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Poverty, Livelihoods and Sustainable Development: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change

  • Global Centre for Climate Mobility


Adverse impacts of climate change, development deficits and inequality exacerbate each other. Existing vulnerabilities and inequalities intensify with adverse impacts of climate change (high confidence1). These impacts disproportionately affect marginalised groups, amplifying inequalities and undermining sustainable development across all regions (high confidence). Due to their socio- economic conditions and the broader development context, many poor communities, especially in regions with high levels of vulnerability and inequality, are less resilient to diverse climate impacts (high con- fidence). {8.2.1, 8.2.2, 8.3.2, 8.3.3} Under all emissions scenarios, climate change reduces capacities for adaptive responses and limits choices and opportunities for sustainable development. Higher levels of global warming lead to greater constraints on societies. Climate change increases the threat of chronic and sudden onset development challenges, such as poverty traps and food insecurity (high confidence). Adaptation interventions and transformative solutions that prioritise inclusive and wide-ranging climate resilient development and the reduction of poverty and inequality are increasingly seen as necessary to minimise loss and damage from climate change (high confidence). {8.2.1, 8.2.2, 8.3.1, 8.3.2, 8.3.3} Observed societal impacts of climate change, such as mortality due to floods, droughts and storms, are much greater for regions with high vulnerability compared to regions with low vulnerability, which reveals the different starting points that regions have in their move towards climate resilient development (high confidence). Observed average mortality from floods, drought and storms is 15 times higher for regions and countries ranked as very high vulnerable, such as Mozambique, Somalia, Nigeria, Afghanistan and Haiti compared to very low vulnerable regions and countries, such as UK, Australia, Canada and Sweden in the last decade (high confidence). Over 3.3 billion people are living in countries classified as very highly or highly vulnerable, while around 1.8 billion people live in countries with low or very low vulnerability (high confidence). Approximately 3.6 billion people live in low and lower middle-income countries, which are most vulnerable and disproportionally bear the human costs of dis- asters due to extreme weather events and hazards (high confidence). The population in most vulnerable countries is projected to increase significantly by 2050 and 2100, while the population in countries with low vulnerability is projected to decrease or grow only slightly.
8Poverty, Livelihoods and
Sustainable Development
This chapter should be cited as:
Birkmann, J., E. Liwenga, R. Pandey, E. Boyd, R. Djalante, F. Gemenne, W. Leal Filho, P.F. Pinho, L. Stringer, and D. Wrathall,
2022: Poverty, Livelihoods and Sustainable Development. In: Climate Change 2022: Impacts, Adaptation and Vulnerability.
Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change
[H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke,
V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 1171–1274,
Coordinating Lead Authors: Joern Birkmann (Germany), Emma Liwenga (Tanzania), Rajiv
Pandey (India)
Lead Authors: Emily Boyd (Sweden), Riyanti Djalante (Indonesia), François Gemenne (Belgium),
Walter Leal Filho (Germany), Patricia Fernanda Pinho (Brazil), Lindsay Stringer (UK), David
Wrathall (USA)
Contributing Authors: Stavros Afionis (Greece), Liana Anderson (Brazil), Desalegn Ayal
(Ethiopia), Connor Joseph Cavanagh (Norway), Jon Ensor (UK), Harald Heubaum (UK), Md.
Monirul Islam (Bangladesh), Rachel James (UK), Emma li Johansson (Sweden), Murukesan
Krishnapillai (The Federated States of Micronesia), Joanna M. McMillan (Germany/Australia);
Nicholas P. Simpson (South Africa), Jamon Van Den Hoek (USA), Emmanuel Raju (Denmark)
Review Editors: Taikan Oki (Japan), Marta G. Rivera-Ferre (Spain), Taha Zatari (Saudi Arabia)
Chapter Scientists: Ali Jamshed (Germany/Pakistan), Joanna M. McMillan (Germany/Australia),
Marvin Ravan (Germany/Iran)
Chapter 8 Poverty, Livelihoods and Sustainable Development
Table of Contents
Executive Summary �������������������������������������������������������������������������������������� 1174
8.1 Introduction ����������������������������������������������������������������������������������� 1176
8.2 Detection and Attribution of Observed Impacts
and Responses ����������������������������������������������������������������������������� 1177
8.2.1 Observed Impacts of Climate Change with
Implications for Poverty, Livelihoods and
Sustainable Development ���������������������������������������������� 1177
Box8.1 | Climate traps: A focus on refugees and
internally displaced people ���������������������������������������������������������� 1183
Box8.2 | Livelihood strategies of internally displaced
atoll communities in Yap ����������������������������������������������������������������� 1185
8.2.2 Poverty–Environment Traps and Observed Responses
to Climate Change with Implications for Poverty,
Livelihoods and Sustainable Development ����������� 1187
Box8.3 | COVID-19 pandemic ����������������������������������������������������� 1188
Box8.4 | Conflict and governance ������������������������������������������ 1190
8.2.3 Observed Impacts and Responses and their
Relevance for Decision Making ���������������������������������� 1192
8.3 Human Vulnerability, Spatial Hotspots, Observed
Loss and Damage, and Livelihood Challenges �� 1193
8.3.1 Assessments of Risk and Vulnerability ������������������� 1193
8.3.2 Global Hotspots of Human Vulnerability to Climate
Change ��������������������������������������������������������������������������������������� 1195
8.3.3 Livelihood Impacts, Shifting Livelihoods and the
Challenges for Equity and Sustainability in the
Context of Climate Change ������������������������������������������� 1204
8.3.4 Observed Disproportionate Impacts According to
Economic and Non-economic Losses and Damages
Due to Climate Change ��������������������������������������������������� 1205
8.3.5 Economic and Non-economic Losses and Damages
Due to Climate Change and their Implications for
Livelihoods and Livelihood Shifts ������������������������������ 1208
Box8.5 | Western Cape Region in South Africa: drought
challenges to equity and sustainability ����������������������������� 1212
8.4 Future Vulnerabilities, Risks and Livelihood
Challenges and Consequences for Equity and
Sustainability �������������������������������������������������������������������������������� 1213
8.4.1 Future Exposure, Climate Change Vulnerability
and Poverty at the Global Scale ��������������������������������� 1213
8.4.2 The Influence of Future Climate Change Impacts
on Future Response Capacities ���������������������������������� 1214
8.4.3 The Influence of Climate Change Responses on
Projected Development Pathways ���������������������������� 1215
8.4.4 Social Tipping Points in the Context of Future
Climate Change �������������������������������������������������������������������� 1215
8.4.5 Projected Risks for Livelihoods and Consequences
for Equity and Sustainability ���������������������������������������� 1216
Box8.6 | Social dimensions of the Amazonia forest
fires and future risks �������������������������������������������������������������������������� 1220
8.5 Adaptation Options and Enabling Environments
for Adaptation with a Particular Focus on the
Poor, Different Livelihood Capitals and Vulnerable
Groups ������������������������������������������������������������������������������������������������ 1227
8.5.1 Adaptation Options to Climate Change Hazards
Focusing on Vulnerable Groups ���������������������������������� 1227
8.5.2 Enabling Environments for Adaptation in
Different Socioeconomic Contexts ��������������������������� 1228
Box8.7 | Addressing inequalities in national capabilities:
common but differentiated responsibilities and
respective capabilities relating to adaptation and
the Paris Agreement ��������������������������������������������������������������������������� 1229
Box8.8 | Cyclone Aila in Bangladesh: impact, adaptation
and way forward ����������������������������������������������������������������������������������� 1236
8.6 Climate Resilient Development for the Poor and
Pro-poor Adaptation Finance: Ensuring Climate
Justice and Sustainable Development ��������������������� 1238
8.6.1 Synergies and Trade-offs Between Adaptation
and Mitigation in Different Sectors with Implications
for Poverty, Livelihoods and Sustainable
Development �������������������������������������������������������������������������� 1239
8.6.2 Decision-making Approaches for Climate Resilient
Development �������������������������������������������������������������������������� 1245
8.6.3. Future Adaptation Finance and Social and Economic
Changes within the Context of Poverty, Livelihoods,
Equity, Equality and Justice ������������������������������������������ 1247
Box8.9 | Adaptation financing for the poor and the
need for systems transition: Eastern Indonesian
Islands ������������������������������������������������������������������������������������������������������������� 1248
8.7 Conclusion ��������������������������������������������������������������������������������������� 1249
Frequently Asked Questions
FAQ 8.1 | Why are people who are poor and
disadvantaged especially vulnerable to climate
change and why do climate change impacts worsen
inequality? �������������������������������������������������������������������������������������������������� 1251
Poverty, Livelihoods and Sustainable Development Chapter 8
FAQ 8.2 | Which world regions are highly vulnerable and
how many people live there? ����������������������������������������������������� 1251
FAQ 8.3 | How does and will climate change interact
with other global trends (e.g., urbanisation, economic
globalisation) and shocks (e.g., COVID-19) to influence
livelihoods of the poor? ������������������������������������������������������������������ 1252
FAQ 8.4 | What can be done to help reduce the risks
from climate change, especially for the poor? ������������ 1253
FAQ 8.5 | How do present adaptation and future
responses to climate change affect poverty and
inequality? �������������������������������������������������������������������������������������������������� 1253
References ����������������������������������������������������������������������������������������������������������� 1254
Chapter 8 Poverty, Livelihoods and Sustainable Development
Executive Summary
Adverse impacts of climate change, development deficits and
inequality exacerbate each other. Existing vulnerabilities and
inequalities intensify with adverse impacts of climate change
(high confidence1). These impacts disproportionately affect margin-
alised groups, amplifying inequalities and undermining sustainable
development across all regions (high confidence). Due to their socio-
economic conditions and the broader development context, many poor
communities, especially in regions with high levels of vulnerability
and inequality, are less resilient to diverse climate impacts (high con-
fidence). {8.2.1, 8.2.2, 8.3.2, 8.3.3}
Under all emissions scenarios, climate change reduces capacities
for adaptive responses and limits choices and opportunities for
sustainable development. Higher levels of global warming lead
to greater constraints on societies. Climate change increases
the threat of chronic and sudden onset development challenges,
such as poverty traps and food insecurity (high confidence).
Adaptation interventions and transformative solutions that prioritise
inclusive and wide-ranging climate resilient development and the
reduction of poverty and inequality are increasingly seen as necessary
to minimise loss and damage from climate change (high confidence).
{8.2.1, 8.2.2, 8.3.1, 8.3.2, 8.3.3}
Observed societal impacts of climate change, such as mortality
due to floods, droughts and storms, are much greater for regions
with high vulnerability compared to regions with low vulner-
ability, which reveals the different starting points that regions
have in their move towards climate resilient development (high
confidence). Observed average mortality from floods, drought and
storms is 15times higher for regions and countries ranked as very high
vulnerable, such as Mozambique, Somalia, Nigeria, Afghanistan and
Haiti compared to very low vulnerable regions and countries, such as
UK, Australia, Canada and Sweden in the last decade (high confidence).
Over 3.3billion people are living in countries classified as very highly
or highly vulnerable, while around 1.8billion people live in countries
with low or very low vulnerability (high confidence). Approximately
3.6billion people live in low and lower middle-income countries, which
are most vulnerable and disproportionally bear the human costs of dis-
asters due to extreme weather events and hazards (high confidence).
The population in most vulnerable countries is projected to increase sig-
nificantly by 2050 and 2100, while the population in countries with low
vulnerability is projected to decrease or grow only slightly. Vulnerability
is a result of many interlinked issues concerning poverty, migration, in-
equality, access to basic services, education, institutions and govern-
ance capacities, often made more complex by past developments, such
as histories of colonialism (high confidence). {8.3.2, 8.3.3}
1 In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. A level of confidence is
expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels
can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.
2 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, Very likely 90–100%, Likely 66–100%, About as
likely as not 33–66%, Unlikely 0–33%, Very unlikely 0–10%, and Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%, More likely than not >50–100%, and Extremely unlikely
0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely). This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome
lies within the 17–83% probability range.
3 Meaning low or moderate emission scenarios.
A growing range of economic and non-economic losses have
been detected and attributed to climate extremes and slow-
onset events under observed increases in global temperatures
(medium evidence, high agreement). If future climate change under
high emissions scenarios continues and increases risks, without strong
adaptation measures, losses and damages will likely2 be concentrated
among the poorest vulnerable populations (high confidence). The
intersection of inequality and poverty presents significant adaptation
limits, resulting in residual risks for people and groups in vulnerable
situations, including women, youth, elderly, ethnic and religious
minorities, Indigenous People and refugees. Climate change is likely
to force economic transitions among the poorest groups, accelerating
the switch from agriculture to other forms of wage labour, with
implications for labour migration and urbanisation (medium evidence,
high agreement). Under an inequality scenario (Shared Socioeconomic
Pathway (SSP) 4) the projected number of people living in extreme
poverty may increase by 122million by 2030 (medium confidence).
{8.2, 8.3.4, 8.4.1, 8.4.5, Figure8.6, Box8.5,}
Both climate change and vulnerability threaten the achievement
of the UN Sustainable Development Goals (SDGs) (medium
confidence). This undermines progress toward various goals such as
no poverty (SDG1), zero hunger (SDG2), gender equality (SDG5) and
reducing inequality (SDG10), among others (medium evidence, high
agreement). Gender inequality and discrimination are among the barriers
to adaptation (high confidence). {8.2.1¸8.4.5} Also maladaptation can
lead to additional complex and compounding future risks and threaten
sustainable development (high confidence). {,}
Under higher emissions scenarios and increasing climate haz-
ards, the potential for social tipping points increases (medium
confidence). Even with moderate climate change3 people in vulner-
able regions will experience a further erosion of livelihood security that
can interact with humanitarian crises, such as displacement and forced
migration (high confidence) and violent conflict, and lead to social
tipping points (medium confidence). Social tipping points can also be
coupled with environmental tipping points. {8.3, 8.4.4}
Vulnerable population groups in most vulnerable regions have
the most urgent need for adaptation (high confidence). The
most vulnerable regions are particularly located in East, Central
and West Africa, South Asia, Micronesia and Melanesia and in
Central America (high confidence). These regions are characterised
by compound challenges of high levels of poverty, a significant number
of people without access to basic services, such as water and sanitation,
and wealth and gender inequalities, as well as governance challenges.
Areas of high human vulnerability are characterised by larger
transboundary regional clusters (high confidence). Additional support
Poverty, Livelihoods and Sustainable Development Chapter 8
and structures are needed to reduce the existing gaps between future
adaptation needs and current capacities, and to support transitions
from vulnerable livelihoods with adequate integration of the
Indigenous knowledge and local knowledge (IKLK) systems. Greater
investments are required under higher levels of global warming and
of inequality (Relative Concentration Pathway (RCP) 4.5; RCP8.5 and
SSP4) (high confidence). {8.3, 8.4, Box8.6}
The direct and indirect consequences of the COVID-19 pandemic
have worsened inequalities within societies, thereby increasing
existing vulnerabilities to climate change and further limiting
the ability of marginalised communities to adapt (medium
confidence). The COVID-19 pandemic is expected to increase the adverse
consequences of climate change since the financial consequences have
led to a shift in priorities and constrain vulnerability reduction (medium
confidence). Moreover, the COVID-19 pandemic is also influencing
the capacities of governmental institutions in developing nations to
support planned adaptation and poverty reduction of most vulnerable
people/groups, since the crisis also means significant reductions in tax
revenues (high confidence). {8.3, 8.4,}
Those with climate-sensitive livelihoods and precarious liveli-
hood conditions are often least able to adapt, afforded limited
adaptation opportunities and have little influence on decision
making (high confidence). Enabling environments that sup-
port sustainable development are essential for adaptation and
climate resilient development (high confidence). Enabling and
supportive environments for adaptation share common governance
characteristics, including multiple actors and assets, multiple centres
of power at different levels and an effective vertical and horizontal
integration between levels (high confidence). Enabling conditions can
support livelihood strategies that do not undermine human well-being
(medium confidence). {8.5.1, 8.5.2, 8.6.3, 5.13}
Mitigation and adaptation responses to climate change influence
inequalities, poverty and livelihood security and thereby aspects
of climate justice (medium confidence). Improving coherence
between adaptations of different social groups and sectors at
different scales can reduce maladaptation, enable mitigation
and advance progress towards climate resilience (medium
confidence). The poor typically have low carbon footprints but are
disproportionately affected by adverse consequences of climate change
and also lack access to adaptation options. In many cases, the poor
and most vulnerable people and groups are most adversely affected by
maladaptation (medium evidence, high agreement). Climate justice and
rights-based approaches are increasingly recognised as key principles
within mitigation and adaptation strategies and projects (medium
confidence). Narrowing gender gaps can play a transformative role
in pursuing climate justice (medium confidence). Climate resilient
development is therefore closely coupled with issues of climate justice.
Synergies between adaptation and mitigation exist, and these can
have benefits for the poor (medium confidence). {8.4,, 8.6}
There is increasing evidence that nature-based solutions (e.g.,
urban green infrastructure, ecosystem-based management) can
provide important livelihood options and reduce poverty while
also supporting mitigation and adaptation (medium confidence).
However, the trade-offs over time between nature-based solutions and
their dynamics are insufficiently understood. Appropriate governance, in-
cluding mainstreaming and policy coherence, supported by adaptation
finance that targets the poor and marginalised, is essential for adaptation
and climate compatible development (medium confidence). {8.5.2, 8.6.3,
Chapter 8 Poverty, Livelihoods and Sustainable Development
8.1 Introduction
The impacts of climate change have already significantly affected
livelihoods and living conditions, especially of the poorest and most
vulnerable, and will continue to undermine development during the
coming century. This chapter assesses the societal consequences of
climate change and related hazards in terms of adverse and irreversible
consequences for the most vulnerable. To understand societal conse-
quences of climate change, we assess impacts through the perspective
of vulnerability, poverty and livelihoods of people. We identify why cli-
mate events trigger sudden and slow-onset disasters, and how the most
severe, acute and chronic impacts cause and deepen human suffering.
We also examine issues of climate justice. Understanding and engaging
with climate justice requires a plural focus on the historical social and
institutional relations and inequalities that produce climate change,
cause people to be vulnerable to climate hazards and shape responses
to them (Newell etal., 2021). An assessment of observed impacts on the
poorest and their strategies for adaptation carries important lessons for
inclusive, broad-based solutions to climate change.
As a starting point, this chapter examines linkages between climate
change, specific climate-related hazards and impacts on multidimen-
sional poverty, vulnerability and livelihoods. Past assessments have
identified the linkages between climate change, poverty, livelihoods
and human vulnerability, and shown how climate change leads to
differential consequences for different communities and populations.
The IPCC Fifth Assessment Report (AR5) identified socially and geo-
graphically disadvantaged people exposed to persistent inequalities
at the intersection of various dimensions of discrimination based on
gender, age, ethnicity, class and caste (IPCC, 2014a). AR5 also showed
evidence that climate change is a universal driver and multiplier of
risk that shapes dynamic interactions between these factors. Climate
change is one stressor that shapes dynamic and differential livelihood
trajectories. Also, the IPCC Special 1.5°C Report (IPCC SR 1.5°C) under-
scored with very high confidence that global mean temperature, harm
and human well-being losses are increasing substantially (Hoegh-
Guldberg etal., 2018; Roy etal., 2018).
This chapter builds on this, examining equitable development, robust
institutions and poverty reduction as essential inputs to societies’
capacity for adaptation (i.e., closes the adaptation gap) in order to avoid
losses and damages (L&Ds) from climate change. It assesses quantitative
spatio-temporal information on human vulnerability at a global scale
and for specific sub-regions, livelihood groups and communities at
the local level. The chapter assesses the newest literature on how
multidimensional poverty and human vulnerability to climate change is
measured and also examined the agreement of different index systems
in terms of global hotspots of human vulnerability.
In addition, the chapter explores how climate change affects different
livelihoods and livelihood assets and also examines factors that
characterise vulnerability to climate change, focusing on different
dimensions of human vulnerability and its subsystems (e.g., access
to infrastructure services). In this context the chapter also assesses
quantitative data to map human vulnerability as well as economic
and non-economic losses that are highly relevant for understanding
adverse impacts of climate change.
The chapter assesses the newest scientific knowledge on how the
most vulnerable and marginalised people are experiencing different
climate-influenced hazards and changes, how these groups prepare for
and adapt to these changes. Hence, it examines how climate change
intersects with broader processes of development. It also considers the
various impacts of climate change on the livelihoods of the poorest,
the capabilities, assets and activities required for a means of living. It
examines the institutional conditions that promote livelihood resilience
in the face of climate change. Quantitative analysis and qualitative data
on observed adverse climate change impacts and future projections and
trends in vulnerability show that societal impacts of climate change
cannot solely be explained by looking at temperature changes or
climatic hazards alone.
The chapter provides due consideration of how societal impacts
of climate change are emerging as a result of climatic changes,
development and vulnerability. In this regard, it also explores how
past and present conditions of poverty, inequality and vulnerability
determine observed and future societal impacts of climate change,
including future adaptive capacities of societies exposed to climate
change. It highlights new entry points to address climate risks and
adaptation needs through the targeted reduction of poverty, inequity
and vulnerability, linking particularly global quantitative information
with local livelihood-orientated qualitative information.
The chapter outlines new approaches for identifying social tipping
points, meaning moments of rapid, destabilising change across scales
that can complement the discussion about physical tipping points in
the climate system. It also addresses new perspectives on the baselines
for assessing future vulnerabilities, and the potential for irreversible
losses, emphasising not only economic but also non-economic losses,
which are linked to past and present development trajectories. There
is robust evidence on non-economic losses, including the loss of
land, livelihoods, social networks, cultural values and the irreversible
degradation of ecosystem functions, as observed, for example, in parts
of the Amazon. Non-economic losses are intertwined with economic
losses to influence human health, nutrition, well-being and social
stability, and therefore also influence present and future vulnerabilities
and adaptive capacities. Non-economic losses from climate change
disproportionately affect the poor. People in vulnerable situations are
often disproportionately affected as they are less resilient and have
less access to institutional support (including protection mechanisms)
and coping strategies. This knowledge is key for informing integrated
strategies for sustainable livelihood transitions and adaptation.
The chapter assesses newer literature about the synergies and trade-
offs for the poorest and most vulnerable people and groups between
adaptation–mitigation and sustainable development strategies, which
societies must negotiate in order to pursue climate resilient develop-
ment. It explores synergies and mismatches in key development sectors
that the poorest rely on, including agriculture, forestry and energy. It
identifies the development strategies, elements of institutional design
and financial mechanisms that could support risk reduction and adap-
tation. Our assessment reveals that successful adaptation is not solely
a question of levels of funding, but depends on broader institutional
design that determine societal development and enabling conditions
for adaptation to and mitigation of climate change. An assessment
Poverty, Livelihoods and Sustainable Development Chapter 8
of enabling conditions for adaptation supports the finding that more
convergent, integrated and comprehensive approaches to adaptation
are needed. The chapter concludes that climate justice requires con-
sideration of the legal, institutional and governance frameworks that
significantly determine whether adaptation is successful in addressing
the needs of the poor.
Thus, intersections between climate hazards and socioeconomic
development are assessed from the point of view of vulnerability,
poverty, livelihoods and inequality (see Figure8.1). Chapter 8 adopts
this wider perspective to examine the differential nature of observed
and future disproportionate vulnerabilities (i.e., who is most susceptible
to climate hazards and events, where, at the core to understanding of
what scale and why?), as well as the inequalities inherent in adaptation
and mitigation solutions as part of a wider climate justice perspective
adopted in Chapter 8, and challenges for climate resilient development.
Finally, our assessment points towards the fact that human vulnerability
to climate change is a complex and multifaceted phenomenon that is
often influenced by historic development processes, such as structures
that originated with colonisation. Also, recent global shocks not
directly related to climate change, such as the COVID-19 pandemic
and its socioeconomic consequences, impact climate vulnerability and
inequitable impacts occurring between countries and within countries.
Recent studies show that COVID-19, and other social, economic and
political crises, have worsened the circumstances of the poor and
further marginalised them.
Overall, the chapter is key in terms of understanding societal impacts
of climate change and factors that determine the various differential
adverse consequences of climate change on societies. The information
presented and assessed is fundamental for informing adaptation and
risk reduction strategies, since climatic information alone cannot
explain sufficiently why some regions, societies or groups are
suffering significantly more under climate change compared to others.
Concepts such as vulnerability, intersectionality and climate justice
provide important insights on how societal impacts of climate change
are influenced and determined by broader societal development
8.2 Detection and Attribution of Observed
Impacts and Responses
8.2.1 Observed Impacts of Climate Change with
Implications for Poverty, Livelihoods and
Sustainable Development
This section reports on new evidence on the observed impacts
of climate change to livelihoods and the poor since the previous
assessment (IPCC, 2014a). New evidence provides additional insight
into the interlinkages between climate change, poverty and livelihoods.
New evidence has been evaluated according to climate change hazard
categories developed for the AR6 (IPCC, 2021), and summarised in
Human dimension of climate change at the nexus of climate change,
climate hazards and socio-economic development
Figure8.1 | The lens of Chapter 8 to better understand the human dimension of climate change at the nexus of climate change, climate hazards and socio-
economic development.
Chapter 8 Poverty, Livelihoods and Sustainable Development Interactions Between Climate Hazards and Non-climatic
Stressors Affecting Livelihoods
New evidence highlights the potential for multi-hazard risks to push
the poor into persistent traps of extreme poverty (Räsänen et al.,
2016). Risk of extreme impoverishment increases for low-income
people experiencing repeated and successive climatic events, whereby
before they have recovered from one disaster, they face another impact
(Forzieri etal., 2016). Cascading and compounding risks arise from
multiple climate hazards coinciding to produce impacts, for example,
in mountainous regions, where the combination of glacier recession
and extreme rainfall result in landslides (Martha etal., 2015). There is
robust evidence that this effect has been observed around slow- and
rapid-onset climate events related to drought (i.e., rising temperatures,
heatwaves and rainfall scarcity), with devastating consequences for
agriculture (Vogt etal., 2018; Bouwer, 2019). In particular, the urban
and rural landless poor face difficulties rebuilding assets following
one-off disasters or a series of shocks (Garcia-Aristizabal etal., 2015).
Climate change is one driver among many that challenges livelihoods
of the rural poor, including economic transitions associated with
industrialisation and urbanisation, and also governance failures such
as unclear property rights and civil conflict (e.g., Nyantakyi-Frimpong
and Bezner-Kerr, 2015). Recent research adds evidence about the ways
that climate hazards impact non-climatic stressors with implications for
poverty reduction (Nelson etal., 2016). The risk that climate hazards may
push the poor into persistent extreme poverty intensifies with stagnant
wages, rising costs of living, mobility traps, and ethnic or religious
discrimination (Cramer et al., 2014; Carter etal., 2016). Likewise in
both urban and rural environments, non-climatic factors related to
governance exacerbate the impacts of climate events among the
poorest, including poor service provisioning (e.g., waste collection), poor
urban planning (e.g., waste water drainage) and water management
failures (Di Baldassarre etal., 2010; Leal Filho etal., 2018), as well as
poor rangeland management, intensification of farming land uses (i.e.,
overgrazing, deforestation), degradation of wetlands, shortage of water
and soil erosion in rural areas (Olsson etal., 2019).
A key risk for the poor is shocks to specific livelihood assets that may
force low-income groups into persistent poverty traps (Figure 8.4;
Chambers and Conway, 1992; Cinner etal., 2018) but research also
suggests that climate change impacts are also driving transient forms
of poverty, a modality of poverty which is recurring (Angelsen etal.,
2014). Recurrent poverty is, for instance, seen in relation to crop losses
and decreasing agricultural production when income losses worsen
living conditions (Ward, 2016; Kihara et al., 2020). Recent research
shows that climate change impacts may exacerbate poverty indirectly
through increasing cost of food, housing and healthcare, among other
rising costs borne by the poor (Islam et al., 2014; Ebi et al., 2017;
Hallegatte etal., 2018) (high confidence). Severe adverse impacts of
climate change at present and future risks may result from permanent,
sudden, destabilising changes accompanying climate events such as
decreases in food security, large-scale migration, changes in labour
capacity or conflict (Bentley etal., 2014). Overall, there is more evidence
that even under medium warming pathways, climate change risks to
poverty would become severe if vulnerability is high and adaptation is
low (limited evidence, high agreement) (see Section
Reliable and precise estimates of the impacts of climate change on
persistent poverty are difficult to generate, for example, due to data
scarcity and data gaps (Hallegatte et al., 2015; Hallegatte et al.,
2018; Kugler etal., 2019). However, progress has been made towards
detection and attribution of climate change impacts on the poorest
by linking standard climate observations in low-income countries with
new non-traditional forms of data (including Indigenous knowledge,
historical archival data, satellite imagery, and data from digital
devices) (Kuffer etal., 2016; Lu etal., 2016; Bennett and Smith, 2017;
Steele etal., 2017). Links Between Climate-related Hazards, Observed
Losses, Poverty and Inequality Globally
There is high confidence that climate-related hazards, including both
slow-onset shifts and extreme events, directly affect the poor through
adverse impacts on livelihoods (see Figure8.2), including reductions
and losses of agricultural yields, impacts on human health and food
security, destruction of homes, and loss of income (Hallegatte etal.,
2015; Connolly-Boutin and Smit, 2016). One of the key factors that
drives disproportionate impacts among poor households globally is
lost agricultural income (high confidence) (Hallegatte et al., 2015;
Islam and Winkel, 2017). Also of concern are the impacts of climate
hazards to human health, which is a primary resource that the poor
rely on (Figure8.2). There are only few robust global estimates of
observed income losses to the poor that comprehensively account for
all climate hazards; nevertheless, (Hallegatte and Rozenberg, 2017),
estimating average impacts of climate change on incomes of the poor,
found that across 92 developing countries, the poorest 40% of the
population experienced losses that were 70% greater than the losses
of people with average wealth.
Overall, our assessment shows (see Figure8.2) high confidence that
two categories of climate hazards pose high risk to a broad range of
livelihood resources that the poor rely on: warming trends and droughts
(Figure 8.2b). Two key livelihood resource categories—life, bodily
health and food security, and crop yield (representing agricultural
productivity) are most at risk to a broad range of climate hazards (high
confidence, Figure 8.2b). In addition to warming and drought, both
pluvial and fluvial flooding, severe storms and sea level rise represent
a high-risk cluster for livelihood impacts (high confidence, Figure8.2b).
Figure8.2 reflects the fundamental threat that climate hazards pose
to the survival of plants, livestock and fish, as well as the people on
which livelihoods depend (high confidence) (see Horton etal., 2021).
The dependence of livelihoods on biological, ecological and human
survival depicted in Figure8.2 is also treated in Chapter 5. Likewise,
impacts to livelihood resources can be compared to impacts to other
key assets (see Working Group I (WGI) Section12.3; WGI Table12.2,
Ranasinghe etal., 2021).
It is revealed that warming trends and droughts pose greatest risks to
the widest array of livelihood resources, and are particularly detrimental
to crops and human health, a long-term requirement for livelihoods
and well-being (high confidence) (see Figure8.2B; Section;
Section; Campbell etal., 2018). A wide range of hazards
also threaten the survival of fish and livestock that livelihoods depend
Poverty, Livelihoods and Sustainable Development Chapter 8
on (high confidence, Figure8.2b), as well as other sources of income
for the poor. Salinity is a secondary hazard related to droughts, coastal
flooding and sea level rise, and poses a fundamental risk to agriculture
(high confidence). There is also robust evidence for rainfall variability
driving short-term impacts to agricultural productivity as well as
permanent loss of agriculture (high confidence).
Summary of confidence on the observed impacts of 23 climate hazards on nine key livelihood resources
on which the poor depend most
Permafrost thawing
Warming trend
River flood
Wet trend
Cold spell
Climate Hazards
Livelíhood resources
Crop yield
Pluvial flood
ice Coastal
Storm OtherWetCold Dry
Dry trend
Heavy snow
Severe storms
Lake/sea ice reduction
Snow avalanche
Snow reduction
Coastal erosion
Sea level rise
Ocean/lake acidification
Coastal flood
Total risk
from all
Housing stock
Farmland/arable cropland
Fisheries and aquaculture
Forest products
Income/financial assets
Life/bodily health/food security
Crop variety
Livelíhood resources
Crop yield
Warming trend
River flood
Pluvial flood
Severe storms
Sea level rise
Fisheries and aquaculture
Income/financial assets
Life/bodily health/food security
Average confidence
Total risk to livelihoods
Average confidence
Total risk to livelihoods
Confidence on the observed impacts
1.4 2.31.4
3.0 2.8 2.2 3.0 2.8 2.2
Total risk
from all
(a) Display of 207
confidence statements
on the total set of
livelihood impacts
Confidence on the observed impacts
Lower Higher
(b) High risk cluster
(c) Spatial distribution of relative confidence
Figure8.2 | Summary of confidence on the observed impacts of 23 climate hazards on nine key livelihood resources on which the poor depend most.
(a) A total of 207 confidence statements on the total set of livelihood impacts. Based on a standardised assessment of available literature since the AR5 (IPCC, 2014a), each impact
category was assigned a confidence statement based on weight of evidence; high confidence is represented with HC, medium confidence with MC and low confidence with LC. An
average numerical confidence score is assigned for impacts from each climate hazard, and for each livelihood resource category, representing total risk.
(b) The ‘high-risk’ cluster of livelihood impacts, where confidence is highest. (c) The spatial distribution of relative confidence. Hotspots represent highest confidence of observed
livelihood impacts; however, the absence of spatial information reflects not an absence of observed livelihood risk, but the relative weight of evidence sampled in this assessment
Chapter 8 Poverty, Livelihoods and Sustainable Development
While severe storms, pluvial and riverine floods, and coastal floods
primarily impact private livelihood resources, such as homes and
income (high confidence, Figure 8.2b), warming and droughts also
affect common pool resources, such as rangeland, fisheries and forests
(high confidence, Figure8.2b). Multiple hazards undermine ecosystems
that Indigenous Peoples and poor communities depend on for food
security and income and have sustainably managed over the long
term, such as forests, grazing land and marine fisheries (Barange etal.,
2014; Leichenko and Silva, 2014; Béné etal., 2016; Jantarasami etal.,
High confidence for observed livelihood impacts is spatially
concentrated in South Asia, Africa, North America, and to a lesser extent
Small Island Developing States (SIDS) (Figure8.2c). The hazards most
prevalent in all regions include warming trends, droughts and sea level
rise (Figure8.2c), and undermine crop productivity, crop varieties, and
cropland in most regions (high confidence). Along coastlines, climate
hazards threaten livelihoods particularly exposed to extreme weather,
flooding and sea level rise, and where poor populations are heavily
dependent on agriculture and fisheries (high confidence). One third of
total sampled evidence on livelihood impacts was observed in just three
countries—Bangladesh, India and Nepal—indicating accumulating
experience with livelihood impacts in South Asia (Figure8.2c). However,
this spatial representation of confidence does not mean that observed
livelihood impacts are not occurring in other regions as well. Relative to
South Asia, in Central Asia and the Caribbean, for example, the weight
of evidence of livelihood impacts though lighter is still robust. Among
industrialised nations, there is high confidence that climate change has
impacted livelihood resources in the USA. Observed Differential Vulnerability to Climate Change,
and Loss and Damage
The negative impacts of climate change on groups of vulnerable or
marginalised communities generate so-called ‘residual impacts’ and
residual risks that can remain a challenge in their lives (Warner and
Van der Geest, 2013; James etal., 2014; Klein etal., 2014; Boyd etal.,
2017). Such ‘unacceptable’ L&Ds include the loss of income sources,
food insecurity, malnutrition, permanent impacts to health and
labour productivity, loss of life and loss of homelands, among others
(McNamara and Jackson, 2019; Schwerdtle etal., 2020). The literature
on L&D provides robust evidence not only on economic dimensions
of global L&Ds, but also experiences of non-economic losses from the
impacts of climate change (see detail in Section8.3; Barnett etal.,
2016; Roy etal., 2018; McNamara and Jackson, 2019). The extreme
events that have occurred in recent years highlight the potential for
L&D, including 2019’s Cyclone Kenneth, the strongest in the recorded
history of the African continent, which made landfall in northern
Mozambique causing 45 deaths and destroying approximately 40,000
houses, leaving hundreds of thousands at risk of acquiring waterborne
diseases such as cholera during a prolonged recovery period (Cambaza
etal., 2019).
In parallel to evidence on L&D, the science of climate event attribution
has evolved from a theoretical possibility into a subfield of climate
science. As attribution science strengthens, with it the evidence base
linking greenhouse gas (GHG) emissions to extreme heat events, heavy
rainfall and wind storms grows and becomes more robust (Otto etal.,
2016; Stott etal., 2016; Otto etal., 2018; Otto, 2020; Clarke etal., 2021;
van Oldenborgh etal., 2021a; van Oldenborgh etal., 2021b; Verschuur
etal., 2021).
Climate justice questions arise about the observed differential L&Ds
due to climatic hazards to affected populations in close connection with
their vulnerability (Wrathall etal., 2015). Individual extreme weather
events attributable to climate change result in L&Ds in communities
and societies, which allow a quantification of the differential impacts
of such events on different groups (Hoegh-Guldberg et al., 2019a).
Considering the disproportionately adverse impacts of climatic hazard
on most vulnerable groups and regions and their relatively minor
contribution to anthropogenic climate change (Mora et al., 2018;
Robinson and Shine, 2018), it is evident that vulnerability reduction
and adaptation to climate change have also to be seen as an issue of
climate justice and climate just development (Byers etal., 2018).
Probabilistic attribution allows an assessment of people’s future
climate risks and estimates about the costs of successfully adapting
to them (James etal., 2014; James etal., 2019). To answer questions
about impacts on people, the vulnerable and poor in particular,
requires attribution, vulnerability and adaptation science need to move
far beyond understanding physical events and incorporate information
(including Indigenous knowledge and local knowledge (IKLK))
on people’s vulnerability and capacities, and exposure and losses
resulting from discrete events (Bellprat etal., 2019). Attribution science
is therefore highly compatible with risk management tools (i.e., risk
reduction, risk transfer, insurance, risk pooling, recovery, rehabilitation
and compensation) suggested in policy (James etal., 2019).
New observations provide greater evidence on the role of extreme
poverty and global inequality, most of the detrimental direct impacts of
climate change (e.g., rising food insecurity) disproportionately affecting
the Global South (Hasegawa etal., 2018; Mbow etal., 2019; Khan and
Zhang, 2021) compared with the Global North. Poor populations in
many countries are also disproportionately facing extreme L&D from
heatwaves, flooding and tropical weather extremes (Gamble et al.,
2016). New case studies, such as the European heatwave of 2018,
illustrate significant negative impacts across crop production in the
Global North (Beillouin etal., 2020), livestock value chain (FAO, 2018;
Godde etal., 2021) and fishing (Plagányi, 2019). Heatwave-induced
intense fires can cause property damage, physical injury and death, as
well as health and psychological harm of the victims. Heatwaves also
create ideal conditions for the prevalence of certain pathogens, increase
the risk of temperature-related health problems and exacerbate many
pre-existing diseases (Rossiello and Szema, 2019).
A focus in the chapter is on the intersections between climate
hazards and differential vulnerability resulting in actual and potential
economic and non-economic losses (Section 8.3, 8.4; Thomas et al.,
2019). Increasingly, intersections of age, gender, socioeconomic class,
ethnicity and race are recognised as important to the climate risks and
differential impacts and losses experienced by vulnerable, marginal and
poor in societies (high confidence).(Section 8.2,2.3; CCB GENDER in
Chapter 18; Nyantakyi-Frimpong and Bezner-Kerr, 2015). For example,
linkages between wildfires and gendered norms and values are real-
Poverty, Livelihoods and Sustainable Development Chapter 8
world examples (Walker etal., 2021). A broader climate agenda which
considers social structures and power relations intersecting with climate
change extremes is important (Versey, 2021), in order to understand
disproportionate impacts of climate hazards, observed and future losses
and vulnerability (see Figure8.3).
Extreme events (e.g., heatwaves, cold periods, icy conditions) occurring
in the Global North illustrate that such events cause disproportionate
impacts among ageing populations, due to their immobility, isolation,
infrastructure deficiencies and poor health assistance (Carter et al.,
2016; Reckien etal., 2018). A well-known example is the heatwave
in 2003 that killed thousands of elderly citizens across Europe
(Poumadere et al., 2005; García-Herrera etal., 2010; Laaidi et al.,
2011). More recently, in the Nordic region, elderly populations have
been experiencing distress associated with heatwaves and extreme
cold events, with significant increases in morbidity and mortality due
to cardiovascular and respiratory failure, showing that both age and
underlying health issues intersect with climate change impacts (Carter
etal., 2016; Li etal., 2016). The elderly also experience severe impacts
from extreme winter seasons, such as in Finland, where of the from
3000 deaths associated with extreme winter weather and 50,000
injuries associated with slippery pavement conditions, the majority
were people over 65 years old (Carter et al., 2016). Adaptation to
extreme events including heatwaves, cold periods and icy conditions
in the Global South and North will increase energy demand and the
individuals’ carbon footprint across all income levels (van Ruijven
etal., 2019).
The 2018 US National Climate Assessment has identified that
southeastern USA is already experiencing more frequent and longer
summer heatwaves and, by 2050, rising global temperatures are expected
to mean that cities in southeastern USA may experience extreme
heat (USGCRP, 2018). This includes disadvantaged African American
communities, who are more exposed and hence disproportionately
experience the impacts of climate change (Shepherd and KC, 2015;
Marsha et al., 2018). The historically discriminated Sami in northern
Sweden and Maasai in Africa are examples of Indigenous People who
also face climate risks and have limited resources, capacity or power to
respond (Leal Filho etal., 2017; Persson etal., 2017). Climate-related Hazards, Livelihood Transitions and
Agricultural livelihoods of the rural poor, especially in Africa, Asia
and Latin America, are already in transition due to the forces of
industrialisation, urbanisation and economic globalisation (De Brauw
etal., 2014; Tacoli etal., 2015). Scientific evidence shows that climate
change is accelerating livelihood transitions from rural agricultural
production to urban wages (Cai etal., 2016; Cattaneo and Peri, 2016;
Kaczan and Orgill-Meyer, 2020).
There is now robust evidence from virtually every region on Earth
showing that the livelihood impacts from a multitude of climate hazards
are driving people to diversify rural income sources (Figure8.2; Cross-
Chapter Box MIGRATE in Chapter 7). Rural households frequently
accomplish the goal of livelihood diversification with an increasing
reliance on migration, urban wage labour and remittances (Marchiori
etal., 2012; Bohra-Mishra etal., 2014; Gray and Wise, 2016; Nawrotzki
and DeWaard, 2016; Banerjee etal., 2019a). What is different about
rural-to-urban livelihood transitions under climate change impacts
is that they accelerate both rural and urban stratification of wealth
(Barrett and Santos, 2014; Thiede etal., 2016). On the one hand, climate
change impacts on rural livelihoods increase the necessity of migration
as an income strategy, accelerating migration (Cai etal., 2016) even
while households that cannot select individuals for migration become
more impoverished (Suckall et al., 2017; Nawrotzki and DeWaard,
On the other hand, climate change impacts widen the range of
households willing or needing to engage in migration to include those
less able to bear the costs of urban migration (Afifi etal., 2016; Hunter
and Simon, 2017). The effect is also greater urban poverty, and a
The interface between climate hazards and factors of human vulnerability
Sea level rise
Actual and potential economic and non-economic losses
Figure8.3 | Illustration of the relationship between climate hazards, their impacts (including economic and non-economic losses and damages) and human
systems leading to systemic vulnerability. We need to understand who is vulnerable, where, at what scale and why. We cannot just look at the climate hazard (e.g., wild
fires, floods, droughts, sea level rise, etc.) but must also look at who is being affected by these hazards and factors that make people and groups vulnerable (e.g., poverty, uneven
power structures, disadvantage and discrimination due to, for example, social location and the intersectionality or the overlapping and compounding risks from ethnicity or racial
discrimination, gender, age, or disability, etc.) (see also Cross-Chapter BoxGENDER in Chapter 18; Section5.12).
Chapter 8 Poverty, Livelihoods and Sustainable Development
higher social burden of migrants seeking urban wages (Singh, 2019).
Evidence suggests that poor households often move in desperation
to make ends meet. In the context of climate hazards, such as coastal
inundation and salinity, economic necessity often drives working-age
adults in poor households to seek outside earnings (Dasgupta etal.,
2016). Labour migration in the context of climate change is also
gendered, and as more men seek employment opportunities away
from home, women are required to acquire new capacities to manage
new challenges, including increasing vulnerability to climate change
(Banerjee etal., 2019b).
Migration and displacement are directly induced by the impacts of
climate change (high confidence) (Cross-Chapter Box MIGRATE in
Chapter 7), however, migration responses to climate change are
differentiated across the spectrum of households’ wealth. In well-
off households, migration can be used as a way to support income
diversification through remittances (Gemenne and Blocher, 2017). High
levels of poverty mean that a large part of the African population does
not have sufficient resources to be mobile (Borderon etal., 2019; Leal
Filho etal., 2020c). The poorest households, conversely, will typically
lack the resources that would allow them to migrate in ways that
maintain an acceptable standard of living, and may find themselves
unable or unwilling to move in the face of climate change impacts
(Sam etal., 2021).
There is high agreement and robust evidence that climate change
impacts also have a major influence on key enabling conditions for
migration, such as sociodemographic, economic and political factors
(Abel et al., 2019; Borderon et al., 2019), and that climate change
impacts to development and governance may affect how people migrate
(Wrathall etal., 2019; CCB MIGRATE in Chapter 7). Mobility, which
was considered the most viable climate change adaptation strategy
to poor pastoralists, is restricted due to the political marginalisation
of pastoral groups, land privatisation, governments’ decentralisation
policies and plantation investment (Blench, 2001; Randall, 2015; Leal
Filho etal., 2020c). While migration can be an adaptation response
to climate change impacts (Black etal., 2011; Gemenne and Blocher,
2017), climate change impacts can also act as a direct driver of forced
displacement (Marchiori etal., 2012). Societal groups that are forced
to involuntarily migrate in response to climate change impacts may
lack resources to invest in planned relocation mainly due to lack of
good governance systems (Reckien etal., 2018). For people displaced
by climate change impacts, policy interventions have a determining
influence on migration outcomes, such as the numbers of migrants,
the timing of migration and destinations (Gemenne and Blocher,
2017; Wrathall etal., 2019).The process of displacement and forced
migration leaves people more exposed to climate change-related
extreme weather events, particularly in low-income countries which
often host the highest number of displaced people (Adger etal., 2018).
Climate change may be accelerating livelihood transitions and
migration in ways that accelerate urbanisation (Adger etal., 2020).
Although a range of climate hazards are noted for accelerating rural-
to-urban livelihood transitions (see Cross-Chapter Box MIGRATE in
Chapter 7), a key theme to emerge across many case studies is the
impact of rising temperatures on agricultural productivity (Mueller
et al., 2014; Cattaneo and Peri, 2016; Call et al., 2017; Wrathall
etal., 2018). In other words, when people cannot farm due to rising
temperatures (and related stressors), they migrate. In this context,
migration as a livelihood diversification strategy may evolve and
take multiple forms over time (Bell etal., 2019), such as temporary
migration (Mueller etal., 2020), seasonal migration (Gautam, 2017) or
permanent migration (Nawrotzki etal., 2017), but generally conforms
to existing patterns of migration (Curtis etal., 2015).
A key concern for the poor is climate change impacts that undermine
livelihood diversification and resilience, narrowing the set of available
livelihood alternatives (Tanner etal., 2015; Bailey and Buck, 2016;
Perfecto etal., 2019). The Long-lasting Effects of Climate Change on Poverty
and Inequality
New studies document the long-term effects of climate change
impacts on people’s livelihoods that persist long after a hazard event.
For example, the impact of drought on livelihoods and food security
is still recognisable in Mali, 30years after 1982–1984, the period of
most intense drought during the protracted late 20th century drying of
the Sahel. The most food secure households associated with persistent
drought-induced famine were those that diversified livelihoods away
from subsistence agriculture during and after the famine (Giannini
etal., 2017). Meanwhile, a larger fraction of households with fewer
livelihood activities, lower food security with higher reliance on
detrimental nutrition-based coping strategies (such as reducing the
quantity or quality of meals) were those unable to diversify livelihoods
30years previously. Sufficient time has passed to consider the long-
term outcomes for the poor in extreme cases featured in previous
IPCC assessments, including Hurricane Katrina (2005) (e.g., Fussell,
2015; Raker etal., 2019) and Hurricane Mitch (1998) (e.g., Alaniz,
2017), forewarning that recovery is complex and requires significant
sustained long-term investment in ‘soft’ aspects of development,
including community organisation and mental health (O’Neill etal.,
2020; Fraser etal., 2021).
The IPCC Special Report on 1.5°C concluded that climate change has
already increased the probability and intensity of individual extreme
weather events occurring (Roy et al., 2018), and our new baseline
consideration should be that serious climate change impacts are
already being experienced by the most vulnerable, with long-term
implications for development (Box 8.1; Roy et al., 2018). In both
developing and developed countries the disproportionate impacts
of the compounding effects of climate change on development are
felt by the most disadvantaged. For example, the residual impacts of
storms like Hurricane Maria (see Section illustrate how rising
temperatures, extreme weather events, coral bleaching and sea level
rise come together and create compounding hazard-cascades to leave
long-lasting effects on the lives of the poor, as well as their food
and water security, health, livelihoods and prospects for sustainable
development—not only in developing countries (Adger et al., 2014;
Olsson et al., 2014; Hoegh-Guldberg etal., 2018; Roy etal., 2018),
but also in highly inequitable industrialised countries within the same
region (Gamble et al., 2016). According to the US National Climate
Assessment (USGCRP, 2018), damages caused to communities
by Hurricanes Irma and Maria in 2017 sparked unprecedented
Poverty, Livelihoods and Sustainable Development Chapter 8
humanitarian crises. Hurricane Maria, a category 5 hurricane, passed
through Dominica, St Croix and Puerto Rico and is considered the worst
climate disaster in recorded history to affect those islands (Rodríguez-
Díaz, 2018). Approximately 200,000 people migrated from Puerto Rico
to the mainland USA in the weeks following the storm (Alexander etal.,
2019). Estimates for direct and indirect casualties in Puerto Rico point
out a total of 4645 excess deaths, equivalent to a 62% increase in the
mortality rate (Kishore etal., 2018). The example of Hurricane Maria
and Puerto Rico illustrates that vulnerability is part of a long history of
discrimination and colonial governance, which led to greater impacts
on the island (Moleti etal., 2020). In Puerto Rico, the economic costs of
the collapse of the island’s energy, water, transport, and communication
infrastructures are estimated to range from USD25 to USD43billion
(USD in 2017), further indebting the island and putting its long-term
development at risk. Meanwhile the economic impacts of Hurricanes
Irma and Maria on the Caribbean region are estimated between USD27
and USD48billion, and have long-term implications for state budgets
for infrastructure supporting development of the poorest.
New evidence provides little expectation of net positive impacts of
climate change for the poor (Hallegatte et al., 2015). Nevertheless,
some benefits of climate change adaptation include improved disaster
preparedness, the accumulation of social assets, economic benefits of
agricultural diversification and benefits associated with migration, as
well as the political benefits of collective action (Pelling etal., 2018).
In contrast, wealthier tiers of society facing climate change impacts
are more able to liquidate assets to avoid losses from climate change,
to be formally compensated for losses (Fang etal., 2019) and employ
social positions to leverage gains from adaptation (Nadiruzzaman and
Wrathall, 2015).
The poor frequently suffer the direct and indirect impacts of climate
change, including the cost of adopting adaptive measures (Atteridge
and Remling, 2018; Bro etal., 2020). Costs to the poor may also include
the secondary impacts of first-order adaptation activities, including the
livelihood consequences to people migrating due to climate change
impacts. The poor frequently bear indirect impacts of adaptation
interventions, such as flood protection barriers, which may displace
flood waters away from high-income populations toward poorer
communities (Mustafa and Wrathall, 2011). Adaptation programming
may also indirectly affect the poor as public resources are drawn into
risk reduction interventions, and away from spending on social welfare
and safety nets (Eriksen et al., 2015). Measures to enhance social
welfare and safety nets themselves help enhance the poor’s resilience
to climate impacts because they focus on non-climatic stressors
affecting livelihoods, which interact with climate hazards. Therefore,
diverting attention away from safety nets may in fact undermine
adaptation efforts (Leichenko and O’Brien, 2019; Tenzing, 2020).
Box8.1 | Climate traps: A focus on refugees and internally displaced people
Populations of concern, who are extremely vulnerable to climate change impacts with limited capacity to adapt, are those displaced
and resettled in the course of conflict or disaster, either internally or across borders (Burrows and Kinney, 2016). The risk for refugees
and internally displaced people (IDPs) is two-fold: on the one hand, refugee and IDP settlements are disproportionately concentrated
in regions (e.g., Central Africa and the Near East) that are exposed to higher-than-average warming levels and specific climate hazards,
including temperature extremes and drought. On the other, these populations frequently inhabit settlements and legal circumstances that
are intended to be temporary but are protracted across generations, and at the same time, face legal and economic barriers on their ability
to migrate away from climate impacts. (Adams, 2016; Devictor and Do, 2016). Large concentrations of these settlements are located in
the Sahel, the Near East and Central Asia, where temperatures will rise higher than the global average, and extreme temperatures
will exceed thresholds for safe habitation (Figure Box8.1.1). Already largely dependent on state and humanitarian intervention, these
immobile populations will require interventions to safely maintain residence in areas exposed to climate hazards. Adaptation planning
should prioritise immobile populations living in an already destabilised development context, on improving their capacities to deal with
the further consequences of climate change.
Refugees and IDPs fit into a global category of extremely structurally vulnerable people that are missing from standard poverty
assessments, officially uncounted or uncountable using traditional census and survey methods (Carr-Hill, 2013). These include highly
mobile populations, internally displaced by war and environmental hazards (UNHCR, 2020; IDMC, 2021); itinerant labourers; urban poor
in informal settlements (Lucci etal., 2018); unauthorised migrants living in countries where they do not hold citizenship (Passel, 2006);
guest workers (Reichel and Morales, 2017); the homeless and institutionalised (Caton etal., 2007); rural nomadic, pastoralist or landless
populations (Randall, 2015); and Indigenous Peoples and forest-dwelling communities (Galappaththi etal., 2020). Frequently living
without social safety nets, such as health care and formal education, these uncounted or ‘missing millions’ are vulnerable to problems
associated with acute and chronic poverty, such as the spread of infectious disease and malnutrition (Ezeh etal., 2017). Because these
‘missing’ populations are not counted, they are frequently not a part of planning (Carr-Hill, 2013), including adaptation planning. In
any particular national context, these missing populations may represent a small fraction of the population (about 5% in South Asian
countries), however cumulatively hundreds of millions of people may be missing from official estimates (Carr-Hill, 2013). Over the last
decade, techniques for estimating the locations, numbers and socioeconomic status of missing populations have moved beyond census
and nationally representative household surveys, leveraging advances in satellite imagery (Kuffer etal., 2016; Bennett and Smith, 2017)
and data from mobile digital devices (Jean etal., 2016; Xie etal., 2016; Steele etal., 2017).
Chapter 8 Poverty, Livelihoods and Sustainable Development
Present-day global distribution of camps for refugees and internally displaced people
Background of days with temperature exceeding 35°C in 2041–2060
Distribution of
3,741 camps registered
to the United Nations High
Commissioner for Refugees
(UNHCR) and 4,012 camps for
Internally Displaced People (IDP).
camps > 500
< 5
of camps
60 70
< 0 10 50
30 40
Days with temperature exceeding 35°C in 2041–2060, relative to 1850–1900
CMIP6, SSP2-8.5
80 90 >100
FigureBox8.1.1 | The global distribution of the United Nations High Commissioner for Refugees (UNHCR) refugee and internally displaced people
(IDP) settlements (as of 2018) overlaid on a gridded map of the days predicted to exceed safe temperature thresholds for human health in the
coming decades (2041–2060 under SSP2 8.5). Semi-circles indicate the presence of refugee and IDP camps in grid cells, with darker semi-circles depicting increasingly
dense concentrations of settlements. Darker background colors indicate increasingly unsafe conditions. Regions of concern include the southern edge of the Sahel, and the
northern edge of the Levant
Box8.1 (continued)
Poverty, Livelihoods and Sustainable Development Chapter 8 Interactions Between Climate Hazards and Social-
ecological Thresholds
Climate change threatens to rapidly transform unique and threatened
ecosystems (Reasons for Concern RFC1), such as tropical rain forests,
coral reefs, arctic and high-mountain ecosystems, as well as the
indigenous and forest-dwelling people whose livelihoods, cultures
and identities are dependent on these ecosystems. In recent years, the
case of Amazonia has illustrated how such systems are transforming,
with detrimental consequences for Indigenous Peoples, and the vital
role that Indigenous Peoples serve in protecting vulnerable ecosystems
(Ricketts etal., 2010; Box8.6). Globally, indigenous territories cover
the greatest area of remaining tropical forest in comparison to other
protected areas. They encompass the bulk of Earth’s biodiversity and
are the locus for a number of key ecosystem services across spatial and
temporal scales (Walker etal., 2020). Specifically, in 2014 indigenous
territories and other protected areas represented the equivalent of
58.5% of all the carbon stored in the Brazilian Amazon biome and had
the lowest deforestation rate (2.1%) and fire incidences, evidencing the
effectiveness in safeguarding important ecosystems services and well-
being (Nogueira etal., 2018). It is estimated that indigenous territories
in the Brazilian Amazon contribute at least USD5billion each year to the
global economy through food and energy production, GHG emissions
offsets, and climate regulation and stability (Siqueira-Gay etal., 2020).
Given the high incidence of poverty of Amazonian countries and high
proportion of traditional and Indigenous Peoples, remoteness and
neglected governance place these unique ecosystems and indigenous
populations as highly vulnerable to climate change impacts (Pinho
etal., 2014; Brondízio etal., 2016; Mansur etal., 2016; Kasecker etal.,
2018). Despite their importance, the survival of Indigenous Peoples in
the Amazon is on the brink in the wake of increasing deforestation,
land conflicts and invasions, cattle ranching, mining, fire incidence,
health problems and human rights violation (Ferrante and Fearnside,
2019). There is robust evidence that both economic and non-economic
L&Ds are currently, and will be, unevenly experienced by populations
in vulnerable conditions, such as children, women, Indigenous Peoples
and traditional communities (Pinho, 2016; Lapola etal., 2018; Roy etal.,
2018; Eloy etal., 2019; Machado-Silva etal., 2020). Increasing wildfires
inside protected areas, in particular, territories of Indigenous Peoples
and traditional communities, is worrisome and presents challenges for
the future of unique and threatened socio-ecological systems, and the
ecosystem services they provide. The Amazonian indigenous territories
and protected areas can deliver protection of biodiversity and important
ecosystem services if appropriate governance mechanisms are in place
and their land tenure rights and livelihoods are secured (Steege etal.,
2015). The role of enabling environments is discussed in Section8.5. Linkages Between Climate Change Impacts and
Sustainable Development Goals
Many of the observed outcomes of climate change, for example,
migration, are also outcomes of multidimensional poverty in low-
income countries (Burrows and Kinney, 2016). Future impacts may be
better understood if the vulnerability and the capacity for adaptation
is understood to be rooted in a sustainable development context (see
Box8.2). The UN Sustainable Development Goals (SDGs), which aim
to reduce poverty and inequality, and identify options for achieving
development progress, also provide insight on reducing climate
vulnerability (United Nations, 2015). First, climate change impacts may
undermine progress toward various SDGs (medium confidence), primarily
poverty reduction (SDG1), zero hunger (SDG2), gender equality (SDG5)
and reducing inequality (SDG10), among others (medium evidence,
high agreement). In both developing and high-income countries,
climate change hazards in connection with other non-climatic drivers
already accelerate trends of wealth inequality (SDG 1) (Leal Filho etal.,
2020b). Climate impacts on SDGs illustrate the complex interrelations in
development. For example, in regions encountering obstacles to SDGs,
characterised by high levels of inequality and poverty, such as in Africa,
Central Asia and Central America, climate change is exacerbating water
insecurity (SDG 6), which may then also drive food insecurity (SDG 2),
impacting the poor directly (e.g., via crop failure), or indirectly (e.g., via
rising food prices) (Conway etal., 2015; Hertel, 2015; Cheeseman, 2016;
Rasul and Sharma, 2016). There is a pressing need to address poverty
issues, since these may negatively influence the implementation of all
SDGs (Leal Filho etal., 2021a).
At the same time, there is increasing evidence that successful adapta-
tion depends on equitable development and climate justice; for exam-
ple, gender inequality (SDG 5) and discrimination (SDG 16) are among
the barriers to effective adaptation (high confidence) (Bryan et al.,
Box8.2 | Livelihood strategies of internally displaced atoll communities in Yap
On Yap Island in the Federated States of Micronesia, displaced atoll communities have been under considerable pressure due to climate
change. This is because of the island’s vulnerability, as a result of its weak economic status, and the little access it has to technologies that
may support adaptation efforts. This trend is seen in many SIDS (see also Chapter 15). On small islands and remote atolls where resources
are often limited, recognising the starting point for action is critical to maximising benefits from adaptation. They do not have uniform
climate risk profiles, and not all adaptations are equally appropriate in all contexts (Nurse etal., 2014) (high confidence).
The recurrences of natural hazards (e.g., El Niño-driven tropical storms, associated coastal erosion and saltwater or seasonal droughts
leading to water scarcity) and crises threaten food and nutrition security through impacts on traditional agriculture, leading to income
losses and causing the forced migration of coastal communities to highlands in search of better living conditions. As many of the
projected climate change impacts are unavoidable, implementing some degree of adaptation becomes crucial for enhancing food and
nutrition security, strengthening livelihoods, preventing poverty traps and increasing the resilience of coastal communities to future
climate risks (Krishnapillai, 2018).
Chapter 8 Poverty, Livelihoods and Sustainable Development
With support from the US Department of Agriculture and the US agency for International Development, the Cooperative Research
and Extension wing of the College of Micronesia- Federated States of Micronesia Yap Campus has been providing outreach, technical
assistance and extension education to regain food and nutrition security and stability. They have done this by improving the soil and
cultivating community vegetable gardens, as well as indigenous trees and traditional crops. This programme implemented a three-
pronged adaptation model to boost household and community resilience under harsh conditions on a degraded landscape, hence
addressing poverty risks and promoting more sustainable livelihoods (Meyer and Jose, 2017).
The following three strategies: (a) gender-focused capacity development on soil health management, (b) good practices in sustainable
land management (SLM) and (c) income-generation activities were employed to mitigate crop production losses and increase resilience
to climate-influenced hazard events within the 258 ha of degraded lands in Gargey Village.
The project first focused on increasing the capacity development for 1100 residents of Gargey Village, including women and youth, in order
to create a base of community knowledge for soil health management. Training on soil health management including the following: use of
cover crops and improved fallow, legumes, composting and agroforestry systems, mulching, minimum tillage and contour farming, as well as
altering production practices (planting time, spacing, pest and disease treatment, harvesting time), alternative crop production methods
(container gardening, raised-bed gardening, small-plot intensive farming), hands-on training on compost preparation and seed germination.
Dissemination and use of good practices in sustainable land management
Following capacity building, the project trained villagers in the use of SLM practices to further soil resilience during ongoing and acute
precipitation events. The SLM practices focused on volcanic soil management and compost preparation and use, along with the planting
of native trees and crops. The protective soil cover was improved through cover crops, crop residues or mulch, and crop diversification
through rotations. Local salt-tolerant crop varieties were introduced. Seed packets and seedlings were distributed to ensure a continuous
supply of resilient traditional plants and to provide for sustainable post-disaster recovery.
Income-generation activities
The project also included training to increase the incomes of households by training household members in the cultivation of vegetables
using various alternative crop production methods. Households were then able to sell their vegetables in the local markets.
Less hunger and more cash from leafy vegetables is a concept adopted at the household level to not only reduce poverty, but also to
empower displaced communities to address the issue of malnutrition. Practices include growing a variety of nutritious vegetables as part
of a large crop portfolio and using alternative crop production methods, such as small-plot intensive farming using container gardening or
raised-bed gardening (Krishnapillai and Gavenda, 2014). In addition, focusing efforts on increasing the sustainable production of staple
crops confers significant nutritional benefits.
More households in the settlements are consuming vegetables since home gardeners started harvesting regularly and sharing their
produce with extended families or selling them to generate income. The location-specific, community-based adaptation model improved
food and nutrition security and livelihoods (Krishnapillai, 2017). People can access more nutritious and reliable food sources, and they are
growing their own food and selling their surplus, creating new optimism about their future.
The climate-smart agriculture (CSA) package increased land cover by more than 50% within Gargey Village. This includes the planting of
42 varieties of native trees and crops. Current major crops that are being successfully grown at this location include coconut, breadfruit,
mango, noni, chestnut, pineapple, sugarcane, land taro, tapioca and sweet potato. There have been additional benefits in terms of
improvement in water availability. These activities have directly benefited the resilience and food security of more than 1000 residents
in Gargey Village, and lessons learnt from this project have helped to scale up similar projects at three locations in Yap that have
experienced equivalent climate-damaging processes.
Overall, this case study illustrates the benefits of promoting resilient crop production in Gargey Village, as an example of displaced atoll
communities. Innovative and sustainable CSA strategies have offered broader insights and lessons for enhancing adaptive capacity
and resilience, on a degraded landscape. The coherent strategies and methods employed have strengthened livelihood opportunities by
improving access to services, knowledge and resources. By its concurrent focus on enhancing food security through traditional crops,
coupled with nutrient-rich vegetables, promoting rainwater harvesting systems and water conservation, and promoting resilient household
livelihood opportunities, atoll communities brought together crucial elements needed to reduce vulnerabilities and to better cope with
disasters and climate extremes, while embracing the traditional culture. The location-specific yet knowledge-intensive CSA methods
deployed, offered opportunities for atoll communities to revitalise themselves, overcoming barriers while adjusting to new landscapes.
Box8.2 (continued)
Poverty, Livelihoods and Sustainable Development Chapter 8
2018; Onwutuebe, 2019; Garcia etal., 2020). Likewise, both climatic
and non-climatic threats to development, such as conflict (SDG 16),
may seriously undermine capacity to formulate and implement adap-
tation policies, and design planning pathways (Hinkel etal., 2018).
The risk of conflict associated with climate change has great potential
to undermine other development goals (Box8.4). Where sustainable
development lags and human vulnerability is high, there is also often
also a severe adaptation gap (Figure8.12; Birkmann etal., 2021a). The
SDGs may provide important cues on how to close the adaptation gap:
climate action needs to be prioritised where past and future climate
change impacts threaten SDGs, and where investment in SDGs im-
prove capacity for adaptation (see Section8.6).
8.2.2 Poverty–Environment Traps and Observed
Responses to Climate Change with Implications for
Poverty, Livelihoods and Sustainable Development
Across all geographical regions, there is evidence that anthropogenic
climate change is hindering poverty alleviation and thereby constraining
responses to climate change in five main ways:
By worsening living conditions (Hallegatte et al., 2017; Hsiang
etal., 2017)
By threatening food and nutrition security due to undernutrition and
reduced opportunities for income generation (Burke etal., 2015)
By disrupting access to basic ecosystems services such as rainwater,
soil moisture (reducing the productivity of agricultural land) or via
the depletion of habitats (e.g., mangroves, fishing grounds) that
particularly vulnerable and poor people are depending on (Malhi
etal., 2020)
By creating favourable conditions for the spread of vector-
transmitted diseases (Liang and Gong, 2017)
By threatening underlying gender inequalities exacerbated by
climate impacts, such as access and control to productive inputs
and reinforcing social-cultural norms that discriminate against
gender, age groups, social classes and race (Singh etal., 2019b).
Responses to observed impacts such as glacier melt, sea level rise
and increases in the frequency of extreme weather events such as
droughts, hurricanes and floods need to take into account how they
influence other policy issues and sectors, including poverty alleviation,
human health and well-being (Orimoloye etal., 2019), water/energy
and the built environment (Andrić et al., 2018), transportation and
mobility (Markolf etal., 2019), agriculture (Hertel and Lobell, 2014)
and biodiversity/ecosystems (Nogués-Bravo et al., 2019), only to
mention a few. Recent literature provides evidence that impacts of
climate change together with non-climatic drivers can create poverty–
environment traps that may increase the probability of long-term and
chronic poverty (Figure8.4; Hallegatte et al., 2015; Djalante et al.,
2020; Malhi etal., 2020; McCloskey etal., 2020) (high confidence).
Schematic representation of a poverty-environment trap that can increase chronic poverty
Figure8.4 | Schematic representation of a poverty–environment trap that can increase chronic poverty.
Chapter 8 Poverty, Livelihoods and Sustainable Development
In addition, observed climate change responses, including autonomous
and planned adaptation, can exacerbate poverty and vulnerability
(Eriksen etal., 2021). There is robust evidence that planned responses
to climate change, such as large-scale adaptation projects, in some
context can also increase vulnerability due to the reinforcement of
inequalities and the effects of further marginalisation (Fritzell etal.,
2015; Eriksen et al., 2021). There is increasing evidence that the
responses to indirect impacts of climate change, such as to shifts in
marine or terrestrial ecosystems due to climate change (Seddon etal.,
2016) also affect different groups differently and impact poverty
and livelihood security. Apart from influences on agriculture trends
(Reichstein etal., 2014) and changes in yields (Reyes-Fox etal., 2014;
Craparo etal., 2015), climate change has significant (direct and indirect)
impacts on livelihood assets and resources such as forests, livestock
production and fisheries, which may undermine the livelihoods security
in the medium and long run. Characteristics of Responses
Many of the observed responses to climate change aim to reduce
exposure of people to climate-related hazards, such as flood defences,
sea walls and embankments (Gralepois et al., 2016), rather than
aiming specifically to address structural vulnerability to climate
change, which means the root causes of vulnerability (e.g., Mikulewicz,
2020; McNamara et al., 2021a). Evidence suggests that responses
to the impacts of climate change should consider the physical
climate event, and also historical and institutional root causes that
make people or systems vulnerable. However, addressing structural
vulnerability must be balanced with the political context and the
range of options available to people, communities or countries (see
Section 8.3). Political frameworks need to consider both types of
responses, to revive democratic debate and citizenship (Pepermans
et al., 2016). In addition to reducing poverty and vulnerability,
planned climate change responses must also be intersectoral, in order
Box8.3 | COVID-19 pandemic
During the COVID-19 pandemic, countries such as India were affected by hydro-meteorological hazards (Raju, 2020) making it extremely
difficult to handle a public health crisis in the context of compounding risks and cascading hazards (Phillips etal., 2020). The COVID-19
pandemic can increase the adverse consequences of climate change since it has the potential to delay some key adaptation actions. On
the other hand, the pandemic also highlights the importance of better preparedness to the impacts of climate change (Djalante etal.,
2020). Overall, the COVID-19 pandemic has worsened the economic situation within many countries and local communities particularly
for already marginalised groups (Gupta etal., 2021). The accumulation of crises, such as the COVID-19 pandemic alongside climate
change impacts, underscore the fact that stressors do not occur in isolation, but are interlinked, with clear implications for structural
vulnerability and adaptation options available to the poorest (Sultana, 2021). Responses to COVID-19 have led to significant economic
and social distress within and across societies and local communities, especially in poorer countries. The direct health and economic
impacts of the lockdowns have further limited the ability of many people across the developing world to pursue income-generating
activities, and sustain livelihoods that are already affected by climate hazards. In addition, poor or most vulnerable groups face further
marginalisation due to misinformation that these groups transmit the virus to other wealthier groups and areas. The pandemic has
intensified inequalities in both developing countries (FAO, 2020) and in industrialised nations (Anderson etal., 2020; McCloskey etal.,
2020), whereby vulnerable groups are especially affected (Raju etal., 2021). Whereas different models and scenarios contain different
data and figures, there is high agreement that it is likely that socioeconomic impacts are particularly severe within selected global regions
and areas that are already characterised by a rather high level of human vulnerability (see also Section8.3). This also implies that the
capacity of people to prepare for present and future climate change impacts will further decrease within these countries and population
groups under the direct and indirect consequences of the COVID-19 pandemic.
Moreover, the COVID-19 pandemic is not only influencing climate change research (Leal Filho etal., 2021b) but is also influencing the
capacities of governmental institutions and nations to support planned adaptation and poverty reduction favouring the most vulnerable
groups, since the crisis also means among other issues a significant reductions in tax revenues (Clemens and Veuger, 2020). COVID-19
may also force people to seek alternative sources of income that can lead to the further erosion of long-term adaptive capacities. In many
settings, the pandemic has had significant impact on businesses and SMEs (Schmid etal., 2021). The important role of governmental
support for buffering crises and periods of income loss of individual households (e.g., unemployment) and private businesses (e.g.,
SMEs) has also been demonstrated during the COVID-19 pandemic in Organisation for Economic Co-operation and Development (OECD)
countries (OECD, 2020b).
Livelihood disruptions and an increasing probability of higher levels of poverty and of structural vulnerability in various countries have
already been observed (Laborde etal., 2020b). These vulnerabilities and the new layers created by the pandemic must be seen with an
intersectional lens (Raju, 2019; Sultana, 2021).
In addition, the COVID-19 pandemic has also revealed the unequal access to vaccine and the importance of national state institutions to
buffer negative impacts, for example, of the lock downs or in terms of unemployment. The COVID-19 pandemic recovery also sets some
basis for a stronger narrative towards a green recovery approach (Djalante etal., 2020; Forster etal., 2020).
Poverty, Livelihoods and Sustainable Development Chapter 8
to increase their effectiveness. This requires higher levels of vertical
and horizontal coordination and integration (GIZ, 2019). Horizontal
coordination encompasses, for example, the integrated coordination
of responses to climate change across different sectors, which requires
suitable governance structures and processes that allow for such a
coordination (Di Gregorio et al., 2017; Burch etal., 2019). Vertical
integration is needed in order to ensure that effective responses also
include different levels of governance and benefit from knowledge at
different scales. The inclusion of local knowledge within national or
provincial adaptation strategies requires such linkages and vertical
coordination. Overall, there is an increasing body of literature that
highlights the importance of improved integration and coordination
also in order to promote a higher effectiveness of strategies and an
improved consideration of social justice and climate justice when
designing and implementing responses (Levy and Patz, 2015).
However, evaluating the effectiveness, social impacts and social justice
of climate change responses is not uniform across locations, nations
and regions for three principal reasons:
Temporal dimensions of responses: effective and appropriate
climate change responses require that strategies and responses
are tested in a specific context and that ongoing learning and
adaptive management is a necessary to avoid maladaptation or
other unintended consequences (Eriksen etal., 2021),
Goal of responses: responses may have distinct and locally specific
goals, such as reducing vulnerability (Sarker etal., 2019), which is
distinct from increasing resilience (Alam etal., 2018). Vulnerability
reduction and the increase of resilience (i.e., raising the ability to
cope) are two different goals and often involve different processes.
Level of responses: there is a need to ascertain the relevant level
at which the responses are needed or expected (e.g., the individual
level, community level, regional level). This analysis, however, also
needs to consider the differential capacities of people, for example,
the limited capacities of poor people or constrained capacities of
most vulnerable countries (see also Section8.3).
Effective responses to climate change impacts for one group could
impose higher costs and negative consequences for other groups, in
terms of shifts in exposure and vulnerability. This category of response
is known as maladaptation. Maladaptation actions defined in the IPCC
SR1.5°C (IPCC, 2018b) and in the Land Report (IPCC, 2019a) are the
ones that usually have unintended consequences, and can lead to
increased negative risk to poor population mostly in the Global South
to climate hazards by either increasing GHG emissions or by increasing
the vulnerabilities to climate change with diminished welfare, now
and in the near future (Roy etal., 2018). For example, migration to
urban centres can represent a significant adaptation opportunity for
the migrants themselves, but can also increase the vulnerability of
their community of origin or destination (e.g., through a depletion
of the workforce or an addition pressure on environmental resources
and infrastructure respectively) (Gemenne and Blocher, 2017). Some
types of observed responses to climate change may not yield long-term
benefits. For example, food imports during droughts or adverse climate
conditions are not a fully adequate response, since they may alleviate
a problem on the one hand (i.e., an imminent food shortage due to
crop failure) but, on the other, lead to no long-lasting improvements
in physical conditions and create new dependencies that can increase
vulnerability in the long run (Zimmermann etal., 2018).
In the AR5, the maladaptation outcomes emerge when climate change
impacts and risks are disproportionately born by the poorest populations
(Olsson etal., 2014). Since then, most maladaptation evidence emerges
as a consequence of failure to address root causes of vulnerabilities
that emerge under high and multiple forms of inequalities. In fact,
the literature shows that adaptation practices can indeed redistribute
vulnerabilities and increase risks to already poor and marginalised
people with risk to maladaptation outcomes mainly in the Global South
countries (Atteridge and Remling, 2018).
The maladaptation outcomes also emerge when responses are not
equitable at the policy level, and exacerbate the precarity of vulnerable
populations by excluding them from benefits and support, while
attending to the needs of people of the most enfranchised segments
of society (Thomas and Warner, 2019; Asplund and Hjerpe 2020). In
Tanzania, the political marginalisation of pastoralist access to critical
riparian wetlands and increasing expansion of agriculture may result
in adaptation pathways that heighten risk for these groups, while
reducing risk for others (Smucker et al., 2015). Salim et al. (2019)
found that adaptation to flooding in Jakarta privileges political
economic elites, while poor infrastructure in poorest neighbourhoods
exacerbates loss of assets, housing and displacements (Salim etal.,
2019). In Bangladesh, intense and consecutive flooding led to national
and regional adaptation plans, that resulted in maladaptive trajectories
as local poverty context and precarities of properties were not carefully
considered and disconnected from local autonomous practice (Rahman
and Hickey, 2019).
Overall, the assessment shows that understanding impacts of climate
change should not be limited to the analysis of direct impacts or
physical changes under different climatic conditions, but needs also
account for the distributional effects that responses to climate change
may imply. For example, responses implemented in order to benefit one
sector or social group (e.g., farmers), should not undermine the well-
being of others (e.g., pastoralists). Documented cases of maladaptation
(see Eriksen etal., 2021) hint that responses to climate change can
exacerbate existing inequality in some cases and may discourage other
types of responses (see also Sections8.5; 8.6). Furthermore, responses
to similar climate change impacts and hazards may be extremely
differentiated according to various social contexts (see Section8.3). In
some cases, responses to climate change (e.g., relocation programmes)
can even trigger social tipping points when climate change responses
lead to major social transformations, such as forced displacement (see
Also the influence of new global phenomena, such as urbanisation,
issues of urban health (Schmid and Raju, 2020) and the consequences
of the COVID-19 pandemic need to be considered when assessing
actual and potential consequences of different responses to climate
change. For example, inequalities, vulnerabilities and poverty pockets
are expected to change and increase, particularly in urban areas in
countries with rapid urbanisation processes and high levels of poverty
(Djalante etal., 2020), hence urban and urbanisation trends need more
attention. Urbanisation processes add another level of complexity (Raju
Chapter 8 Poverty, Livelihoods and Sustainable Development
Box8.4 | Conflict and governance
Climate change impacts carry the risk of amplifying or aggravating existing tensions within and between communities or countries
(Sakaguchi etal., 2017). There is, however, limited evidence for a universal direct causal linkage between climate change and violent
conflicts (Mach etal., 2019). The triggering of conflicts related to climate impacts is strongly determined by contextual factors, such as
the type of government or the level of development (Mach etal., 2019). A study of 156countries (Abel etal., 2019) showed that an
increase in periods of drought exacerbate the risk of conflict, especially in democratic countries. This influence was particularly marked
during the period 2010–2012 in countries of western Asia and northern Africa that were undergoing political transformations, such as the
Arab Spring. Conflict can then represent people’s discontent in governments’ inefficient responses to climate impacts (Abel etal., 2019).
Research has noted conditions under which climate change can increase the risk of armed conflict, which includes ethnic exclusion,
agricultural dependence, large populations, insufficient infrastructure, dysfunctional local institutions and low levels of development (von
Uexkull etal., 2016; Ide etal., 2020).
Since the AR5, there is robust evidence of the socially destabilising measures and high-risk income alternatives that the world’s poorest
commonly take to cope with the impacts of climate change on livelihoods (Blattman and Annan, 2016). To avoid impoverishment,
households often pursue risky livelihood alternatives, with high potential for return on investment (Sovacool etal., 2018), but which in
some cases undermine environmental quality (Bolognesi etal., 2015), violate laws (Ahmed etal., 2019), contradict social norms (Hagerman
and Satterfield, 2014), erode institutions (Sovacool etal., 2018), or affect intra- and inter-community cooperation (Nadiruzzaman and
Wrathall, 2015). At the same time, a narrowing of livelihood options carries a strong potential for participation and association with
violent non-state organisations and movements, either criminal or ideological (Nett and Rüttinger, 2016). In order to reduce the risk of
instability and violence associated with climate change, a broadening of livelihood options among the most vulnerable people appears
to be an effective policy approach (Miguel etal., 2004).
The determinants of violence in the context of climate shocks are primarily poor institutional planning and response to impacts, such as
the capacity of a government to respond to and manage environmental risk (Selby etal., 2017). In Latin America, for example, evidence on
social conflicts related to disputes over access to water in the context of drought and decreasing water availability point to institutional
failures, such as poor, inequitable or corrupt water governance (Poupeau etal., 2017). Such observations are not confined to low-income
countries. In industrialised countries, failure of governments to address climate change is likely to fuel discontent, a condition in which
violent outcomes are possible (Ide etal., 2020).
In this regard, specific attention ought to be paid to how responses to climate change exacerbate inequalities within societies and create
tensions between different groups—typically between those who are able to protect themselves from climate change impacts and those
who do not have sufficient resources or are not prioritised in the responses to climate change. Frequently the possibility of migration
from climate change is conflated with conflict outcomes from climate change; however, there is limited evidence and low agreement
that climate change and migration will result in increased conflict (Okpara etal., 2016b), while there is robust evidence and medium
agreement that climate change can exacerbate existing tensions, which can in turn result in political violence and an increase in asylum-
seeking (Marchiori etal., 2012). In the future, conflict in the context of climate change impacts may increase the number of migrants
seeking asylum, although at present there is scant empirical evidence for this (Schutte etal., 2021). Recent evidence also provides
support for social conflict around inequitable climate mitigation policy as well (e.g., fossil fuel subsidies and emissions reductions targets)
(Rentschler, 2016).
In recent years, research on the climate–security nexus has developed considerably, and has highlighted risks pertaining to conflicts,
geo-political rivalries, critical infrastructure, terrorism or human security (Gemenne etal., 2014). While different studies have identified
strong past correlations between climatic variations (of temperature and rainfall in particular) and the occurrence of violent conflicts
(Hsiang etal., 2013), others have stressed the need for stronger explanatory models or the risk of a selection bias (Benjaminsen etal.,
2012; Solow, 2013; Buhaug etal., 2014).
While climate change may increase armed conflict risks in certain contexts (Mach etal., 2019), responses to climate change will be crucial
to mitigate these risks. Poor institutional responses can directly drive violence, and there is robust evidence that inequitable responses
further exacerbate marginalisation, exclusion or disenfranchisement of some populations, which are commonly recognised drivers of
violent conflict.
Robust evidence suggests environmental problems (related to climate change) can be dealt with cooperatively, hence leading to more
positive and peaceful relations between groups (Wolf etal., 2003; Ide, 2019). To avert violent outcomes induced by climate change,
stronger local and national climate adaptation institutions within vulnerable societies, and stronger cooperative resource governance
mechanisms between vulnerable countries (such as transboundary water governance agreements) are needed.
Poverty, Livelihoods and Sustainable Development Chapter 8
etal., 2021). This is particularly the case in rapidly growing medium-
sized cities in Africa that at present do not have sufficient resources to
cope and adapt, and to implement climate-sensitive land use planning
(Birkmann etal., 2016).
Tables8.1 and 8.2 present a summary of a set of common climate change
responses observed, classified according to their main approach. All
these responses demand a certain level of commitment, the support of
adequate policies and enough budget for their implementation (Archie
et al., 2018). The observed climate change adaptation responses—
differentiated along urban and rural settings—underscore the very
different nature of various responses and the need for cross-sectoral
While Table8.1 shows selected adaptation responses, Table8.2 shows
selected mitigation responses that highlight that some mitigation
responses (e.g., increasing energy efficiency) also have a potential
benefit for the poor or more vulnerable groups, for example, through
the reduction of costs for electricity. Both tables underscore that
climate change mitigation and adaptation responses are strongly
interlinked with broader development issues (industrial production,
land use planning, education, etc.) at different scales. Observed Impacts and Implications for Structural
Inequalities, Gender and Access to Resources
This section examines the mutual reinforcement of climate change
impacts and structural inequalities. There is robust evidence that
negative impacts and harm posed by climate change are also a result
of social and political processes and existing structural inequalities
(Sealey-Huggins, 2018). Climate change encompasses unevenly
distributed impacts on women, youth, elderly, Indigenous Peoples,
communities of colour, urban poor and socially excluded groups,
exacerbated by unequal distribution of resources and poor access
for some (Rufat etal., 2015; McNeeley, 2017; Sealey-Huggins, 2018).
Structurally disadvantaged people, who are subject to social, economic
and political inequalities resulting historically from discrimination,
marginality or disenfranchisement because of gender, age, ethnicity,
class, language, ability and/or sexual orientation, are disproportionately
vulnerable to the negative impacts of climate change hazards (Kaijser
Table8.2 | Selected climate change mitigation responses.
Modality of response Impacts on urban communities Impacts on rural communities (e.g., farmers, pastoralists)
Land use planning
(e.g., Frose and Schiling, 2019)
Helps to reduce GHG emissions and support environmental
conservation, preventing urban heat islands
Helps to reduce pressure on the natural resources (deforestation, land
filling, damaging wetland) and promotes carbon sequestration
Improving industrial processes
(e.g., van Vuuren etal., 2018)
Unlocks many opportunities for improvement, including the
optimised use of energy, reuse of waste in production, reducing
GHG emissions, use of biomass and more efficient equipment
In rural settings, industrialisation and technological innovation may
directly assist vulnerable communities through provision of inputs
(e.g., water storage, drip irrigation, forecast information), or reuse of
biowaste in agriculture or energy production, hence reducing costs
and pollution levels
Renewable energy
(e.g., Cronin etal., 2018) Reduction of GHG emissions and reduction of the cost of electricity Some options (e.g., solar, wind) may help to reduce deforestation,
reduce GHG emissions and promote healthier air within households
Energy efficiency
(e.g., Abrahamse and Shwom, 2018)
Efficient end-users’ energy utilisation reduces energy wastage,
reduces costs and lowers carbon emissions
Efficient end-users’ energy utilisation leads to natural resource
conservation and a reduction of GHG emissions
Local/individual actions
(e.g., Shaffril etal., 2018; Tvinnereim
etal., 2018)
Can contribute to reduce carbon footprints
Fosters personal and community motivation to manage individually
and communally owned resources, helps to reduce GHG emissions
and foster resources conservation
Table8.1 |Selected observed climate change adaptation responses in urban and rural areas commonly associated with positive implications for poverty, livelihoods and sustainable
Modality of response Impacts to urban communities Impacts to rural communities (e.g., farmers, pastoralists)
Integrated natural resource management
(e.g., van Noordwijk, 2019) Better conservation of green areas and reduced exposure to floods Conservation of natural resources (e.g., water, soil, pasture, forest,
wildlife, biodiversity, aquatic life)
Disaster risk management
(e.g., Mall etal., 2019)
Pre-disaster risk management and post-disaster risk management
measures reduce loss of life and damage to property
Disaster risk management may play an important role in avoiding or
limiting the impacts of floods, droughts and other extreme events
Physical/structural improvements
(e.g., Vallejo and Mullan, 2017)
Improving physical/structural measures to prevent property damage
and foster ecosystems integrity
Flood defences may help to prevent property losses, planting of trees
may stabilise slopes, reduce soil erosion and siltation, rainwater
harvesting increases water availability, protection of biotopes
supports biodiversity
Relocation of vulnerable communities
(e.g., McNamara and Des Combes, 2015)
Moving vulnerable communities before and during climate-induced
hazards may reduce loss of life
Reduces the exposure of vulnerable communities to climate change
and extremes hazards (e.g., floods and droughts), lessens their
vulnerability, improves access to better resources and builds their
capacity to adjust to a new context
Education and communication
(e.g., Monroe etal., 2017)
Public education and awareness, improved communication may
reduce the damages and losses from adverse impacts of climate
change and from extreme events
Fosters awareness creation, reducing the degree of vulnerability to
certain climate-induced hazards and help build the capacity to adapt
Chapter 8 Poverty, Livelihoods and Sustainable Development
and Kronsell, 2014; Otto etal., 2016). High levels of vulnerability at
national scale (see Section8.3) are often linked to complex histories,
including long-term economic dependencies established and reinforced
in the context of colonisation.
Links between climate change, structural racism and development
are less well established as an element of disproportionate impacts
of climate change (Sealey-Huggins, 2018). Discrimination is not
restricted to structural racism and includes discrimination of all kinds,
including that of gender and caste, because of which a considerable
population is directly bound to suffer the harsh impacts of the
climate change. The climate change and gender literature has come
a long way in demonstrating concrete examples of how structural
inequalities operate. The political and micro-political aspects and
how they interact with structural inequalities are also important to
understand vulnerability. Henrique and Tschakert (2020) shows how
the many adaptation efforts benefit powerful actors, while further
entrenching the poor and disadvantaged in cycles of dispossession.
This critical analysis recommends acknowledging injustices, embracing
deliberation and nurturing responsibility for human and more-than-
human others. Garcia etal. (2020) describes the socio-political drivers
of gendered inequalities that produce discriminatory opportunities for
adaptation. They use an intersectional subjectivities lens to examine
how entrenched power dynamics and social norms related to gender
create barriers to adaptation, such as lack of resources and agency.
The analysis shows a pronounced dichotomy as women experience the
brunt of these barriers and a persistent power imbalance that positions
them as ‘less able’ to adapt than men.
Historical marginality and exclusion are context-specific conditions that
shape vulnerability (Leichenko and Silva, 2014). There is also robust
evidence that gender inequalities contribute to climate vulnerability,
and that consideration of gender is a key approach to climate justice
(see Cross-Chapter Box GENDER in Chapter 18). There is robust
evidence for the differentiated impacts of climate change and climate-
orientated policies on women (McOmber, 2020). For example, Friedman
etal. (2019) show that, in Ghana, homogeneous representations of
women farmers and a technical focus of climate-orientated policy
interventions may threaten to further marginalise the most vulnerable
and exacerbate existing inequalities. Climate change impacts can also
heighten existing gender inequalities (Jost et al., 2016; Glazebrook
etal., 2020). On the one hand, climate change impacts can be gendered
as a result of customary roles in society, such as triple workloads for
women (i.e., economic labour, household and family labour, and duties
of community participation), and occupational hazards from gendered
work indoors and outdoors (Murray etal., 2016). On the other, climate
change hazards interact with changing gender roles in society, such
as urban migration of both men and women in ways that break with
tradition (Bhatta etal., 2016).
Gender influences the way that people also experience loss and
process psychological and emotional distress of losses, such as
mortality of children and other relatives in climate-related disasters
(Chandra etal., 2017).Women’s capacities are often constrained due
to their roles in their household and society, institutional barriers
and social norms. These constraints result in low adaptive capacity of
women, which make them more vulnerable to hazards. As more men
seek employment opportunities away from home, women are required
to acquire new capacities to manage new challenges, including risks
from climate change. Banerjee et al. (2019b) finds that capacity-
building interventions for women staying behind, which aimed to
strengthen autonomous adaptation measures (e.g. precautionary
savings and flood preparedness), also positively influenced women
to approach formal institutions. Besides, the intervention households
were more likely to invest a part of the precautionary savings in flood
preparedness measures than control households.
Next to the direct differential impacts of climate change on different
social groups, the impacts of climate change can also exacerbate
inequality due to the lower access and limited ability to benefit from
services provided by ecosystems. Marginalised poor people often
significantly depend on the access to surrounding environments,
natural resources and ecosystem services for their livelihoods, for
leisure or cultural practices. Thus shifts in such resources, for example,
due to the bleaching of coral reefs or shifts in fish stock, also cause
severe challenges and risks to these communities (Leal Filho, 2018; Le,
2019; UNTTSDCC, 2014).
Overall, the assessed literature highlights that climate change
impacts are not emerging in isolation from development context and
development pathways. Economic and social ramifications mean that
they may exacerbate poverty and marginalisation (Finkbeiner etal.,
2018; Dogru etal., 2019). Choudhary etal. (2019) and Orimoloye etal.
(2019) highlight how the effects of climate change can be even more
prejudicial to poor countries, which, in most cases, already suffer from
weak governance, high prevalence of informal settlements and lack
of resources. Health, livelihood assets and economy are examples of
aspects that will worsen as a result of the negative impacts of climate
change and failure to provide opportunities for sustainable adaptation
(United Nations, 2015). These facts highlight the importance of
mitigation and adaptation measures especially in these regions
characterised by high levels of vulnerability (see also Section8.3).
8.2.3 Observed Impacts and Responses and their
Relevance for Decision Making
Many countries base their adaptation strategies on National Adaptation
Programmes of Action (NAPAs), which often correlate different levels
of decision making and governance (Golrokhian etal., 2016). Whereas
the involvement of national governments is needed for designing
appropriate responses to climate change, recent studies underscore
the need to also consider IKLK within adaptation and risk reduction
strategies, thus fostering stronger linkages with local communities,
leading to improved vertical integration between different strategies,
programmes and actors (Ford etal., 2016; Vij etal., 2017; Singh etal.,
2020). The relevance of addressing the issue of vulnerability and poverty
to reduce the climate change risks has been demonstrated within the
assessed literature on the impact of climate change (Hallegatte etal.,
2017). In this regard, it is noticeable that not many NAPAs explicitly aim
to reduce poverty, even though poverty reduction is associated with
vulnerability reduction to climate change (Demski etal., 2017).
Poverty, Livelihoods and Sustainable Development Chapter 8
Next to issues of observed impacts and responses to climate change,
it is important to assess observed barriers in implementing climate
change responses. The discussion of barriers is complemented later
in the chapter with an assessment of the enabling environments for
adaptation (see Section 8.5.1). Some of the most common barriers
outlined in the scientific literature are summarised in Table8.3.
There are various characteristics of responses to climate change,
which aim to protect livelihoods and prevent poverty expansion (i.e.,
an enlargement of the group of people already affected by poverty).
Some of them are:
Timely: meaning that responses need to take place within a matter
of weeks or months and not over years (Wise etal., 2014).
Targeted: with a focus on the affected communities and groups,
to help alleviate the pressures they are under (e.g., Aleksandrova,
Sustainable: with long-lasting results leading to self-sufficiency of
the affected communities and their resource base, as opposed to
short-term ones relying on external support (e.g., Caetano etal.,
Integrated: the impact of climate change is multifaceted and far
reaching and requires the engagement of various actors (e.g., the
vulnerable community, government agencies, local and international
nongovernmental organisations, civil societies, media) (Ayal etal.,
Finally, responses such as those outlined in Table8.1 and Table8.2,
need to ensure the active participation of local stakeholders considering
their diverse interests, so that they are grounded in reality. In addition,
responses need to be complemented with operational procedures
and time frames so that they can be more systematically pursued and
implemented (Alves etal., 2020).
8.3 Human Vulnerability, Spatial Hotspots,
Observed Loss and Damage, and
Livelihood Challenges
This section assesses the literature on vulnerability—the assessment
of vulnerability at global and national scales—and explores economic
and non-economic losses of people and livelihoods exposed to and
impacted by climate change. The section examines how climate change
threatens livelihoods and juxtaposes global and local level assessments
of vulnerability based on empirical data at different scales. The analysis
of recent literature underscores that climate change impacts and
adaptation needs cannot be understood by looking at climate change
only. Vulnerability and livelihood security are seen as an important
component for understanding the human dimension of climate change
(Rhiney etal., 2016; Cardona, 2017; Byers etal., 2018; Eriksen etal.,
2020; Wisner, 2020; Birkmann etal., 2021a; Cole etal., 2021).
Linkages between global and individual vulnerability and livelihood
security, including aspects of intersectionality are also assessed.
Overall, this Section8.3 reveals that different countries, societies and
specific groups within a society have very different starting points on
their move towards climate resilience.
8.3.1 Assessments of Risk and Vulnerability
Conventional assessments of risks and the benefits of adaptation and
risk reduction measures in the context of climate change primarily
focus on the financial value of the avoided losses (in USD) and the
assets that are going to be protected from adverse consequences
of climate change or extreme events due to specific measures (e.g.,
dyke construction). Even though these assessments fall short of
measuring the real costs of addressing climate change impacts (see
DeFries etal., 2019), they often support the definition of priorities
in terms of protecting economic values and assets. However, these
Table8.3 | Some common barriers in implementing climate change responses and their implications.
Dimensions Barriers in implementing effective
climate change responses Implications
Governance Unfavourable political frameworks (Gupta, 2016) Governance structures can undermine autonomous adaptation (Section8.4; Table8.6); inability to
include gender differentiated vulnerabilities in governance schemes (Bryan etal., 2017)
Social Attitudes to risks and cultural values may hamper responses
(Billi etal., 2019)
Social norms of reciprocity and cohesion may erode as a consequence of climate change
responses (Volpato and King, 2019); socio-cultural conditions as key barriers to gender
differentiated support to impact reduction (Bryan etal., 2017)
Institutional Limited availability coordination and prioritisation processes
(Patterson and Huitema, 2019) Lack of anticipatory risks undermining local efforts to cope with hazards (Singh etal., 2019a)
Behavioural Psychological distress may cause insecurity and behaviour of
some groups may increase vulnerability (Van Lange etal., 2018)
Psychological distress associated with loss of attachment to a place has also been observed
among vulnerable communities in regions such as South Asia (Maharjan etal., 2020)
Financial Limited financial resources to support adaptation projects
(Khan etal., 2019)
Lack of financial resources and assets among urban poor increase their exposure and
vulnerabilities to the increasing climate hazards (Salim etal., 2019)
Structural Unsuitable infrastructure may increase exposure
(Chinowsky etal., 2015; Vallejo and Mullan, 2017)
Structural marginalisation of Indigenous Peoples and their IKLK can exacerbate risks of
maladaptation among SIDS countries (McNamara and Prasad, 2014; Aipira etal., 2017;
Granderson, 2017); infrastructure projects to adapt to climate change impacts may increase the
vulnerability of poor slum people
Technical Lack of access to technologies which may support adaptation
(e.g., climate services) (Bel and Joseph, 2018)
The highest level of illiteracy among women prevent their engagement to access technology and
risk reductions in vulnerable communities (Balehey etal., 2018)
Chapter 8 Poverty, Livelihoods and Sustainable Development
assessments do not sufficiently account for how climate change
impacts and imposes risks on poor people, nor do they capture issues
of climate justice and more complex societal impacts and future
risks. For example, various observed losses in the context of climate
change cannot be sufficiently expressed in terms of an economic value
(see Section8.3.5), but these items or assets are highly relevant for
various people with limited economic resources (Hallegatte et al.,
2017). Consequently, the assessment of risks from climate change
facing particularly poor people requires comprehensive assessments
of human vulnerability, resilience and the impacts of climate change
on human well-being going beyond a simple temperature–societal-
impact understanding. Knowledge about methods and approaches to
assess human or human–environmental vulnerability and livelihood
security, including aspects of intersectionality, is important in order
to explore whether or not adaptation and development programmes
are able to reduce vulnerability. The body of literature on these issues
has grown significantly since the AR5 publication (IPCC, 2014a; Moser,
This literature underscores that approaches to assess resilience,
vulnerability and human well-being include global assessments that
can inform strategies and priority settings for adaptation and risk
reduction in the context of climate change (high confidence) (WHO,
2014b; Young etal., 2015; Feldmeyer etal., 2017; GIZ and BMZ, 2017;
Hallegatte etal., 2017; Birkmann etal., 2021a; Garschagen etal., 2021;
Toolkit, 2021).
These quantitative global assessments that have emerged within
the last decades have not been sufficiently assessed in former IPCC
reports, for example, in terms of the agreement on spatial hotspots or
in terms of regional clusters of vulnerability and the linkages between
past societal impacts and levels of vulnerability. The assessed literature
shows that conditions and phenomena that characterise systemic
vulnerability (hazard independent vulnerability), such as high levels of
poverty and gender inequality, limited access to basic infrastructure
services or state fragility are highly relevant for understanding societal
impacts of climatic hazards and future risks of climate change (e.g.,
Cutter etal., 2003; ADB, 2005; Cutter and Finch, 2008; World Bank,
2008; UNISDR, 2009; Crawford etal., 2015; Rufat etal., 2015; Carrao
et al., 2016; Gupta, 2016; Rahman, 2018; Andrijevic et al., 2020;
Jamshed et al., 2020a; Feldmeyer et al., 2021; Garschagen et al.,
2021). These factors and context conditions also influence individual
vulnerability at household or community level. Access to basic services,
such as water and sanitation, are linked to human rights and if not
granted increase the likelihood that people disproportionately suffer
from climate-induced hazards, due to their pre-existing lack of access
to such services. In addition, increasing climate hazards further
constrain the access to such services (United Nations, 2018; Kohlitz
etal., 2019; Gupta etal., 2020).
There is an increasing evidence base that successful adaptation and risk
reduction strategies need to acknowledge not only climate change and/
or specific climate hazards (sea level rise, flooding, droughts, etc.), but
also human vulnerability and existing adaptation gaps and thereby the
different starting points that societies or different groups have towards
climate resilience (see UNEP, 2016; Birkmann et al., 2021a). Recent
reports underscore that development and capacity indicators are useful
to assess the broader adaptation challenges and adaptive capacities
at global scale independent of a specific climatic hazard. Examples
include the percentage of population with access to improved water
sources and improved sanitation, the number of physicians per 1000
people or the dependency ratio (UNEP, 2018). These indicators are also
part of more comprehensive vulnerability assessments, such as those
assessed within this section namely the vulnerability components of
the INFORM risk index (e.g., INFORM, 2019) and of the WorldRiskIndex
(e.g., Birkmann and Welle, 2016; Birkmann etal., 2021a; Feldmeyer
etal., 2021). Recent literature underscores that measuring vulnerability
is key for assessing factors that significantly determine actual and
future adverse consequences of climate change and complex risks
(Cutter and Finch, 2008; Cardona etal., 2012; de Sherbinin etal., 2019;
Peters etal., 2019; Jamshed etal., 2020c; Visser etal., 2020; Feldmeyer
etal., 2021). However, there is also important critique on indicator-
based assessments of vulnerability (see de Sherbinin etal., 2019; Rufat
etal., 2019; Visser etal., 2020), particularly with regard to issues of
validation and its use in decision-making processes. Nevertheless,
we observe an emerging agreement in the literature that resilience
building and adaptation to climate change has to be informed by
climate and multidimensional assessment of the vulnerability of
people, different groups and coupled human–environmental systems,
including both quantitative and qualitative assessment approaches
(IPCC, 2014b; UNEP, 2018; Singleton etal., 2021; Birkmann et al.,
2022). Since, interdependencies between regional (supranational/sub-
continental), national, community and individual vulnerability have
often been overlooked, this chapter assesses both global and regional
vulnerability, as well as local livelihood vulnerabilities.
While past research regarding the nexus between climate change and
poverty often focused on vulnerable groups in rural areas of low-income
countries (de Sherbinin, 2014; IPCC, 2014a; Barbier and Hochard, 2018),
new global mega-trends, such as urbanisation, underscore the need to
assess both rural and urban communities and their vulnerability. In
many rapidly growing cities in the Global South, access to land and
to housing is a challenge, particularly for the poor and marginalised,
contributing to a further increase in informal settlements that often
emerge in highly hazard-exposed areas (Jeschonnek etal., 2014; Rana
etal., 2021). In addition, migration from rural areas to urban centres,
also due to increasing adverse impacts of climate change on rural
livelihoods, can add another level of complexity (Flavell etal., 2020).
Moreover, the context in which such urbanisation processes take place
is key. For example, rapidly growing medium-sized cities, for example
in West Africa, often do not have sufficient financial, technical and
institutional resources to adapt urban structures to climate change
(Birkmann and Welle, 2016; Birkmann etal., 2016; de Sherbinin etal.,
2017). Hence, vulnerability in urban contexts is an emerging issue for
international, national and local adaptation programmes. Rather than
focusing on mega-cities and their exposure as primary hotspots, more
attention has to be given to rapidly growing small- and medium-sized
cities and their adaptation needs from the perspective of vulnerability
reduction and poverty.
Poverty, Livelihoods and Sustainable Development Chapter 8
8.3.2 Global Hotspots of Human Vulnerability to Climate
Change Hotspots and Spatial Patterns of Multidimensional
The assessment of literature published since the AR5 suggests that
alongside already deteriorated specific conditions that determine
individual vulnerability and livelihood security to climate change (see
Section8.2), high levels of poverty, lack of access to basic services
(human rights to water and sanitation), poor governance and conflicts
are important factors that characterise vulnerability and systemic
human vulnerability in particular (EC-DRMKC, 2020; Wisner, 2020;
Feldmeyer et al., 2021; Garschagen et al., 2021; GIZ, 2021). These
context conditions within a country or region limit the access to
effective adaptation options particularly for the poor and marginalised
Recent studies underscore that human vulnerability—thus the
predisposition to be adversely affected—is largely determined by past
and present development processes, rather than by the occurrence of
individual events (Wisner, 2016; Cutter, 2018; Birkmann etal., 2020).
Also the consequences of the COVID-19 pandemic will create newly
poor, particularly in countries that are already characterised by high
levels of vulnerability (see Box8.3; Laborde etal., 2020b; Lakner etal.,
Quantitative studies and assessments published since AR5 provide
additional insights about human vulnerability to climate change and
resilience of societies at different scales using different indicator sets and
approaches (Feldmeyer etal., 2017; Hallegatte etal., 2017; EC-DRMKC,
2020; Birkmann etal., 2021a; Feldmeyer etal., 2021; Garschagen etal.,
While quantitative measures of vulnerability are widely used at
different scales (Cutter etal., 2016; Garschagen etal., 2021), there
are also studies that caution the use of such indices in policy making
or risk reduction efforts (Rufat et al., 2019; Spielman et al., 2020).
Such assessments of vulnerability have to be internally and externally
validated and handled with care when applied in decision-making
processes in terms of their options and limits. At the same time, these
assessments capture important conditions and structures that make
people more susceptible to various climate hazards and climate change
impacts. The relevance of these conditions is confirmed by quantitative
impact assessments as well as many specific case study assessments
(Welle and Birkmann, 2015; Feldmeyer etal., 2021; Birkmann etal.,
2022). For example, the access to basic services (e.g., water and
sanitation) (Bollin and Hidajat, 2013; Pandey et al., 2017b; UNEP,
2018; United Nations, 2018; Gupta etal., 2020; Jamshed etal., 2020a)
and broader modes of engagement in governance and governance
fragility (Crawford etal., 2015; Rahman, 2018; Andrijevic etal., 2020)
significantly influence how climatic hazards translate into severe or
non-severe losses and harm (see Section8.5.2).
The lack of such support structures and resources can severely constrain
opportunities of people to cope with and adapt to climate change, since
it is not only the climate hazard, but also exposure and particularly
the vulnerability of a society, a specific community or an individual
household that determine adverse societal consequences of climatic
hazards. International vulnerability and resilience assessments show
that vulnerability varies across countries of similar wealth or income
because multidimensional vulnerability, well-being and resilience
depend on a larger set of factors (Birkmann and Welle, 2016; Hallegatte
etal., 2017; INFORM, 2019). In this regard, vulnerability assessment is
significantly different from climate exposure mapping.
The findings of these global assessments suggest, among other
issues, that options to reduce vulnerability and enhance resilience
do exist in various countries at different levels, in part irrespective
of their income level (Feldmeyer etal., 2017; Hallegatte etal., 2017).
Vulnerabilities at national and regional-level influence community and
individual vulnerability, particularly through structures that determine
entitlements, the access to resources and processes of marginalisation
(Watts and Bohle, 1993; Thomas and Warner, 2019).
While different assessments use different sets of indicators, most
of the global assessments with national-scale resolution (Birkmann
and Welle, 2016; Kreft etal., 2016; Feldmeyer etal., 2017; Hallegatte
et al., 2017; Eckstein et al., 2019; INFORM, 2019; ND-GAIN, 2019;
Garschagen et al., 2021), contain indicators that cover different
aspects of economic poverty, inequality, access to basic infrastructure
services, education and human capital (e.g., adult literacy rate) and
some also include issues of gender inequality, specific vulnerable
groups or insurance against extreme events. The assessments also
differ, for example, in terms of their consideration of aspects of
governance, such as corruption and conflict, or the consideration
of social safety nets, such as insurance coverage, or the number of
people affected by hazards (Feldmeyer etal., 2017; INFORM, 2019),
as well as in terms of the consideration of losses experienced in the
past or issues such as biodiversity as an aspect of adaptive capacity
(Hallegatte et al., 2017; Birkmann et al., 2022). Moreover, the
assessments differ in terms of the consideration of specific indicators
and the inclusion or non-inclusion of specific hazard exposure (Welle
and Birkmann, 2015; Hallegatte etal., 2017; INFORM, 2019; ND-GAIN,
2019; Birkmann etal., 2022).
Recent comparative studies of global assessments of vulnerability
show high agreement on the spatial clusters that have very high
or very low vulnerability to climate change, compared to larger
differences in terms of exposure and risk (Birkmann and Welle,
2016; Hallegatte etal., 2017; INFORM, 2019; Feldmeyer etal., 2021;
Garschagen etal., 2021; Schleussner etal., 2021). The comparison of
the averaged ranking results at the scale of ‘climate regions’ using the
vulnerability components of INFORM and the WorldRiskIndex—as two
comprehensive global assessment approaches of systemic vulnerability
(hazard independent vulnerability) (see Figures8.5; 8.6)—also finds a
high agreement in terms of most vulnerable regions and regions with
low vulnerability (Figure8.5; Feldmeyer etal., 2021). The assessment
at this scale reveals that global hotspots of human vulnerability can be
found in climate regions in East Africa, Central Africa and West Africa,
followed by high vulnerability in Central America, South Asia and
Southeast Asia, for example. Garschagen etal. (2021) in a comparison
of further risk indices also found that there is high agreement on global
assessments of vulnerability compared to exposure or overall risk.
Chapter 8 Poverty, Livelihoods and Sustainable Development
The analysis of vulnerability assessment results of the INFORM Risk
Index and WorldRiskIndex4 at the level of countries coupled with
population data confirms a high agreement on most vulnerable
countries. It also shows that global hotspots of human vulnerability
are not just single countries, but often emerge within regional
clusters, particularly in Africa, but also in Asia and Central America
(see Figure8.6 and Birkmann etal., 2021a). These regional clusters
(Figure8.6) are characterised by high levels of vulnerability in terms
of socioeconomic, demographic, environmental and governance
conditions that make people more likely to face adverse consequences
once a climate hazard occurs. The internal and external validation of
these index systems shows its statistical validity and robustness (Welle
and Birkmann, 2015; Marin-Ferrer etal., 2017; Birkmann etal., 2022).
It also confirms a quantitative relationship between most vulnerable
regions and fatalities and severely affected people due to climate-
influenced hazards (Birkmann etal., 2022). The vulnerability map in
Figure8.5 shows the vulnerability level (systemic societal vulnerability)
linked to national scale and provides additional information about the
population density within these countries. The background map does
not show specific vulnerable populations within countries. Selected
examples of sub-national human vulnerabilities have been added as
additional information in terms of case studies based on information
from other chapters within this report (see, for example, Box 8.7;
Sections 5.12; 10.3.3; 10.5.1; 13.8.1; 14.4.7; 15.3.4; Cross-Chapter
Paper 6.2.7).
4 Both index system analyse risk and vulnerability at the country level and are updated yearly. The WorldRiskIndex (WRI) conceptualizes vulnerability as having susceptibility, lack of coping capacity and
lack of adaptive capacity components. It is based on 28 indicators (23 vulnerability indicators) for 171countries. It uses different weights based on statistical tools complemented by expert judgements
and equal weights for the three components. The index is composed of additive functions for vulnerability components (Welle and Birkmann_2015). The INFORM Risk Index conceptualizes vulnerability
as having two components namely socioeconomic vulnerability and vulnerable groups while lack of coping capacity is considered as a separate component. The INFORM index consists of 18 indicators
to assess vulnerability and 14 indicators for measuring lack of coping capacity. It analyses risk and vulnerability for 191countries. It uses equal weights for indicators and components and uses a
multiplicative function for aggregating components to compose the final index (Marin-Ferrer etal., 2017). In this chapter, the lack of coping capacity component of INFORM is included in vulnerability
calculations in line with the IPCC framing of vulnerability. The vulnerability map presented in this report is based on both WRI and INFORM indices (see Birkmann etal. (2022), Feldmeyer etal. (2021),
Garschagen etal. (2021) for agreement between the WRI and INFORM indices)
Figure 8.7 provides an aggregated regional overview of selected
indicators used within the vulnerability index mapped in Figure8.6.
The overview shows that the many compounded challenges faced by
African countries are starkly pronounced, but also in other regions,
especially Asia, Central and South America, and among SIDS, there
are several challenges such as inequality, governance issues and
displacement, which all increase the vulnerability and constrain
adaptive capacities of these regions to climate change.
However, it is also important to note that vulnerability assessments
do have their limitations (Heesen et al., 2014; Rufat et al., 2019).
For example, in high-income countries, specific groups can be highly
vulnerable to climate change due to marginalisation and discrimination
due to ethnicity or gender. Gender inequality, for example, is also high
in some countries classified in the literature as having low vulnerability
(see Birkmann etal., 2021a; Birkmann etal., 2022). Nevertheless, these
countries have, in theory, sufficient financial resources and governance
capacities to deal with these challenges, while this is different for
many country clusters classified as highly vulnerable.
Countries and regional clusters with low vulnerability (see Figures8.5;
Figure8.6), such as Australia and New Zealand or Iceland and North
Europe, encompass population groups that are exposed and vulnerable
to climate hazards, such as sea level rise or droughts but, within these
regions’ context, conditions exist that allow the negative impacts
Agreement between global vulnerability indices
World Risk Index
Vulnerability rank
10 20 30
INFORM Risk Index
Vulnerability rank
Climate regions in the map are each represented with one circle in the diagram.
Climate regions with very high agreement fall within the center line area.
Figure8.5 | Aggregated vulnerability map at the scale of climate regions based on the averaged ranking of the INFORM Index’s vulnerability component
and the averaged ranking of the vulnerability component of the WorldRiskIndex. Based on the rankings of the INFORM index (INFORM, 2019) and the WorldRiskIndex
(Birkmann and Welle, 2016; Feldmeyer etal., 2017). The map and diagram show agreement between the two global vulnerability indices when ranking climate regions according
to their vulnerability—darker colours show regions of higher vulnerability. The diagram shows how the 35 climate regions are ranked by each index and also serves as a legend
for the map above.
Poverty, Livelihoods and Sustainable Development Chapter 8
and losses to be buffered (also for most vulnerable groups). These
regions have higher financial and institutional capacities to support
people at risk and planned adaptation at a different magnitude
within their region, for example, as seen in compensation payments
for drought exposed farmers (Hochrainer-Stigler and Hanger-Kopp,
2017; Australian-Government, 2021) or flood affected households in
Germany in 2021. Also, the percentage of households insured against
climate-influenced hazards, such as floods or storms, is significantly
higher in these regions (North America, Western Europe) compared to
regions such as Western Africa or Micronesia (Welle and Birkmann,
2015; Feldmeyer etal., 2021; Birkmann etal., 2022).
While climate change differentially impacts people in vulnerable
situations within countries, including the poor, children, women,
marginalised Indigenous or other ethnic minority people (Rhiney etal.,
2016; Méndez etal., 2020), the global assessment results underscore
that, in most vulnerable regions and countries, very limited resources
and structures exist to support these groups when droughts, floods or
storms occur and place an additional burden on these groups.
The assessments of human vulnerability also point towards important
adaptation options that are not visible if one focuses on climatic
hazards or temperature changes alone (Figure 8.9; Dückers et al.,
2015; Cutter etal., 2016; Birkmann etal., 2021a). Fundamental for
vulnerability reduction and adaptation are social insurances and
infrastructure programmes, as well as legislation that improves the
access of poor and marginalised groups to basic infrastructure services
and security. For example, the ‘free basic service programme’ of the
national government of South Africa (GovSA, 2021) is one example
where a national government (Government of South Africa) has
committed itself to providing a basic amount of free water, electricity
and sanitation to low-income households, particularly indigent
people, such as those living in informal settlements or remote rural
areas. Coupled with incentives, for example in terms of a higher use
of renewable energy (e.g., solar home systems in rural areas) (see
GovSA, 2021), these investments can support vulnerability reduction
and mitigation of GHG emissions. However, the programme design and
implementation has also been criticised (see Nel and Rogerson, 2005;
Muller, 2008), as is witnessed by ongoing service delivery protests
(Mutyambizi etal., 2020). This example shows that current national
programmes can—even if they are not classified as adaptation
measures—provide important entry points to reduce human
vulnerability to climate change.
The relevance of human vulnerability has also been confirmed by
recent assessments. The assessment of vulnerability studies and
mortality data found that the average mortality5 from floods, storms
and droughts is 15 times higher in regions and countries ranked
as very highly vulnerable (e.g., Afghanistan, Haiti, Mozambique,
Nigeria, Somalia) compared to regions and countries with very low
vulnerability (e.g., Canada, Italy, Sweden, UK) (Birkmann etal., 2022).
These patterns are confirmed by other studies (e.g., CRED and UNDRR,
2015; CRED and UNDRR, 2016; CRED and UNDRR, 2020b) that
examined disaster mortality per hazard event in low and lower middle
income countries compared to high income countries and therewith
5 Measured as death per hazard event and calculated by averaging the country values of mortality per event falling in different vulnerability categories.
also point towards major differences between countries with high and
low vulnerability (Pelling etal., 2004; CRED and UNDRR, 2015; CRED
and UNDRR, 2016; CRED and UNDRR, 2020b). Even if one takes solely
‘highly vulnerable countries’ such as India, Pakistan and the Philippines
(and not ‘very highly’ vulnerable countries), mortality is still nine times
higher compared to very low vulnerability countries. Similarly, studies
further revealed that average number of adversely affected people per
hazard event (e.g., loss of the house) is 11times higher in regions and
countries categorised as having very high vulnerability compared to
very low vulnerability (Birkmann etal., 2022). In addition to floods,
droughts and storms, published EM-DAT data for wildfires and heat
stress, confirmed higher suffering (higher average mortality) in more
vulnerable regions compared to less vulnerable regions, particularly
when excluding extreme outliers (CRED, 2020). These findings point
towards the fact that in regions identified as highly vulnerable in the
assessments even moderate future climate change and future climate
hazards are likely to push people further into poverty and lead to
significant destabilisation processes in terms of livelihoods security
(Wallemacq and House, 2018; Birkmann etal., 2022). Historic roots of vulnerability in regions classified as highly
While increasing attention is given to issues of human vulnerability,
less attention has been given to the historical conditions that foster
systemic vulnerability of societies. It is important to acknowledge
that drivers and root causes of systemic human vulnerabilities and
development challenges are not always new, and sometimes—for
example in various countries in Africa, Asia and the Caribbean—can
be linked to histories of imperialism, colonial structures (Grasham
etal., 2019), and subsequent development and governance contexts
(Southard, 2017; Zhukova, 2020). Thus, root causes of present
structures of human and human–environmental vulnerability often
have historic dimensions, for example, chronic poverty and structural
inequality in Africa (Grasham etal., 2019) or the Caribbean are still
influenced by the colonial power relations outside of these countries
making solutions for vulnerability reduction more difficult (see e.g.,
Douglass and Cooper, 2020). In addition, national borders, such as
in many regions in Africa, sometimes cut through ethnic groups and
therewith ignore important interrelations between communities on
both sides of the border. People residing in most vulnerable versus least vulnerable
While global assessments often allow for country rankings, it is
similarly important to better understand how many people are living
in these different levels of vulnerability. The quantitative assessments
underscore that a significantly higher number of people live in countries
with very high and high vulnerability compared to the population
living in countries classified as having low and very low vulnerability.
An analysis that measured the vulnerability of countries according
to the INFORM Risk Index and the WorldRiskIndex vulnerability
index components, differentiating vulnerability values into seven
vulnerability classes found that nearly twice as many people are living
Chapter 8 Poverty, Livelihoods and Sustainable Development
ulnerability at the national level varies. Vulnerability also greatly differs within countries.
Countries with moderate or low average vulnerability have sub-populations with high vulnerability and vice versa.
Population density
Observed human vulnerability to climate change is a key risk factor and differs globally Relative vulnerability
Very high
Very low
Children in rural low-income communities | food insecurity, sensitivity to undernutrition and
disease | 5.12.3
People uprooted by conict in the Near East and Sahel | prolonged temporary status, limited
mobility | Box 8.1, Box 8.4
Women & non-binary | limited access to & control over resources, e.g. water, land, credit |
Box 9.1, CCB-GENDER, 4.8.3, 5.4.2, 10.3.3
Migrants | informal status, limited access to health services & shelter, exclusion from
decision-making processes | 6.3.6, Box 10.2
Aboriginal and Torres Strait Islander Peoples | poverty, food & housing insecurity,
dislocation from community | 11.4.1
People living in informal settlements | poverty, limited basic services & often located in areas
with high exposure to climate hazards | 6.2.3, Box 9.1, 9.9, 10.4.6, 12.3.2, 12.3.5, 15.3.4
Examples of
Indigenous Peoples with
high vulnerability to
climate change and
climate change responses
(4.3.8, 5.10.2, 5.13.5,
Box7.1, 8.2.1, 15.6.4) and
the importance of
Indigenous Knowledge
(Box9.2.1, 11.4, 14.4,
Cross-Chapter Box INDIG)
Indigenous Peoples of the Arctic | health inequality, limited access to subsistence resources and
culture | CCP 6.2.3, CCP 6.3.1
Urban ethnic minorities | structural inequality, marginalisation, exclusion from planning processes |
14.5.9, 14.5.5, 6.3.6
Smallholder coffee producers | limited market access & stability, single crop dependency, limited
institutional support | 5.4.2
Indigenous Peoples in the Amazon | land degradation, deforestation, poverty, lack of support |
8.2.1, Box 8.6
Older people, especially those poor & socially isolated | health issues, disability, limited access
to support | 8.2.1, 13.7.1, 6.2.3, 7.1.7
Island communities | limited land, population growth and coastal ecosystem degradation | 15.3.2
Examples of vulnerable local groups across different contexts include the following:
North America
Central &
South America
Small islands
Small islands
Small islands
10 12
36 12
36 12
36 12
Pie charts
StormFlood Drought Wild FiresHeat
The size of the pie charts show average mortality per hazard event per region between 2010 and 2020.
The slices of pie charts show the distribution of deaths from a particular hazard.
Poverty, Livelihoods and Sustainable Development Chapter 8
in most vulnerable countries compared to the number living in less
vulnerable countries (Birkmann etal., 2021a). Another study that uses
the same data and differentiates vulnerability into five classes (also
considering the lack of coping capacity within the INFORM index, see
(Marin-Ferrer etal., 2017)) concludes that about 3.3billion people are
living in countries classified as highly vulnerable, while approximately
1.8billion people live in countries with low vulnerability (Birkmann
et al., 2022). Additional assessments based on the classification of
Africa Australasia
Different aspects and dimensions of vulnerability (regional averages of selected vulnerability indicators)
North America
Central & South America
Europe Small Islands
Relatively moderate challenges
Health status
Access to health care
Relatively severe challenges
Relatively mild challenges
Gender inequality
Food security
Extreme poverty Adult literacy rate
Dependency ratio
Access to basic infrastructure
Figure8.7 | The figure shows selected aspects of human vulnerability, such as extreme poverty and inequality, and access to health care and basic infra-
structure as regional averages. These vulnerability aspects are a selection of indicators from the indicator systems (the INFORM Risk Index and WorldRiskIndex 2019) used
for the global vulnerability map (Figure8.6). These normalized indicator scores were averaged for each region and classified into three levels of severity using the natural breaks
method. This figure provides a more differentiated picture about the various dimensions of vulnerability that different regions and countries face and the severity of such challenges
in each region. Such vulnerability challenges increase the risk of severe adverse impacts of climate change and related hazards (Birkmann etal., 2022).
income groups of countries reveal that approximately 3.6 billion
people live in low and lower middle-income countries, which are most
vulnerable and disproportionally bear the human costs of disasters due
to extreme weather events and hazards (World Bank, 2019b; CRED
and UNDRR, 2020b; EC-DRMKC, 2020; UN-DESA, 2020a; UN-DESA,
2021; Birkmann etal., 2022). While these numbers are different, both
results underscore that the absolute and relative number of people
living in most vulnerable contexts is significantly higher compared to
Figure8.6 | Global map of vulnerability. This map shows the relative level of average national vulnerability as calculated by global indices (INFORM and WRI see details
in 8.3.2). Areas shaded light yellow are on average the least vulnerable and those shaded darker red are the most vulnerable. The map combines information about the level of
vulnerability (independent of the population size) with the population density (see legend) to show where both high vulnerability and high population density coincide. The map
reveals that there are densely populated areas of the world that are highly vulnerable, but also highly vulnerable populations in more sparsely populated areas. There are also
highly vulnerable communities and populations in countries with overall low vulnerability as shown with local case studies alongside the map. The pie charts show the number
of deaths (mortality) per hazard (storm, flood, drought, heatwaves and wildfires) event per continental region based on EM-DAT Data (CRED, 2020). The size of the pie chart
represents the average mortality per hazard event while slices of each pie chart show the absolute number of deaths from each hazard. This reveals that over the past decade, there
were significantly more fatalities per hazard in the more vulnerable regions, e.g., Africa and Asia. The analysis of the data shown in this map revealed that over 3.3billion people
are living in countries classified as very highly and highly vulnerable, while approximately 1.8billion people live in countries with low and very low vulnerability (Birkmann etal.,
2022). These vulnerability values are based on the average of the vulnerability components of the INFORM Index (INFORM, 2019) and WorldRiskIndex (Birkmann and Welle, 2016;
Feldmeyer etal., 2017) with updated data from 2019 classified into five classes using the quantile method. Other studies applied more vulnerability classes within their assessment
and therefore provide slightly different numbers (Birkmann etal., 2021a). However, despite different calculation methods, the conclusion remains that there are significantly more
people residing in countries with very high and highly vulnerability compared to those living in countries classified as having low or very low vulnerability.
Chapter 8 Poverty, Livelihoods and Sustainable Development
those that live in a country with a low vulnerability status (Birkmann
etal., 2021a; Birkmann etal., 2022). These differences have also been
observed in former years (Welle and Birkmann, 2015; Feldmeyer etal.,
That means, even moderate changes in the global mean temperature,
as identified in the recent IPCC report SR1.5°C (IPCC, 2018c) and
in scientific literature (Hoegh-Guldberg et al., 2019a), can mean
substantial increases in risks for more than 3billion people due to high
levels of vulnerability.
Overall, there is robust evidence and high agreement in the recent
literature that countries and regions classified as highly vulnerable
face multiple development challenges at once, in which high levels of
poverty interact with limited access to water and sanitation or with
high levels of forced migration and, in some cases, with state fragility
making solutions difficult (Hallegatte etal., 2017; Marin-Ferrer etal.,
2017; Feldmeyer etal., 2021; Garschagen etal., 2021; Birkmann etal.,
2022). High levels of vulnerability within these regional clusters are
the product of current development challenges, but are often caused
by long and complex histories, including issues of colonisation and
marginalisation, for example, in hotspots in Africa (Birkmann etal.,
2021a). Transboundary Vulnerability and Adaptation
Next to the identification of the level of agreement between different
vulnerability assessments (Garschagen et al., 2021) and the spatial
hotspots, global assessments of vulnerability and adaptation readiness
also point towards the need for a transboundary perspective and
transboundary cooperation in terms of vulnerability reduction and
adaptation (Tilleard and Ford, 2016; Birkmann etal., 2021a). Newer
research points towards the fact that various phenomena of vulnerability,
particularly in highly vulnerable regions, spill over national borders and
emerge in rather regional clusters, such as forced migration and poverty
in West and Central Africa, as well as conflicts in the Near East and Asia
(IDMC, 2020). This means that regional and transboundary challenges
contribute to the formation of systemic human vulnerability, for
example, forced migration that is occurring within countries, but also
across international borders that is also influenced by climate change
(Kaczan and Orgill-Meyer, 2020). In summary, these findings point
towards the need for more transboundary approaches in vulnerability
and risk reduction, adaptation and development. Recent literature and
data presented in Figure8.6 and (Birkmann and Welle, 2016; Feldmeyer
etal., 2017; Hallegatte etal., 2017; INFORM, 2019; Birkmann etal.,
2021a) demonstrate the need to strengthen approaches to monitor
the regional dimensions of vulnerability and to develop strategies
and programmes that consider transboundary vulnerability in risk
reduction and cooperation at different scales. This includes, for example,
cooperation between national-level institutions, but also transboundary
networks of cities or communities (Tilleard and Ford, 2016; Benzie and
Persson, 2019; Birkmann et al., 2021a). The transnational nature of
climate change impacts means that addressing them requires concerted
efforts among nations (IPCC, 2014b; Dzebo, 2019).
In addition, national response strategies for specific transboundary
climate-influenced hazards, such as river flooding, droughts or coastal
flooding can also significantly influence neighbouring countries and
can affect exposure and vulnerability of the respective country (Nadin
and Roberts, 2018; Booth etal., 2020). Likewise, climate change may
affect transboundary resources (e.g., underground water reserves) and
transboundary ecosystems (e.g., in terms of the migration of species)
(Vij etal., 2017) and thereby further reduce the capacity of vulnerable
groups to cope and adapt. In addition, recent research indicates
that social inequities are also coupled with access to and quality of
environmental resources in urban environments—meaning social and
environmental justices are interconnected (see Schell etal., 2020).
Individual adaptation projects to specific climate hazards in regions
classified as highly vulnerable are needed. However, recent studies
underscore that deeper development challenges need to be addressed
in order to make progress towards adaptation and vulnerability
reduction and to avoid maladaptation (Eriksen etal., 2021). Adaptation
and development projects, such as the construction of a dam as a
response to water shortages in one country can significantly influence
the exposure to water shortages and the response capacities of
another country downstream. Often, transboundary challenges are a
result of policy and resource management choices or uncertainty, and
addressing them requires a greater engagement between governing
bodies, which may also guide more suitable responses in the context
of climate change adaptation and vulnerability reduction (Earle etal.,
2015; Tilleard and Ford, 2016; McLeman, 2018; Birkmann etal., 2021a).
Most of those countries and regional clusters identified as highly
vulnerable have contributed little to the overall amount of GHG
emissions and therefore support for (transboundary) adaptation from
the international community is required in these places and for those
living under these conditions in order to support and achieve climate
justice. The Effect of Higher Levels of Global Warming for Most
Vulnerable Regions and Specific Livelihoods
Evidence exists that threats to land-based livelihoods and risks of
undernutrition increase significantly with higher levels of global
warming (Hoegh-Guldberg etal., 2019a). With global warming of 1.5°C
or less, impacts of climate change on livelihoods are still significant,
for example, for West Africa and the Sahel there will be an estimated
reduction of the area suitable for maize production of about 40%. The
consequences of global warming of up to 3°C would mean a high
risk of undernutrition for entire regions (see Hoegh-Guldberg etal.,
2019a) that are already classified as most vulnerable (see Figure8.6).
That means the consequences of significant warming are a particular
challenge for regional hotspots of vulnerability. Small changes in crop
productivity, already observed due to increasing droughts, floods or
changes in rainfall patterns, could lead to severe health risks and
undernutrition. This is because of existing precarious living conditions
and the limited capacities that people and institutions have to build
and enhance coping and adaptive capacities at the level of individual
households, communities and state institutions (see UNEP, 2018;
Birkmann et al., 2021a). The risk of loss of life, displacement and
adverse health consequences due to climate change in these most
vulnerable regions (such as Micronesia, South Asia, West Africa—see
Figures8.5; 8.6) is higher compared to regions classified as having
Poverty, Livelihoods and Sustainable Development Chapter 8
medium or low vulnerability (Birkmann et al., 2022). Nevertheless,
other regions and countries classified as less vulnerable, for example
in Asia, are experiencing disasters and have a relative high share of
the observed global fatalities or losses, when considering non-climatic
natural hazards (CRED and UNDRR, 2020a; see also Section In
addition, changing climatic hazard and exposure patterns have to be
considered. Howeve