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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

August 2022

Authors:

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.

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1171

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,

doi:10.1017/9781009325844.010.

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)

1172

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

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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

1174

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, 16.5.2.3.4}

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). {8.4.5.5, 8.2.1.7}

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

1175

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, 8.4.5.5}

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, 8.4.5.5, 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,

5.14}

1176

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

1177

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

contexts.

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

Figure8.2.

Human dimension of climate change at the nexus of climate change,

climate hazards and socio-economic development

and

socio-economic

devel

ment

LIVELIHOODS

INEQUALITY

VULNERABILITY

POVERTY

CLIMATE HAZARDS

SOCIO-ECONOMIC

DEVELOPMENT

MITIGATION & ADAPTATION

GOVERNANCE

ADAPTATION

& MITIGATION

CLIMATE JUSTICE

& GOVERNANCE

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.

1178

Chapter 8 Poverty, Livelihoods and Sustainable Development

8.2.1.1 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16.5.2.3.4)

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).

8.2.1.2 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 8.4.5.3;

Section16.5.2.3.4; Campbell etal., 2018). A wide range of hazards

also threaten the survival of ﬁsh and livestock that livelihoods depend

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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

Frost

River flood

Wet trend

Drought

Cold spell

Landslides

Climate Hazards

Livelíhood resources

Crop yield

Pluvial flood

Heat

Snow/

ice Coastal

Wind/

Storm OtherWetCold Dry

Dry trend

Wildfire

Hail

Heavy snow

Severe storms

Lake/sea ice reduction

Snow avalanche

Snow reduction

Coastal erosion

Sea level rise

Ocean/lake acidification

Average

confidence

Coastal flood

Salinity

Total risk

from all

hazards

Housing stock

Farmland/arable cropland

Fisheries and aquaculture

Forest products

Income/financial assets

Life/bodily health/food security

Pasture/rangeland/livestock

Crop variety

Heatwave

Livelíhood resources

Crop yield

Warming trend

River flood

Drought

Pluvial flood

Severe storms

Sea level rise

Fisheries and aquaculture

Income/financial assets

Life/bodily health/food security

Pasture/rangeland/livestock

Average confidence

Total risk to livelihoods

Average confidence

Total risk to livelihoods

Confidence on the observed impacts

2.1

1.6

1.5

1.7

1.4

1.8

2.2

1.8

1.4

2.12.01.31.71.31.01.12.31.31.42.32.61.22.02.41.61.41.12.61.21.8 2.31.4

3.0 2.8 2.2 3.0 2.8 2.2

2.3

3.0

2.7

2.5

2.8

Average

confidence

Total risk

from all

hazards

(a) Display of 207

confidence statements

on the total set of

livelihood impacts

Confidence on the observed impacts

Lower Higher

(b) High risk cluster

HighMediumLow

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

exercise.

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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.,

2018).

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.

8.2.1.3. 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-

1181

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).

8.2.1.4 Climate-related Hazards, Livelihood Transitions and

Migration

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,

2018).

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

CLIMATE HAZARDS HUMAN SYSTEM

Wildfires

Floods

Sea level rise

Fast

Slow

Intersectionality

Droughts

Age

Ethnicity

Actual and potential economic and non-economic losses

HAZARD-VULNERABILITY INTERFACE

Examples

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).

1182

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).

8.2.1.5 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8.2.1.1) 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

1183

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).

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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).

UNHCR

camps

IDP

camps > 500

100

< 5

Number

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)

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Poverty, Livelihoods and Sustainable Development Chapter 8

8.2.1.6 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.

8.2.1.7 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).

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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)

1187

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

POVERTY TRAPPOOR NUTRITION

LIMITED INCOME

GENERATION

STREAMS

LIVESTOCK AND

CROP LOSSES

LOW

PRODUCTIVITY

AND OUTPUT

LOW ASSET AND

SECURITY

LOW LEVELS OF

HUMAN CAPITAL

LIMITED OPPORTUNITY

TO ADAPT

Figure8.4 | Schematic representation of a poverty–environment trap that can increase chronic poverty.

1188

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.

8.2.2.1 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).

1189

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

Section8.4).

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

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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.

1191

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

approaches.

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.

8.2.2.2 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

development.

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

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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).

1193

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,

2020).

• 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.,

2020).

• 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.,

2020).

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)

1194

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,

2014).

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.

1195

Poverty, Livelihoods and Sustainable Development Chapter 8

8.3.2 Global Hotspots of Human Vulnerability to Climate

Change

8.3.2.1 Hotspots and Spatial Patterns of Multidimensional

Vulnerability

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

groups.

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.,

2020).

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.,

2021).

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.

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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

High

Low

High

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.

1197

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).

8.3.2.1.1 Historic roots of vulnerability in regions classiﬁed as highly

vulnerable

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.

8.3.2.1.2 People residing in most vulnerable versus least vulnerable

regions

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

1198

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

Low

High

Observed human vulnerability to climate change is a key risk factor and differs globally Relative vulnerability

Medium

Very high

Low

Very low

High

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:

Africa

North America

Central &

South America

Australasia

Small islands

Europe

Small islands

Asia

Small islands

78912

10 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.

1199

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

Asia

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

Governance

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.

1200

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.,

2017).

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).

8.3.2.2 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.

8.3.2.3 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

1201

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8.3.2.1). In

addition, changing climatic hazard and exposure patterns have to be

considered. Howeve

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

Abstract