G (eds) The
IPBES regional assessment report on biodiversity and ecosystem
services for Africa. Secretariat of the Intergovernmental Science-
Policy Platform on Biodiversity and Ecosystem Services, Bonn,
Germany, pp 297–352
Black R, Adger WN, Arnell NW et al (2011) The effect of environmen-
tal change on human migration. Glob Environ Change 21:S3–S11.
https://doi.org/10.1016/j.gloenvcha.2011.10.001
Blunden J, Arndt DS (2020) State of the climate in 2019. Bull Am
Meteorol Soc 101:S1–S429. https://doi.org/10.1175/2020B
AMSStateoftheClimate.1
Boas I, Farbotko C, Adams H et al (2019) Climate migration
myths. Nat Clim Change 9:901–903. https://doi.org/10.1038/
s41558-019-0633-3
Burke M, Hsiang SM, Miguel E (2015) Global non-linear effect of tem-
perature on economic production. Nature 527:235–239. https://
doi.org/10.1038/nature15725
Carney MA, Krause KC (2020) Immigration/migration and healthy
publics: the threat of food insecurity. Palgrave Commun 6:93.
https://doi.org/10.1057/s41599-020-0461-0
Castelli F (2018) Drivers of migration: why do people move? J Travel
Med https://doi.org/10.1093/jtm/tay040
Cattaneo C, Peri G (2016) The migration response to increasing tem-
peratures. J Dev Econ 122:127–146. https://doi.org/10.1016/j.
jdeveco.2016.05.004
Challinor AJ, Adger WN, Benton TG et al (2018) Transmission of
climate risks across sectors and borders. Philos Trans R Soc Math
Phys Eng Sci 376:20170301. https://doi.org/10.1098/rsta.2017.
0301
Chapin FS, Carpenter SR, Kofinas GP et al (2010) Ecosystem steward-
ship: sustainability strategies for a rapidly changing planet. Trends
Ecol Evol 25:241–249. https://doi.org/10.1016/j.tree.2009.10.008
Chort I, de la Rupelle M (2016) Determinants of Mexico-U.S. out-
ward and return migration flows: a state-level panel data anal-
ysis. Demography 53:1453–1476. https://doi.org/10.1007/
s13524-016-0503-9
Cottier F, Salehyan I (2021) Climate variability and irregular migration
to the European Union. Glob Environ Change 69:102275. https://
doi.org/10.1016/j.gloenvcha.2021.102275
Cumming GS, Buerkert A, Hoffmann EM et al (2014) Implications of
agricultural transitions and urbanisation for ecosystem services.
Nature 515:50–57. https://doi.org/10.1038/nature13945
Cumming GS, Morrison TH, Hughes TP (2017) New directions
for understanding the spatial resilience of social-ecological
systems. Ecosystems 20:649–664. https://doi.org/10.1007/
s10021-016-0089-5
Davis TJ (2017) Good governance as a foundation for sustainable
human development in sub-Saharan Africa. Third World Q
38:636–654. https://doi.org/10.1080/01436597.2016.1191340
Dell M, Jones BF, Olken BA (2012) Temperature shocks and economic
growth: evidence from the last half century. Am Econ J Macro-
econ 4:66–95. https://doi.org/10.1257/mac.4.3.66
Dell M, Jones BF, Olken BA (2009) Temperature and income: rec-
onciling new cross-sectional and panel estimates. Am Econ Rev
99:198–204. https://doi.org/10.1257/aer.99.2.198
Diamantides ND (1994) International migration as a dynamic process.
Kybernetes 23:37–55. https://doi.org/10.1108/036849294100644
91
Eklund L, Romankiewicz C, Brandt M (2016) Data and methods in
the environment-migration nexus: a scale perspective, 2nd edn.
Gesellschaft für Erdkunde zu Berlin, DE
Exploring Africa (2021) Map 2: Colonial Languages. In: Explor. Afr.
http://exploringafrica.matrix.msu.edu/3061-2/. Accessed 1 Sep
2020
Flahaux M-L, De Haas H (2016) African migration: trends, pat-
terns, drivers. Comp Migr Stud 4:1. https://doi.org/10.1186/
s40878-015-0015-6
Folke C (2016) Resilience (republished). Ecol Soc. https://doi.org/10.
5751/ES-09088-210444
Folke C, Biggs R, Norström AV et al (2016) Social-ecological resil-
ience and biosphere-based sustainability science. Ecol Soc.
https://doi.org/10.5751/ES-08748-210341
Folke C, Carpenter SR, Walker B et al (2010) Resilience thinking:
integrating resilience, adaptability and transformability. Ecol Soc
15:art20. https://doi.org/10.5751/ES-03610-150420
Food and Agriculture Organization of the United Nations, Rome, Italy
(2020) FAOSTAT. http://www.fao.org/faostat/en/#data. Accessed
1 Sep 2020
Forster PM, Forster HI, Evans MJ et al (2020) Current and future
global climate impacts resulting from COVID-19. Nat Clim
Change 10:913–919. https://doi.org/10.1038/s41558-020-0883-0
Garcés-Mascareñas B, Penninx R (eds) (2016) Integration processes
and policies in Europe. Springer International Publishing, Cham
Gebremedhin TA, Mavisakalyan A (2013) Immigration and political
instability: immigration and political instability. Kyklos 66:317–
341. https://doi.org/10.1111/kykl.12024
Gray C (2012) Drought and population mobility in rural Ethiopia.
World Dev 40:134–145. https://doi.org/10.1016/j.worlddev.2011.
05.023
Gray CL (2011) Soil quality and human migration in Kenya and
Uganda. Glob Environ Change 21:421–430. https://doi.org/10.
1016/j.gloenvcha.2011.02.004
Grêt-Regamey A, Huber SH, Huber R (2019) Actors’ diversity and
the resilience of social-ecological systems to global change. Nat
Sustain. https://doi.org/10.1038/s41893-019-0236-z
Grothmann T, Patt A (2005) Adaptive capacity and human cognition:
the process of individual adaptation to climate change. Glob
Environ Change 15:199–213. https://doi.org/10.1016/j.gloenvcha.
2005.01.002
Heckman JJ (1979) Sample selection bias as a specification error.
Econometrica 47:153. https://doi.org/10.2307/1912352
Higgins SI (2017) Ecosystem assembly: a mission for terrestrial earth
system science. Ecosystems 20:69–77. https://doi.org/10.1007/
s10021-016-0054-3
Hoffmann R, Dimitrova A, Muttarak R et al (2020) A meta-analysis
of country-level studies on environmental change and migra-
tion. Nat Clim Change 10:904–912. https://doi.org/10.1038/
s41558-020-0898-6
Holling CS (2001) Understanding the complexity of economic, eco-
logical, and social systems. Ecosystems 4:390–405. https://doi.
org/10.1007/s10021-001-0101-5
Huang C (2012) The impact of temperature on years of life lost in
Brisbane Australia. Nat Clim Change 2:6
IOM’s GMDAC (2019) Migration and Development, Sustain-
able Development Goals. In: Migr. Data Portal Bigger Pict.
https://migrationdataportal.org/themes/sustainable-devel
opment-goals-sdgs-0
Jayne TS, Chamberlin J, Benfica R (2018) Africa’s unfolding economic
transformation. J Dev Stud 54:777–787. https://doi.org/10.1080/
00220388.2018.1430774
Kelley CP, Mohtadi S, Cane MA et al (2015) Climate change in the
Fertile Crescent and implications of the recent Syrian drought.
Proc Natl Acad Sci 112:3241–3246. https://doi.org/10.1073/pnas.
1421533112
Kniveton DR, Smith CD, Black R (2012) Emerging migration flows
in a changing climate in dryland Africa. Nat Clim Change 2:444–
447. https://doi.org/10.1038/nclimate1447
1896
Sustainability Science (2022) 17:1873–1897
1 3
Kodongo O, Ojah K (2016) Does infrastructure really explain eco-
nomic growth in Sub-Saharan Africa? Rev Dev Fin 6:105–125.
https://doi.org/10.1016/j.rdf.2016.12.001
Lambert H, Gupte J, Fletcher H et al (2020) COVID-19 as a global
challenge: towards an inclusive and sustainable future. Lancet
Planet Health 4:e312–e314. https://doi.org/10.1016/S2542-
5196(20)30168-6
Li Q (2020) Resilience thinking as a system approach to promote Chi-
na’s sustainability transitions. Sustainability 12:5008. https://doi.
org/10.3390/su12125008
Liu J, Dietz T, Carpenter SR et al (2007) Coupled human and natural
systems. AMBIO J Hum Environ 36:639–649. https://doi.org/10.
1579/0044-7447(2007)36[639:CHANS]2.0.CO;2
Mahony CR, Cannon AJ (2018) Wetter summers can intensify depar-
tures from natural variability in a warming climate. Nat Commun
9:783. https://doi.org/10.1038/s41467-018-03132-z
Marchese D, Reynolds E, Bates ME et al (2018) Resilience and sustain-
ability: similarities and differences in environmental management
applications. Sci Total Environ 613–614:1275–1283. https://doi.
org/10.1016/j.scitotenv.2017.09.086
Massey DS (1988) Econ Dev Int Migr Compar Perspect 14:383–413
Massey DS (1990) The social and economic origins of immigration.
Am Acad Polit Soc Sci 510:60–72. https://doi.org/10.1177/00027
16290510001005
Massey DS, Zenteno RM (1999) The dynamics of mass migration.
Proc Natl Acad Sci 96:5328–5335. https://doi.org/10.1073/pnas.
96.9.5328
Mastrorillo M, Licker R, Bohra-Mishra P et al (2016) The influence
of climate variability on internal migration flows in South Africa.
Glob Environ Change 39:155–169. https://doi.org/10.1016/j.gloen
vcha.2016.04.014
Mayda AM (2010) International migration: a panel data analysis of
the determinants of bilateral flows. J Popul Econ 23:1249–1274.
https://doi.org/10.1007/s00148-009-0251-x
Maystadt J-F, Ecker O (2014) Extreme weather and civil war: does
drought fuel conflict in Somalia through Livestock Price Shocks?
Am J Agric Econ 96:1157–1182. https://doi.org/10.1093/ajae/
aau010
McLeman R (2013) Developments in modelling of climate change-
related migration. Clim Change 117:599–611. https://doi.org/10.
1007/s10584-012-0578-2
McMaken R (2019) If Climate Change Is Killing Us, Why Is Life
Expectancy Increasing? In: MISES WIRE. https://mises.org/wire/
if-climate-change-killing-us-why-life-expectancy-increasing.
Accessed 16 Dec 2020
Moore FC, Diaz DB (2015) Temperature impacts on economic growth
warrant stringent mitigation policy. Nat Clim Change 5:6
Mueller ND, Binder S (2015) Closing yield gaps: consequences for
the global food supply, environmental quality and food security.
Daedalus 144:45–56. https://doi.org/10.1162/DAED_a_00353
Ndulu BJ, O’Connell SA (1999) Governance and growth in Sub-Saha-
ran Africa. J Econ Perspect 13:41–66. https://doi.org/10.1257/jep.
13.3.41
Niang I, Ruppel OC, Abdrabo MA et al (2014a) Africa. Climate change
2014: impacts, adaptation, and vulnerability. Cambridge Univer-
sity Press, Cambridge, pp 1199–1265
Niang I, Ruppel OC, Abdrabo MA et al (2014b) Africa. In: Barros VR,
Field CB, Dokken DJ et al (eds) Climate change 2014: impacts,
adaptation, and vulnerability. Cambridge University Press, pp
1199–1265
Nour AE, Eziakonwa A, Krivonos D (2020) Migration Futures for
Africa. In: Africa Migration Report: Challenging the Narrative.
International Organization for Migration, Addis Ababa, Ethiopia,
p 217 of 232
OECD (2015) International Migration Database, Online Edition.
https://stats.oecd.org/. Accessed 1 Sep 2020
Ottersen OP, Engebretsen E (2020) COVID-19 puts the Sustainable
Development Goals center stage. Nat Med 26:1672–1673. https://
doi.org/10.1038/s41591-020-1094-y
Reuveny R (2007) Climate change-induced migration and violent con-
flict. Polit Geogr 26:656–673. https://doi.org/10.1016/j.polgeo.
2007.05.001
Reuveny R (2008) Ecomigration and violent conflict: case studies and
public policy implications. Hum Ecol 36:1–13. https://doi.org/10.
1007/s10745-007-9142-5
Rockenbauch T, Sakdapolrak P (2017) Social networks and the resil-
ience of rural communities in the Global South: a critical review
and conceptual reflections. Ecol Soc 22:art10. https://doi.org/10.
5751/ES-09009-220110
Sadiddin A, Cattaneo A, Cirillo M, Miller M (2019) Food insecurity
as a determinant of international migration: evidence from Sub-
Saharan Africa. Food Secur 11:515–530. https://doi.org/10.1007/
s12571-019-00927-w
Schapendonk J, van Liempt I, Schwarz I, Steel G (2020) Re-routing
migration geographies: migrants, trajectories and mobility
regimes. Geoforum 116:211–216. https://doi.org/10.1016/j.geofo
rum.2018.06.007
Schleussner C-F, Donges JF, Donner RV, Schellnhuber HJ (2016)
Armed-conflict risks enhanced by climate-related disasters in
ethnically fractionalised countries. Proc Natl Acad Sci 113:9216–
9221. https://doi.org/10.1073/pnas.1601611113
Serdeczny O, Adams S, Baarsch F et al (2017) Climate change impacts
in Sub-Saharan Africa: from physical changes to their social
repercussions. Reg Environ Change 17:1585–1600. https://doi.
org/10.1007/s10113-015-0910-2
Sesay L (1977) Crime and development in Africa. Ann Am Acad Pol
Soc Sci 432:42–51. https://doi.org/10.1177/000271627743200105
Sewe MO, Bunker A, Ingole V et al (2018) Estimated effect of tem-
perature on years of life lost: a retrospective time-series study of
low-, middle-, and high-income regions. Environ Health Perspect
126:12
Steffen W, Richardson K, Rockstrom J et al (2015) Planetary bounda-
ries: guiding human development on a changing planet. Science
347:1259855–1259855. https://doi.org/10.1126/science.1259855
The World Bank (2020a) World development indicators. In: World
Bank Natl. Acc. Data OECD Natl. Acc. Data Files. https://datab
ank.worldbank.org/reports.aspx?source=2&series=NY.GDP.
PCAP.CD&country=. Accessed 1 May 2020a
The World Bank (2020b) GDP per capita (current US$) - Sub-Saharan
Africa. In: World Bank Natl. Acc. Data OECD Natl. Acc. Data
Files. https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?
locations=ZG. Accessed 1 Dec 2020b
UCLA: Statistical Consulting Group (2006) Negative Binomial Regres-
sion | Stata Annotated Output. https://stats.idre.ucla.edu/stata/out-
put/negative-binomial-regression/
United Nations (2015) Transforming our World: The 2030 Agenda
for Sustainable Development. United Nations General Assembly,
New York, New York, USA.
United Nations (2019) World Population Prospects 2019, Online Edi-
tion. In: Dep. Econ. Soc. Aff. Popul. Div. https://population.un.
org/wpp/Download/Standard/Population/. Accessed 10 Apr 2020
United Nations (2020) International migrant stock 2019, Online Edi-
tion. In: Dep. Econ. Soc. Aff. Popul. Div. Int. Migr. https://www.
un.org/en/development/desa/population/migration/data/estim
ates2/estimates19.asp. Accessed 15 Apr 2020
United Nations Office on Drugs and Crime (2005) CRIME AND
DEVELOPMENT IN AFRICA
United Nations Statistics Division (2017) Global Indicator Framework
for the Sustainable Development Goals and Targets of the 2030
Agenda for Sustainable Development Report No. A/RES/71/313.
United Nations Statistics Division,
1897
Sustainability Science (2022) 17:1873–1897
1 3
van der Schrier G, Barichivich J, Briffa KR, Jones PD (2013) A scP-
DSI-based global data set of dry and wet spells for 1901–2009:
variations in the self-calibrating PDSI. J Geophys Res Atmos
118:4025–4048. https://doi.org/10.1002/jgrd.50355
van Ittersum MK, van Bussel LGJ, Wolf J et al (2016) Can sub-Saharan
Africa feed itself? Proc Natl Acad Sci 113:14964–14969. https://
doi.org/10.1073/pnas.1610359113
World Bank (ed) (1989) Sub-Saharan Africa: from crisis to sustainable
growth: along-term perspective study. World Bank, Washington,
D.C
World Bank Data Team (2019) New country classifications by
income level: 2019–2020. https://blogs.worldbank.org/opendata/
new-country-classifications-income-level-2019-2020. Accessed
1 Dec 2020
World Health Organization (2020) Global Health Observatory, Global
Health Observatory data repository, Homicide Estimates by coun-
try. https://apps.who.int/gho/data/view.main.VIOLENCEHO
MICIDEv. Accessed 1 Sep 2020
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